system

Abstract

We provide the system. [Solution] A means of automatically collecting information from publicly available sources, Means for filtering and cleaning the collected information, A means of analyzing filtered and cleaned information to extract potential customer needs and challenges, A means of automatically generating a proposal based on the analysis results, A means of sending the generated proposal to the user, 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 persona chatbot control method 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] It is an object to solve the problem that it takes a lot of time and labor for corporate sales representatives to collect information, analyze competing companies, and investigate industry information before visiting customers, and that preparing a proposal becomes a burden involving overtime work after business. Thereby, it is required to improve the work efficiency of sales representatives and realize an environment in which they can concentrate on creative sales activities.

Means for Solving the Problems

[0005] The system according to the present invention includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information, and means for extracting customer needs and challenges using natural language processing technology as an analysis means. By providing means for automatically generating a proposal based on the analysis results and means for sending the generated proposal to the user, the system significantly reduces the time and effort required to create proposals. This system reduces the burden on sales representatives in preparing information before visits and improves the quality of sales activities.

[0006] "Public information sources" refer to a collection of information that is publicly accessible and available, such as information found on the internet, from news organizations, and from official company press releases.

[0007] "Means of automatically collecting information" refers to the function of a program or system that executes a process to obtain necessary information from specified sources without human intervention.

[0008] "Filtering" refers to the process of removing unnecessary data from collected information.

[0009] "Cleaning" refers to the process of organizing collected information into an appropriate format and correcting or removing duplicate or inaccurate data.

[0010] "Analysis means" refers to the function of deriving the value of information using technologies and methods for processing and analyzing data.

[0011] "Natural language processing technology" refers to artificial intelligence technology used to understand, interpret, and generate human language.

[0012] "Means for extracting customer needs and challenges" refers to the process of identifying customer demands and problems from analyzed data.

[0013] "Means for generating proposals" refers to a function that automatically creates proposals to be presented to customers based on collected and analyzed information.

[0014] "Means of sending to the user" refers to the means of communication that delivers the generated proposal to the user's device in an appropriate format. [Brief explanation of the drawing]

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

Embodiments for Carrying out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

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

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

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

[0036] One embodiment of this invention is a system that enables sales representatives to efficiently create proposals. This system operates server-centric, and users access the system via terminals.

[0037] The server first collects information from publicly available sources on the internet. Based on pre-configured keywords and conditions, the server automatically retrieves corporate investor relations information, industry news, and press releases from competitors. The collected data is then filtered by the server to remove unnecessary information.

[0038] Next, the server cleans up the information and then analyzes it using natural language processing techniques. The purpose of the analysis is to identify customer needs and potential challenges. For example, the server can analyze industry trends and competitor activities using natural language processing techniques to extract keywords and topics that are important to the customer.

[0039] Based on the analysis results, the server automatically generates a proposal. This proposal is structured according to a template set by the user in advance, and organizes the information necessary for sales activities. The proposal includes an overview of the customer's challenges, industry trends, competitor analysis, and proposed solutions, and covers all the essentials to support sales activities.

[0040] The completed proposal is sent from the server to the user's terminal using a secure communication method. The user can then review the proposal on their terminal and use it as reference material during client visits. This significantly reduces the time sales representatives spend gathering information and creating proposals, allowing them to focus on more value-added sales activities.

[0041] For example, when a sales representative is preparing to visit a new customer, the server automatically gathers and analyzes the latest information and competitive landscape of the relevant industry, and creates a proposal tailored to the purpose of the visit. The user can then efficiently prepare a presentation based on this proposal and conduct an effective business negotiation.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server automatically collects information by accessing publicly available information sources on the internet (such as company IR pages, news sites, and industry reports) based on specified keywords and conditions. The information is obtained using APIs and web scraping techniques.

[0045] Step 2:

[0046] The server filters out irrelevant and duplicate data from the collected information, retaining only the necessary information. This is done using keyword matching and data deduplication algorithms.

[0047] Step 3:

[0048] The server cleans the filtered information. This process includes formatting the data, correcting outliers, and removing unnecessary strings.

[0049] Step 4:

[0050] The server uses natural language processing techniques to analyze clean data. Specifically, it performs topic modeling, keyword extraction, and sentiment analysis to identify data points for gaining important insights.

[0051] Step 5:

[0052] The server extracts customer needs and potential challenges based on the analysis results. This generates analysis results that combine customer-specific information with relevant industry trends and competitive information.

[0053] Step 6:

[0054] The server automatically generates a proposal based on the extracted information. The proposal is structured using a template set by the user and includes comprehensive analysis results and proposed content.

[0055] Step 7:

[0056] The server sends the generated proposal to the user's terminal. The terminal can then open the received proposal and view its contents, allowing the user to use it as reference material during client visits.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In today's business environment, sales representatives need to quickly and accurately grasp large amounts of information and make effective proposals based on that information. However, information gathering and analysis require considerable time and effort, and the accuracy of the analysis directly impacts the success or failure of sales activities. Therefore, there is a need for efficient and highly accurate information gathering and analysis, and the rapid creation of proposals based on the results.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information using data processing technology, and means for analyzing the filtered and cleaned information using natural language processing technology to extract and process needs and issues of interest. This makes it possible to quickly collect and analyze the information needed by sales representatives and automatically generate accurate proposals.

[0062] A "public information source" is a medium or platform that provides publicly available information accessible to a large number of users.

[0063] "Means of automatically collecting information" refers to systems and processes that collect digital information via a network without user intervention.

[0064] "Filtering" is the process of selecting necessary information from collected data and removing unnecessary information.

[0065] "Cleaning" is the process of formatting collected data, removing noise and redundancy, to make it usable.

[0066] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0067] "Methods for extracting and processing needs and challenges" refer to methods for analyzing and clarifying the requirements and problems that users and customers seek or need to solve, based on the analyzed information.

[0068] "Means of automatically generating documents" refers to a system for generating structured documents using a computer program.

[0069] "Communication methods" refer to methods and technologies for electronically sending and receiving information.

[0070] "Example configuration" refers to an arrangement or configuration assembled to suit specific conditions or purposes.

[0071] One embodiment of this invention is a system for enabling sales representatives to efficiently create proposals. The system operates server-centric, and users access the system via terminals. The server is responsible for information gathering, data filtering, natural language processing, and proposal generation.

[0072] The server utilizes APIs to automatically collect information from publicly available sources on the internet. For example, it can use APIs from platforms that provide news and financial information. Furthermore, Python libraries (e.g., Pandas, Numpy) are used for filtering and cleaning the information, performing noise reduction and selecting the necessary data.

[0073] By utilizing natural language processing technologies such as Google® Cloud Natural Language API and IBM Watson® Natural Language Understanding, we enhance analysis accuracy and extract key customer needs and challenges. This allows us to accurately grasp industry trends and the actions of competitors.

[0074] To generate proposals based on the analysis results, we utilize the Microsoft Word API and LaTeX templates. This ensures that the proposals clearly outline customer challenges, industry trends, competitor analysis, and proposed solutions. Finally, the generated proposals are sent from the server to the user's terminal via a secure communication method. This communication is secured using the SSL / TLS protocol.

[0075] As a concrete example, when a user visits a new customer, the server collects and analyzes the latest industry information and competitive landscape, automatically generating a proposal based on the results. The user can then review it on their terminal and efficiently prepare for the visit. An example of a prompt message would be, "Please automatically generate a proposal based on the latest industry trends and competitive analysis." This system makes sales activities more effective and faster.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server collects information from publicly available sources on the internet. Specifically, it uses configured keywords and a data collection API to retrieve corporate financial information, industry news, and competitor press releases. The input is raw data from public sources, and the output is the collected, unorganized data. This operation allows users to obtain the basis for the information they need.

[0079] Step 2:

[0080] The server filters and cleans the collected information using data processing techniques. Specifically, it uses the Python Pandas library to remove noise and select relevant information. The input is unprocessed raw data, and the output is formatted and refined data. This process clarifies only the necessary information and eliminates irrelevant information.

[0081] Step 3:

[0082] The server analyzes the filtered information using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract keywords related to customer needs and challenges. The input is formatted data, and the output is data with key topics and keywords extracted. This analysis allows users to identify potential business opportunities.

[0083] Step 4:

[0084] The server automatically generates a proposal based on the analysis results. Specifically, it utilizes the Microsoft Word API to create a proposal incorporating the obtained topics and keywords, following a pre-specified template. The input is the extracted keywords and analysis data, and the output is the completed proposal. This generation process allows users to obtain a proposal quickly and effectively.

[0085] Step 5:

[0086] The server securely sends the generated proposal to the user's terminal. Specifically, it uses the SSL / TLS protocol to send the proposal via email or cloud storage over a secure communication channel. The input is the completed proposal, and the output is the complete proposal saved on the user's terminal. This transmission allows the user to review the proposal and prepare for negotiations with clients.

[0087] (Application Example 1)

[0088] 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."

[0089] In existing sales activities, sales representatives spend a significant amount of time and effort gathering information and creating proposals. In particular, when customized information needs to be provided to each customer, manual processing has its limitations. This creates a challenge in efficiently and quickly providing customer-appropriate proposals.

[0090] 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.

[0091] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information, and means for analyzing the filtered and cleaned information to extract potential requests and issues. This makes it possible to efficiently provide customers with immediately customized proposals.

[0092] "Public information sources" refer to information media that are generally accessible, such as the internet, and include official company announcements, news articles, and online documents.

[0093] "Filtering" is the process of selecting necessary information from collected data and removing unnecessary information.

[0094] "Cleaning" is the process of organizing information, removing inaccurate data and noise, and preparing it for analysis.

[0095] "Analysis" refers to the act of data processing and analysis to evaluate obtained information and discover potential demands and needs.

[0096] "Potential requirements and challenges" refer to data and problems that users or customers may have as needs, even if they haven't explicitly stated them.

[0097] "Presented materials" refer to proposals and informational documents created based on analysis results, and are generated for the purpose of user use.

[0098] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[0099] "Important terms" refer to keywords or topics that are particularly noteworthy among the information identified during the analysis process.

[0100] "User-defined format" refers to a format or template that the user has predetermined, meaning that the presentation materials will be organized in this format.

[0101] This invention is implemented as a system for sales representatives to efficiently generate customized proposal materials. The system includes a server, a user terminal, and an internet connection.

[0102] The server has the capability to automatically collect information from publicly available sources on the internet. This information includes official company announcements, news articles, and industry reports. The collected information is then filtered and cleaned by the server to remove unnecessary information and extract accurate and useful data.

[0103] The server then analyzes this data using natural language processing techniques to identify potential requirements and challenges. These techniques include, for example, Python and the NLTK library. Key terms identified through the analysis are then used as keywords when generating proposal documents.

[0104] The generated presentation materials are structured according to the user's specified format and sent to the terminal. Users can then use these materials to conduct sales activities efficiently. Specifically, security service sales representatives can quickly prepare proposals for new customers that incorporate the latest industry and competitor information.

[0105] For example, when a sales representative is proposing a security solution to a specific customer, they could use a prompt like, "Generate a proposal based on the latest industry news and competitive information to create a state-of-the-art security solution proposal for a new customer."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server automatically collects necessary information from publicly available sources on the internet. Based on specific keywords set by the user, it retrieves data from sources such as company websites and news portals. The input for this step is keywords, and the output is a collection of unorganized information data.

[0109] Step 2:

[0110] The server filters and cleans the acquired information. In this step, redundant and inaccurate information is removed from the collected data, leaving only the necessary information. The input is the information data set from step 1, and the output is a clean dataset.

[0111] Step 3:

[0112] The server analyzes a clean dataset using natural language processing techniques. This analysis extracts keywords to identify potential requirements and challenges. The input is the clean dataset from step 2, and the output is a list of keywords identified in the analysis.

[0113] Step 4:

[0114] The server generates presentation materials in a user-defined format based on keywords obtained through analysis. This process involves using templates to organize the information and prepare it as presentation material. The input consists of the keyword list and template information from step 3, and the output is the presentation material.

[0115] Step 5:

[0116] The terminal receives presentation materials sent from the server. Users can operate the terminal to review the generated materials and utilize them in their sales activities. The input is the presentation materials provided by the server, and the output is the final document that the user can view.

[0117] 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.

[0118] This invention relates to a system that has an emotion engine that recognizes and incorporates user emotions to enable sales representatives to efficiently create customer proposals. This system is mainly server-centric and interfaces with users through terminals.

[0119] The server first automatically collects relevant data from various publicly available sources on the internet. This includes corporate investor relations information, the latest industry news, and press releases from competitors. The collected information is filtered and cleaned to remove irrelevant data and noise.

[0120] Next, the server uses natural language processing techniques to analyze the filtered and cleaned data. The analysis extracts key keywords and topics, identifying potential customer needs and challenges. This data is then used to deepen the understanding of customer scenarios and market trends.

[0121] Furthermore, the emotion engine built into the server recognizes the user's emotions in real time. Emotional states are read, for example, from the user's voice and actions, and the system dynamically adjusts the content of the proposal based on this data. In this way, it becomes possible to create effective proposals that leverage emotional insights.

[0122] Finally, based on the analysis results and information obtained from the emotion engine, the server automatically generates a proposal. The generated proposal is structured according to a template set by the user and includes industry analysis, competitor information, and suggestions based on the specific needs of the customer. The server sends the completed proposal to the terminal, where the user can view it and use it as reference material during customer visits.

[0123] As a concrete example, when a user approaches a new customer, the emotion engine can recognize which elements the user is interested in at an emotional level and customize the proposal based on that feedback. This makes the proposal more personalized and more likely to capture the customer's attention.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server periodically crawls publicly available information sources on the internet (e.g., news sites, official company pages, industry reports, etc.) based on pre-configured keyword lists and conditions, and automatically collects the necessary information.

[0127] Step 2:

[0128] The server filters and cleans the collected information. Specifically, it removes duplicate and irrelevant information from the database and deletes inappropriate elements to maintain the integrity of the information.

[0129] Step 3:

[0130] The server uses natural language processing technology to analyze filtered and cleaned information, extracting key keywords and topics related to industry trends, competitive strategies, and customer needs.

[0131] Step 4:

[0132] The server identifies the customer's potential needs and current challenges based on the analyzed information, and uses this as foundational data for proposals.

[0133] Step 5:

[0134] The emotion engine recognizes the user's emotions in real time and provides data to the server for refining proposals. Emotions are primarily obtained from the user's voice and interface interactions.

[0135] Step 6:

[0136] The server dynamically adjusts the analysis results based on the user's emotional state obtained from the emotion engine, and reflects this in the proposal. This makes the proposal more aligned with the user's emotions.

[0137] Step 7:

[0138] The server organizes the generated proposals according to the user's template and completes the final proposal that includes industry trends, competitive analysis, and customer needs.

[0139] Step 8:

[0140] The server sends the completed proposal to the user's terminal. The user reviews the proposal on their terminal and uses it for sales activities. The proposal includes visualized information and data to enhance the effectiveness of presentations and discussions.

[0141] (Example 2)

[0142] 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".

[0143] In modern sales activities, creating customer proposals quickly and effectively is essential for maintaining a competitive edge. However, existing methods make it difficult to efficiently generate personalized proposals that fully reflect the customer's latent needs and emotions, and sales representatives still have to expend considerable effort. In particular, adjusting proposal content to take customer emotions into account is a factor that greatly influences sales success, but conventional systems often do not perform such integrated processing. There is a need to improve this situation and develop a system that more effectively supports sales activities.

[0144] 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.

[0145] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information, means for analyzing the filtered and cleaned information using natural language processing technology to extract the customer's potential needs and challenges, means for automatically generating a proposal using an artificial intelligence model generated based on the analysis results, and means for recognizing the user's emotions in real time and applying that information to the proposal. This enables sales representatives to automatically generate effective proposals tailored to the customer's emotions, making it easier to attract the customer's interest with personalized proposal content, and improving the efficiency and results of sales activities.

[0146] "Public information sources" refer to a collection of information accessible to the general public, including company information, industry news, and announcements from competitors.

[0147] "Filtering" is the process of selecting the elements necessary for a given purpose from acquired information, and it is a means of improving data quality by removing irrelevant data and noise.

[0148] "Cleaning" is a process performed to remove further duplication and errors from filtered information in order to maintain the accuracy and consistency of the dataset.

[0149] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract keywords and topics from text.

[0150] A "generating artificial intelligence model" is an algorithm trained to produce a specific output based on defined input data, and in this case, it is used to automatically generate proposals.

[0151] "Recognizing emotions in real time" refers to the process of evaluating a user's emotional state on the spot, such as analyzing emotional data obtained from voice or operation information.

[0152] This invention is a system for sales representatives to quickly create customer proposals, and it includes a series of functions including information gathering from public sources, natural language processing, sentiment recognition, and automated proposal generation using artificial intelligence.

[0153] The server first uses technologies such as web crawlers and API access to automatically collect relevant information from publicly available sources on the internet. The hardware used in this process includes computing servers that enable high-speed processing and data storage. The collected information is filtered and cleaned using the Python Pandas library, which improves the accuracy and usefulness of the data.

[0154] Furthermore, the server analyzes the cleaned information using natural language processing technology. This analysis utilizes natural language processing libraries such as SpaCy and NLTK to extract potential customer needs and challenges from the information. Based on the analysis results, a generative AI model automatically generates a proposal. The generated proposal is structured according to a user-defined template and incorporates specific sales strategies.

[0155] To recognize user emotions, the server is equipped with an emotion analysis engine that analyzes user voice and operation information in real time. The technologies used here include a speech recognition engine and emotion analysis tools. Based on this data, the content of the proposal can be dynamically adjusted.

[0156] For example, if a user uses this system to create a proposal for a new product launch, the server prompts the AI ​​model with "Create a proposal that highlights the features of the new product. If possible, add graphs that will attract customer attention," and then constructs the optimal proposal.

[0157] In this way, this system can efficiently and effectively generate personalized sales proposals, significantly supporting the work of sales representatives.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server automatically collects information from publicly available sources. Specific URLs or APIs, such as company information, industry news, and competitor press releases, are provided as input. The server utilizes web crawlers and APIs to collect this information into its own database. The output is the collected, raw dataset.

[0161] Step 2:

[0162] The server filters and cleans the collected information. The input is the dataset collected in step 1. The server uses the Python Pandas library to apply filtering conditions to the data, removing duplicate data and irrelevant information. The output is purified data that has been filtered and cleaned.

[0163] Step 3:

[0164] The server analyzes the refined data using natural language processing techniques. The refined data obtained in step 2 is used as input. Here, text analysis is performed using the SpaCy library, including grammatical analysis and topic extraction, to identify the customer's potential needs and challenges. The output is a list of keywords and topics containing the analysis results.

[0165] Step 4:

[0166] The server uses an emotion engine to recognize the user's emotions in real time. User voice commands and operation log data are used as input. A speech recognition engine converts this data into text, and an emotion analysis tool analyzes that text to identify the user's emotional state. The output is emotional state data obtained from the emotion engine.

[0167] Step 5:

[0168] The server automatically generates a proposal using a generative AI model. The input consists of the analysis results from step 3 and the emotional state data from step 4. Based on this data, the generative AI model constructs the content according to the prompt: "Create a proposal that highlights the features of the new product. If possible, add graphs that will attract customer attention." The output is an automatically generated proposal following a template.

[0169] Step 6:

[0170] The server sends the generated proposal to the terminal. The input is the proposal generated in step 5. The server converts this data to the appropriate format and transmits it to the terminal. The terminal displays the received proposal, allowing the user to edit and print it in preparation for the customer visit. The output provides the proposal in a user-friendly format.

[0171] (Application Example 2)

[0172] 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".

[0173] In creating customer proposals, sales representatives are required to accurately understand the customer's emotions and needs and personalize the proposal content accordingly. However, traditional systems have made it difficult to dynamically adjust proposals in this way. Furthermore, in electronic payment services, the lack of real-time product suggestions and coupon offers that respond to the customer's immediate emotions makes improving the customer experience a challenge.

[0174] 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.

[0175] In this invention, the server includes means for automatically collecting information from public sources, means for filtering and cleaning the information, means for analyzing the filtered information and extracting the customer's potential needs and challenges, and means for recognizing the user's emotions and dynamically adjusting the proposed content based on them. This enables the generation of personalized proposals that respond to the customer's emotions and real-time product recommendations.

[0176] A "public information source" is a collection of data that is made publicly available on a network for automated information gathering.

[0177] "Filtering" is the process of removing irrelevant or harmful data from collected information and extracting highly relevant information.

[0178] "Cleaning" is the process of removing noise and inaccurate data from collected information to improve its accuracy and consistency.

[0179] "Analysis" refers to the processing and analysis techniques used to obtain meaningful patterns and insights from collected data.

[0180] "Customer latent needs" refer to customer requests and demands that have not yet been made apparent, and are revealed through data analysis.

[0181] A "challenge" is a problem or situation that the customer is facing that requires improvement, and it serves as an indicator that guides the direction of the proposed solution.

[0182] "Recognizing user emotions" refers to real-time technology that uses data such as audio and video to determine the emotional state of a user.

[0183] "Dynamic adjustment" means adaptively changing a system or process in response to the passage of time or changes in circumstances.

[0184] "Generating" refers to the act of creating new outputs or content based on various data and algorithms.

[0185] A system for realizing an application of this invention consists primarily of a server, a user terminal, and an emotion recognition engine. The server automatically collects information about companies and markets from publicly available information sources on the internet. The collected data is filtered and cleaned to remove noise and unnecessary information. Database management systems (e.g., MySQL®) and natural language processing libraries (e.g., NLTK) are used for this processing.

[0186] The analysis is performed using natural language processing technology on the server to extract the customer's potential needs and challenges. This allows for more appropriate customization of the proposal. The user terminal is typically a smartphone or tablet device, which receives the proposal sent from the server. The proposal is structured based on a pre-configured template and is provided in a state that can be immediately used by the user in business negotiations or customer visits.

[0187] Furthermore, the server is equipped with an emotion recognition engine that analyzes the user's emotional state from their actions and voice. Based on this emotion data, a generative AI model generates offers in real time that are tailored to the user's mood. Software such as OpenCV and TENSORFLOW® are used for this purpose. For example, if a customer's facial expression in a cafe indicates that they want to relax, the generative AI model will offer an offer such as "a discount on relaxing herbal tea."

[0188] A possible example of a specific prompt message would be: "We have analyzed that the user is currently in an emotional state seeking relaxation. Please generate product offers related to this emotion."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The server collects data from publicly available sources. The input consists of URLs of corporate investor relations (IR) information and industry news, and the server uses web scraping techniques to collect this information. The output is raw text data.

[0192] Step 2:

[0193] The server filters and cleans the collected text data. The input is raw text data, and irrelevant information is removed using regular expressions and denoising algorithms. The output is clean text data containing only meaningful information.

[0194] Step 3:

[0195] The server analyzes clean text data using natural language processing techniques. The input is filtered text data, and keywords and topics are extracted using natural language processing libraries. The output is analytical data regarding the customer's potential needs and challenges.

[0196] Step 4:

[0197] To recognize the user's emotional state in real time, the emotion recognition engine analyzes voice and operation data. The input consists of user voice data and operation logs, and machine learning algorithms are used to identify emotions. The output is data indicating the user's emotional state.

[0198] Step 5:

[0199] The server dynamically adjusts the proposal content based on analysis results and sentiment data, and generates personalized proposals using a generative AI model. The inputs are needs analysis and sentiment data, and dynamic content generation technology is used. The output is a proposal tailored to the customer.

[0200] Step 6:

[0201] The generated proposal is sent to the user's terminal. The input is the generated proposal, which is securely transferred using a communication protocol. The output is the proposal that the user can utilize.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] One embodiment of this invention is a system that enables sales representatives to efficiently create proposals. This system operates server-centric, and users access the system via terminals.

[0219] The server first collects information from publicly available sources on the internet. Based on pre-configured keywords and conditions, the server automatically retrieves corporate investor relations information, industry news, and press releases from competitors. The collected data is then filtered by the server to remove unnecessary information.

[0220] Next, the server cleans up the information and then analyzes it using natural language processing techniques. The purpose of the analysis is to identify customer needs and potential challenges. For example, the server can analyze industry trends and competitor activities using natural language processing techniques to extract keywords and topics that are important to the customer.

[0221] Based on the analysis results, the server automatically generates a proposal. This proposal is structured according to a template set by the user in advance, and organizes the information necessary for sales activities. The proposal includes an overview of the customer's challenges, industry trends, competitor analysis, and proposed solutions, and covers all the essentials to support sales activities.

[0222] The completed proposal is sent from the server to the user's terminal using a secure communication method. The user can then review the proposal on their terminal and use it as reference material during client visits. This significantly reduces the time sales representatives spend gathering information and creating proposals, allowing them to focus on more value-added sales activities.

[0223] For example, when a sales representative is preparing to visit a new customer, the server automatically gathers and analyzes the latest information and competitive landscape of the relevant industry, and creates a proposal tailored to the purpose of the visit. The user can then efficiently prepare a presentation based on this proposal and conduct an effective business negotiation.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The server automatically collects information by accessing publicly available information sources on the internet (such as company IR pages, news sites, and industry reports) based on specified keywords and conditions. The information is obtained using APIs and web scraping techniques.

[0227] Step 2:

[0228] The server filters out irrelevant and duplicate data from the collected information, retaining only the necessary information. This is done using keyword matching and data deduplication algorithms.

[0229] Step 3:

[0230] The server cleans the filtered information. This process includes formatting the data, correcting outliers, and removing unnecessary strings.

[0231] Step 4:

[0232] The server uses natural language processing techniques to analyze clean data. Specifically, it performs topic modeling, keyword extraction, and sentiment analysis to identify data points for gaining important insights.

[0233] Step 5:

[0234] The server extracts customer needs and potential challenges based on the analysis results. This generates analysis results that combine customer-specific information with relevant industry trends and competitive information.

[0235] Step 6:

[0236] The server automatically generates a proposal based on the extracted information. The proposal is structured using a template set by the user and includes comprehensive analysis results and proposed content.

[0237] Step 7:

[0238] The server sends the generated proposal to the user's terminal. The terminal can then open the received proposal and view its contents, allowing the user to use it as reference material during client visits.

[0239] (Example 1)

[0240] 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."

[0241] In today's business environment, sales representatives need to quickly and accurately grasp large amounts of information and make effective proposals based on that information. However, information gathering and analysis require considerable time and effort, and the accuracy of the analysis directly impacts the success or failure of sales activities. Therefore, there is a need for efficient and highly accurate information gathering and analysis, and the rapid creation of proposals based on the results.

[0242] 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.

[0243] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information using data processing technology, and means for analyzing the filtered and cleaned information using natural language processing technology to extract and process needs and issues of interest. This makes it possible to quickly collect and analyze the information needed by sales representatives and automatically generate accurate proposals.

[0244] A "public information source" is a medium or platform that provides publicly available information accessible to a large number of users.

[0245] "Means of automatically collecting information" refers to systems and processes that collect digital information via a network without user intervention.

[0246] "Filtering" is the process of selecting necessary information from collected data and removing unnecessary information.

[0247] "Cleaning" is the process of formatting collected data, removing noise and redundancy, to make it usable.

[0248] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0249] "Methods for extracting and processing needs and challenges" refer to methods for analyzing and clarifying the requirements and problems that users and customers seek or need to solve, based on the analyzed information.

[0250] "Means of automatically generating documents" refers to a system for generating structured documents using a computer program.

[0251] "Communication methods" refer to methods and technologies for electronically sending and receiving information.

[0252] "Example configuration" refers to an arrangement or configuration assembled to suit specific conditions or purposes.

[0253] One embodiment of this invention is a system for enabling sales representatives to efficiently create proposals. The system operates server-centric, and users access the system via terminals. The server is responsible for information gathering, data filtering, natural language processing, and proposal generation.

[0254] The server utilizes APIs to automatically collect information from publicly available sources on the internet. For example, it can use APIs from platforms that provide news and financial information. Furthermore, Python libraries (e.g., Pandas, Numpy) are used for filtering and cleaning the information, performing noise reduction and selecting the necessary data.

[0255] By utilizing natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding, we can improve analysis accuracy and extract key customer needs and challenges. This allows us to accurately grasp industry trends and the actions of our competitors.

[0256] To generate proposals based on the analysis results, we utilize the Microsoft Word API and LaTeX templates. This ensures that the proposals clearly outline customer challenges, industry trends, competitor analysis, and proposed solutions. Finally, the generated proposals are sent from the server to the user's terminal via a secure communication method. This communication is secured using the SSL / TLS protocol.

[0257] As a concrete example, when a user visits a new customer, the server collects and analyzes the latest industry information and competitive landscape, automatically generating a proposal based on the results. The user can then review it on their terminal and efficiently prepare for the visit. An example of a prompt message would be, "Please automatically generate a proposal based on the latest industry trends and competitive analysis." This system makes sales activities more effective and faster.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server collects information from publicly available sources on the internet. Specifically, it uses configured keywords and a data collection API to retrieve corporate financial information, industry news, and competitor press releases. The input is raw data from public sources, and the output is the collected, unorganized data. This operation allows users to obtain the basis for the information they need.

[0261] Step 2:

[0262] The server filters and cleans the collected information using data processing techniques. Specifically, it uses the Python Pandas library to remove noise and select relevant information. The input is unprocessed raw data, and the output is formatted and refined data. This process clarifies only the necessary information and eliminates irrelevant information.

[0263] Step 3:

[0264] The server analyzes the filtered information using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract keywords related to customer needs and challenges. The input is formatted data, and the output is data with key topics and keywords extracted. This analysis allows users to identify potential business opportunities.

[0265] Step 4:

[0266] The server automatically generates a proposal based on the analysis results. Specifically, it utilizes the Microsoft Word API to create a proposal incorporating the obtained topics and keywords, following a pre-specified template. The input is the extracted keywords and analysis data, and the output is the completed proposal. This generation process allows users to obtain a proposal quickly and effectively.

[0267] Step 5:

[0268] The server securely sends the generated proposal to the user's terminal. Specifically, it uses the SSL / TLS protocol to send the proposal via email or cloud storage over a secure communication channel. The input is the completed proposal, and the output is the complete proposal saved on the user's terminal. This transmission allows the user to review the proposal and prepare for negotiations with clients.

[0269] (Application Example 1)

[0270] 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."

[0271] In existing sales activities, sales representatives spend a significant amount of time and effort gathering information and creating proposals. In particular, when customized information needs to be provided to each customer, manual processing has its limitations. This creates a challenge in efficiently and quickly providing customer-appropriate proposals.

[0272] 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.

[0273] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information, and means for analyzing the filtered and cleaned information to extract potential requests and issues. This makes it possible to efficiently provide customers with immediately customized proposals.

[0274] "Public information sources" refer to information media that are generally accessible, such as the internet, and include official company announcements, news articles, and online documents.

[0275] "Filtering" is the process of selecting necessary information from collected data and removing unnecessary information.

[0276] "Cleaning" is the process of organizing information, removing inaccurate data and noise, and preparing it for analysis.

[0277] "Analysis" refers to the act of data processing and analysis to evaluate obtained information and discover potential demands and needs.

[0278] "Potential requirements and challenges" refer to data and problems that users or customers may have as needs, even if they haven't explicitly stated them.

[0279] "Prompt materials" refer to proposal documents and information documents created based on analysis results and are generated for the purpose of being used by users.

[0280] "Natural language processing technology" refers to technology for a computer to understand, interpret, and generate human natural language.

[0281] "Important phrases" refer to the keywords and topics that should be particularly noted among the information identified in the analysis process.

[0282] "User-set format" refers to the format and template predefined by the user, meaning that the prompt materials are arranged in this form.

[0283] This invention is implemented as a system for salespersons to efficiently generate customized proposal materials. This system includes a server, a user's terminal, and an Internet connection.

[0284] The server has a function of automatically collecting information from public information sources on the Internet. These information includes corporate official announcements, news articles, industry reports, etc. Next, the collected information is filtered and cleaned by the server, unnecessary information is removed, and accurate and useful data is extracted.

[0285] The server then analyzes this data using natural language processing technology to identify potential requirements and issues. The natural language processing technology used here includes, for example, Python and the NLTK library. Also, the important phrases identified by the analysis are used as keywords when generating proposal materials.

[0286] The generated prompt materials are configured according to the format set by the user and sent to the terminal. The user can efficiently conduct sales activities using this material. Specifically, it becomes possible for a salesperson of a security service to quickly prepare a proposal incorporating the latest industry information and competitive information for a new customer.

[0287] For example, when a salesperson proposes a security solution to a specific customer, a prompt sentence such as "Please generate a proposal based on the latest news and competitive information in the industry to create a proposal for the latest security solution for new customers." can be used.

[0288] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0289] Step 1:

[0290] The server automatically collects the necessary information from public information sources on the Internet. Based on specific keywords set by the user, data is obtained from corporate websites, news portals, etc. The input for this step is the keyword, and the output is an unorganized group of information data.

[0291] Step 2:

[0292] The server filters and cleans the acquired information. In this step, redundant parts and inaccurate information are removed from the collected data, leaving only the necessary information. The input is the group of information data from Step 1, and the output is a clean data set.

[0293] Step 3:

[0294] The server analyzes the clean data set using natural language processing technology. In this analysis, keywords for identifying potential requirements and issues are extracted. The input is the clean data set from Step 2, and the output is a list of keywords identified by the analysis result.

[0295] Step 4:

[0296] The server generates presentation materials in a user-defined format based on keywords obtained through analysis. This process involves using templates to organize the information and prepare it as presentation material. The input consists of the keyword list and template information from step 3, and the output is the presentation material.

[0297] Step 5:

[0298] The terminal receives presentation materials sent from the server. Users can operate the terminal to review the generated materials and utilize them in their sales activities. The input is the presentation materials provided by the server, and the output is the final document that the user can view.

[0299] 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.

[0300] This invention relates to a system that has an emotion engine that recognizes and incorporates user emotions to enable sales representatives to efficiently create customer proposals. This system is mainly server-centric and interfaces with users through terminals.

[0301] The server first automatically collects relevant data from various publicly available sources on the internet. This includes corporate investor relations information, the latest industry news, and press releases from competitors. The collected information is filtered and cleaned to remove irrelevant data and noise.

[0302] Next, the server uses natural language processing techniques to analyze the filtered and cleaned data. The analysis extracts key keywords and topics, identifying potential customer needs and challenges. This data is then used to deepen the understanding of customer scenarios and market trends.

[0303] Furthermore, the emotion engine built into the server recognizes the user's emotions in real time. The emotional state is read, for example, from the user's voice and operations, and the system dynamically adjusts the content of the proposal based on that data. In this way, it becomes possible to create an effective proposal that makes use of emotional insights.

[0304] Finally, based on the analysis results and the information obtained from the emotion engine, the server automatically generates a proposal. The generated proposal is configured according to the template set by the user, and the content includes industry analysis, competitive information, and proposals based on the specific needs of the customer. The server sends the completed proposal to the terminal, and the user can view it and use it as materials for customer visits.

[0305] As a specific example, when the user approaches a new customer, the emotion engine can recognize at an emotional level what elements the user is interested in, and customize the proposal based on that feedback. As a result, the proposal becomes more personalized and is likely to attract the customer's attention.

[0306] The following describes the processing flow.

[0307] Step 1:

[0308] Based on a pre-set keyword list and conditions, the server periodically crawls public information sources on the Internet (e.g., news sites, corporate official pages, industry reports, etc.) and automatically collects the necessary information.

[0309] Step 2:

[0310] The server filters and cleans the collected information. Specifically, it excludes duplicate information and irrelevant information on the database, and deletes inappropriate elements to maintain the integrity of the information.

[0311] Step 3:

[0312] The server uses natural language processing technology to analyze filtered and cleaned information, extracting key keywords and topics related to industry trends, competitive strategies, and customer needs.

[0313] Step 4:

[0314] The server identifies the customer's potential needs and current challenges based on the analyzed information, and uses this as foundational data for proposals.

[0315] Step 5:

[0316] The emotion engine recognizes the user's emotions in real time and provides data to the server for refining proposals. Emotions are primarily obtained from the user's voice and interface interactions.

[0317] Step 6:

[0318] The server dynamically adjusts the analysis results based on the user's emotional state obtained from the emotion engine, and reflects this in the proposal. This makes the proposal more aligned with the user's emotions.

[0319] Step 7:

[0320] The server organizes the generated proposals according to the user's template and completes the final proposal that includes industry trends, competitive analysis, and customer needs.

[0321] Step 8:

[0322] The server sends the completed proposal to the user's terminal. The user reviews the proposal on their terminal and uses it for sales activities. The proposal includes visualized information and data to enhance the effectiveness of presentations and discussions.

[0323] (Example 2)

[0324] 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".

[0325] In modern sales activities, creating customer proposals quickly and effectively is essential for maintaining a competitive edge. However, existing methods make it difficult to efficiently generate personalized proposals that fully reflect the customer's latent needs and emotions, and sales representatives still have to expend considerable effort. In particular, adjusting proposal content to take customer emotions into account is a factor that greatly influences sales success, but conventional systems often do not perform such integrated processing. There is a need to improve this situation and develop a system that more effectively supports sales activities.

[0326] 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.

[0327] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information, means for analyzing the filtered and cleaned information using natural language processing technology to extract the customer's potential needs and challenges, means for automatically generating a proposal using an artificial intelligence model generated based on the analysis results, and means for recognizing the user's emotions in real time and applying that information to the proposal. This enables sales representatives to automatically generate effective proposals tailored to the customer's emotions, making it easier to attract the customer's interest with personalized proposal content, and improving the efficiency and results of sales activities.

[0328] "Public information sources" refer to a collection of information accessible to the general public, including company information, industry news, and announcements from competitors.

[0329] "Filtering" is the process of selecting the elements necessary for a given purpose from acquired information, and it is a means of improving data quality by removing irrelevant data and noise.

[0330] "Cleaning" is a process performed to remove further duplication and errors from filtered information in order to maintain the accuracy and consistency of the dataset.

[0331] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract keywords and topics from text.

[0332] A "generating artificial intelligence model" is an algorithm trained to produce a specific output based on defined input data, and in this case, it is used to automatically generate proposals.

[0333] "Recognizing emotions in real time" refers to the process of evaluating a user's emotional state on the spot, such as analyzing emotional data obtained from voice or operation information.

[0334] This invention is a system for sales representatives to quickly create customer proposals, and it includes a series of functions including information gathering from public sources, natural language processing, sentiment recognition, and automated proposal generation using artificial intelligence.

[0335] The server first uses technologies such as web crawlers and API access to automatically collect relevant information from publicly available sources on the internet. The hardware used in this process includes computing servers that enable high-speed processing and data storage. The collected information is filtered and cleaned using the Python Pandas library, which improves the accuracy and usefulness of the data.

[0336] Furthermore, the server analyzes the cleaned information using natural language processing technology. This analysis utilizes natural language processing libraries such as SpaCy and NLTK to extract potential customer needs and challenges from the information. Based on the analysis results, a generative AI model automatically generates a proposal. The generated proposal is structured according to a user-defined template and incorporates specific sales strategies.

[0337] To recognize user emotions, the server is equipped with an emotion analysis engine that analyzes user voice and operation information in real time. The technologies used here include a speech recognition engine and emotion analysis tools. Based on this data, the content of the proposal can be dynamically adjusted.

[0338] For example, if a user uses this system to create a proposal for a new product launch, the server prompts the AI ​​model with "Create a proposal that highlights the features of the new product. If possible, add graphs that will attract customer attention," and then constructs the optimal proposal.

[0339] In this way, this system can efficiently and effectively generate personalized sales proposals, significantly supporting the work of sales representatives.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] The server automatically collects information from publicly available sources. Specific URLs or APIs, such as company information, industry news, and competitor press releases, are provided as input. The server utilizes web crawlers and APIs to collect this information into its own database. The output is the collected, raw dataset.

[0343] Step 2:

[0344] The server filters and cleans the collected information. The input is the dataset collected in step 1. The server uses the Python Pandas library to apply filtering conditions to the data, removing duplicate data and irrelevant information. The output is purified data that has been filtered and cleaned.

[0345] Step 3:

[0346] The server analyzes the refined data using natural language processing techniques. The refined data obtained in step 2 is used as input. Here, text analysis is performed using the SpaCy library, including grammatical analysis and topic extraction, to identify the customer's potential needs and challenges. The output is a list of keywords and topics containing the analysis results.

[0347] Step 4:

[0348] The server uses an emotion engine to recognize the user's emotions in real time. User voice commands and operation log data are used as input. A speech recognition engine converts this data into text, and an emotion analysis tool analyzes that text to identify the user's emotional state. The output is emotional state data obtained from the emotion engine.

[0349] Step 5:

[0350] The server automatically generates a proposal using a generative AI model. The input consists of the analysis results from step 3 and the emotional state data from step 4. Based on this data, the generative AI model constructs the content according to the prompt: "Create a proposal that highlights the features of the new product. If possible, add graphs that will attract customer attention." The output is an automatically generated proposal following a template.

[0351] Step 6:

[0352] The server sends the generated proposal to the terminal. The input is the proposal generated in step 5. The server converts this data to the appropriate format and transmits it to the terminal. The terminal displays the received proposal, allowing the user to edit and print it in preparation for the customer visit. The output provides the proposal in a user-friendly format.

[0353] (Application Example 2)

[0354] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0355] In creating customer proposals, sales representatives are required to accurately understand the customer's emotions and needs and personalize the proposal content accordingly. However, traditional systems have made it difficult to dynamically adjust proposals in this way. Furthermore, in electronic payment services, the lack of real-time product suggestions and coupon offers that respond to the customer's immediate emotions makes improving the customer experience a challenge.

[0356] 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.

[0357] In this invention, the server includes means for automatically collecting information from public sources, means for filtering and cleaning the information, means for analyzing the filtered information and extracting the customer's potential needs and challenges, and means for recognizing the user's emotions and dynamically adjusting the proposed content based on them. This enables the generation of personalized proposals that respond to the customer's emotions and real-time product recommendations.

[0358] A "public information source" is a collection of data that is made publicly available on a network for automated information gathering.

[0359] "Filtering" is the process of removing irrelevant or harmful data from collected information and extracting highly relevant information.

[0360] "Cleaning" is the process of removing noise and inaccurate data from collected information to improve its accuracy and consistency.

[0361] "Analysis" refers to the processing and analysis techniques used to obtain meaningful patterns and insights from collected data.

[0362] "Customer latent needs" refer to customer requests and demands that have not yet been made apparent, and are revealed through data analysis.

[0363] A "challenge" is a problem or situation that the customer is facing that requires improvement, and it serves as an indicator that guides the direction of the proposed solution.

[0364] "Recognizing user emotions" refers to real-time technology that uses data such as audio and video to determine the emotional state of a user.

[0365] "Dynamic adjustment" means adaptively changing a system or process in response to the passage of time or changes in circumstances.

[0366] "Generating" refers to the act of creating new outputs or content based on various data and algorithms.

[0367] A system for realizing an application of this invention consists primarily of a server, a user terminal, and an emotion recognition engine. The server automatically collects information about companies and markets from publicly available information sources on the internet. The collected data is filtered and cleaned to remove noise and unnecessary information. A database management system (e.g., MySQL) and a natural language processing library (e.g., NLTK) are used for this processing.

[0368] The analysis is performed using natural language processing technology on the server to extract the customer's potential needs and challenges. This allows for more appropriate customization of the proposal. The user terminal is typically a smartphone or tablet device, which receives the proposal sent from the server. The proposal is structured based on a pre-configured template and is provided in a state that can be immediately used by the user in business negotiations or customer visits.

[0369] Furthermore, the server is equipped with an emotion recognition engine that analyzes the user's emotional state from their actions and voice. Based on this emotion data, a generative AI model generates offers in real time that are tailored to the user's mood. Software such as OpenCV and TensorFlow are used for this purpose. For example, if a customer's facial expression in a cafe indicates that they want to relax, the generative AI model will offer an offer such as "a discount on relaxing herbal tea."

[0370] A possible example of a specific prompt message would be: "We have analyzed that the user is currently in an emotional state seeking relaxation. Please generate product offers related to this emotion."

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server collects data from publicly available sources. The input consists of URLs of corporate investor relations (IR) information and industry news, and the server uses web scraping techniques to collect this information. The output is raw text data.

[0374] Step 2:

[0375] The server filters and cleans the collected text data. The input is raw text data, and irrelevant information is removed using regular expressions and denoising algorithms. The output is clean text data containing only meaningful information.

[0376] Step 3:

[0377] The server analyzes clean text data using natural language processing techniques. The input is filtered text data, and keywords and topics are extracted using natural language processing libraries. The output is analytical data regarding the customer's potential needs and challenges.

[0378] Step 4:

[0379] To recognize the user's emotional state in real time, the emotion recognition engine analyzes voice and operation data. The input consists of user voice data and operation logs, and machine learning algorithms are used to identify emotions. The output is data indicating the user's emotional state.

[0380] Step 5:

[0381] The server dynamically adjusts the proposal content based on analysis results and sentiment data, and generates personalized proposals using a generative AI model. The inputs are needs analysis and sentiment data, and dynamic content generation technology is used. The output is a proposal tailored to the customer.

[0382] Step 6:

[0383] The generated proposal is sent to the user's terminal. The input is the generated proposal, which is securely transferred using a communication protocol. The output is the proposal that the user can utilize.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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".

[0400] One embodiment of this invention is a system that enables sales representatives to efficiently create proposals. This system operates server-centric, and users access the system via terminals.

[0401] The server first collects information from publicly available sources on the internet. Based on pre-configured keywords and conditions, the server automatically retrieves corporate investor relations information, industry news, and press releases from competitors. The collected data is then filtered by the server to remove unnecessary information.

[0402] Next, the server cleans up the information and then analyzes it using natural language processing techniques. The purpose of the analysis is to identify customer needs and potential challenges. For example, the server can analyze industry trends and competitor activities using natural language processing techniques to extract keywords and topics that are important to the customer.

[0403] Based on the analysis results, the server automatically generates a proposal. This proposal is structured according to a template set by the user in advance, and organizes the information necessary for sales activities. The proposal includes an overview of the customer's challenges, industry trends, competitor analysis, and proposed solutions, and covers all the essentials to support sales activities.

[0404] The completed proposal is sent from the server to the user's terminal using a secure communication method. The user can then review the proposal on their terminal and use it as reference material during client visits. This significantly reduces the time sales representatives spend gathering information and creating proposals, allowing them to focus on more value-added sales activities.

[0405] For example, when a sales representative is preparing to visit a new customer, the server automatically gathers and analyzes the latest information and competitive landscape of the relevant industry, and creates a proposal tailored to the purpose of the visit. The user can then efficiently prepare a presentation based on this proposal and conduct an effective business negotiation.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The server automatically collects information by accessing publicly available information sources on the internet (such as company IR pages, news sites, and industry reports) based on specified keywords and conditions. The information is obtained using APIs and web scraping techniques.

[0409] Step 2:

[0410] The server filters out irrelevant and duplicate data from the collected information, retaining only the necessary information. This is done using keyword matching and data deduplication algorithms.

[0411] Step 3:

[0412] The server cleans the filtered information. This process includes formatting the data, correcting outliers, and removing unnecessary strings.

[0413] Step 4:

[0414] The server uses natural language processing techniques to analyze clean data. Specifically, it performs topic modeling, keyword extraction, and sentiment analysis to identify data points for gaining important insights.

[0415] Step 5:

[0416] The server extracts customer needs and potential challenges based on the analysis results. This generates analysis results that combine customer-specific information with relevant industry trends and competitive information.

[0417] Step 6:

[0418] The server automatically generates a proposal based on the extracted information. The proposal is structured using a template set by the user and includes comprehensive analysis results and proposed content.

[0419] Step 7:

[0420] The server sends the generated proposal to the user's terminal. The terminal can then open the received proposal and view its contents, allowing the user to use it as reference material during client visits.

[0421] (Example 1)

[0422] 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."

[0423] In today's business environment, sales representatives need to quickly and accurately grasp large amounts of information and make effective proposals based on that information. However, information gathering and analysis require considerable time and effort, and the accuracy of the analysis directly impacts the success or failure of sales activities. Therefore, there is a need for efficient and highly accurate information gathering and analysis, and the rapid creation of proposals based on the results.

[0424] 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.

[0425] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information using data processing technology, and means for analyzing the filtered and cleaned information using natural language processing technology to extract and process needs and issues of interest. This makes it possible to quickly collect and analyze the information needed by sales representatives and automatically generate accurate proposals.

[0426] A "public information source" is a medium or platform that provides publicly available information accessible to a large number of users.

[0427] "Means of automatically collecting information" refers to systems and processes that collect digital information via a network without user intervention.

[0428] "Filtering" is the process of selecting necessary information from collected data and removing unnecessary information.

[0429] "Cleaning" is the process of formatting collected data, removing noise and redundancy, to make it usable.

[0430] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0431] "Methods for extracting and processing needs and challenges" refer to methods for analyzing and clarifying the requirements and problems that users and customers seek or need to solve, based on the analyzed information.

[0432] "Means of automatically generating documents" refers to a system for generating structured documents using a computer program.

[0433] "Communication methods" refer to methods and technologies for electronically sending and receiving information.

[0434] "Example configuration" refers to an arrangement or configuration assembled to suit specific conditions or purposes.

[0435] One embodiment of this invention is a system for enabling sales representatives to efficiently create proposals. The system operates server-centric, and users access the system via terminals. The server is responsible for information gathering, data filtering, natural language processing, and proposal generation.

[0436] The server utilizes APIs to automatically collect information from publicly available sources on the internet. For example, it can use APIs from platforms that provide news and financial information. Furthermore, Python libraries (e.g., Pandas, Numpy) are used for filtering and cleaning the information, performing noise reduction and selecting the necessary data.

[0437] By utilizing natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding, we can improve analysis accuracy and extract key customer needs and challenges. This allows us to accurately grasp industry trends and the actions of our competitors.

[0438] To generate proposals based on the analysis results, we utilize the Microsoft Word API and LaTeX templates. This ensures that the proposals clearly outline customer challenges, industry trends, competitor analysis, and proposed solutions. Finally, the generated proposals are sent from the server to the user's terminal via a secure communication method. This communication is secured using the SSL / TLS protocol.

[0439] As a concrete example, when a user visits a new customer, the server collects and analyzes the latest industry information and competitive landscape, automatically generating a proposal based on the results. The user can then review it on their terminal and efficiently prepare for the visit. An example of a prompt message would be, "Please automatically generate a proposal based on the latest industry trends and competitive analysis." This system makes sales activities more effective and faster.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The server collects information from publicly available sources on the internet. Specifically, it uses configured keywords and a data collection API to retrieve corporate financial information, industry news, and competitor press releases. The input is raw data from public sources, and the output is the collected, unorganized data. This operation allows users to obtain the basis for the information they need.

[0443] Step 2:

[0444] The server filters and cleans the collected information using data processing techniques. Specifically, it uses the Python Pandas library to remove noise and select relevant information. The input is unprocessed raw data, and the output is formatted and refined data. This process clarifies only the necessary information and eliminates irrelevant information.

[0445] Step 3:

[0446] The server analyzes the filtered information using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract keywords related to customer needs and challenges. The input is formatted data, and the output is data with key topics and keywords extracted. This analysis allows users to identify potential business opportunities.

[0447] Step 4:

[0448] The server automatically generates a proposal based on the analysis results. Specifically, it utilizes the Microsoft Word API to create a proposal incorporating the obtained topics and keywords, following a pre-specified template. The input is the extracted keywords and analysis data, and the output is the completed proposal. This generation process allows users to obtain a proposal quickly and effectively.

[0449] Step 5:

[0450] The server securely sends the generated proposal to the user's terminal. Specifically, it uses the SSL / TLS protocol to send the proposal via email or cloud storage over a secure communication channel. The input is the completed proposal, and the output is the complete proposal saved on the user's terminal. This transmission allows the user to review the proposal and prepare for negotiations with clients.

[0451] (Application Example 1)

[0452] 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."

[0453] In existing sales activities, sales representatives spend a significant amount of time and effort gathering information and creating proposals. In particular, when customized information needs to be provided to each customer, manual processing has its limitations. This creates a challenge in efficiently and quickly providing customer-appropriate proposals.

[0454] 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.

[0455] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information, and means for analyzing the filtered and cleaned information to extract potential requests and issues. This makes it possible to efficiently provide customers with immediately customized proposals.

[0456] "Public information sources" refer to information media that are generally accessible, such as the internet, and include official company announcements, news articles, and online documents.

[0457] "Filtering" is the process of selecting necessary information from collected data and removing unnecessary information.

[0458] "Cleaning" is the process of organizing information, removing inaccurate data and noise, and preparing it for analysis.

[0459] "Analysis" refers to the act of data processing and analysis to evaluate obtained information and discover potential demands and needs.

[0460] "Potential requirements and challenges" refer to data and problems that users or customers may have as needs, even if they haven't explicitly stated them.

[0461] "Presented materials" refer to proposals and informational documents created based on analysis results, and are generated for the purpose of user use.

[0462] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[0463] "Important terms" refer to keywords or topics that are particularly noteworthy among the information identified during the analysis process.

[0464] "User-defined format" refers to a format or template that the user has predetermined, meaning that the presentation materials will be organized in this format.

[0465] This invention is implemented as a system for sales representatives to efficiently generate customized proposal materials. The system includes a server, a user terminal, and an internet connection.

[0466] The server has the capability to automatically collect information from publicly available sources on the internet. This information includes official company announcements, news articles, and industry reports. The collected information is then filtered and cleaned by the server to remove unnecessary information and extract accurate and useful data.

[0467] The server then analyzes this data using natural language processing techniques to identify potential requirements and challenges. These techniques include, for example, Python and the NLTK library. Key terms identified through the analysis are then used as keywords when generating proposal documents.

[0468] The generated presentation materials are structured according to the user's specified format and sent to the terminal. Users can then use these materials to conduct sales activities efficiently. Specifically, security service sales representatives can quickly prepare proposals for new customers that incorporate the latest industry and competitor information.

[0469] For example, when a sales representative is proposing a security solution to a specific customer, they could use a prompt like, "Generate a proposal based on the latest industry news and competitive information to create a state-of-the-art security solution proposal for a new customer."

[0470] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0471] Step 1:

[0472] The server automatically collects necessary information from publicly available sources on the internet. Based on specific keywords set by the user, it retrieves data from sources such as company websites and news portals. The input for this step is keywords, and the output is a collection of unorganized information data.

[0473] Step 2:

[0474] The server filters and cleans the acquired information. In this step, redundant and inaccurate information is removed from the collected data, leaving only the necessary information. The input is the information data set from step 1, and the output is a clean dataset.

[0475] Step 3:

[0476] The server analyzes a clean dataset using natural language processing techniques. This analysis extracts keywords to identify potential requirements and challenges. The input is the clean dataset from step 2, and the output is a list of keywords identified in the analysis.

[0477] Step 4:

[0478] The server generates presentation materials in a user-defined format based on keywords obtained through analysis. This process involves using templates to organize the information and prepare it as presentation material. The input consists of the keyword list and template information from step 3, and the output is the presentation material.

[0479] Step 5:

[0480] The terminal receives presentation materials sent from the server. Users can operate the terminal to review the generated materials and utilize them in their sales activities. The input is the presentation materials provided by the server, and the output is the final document that the user can view.

[0481] 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.

[0482] This invention relates to a system that has an emotion engine that recognizes and incorporates user emotions to enable sales representatives to efficiently create customer proposals. This system is mainly server-centric and interfaces with users through terminals.

[0483] The server first automatically collects relevant data from various publicly available sources on the internet. This includes corporate investor relations information, the latest industry news, and press releases from competitors. The collected information is filtered and cleaned to remove irrelevant data and noise.

[0484] Next, the server uses natural language processing techniques to analyze the filtered and cleaned data. The analysis extracts key keywords and topics, identifying potential customer needs and challenges. This data is then used to deepen the understanding of customer scenarios and market trends.

[0485] Furthermore, the emotion engine built into the server recognizes the user's emotions in real time. Emotional states are read, for example, from the user's voice and actions, and the system dynamically adjusts the content of the proposal based on this data. In this way, it becomes possible to create effective proposals that leverage emotional insights.

[0486] Finally, based on the analysis results and information obtained from the emotion engine, the server automatically generates a proposal. The generated proposal is structured according to a template set by the user and includes industry analysis, competitor information, and suggestions based on the specific needs of the customer. The server sends the completed proposal to the terminal, where the user can view it and use it as reference material during customer visits.

[0487] As a concrete example, when a user approaches a new customer, the emotion engine can recognize which elements the user is interested in at an emotional level and customize the proposal based on that feedback. This makes the proposal more personalized and more likely to capture the customer's attention.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The server periodically crawls publicly available information sources on the internet (e.g., news sites, official company pages, industry reports, etc.) based on pre-configured keyword lists and conditions, and automatically collects the necessary information.

[0491] Step 2:

[0492] The server filters and cleans the collected information. Specifically, it removes duplicate and irrelevant information from the database and deletes inappropriate elements to maintain the integrity of the information.

[0493] Step 3:

[0494] The server uses natural language processing technology to analyze filtered and cleaned information, extracting key keywords and topics related to industry trends, competitive strategies, and customer needs.

[0495] Step 4:

[0496] The server identifies the customer's potential needs and current challenges based on the analyzed information, and uses this as foundational data for proposals.

[0497] Step 5:

[0498] The emotion engine recognizes the user's emotions in real time and provides data to the server for refining proposals. Emotions are primarily obtained from the user's voice and interface interactions.

[0499] Step 6:

[0500] The server dynamically adjusts the analysis results based on the user's emotional state obtained from the emotion engine, and reflects this in the proposal. This makes the proposal more aligned with the user's emotions.

[0501] Step 7:

[0502] The server organizes the generated proposals according to the user's template and completes the final proposal, which includes industry trends, competitive analysis, and customer needs.

[0503] Step 8:

[0504] The server sends the completed proposal to the user's terminal. The user reviews the proposal on their terminal and uses it for sales activities. The proposal includes visualized information and data to enhance the effectiveness of presentations and discussions.

[0505] (Example 2)

[0506] 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."

[0507] In modern sales activities, creating customer proposals quickly and effectively is essential for maintaining a competitive edge. However, existing methods make it difficult to efficiently generate personalized proposals that fully reflect the customer's latent needs and emotions, and sales representatives still have to expend considerable effort. In particular, adjusting proposal content to take customer emotions into account is a factor that greatly influences sales success, but conventional systems often do not perform such integrated processing. There is a need to improve this situation and develop a system that more effectively supports sales activities.

[0508] 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.

[0509] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information, means for analyzing the filtered and cleaned information using natural language processing technology to extract the customer's potential needs and challenges, means for automatically generating a proposal using an artificial intelligence model generated based on the analysis results, and means for recognizing the user's emotions in real time and applying that information to the proposal. This enables sales representatives to automatically generate effective proposals tailored to the customer's emotions, making it easier to attract the customer's interest with personalized proposal content, and improving the efficiency and results of sales activities.

[0510] "Public information sources" refer to a collection of information accessible to the general public, including company information, industry news, and announcements from competitors.

[0511] "Filtering" is the process of selecting the elements necessary for a given purpose from acquired information, and it is a means of improving data quality by removing irrelevant data and noise.

[0512] "Cleaning" is a process performed to remove further duplication and errors from filtered information in order to maintain the accuracy and consistency of the dataset.

[0513] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract keywords and topics from text.

[0514] A "generating artificial intelligence model" is an algorithm trained to produce a specific output based on defined input data, and in this case, it is used to automatically generate proposals.

[0515] "Recognizing emotions in real time" refers to the process of evaluating a user's emotional state on the spot, such as analyzing emotional data obtained from voice or operation information.

[0516] This invention is a system for sales representatives to quickly create customer proposals, and it includes a series of functions including information gathering from public sources, natural language processing, sentiment recognition, and automated proposal generation using artificial intelligence.

[0517] The server first uses technologies such as web crawlers and API access to automatically collect relevant information from publicly available sources on the internet. The hardware used in this process includes computing servers that enable high-speed processing and data storage. The collected information is filtered and cleaned using the Python Pandas library, which improves the accuracy and usefulness of the data.

[0518] Furthermore, the server analyzes the cleaned information using natural language processing technology. This analysis utilizes natural language processing libraries such as SpaCy and NLTK to extract potential customer needs and challenges from the information. Based on the analysis results, a generative AI model automatically generates a proposal. The generated proposal is structured according to a user-defined template and incorporates specific sales strategies.

[0519] To recognize user emotions, the server is equipped with an emotion analysis engine that analyzes user voice and operation information in real time. The technologies used here include a speech recognition engine and emotion analysis tools. Based on this data, the content of the proposal can be dynamically adjusted.

[0520] For example, if a user uses this system to create a proposal for a new product launch, the server prompts the AI ​​model with "Create a proposal that highlights the features of the new product. If possible, add graphs that will attract customer attention," and then constructs the optimal proposal.

[0521] In this way, this system can efficiently and effectively generate personalized sales proposals, significantly supporting the work of sales representatives.

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] The server automatically collects information from publicly available sources. Specific URLs or APIs, such as company information, industry news, and competitor press releases, are provided as input. The server utilizes web crawlers and APIs to collect this information into its own database. The output is the collected, raw dataset.

[0525] Step 2:

[0526] The server filters and cleans the collected information. The input is the dataset collected in step 1. The server uses the Python Pandas library to apply filtering conditions to the data, removing duplicate data and irrelevant information. The output is purified data that has been filtered and cleaned.

[0527] Step 3:

[0528] The server analyzes the refined data using natural language processing techniques. The refined data obtained in step 2 is used as input. Here, text analysis is performed using the SpaCy library, including grammatical analysis and topic extraction, to identify the customer's potential needs and challenges. The output is a list of keywords and topics containing the analysis results.

[0529] Step 4:

[0530] The server uses an emotion engine to recognize the user's emotions in real time. User voice commands and operation log data are used as input. A speech recognition engine converts this data into text, and an emotion analysis tool analyzes that text to identify the user's emotional state. The output is emotional state data obtained from the emotion engine.

[0531] Step 5:

[0532] The server automatically generates a proposal using a generative AI model. The input consists of the analysis results from step 3 and the emotional state data from step 4. Based on this data, the generative AI model constructs the content according to the prompt: "Create a proposal that highlights the features of the new product. If possible, add graphs that will attract customer attention." The output is an automatically generated proposal following a template.

[0533] Step 6:

[0534] The server sends the generated proposal to the terminal. The input is the proposal generated in step 5. The server converts this data to the appropriate format and transmits it to the terminal. The terminal displays the received proposal, allowing the user to edit and print it in preparation for the customer visit. The output provides the proposal in a user-friendly format.

[0535] (Application Example 2)

[0536] 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."

[0537] In creating customer proposals, sales representatives are required to accurately understand the customer's emotions and needs and personalize the proposal content accordingly. However, traditional systems have made it difficult to dynamically adjust proposals in this way. Furthermore, in electronic payment services, the lack of real-time product suggestions and coupon offers that respond to the customer's immediate emotions makes improving the customer experience a challenge.

[0538] 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.

[0539] In this invention, the server includes means for automatically collecting information from public sources, means for filtering and cleaning the information, means for analyzing the filtered information and extracting the customer's potential needs and challenges, and means for recognizing the user's emotions and dynamically adjusting the proposed content based on them. This enables the generation of personalized proposals that respond to the customer's emotions and real-time product recommendations.

[0540] A "public information source" is a collection of data that is made publicly available on a network for automated information gathering.

[0541] "Filtering" is the process of removing irrelevant or harmful data from collected information and extracting highly relevant information.

[0542] "Cleaning" is the process of removing noise and inaccurate data from collected information to improve its accuracy and consistency.

[0543] "Analysis" refers to the processing and analysis techniques used to obtain meaningful patterns and insights from collected data.

[0544] "Customer latent needs" refer to customer requests and demands that have not yet been made apparent, and are revealed through data analysis.

[0545] A "challenge" is a problem or situation that the customer is facing that requires improvement, and it serves as an indicator that guides the direction of the proposed solution.

[0546] "Recognizing user emotions" refers to real-time technology that uses data such as audio and video to determine the emotional state of a user.

[0547] "Dynamic adjustment" means adaptively changing a system or process in response to the passage of time or changes in circumstances.

[0548] "Generating" refers to the act of creating new outputs or content based on various data and algorithms.

[0549] A system for realizing an application of this invention consists primarily of a server, a user terminal, and an emotion recognition engine. The server automatically collects information about companies and markets from publicly available information sources on the internet. The collected data is filtered and cleaned to remove noise and unnecessary information. A database management system (e.g., MySQL) and a natural language processing library (e.g., NLTK) are used for this processing.

[0550] The analysis is performed using natural language processing technology on the server to extract the customer's potential needs and challenges. This allows for more appropriate customization of the proposal. The user terminal is typically a smartphone or tablet device, which receives the proposal sent from the server. The proposal is structured based on a pre-configured template and is provided in a state that can be immediately used by the user in business negotiations or customer visits.

[0551] Furthermore, the server is equipped with an emotion recognition engine that analyzes the user's emotional state from their actions and voice. Based on this emotion data, a generative AI model generates offers in real time that are tailored to the user's mood. Software such as OpenCV and TensorFlow are used for this purpose. For example, if a customer's facial expression in a cafe indicates that they want to relax, the generative AI model will offer an offer such as "a discount on relaxing herbal tea."

[0552] A possible example of a specific prompt message would be: "We have analyzed that the user is currently in an emotional state seeking relaxation. Please generate product offers related to this emotion."

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] The server collects data from publicly available sources. The input consists of URLs of corporate investor relations (IR) information and industry news, and the server uses web scraping techniques to collect this information. The output is raw text data.

[0556] Step 2:

[0557] The server filters and cleans the collected text data. The input is raw text data, and irrelevant information is removed using regular expressions and denoising algorithms. The output is clean text data containing only meaningful information.

[0558] Step 3:

[0559] The server analyzes clean text data using natural language processing techniques. The input is filtered text data, and keywords and topics are extracted using natural language processing libraries. The output is analytical data regarding the customer's potential needs and challenges.

[0560] Step 4:

[0561] To recognize the user's emotional state in real time, the emotion recognition engine analyzes voice and operation data. The input consists of user voice data and operation logs, and machine learning algorithms are used to identify emotions. The output is data indicating the user's emotional state.

[0562] Step 5:

[0563] The server dynamically adjusts the proposal content based on analysis results and sentiment data, and generates personalized proposals using a generative AI model. The inputs are needs analysis and sentiment data, and dynamic content generation technology is used. The output is a proposal tailored to the customer.

[0564] Step 6:

[0565] The generated proposal is sent to the user's terminal. The input is the generated proposal, which is securely transferred using a communication protocol. The output is the proposal that the user can utilize.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] [Fourth Embodiment]

[0570] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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".

[0583] One embodiment of this invention is a system that enables sales representatives to efficiently create proposals. This system operates server-centric, and users access the system via terminals.

[0584] The server first collects information from publicly available sources on the internet. Based on pre-configured keywords and conditions, the server automatically retrieves corporate investor relations information, industry news, and press releases from competitors. The collected data is then filtered by the server to remove unnecessary information.

[0585] Next, the server cleans up the information and then analyzes it using natural language processing techniques. The purpose of the analysis is to identify customer needs and potential challenges. For example, the server can analyze industry trends and competitor activities using natural language processing techniques to extract keywords and topics that are important to the customer.

[0586] Based on the analysis results, the server automatically generates a proposal. This proposal is structured according to a template set by the user in advance, and organizes the information necessary for sales activities. The proposal includes an overview of the customer's challenges, industry trends, competitor analysis, and proposed solutions, and covers all the essentials to support sales activities.

[0587] The completed proposal is sent from the server to the user's terminal using a secure communication method. The user can then review the proposal on their terminal and use it as reference material during client visits. This significantly reduces the time sales representatives spend gathering information and creating proposals, allowing them to focus on more value-added sales activities.

[0588] For example, when a sales representative is preparing to visit a new customer, the server automatically gathers and analyzes the latest information and competitive landscape of the relevant industry, and creates a proposal tailored to the purpose of the visit. The user can then efficiently prepare a presentation based on this proposal and conduct an effective business negotiation.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The server automatically collects information by accessing publicly available information sources on the internet (such as company IR pages, news sites, and industry reports) based on specified keywords and conditions. The information is obtained using APIs and web scraping techniques.

[0592] Step 2:

[0593] The server filters out irrelevant and duplicate data from the collected information, retaining only the necessary information. This is done using keyword matching and data deduplication algorithms.

[0594] Step 3:

[0595] The server cleans the filtered information. This process includes formatting the data, correcting outliers, and removing unnecessary strings.

[0596] Step 4:

[0597] The server uses natural language processing techniques to analyze clean data. Specifically, it performs topic modeling, keyword extraction, and sentiment analysis to identify data points for gaining important insights.

[0598] Step 5:

[0599] The server extracts customer needs and potential challenges based on the analysis results. This generates analysis results that combine customer-specific information with relevant industry trends and competitive information.

[0600] Step 6:

[0601] The server automatically generates a proposal based on the extracted information. The proposal is structured using a template set by the user and includes comprehensive analysis results and proposed content.

[0602] Step 7:

[0603] The server sends the generated proposal to the user's terminal. The terminal can then open the received proposal and view its contents, allowing the user to use it as reference material during client visits.

[0604] (Example 1)

[0605] 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".

[0606] In today's business environment, sales representatives need to quickly and accurately grasp large amounts of information and make effective proposals based on that information. However, information gathering and analysis require considerable time and effort, and the accuracy of the analysis directly impacts the success or failure of sales activities. Therefore, there is a need for efficient and highly accurate information gathering and analysis, and the rapid creation of proposals based on the results.

[0607] 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.

[0608] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information using data processing technology, and means for analyzing the filtered and cleaned information using natural language processing technology to extract and process needs and issues of interest. This makes it possible to quickly collect and analyze the information needed by sales representatives and automatically generate accurate proposals.

[0609] A "public information source" is a medium or platform that provides publicly available information accessible to a large number of users.

[0610] "Means of automatically collecting information" refers to systems and processes that collect digital information via a network without user intervention.

[0611] "Filtering" is the process of selecting necessary information from collected data and removing unnecessary information.

[0612] "Cleaning" is the process of formatting collected data, removing noise and redundancy, to make it usable.

[0613] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0614] "Methods for extracting and processing needs and challenges" refer to methods for analyzing and clarifying the requirements and problems that users and customers seek or need to solve, based on the analyzed information.

[0615] "Means of automatically generating documents" refers to a system for generating structured documents using a computer program.

[0616] "Communication methods" refer to methods and technologies for electronically sending and receiving information.

[0617] "Example configuration" refers to an arrangement or configuration assembled to suit specific conditions or purposes.

[0618] One embodiment of this invention is a system for enabling sales representatives to efficiently create proposals. The system operates server-centric, and users access the system via terminals. The server is responsible for information gathering, data filtering, natural language processing, and proposal generation.

[0619] The server utilizes APIs to automatically collect information from publicly available sources on the internet. For example, it can use APIs from platforms that provide news and financial information. Furthermore, Python libraries (e.g., Pandas, Numpy) are used for filtering and cleaning the information, performing noise reduction and selecting the necessary data.

[0620] By utilizing natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding, we can improve analysis accuracy and extract key customer needs and challenges. This allows us to accurately grasp industry trends and the actions of our competitors.

[0621] To generate proposals based on the analysis results, we utilize the Microsoft Word API and LaTeX templates. This ensures that the proposals clearly outline customer challenges, industry trends, competitor analysis, and proposed solutions. Finally, the generated proposals are sent from the server to the user's terminal via a secure communication method. This communication is secured using the SSL / TLS protocol.

[0622] As a concrete example, when a user visits a new customer, the server collects and analyzes the latest industry information and competitive landscape, automatically generating a proposal based on the results. The user can then review it on their terminal and efficiently prepare for the visit. An example of a prompt message would be, "Please automatically generate a proposal based on the latest industry trends and competitive analysis." This system makes sales activities more effective and faster.

[0623] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0624] Step 1:

[0625] The server collects information from publicly available sources on the internet. Specifically, it uses configured keywords and a data collection API to retrieve corporate financial information, industry news, and competitor press releases. The input is raw data from public sources, and the output is the collected, unorganized data. This operation allows users to obtain the basis for the information they need.

[0626] Step 2:

[0627] The server filters and cleans the collected information using data processing techniques. Specifically, it uses the Python Pandas library to remove noise and select relevant information. The input is unprocessed raw data, and the output is formatted and refined data. This process clarifies only the necessary information and eliminates irrelevant information.

[0628] Step 3:

[0629] The server analyzes the filtered information using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract keywords related to customer needs and challenges. The input is formatted data, and the output is data with key topics and keywords extracted. This analysis allows users to identify potential business opportunities.

[0630] Step 4:

[0631] The server automatically generates a proposal based on the analysis results. Specifically, it utilizes the Microsoft Word API to create a proposal incorporating the obtained topics and keywords, following a pre-specified template. The input is the extracted keywords and analysis data, and the output is the completed proposal. This generation process allows users to obtain a proposal quickly and effectively.

[0632] Step 5:

[0633] The server securely sends the generated proposal to the user's terminal. Specifically, it uses the SSL / TLS protocol to send the proposal via email or cloud storage over a secure communication channel. The input is the completed proposal, and the output is the complete proposal saved on the user's terminal. This transmission allows the user to review the proposal and prepare for negotiations with clients.

[0634] (Application Example 1)

[0635] 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".

[0636] In existing sales activities, sales representatives spend a significant amount of time and effort gathering information and creating proposals. In particular, when customized information needs to be provided to each customer, manual processing has its limitations. This creates a challenge in efficiently and quickly providing customer-appropriate proposals.

[0637] 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.

[0638] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information, and means for analyzing the filtered and cleaned information to extract potential requests and issues. This makes it possible to efficiently provide customers with immediately customized proposals.

[0639] "Public information sources" refer to information media that are generally accessible, such as the internet, and include official company announcements, news articles, and online documents.

[0640] "Filtering" is the process of selecting necessary information from collected data and removing unnecessary information.

[0641] "Cleaning" is the process of organizing information, removing inaccurate data and noise, and preparing it for analysis.

[0642] "Analysis" refers to the act of data processing and analysis to evaluate obtained information and discover potential demands and needs.

[0643] "Potential requirements and challenges" refer to data and problems that users or customers may have as needs, even if they haven't explicitly stated them.

[0644] "Presented materials" refer to proposals and informational documents created based on analysis results, and are generated for the purpose of user use.

[0645] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human natural language.

[0646] "Important terms" refer to keywords or topics that are particularly noteworthy among the information identified during the analysis process.

[0647] "User-defined format" refers to a format or template that the user has predetermined, meaning that the presentation materials will be organized in this format.

[0648] This invention is implemented as a system for sales representatives to efficiently generate customized proposal materials. The system includes a server, a user terminal, and an internet connection.

[0649] The server has the capability to automatically collect information from publicly available sources on the internet. This information includes official company announcements, news articles, and industry reports. The collected information is then filtered and cleaned by the server to remove unnecessary information and extract accurate and useful data.

[0650] The server then analyzes this data using natural language processing techniques to identify potential requirements and challenges. These techniques include, for example, Python and the NLTK library. Key terms identified through the analysis are then used as keywords when generating proposal documents.

[0651] The generated presentation materials are structured according to the user's specified format and sent to the terminal. Users can then use these materials to conduct sales activities efficiently. Specifically, security service sales representatives can quickly prepare proposals for new customers that incorporate the latest industry and competitor information.

[0652] For example, when a sales representative is proposing a security solution to a specific customer, they could use a prompt like, "Generate a proposal based on the latest industry news and competitive information to create a state-of-the-art security solution proposal for a new customer."

[0653] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0654] Step 1:

[0655] The server automatically collects necessary information from publicly available sources on the internet. Based on specific keywords set by the user, it retrieves data from sources such as company websites and news portals. The input for this step is keywords, and the output is a collection of unorganized information data.

[0656] Step 2:

[0657] The server filters and cleans the acquired information. In this step, redundant and inaccurate information is removed from the collected data, leaving only the necessary information. The input is the information data set from step 1, and the output is a clean dataset.

[0658] Step 3:

[0659] The server analyzes a clean dataset using natural language processing techniques. This analysis extracts keywords to identify potential requirements and challenges. The input is the clean dataset from step 2, and the output is a list of keywords identified in the analysis.

[0660] Step 4:

[0661] The server generates presentation materials in a user-defined format based on keywords obtained through analysis. This process involves using templates to organize the information and prepare it as presentation material. The input consists of the keyword list and template information from step 3, and the output is the presentation material.

[0662] Step 5:

[0663] The terminal receives presentation materials sent from the server. Users can operate the terminal to review the generated materials and utilize them in their sales activities. The input is the presentation materials provided by the server, and the output is the final document that the user can view.

[0664] 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.

[0665] This invention relates to a system that has an emotion engine that recognizes and incorporates user emotions to enable sales representatives to efficiently create customer proposals. This system is mainly server-centric and interfaces with users through terminals.

[0666] The server first automatically collects relevant data from various publicly available sources on the internet. This includes corporate investor relations information, the latest industry news, and press releases from competitors. The collected information is filtered and cleaned to remove irrelevant data and noise.

[0667] Next, the server uses natural language processing techniques to analyze the filtered and cleaned data. The analysis extracts key keywords and topics, identifying potential customer needs and challenges. This data is then used to deepen the understanding of customer scenarios and market trends.

[0668] Furthermore, the emotion engine built into the server recognizes the user's emotions in real time. Emotional states are read, for example, from the user's voice and actions, and the system dynamically adjusts the content of the proposal based on this data. In this way, it becomes possible to create effective proposals that leverage emotional insights.

[0669] Finally, based on the analysis results and information obtained from the emotion engine, the server automatically generates a proposal. The generated proposal is structured according to a template set by the user and includes industry analysis, competitor information, and suggestions based on the specific needs of the customer. The server sends the completed proposal to the terminal, where the user can view it and use it as reference material during customer visits.

[0670] As a concrete example, when a user approaches a new customer, the emotion engine can recognize which elements the user is interested in at an emotional level and customize the proposal based on that feedback. This makes the proposal more personalized and more likely to capture the customer's attention.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The server periodically crawls publicly available information sources on the internet (e.g., news sites, official company pages, industry reports, etc.) based on pre-configured keyword lists and conditions, and automatically collects the necessary information.

[0674] Step 2:

[0675] The server filters and cleans the collected information. Specifically, it removes duplicate and irrelevant information from the database and deletes inappropriate elements to maintain the integrity of the information.

[0676] Step 3:

[0677] The server uses natural language processing technology to analyze filtered and cleaned information, extracting key keywords and topics related to industry trends, competitive strategies, and customer needs.

[0678] Step 4:

[0679] The server identifies the customer's potential needs and current challenges based on the analyzed information, and uses this as foundational data for proposals.

[0680] Step 5:

[0681] The emotion engine recognizes the user's emotions in real time and provides data to the server for refining proposals. Emotions are primarily obtained from the user's voice and interface interactions.

[0682] Step 6:

[0683] The server dynamically adjusts the analysis results based on the user's emotional state obtained from the emotion engine, and reflects this in the proposal. This makes the proposal more aligned with the user's emotions.

[0684] Step 7:

[0685] The server organizes the generated proposals according to the user's template and completes the final proposal, which includes industry trends, competitive analysis, and customer needs.

[0686] Step 8:

[0687] The server sends the completed proposal to the user's terminal. The user reviews the proposal on their terminal and uses it for sales activities. The proposal includes visualized information and data to enhance the effectiveness of presentations and discussions.

[0688] (Example 2)

[0689] 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".

[0690] In modern sales activities, creating customer proposals quickly and effectively is essential for maintaining a competitive edge. However, existing methods make it difficult to efficiently generate personalized proposals that fully reflect the customer's latent needs and emotions, and sales representatives still have to expend considerable effort. In particular, adjusting proposal content to take customer emotions into account is a factor that greatly influences sales success, but conventional systems often do not perform such integrated processing. There is a need to improve this situation and develop a system that more effectively supports sales activities.

[0691] 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.

[0692] In this invention, the server includes means for automatically collecting information from publicly available sources, means for filtering and cleaning the collected information, means for analyzing the filtered and cleaned information using natural language processing technology to extract the customer's potential needs and challenges, means for automatically generating a proposal using an artificial intelligence model generated based on the analysis results, and means for recognizing the user's emotions in real time and applying that information to the proposal. This enables sales representatives to automatically generate effective proposals tailored to the customer's emotions, making it easier to attract the customer's interest with personalized proposal content, and improving the efficiency and results of sales activities.

[0693] "Public information sources" refer to a collection of information accessible to the general public, including company information, industry news, and announcements from competitors.

[0694] "Filtering" is the process of selecting the elements necessary for a given purpose from acquired information, and it is a means of improving data quality by removing irrelevant data and noise.

[0695] "Cleaning" is a process performed to remove further duplication and errors from filtered information in order to maintain the accuracy and consistency of the dataset.

[0696] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract keywords and topics from text.

[0697] A "generating artificial intelligence model" is an algorithm trained to produce a specific output based on defined input data, and in this case, it is used to automatically generate proposals.

[0698] "Recognizing emotions in real time" refers to the process of evaluating a user's emotional state on the spot, such as analyzing emotional data obtained from voice or operation information.

[0699] This invention is a system for sales representatives to quickly create customer proposals, and it includes a series of functions including information gathering from public sources, natural language processing, sentiment recognition, and automated proposal generation using artificial intelligence.

[0700] The server first uses technologies such as web crawlers and API access to automatically collect relevant information from publicly available sources on the internet. The hardware used in this process includes computing servers that enable high-speed processing and data storage. The collected information is filtered and cleaned using the Python Pandas library, which improves the accuracy and usefulness of the data.

[0701] Furthermore, the server analyzes the cleaned information using natural language processing technology. This analysis utilizes natural language processing libraries such as SpaCy and NLTK to extract potential customer needs and challenges from the information. Based on the analysis results, a generative AI model automatically generates a proposal. The generated proposal is structured according to a user-defined template and incorporates specific sales strategies.

[0702] To recognize user emotions, the server is equipped with an emotion analysis engine that analyzes user voice and operation information in real time. The technologies used here include a speech recognition engine and emotion analysis tools. Based on this data, the content of the proposal can be dynamically adjusted.

[0703] For example, if a user uses this system to create a proposal for a new product launch, the server prompts the AI ​​model with "Create a proposal that highlights the features of the new product. If possible, add graphs that will attract customer attention," and then constructs the optimal proposal.

[0704] In this way, this system can efficiently and effectively generate personalized sales proposals, significantly supporting the work of sales representatives.

[0705] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0706] Step 1:

[0707] The server automatically collects information from publicly available sources. Specific URLs or APIs, such as company information, industry news, and competitor press releases, are provided as input. The server utilizes web crawlers and APIs to collect this information into its own database. The output is the collected, raw dataset.

[0708] Step 2:

[0709] The server filters and cleans the collected information. The input is the dataset collected in step 1. The server uses the Python Pandas library to apply filtering conditions to the data, removing duplicate data and irrelevant information. The output is purified data that has been filtered and cleaned.

[0710] Step 3:

[0711] The server analyzes the refined data using natural language processing techniques. The refined data obtained in step 2 is used as input. Here, text analysis is performed using the SpaCy library, including grammatical analysis and topic extraction, to identify the customer's potential needs and challenges. The output is a list of keywords and topics containing the analysis results.

[0712] Step 4:

[0713] The server uses an emotion engine to recognize the user's emotions in real time. User voice commands and operation log data are used as input. A speech recognition engine converts this data into text, and an emotion analysis tool analyzes that text to identify the user's emotional state. The output is emotional state data obtained from the emotion engine.

[0714] Step 5:

[0715] The server automatically generates a proposal using a generative AI model. The input consists of the analysis results from step 3 and the emotional state data from step 4. Based on this data, the generative AI model constructs the content according to the prompt: "Create a proposal that highlights the features of the new product. If possible, add graphs that will attract customer attention." The output is an automatically generated proposal following a template.

[0716] Step 6:

[0717] The server sends the generated proposal to the terminal. The input is the proposal generated in step 5. The server converts this data to the appropriate format and transmits it to the terminal. The terminal displays the received proposal, allowing the user to edit and print it in preparation for the customer visit. The output provides the proposal in a user-friendly format.

[0718] (Application Example 2)

[0719] 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".

[0720] In creating customer proposals, sales representatives are required to accurately understand the customer's emotions and needs and personalize the proposal content accordingly. However, traditional systems have made it difficult to dynamically adjust proposals in this way. Furthermore, in electronic payment services, the lack of real-time product suggestions and coupon offers that respond to the customer's immediate emotions makes improving the customer experience a challenge.

[0721] 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.

[0722] In this invention, the server includes means for automatically collecting information from public sources, means for filtering and cleaning the information, means for analyzing the filtered information and extracting the customer's potential needs and challenges, and means for recognizing the user's emotions and dynamically adjusting the proposed content based on them. This enables the generation of personalized proposals that respond to the customer's emotions and real-time product recommendations.

[0723] A "public information source" is a collection of data that is made publicly available on a network for automated information gathering.

[0724] "Filtering" is the process of removing irrelevant or harmful data from collected information and extracting highly relevant information.

[0725] "Cleaning" is the process of removing noise and inaccurate data from collected information to improve its accuracy and consistency.

[0726] "Analysis" refers to the processing and analysis techniques used to obtain meaningful patterns and insights from collected data.

[0727] "Customer latent needs" refer to customer requests and demands that have not yet been made apparent, and are revealed through data analysis.

[0728] A "challenge" is a problem or situation that the customer is facing that requires improvement, and it serves as an indicator that guides the direction of the proposed solution.

[0729] "Recognizing user emotions" refers to real-time technology that uses data such as audio and video to determine the emotional state of a user.

[0730] "Dynamic adjustment" means adaptively changing a system or process in response to the passage of time or changes in circumstances.

[0731] "Generating" refers to the act of creating new outputs or content based on various data and algorithms.

[0732] A system for realizing an application of this invention consists primarily of a server, a user terminal, and an emotion recognition engine. The server automatically collects information about companies and markets from publicly available information sources on the internet. The collected data is filtered and cleaned to remove noise and unnecessary information. A database management system (e.g., MySQL) and a natural language processing library (e.g., NLTK) are used for this processing.

[0733] The analysis is performed using natural language processing technology on the server to extract the customer's potential needs and challenges. This allows for more appropriate customization of the proposal. The user terminal is typically a smartphone or tablet device, which receives the proposal sent from the server. The proposal is structured based on a pre-configured template and is provided in a state that can be immediately used by the user in business negotiations or customer visits.

[0734] Furthermore, the server is equipped with an emotion recognition engine that analyzes the user's emotional state from their actions and voice. Based on this emotion data, a generative AI model generates offers in real time that are tailored to the user's mood. Software such as OpenCV and TensorFlow are used for this purpose. For example, if a customer's facial expression in a cafe indicates that they want to relax, the generative AI model will offer an offer such as "a discount on relaxing herbal tea."

[0735] A possible example of a specific prompt message would be: "We have analyzed that the user is currently in an emotional state seeking relaxation. Please generate product offers related to this emotion."

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The server collects data from publicly available sources. The input consists of URLs of corporate investor relations (IR) information and industry news, and the server uses web scraping techniques to collect this information. The output is raw text data.

[0739] Step 2:

[0740] The server filters and cleans the collected text data. The input is raw text data, and irrelevant information is removed using regular expressions and denoising algorithms. The output is clean text data containing only meaningful information.

[0741] Step 3:

[0742] The server analyzes clean text data using natural language processing techniques. The input is filtered text data, and keywords and topics are extracted using natural language processing libraries. The output is analytical data regarding the customer's potential needs and challenges.

[0743] Step 4:

[0744] To recognize the user's emotional state in real time, the emotion recognition engine analyzes voice and operation data. The input consists of user voice data and operation logs, and machine learning algorithms are used to identify emotions. The output is data indicating the user's emotional state.

[0745] Step 5:

[0746] The server dynamically adjusts the proposal content based on analysis results and sentiment data, and generates personalized proposals using a generative AI model. The inputs are needs analysis and sentiment data, and dynamic content generation technology is used. The output is a proposal tailored to the customer.

[0747] Step 6:

[0748] The generated proposal is sent to the user's terminal. The input is the generated proposal, which is securely transferred using a communication protocol. The output is the proposal that the user can utilize.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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."

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] The following is further disclosed regarding the embodiments described above.

[0771] (Claim 1)

[0772] A means of automatically collecting information from publicly available sources,

[0773] Means for filtering and cleaning the collected information,

[0774] A means of analyzing filtered and cleaned information to extract potential customer needs and challenges,

[0775] A means of automatically generating a proposal based on the analysis results,

[0776] A means of sending the generated proposal to the user,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, characterized in that the analysis means analyzes information using natural language processing technology and extracts keywords.

[0780] (Claim 3)

[0781] The system according to claim 1, characterized in that the generated proposal is configured according to a user-defined template.

[0782] "Example 1"

[0783] (Claim 1)

[0784] A means of automatically collecting information from publicly available sources,

[0785] A means for filtering and cleaning the collected information using data processing technology,

[0786] A means for analyzing filtered and cleaned information using natural language processing techniques to extract and process needs and issues of interest,

[0787] A means for automatically generating documents based on analysis results,

[0788] A means for sending the generated document to the user via a communication means,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, characterized in that the analysis means analyzes information using natural language processing technology and extracts important words.

[0792] (Claim 3)

[0793] The system according to claim 1, characterized in that the generated document is generated according to a configuration example set by the user.

[0794] "Application Example 1"

[0795] (Claim 1)

[0796] A means of automatically collecting information from publicly available sources,

[0797] Means for filtering and cleaning the collected information,

[0798] A means of analyzing filtered and cleaned information to extract potential requirements and issues,

[0799] A means for automatically generating presentation materials based on analysis results,

[0800] A means of sending the generated presentation materials to the user,

[0801] A means of analyzing relevant information based on user input and providing customized information,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, characterized in that the analysis means analyzes information using natural language processing technology and extracts important words and phrases.

[0805] (Claim 3)

[0806] The system according to claim 1, characterized in that the generated presentation materials are configured according to a user-defined format.

[0807] "Example 2 of combining an emotion engine"

[0808] (Claim 1)

[0809] A means of automatically collecting information from publicly available sources,

[0810] Means for filtering and cleaning the collected information,

[0811] A means of analyzing filtered and cleaned information to extract potential customer needs and challenges,

[0812] A means for automatically generating a proposal using an artificial intelligence model generated based on the analysis results,

[0813] A means of recognizing user emotions in real time and applying that information to proposals,

[0814] A means of sending the generated proposal to the user,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, characterized in that the analysis means analyzes information using natural language processing technology and extracts keywords.

[0818] (Claim 3)

[0819] The system according to claim 1, characterized in that the generated proposal is structured according to a user-defined template and dynamically adjusts its content based on the user's sentiment data.

[0820] "Application example 2 when combining with an emotional engine"

[0821] (Claim 1)

[0822] A means of automatically collecting information from publicly available sources,

[0823] Means for filtering and cleaning the collected information,

[0824] A means of analyzing filtered and cleaned information to extract potential customer needs and challenges,

[0825] A means of recognizing the user's emotions and dynamically adjusting the suggested content based on those emotions,

[0826] A means for automatically generating a proposal based on analysis results and emotion recognition,

[0827] A means of sending the generated proposal to the user,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, characterized in that the analysis means analyzes information using natural language processing technology and extracts keywords.

[0831] (Claim 3)

[0832] The system according to claim 1, characterized in that the generated proposal is configured according to a user-defined template. [Explanation of symbols]

[0833] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically collecting information from publicly available sources, Means for filtering and cleaning the collected information, A means of analyzing filtered and cleaned information to extract potential customer needs and challenges, A means of automatically generating a proposal based on the analysis results, A means of sending the generated proposal to the user, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means analyzes information using natural language processing technology and extracts keywords.

3. The system according to claim 1, characterized in that the generated proposal is configured according to a user-defined template.

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