system

Abstract

We provide the system. [Solution] Means of collecting management plan information of the target organization, A means for analyzing past proposal activity data and contact person information of the aforementioned proposed organization, Means for optimizing the proposed content based on the information collected and analyzed, A means for automatically generating presentation materials based on the optimized proposal content, A means of monitoring the progress of the sales process and advising on the steps to be taken, 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, which is 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] Regarding the optimization of proposals in business activities, monitoring of progress, and strengthening of engagement with customers, while many companies are seeking efficiency and effectiveness, there is a problem that a system for managing and utilizing these in a unified manner is lacking. In such a situation, it is difficult to individually analyze a vast amount of data and construct a quick and appropriate business strategy, and as a result, there is a possibility that business efficiency will decline. Therefore, there is a demand for a system that effectively utilizes information of the proposed organization and realizes optimization of the entire business activity.

Means for Solving the Problems

[0005] This invention provides a means for collecting management plan information of the target organization and analyzing past proposal activity data and personnel information. This allows for the optimization of proposal content based on the collected and analyzed information, and further provides a means for automatically generating presentation materials based on the optimized proposal content. It also has a function to monitor the progress of the sales process and advise on the procedures to be followed, thereby significantly improving the efficiency and effectiveness of sales activities. Furthermore, it provides a system that extracts relevant internal resources based on past sales activity data, enabling the use of optimal personnel and tools in proposal activities. This enables the analysis of customer contact history and relationship data, and provides optimal strategies for strengthening customer-organizational engagement.

[0006] A "recipient organization" refers to a group or company that will consider a proposal for purposes such as commercial transactions or cooperative relationships.

[0007] "Management plan information" refers to a collection of data related to the goals, strategies, and activity plans that an organization aims to achieve in the medium to long term.

[0008] "Proposal activity data" refers to a dataset containing information about past and present proposals, their progress, success rates, and other related data.

[0009] "Contact person information" refers to information about the experience, skills, and performance of individual sales representatives involved in proposal activities.

[0010] "Optimization" refers to adjusting something to achieve the greatest effect for a specific purpose.

[0011] "Presentation materials" refer to prepared materials such as slides and documents used to visually and effectively convey the content of a proposal.

[0012] "Monitoring" refers to continuously observing the progress of a particular activity or process and understanding its status.

[0013] "Advice" refers to guidance or instructions that suggest appropriate and actionable behaviors in a particular situation.

[0014] "Internal resources" refer to resources available within an organization, including personnel, technology, tools, and data.

[0015] "Engagement" refers to a metric that measures the depth of relationships and communication built between customers and an organization. [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 the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one 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 numbered 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 according to the present invention consists of a server and a user terminal. The server collects management plan information of the proposed organization from various data sources and stores it in a database. This data is collected through APIs and data feeds. The server also retrieves past proposal activity data and personnel information from an internal database and analyzes the overall trends of proposal activities. This analysis uses machine learning models and natural language processing techniques to identify relationships and patterns in the information.

[0038] The server generates optimized proposals based on the collected information. This ensures that the proposal accurately meets the needs of the target organization. Based on these optimized proposals, the server uses an algorithm to automatically generate presentation materials. These materials, incorporating the necessary data and visual elements for the proposal, are then sent to the user's device.

[0039] To monitor the progress of the sales process in real time, the server utilizes input information from user terminals and data collected through integration with external systems. Based on the progress monitoring results, the server provides the user with advice on the next course of action. This advice is based on the most effective methods predicted from the analyzed data.

[0040] Furthermore, the server extracts relevant internal resources based on past sales activity data. This allows the user to see how the suggested personnel and tools can be effective in current proposal activities. Optimizing internal resources improves sales efficiency and enables more effective proposal activities.

[0041] Furthermore, the server continuously analyzes customer contact history and relationship data to provide strategies for strengthening customer-organizational engagement. These strategies are optimized based on customer behavior patterns and needs, suggesting effective timing and methods for relationship building.

[0042] For example, if the server retrieves the target organization's annual plan and that plan includes expansion into new markets, a proposal is automatically generated based on detailed data and past success stories related to those markets. The user's terminal displays the optimal slide structure for the presentation, allowing for efficient preparation for the proposal meeting.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects management plan information for the proposed organization from publicly available company information, internal databases, and API connections. The collected information is stored in a database to prepare for subsequent analysis.

[0046] Step 2:

[0047] The server retrieves past proposal activity data and contact person information from a database. Using this data, it analyzes success patterns and organization-specific trends using natural language processing and machine learning algorithms.

[0048] Step 3:

[0049] The server creates an optimized proposal tailored to the needs of the target organization, based on the collected business plan information and analysis results. This proposal includes data points selected by an algorithm and a proposal strategy.

[0050] Step 4:

[0051] The server automatically generates presentation materials based on optimized proposals. The generated materials are structured in a visually appealing format and prepared for user use.

[0052] Step 5:

[0053] The user's device receives presentation materials provided by the server, reviews them, and edits them. Users can customize the materials based on customer requests and prior feedback.

[0054] Step 6:

[0055] The server monitors the progress of users' sales activities in real time. This is done through user input and integration with sales management systems.

[0056] Step 7:

[0057] The server advises the user on the next steps based on sales progress. If a specific action is required, it notifies the user's terminal with a suggestion including the timing and method of that action.

[0058] Step 8:

[0059] The server references the company's past sales data and extracts personnel and resources relevant to the current project. It then recommends that users utilize these resources to support efficient sales activities.

[0060] Step 9:

[0061] The server analyzes customer contact history and relationship data to provide strategies for strengthening customer engagement. Users can then use these strategies to take actions that deepen their relationships with customers.

[0062] (Example 1)

[0063] 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."

[0064] In today's highly competitive business environment, effectively utilizing business plans and proposal histories to build deep engagement with customers is essential for improving the efficiency of proposal activities. However, processing and visualizing vast amounts of data in a timely and appropriate manner is challenging, and advanced technology is required to grasp progress in real time and propose effective strategies. Therefore, there is a need to develop new systems that efficiently optimize proposal activities and improve the sales process.

[0065] 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.

[0066] In this invention, the server includes means for collecting business plan data of the proposed organization using an information processing device, means for analyzing past proposal activity data and personnel data of the proposed organization using statistical techniques, and means for using a generative AI model to optimize the proposal content based on the collected and analyzed data. This enables the efficient analysis and optimization of vast amounts of data, thereby improving the quality of proposals and enabling strategic proposals to deepen relationships with customers.

[0067] "Information processing equipment" refers to all computing devices used for data collection, analysis, and optimization.

[0068] "Business plan data" refers to information that describes the management policies and strategies that the proposed organization aims to achieve in the future.

[0069] "Proposal activity data" refers to information that includes records of past proposal processes and their results.

[0070] "Contact person data" refers to information about individuals involved in proposal activities, such as their job title and assigned duties.

[0071] "Statistical techniques" refer to mathematical methods and algorithms used to understand the characteristics of data and find patterns.

[0072] A "generative AI model" refers to an artificial intelligence system that generates responses in natural language from input data.

[0073] "Visualized data" refers to a format that uses graphics and charts to present information in a way that makes it easier to understand.

[0074] "Status monitoring" refers to the act of tracking and evaluating the progress or status of a process in real time.

[0075] "Action sequence" refers to a plan of steps or actions to be taken in order to achieve a specific objective.

[0076] "Resources" refers to all elements necessary for carrying out business operations, including personnel, equipment, and information.

[0077] "Communication history" refers to the record of all communications and interactions that took place with the customer.

[0078] "Relational data" refers to information about the connections and interactions between individual entities.

[0079] In order to implement this invention, it is necessary for the server and the user's terminal to work together to enable efficient proposal activities and optimize the sales process.

[0080] The server first uses APIs and data feeds to collect business plan data from the target organization and stores the latest data in a database. Next, the server retrieves past proposal activity data and personnel data from its internal database and analyzes it using statistical techniques. Specifically, data analysis is performed using Python and R to reveal patterns and relationships between pieces of information.

[0081] Subsequently, the server uses a generative AI model to generate optimized proposals based on the collected and analyzed data. This AI model takes text-based prompts as input and generates optimal proposal documents in natural language. For example, using the prompt "Automatically generate proposals for organizations planning to expand into new markets. Create the optimal slide structure based on the necessary data and past success stories," the AI ​​can derive specific proposal content.

[0082] Based on this information, the server automatically creates a presentation document including visualization data and sends it to the user's device. The document creation process utilizes document generation tools such as Google® Slides API and LaTeX to create slides incorporating data and visual elements.

[0083] The user's device provides information to the server for progress management and monitoring, tracking the sales process in real time. The server uses this information to suggest the optimal sequence of actions and provide progress advice to the user. Taking the necessary actions at the optimal time increases the success rate of the proposal.

[0084] This format significantly improves the efficiency of proposal activities and sales processes, enabling data-driven, strategic proposals.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server collects business plan data for the proposed organization from external data sources. It uses API and data feed endpoint information as input. The server sends HTTP requests, receives response data, parses the data in JSON format, and stores it in the database. The output of this step is that the latest business plan data is saved in the database.

[0088] Step 2:

[0089] The server retrieves past proposal activity data and assignee data from its internal database. It uses SQL queries to retrieve specific records as input. The server analyzes this data using statistical techniques to extract patterns and relationships between the information. As a result of this process, a report of the analyzed patterns and relationships is output.

[0090] Step 3:

[0091] The server optimizes the proposal content using a generative AI model based on the collected and analyzed data. The input consists of prompt sentences and analyzed data. The server inputs these into the AI ​​model and generates optimized proposal content. The output of this step is the result of natural language generation as a proposal document.

[0092] Step 4:

[0093] The server automatically creates visualization materials based on the optimized proposal. It uses the proposal document and template information as input. Tools such as the Google Slides API and LaTeX are used to create the visualization materials, generating slides that match the proposal. The output of this process is a presentation document containing visual elements.

[0094] Step 5:

[0095] The user's terminal sends progress information about the sales process to the server. The server uses user progress data and feedback as input. The server processes the received data and monitors progress in real time. This process allows the server to advise the user on the next steps and support timely decision-making. The output of this step is actionable guidance and alert notifications for the user.

[0096] (Application Example 1)

[0097] 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."

[0098] In the field of electronic payment services, it is challenging to propose payment solutions that are accurately and quickly optimized to meet the diverse business needs of clients. Furthermore, there is a need to streamline sales activities and strengthen customer relationships. It is necessary to overcome these challenges and maximize the results of commercial activities.

[0099] 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.

[0100] In this invention, the server includes a device for collecting management plan information of the proposed organization, a device for analyzing past proposal activity data and personnel information of the proposed organization, and a device for automatically generating and providing solutions based on the customer's industry data in proposal activities in the payment system domain. This enables the proposal of quick and accurate payment solutions to clients.

[0101] "Management plan information" refers to data related to the goals and strategies set by an organization, as well as the activity plans based on them.

[0102] "Proposal activity data" refers to records of past sales proposals, including information about the content of the proposals and their results.

[0103] "Information on personnel involved in sales activities within your organization" refers to data on the names, positions, and related skills and achievements of the personnel involved in sales activities within your organization.

[0104] "Optimization" is the process of improving proposals and systems based on existing information and conditions to make them function more effectively and efficiently.

[0105] "Presentation materials" refer to documents and slides created to visually represent a proposal and facilitate understanding and empathy among stakeholders.

[0106] "Sales activities" refer to a series of organizational activities and processes carried out with the aim of selling products or services.

[0107] "Monitoring progress" means constantly checking the status of activities or projects and observing whether the objectives are being achieved.

[0108] A "solution" refers to an optimized solution or proposal provided for a specific problem or need.

[0109] "Connection" refers to the mutual relationship and trust between customers and the organization, and includes long-term cooperation and partnerships.

[0110] The system for realizing this invention mainly consists of a server and user terminals. The server collects organizational management plan information, past proposal activity data, and information on personnel in charge of operations, and stores this information in a MySQL® database. For data analysis, Scikit-learn and NLTK are used for natural language processing to optimize the proposal content. Based on the optimized proposal content, presentation materials are automatically generated using the Python-PPTX library.

[0111] The user's device is a smartphone or tablet, which receives optimized proposals and presentation materials sent from the server. Through this device, the progress of the sales process is transmitted to the server in real time, and advice on the next action is provided. In the payment system domain, the server provides users with customized payment solutions based on industry data via a push notification system.

[0112] For example, if a user receives a request from a new client for a payment solution specific to a particular industry, the server analyzes past success stories specific to that industry and generates an optimal proposal. The resulting document becomes immediately available on the user's device.

[0113] Examples of prompts for a generative AI model include the following:

[0114] "Please generate documentation to propose the optimal payment solution to a client who is considering launching a new e-commerce platform. This solution must be optimized for the client's business needs."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server collects management plan information, past proposal activity data, and personnel information of the target organization through APIs and data feeds. It receives management plan and past proposal activity data from external systems as input and stores this data in a MySQL database. Specifically, it performs API calls, parses the retrieved data, and stores it in the appropriate fields in the database.

[0118] Step 2:

[0119] The server uses Scikit-learn and NLTK to analyze the collected data. The input consists of business plan information and proposal activity data stored in the database in Step 1. Machine learning algorithms are applied to generate optimal proposals. Specifically, the process involves preprocessing the data, extracting features, inputting them into an optimization model, and obtaining the output.

[0120] Step 3:

[0121] The server automatically generates presentation materials using the Python-PPTX library based on the output of the optimization algorithm. The input is the optimal suggestion generated in step 2. This is converted into a slide format, visual elements are incorporated, and a completed presentation is created. Specifically, the data is fed into a slide template, and the necessary text and graphics are placed on each slide.

[0122] Step 4:

[0123] The user's device receives presentation materials sent from the server. The input is the generated material sent from the server. The user presents this material to the client and obtains feedback. Specifically, the user can view the downloaded material on the device and add annotations and comments as needed.

[0124] Step 5:

[0125] The server receives data sent from the user's terminal in real time for progress monitoring and provides advice on the next action. It uses user operation logs and client feedback as input. Based on this, it presents a sales strategy. Specifically, it has the function of analyzing log data and displaying appropriate actions on a dashboard.

[0126] Step 6:

[0127] The server uses a generative AI model to generate optimal payment solutions based on specific industry data. It takes prompt text as input to run the AI ​​model and output customized suggestions. Specifically, it passes the prompt text to an internal API, saves the received solution to a database, and sends the result to the user's terminal.

[0128] 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.

[0129] The system according to the present invention comprises a server, a terminal, and an emotion engine that recognizes the user's emotions. The server has the function of collecting management plan information of the proposed organization from various data sources and storing this information in a database. The server also retrieves past proposal activity data and personnel information from the database and performs analysis using machine learning algorithms and natural language processing techniques based on this data.

[0130] The server uses these analysis results to optimize the proposal and automatically generates presentation materials based on that content. The generated materials are structured to meet the needs of the target audience and are sent to the user's device in a visually appealing format.

[0131] Sales activities are monitored in real time by the server, and feedback is provided to the user as needed. This feedback includes advice on the next actions to take and when to take them.

[0132] Furthermore, by incorporating an emotion engine, this system recognizes the user's emotions in real time and dynamically adjusts the information provided by the system according to the sales situation. The emotion engine analyzes the user's voice tone, facial expressions, input text, etc., and evaluates their emotional state.

[0133] For example, if the emotion engine detects that a user is experiencing emotional stress during a presentation, the server dynamically adjusts the presentation, either by concisely summarizing the proposal or quickly providing supporting information. Conversely, if the user is showing positive emotions, additional information or detailed data analysis results are displayed to increase the success rate of the sales pitch.

[0134] By leveraging this emotion engine, users can adjust their approach to suit their situation in real time, improving the efficiency and results of their sales activities. This brings a level of flexibility and personalization not found in traditional sales support systems.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The server collects management plan information for the proposed organization from external data sources and publicly available company documents. This information includes medium- to long-term strategies, important projects, and financial data, and is stored in a database.

[0138] Step 2:

[0139] The server retrieves past proposal activity data and contact person information from an internal database. This includes proposal success rates, contact person feedback, and customer internal communication history.

[0140] Step 3:

[0141] The server utilizes machine learning algorithms and natural language processing technology to analyze the target company's business plan information and historical data. Based on the analyzed information, it generates insights to optimize the proposal.

[0142] Step 4:

[0143] The server automatically generates presentation materials based on the optimized proposal. The slides and data visualizations included are customized to the user's needs and sent to the user's device.

[0144] Step 5:

[0145] The user's device receives presentation materials, reviews their content, and edits them as needed. The user can then prepare the materials for the proposal meeting.

[0146] Step 6:

[0147] The emotion engine extracts emotions in real time from the user's voice, facial expressions, and input text, and evaluates the user's emotional state. The emotional state is recorded according to the situation of the presentation or sales activity.

[0148] Step 7:

[0149] The server dynamically adjusts the presentation and the suggestions it provides based on the evaluation results of the emotion engine. If the user's emotions indicate a stressed state, it simplifies the materials and strengthens the advice for the next action.

[0150] Step 8:

[0151] The user conducts conversations with customers based on the tailored proposals. If the user's sentiment is positive, the server provides more detailed information and success stories to help improve the effectiveness of sales activities.

[0152] (Example 2)

[0153] 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".

[0154] In proposal activities, it is necessary to effectively utilize organizational planning information and historical data to optimize proposal content. Furthermore, while it is important to monitor the progress of sales activities in real time and provide appropriate advice, traditional systems have the challenge of not being able to adequately address individual situations. Additionally, the inability to provide information that takes user emotions into account has made it difficult to maximize sales effectiveness.

[0155] 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.

[0156] In this invention, the server includes means for acquiring planning information of the target organization, means for evaluating past activity data and personnel data, and means for incorporating an emotion recognition engine to determine the user's emotional state and dynamically adjust the information. This makes it possible to optimize proposals by utilizing organizational data, monitor the progress of sales activities, and provide flexible advice and information tailored to the user's emotions.

[0157] "The organization being proposed to" refers to the business entity that receives the proposal for products or services.

[0158] "Planning information" refers to data related to an organization's management policies, strategies, financial plans, etc.

[0159] "Past activity data" refers to records of sales activities and proposals that an organization has conducted in the past.

[0160] "Contact person data" refers to information about individuals involved in sales activities, such as their role, past performance, and skill set.

[0161] An "emotion recognition engine" refers to a technology that analyzes a user's voice, facial expressions, and text input to evaluate their emotional state.

[0162] "Optimization" refers to making adjustments to a proposal to most effectively match the needs of the organization and its customers.

[0163] "Dynamic adjustment" refers to changing the information and methods provided in real time according to the situation.

[0164] "Flexible advice" refers to providing appropriate advice based on the user's current emotional state and business situation.

[0165] "Information provision" refers to the act of presenting data or knowledge that is beneficial to the user or customer.

[0166] This invention is implemented by a system including a server, a terminal, and an emotion recognition engine. The server retrieves planning information for the proposed organization and uses a dedicated database to evaluate past activity data and personnel data. Internet connectivity and API access are often used for data retrieval.

[0167] The server analyzes this data using machine learning algorithms and natural language processing technology, and optimizes the proposal content using generative AI models. This automatically generates presentation materials that are tailored to the needs of the target audience and industry trends. The generated materials are sent to the terminal in common formats such as PowerPoint and PDF, making them easily accessible to the user.

[0168] The terminal is equipped with an emotion recognition engine that analyzes the user's voice, facial expressions, and entered text in real time. This determines the user's emotional state, and the server dynamically adjusts the information to support the user's decision-making in sales activities. For example, if the user is showing signs of stress, the server provides information that concisely summarizes the proposal.

[0169] For example, if a user enters the prompt "What should I do next?" during a proposal, appropriate advice and information based on their emotional state will be immediately provided. This improves the success rate of proposal activities, allowing users to conduct sales activities more effectively and efficiently.

[0170] In this way, this invention realizes flexible and personalized sales support tailored to the user's situation.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The server retrieves planning information about the proposed organization from external and internal data sources. Inputs include API calls and database queries, and this information is compiled into data that reflects the organization's current state and future goals. The output is a dataset summarizing this retrieved information.

[0174] Step 2:

[0175] The server retrieves past activity data and employee data from the database. Past sales records and employee history information, which serve as explanatory variables, are used as input, and the server filters the data based on this information. The output is historical data organized for use in analysis.

[0176] Step 3:

[0177] The server begins analyzing acquired planning information and historical activity data using machine learning algorithms and natural language processing techniques. The input is the dataset and historical data acquired in the previous step, and the output is an optimized proposal based on the analysis results. Specifically, a generative AI model performs selection and weighting to determine the appropriate approach.

[0178] Step 4:

[0179] The server automatically generates presentation materials based on the optimized proposal. The input is the optimized proposal obtained through analysis, and the server then performs specific actions such as combining templates and slide formats based on this. The output is a visually organized presentation tailored to the target organization.

[0180] Step 5:

[0181] The generated presentation materials are delivered to the terminal. The user receives these materials and uses them in their proposal activities. The concrete action is that the materials are displayed on the terminal, making them immediately ready for the user to use in their proposal.

[0182] Step 6:

[0183] The server monitors the progress of the user's sales activities in real time. Input information includes user logs and feedback data, while output is advice for the next steps, generated based on the monitoring.

[0184] Step 7:

[0185] On the device, an emotion recognition engine analyzes the user's voice, facial expressions, and text input in real time. The input is the user's voice and text information, and the output is the user's emotional state based on these analysis results. Specifically, the system analyzes the user's state and optimizes its actions accordingly.

[0186] Step 8:

[0187] The server dynamically adjusts information based on the user's emotional state, providing flexible advice and additional data. The input is the output of the emotion recognition engine, and the output is specific advice and information for the user. This functionality enables users to respond appropriately to different situations.

[0188] (Application Example 2)

[0189] 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".

[0190] The problem that this invention aims to solve is to prevent the loss of sales opportunities due to insufficient communication with customers and inefficient proposals during sales activities. Furthermore, there is a need to improve the success rate and efficiency of sales activities by accurately understanding the emotional state of customers and optimizing sales activities in real time.

[0191] 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.

[0192] In this invention, the server includes means for collecting management strategy information of the target organization, means for analyzing past proposal activity information and personnel information of the target organization, and means for optimizing proposal content based on the collected and analyzed information. This enables dynamic adjustment of sales content according to the customer's emotional state and automatic generation of optimal proposals.

[0193] "Management strategy information of the proposed organization" refers to information related to the long-term and short-term business policies and activity plans set by a specific organization.

[0194] "Past proposal activity information" refers to records including past sales activities and negotiation history with the target organization.

[0195] "Contact person information" refers to data regarding the career history and performance of sales representatives involved in proposal activities.

[0196] An "emotion analysis engine" is a component of a system that analyzes the user's voice tone, facial expressions, input text, etc., to evaluate their emotional state.

[0197] "Evaluating in real time and dynamically adjusting presentation content" means instantly reading the user's emotional state and flexibly changing the proposed content based on that information.

[0198] "Monitoring the progress of sales activities and providing guidance on the procedures to be followed" means tracking the progress of sales and giving instructions on the next steps and timing of actions to take.

[0199] "Identifying relevant resources within the organization and presenting the most suitable personnel and equipment for proposal activities" means using past sales data to identify and present the internal human resources and technologies that will be effective for a particular proposal.

[0200] "Customer contact history and relationship information" refers to information that shows the past interactions between customers and the organization and the depth of those relationships.

[0201] This invention is a system for optimizing sales activities to target organizations. The system comprises a server, terminals, and an emotion analysis engine.

[0202] The server first collects management strategy information of the target organization from various data sources. This information is stored in a database and analyzed along with past proposal activity information and contact person information. For the analysis, TENSORFLOW® is used to implement machine learning algorithms, and NLTK is used for natural language processing. Based on the analysis results, the proposal content is optimized and presentation information is automatically generated. This generated information is provided to the sales representative's smartphone or other device in a visually appealing format. On the device side, image processing using OpenCV and audio data acquisition using a microphone are performed to capture the user's voice tone and facial expression data.

[0203] The emotion analysis engine acquires the user's visual and auditory information in real time and evaluates their emotional state based on that data. Based on the evaluated emotion data, the server dynamically adjusts the presentation and advises on actions. This enables flexible responses that increase the success rate of sales activities.

[0204] As a concrete example, if a customer shows signs of anxiety while a sales representative is giving a presentation on a new product, the emotion analysis engine evaluates that emotion. Based on this information, the server immediately provides information emphasizing the product's security advantages. This helps to alleviate the customer's anxiety and enhance the effectiveness of the proposal.

[0205] An example of a prompt for a generative AI model is, "If a customer hears information and becomes suspicious, what data should be presented to regain their trust?"

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The server collects management strategy information for the proposed organization from multiple data sources. Inputs are publicly available management information and internal databases, while output is integrated management strategy information. This information is stored in a database for subsequent analysis.

[0209] Step 2:

[0210] The server analyzes collected business strategy information, past proposal activity information, and personnel information. The input is integrated business strategy information and historical sales data, and the output is the optimized proposal content based on the analysis. TensorFlow is used to perform data analysis with machine learning models to derive effective proposals.

[0211] Step 3:

[0212] The server automatically generates presentation information based on the optimized proposal. The input is the optimized result, and the output is structured presentation material. The presentation material is generated using natural language processing technology with NLTK and sent to the user's terminal.

[0213] Step 4:

[0214] The device uses an emotion analysis engine to collect user voice tone and facial expression data in real time. Input is voice and video data acquired from the user, and output is the user's emotional state after analysis. Data is captured using a camera and microphone, and image processing is performed using OpenCV.

[0215] Step 5:

[0216] The server dynamically adjusts the presentation based on the user's emotional state, as determined by an emotion analysis engine. The input is the analyzed emotional state, and the output is the adjusted presentation content. The server uses an AI model to determine the next action and provide the customer with the necessary information in a timely manner.

[0217] Step 6:

[0218] The server monitors the progress of sales activities in real time and advises on the next course of action. Inputs are the status of sales activities and user feedback, while output is the recommended next action. This allows sales representatives to take appropriate steps on the spot, increasing their sales success rate.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] The system according to the present invention consists of a server and a user terminal. The server collects management plan information of the proposed organization from various data sources and stores it in a database. This data is collected through APIs and data feeds. The server also retrieves past proposal activity data and personnel information from an internal database and analyzes the overall trends of proposal activities. This analysis uses machine learning models and natural language processing techniques to identify relationships and patterns in the information.

[0236] The server generates optimized proposals based on the collected information. This ensures that the proposal accurately meets the needs of the target organization. Based on these optimized proposals, the server uses an algorithm to automatically generate presentation materials. These materials, incorporating the necessary data and visual elements for the proposal, are then sent to the user's device.

[0237] To monitor the progress of the sales process in real time, the server utilizes input information from user terminals and data collected through integration with external systems. Based on the progress monitoring results, the server provides the user with advice on the next course of action. This advice is based on the most effective methods predicted from the analyzed data.

[0238] Furthermore, the server extracts relevant internal resources based on past sales activity data. This allows the user to see how the suggested personnel and tools can be effective in current proposal activities. Optimizing internal resources improves sales efficiency and enables more effective proposal activities.

[0239] Furthermore, the server continuously analyzes customer contact history and relationship data to provide strategies for strengthening customer-organizational engagement. These strategies are optimized based on customer behavior patterns and needs, suggesting effective timing and methods for relationship building.

[0240] For example, if the server retrieves the target organization's annual plan and that plan includes expansion into new markets, a proposal is automatically generated based on detailed data and past success stories related to those markets. The user's terminal displays the optimal slide structure for the presentation, allowing for efficient preparation for the proposal meeting.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server collects management plan information for the proposed organization from publicly available company information, internal databases, and API connections. The collected information is stored in a database to prepare for subsequent analysis.

[0244] Step 2:

[0245] The server retrieves past proposal activity data and contact person information from a database. Using this data, it analyzes success patterns and organization-specific trends using natural language processing and machine learning algorithms.

[0246] Step 3:

[0247] The server creates an optimized proposal tailored to the needs of the target organization, based on the collected business plan information and analysis results. This proposal includes data points selected by an algorithm and a proposal strategy.

[0248] Step 4:

[0249] The server automatically generates presentation materials based on optimized proposals. The generated materials are structured in a visually appealing format and prepared for user use.

[0250] Step 5:

[0251] The user's device receives presentation materials provided by the server, reviews them, and edits them. Users can customize the materials based on customer requests and prior feedback.

[0252] Step 6:

[0253] The server monitors the progress of users' sales activities in real time. This is done through user input and integration with sales management systems.

[0254] Step 7:

[0255] The server advises the user on the next steps based on sales progress. If a specific action is required, it notifies the user's terminal with a suggestion including the timing and method of that action.

[0256] Step 8:

[0257] The server references the company's past sales data and extracts personnel and resources relevant to the current project. It then recommends that users utilize these resources to support efficient sales activities.

[0258] Step 9:

[0259] The server analyzes customer contact history and relationship data to provide strategies for strengthening customer engagement. Users can then use these strategies to take actions that deepen their relationships with customers.

[0260] (Example 1)

[0261] 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."

[0262] In today's highly competitive business environment, effectively utilizing business plans and proposal histories to build deep engagement with customers is essential for improving the efficiency of proposal activities. However, processing and visualizing vast amounts of data in a timely and appropriate manner is challenging, and advanced technology is required to grasp progress in real time and propose effective strategies. Therefore, there is a need to develop new systems that efficiently optimize proposal activities and improve the sales process.

[0263] 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.

[0264] In this invention, the server includes means for collecting business plan data of the proposed organization using an information processing device, means for analyzing past proposal activity data and personnel data of the proposed organization using statistical techniques, and means for using a generative AI model to optimize the proposal content based on the collected and analyzed data. This enables the efficient analysis and optimization of vast amounts of data, thereby improving the quality of proposals and enabling strategic proposals to deepen relationships with customers.

[0265] "Information processing equipment" refers to all computing devices used for data collection, analysis, and optimization.

[0266] "Business plan data" refers to information that describes the management policies and strategies that the proposed organization aims to achieve in the future.

[0267] "Proposal activity data" refers to information that includes records of past proposal processes and their results.

[0268] "Contact person data" refers to information about individuals involved in proposal activities, such as their job title and assigned duties.

[0269] "Statistical techniques" refer to mathematical methods and algorithms used to understand the characteristics of data and find patterns.

[0270] A "generative AI model" refers to an artificial intelligence system that generates responses in natural language from input data.

[0271] "Visualized data" refers to a format that uses graphics and charts to present information in a way that makes it easier to understand.

[0272] "Status monitoring" refers to the act of tracking and evaluating the progress or status of a process in real time.

[0273] "Action sequence" refers to a plan of steps or actions to be taken in order to achieve a specific objective.

[0274] "Resources" refers to all elements necessary for carrying out business operations, including personnel, equipment, and information.

[0275] "Communication history" refers to the record of all communications and interactions that took place with the customer.

[0276] "Relational data" refers to information about the connections and interactions between individual entities.

[0277] In order to implement this invention, it is necessary for the server and the user's terminal to work together to enable efficient proposal activities and optimize the sales process.

[0278] The server first uses APIs and data feeds to collect business plan data from the target organization and stores the latest data in a database. Next, the server retrieves past proposal activity data and personnel data from its internal database and analyzes it using statistical techniques. Specifically, data analysis is performed using Python and R to reveal patterns and relationships between pieces of information.

[0279] Subsequently, the server uses a generative AI model to generate optimized proposals based on the collected and analyzed data. This AI model takes text-based prompts as input and generates optimal proposal documents in natural language. For example, using the prompt "Automatically generate proposals for organizations planning to expand into new markets. Create the optimal slide structure based on the necessary data and past success stories," the AI ​​can derive specific proposal content.

[0280] Based on this information, the server automatically creates a presentation document containing visualization data and sends it to the user's device. Document generation tools such as the Google Slides API and LaTeX are used to create slides incorporating data and visual elements.

[0281] The user's terminal provides information for progress management and monitoring to the server and tracks the sales process in real time. The server uses this information to propose an optimal action sequence and provide advice on the progress to the user. By taking actions at the optimal timing, the success rate of the proposal increases.

[0282] With this form, the efficiency of the proposal activity and the sales process is greatly improved, and strategic proposals based on data become possible.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The server collects business plan data of the organization to be proposed from an external data source. As input, API and endpoint information of the data feed are used. The server sends an HTTP request to receive response data, parses the JSON-formatted data, and stores it in the database. The output of this step is that the latest business plan data is saved in the database.

[0286] Step 2:

[0287] The server obtains past proposal activity data and staff data from the internal database. As input, SQL queries are used to call specific records required. The server analyzes these data using statistical techniques and extracts patterns and correlations between information. As a result of this process, a report on the analyzed patterns and relationships is output.

[0288] Step 3:

[0289] Based on the collected and analyzed data, the server optimizes the proposed content using a generative AI model. As input, prompt sentences and the analyzed data are used. The server inputs these into the AI model and generates optimized proposed content. The output of this step is the result of natural language generation as a proposal document.

[0290] Step 4:

[0291] The server automatically creates visualization materials based on the optimized proposal. It uses the proposal document and template information as input. Tools such as the Google Slides API and LaTeX are used to create the visualization materials, generating slides that match the proposal. The output of this process is a presentation document containing visual elements.

[0292] Step 5:

[0293] The user's terminal sends progress information about the sales process to the server. The server uses user progress data and feedback as input. The server processes the received data and monitors progress in real time. This process allows the server to advise the user on the next steps and support timely decision-making. The output of this step is actionable guidance and alert notifications for the user.

[0294] (Application Example 1)

[0295] 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."

[0296] In the field of electronic payment services, it is challenging to propose payment solutions that are accurately and quickly optimized to meet the diverse business needs of clients. Furthermore, there is a need to streamline sales activities and strengthen customer relationships. It is necessary to overcome these challenges and maximize the results of commercial activities.

[0297] 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.

[0298] In this invention, the server includes a device for collecting management plan information of the proposed organization, a device for analyzing past proposal activity data and personnel information of the proposed organization, and a device for automatically generating and providing solutions based on the customer's industry data in proposal activities in the payment system domain. This enables the proposal of quick and accurate payment solutions to clients.

[0299] "Management plan information" refers to data related to the goals and strategies set by an organization, as well as the activity plans based on them.

[0300] "Proposal activity data" refers to records of past sales proposals, including information about the content of the proposals and their results.

[0301] "Information on personnel involved in sales activities within your organization" refers to data on the names, positions, and related skills and achievements of the personnel involved in sales activities within your organization.

[0302] "Optimization" is the process of improving proposals and systems based on existing information and conditions to make them function more effectively and efficiently.

[0303] "Presentation materials" refer to documents and slides created to visually represent a proposal and facilitate understanding and empathy among stakeholders.

[0304] "Sales activities" refer to a series of organizational activities and processes carried out with the aim of selling products or services.

[0305] "Monitoring progress" means constantly checking the status of activities or projects and observing whether the objectives are being achieved.

[0306] A "solution" refers to an optimized solution or proposal provided for a specific problem or need.

[0307] "Connection" refers to the mutual relationship and trust between customers and the organization, and includes long-term cooperation and partnerships.

[0308] The system for realizing this invention mainly consists of a server and a user's terminal. The server collects the organization's business plan information, past proposal activity data, and operator information, and stores this information in a MySQL database. For data analysis, Scikit-learn is utilized, and NLTK is used for natural language processing to optimize the proposal content. Based on the optimized proposal content, the Python-PPTX library is used to automatically generate presentation materials.

[0309] The user's terminal is a smartphone or a tablet, which receives the optimized proposal document and presentation materials sent from the server. Through this terminal, the progress of the sales process is sent to the server in real time, and advice on the next action is provided. In the payment system area, the server provides a customized payment solution based on industry data to the user in a push type.

[0310] As a specific example, when a user is requested by a new client for a payment solution for a specific industry, the server analyzes past successful cases specialized in that industry and generates an optimal proposal. The materials generated as a result are immediately available on the user's terminal.

[0311] Examples of prompt texts for the generation AI model are as follows.

[0312] "Please generate materials for proposing an optimal payment solution for a client considering launching a new e-commerce platform. This solution needs to be optimized for the client's business needs."

[0313] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0314] Step 1:

[0315] The server collects management plan information, past proposal activity data, and personnel information of the target organization through APIs and data feeds. It receives management plan and past proposal activity data from external systems as input and stores this data in a MySQL database. Specifically, it performs API calls, parses the retrieved data, and stores it in the appropriate fields in the database.

[0316] Step 2:

[0317] The server uses Scikit-learn and NLTK to analyze the collected data. The input consists of business plan information and proposal activity data stored in the database in Step 1. Machine learning algorithms are applied to generate optimal proposals. Specifically, the process involves preprocessing the data, extracting features, inputting them into an optimization model, and obtaining the output.

[0318] Step 3:

[0319] The server automatically generates presentation materials using the Python-PPTX library based on the output of the optimization algorithm. The input is the optimal suggestion generated in step 2. This is converted into a slide format, visual elements are incorporated, and a completed presentation is created. Specifically, the data is fed into a slide template, and the necessary text and graphics are placed on each slide.

[0320] Step 4:

[0321] The user's device receives presentation materials sent from the server. The input is the generated material sent from the server. The user presents this material to the client and obtains feedback. Specifically, the user can view the downloaded material on the device and add annotations and comments as needed.

[0322] Step 5:

[0323] The server receives data sent from the user's terminal in real time for progress monitoring and provides advice on the next action. It uses user operation logs and client feedback as input. Based on this, it presents a sales strategy. Specifically, it has the function of analyzing log data and displaying appropriate actions on a dashboard.

[0324] Step 6:

[0325] The server uses a generative AI model to generate optimal payment solutions based on specific industry data. It takes prompt text as input to run the AI ​​model and output customized suggestions. Specifically, it passes the prompt text to an internal API, saves the received solution to a database, and sends the result to the user's terminal.

[0326] 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.

[0327] The system according to the present invention comprises a server, a terminal, and an emotion engine that recognizes the user's emotions. The server has the function of collecting management plan information of the proposed organization from various data sources and storing this information in a database. The server also retrieves past proposal activity data and personnel information from the database and performs analysis using machine learning algorithms and natural language processing techniques based on this data.

[0328] The server uses these analysis results to optimize the proposal and automatically generates presentation materials based on that content. The generated materials are structured to meet the needs of the target audience and are sent to the user's device in a visually appealing format.

[0329] Sales activities are monitored in real time by the server, and feedback is provided to the user as needed. This feedback includes advice on the next actions to take and when to take them.

[0330] Furthermore, by incorporating an emotion engine, this system recognizes the user's emotions in real time and dynamically adjusts the information provided by the system according to the sales situation. The emotion engine analyzes the user's voice tone, facial expressions, input text, etc., and evaluates their emotional state.

[0331] For example, if the emotion engine detects that a user is experiencing emotional stress during a presentation, the server dynamically adjusts the presentation, either by concisely summarizing the proposal or quickly providing supporting information. Conversely, if the user is showing positive emotions, additional information or detailed data analysis results are displayed to increase the success rate of the sales pitch.

[0332] By leveraging this emotion engine, users can adjust their approach to suit their situation in real time, improving the efficiency and results of their sales activities. This brings a level of flexibility and personalization not found in traditional sales support systems.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The server collects management plan information for the proposed organization from external data sources and publicly available company documents. This information includes medium- to long-term strategies, important projects, and financial data, and is stored in a database.

[0336] Step 2:

[0337] The server retrieves past proposal activity data and contact person information from an internal database. This includes proposal success rates, contact person feedback, and customer internal communication history.

[0338] Step 3:

[0339] The server utilizes machine learning algorithms and natural language processing technology to analyze the target company's business plan information and historical data. Based on the analyzed information, it generates insights to optimize the proposal.

[0340] Step 4:

[0341] The server automatically generates presentation materials based on the optimized proposal. The slides and data visualizations included are customized to the user's needs and sent to the user's device.

[0342] Step 5:

[0343] The user's device receives presentation materials, reviews their content, and edits them as needed. The user can then prepare the materials for the proposal meeting.

[0344] Step 6:

[0345] The emotion engine extracts emotions in real time from the user's voice, facial expressions, and input text, and evaluates the user's emotional state. The emotional state is recorded according to the situation of the presentation or sales activity.

[0346] Step 7:

[0347] The server dynamically adjusts the presentation and the suggestions it provides based on the evaluation results of the emotion engine. If the user's emotions indicate a stressed state, it simplifies the materials and strengthens the advice for the next action.

[0348] Step 8:

[0349] The user conducts conversations with customers based on the tailored proposals. If the user's sentiment is positive, the server provides more detailed information and success stories to help improve the effectiveness of sales activities.

[0350] (Example 2)

[0351] 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".

[0352] In proposal activities, it is necessary to effectively utilize organizational planning information and historical data to optimize proposal content. Furthermore, while it is important to monitor the progress of sales activities in real time and provide appropriate advice, traditional systems have the challenge of not being able to adequately address individual situations. Additionally, the inability to provide information that takes user emotions into account has made it difficult to maximize sales effectiveness.

[0353] 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.

[0354] In this invention, the server includes means for acquiring planning information of the target organization, means for evaluating past activity data and personnel data, and means for incorporating an emotion recognition engine to determine the user's emotional state and dynamically adjust the information. This makes it possible to optimize proposals by utilizing organizational data, monitor the progress of sales activities, and provide flexible advice and information tailored to the user's emotions.

[0355] "The organization being proposed to" refers to the business entity that receives the proposal for products or services.

[0356] "Planning information" refers to data related to an organization's management policies, strategies, financial plans, etc.

[0357] "Past activity data" refers to records of sales activities and proposals that an organization has conducted in the past.

[0358] "Contact person data" refers to information about individuals involved in sales activities, such as their role, past performance, and skill set.

[0359] An "emotion recognition engine" refers to a technology that analyzes a user's voice, facial expressions, and text input to evaluate their emotional state.

[0360] "Optimization" refers to making adjustments to a proposal to most effectively match the needs of the organization and its customers.

[0361] "Dynamic adjustment" refers to changing the information and methods provided in real time according to the situation.

[0362] "Flexible advice" refers to providing appropriate advice based on the user's current emotional state and business situation.

[0363] "Information provision" refers to the act of presenting data or knowledge that is beneficial to the user or customer.

[0364] This invention is implemented by a system including a server, a terminal, and an emotion recognition engine. The server retrieves planning information for the proposed organization and uses a dedicated database to evaluate past activity data and personnel data. Internet connectivity and API access are often used for data retrieval.

[0365] The server analyzes this data using machine learning algorithms and natural language processing technology, and optimizes the proposal content using generative AI models. This automatically generates presentation materials that are tailored to the needs of the target audience and industry trends. The generated materials are sent to the terminal in common formats such as PowerPoint and PDF, making them easily accessible to the user.

[0366] The terminal is equipped with an emotion recognition engine that analyzes the user's voice, facial expressions, and entered text in real time. This determines the user's emotional state, and the server dynamically adjusts the information to support the user's decision-making in sales activities. For example, if the user is showing signs of stress, the server provides information that concisely summarizes the proposal.

[0367] For example, if a user enters the prompt "What should I do next?" during a proposal, appropriate advice and information based on their emotional state will be immediately provided. This improves the success rate of proposal activities, allowing users to conduct sales activities more effectively and efficiently.

[0368] In this way, this invention realizes flexible and personalized sales support tailored to the user's situation.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The server retrieves planning information about the proposed organization from external and internal data sources. Inputs include API calls and database queries, and this information is compiled into data that reflects the organization's current state and future goals. The output is a dataset summarizing this retrieved information.

[0372] Step 2:

[0373] The server retrieves past activity data and employee data from the database. Past sales records and employee history information, which serve as explanatory variables, are used as input, and the server filters the data based on this information. The output is historical data organized for use in analysis.

[0374] Step 3:

[0375] The server begins analyzing acquired planning information and historical activity data using machine learning algorithms and natural language processing techniques. The input is the dataset and historical data acquired in the previous step, and the output is an optimized proposal based on the analysis results. Specifically, a generative AI model performs selection and weighting to determine the appropriate approach.

[0376] Step 4:

[0377] The server automatically generates presentation materials based on the optimized proposal. The input is the optimized proposal obtained through analysis, and the server then performs specific actions such as combining templates and slide formats based on this. The output is a visually organized presentation tailored to the target organization.

[0378] Step 5:

[0379] The generated presentation materials are delivered to the terminal. The user receives these materials and uses them in their proposal activities. The concrete action is that the materials are displayed on the terminal, making them immediately ready for the user to use in their proposal.

[0380] Step 6:

[0381] The server monitors the progress of the user's sales activities in real time. Input information includes user logs and feedback data, while output is advice for the next steps, generated based on the monitoring.

[0382] Step 7:

[0383] On the device, an emotion recognition engine analyzes the user's voice, facial expressions, and text input in real time. The input is the user's voice and text information, and the output is the user's emotional state based on these analysis results. Specifically, the system analyzes the user's state and optimizes its actions accordingly.

[0384] Step 8:

[0385] The server dynamically adjusts information based on the user's emotional state, providing flexible advice and additional data. The input is the output of the emotion recognition engine, and the output is specific advice and information for the user. This functionality enables users to respond appropriately to different situations.

[0386] (Application Example 2)

[0387] 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 as the "terminal".

[0388] The problem that this invention aims to solve is to prevent the loss of sales opportunities due to insufficient communication with customers and inefficient proposals during sales activities. Furthermore, there is a need to improve the success rate and efficiency of sales activities by accurately understanding the emotional state of customers and optimizing sales activities in real time.

[0389] 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.

[0390] In this invention, the server includes means for collecting management strategy information of the target organization, means for analyzing past proposal activity information and personnel information of the target organization, and means for optimizing proposal content based on the collected and analyzed information. This enables dynamic adjustment of sales content according to the customer's emotional state and automatic generation of optimal proposals.

[0391] "Management strategy information of the proposed organization" refers to information related to the long-term and short-term business policies and activity plans set by a specific organization.

[0392] "Past proposal activity information" refers to records including past sales activities and negotiation history with the target organization.

[0393] "Contact person information" refers to data regarding the career history and performance of sales representatives involved in proposal activities.

[0394] An "emotion analysis engine" is a component of a system that analyzes the user's voice tone, facial expressions, input text, etc., to evaluate their emotional state.

[0395] "Evaluating in real time and dynamically adjusting presentation content" means instantly reading the user's emotional state and flexibly changing the proposed content based on that information.

[0396] "Monitoring the progress of sales activities and providing guidance on the procedures to be followed" means tracking the progress of sales and giving instructions on the next steps and timing of actions to take.

[0397] "Identifying relevant resources within the organization and presenting the most suitable personnel and equipment for proposal activities" means using past sales data to identify and present the internal human resources and technologies that will be effective for a particular proposal.

[0398] "Customer contact history and relationship information" refers to information that shows the past interactions between customers and the organization and the depth of those relationships.

[0399] This invention is a system for optimizing sales activities to target organizations. The system comprises a server, terminals, and an emotion analysis engine.

[0400] The server first collects management strategy information of the target organization from various data sources. This information is stored in a database and analyzed along with past proposal activity information and contact person information. TensorFlow is used to implement machine learning algorithms for the analysis, and NLTK is used for natural language processing. Based on the analysis results, the proposal content is optimized and presentation information is automatically generated. This generated information is provided to the sales representative's smartphone or other device in a visually appealing format. On the device side, image processing using OpenCV and audio data acquisition using a microphone are performed to capture the user's voice tone and facial expression data.

[0401] The emotion analysis engine acquires the user's visual and auditory information in real time and evaluates their emotional state based on that data. Based on the evaluated emotion data, the server dynamically adjusts the presentation and advises on actions. This enables flexible responses that increase the success rate of sales activities.

[0402] As a concrete example, if a customer shows signs of anxiety while a sales representative is giving a presentation on a new product, the emotion analysis engine evaluates that emotion. Based on this information, the server immediately provides information emphasizing the product's security advantages. This helps to alleviate the customer's anxiety and enhance the effectiveness of the proposal.

[0403] An example of a prompt for a generative AI model is, "If a customer hears information and becomes suspicious, what data should be presented to regain their trust?"

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The server collects management strategy information for the proposed organization from multiple data sources. Inputs are publicly available management information and internal databases, while output is integrated management strategy information. This information is stored in a database for subsequent analysis.

[0407] Step 2:

[0408] The server analyzes collected business strategy information, past proposal activity information, and personnel information. The input is integrated business strategy information and historical sales data, and the output is the optimized proposal content based on the analysis. TensorFlow is used to perform data analysis with machine learning models to derive effective proposals.

[0409] Step 3:

[0410] The server automatically generates presentation information based on the optimized proposal. The input is the optimized result, and the output is structured presentation material. The presentation material is generated using natural language processing technology with NLTK and sent to the user's terminal.

[0411] Step 4:

[0412] The device uses an emotion analysis engine to collect user voice tone and facial expression data in real time. Input is voice and video data acquired from the user, and output is the user's emotional state after analysis. Data is captured using a camera and microphone, and image processing is performed using OpenCV.

[0413] Step 5:

[0414] The server dynamically adjusts the presentation based on the user's emotional state, as determined by an emotion analysis engine. The input is the analyzed emotional state, and the output is the adjusted presentation content. The server uses an AI model to determine the next action and provide the customer with the necessary information in a timely manner.

[0415] Step 6:

[0416] The server monitors the progress of sales activities in real time and advises on the next course of action. Inputs are the status of sales activities and user feedback, while output is the recommended next action. This allows sales representatives to take appropriate steps on the spot, increasing their sales success rate.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] The system according to the present invention consists of a server and a user terminal. The server collects management plan information of the proposed organization from various data sources and stores it in a database. This data is collected through APIs and data feeds. The server also retrieves past proposal activity data and personnel information from an internal database and analyzes the overall trends of proposal activities. This analysis uses machine learning models and natural language processing techniques to identify relationships and patterns in the information.

[0434] The server generates optimized proposals based on the collected information. This ensures that the proposal accurately meets the needs of the target organization. Based on these optimized proposals, the server uses an algorithm to automatically generate presentation materials. These materials, incorporating the necessary data and visual elements for the proposal, are then sent to the user's device.

[0435] To monitor the progress of the sales process in real time, the server utilizes input information from user terminals and data collected through integration with external systems. Based on the progress monitoring results, the server provides the user with advice on the next course of action. This advice is based on the most effective methods predicted from the analyzed data.

[0436] Furthermore, the server extracts relevant internal resources based on past sales activity data. This allows the user to see how the suggested personnel and tools can be effective in current proposal activities. Optimizing internal resources improves sales efficiency and enables more effective proposal activities.

[0437] Furthermore, the server continuously analyzes customer contact history and relationship data to provide strategies for strengthening customer-organizational engagement. These strategies are optimized based on customer behavior patterns and needs, suggesting effective timing and methods for relationship building.

[0438] For example, if the server retrieves the target organization's annual plan and that plan includes expansion into new markets, a proposal is automatically generated based on detailed data and past success stories related to those markets. The user's terminal displays the optimal slide structure for the presentation, allowing for efficient preparation for the proposal meeting.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The server collects management plan information for the proposed organization from publicly available company information, internal databases, and API connections. The collected information is stored in a database to prepare for subsequent analysis.

[0442] Step 2:

[0443] The server retrieves past proposal activity data and contact person information from a database. Using this data, it analyzes success patterns and organization-specific trends using natural language processing and machine learning algorithms.

[0444] Step 3:

[0445] The server creates an optimized proposal tailored to the needs of the target organization, based on the collected business plan information and analysis results. This proposal includes data points selected by an algorithm and a proposal strategy.

[0446] Step 4:

[0447] The server automatically generates presentation materials based on optimized proposals. The generated materials are structured in a visually appealing format and prepared for user use.

[0448] Step 5:

[0449] The user's device receives presentation materials provided by the server, reviews them, and edits them. Users can customize the materials based on customer requests and prior feedback.

[0450] Step 6:

[0451] The server monitors the progress of users' sales activities in real time. This is done through user input and integration with sales management systems.

[0452] Step 7:

[0453] The server advises the user on the next steps based on sales progress. If a specific action is required, it notifies the user's terminal with a suggestion including the timing and method of that action.

[0454] Step 8:

[0455] The server references the company's past sales data and extracts personnel and resources relevant to the current project. It then recommends that users utilize these resources to support efficient sales activities.

[0456] Step 9:

[0457] The server analyzes customer contact history and relationship data to provide strategies for strengthening customer engagement. Users can then use these strategies to take actions that deepen their relationships with customers.

[0458] (Example 1)

[0459] 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."

[0460] In today's highly competitive business environment, effectively utilizing business plans and proposal histories to build deep engagement with customers is essential for improving the efficiency of proposal activities. However, processing and visualizing vast amounts of data in a timely and appropriate manner is challenging, and advanced technology is required to grasp progress in real time and propose effective strategies. Therefore, there is a need to develop new systems that efficiently optimize proposal activities and improve the sales process.

[0461] 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.

[0462] In this invention, the server includes means for collecting business plan data of the proposed organization using an information processing device, means for analyzing past proposal activity data and personnel data of the proposed organization using statistical techniques, and means for using a generative AI model to optimize the proposal content based on the collected and analyzed data. This enables the efficient analysis and optimization of vast amounts of data, thereby improving the quality of proposals and enabling strategic proposals to deepen relationships with customers.

[0463] "Information processing equipment" refers to all computing devices used for data collection, analysis, and optimization.

[0464] "Business plan data" refers to information that describes the management policies and strategies that the proposed organization aims to achieve in the future.

[0465] "Proposal activity data" refers to information that includes records of past proposal processes and their results.

[0466] "Contact person data" refers to information about individuals involved in proposal activities, such as their job title and assigned duties.

[0467] "Statistical techniques" refer to mathematical methods and algorithms used to understand the characteristics of data and find patterns.

[0468] A "generative AI model" refers to an artificial intelligence system that generates responses in natural language from input data.

[0469] "Visualized data" refers to a format that uses graphics and charts to present information in a way that makes it easier to understand.

[0470] "Status monitoring" refers to the act of tracking and evaluating the progress or status of a process in real time.

[0471] "Action sequence" refers to a plan of steps or actions to be taken in order to achieve a specific objective.

[0472] "Resources" refers to all elements necessary for carrying out business operations, including personnel, equipment, and information.

[0473] "Communication history" refers to the record of all communications and interactions that took place with the customer.

[0474] "Relational data" refers to information about the connections and interactions between individual entities.

[0475] In order to implement this invention, it is necessary for the server and the user's terminal to work together to enable efficient proposal activities and optimize the sales process.

[0476] The server first uses APIs and data feeds to collect business plan data from the target organization and stores the latest data in a database. Next, the server retrieves past proposal activity data and personnel data from its internal database and analyzes it using statistical techniques. Specifically, data analysis is performed using Python and R to reveal patterns and relationships between pieces of information.

[0477] Subsequently, the server uses a generative AI model to generate optimized proposals based on the collected and analyzed data. This AI model takes text-based prompts as input and generates optimal proposal documents in natural language. For example, using the prompt "Automatically generate proposals for organizations planning to expand into new markets. Create the optimal slide structure based on the necessary data and past success stories," the AI ​​can derive specific proposal content.

[0478] Based on this information, the server automatically creates a presentation document containing visualization data and sends it to the user's device. Document generation tools such as the Google Slides API and LaTeX are used to create slides incorporating data and visual elements.

[0479] The user's device provides information to the server for progress management and monitoring, tracking the sales process in real time. The server uses this information to suggest the optimal sequence of actions and provide progress advice to the user. Taking the necessary actions at the optimal time increases the success rate of the proposal.

[0480] This format significantly improves the efficiency of proposal activities and sales processes, enabling data-driven, strategic proposals.

[0481] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0482] Step 1:

[0483] The server collects business plan data for the proposed organization from external data sources. It uses API and data feed endpoint information as input. The server sends HTTP requests, receives response data, parses the data in JSON format, and stores it in the database. The output of this step is that the latest business plan data is saved in the database.

[0484] Step 2:

[0485] The server retrieves past proposal activity data and assignee data from its internal database. It uses SQL queries to retrieve specific records as input. The server analyzes this data using statistical techniques to extract patterns and relationships between the information. As a result of this process, a report of the analyzed patterns and relationships is output.

[0486] Step 3:

[0487] The server optimizes the proposal content using a generative AI model based on the collected and analyzed data. The input consists of prompt sentences and analyzed data. The server inputs these into the AI ​​model and generates optimized proposal content. The output of this step is the result of natural language generation as a proposal document.

[0488] Step 4:

[0489] The server automatically creates visualization materials based on the optimized proposal. It uses the proposal document and template information as input. Tools such as the Google Slides API and LaTeX are used to create the visualization materials, generating slides that match the proposal. The output of this process is a presentation document containing visual elements.

[0490] Step 5:

[0491] The user's terminal sends progress information about the sales process to the server. The server uses user progress data and feedback as input. The server processes the received data and monitors progress in real time. This process allows the server to advise the user on the next steps and support timely decision-making. The output of this step is actionable guidance and alert notifications for the user.

[0492] (Application Example 1)

[0493] 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."

[0494] In the field of electronic payment services, it is challenging to propose payment solutions that are accurately and quickly optimized to meet the diverse business needs of clients. Furthermore, there is a need to streamline sales activities and strengthen customer relationships. It is necessary to overcome these challenges and maximize the results of commercial activities.

[0495] 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.

[0496] In this invention, the server includes a device for collecting management plan information of the proposed organization, a device for analyzing past proposal activity data and personnel information of the proposed organization, and a device for automatically generating and providing solutions based on the customer's industry data in proposal activities in the payment system domain. This enables the proposal of quick and accurate payment solutions to clients.

[0497] "Management plan information" refers to data related to the goals and strategies set by an organization, as well as the activity plans based on them.

[0498] "Proposal activity data" refers to records of past sales proposals, including information about the content of the proposals and their results.

[0499] "Information on personnel involved in sales activities within your organization" refers to data on the names, positions, and related skills and achievements of the personnel involved in sales activities within your organization.

[0500] "Optimization" is the process of improving proposals and systems based on existing information and conditions to make them function more effectively and efficiently.

[0501] "Presentation materials" refer to documents and slides created to visually represent a proposal and facilitate understanding and empathy among stakeholders.

[0502] "Sales activities" refer to a series of organizational activities and processes carried out with the aim of selling products or services.

[0503] "Monitoring progress" means constantly checking the status of activities or projects and observing whether the objectives are being achieved.

[0504] A "solution" refers to an optimized solution or proposal provided for a specific problem or need.

[0505] "Connection" refers to the mutual relationship and trust between customers and the organization, and includes long-term cooperation and partnerships.

[0506] The system for realizing this invention mainly consists of a server and user terminals. The server collects organizational management plan information, past proposal activity data, and information on personnel in charge of operations, and stores this information in a MySQL database. For data analysis, Scikit-learn and NLTK are used for natural language processing to optimize the proposal content. Based on the optimized proposal content, presentation materials are automatically generated using the Python-PPTX library.

[0507] The user's device is a smartphone or tablet, which receives optimized proposals and presentation materials sent from the server. Through this device, the progress of the sales process is transmitted to the server in real time, and advice on the next action is provided. In the payment system domain, the server provides users with customized payment solutions based on industry data via a push notification system.

[0508] For example, if a user receives a request from a new client for a payment solution specific to a particular industry, the server analyzes past success stories specific to that industry and generates an optimal proposal. The resulting document becomes immediately available on the user's device.

[0509] Examples of prompts for a generative AI model include the following:

[0510] "Please generate documentation to propose the optimal payment solution to a client who is considering launching a new e-commerce platform. This solution must be optimized for the client's business needs."

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The server collects management plan information, past proposal activity data, and personnel information of the target organization through APIs and data feeds. It receives management plan and past proposal activity data from external systems as input and stores this data in a MySQL database. Specifically, it performs API calls, parses the retrieved data, and stores it in the appropriate fields in the database.

[0514] Step 2:

[0515] The server uses Scikit-learn and NLTK to analyze the collected data. The input consists of business plan information and proposal activity data stored in the database in Step 1. Machine learning algorithms are applied to generate optimal proposals. Specifically, the process involves preprocessing the data, extracting features, inputting them into an optimization model, and obtaining the output.

[0516] Step 3:

[0517] The server automatically generates presentation materials using the Python-PPTX library based on the output of the optimization algorithm. The input is the optimal suggestion generated in step 2. This is converted into a slide format, visual elements are incorporated, and a completed presentation is created. Specifically, the data is fed into a slide template, and the necessary text and graphics are placed on each slide.

[0518] Step 4:

[0519] The user's device receives presentation materials sent from the server. The input is the generated material sent from the server. The user presents this material to the client and obtains feedback. Specifically, the user can view the downloaded material on the device and add annotations and comments as needed.

[0520] Step 5:

[0521] The server receives data sent from the user's terminal in real time for progress monitoring and provides advice on the next action. It uses user operation logs and client feedback as input. Based on this, it presents a sales strategy. Specifically, it has the function of analyzing log data and displaying appropriate actions on a dashboard.

[0522] Step 6:

[0523] The server uses a generative AI model to generate optimal payment solutions based on specific industry data. It takes prompt text as input to run the AI ​​model and output customized suggestions. Specifically, it passes the prompt text to an internal API, saves the received solution to a database, and sends the result to the user's terminal.

[0524] 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.

[0525] The system according to the present invention comprises a server, a terminal, and an emotion engine that recognizes the user's emotions. The server has the function of collecting management plan information of the proposed organization from various data sources and storing this information in a database. The server also retrieves past proposal activity data and personnel information from the database and performs analysis using machine learning algorithms and natural language processing techniques based on this data.

[0526] The server uses these analysis results to optimize the proposal and automatically generates presentation materials based on that content. The generated materials are structured to meet the needs of the target audience and are sent to the user's device in a visually appealing format.

[0527] Sales activities are monitored in real time by the server, and feedback is provided to the user as needed. This feedback includes advice on the next actions to take and when to take them.

[0528] Furthermore, by incorporating an emotion engine, this system recognizes the user's emotions in real time and dynamically adjusts the information provided by the system according to the sales situation. The emotion engine analyzes the user's voice tone, facial expressions, input text, etc., and evaluates their emotional state.

[0529] For example, if the emotion engine detects that a user is experiencing emotional stress during a presentation, the server dynamically adjusts the presentation, either by concisely summarizing the proposal or quickly providing supporting information. Conversely, if the user is showing positive emotions, additional information or detailed data analysis results are displayed to increase the success rate of the sales pitch.

[0530] By leveraging this emotion engine, users can adjust their approach to suit their situation in real time, improving the efficiency and results of their sales activities. This brings a level of flexibility and personalization not found in traditional sales support systems.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The server collects management plan information for the proposed organization from external data sources and publicly available company documents. This information includes medium- to long-term strategies, important projects, and financial data, and is stored in a database.

[0534] Step 2:

[0535] The server retrieves past proposal activity data and contact person information from an internal database. This includes proposal success rates, contact person feedback, and customer internal communication history.

[0536] Step 3:

[0537] The server utilizes machine learning algorithms and natural language processing technology to analyze the target company's business plan information and historical data. Based on the analyzed information, it generates insights to optimize the proposal.

[0538] Step 4:

[0539] The server automatically generates presentation materials based on the optimized proposal. The slides and data visualizations included are customized to the user's needs and sent to the user's device.

[0540] Step 5:

[0541] The user's device receives presentation materials, reviews their content, and edits them as needed. The user can then prepare the materials for the proposal meeting.

[0542] Step 6:

[0543] The emotion engine extracts emotions in real time from the user's voice, facial expressions, and input text, and evaluates the user's emotional state. The emotional state is recorded according to the situation of the presentation or sales activity.

[0544] Step 7:

[0545] The server dynamically adjusts the presentation and the suggestions it provides based on the evaluation results of the emotion engine. If the user's emotions indicate a stressed state, it simplifies the materials and strengthens the advice for the next action.

[0546] Step 8:

[0547] The user conducts conversations with customers based on the tailored proposals. If the user's sentiment is positive, the server provides more detailed information and success stories to help improve the effectiveness of sales activities.

[0548] (Example 2)

[0549] 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."

[0550] In proposal activities, it is necessary to effectively utilize organizational planning information and historical data to optimize proposal content. Furthermore, while it is important to monitor the progress of sales activities in real time and provide appropriate advice, traditional systems have the challenge of not being able to adequately address individual situations. Additionally, the inability to provide information that takes user emotions into account has made it difficult to maximize sales effectiveness.

[0551] 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.

[0552] In this invention, the server includes means for acquiring planning information of the target organization, means for evaluating past activity data and personnel data, and means for incorporating an emotion recognition engine to determine the user's emotional state and dynamically adjust the information. This makes it possible to optimize proposals by utilizing organizational data, monitor the progress of sales activities, and provide flexible advice and information tailored to the user's emotions.

[0553] "The organization being proposed to" refers to the business entity that receives the proposal for products or services.

[0554] "Planning information" refers to data related to an organization's management policies, strategies, financial plans, etc.

[0555] "Past activity data" refers to records of sales activities and proposals that an organization has conducted in the past.

[0556] "Contact person data" refers to information about individuals involved in sales activities, such as their role, past performance, and skill set.

[0557] An "emotion recognition engine" refers to a technology that analyzes a user's voice, facial expressions, and text input to evaluate their emotional state.

[0558] "Optimization" refers to making adjustments to a proposal to most effectively match the needs of the organization and its customers.

[0559] "Dynamic adjustment" refers to changing the information and methods provided in real time according to the situation.

[0560] "Flexible advice" refers to providing appropriate advice based on the user's current emotional state and business situation.

[0561] "Information provision" refers to the act of presenting data or knowledge that is beneficial to the user or customer.

[0562] This invention is implemented by a system including a server, a terminal, and an emotion recognition engine. The server retrieves planning information for the proposed organization and uses a dedicated database to evaluate past activity data and personnel data. Internet connectivity and API access are often used for data retrieval.

[0563] The server analyzes this data using machine learning algorithms and natural language processing technology, and optimizes the proposal content using generative AI models. This automatically generates presentation materials that are tailored to the needs of the target audience and industry trends. The generated materials are sent to the terminal in common formats such as PowerPoint and PDF, making them easily accessible to the user.

[0564] The terminal is equipped with an emotion recognition engine that analyzes the user's voice, facial expressions, and entered text in real time. This determines the user's emotional state, and the server dynamically adjusts the information to support the user's decision-making in sales activities. For example, if the user is showing signs of stress, the server provides information that concisely summarizes the proposal.

[0565] For example, if a user enters the prompt "What should I do next?" during a proposal, appropriate advice and information based on their emotional state will be immediately provided. This improves the success rate of proposal activities, allowing users to conduct sales activities more effectively and efficiently.

[0566] In this way, this invention realizes flexible and personalized sales support tailored to the user's situation.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The server retrieves planning information about the proposed organization from external and internal data sources. Inputs include API calls and database queries, and this information is compiled into data that reflects the organization's current state and future goals. The output is a dataset summarizing this retrieved information.

[0570] Step 2:

[0571] The server retrieves past activity data and employee data from the database. Past sales records and employee history information, which serve as explanatory variables, are used as input, and the server filters the data based on this information. The output is historical data organized for use in analysis.

[0572] Step 3:

[0573] The server begins analyzing acquired planning information and historical activity data using machine learning algorithms and natural language processing techniques. The input is the dataset and historical data acquired in the previous step, and the output is an optimized proposal based on the analysis results. Specifically, a generative AI model performs selection and weighting to determine the appropriate approach.

[0574] Step 4:

[0575] The server automatically generates presentation materials based on the optimized proposal. The input is the optimized proposal obtained through analysis, and the server then performs specific actions such as combining templates and slide formats based on this. The output is a visually organized presentation tailored to the target organization.

[0576] Step 5:

[0577] The generated presentation materials are delivered to the terminal. The user receives these materials and uses them in their proposal activities. The concrete action is that the materials are displayed on the terminal, making them immediately ready for the user to use in their proposal.

[0578] Step 6:

[0579] The server monitors the progress of the user's sales activities in real time. Input information includes user logs and feedback data, while output is advice for the next steps, generated based on the monitoring.

[0580] Step 7:

[0581] On the device, an emotion recognition engine analyzes the user's voice, facial expressions, and text input in real time. The input is the user's voice and text information, and the output is the user's emotional state based on these analysis results. Specifically, the system analyzes the user's state and optimizes its actions accordingly.

[0582] Step 8:

[0583] The server dynamically adjusts information based on the user's emotional state, providing flexible advice and additional data. The input is the output of the emotion recognition engine, and the output is specific advice and information for the user. This functionality enables users to respond appropriately to different situations.

[0584] (Application Example 2)

[0585] 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."

[0586] The problem that this invention aims to solve is to prevent the loss of sales opportunities due to insufficient communication with customers and inefficient proposals during sales activities. Furthermore, there is a need to improve the success rate and efficiency of sales activities by accurately understanding the emotional state of customers and optimizing sales activities in real time.

[0587] 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.

[0588] In this invention, the server includes means for collecting management strategy information of the target organization, means for analyzing past proposal activity information and personnel information of the target organization, and means for optimizing proposal content based on the collected and analyzed information. This enables dynamic adjustment of sales content according to the customer's emotional state and automatic generation of optimal proposals.

[0589] "Management strategy information of the proposed organization" refers to information related to the long-term and short-term business policies and activity plans set by a specific organization.

[0590] "Past proposal activity information" refers to records including past sales activities and negotiation history with the target organization.

[0591] "Contact person information" refers to data regarding the career history and performance of sales representatives involved in proposal activities.

[0592] An "emotion analysis engine" is a component of a system that analyzes the user's voice tone, facial expressions, input text, etc., to evaluate their emotional state.

[0593] "Evaluating in real time and dynamically adjusting presentation content" means instantly reading the user's emotional state and flexibly changing the proposed content based on that information.

[0594] "Monitoring the progress of sales activities and providing guidance on the procedures to be followed" means tracking the progress of sales and giving instructions on the next steps and timing of actions to take.

[0595] "Identifying relevant resources within the organization and presenting the most suitable personnel and equipment for proposal activities" means using past sales data to identify and present the internal human resources and technologies that will be effective for a particular proposal.

[0596] "Customer contact history and relationship information" refers to information that shows the past interactions between customers and the organization and the depth of those relationships.

[0597] This invention is a system for optimizing sales activities to target organizations. The system comprises a server, terminals, and an emotion analysis engine.

[0598] The server first collects management strategy information of the target organization from various data sources. This information is stored in a database and analyzed along with past proposal activity information and contact person information. TensorFlow is used to implement machine learning algorithms for the analysis, and NLTK is used for natural language processing. Based on the analysis results, the proposal content is optimized and presentation information is automatically generated. This generated information is provided to the sales representative's smartphone or other device in a visually appealing format. On the device side, image processing using OpenCV and audio data acquisition using a microphone are performed to capture the user's voice tone and facial expression data.

[0599] The emotion analysis engine acquires the user's visual and auditory information in real time and evaluates their emotional state based on that data. Based on the evaluated emotion data, the server dynamically adjusts the presentation and advises on actions. This enables flexible responses that increase the success rate of sales activities.

[0600] As a concrete example, if a customer shows signs of anxiety while a sales representative is giving a presentation on a new product, the emotion analysis engine evaluates that emotion. Based on this information, the server immediately provides information emphasizing the product's security advantages. This helps to alleviate the customer's anxiety and enhance the effectiveness of the proposal.

[0601] An example of a prompt for a generative AI model is, "If a customer hears information and becomes suspicious, what data should be presented to regain their trust?"

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The server collects management strategy information for the proposed organization from multiple data sources. Inputs are publicly available management information and internal databases, while output is integrated management strategy information. This information is stored in a database for subsequent analysis.

[0605] Step 2:

[0606] The server analyzes collected business strategy information, past proposal activity information, and personnel information. The input is integrated business strategy information and historical sales data, and the output is the optimized proposal content based on the analysis. TensorFlow is used to perform data analysis with machine learning models to derive effective proposals.

[0607] Step 3:

[0608] The server automatically generates presentation information based on the optimized proposal. The input is the optimized result, and the output is structured presentation material. The presentation material is generated using natural language processing technology with NLTK and sent to the user's terminal.

[0609] Step 4:

[0610] The device uses an emotion analysis engine to collect user voice tone and facial expression data in real time. Input is voice and video data acquired from the user, and output is the user's emotional state after analysis. Data is captured using a camera and microphone, and image processing is performed using OpenCV.

[0611] Step 5:

[0612] The server dynamically adjusts the presentation based on the user's emotional state, as determined by an emotion analysis engine. The input is the analyzed emotional state, and the output is the adjusted presentation content. The server uses an AI model to determine the next action and provide the customer with the necessary information in a timely manner.

[0613] Step 6:

[0614] The server monitors the progress of sales activities in real time and advises on the next course of action. Inputs are the status of sales activities and user feedback, while output is the recommended next action. This allows sales representatives to take appropriate steps on the spot, increasing their sales success rate.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] The system according to the present invention consists of a server and a user terminal. The server collects management plan information of the proposed organization from various data sources and stores it in a database. This data is collected through APIs and data feeds. The server also retrieves past proposal activity data and personnel information from an internal database and analyzes the overall trends of proposal activities. This analysis uses machine learning models and natural language processing techniques to identify relationships and patterns in the information.

[0633] The server generates optimized proposals based on the collected information. This ensures that the proposal accurately meets the needs of the target organization. Based on these optimized proposals, the server uses an algorithm to automatically generate presentation materials. These materials, incorporating the necessary data and visual elements for the proposal, are then sent to the user's device.

[0634] To monitor the progress of the sales process in real time, the server utilizes input information from user terminals and data collected through integration with external systems. Based on the progress monitoring results, the server provides the user with advice on the next course of action. This advice is based on the most effective methods predicted from the analyzed data.

[0635] Furthermore, the server extracts relevant internal resources based on past sales activity data. This allows the user to see how the suggested personnel and tools can be effective in current proposal activities. Optimizing internal resources improves sales efficiency and enables more effective proposal activities.

[0636] Furthermore, the server continuously analyzes customer contact history and relationship data to provide strategies for strengthening customer-organizational engagement. These strategies are optimized based on customer behavior patterns and needs, suggesting effective timing and methods for relationship building.

[0637] For example, if the server retrieves the target organization's annual plan and that plan includes expansion into new markets, a proposal is automatically generated based on detailed data and past success stories related to those markets. The user's terminal displays the optimal slide structure for the presentation, allowing for efficient preparation for the proposal meeting.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The server collects management plan information for the proposed organization from publicly available company information, internal databases, and API connections. The collected information is stored in a database to prepare for subsequent analysis.

[0641] Step 2:

[0642] The server retrieves past proposal activity data and contact person information from a database. Using this data, it analyzes success patterns and organization-specific trends using natural language processing and machine learning algorithms.

[0643] Step 3:

[0644] The server creates an optimized proposal tailored to the needs of the target organization, based on the collected business plan information and analysis results. This proposal includes data points selected by an algorithm and a proposal strategy.

[0645] Step 4:

[0646] The server automatically generates presentation materials based on optimized proposals. The generated materials are structured in a visually appealing format and prepared for user use.

[0647] Step 5:

[0648] The user's device receives presentation materials provided by the server, reviews them, and edits them. Users can customize the materials based on customer requests and prior feedback.

[0649] Step 6:

[0650] The server monitors the progress of users' sales activities in real time. This is done through user input and integration with sales management systems.

[0651] Step 7:

[0652] The server advises the user on the next steps based on sales progress. If a specific action is required, it notifies the user's terminal with a suggestion including the timing and method of that action.

[0653] Step 8:

[0654] The server references the company's past sales data and extracts personnel and resources relevant to the current project. It then recommends that users utilize these resources to support efficient sales activities.

[0655] Step 9:

[0656] The server analyzes customer contact history and relationship data to provide strategies for strengthening customer engagement. Users can then use these strategies to take actions that deepen their relationships with customers.

[0657] (Example 1)

[0658] 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".

[0659] In today's highly competitive business environment, effectively utilizing business plans and proposal histories to build deep engagement with customers is essential for improving the efficiency of proposal activities. However, processing and visualizing vast amounts of data in a timely and appropriate manner is challenging, and advanced technology is required to grasp progress in real time and propose effective strategies. Therefore, there is a need to develop new systems that efficiently optimize proposal activities and improve the sales process.

[0660] 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.

[0661] In this invention, the server includes means for collecting business plan data of the proposed organization using an information processing device, means for analyzing past proposal activity data and personnel data of the proposed organization using statistical techniques, and means for using a generative AI model to optimize the proposal content based on the collected and analyzed data. This enables the efficient analysis and optimization of vast amounts of data, thereby improving the quality of proposals and enabling strategic proposals to deepen relationships with customers.

[0662] "Information processing equipment" refers to all computing devices used for data collection, analysis, and optimization.

[0663] "Business plan data" refers to information that describes the management policies and strategies that the proposed organization aims to achieve in the future.

[0664] "Proposal activity data" refers to information that includes records of past proposal processes and their results.

[0665] "Contact person data" refers to information about individuals involved in proposal activities, such as their job title and assigned duties.

[0666] "Statistical techniques" refer to mathematical methods and algorithms used to understand the characteristics of data and find patterns.

[0667] A "generative AI model" refers to an artificial intelligence system that generates responses in natural language from input data.

[0668] "Visualized data" refers to a format that uses graphics and charts to present information in a way that makes it easier to understand.

[0669] "Status monitoring" refers to the act of tracking and evaluating the progress or status of a process in real time.

[0670] "Action sequence" refers to a plan of steps or actions to be taken in order to achieve a specific objective.

[0671] "Resources" refers to all elements necessary for carrying out business operations, including personnel, equipment, and information.

[0672] "Communication history" refers to the record of all communications and interactions that took place with the customer.

[0673] "Relational data" refers to information about the connections and interactions between individual entities.

[0674] In order to implement this invention, it is necessary for the server and the user's terminal to work together to enable efficient proposal activities and optimize the sales process.

[0675] The server first uses APIs and data feeds to collect business plan data from the target organization and stores the latest data in a database. Next, the server retrieves past proposal activity data and personnel data from its internal database and analyzes it using statistical techniques. Specifically, data analysis is performed using Python and R to reveal patterns and relationships between pieces of information.

[0676] Subsequently, the server uses a generative AI model to generate optimized proposals based on the collected and analyzed data. This AI model takes text-based prompts as input and generates optimal proposal documents in natural language. For example, using the prompt "Automatically generate proposals for organizations planning to expand into new markets. Create the optimal slide structure based on the necessary data and past success stories," the AI ​​can derive specific proposal content.

[0677] Based on this information, the server automatically creates a presentation document containing visualization data and sends it to the user's device. Document generation tools such as the Google Slides API and LaTeX are used to create slides incorporating data and visual elements.

[0678] The user's device provides information to the server for progress management and monitoring, tracking the sales process in real time. The server uses this information to suggest the optimal sequence of actions and provide progress advice to the user. Taking the necessary actions at the optimal time increases the success rate of the proposal.

[0679] This format significantly improves the efficiency of proposal activities and sales processes, enabling data-driven, strategic proposals.

[0680] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0681] Step 1:

[0682] The server collects business plan data for the proposed organization from external data sources. It uses API and data feed endpoint information as input. The server sends HTTP requests, receives response data, parses the data in JSON format, and stores it in the database. The output of this step is that the latest business plan data is saved in the database.

[0683] Step 2:

[0684] The server retrieves past proposal activity data and assignee data from its internal database. It uses SQL queries to retrieve specific records as input. The server analyzes this data using statistical techniques to extract patterns and relationships between the information. As a result of this process, a report of the analyzed patterns and relationships is output.

[0685] Step 3:

[0686] The server optimizes the proposal content using a generative AI model based on the collected and analyzed data. The input consists of prompt sentences and analyzed data. The server inputs these into the AI ​​model and generates optimized proposal content. The output of this step is the result of natural language generation as a proposal document.

[0687] Step 4:

[0688] The server automatically creates visualization materials based on the optimized proposal. It uses the proposal document and template information as input. Tools such as the Google Slides API and LaTeX are used to create the visualization materials, generating slides that match the proposal. The output of this process is a presentation document containing visual elements.

[0689] Step 5:

[0690] The user's terminal sends progress information about the sales process to the server. The server uses user progress data and feedback as input. The server processes the received data and monitors progress in real time. This process allows the server to advise the user on the next steps and support timely decision-making. The output of this step is actionable guidance and alert notifications for the user.

[0691] (Application Example 1)

[0692] 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".

[0693] In the field of electronic payment services, it is challenging to propose payment solutions that are accurately and quickly optimized to meet the diverse business needs of clients. Furthermore, there is a need to streamline sales activities and strengthen customer relationships. It is necessary to overcome these challenges and maximize the results of commercial activities.

[0694] 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.

[0695] In this invention, the server includes a device for collecting management plan information of the proposed organization, a device for analyzing past proposal activity data and personnel information of the proposed organization, and a device for automatically generating and providing solutions based on the customer's industry data in proposal activities in the payment system domain. This enables the proposal of quick and accurate payment solutions to clients.

[0696] "Management plan information" refers to data related to the goals and strategies set by an organization, as well as the activity plans based on them.

[0697] "Proposal activity data" refers to records of past sales proposals, including information about the content of the proposals and their results.

[0698] "Information on personnel involved in sales activities within your organization" refers to data on the names, positions, and related skills and achievements of the personnel involved in sales activities within your organization.

[0699] "Optimization" is the process of improving proposals and systems based on existing information and conditions to make them function more effectively and efficiently.

[0700] "Presentation materials" refer to documents and slides created to visually represent a proposal and facilitate understanding and empathy among stakeholders.

[0701] "Sales activities" refer to a series of organizational activities and processes carried out with the aim of selling products or services.

[0702] "Monitoring progress" means constantly checking the status of activities or projects and observing whether the objectives are being achieved.

[0703] A "solution" refers to an optimized solution or proposal provided for a specific problem or need.

[0704] "Connection" refers to the mutual relationship and trust between customers and the organization, and includes long-term cooperation and partnerships.

[0705] The system for realizing this invention mainly consists of a server and user terminals. The server collects organizational management plan information, past proposal activity data, and information on personnel in charge of operations, and stores this information in a MySQL database. For data analysis, Scikit-learn and NLTK are used for natural language processing to optimize the proposal content. Based on the optimized proposal content, presentation materials are automatically generated using the Python-PPTX library.

[0706] The user's device is a smartphone or tablet, which receives optimized proposals and presentation materials sent from the server. Through this device, the progress of the sales process is transmitted to the server in real time, and advice on the next action is provided. In the payment system domain, the server provides users with customized payment solutions based on industry data via a push notification system.

[0707] For example, if a user receives a request from a new client for a payment solution specific to a particular industry, the server analyzes past success stories specific to that industry and generates an optimal proposal. The resulting document becomes immediately available on the user's device.

[0708] Examples of prompts for a generative AI model include the following:

[0709] "Please generate documentation to propose the optimal payment solution to a client who is considering launching a new e-commerce platform. This solution must be optimized for the client's business needs."

[0710] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0711] Step 1:

[0712] The server collects management plan information, past proposal activity data, and personnel information of the target organization through APIs and data feeds. It receives management plan and past proposal activity data from external systems as input and stores this data in a MySQL database. Specifically, it performs API calls, parses the retrieved data, and stores it in the appropriate fields in the database.

[0713] Step 2:

[0714] The server uses Scikit-learn and NLTK to analyze the collected data. The input consists of business plan information and proposal activity data stored in the database in Step 1. Machine learning algorithms are applied to generate optimal proposals. Specifically, the process involves preprocessing the data, extracting features, inputting them into an optimization model, and obtaining the output.

[0715] Step 3:

[0716] The server automatically generates presentation materials using the Python-PPTX library based on the output of the optimization algorithm. The input is the optimal suggestion generated in step 2. This is converted into a slide format, visual elements are incorporated, and a completed presentation is created. Specifically, the data is fed into a slide template, and the necessary text and graphics are placed on each slide.

[0717] Step 4:

[0718] The user's device receives presentation materials sent from the server. The input is the generated material sent from the server. The user presents this material to the client and obtains feedback. Specifically, the user can view the downloaded material on the device and add annotations and comments as needed.

[0719] Step 5:

[0720] The server receives data sent from the user's terminal in real time for progress monitoring and provides advice on the next action. It uses user operation logs and client feedback as input. Based on this, it presents a sales strategy. Specifically, it has the function of analyzing log data and displaying appropriate actions on a dashboard.

[0721] Step 6:

[0722] The server uses a generative AI model to generate optimal payment solutions based on specific industry data. It takes prompt text as input to run the AI ​​model and output customized suggestions. Specifically, it passes the prompt text to an internal API, saves the received solution to a database, and sends the result to the user's terminal.

[0723] 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.

[0724] The system according to the present invention comprises a server, a terminal, and an emotion engine that recognizes the user's emotions. The server has the function of collecting management plan information of the proposed organization from various data sources and storing this information in a database. The server also retrieves past proposal activity data and personnel information from the database and performs analysis using machine learning algorithms and natural language processing techniques based on this data.

[0725] The server uses these analysis results to optimize the proposal and automatically generates presentation materials based on that content. The generated materials are structured to meet the needs of the target audience and are sent to the user's device in a visually appealing format.

[0726] Sales activities are monitored in real time by the server, and feedback is provided to the user as needed. This feedback includes advice on the next actions to take and when to take them.

[0727] Furthermore, by incorporating an emotion engine, this system recognizes the user's emotions in real time and dynamically adjusts the information provided by the system according to the sales situation. The emotion engine analyzes the user's voice tone, facial expressions, input text, etc., and evaluates their emotional state.

[0728] For example, if the emotion engine detects that a user is experiencing emotional stress during a presentation, the server dynamically adjusts the presentation, either by concisely summarizing the proposal or quickly providing supporting information. Conversely, if the user is showing positive emotions, additional information or detailed data analysis results are displayed to increase the success rate of the sales pitch.

[0729] By leveraging this emotion engine, users can adjust their approach to suit their situation in real time, improving the efficiency and results of their sales activities. This brings a level of flexibility and personalization not found in traditional sales support systems.

[0730] The following describes the processing flow.

[0731] Step 1:

[0732] The server collects management plan information for the proposed organization from external data sources and publicly available company documents. This information includes medium- to long-term strategies, important projects, and financial data, and is stored in a database.

[0733] Step 2:

[0734] The server retrieves past proposal activity data and contact person information from an internal database. This includes proposal success rates, contact person feedback, and customer internal communication history.

[0735] Step 3:

[0736] The server utilizes machine learning algorithms and natural language processing technology to analyze the target company's business plan information and historical data. Based on the analyzed information, it generates insights to optimize the proposal.

[0737] Step 4:

[0738] The server automatically generates presentation materials based on the optimized proposal. The slides and data visualizations included are customized to the user's needs and sent to the user's device.

[0739] Step 5:

[0740] The user's device receives presentation materials, reviews their content, and edits them as needed. The user can then prepare the materials for the proposal meeting.

[0741] Step 6:

[0742] The emotion engine extracts emotions in real time from the user's voice, facial expressions, and input text, and evaluates the user's emotional state. The emotional state is recorded according to the situation of the presentation or sales activity.

[0743] Step 7:

[0744] The server dynamically adjusts the presentation and the suggestions it provides based on the evaluation results of the emotion engine. If the user's emotions indicate a stressed state, it simplifies the materials and strengthens the advice for the next action.

[0745] Step 8:

[0746] The user conducts conversations with customers based on the tailored proposals. If the user's sentiment is positive, the server provides more detailed information and success stories to help improve the effectiveness of sales activities.

[0747] (Example 2)

[0748] 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".

[0749] In proposal activities, it is necessary to effectively utilize organizational planning information and historical data to optimize proposal content. Furthermore, while it is important to monitor the progress of sales activities in real time and provide appropriate advice, traditional systems have the challenge of not being able to adequately address individual situations. Additionally, the inability to provide information that takes user emotions into account has made it difficult to maximize sales effectiveness.

[0750] 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.

[0751] In this invention, the server includes means for acquiring planning information of the target organization, means for evaluating past activity data and personnel data, and means for incorporating an emotion recognition engine to determine the user's emotional state and dynamically adjust the information. This makes it possible to optimize proposals by utilizing organizational data, monitor the progress of sales activities, and provide flexible advice and information tailored to the user's emotions.

[0752] "The organization being proposed to" refers to the business entity that receives the proposal for products or services.

[0753] "Planning information" refers to data related to an organization's management policies, strategies, financial plans, etc.

[0754] "Past activity data" refers to records of sales activities and proposals that an organization has conducted in the past.

[0755] "Contact person data" refers to information about individuals involved in sales activities, such as their role, past performance, and skill set.

[0756] An "emotion recognition engine" refers to a technology that analyzes a user's voice, facial expressions, and text input to evaluate their emotional state.

[0757] "Optimization" refers to making adjustments to a proposal to most effectively match the needs of the organization and its customers.

[0758] "Dynamic adjustment" refers to changing the information and methods provided in real time according to the situation.

[0759] "Flexible advice" refers to providing appropriate advice based on the user's current emotional state and business situation.

[0760] "Information provision" refers to the act of presenting data or knowledge that is beneficial to the user or customer.

[0761] This invention is implemented by a system including a server, a terminal, and an emotion recognition engine. The server retrieves planning information for the proposed organization and uses a dedicated database to evaluate past activity data and personnel data. Internet connectivity and API access are often used for data retrieval.

[0762] The server analyzes this data using machine learning algorithms and natural language processing technology, and optimizes the proposal content using generative AI models. This automatically generates presentation materials that are tailored to the needs of the target audience and industry trends. The generated materials are sent to the terminal in common formats such as PowerPoint and PDF, making them easily accessible to the user.

[0763] The terminal is equipped with an emotion recognition engine that analyzes the user's voice, facial expressions, and entered text in real time. This determines the user's emotional state, and the server dynamically adjusts the information to support the user's decision-making in sales activities. For example, if the user is showing signs of stress, the server provides information that concisely summarizes the proposal.

[0764] For example, if a user enters the prompt "What should I do next?" during a proposal, appropriate advice and information based on their emotional state will be immediately provided. This improves the success rate of proposal activities, allowing users to conduct sales activities more effectively and efficiently.

[0765] In this way, this invention realizes flexible and personalized sales support tailored to the user's situation.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] The server retrieves planning information about the proposed organization from external and internal data sources. Inputs include API calls and database queries, and this information is compiled into data that reflects the organization's current state and future goals. The output is a dataset summarizing this retrieved information.

[0769] Step 2:

[0770] The server retrieves past activity data and employee data from the database. Past sales records and employee history information, which serve as explanatory variables, are used as input, and the server filters the data based on this information. The output is historical data organized for use in analysis.

[0771] Step 3:

[0772] The server begins analyzing acquired planning information and historical activity data using machine learning algorithms and natural language processing techniques. The input is the dataset and historical data acquired in the previous step, and the output is an optimized proposal based on the analysis results. Specifically, a generative AI model performs selection and weighting to determine the appropriate approach.

[0773] Step 4:

[0774] The server automatically generates presentation materials based on the optimized proposal. The input is the optimized proposal obtained through analysis, and the server then performs specific actions such as combining templates and slide formats based on this. The output is a visually organized presentation tailored to the target organization.

[0775] Step 5:

[0776] The generated presentation materials are delivered to the terminal. The user receives these materials and uses them in their proposal activities. The concrete action is that the materials are displayed on the terminal, making them immediately ready for the user to use in their proposal.

[0777] Step 6:

[0778] The server monitors the progress of the user's sales activities in real time. Input information includes user logs and feedback data, while output is advice for the next steps, generated based on the monitoring.

[0779] Step 7:

[0780] On the device, an emotion recognition engine analyzes the user's voice, facial expressions, and text input in real time. The input is the user's voice and text information, and the output is the user's emotional state based on these analysis results. Specifically, the system analyzes the user's state and optimizes its actions accordingly.

[0781] Step 8:

[0782] The server dynamically adjusts information based on the user's emotional state, providing flexible advice and additional data. The input is the output of the emotion recognition engine, and the output is specific advice and information for the user. This functionality enables users to respond appropriately to different situations.

[0783] (Application Example 2)

[0784] 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".

[0785] The problem that this invention aims to solve is to prevent the loss of sales opportunities due to insufficient communication with customers and inefficient proposals during sales activities. Furthermore, there is a need to improve the success rate and efficiency of sales activities by accurately understanding the emotional state of customers and optimizing sales activities in real time.

[0786] 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.

[0787] In this invention, the server includes means for collecting management strategy information of the target organization, means for analyzing past proposal activity information and personnel information of the target organization, and means for optimizing proposal content based on the collected and analyzed information. This enables dynamic adjustment of sales content according to the customer's emotional state and automatic generation of optimal proposals.

[0788] "Management strategy information of the proposed organization" refers to information related to the long-term and short-term business policies and activity plans set by a specific organization.

[0789] "Past proposal activity information" refers to records including past sales activities and negotiation history with the target organization.

[0790] "Contact person information" refers to data regarding the career history and performance of sales representatives involved in proposal activities.

[0791] An "emotion analysis engine" is a component of a system that analyzes the user's voice tone, facial expressions, input text, etc., to evaluate their emotional state.

[0792] "Evaluating in real time and dynamically adjusting presentation content" means instantly reading the user's emotional state and flexibly changing the proposed content based on that information.

[0793] "Monitoring the progress of sales activities and providing guidance on the procedures to be followed" means tracking the progress of sales and giving instructions on the next steps and timing of actions to take.

[0794] "Identifying relevant resources within the organization and presenting the most suitable personnel and equipment for proposal activities" means using past sales data to identify and present the internal human resources and technologies that will be effective for a particular proposal.

[0795] "Customer contact history and relationship information" refers to information that shows the past interactions between customers and the organization and the depth of those relationships.

[0796] This invention is a system for optimizing sales activities to target organizations. The system comprises a server, terminals, and an emotion analysis engine.

[0797] The server first collects management strategy information of the target organization from various data sources. This information is stored in a database and analyzed along with past proposal activity information and contact person information. TensorFlow is used to implement machine learning algorithms for the analysis, and NLTK is used for natural language processing. Based on the analysis results, the proposal content is optimized and presentation information is automatically generated. This generated information is provided to the sales representative's smartphone or other device in a visually appealing format. On the device side, image processing using OpenCV and audio data acquisition using a microphone are performed to capture the user's voice tone and facial expression data.

[0798] The emotion analysis engine acquires the user's visual and auditory information in real time and evaluates their emotional state based on that data. Based on the evaluated emotion data, the server dynamically adjusts the presentation and advises on actions. This enables flexible responses that increase the success rate of sales activities.

[0799] As a concrete example, if a customer shows signs of anxiety while a sales representative is giving a presentation on a new product, the emotion analysis engine evaluates that emotion. Based on this information, the server immediately provides information emphasizing the product's security advantages. This helps to alleviate the customer's anxiety and enhance the effectiveness of the proposal.

[0800] An example of a prompt for a generative AI model is, "If a customer hears information and becomes suspicious, what data should be presented to regain their trust?"

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The server collects management strategy information for the proposed organization from multiple data sources. Inputs are publicly available management information and internal databases, while output is integrated management strategy information. This information is stored in a database for subsequent analysis.

[0804] Step 2:

[0805] The server analyzes collected business strategy information, past proposal activity information, and personnel information. The input is integrated business strategy information and historical sales data, and the output is the optimized proposal content based on the analysis. TensorFlow is used to perform data analysis with machine learning models to derive effective proposals.

[0806] Step 3:

[0807] The server automatically generates presentation information based on the optimized proposal. The input is the optimized result, and the output is structured presentation material. The presentation material is generated using natural language processing technology with NLTK and sent to the user's terminal.

[0808] Step 4:

[0809] The device uses an emotion analysis engine to collect user voice tone and facial expression data in real time. Input is voice and video data acquired from the user, and output is the user's emotional state after analysis. Data is captured using a camera and microphone, and image processing is performed using OpenCV.

[0810] Step 5:

[0811] The server dynamically adjusts the presentation based on the user's emotional state, as determined by an emotion analysis engine. The input is the analyzed emotional state, and the output is the adjusted presentation content. The server uses an AI model to determine the next action and provide the customer with the necessary information in a timely manner.

[0812] Step 6:

[0813] The server monitors the progress of sales activities in real time and advises on the next course of action. Inputs are the status of sales activities and user feedback, while output is the recommended next action. This allows sales representatives to take appropriate steps on the spot, increasing their sales success rate.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] Means of collecting management plan information of the target organization,

[0838] A means for analyzing past proposal activity data and contact person information of the aforementioned proposed organization,

[0839] Means for optimizing the proposed content based on the information collected and analyzed,

[0840] A means for automatically generating presentation materials based on the optimized proposal content,

[0841] A means of monitoring the progress of the sales process and advising on the steps to be taken,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for extracting relevant internal resources based on past sales activity data and for presenting the most suitable personnel and tools for proposal activities.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for analyzing customer contact history and relationship data to provide optimal strategies for strengthening customer-organizational engagement.

[0847] "Example 1"

[0848] (Claim 1)

[0849] A means of collecting business plan data of the proposed organization using an information processing device,

[0850] A means for analyzing past proposal activity data and personnel data of the aforementioned proposed organization using statistical techniques,

[0851] Means of using a generative AI model that optimizes the proposed content based on the collected and analyzed data,

[0852] A means for automatically generating visualization data using a document creation means based on the optimized proposal content,

[0853] A means of monitoring the progress of work and recommending the order of actions,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising means for identifying relevant resources within the organization based on past business activity data and for presenting the optimal resources and systems in the proposal activity.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising means for analyzing customer communication history and relationship data to provide an optimal method for promoting customer and organizational engagement.

[0859] "Application Example 1"

[0860] (Claim 1)

[0861] A device for collecting management plan information of the proposed organization,

[0862] A device for analyzing past proposal activity data and personnel information of the aforementioned proposed organization,

[0863] A device for optimizing the proposed content based on the collected and analyzed information,

[0864] A device that automatically generates presentation materials based on the optimized proposal content,

[0865] A device that monitors the progress of sales activities and advises on the procedures to be implemented,

[0866] In proposal activities in the payment system domain, a device that automatically generates and provides solutions based on customer industry data,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising a device that extracts relevant internal resources based on past sales activity data and presents the most suitable personnel and tools for work activities.

[0870] (Claim 3)

[0871] The system according to claim 1, further comprising a device for analyzing customer contact history and relevance data and providing optimal strategies for strengthening customer-organizational connections.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] Means for obtaining planning information of the target organization,

[0875] A means for evaluating the past activity data and personnel data of the aforementioned organization,

[0876] Means for optimizing the content based on the aforementioned acquisition and evaluation,

[0877] A means for automatically generating documents based on the optimized content,

[0878] A means of monitoring the progress of business activities and advising on procedures,

[0879] A means of incorporating an emotion recognition engine to determine the user's emotional state and dynamically adjust information,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, further comprising means for selecting relevant internal resources based on past activity data and for suggesting the most suitable personnel and equipment for the activity.

[0883] (Claim 3)

[0884] The system according to claim 1, further comprising means for analyzing customer contact history and relationship data and providing strategies for strengthening relationships with customers.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] Means of collecting management strategy information of the target organization,

[0888] A means for analyzing past proposal activity information and contact person information of the aforementioned proposed organization,

[0889] Means for optimizing the proposed content based on the collected and analyzed information,

[0890] A means for automatically generating presentation information based on the optimized proposal content,

[0891] A means of monitoring the progress of sales activities and providing guidance on the procedures for their implementation,

[0892] A means of dynamically adjusting presentation content by evaluating the user's emotional state in real time using an emotion analysis engine,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, further comprising means for extracting relevant resources within the organization based on past sales activity information and for presenting the most suitable personnel and equipment for proposal activities.

[0896] (Claim 3)

[0897] The system according to claim 1, further comprising means for analyzing customer contact history and relationship information and providing optimal strategies for strengthening customer and organizational engagement. [Explanation of symbols]

[0898] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting management plan information of the target organization, A means for analyzing past proposal activity data and contact person information of the aforementioned proposed organization, Means for optimizing the proposed content based on the information collected and analyzed, A means for automatically generating presentation materials based on the optimized proposal content, A means of monitoring the progress of the sales process and advising on the steps to be taken, A system that includes this.

2. The system according to claim 1, further comprising means for extracting relevant internal resources based on past sales activity data and for presenting the most suitable personnel and tools for proposal activities.

3. The system according to claim 1, further comprising means for analyzing customer contact history and relationship data to provide optimal strategies for strengthening customer-organizational engagement.

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