system

Abstract

The system according to this embodiment aims to facilitate efficient transactions and collaborations between companies. [Solution] The system according to this embodiment comprises an order-taking AI agent, a purchase-ordering AI agent, and a personalization unit. The order-taking AI agent automatically generates and posts content for the ordering company. The purchase-ordering AI agent supports the process from information gathering for the purchasing company to placing an order. The personalization unit provides personalized content to all users.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is not enough support for efficiently conducting transactions and collaborations between enterprises, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently conduct transactions and collaborations between enterprises.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an order-taking AI agent, a purchase-making AI agent, and a personalization unit. The order-taking AI agent automatically generates and posts content for the ordering company. The purchase-making AI agent supports the process from information gathering to placing an order for the purchasing company. The personalization unit provides personalized content to all users. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently facilitate transactions and collaborations between companies. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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. 5> The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that promotes collaboration between companies by utilizing an AI agent. In this system, the AI ​​agent provides support tailored to the needs of both the receiving company and the ordering company. Receiving companies can easily create content and find new customers through the receiving AI agent. On the other hand, ordering companies can quickly find their partners while gaining business insights through the ordering AI agent. This solves the cost-effectiveness problem in conventional B2B marketing and streamlines transactions between companies. For example, receiving companies can use the ordering AI agent to automatically generate and post content about their products and services, thereby contributing to increased sales. For example, the AI ​​agent can generate SEO articles, and by ranking highly in search engines, the receiving company's brand awareness will improve. Next, ordering companies can use the ordering AI agent to support the process from information gathering and document inquiries to Q&A with sales, meeting scheduling, and placing orders. This allows ordering companies to efficiently find partners. For example, the AI ​​agent can suggest the most suitable partner based on the ordering company's needs, enabling quick transactions. Furthermore, personalized content is displayed to all users, providing business-oriented insights in a social media-like format. This allows the system to be used even when there are no specific ordering needs. For example, the AI ​​agent provides relevant business information based on the user's interests and preferences, creating new business opportunities. In this way, the system leverages the AI ​​agent to promote collaboration between companies and solve B2B marketing challenges. This contributes to the growth of companies and the revitalization of society.

[0029] The system according to this embodiment comprises an order-taking AI agent, a purchase-making AI agent, and a personalization unit. The order-taking AI agent automatically generates and posts content for the ordering company. For example, the order-taking AI agent can automatically generate and post content related to products and services. The order-taking AI agent can also generate SEO articles and ensure they rank highly in search engine results. For example, the order-taking AI agent generates SEO articles and ensures they rank highly in search engine results. The order-taking AI agent can also analyze the ordering company's past content generation history and select the optimal content generation method. For example, the order-taking AI agent analyzes the style of content that has received high ratings in the past and generates new content in a similar style. The purchase-making AI agent supports the ordering company from information gathering to placing orders. For example, the purchase-making AI agent can suggest the optimal partner based on the ordering company's needs. The purchase-making AI agent can also analyze the ordering company's past transaction history and select the optimal partner suggestion method. For example, the purchase-making AI agent suggests similar partners based on partners from successful past transactions. The ordering AI agent can also support the entire process, from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. For example, the ordering AI agent supports the entire process from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. The personalization unit provides personalized content to all users. For example, the personalization unit can provide relevant business information based on the user's interests. The personalization unit can also analyze the user's past browsing history and select the optimal content display method. For example, the personalization unit displays relevant content based on content the user has previously viewed. The personalization unit can also estimate the user's emotions and adjust the tone and style of the content displayed based on the estimated emotions. For example, if the user is feeling stressed, the personalization unit will display content in a relaxing tone.As a result, the system according to this embodiment can facilitate collaboration between companies and solve the challenges of B2B marketing.

[0030] The AI-powered order fulfillment agent automatically generates and posts content for client companies. Specifically, the AI-powered order fulfillment agent generates engaging content based on detailed information about products and services. For example, it can automatically create blog posts including new product introductions and service usage examples, social media posts, and email marketing content. This content is generated with SEO (search engine optimization) in mind and optimized to rank highly in search engine results. The AI-powered order fulfillment agent utilizes natural language processing technology to select keywords and structure sentences, creating content that is suitable for search engine algorithms. Furthermore, the AI-powered order fulfillment agent analyzes the client company's past content creation history and learns which styles and tones were most effective. For example, it analyzes blog posts and social media posts that received high ratings in the past and generates new content that follows that style and tone. This allows the AI-powered order fulfillment agent to provide effective content while maintaining the client company's brand image. The AI-powered order fulfillment agent also has a function to automatically post the generated content, which can be posted to social media and blog platforms at specified dates and times. This allows client companies to centrally manage the process from content creation to posting and conduct marketing activities efficiently.

[0031] The AI ​​ordering agent supports client companies from information gathering to placing orders. Specifically, the AI ​​ordering agent proposes the most suitable partners based on the client company's needs. For example, if a client company is planning a new marketing campaign, the AI ​​ordering agent analyzes past transaction history and market trends to propose the most suitable advertising agencies and creative agencies. The AI ​​ordering agent can analyze the client company's past transaction history and propose similar partners based on successful transactions. This allows client companies to efficiently conduct business with reliable partners. The AI ​​ordering agent also supports the process from information gathering and document requests to Q&A with sales representatives, meeting scheduling, and placing orders. For example, if a client company requests documents for a new project, the AI ​​ordering agent automatically collects the relevant documents and provides them to the client company. Furthermore, it automatically handles Q&A with sales representatives, allowing for quick acquisition of necessary information. The AI ​​ordering agent also handles meeting scheduling, coordinating schedules between the client company and partner companies to efficiently set up meetings. This allows client companies to eliminate cumbersome processes and place orders quickly and efficiently.

[0032] The Personalization Department provides personalized content to all users. Specifically, it provides relevant business information based on the user's interests. For example, if a user is interested in a particular industry, it will provide the latest news and trend information related to that industry. The Personalization Department can also analyze a user's past browsing history and select the optimal content display method. For example, it can display relevant content based on articles and videos the user has previously viewed. This allows users to efficiently obtain information that matches their interests. Furthermore, the Personalization Department can estimate a user's emotions and adjust the tone and style of the content displayed based on the estimated emotions. For example, if a user is feeling stressed, it will display content in a relaxing tone. This uses natural language processing and sentiment analysis technologies to accurately estimate the user's emotional state. This allows the Personalization Department to provide more appropriate content to users and improve the user experience. In addition, the Personalization Department can collect user feedback and continuously improve the accuracy and effectiveness of the content it provides. For example, it can analyze how users reacted to the content provided and revise the content display method based on the results. This allows the Personalization Department to always provide the most optimal content to users.

[0033] The AI-powered order fulfillment agent can generate SEO articles and ensure they rank highly in search engine results. For example, the AI-powered order fulfillment agent can generate SEO articles and ensure they rank highly in search engine results. The AI-powered order fulfillment agent can generate optimal SEO articles by considering keyword selection methods and article structure. For example, the AI-powered order fulfillment agent generates SEO articles based on keyword selection methods. The AI-powered order fulfillment agent can also generate SEO articles by considering article structure. This improves the brand awareness of the client company.

[0034] The AI ​​ordering agent can propose the most suitable partner based on the needs of the ordering company. For example, the AI ​​ordering agent can propose the most suitable partner based on the ordering company's needs. The AI ​​ordering agent can also propose the most suitable partner by considering the partner's evaluation criteria and selection process. For example, the AI ​​ordering agent can propose the most suitable partner based on the partner's evaluation criteria. The AI ​​ordering agent can also propose the most suitable partner by considering the selection process. This allows the ordering company to find a partner efficiently.

[0035] The personalization function can provide relevant business information based on the user's interests. For example, the personalization function can provide relevant business information based on the user's interests. The personalization function can also provide relevant business information considering the type of information provided and the method of information collection. For example, the personalization function can provide relevant business information based on the type of information provided. The personalization function can also provide relevant business information considering the method of information collection. This creates new business opportunities.

[0036] The order processing AI agent can automatically generate and post content related to products and services. For example, it can automatically generate and post content about products and services. The order processing AI agent can generate optimal content by considering product features and service details. For example, it can generate product-related content based on product features. It can also generate service-related content by considering service details. This contributes to new sales growth.

[0037] The AI ​​ordering agent can support the entire process, from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. For example, the AI ​​ordering agent can support the entire process, from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. The AI ​​ordering agent can provide optimal support by considering the means of information gathering and the details of the ordering process. For example, the AI ​​ordering agent can support information gathering based on the means of information gathering. The AI ​​ordering agent can also support ordering by considering the details of the ordering process. This allows ordering companies to conduct transactions efficiently.

[0038] The AI-powered order fulfillment agent can analyze the past content creation history of client companies and select the optimal content creation method. For example, the AI-powered order fulfillment agent can analyze the style of content that has received high ratings in the past and generate new content in a similar style. The AI-powered order fulfillment agent can also select the most effective content creation method based on the number of views and shares of past content. For example, the AI-powered order fulfillment agent selects the optimal content creation method based on the number of views and shares of past content. The AI-powered order fulfillment agent can also analyze the response to past content and select a content creation method that matches user preferences. For example, the AI-powered order fulfillment agent selects a content creation method that matches user preferences based on the response to past content. In this way, by selecting the optimal content creation method based on past data, effective content can be generated.

[0039] The order-receiving AI agent can filter content generation based on the client company's current marketing campaigns and promotions. For example, it can generate content containing keywords related to ongoing campaigns. It can also generate content focused on products or services being promoted. Furthermore, it can generate content aligned with the marketing campaign's theme. This enables effective marketing by generating content relevant to current marketing campaigns and promotions.

[0040] The order-taking AI agent can prioritize generating highly relevant content by considering the geographical location information of the client company during content generation. For example, the order-taking AI agent can generate content based on local news and events related to the client company's location. The order-taking AI agent can also prioritize generating content targeted at geographically nearby customer segments. For example, the order-taking AI agent can generate content targeted at geographically nearby customer segments. The order-taking AI agent can also generate content tailored to the specific culture and customs of a region. For example, the order-taking AI agent can generate content tailored to the specific culture and customs of a region. This allows the agent to address region-specific needs by generating geographically relevant content.

[0041] The AI ​​agent for order fulfillment can analyze the social media activities of client companies and generate relevant content during content creation. For example, the AI ​​agent can generate content based on popular posts on the client company's social media. The AI ​​agent can also analyze social media trends and generate relevant content. For example, the AI ​​agent can analyze social media trends and generate relevant content. The AI ​​agent can also generate optimal content based on user reactions on social media. For example, the AI ​​agent can generate optimal content based on user reactions on social media. This enables more effective marketing by generating content based on social media activity.

[0042] The AI ​​ordering agent can analyze the past transaction history of the ordering company and select the most suitable partner proposal method. For example, the AI ​​ordering agent can propose similar partners based on partners from successful past transactions. The AI ​​ordering agent can also select the most effective proposal method from past transaction history. For example, the AI ​​ordering agent selects the most suitable proposal method from past transaction history. The AI ​​ordering agent can also analyze past transaction history and propose the partner best suited to the ordering company's needs. For example, the AI ​​ordering agent proposes the partner best suited to the ordering company's needs based on past transaction history. This enables effective transactions by proposing the most suitable partner based on past transaction history.

[0043] The ordering AI agent can filter partner proposals based on the client company's current business needs and projects. For example, the ordering AI agent can prioritize proposing partners related to ongoing projects. The ordering AI agent can also propose partners that are best suited to the client company's business needs. For example, the ordering AI agent can propose partners that are best suited to the client company's business needs. The ordering AI agent can also propose partners that are best suited to the client company's current resource situation. For example, the ordering AI agent can propose partners that are best suited to the client company's current resource situation. This enables effective transactions by proposing partners that are relevant to current business needs and projects.

[0044] The ordering AI agent can prioritize suggesting highly relevant partners by considering the geographical location information of the ordering company when proposing partners. For example, the ordering AI agent can prioritize suggesting partners located close to the ordering company's location. The ordering AI agent can also prioritize suggesting geographically close partners. For example, the ordering AI agent can suggest geographically close partners. The ordering AI agent can also prioritize suggesting partners who can meet region-specific business needs. For example, the ordering AI agent can suggest partners who can meet region-specific business needs. In this way, by suggesting geographically relevant partners, it is possible to address region-specific needs.

[0045] The ordering AI agent can analyze the ordering company's social media activities and suggest relevant partners when proposing partners. For example, the ordering AI agent can suggest relevant partners based on the ordering company's social media activities. The ordering AI agent can also analyze social media trends and suggest relevant partners. For example, the ordering AI agent can analyze social media trends and suggest relevant partners. The ordering AI agent can also suggest the best partner based on user reactions on social media. For example, the ordering AI agent can suggest the best partner based on user reactions on social media. This allows for more effective transactions by suggesting partners based on social media activities.

[0046] The personalization unit can analyze a user's past browsing history and select the optimal content display method. For example, the personalization unit can display relevant content based on content the user has previously viewed. The personalization unit can also display the most interesting content based on past browsing history. For example, the personalization unit selects the optimal content display method based on past browsing history. The personalization unit can also analyze past browsing history and select a content display method tailored to the user's preferences. For example, the personalization unit selects a content display method tailored to the user's preferences based on past browsing history. This makes it possible to provide information that is of interest to the user by displaying the most suitable content based on past browsing history.

[0047] The personalization feature can filter content based on the user's current business needs and areas of interest when displaying it. For example, it can prioritize displaying content related to the user's current business needs. The personalization feature can also display relevant content based on the user's areas of interest. For example, it can display relevant content based on the user's areas of interest. The personalization feature can also prioritize displaying content related to the user's current project. For example, it can display content related to the user's current project. This makes it possible to provide users with useful information by displaying content related to their current business needs and areas of interest.

[0048] The personalization feature can prioritize displaying highly relevant content by considering the user's geographical location when displaying content. For example, it can display content based on local news and events related to the user's location. The personalization feature can also prioritize displaying business information that is geographically close. For example, it can display business information that is geographically close. The personalization feature can also display content tailored to the specific culture and customs of a region. For example, it can display content tailored to the specific culture and customs of a region. This allows the display of geographically relevant content to address region-specific needs.

[0049] The personalization function can analyze a user's social media activity when displaying content and show relevant content. For example, it can display relevant content based on a user's social media activity. The personalization function can also analyze social media trends and display relevant content. For example, it can analyze social media trends and display relevant content. The personalization function can also display optimal content based on user reactions on social media. For example, it can display optimal content based on user reactions on social media. This enables more effective information delivery by displaying content based on social media activity.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The AI-powered order fulfillment agent can filter content generation based on the client company's current marketing campaigns and promotions. For example, it can generate content containing keywords related to ongoing campaigns. It can also generate content focusing on products or services being promoted. Furthermore, it can generate content aligned with the marketing campaign's theme. This enables effective marketing by generating content relevant to current marketing campaigns and promotions.

[0052] The personalization function can analyze a user's past browsing history and select the optimal content display method. For example, it can display relevant content based on what the user has viewed in the past. It can also display the content that is most likely to interest the user based on their past browsing history. Furthermore, it can analyze past browsing history and select a content display method tailored to the user's preferences. This makes it possible to provide information that is most interesting to the user by displaying the most suitable content based on their past browsing history.

[0053] The AI ​​ordering agent can filter partner proposals based on the client company's current business needs and projects. For example, it can prioritize partners related to ongoing projects. It can also propose partners best suited to the client company's business needs. Furthermore, it can propose the most suitable partners considering the client company's current resource situation. This enables more effective transactions by proposing partners relevant to current business needs and projects.

[0054] The ordering AI agent can prioritize suggesting highly relevant partners by considering the geographical location of the ordering company when proposing partners. For example, it can prioritize suggesting partners located close to the ordering company's location. It can also prioritize suggesting partners that are geographically close. Furthermore, it can prioritize suggesting partners that can address region-specific business needs. In this way, by suggesting geographically relevant partners, it can address region-specific needs.

[0055] The personalization feature can prioritize displaying highly relevant content by considering the user's geographical location. For example, it can display content based on local news and events related to the user's location. It can also prioritize displaying business information that is geographically close. Furthermore, it can display content tailored to the specific culture and customs of a region. This allows the display of geographically relevant content to address region-specific needs.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The AI ​​order-taking agent automatically generates and posts content for the client company. For example, it automatically generates content about products and services, and creates SEO articles to ensure they rank highly in search engine results. It also analyzes the client company's past content generation history and selects the optimal content generation method. Step 2: The AI ​​ordering agent supports the ordering company from information gathering to placing an order. For example, it proposes the most suitable partner based on the ordering company's needs and selects the best partner proposal method by analyzing past transaction history. It also supports the process from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing an order. Step 3: The personalization section provides personalized content to all users. For example, it provides relevant business information based on the user's interests and selects the optimal content display method by analyzing past browsing history. It also estimates the user's emotions and adjusts the tone and style of the content displayed based on those emotions.

[0058] (Example of form 2) The system according to an embodiment of the present invention is a system that promotes collaboration between companies by utilizing an AI agent. In this system, the AI ​​agent provides support tailored to the needs of both the receiving company and the ordering company. Receiving companies can easily create content and find new customers through the receiving AI agent. On the other hand, ordering companies can quickly find their partners while gaining business insights through the ordering AI agent. This solves the cost-effectiveness problem in conventional B2B marketing and streamlines transactions between companies. For example, receiving companies can use the ordering AI agent to automatically generate and post content about their products and services, thereby contributing to increased sales. For example, the AI ​​agent can generate SEO articles, and by ranking highly in search engines, the receiving company's brand awareness will improve. Next, ordering companies can use the ordering AI agent to support the process from information gathering and document inquiries to Q&A with sales, meeting scheduling, and placing orders. This allows ordering companies to efficiently find partners. For example, the AI ​​agent can suggest the most suitable partner based on the ordering company's needs, enabling quick transactions. Furthermore, personalized content is displayed to all users, providing business-oriented insights in a social media-like format. This allows the system to be used even when there are no specific ordering needs. For example, the AI ​​agent provides relevant business information based on the user's interests and preferences, creating new business opportunities. In this way, the system leverages the AI ​​agent to promote collaboration between companies and solve B2B marketing challenges. This contributes to the growth of companies and the revitalization of society.

[0059] The system according to this embodiment comprises an order-taking AI agent, a purchase-making AI agent, and a personalization unit. The order-taking AI agent automatically generates and posts content for the ordering company. For example, the order-taking AI agent can automatically generate and post content related to products and services. The order-taking AI agent can also generate SEO articles and ensure they rank highly in search engine results. For example, the order-taking AI agent generates SEO articles and ensures they rank highly in search engine results. The order-taking AI agent can also analyze the ordering company's past content generation history and select the optimal content generation method. For example, the order-taking AI agent analyzes the style of content that has received high ratings in the past and generates new content in a similar style. The purchase-making AI agent supports the ordering company from information gathering to placing orders. For example, the purchase-making AI agent can suggest the optimal partner based on the ordering company's needs. The purchase-making AI agent can also analyze the ordering company's past transaction history and select the optimal partner suggestion method. For example, the purchase-making AI agent suggests similar partners based on partners from successful past transactions. The ordering AI agent can also support the entire process, from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. For example, the ordering AI agent supports the entire process from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. The personalization unit provides personalized content to all users. For example, the personalization unit can provide relevant business information based on the user's interests. The personalization unit can also analyze the user's past browsing history and select the optimal content display method. For example, the personalization unit displays relevant content based on content the user has previously viewed. The personalization unit can also estimate the user's emotions and adjust the tone and style of the content displayed based on the estimated emotions. For example, if the user is feeling stressed, the personalization unit will display content in a relaxing tone.As a result, the system according to this embodiment can facilitate collaboration between companies and solve the challenges of B2B marketing.

[0060] The AI-powered order fulfillment agent automatically generates and posts content for client companies. Specifically, the AI-powered order fulfillment agent generates engaging content based on detailed information about products and services. For example, it can automatically create blog posts including new product introductions and service usage examples, social media posts, and email marketing content. This content is generated with SEO (search engine optimization) in mind and optimized to rank highly in search engine results. The AI-powered order fulfillment agent utilizes natural language processing technology to select keywords and structure sentences, creating content that is suitable for search engine algorithms. Furthermore, the AI-powered order fulfillment agent analyzes the client company's past content creation history and learns which styles and tones were most effective. For example, it analyzes blog posts and social media posts that received high ratings in the past and generates new content that follows that style and tone. This allows the AI-powered order fulfillment agent to provide effective content while maintaining the client company's brand image. The AI-powered order fulfillment agent also has a function to automatically post the generated content, which can be posted to social media and blog platforms at specified dates and times. This allows client companies to centrally manage the process from content creation to posting and conduct marketing activities efficiently.

[0061] The AI ​​ordering agent supports client companies from information gathering to placing orders. Specifically, the AI ​​ordering agent proposes the most suitable partners based on the client company's needs. For example, if a client company is planning a new marketing campaign, the AI ​​ordering agent analyzes past transaction history and market trends to propose the most suitable advertising agencies and creative agencies. The AI ​​ordering agent can analyze the client company's past transaction history and propose similar partners based on successful transactions. This allows client companies to efficiently conduct business with reliable partners. The AI ​​ordering agent also supports the process from information gathering and document requests to Q&A with sales representatives, meeting scheduling, and placing orders. For example, if a client company requests documents for a new project, the AI ​​ordering agent automatically collects the relevant documents and provides them to the client company. Furthermore, it automatically handles Q&A with sales representatives, allowing for quick acquisition of necessary information. The AI ​​ordering agent also handles meeting scheduling, coordinating schedules between the client company and partner companies to efficiently set up meetings. This allows client companies to eliminate cumbersome processes and place orders quickly and efficiently.

[0062] The Personalization Department provides personalized content to all users. Specifically, it provides relevant business information based on the user's interests. For example, if a user is interested in a particular industry, it will provide the latest news and trend information related to that industry. The Personalization Department can also analyze a user's past browsing history and select the optimal content display method. For example, it can display relevant content based on articles and videos the user has previously viewed. This allows users to efficiently obtain information that matches their interests. Furthermore, the Personalization Department can estimate a user's emotions and adjust the tone and style of the content displayed based on the estimated emotions. For example, if a user is feeling stressed, it will display content in a relaxing tone. This uses natural language processing and sentiment analysis technologies to accurately estimate the user's emotional state. This allows the Personalization Department to provide more appropriate content to users and improve the user experience. In addition, the Personalization Department can collect user feedback and continuously improve the accuracy and effectiveness of the content it provides. For example, it can analyze how users reacted to the content provided and revise the content display method based on the results. This allows the Personalization Department to always provide the most optimal content to users.

[0063] The AI-powered order fulfillment agent can generate SEO articles and ensure they rank highly in search engine results. For example, the AI-powered order fulfillment agent can generate SEO articles and ensure they rank highly in search engine results. The AI-powered order fulfillment agent can generate optimal SEO articles by considering keyword selection methods and article structure. For example, the AI-powered order fulfillment agent generates SEO articles based on keyword selection methods. The AI-powered order fulfillment agent can also generate SEO articles by considering article structure. This improves the brand awareness of the client company.

[0064] The AI ​​ordering agent can propose the most suitable partner based on the needs of the ordering company. For example, the AI ​​ordering agent can propose the most suitable partner based on the ordering company's needs. The AI ​​ordering agent can also propose the most suitable partner by considering the partner's evaluation criteria and selection process. For example, the AI ​​ordering agent can propose the most suitable partner based on the partner's evaluation criteria. The AI ​​ordering agent can also propose the most suitable partner by considering the selection process. This allows the ordering company to find a partner efficiently.

[0065] The personalization function can provide relevant business information based on the user's interests. For example, the personalization function can provide relevant business information based on the user's interests. The personalization function can also provide relevant business information considering the type of information provided and the method of information collection. For example, the personalization function can provide relevant business information based on the type of information provided. The personalization function can also provide relevant business information considering the method of information collection. This creates new business opportunities.

[0066] The order processing AI agent can automatically generate and post content related to products and services. For example, it can automatically generate and post content about products and services. The order processing AI agent can generate optimal content by considering product features and service details. For example, it can generate product-related content based on product features. It can also generate service-related content by considering service details. This contributes to new sales growth.

[0067] The AI ​​ordering agent can support the entire process, from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. For example, the AI ​​ordering agent can support the entire process, from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. The AI ​​ordering agent can provide optimal support by considering the means of information gathering and the details of the ordering process. For example, the AI ​​ordering agent can support information gathering based on the means of information gathering. The AI ​​ordering agent can also support ordering by considering the details of the ordering process. This allows ordering companies to conduct transactions efficiently.

[0068] The AI ​​agent can estimate the user's emotions and adjust the tone and style of the content it generates based on those estimated emotions. For example, the AI ​​agent can estimate the user's emotions and adjust the tone and style of the content it generates based on those estimated emotions. The AI ​​agent can select the optimal tone and style by considering the emotion estimation algorithm and the data used. For example, the AI ​​agent adjusts the tone and style based on the emotion estimation algorithm. The AI ​​agent can also adjust the tone and style by considering the data used. This enables more effective communication by generating content that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The AI-powered order fulfillment agent can analyze the past content creation history of client companies and select the optimal content creation method. For example, the AI-powered order fulfillment agent can analyze the style of content that has received high ratings in the past and generate new content in a similar style. The AI-powered order fulfillment agent can also select the most effective content creation method based on the number of views and shares of past content. For example, the AI-powered order fulfillment agent selects the optimal content creation method based on the number of views and shares of past content. The AI-powered order fulfillment agent can also analyze the response to past content and select a content creation method that matches user preferences. For example, the AI-powered order fulfillment agent selects a content creation method that matches user preferences based on the response to past content. In this way, by selecting the optimal content creation method based on past data, effective content can be generated.

[0070] The order-receiving AI agent can filter content generation based on the client company's current marketing campaigns and promotions. For example, it can generate content containing keywords related to ongoing campaigns. It can also generate content focused on products or services being promoted. Furthermore, it can generate content aligned with the marketing campaign's theme. This enables effective marketing by generating content relevant to current marketing campaigns and promotions.

[0071] The order-taking AI agent can estimate the user's emotions and determine the priority of content to generate based on those estimated emotions. For example, the order-taking AI agent can estimate the user's emotions and determine the priority of content to generate based on those estimated emotions. The order-taking AI agent can select the optimal priority by considering the priority evaluation criteria and decision-making process. For example, the order-taking AI agent determines content priority based on priority evaluation criteria. The order-taking AI agent can also determine content priority by considering the decision-making process. This enables more effective information delivery by generating content with priorities that align with the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The order-taking AI agent can prioritize generating highly relevant content by considering the geographical location information of the client company during content generation. For example, the order-taking AI agent can generate content based on local news and events related to the client company's location. The order-taking AI agent can also prioritize generating content targeted at geographically nearby customer segments. For example, the order-taking AI agent can generate content targeted at geographically nearby customer segments. The order-taking AI agent can also generate content tailored to the specific culture and customs of a region. For example, the order-taking AI agent can generate content tailored to the specific culture and customs of a region. This allows the agent to address region-specific needs by generating geographically relevant content.

[0073] The AI ​​agent for order fulfillment can analyze the social media activities of client companies and generate relevant content during content creation. For example, the AI ​​agent can generate content based on popular posts on the client company's social media. The AI ​​agent can also analyze social media trends and generate relevant content. For example, the AI ​​agent can analyze social media trends and generate relevant content. The AI ​​agent can also generate optimal content based on user reactions on social media. For example, the AI ​​agent can generate optimal content based on user reactions on social media. This enables more effective marketing by generating content based on social media activity.

[0074] The ordering AI agent can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, the ordering AI agent can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. The ordering AI agent can select the optimal presentation method by considering the selection criteria and adjustment process. For example, the ordering AI agent adjusts the presentation method based on the selection criteria. The ordering AI agent can also adjust the presentation method by considering the adjustment process. This enables more effective communication by providing proposals tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The AI ​​ordering agent can analyze the past transaction history of the ordering company and select the most suitable partner proposal method. For example, the AI ​​ordering agent can propose similar partners based on partners from successful past transactions. The AI ​​ordering agent can also select the most effective proposal method from past transaction history. For example, the AI ​​ordering agent selects the most suitable proposal method from past transaction history. The AI ​​ordering agent can also analyze past transaction history and propose the partner best suited to the ordering company's needs. For example, the AI ​​ordering agent proposes the partner best suited to the ordering company's needs based on past transaction history. This enables effective transactions by proposing the most suitable partner based on past transaction history.

[0076] The ordering AI agent can filter partner proposals based on the client company's current business needs and projects. For example, the ordering AI agent can prioritize proposing partners related to ongoing projects. The ordering AI agent can also propose partners that are best suited to the client company's business needs. For example, the ordering AI agent can propose partners that are best suited to the client company's business needs. The ordering AI agent can also propose partners that are best suited to the client company's current resource situation. For example, the ordering AI agent can propose partners that are best suited to the client company's current resource situation. This enables effective transactions by proposing partners that are relevant to current business needs and projects.

[0077] The ordering AI agent can estimate the user's emotions and determine the priority of proposals based on those emotions. For example, the ordering AI agent can estimate the user's emotions and determine the priority of proposals based on those emotions. The ordering AI agent can also select the optimal priority by considering the priority evaluation criteria and decision-making process. For example, the ordering AI agent determines the priority of proposals based on the priority evaluation criteria. The ordering AI agent can also determine the priority of proposals by considering the decision-making process. This allows for more effective information provision by offering proposals with priorities that align with the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The ordering AI agent can prioritize suggesting highly relevant partners by considering the geographical location information of the ordering company when proposing partners. For example, the ordering AI agent can prioritize suggesting partners located close to the ordering company's location. The ordering AI agent can also prioritize suggesting geographically close partners. For example, the ordering AI agent can suggest geographically close partners. The ordering AI agent can also prioritize suggesting partners who can meet region-specific business needs. For example, the ordering AI agent can suggest partners who can meet region-specific business needs. In this way, by suggesting geographically relevant partners, it is possible to address region-specific needs.

[0079] The ordering AI agent can analyze the ordering company's social media activities and suggest relevant partners when proposing partners. For example, the ordering AI agent can suggest relevant partners based on the ordering company's social media activities. The ordering AI agent can also analyze social media trends and suggest relevant partners. For example, the ordering AI agent can analyze social media trends and suggest relevant partners. The ordering AI agent can also suggest the best partner based on user reactions on social media. For example, the ordering AI agent can suggest the best partner based on user reactions on social media. This allows for more effective transactions by suggesting partners based on social media activities.

[0080] The personalization unit can estimate the user's emotions and adjust the tone and style of the content displayed based on those emotions. For example, the personalization unit can estimate the user's emotions and adjust the tone and style of the content displayed based on those emotions. The personalization unit can select the optimal tone and style by considering the adjustment criteria for tone and style and the data used. For example, the personalization unit adjusts the tone and style of the content based on the adjustment criteria. The personalization unit can also adjust the tone and style of the content by considering the data used. This enables more effective information delivery by displaying content that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The personalization unit can analyze a user's past browsing history and select the optimal content display method. For example, the personalization unit can display relevant content based on content the user has previously viewed. The personalization unit can also display the most interesting content based on past browsing history. For example, the personalization unit selects the optimal content display method based on past browsing history. The personalization unit can also analyze past browsing history and select a content display method tailored to the user's preferences. For example, the personalization unit selects a content display method tailored to the user's preferences based on past browsing history. This makes it possible to provide information that is of interest to the user by displaying the most suitable content based on past browsing history.

[0082] The personalization feature can filter content based on the user's current business needs and areas of interest when displaying it. For example, it can prioritize displaying content related to the user's current business needs. The personalization feature can also display relevant content based on the user's areas of interest. For example, it can display relevant content based on the user's areas of interest. The personalization feature can also prioritize displaying content related to the user's current project. For example, it can display content related to the user's current project. This makes it possible to provide users with useful information by displaying content related to their current business needs and areas of interest.

[0083] The personalization unit can estimate the user's emotions and determine the priority of content to display based on those emotions. For example, the personalization unit can estimate the user's emotions and determine the priority of content to display based on those emotions. The personalization unit can select the optimal priority by considering the priority evaluation criteria and decision-making process. For example, the personalization unit determines content priority based on priority evaluation criteria. The personalization unit can also determine content priority by considering the decision-making process. This enables more effective information delivery by displaying content with priorities that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The personalization feature can prioritize displaying highly relevant content by considering the user's geographical location when displaying content. For example, it can display content based on local news and events related to the user's location. The personalization feature can also prioritize displaying business information that is geographically close. For example, it can display business information that is geographically close. The personalization feature can also display content tailored to the specific culture and customs of a region. For example, it can display content tailored to the specific culture and customs of a region. This allows the display of geographically relevant content to address region-specific needs.

[0085] The personalization function can analyze a user's social media activity when displaying content and show relevant content. For example, it can display relevant content based on a user's social media activity. The personalization function can also analyze social media trends and display relevant content. For example, it can analyze social media trends and display relevant content. The personalization function can also display optimal content based on user reactions on social media. For example, it can display optimal content based on user reactions on social media. This enables more effective information delivery by displaying content based on social media activity.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The AI ​​agent can estimate the user's emotions and adjust the tone and style of the content it generates based on those emotions. For example, if the user is stressed, it can generate content in a relaxing tone. If the user is excited, it can generate content in an energetic tone. Furthermore, if the user is sad, it can generate content in a comforting tone. This allows for more effective communication by generating content that matches the user's emotions.

[0088] The AI-powered order fulfillment agent can filter content generation based on the client company's current marketing campaigns and promotions. For example, it can generate content containing keywords related to ongoing campaigns. It can also generate content focusing on products or services being promoted. Furthermore, it can generate content aligned with the marketing campaign's theme. This enables effective marketing by generating content relevant to current marketing campaigns and promotions.

[0089] The ordering AI agent can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is feeling anxious, it will present suggestions in a reassuring way. If the user is excited, it can present suggestions in an energetic way. Furthermore, if the user is calm, it can present suggestions in a calm way. This allows for more effective communication by providing suggestions tailored to the user's emotions.

[0090] The personalization function can analyze a user's past browsing history and select the optimal content display method. For example, it can display relevant content based on what the user has viewed in the past. It can also display the content that is most likely to interest the user based on their past browsing history. Furthermore, it can analyze past browsing history and select a content display method tailored to the user's preferences. This makes it possible to provide information that is most interesting to the user by displaying the most suitable content based on their past browsing history.

[0091] The AI ​​ordering agent can filter partner proposals based on the client company's current business needs and projects. For example, it can prioritize partners related to ongoing projects. It can also propose partners best suited to the client company's business needs. Furthermore, it can propose the most suitable partners considering the client company's current resource situation. This enables more effective transactions by proposing partners relevant to current business needs and projects.

[0092] The AI ​​agent can estimate the user's emotions and prioritize the content it generates based on those emotions. For example, if the user is excited, it will prioritize displaying energetic content. If the user is calm, it can prioritize displaying calming content. Furthermore, if the user is stressed, it can prioritize displaying relaxing content. This allows for more effective information delivery by generating content with priorities tailored to the user's emotions.

[0093] The ordering AI agent can prioritize suggesting highly relevant partners by considering the geographical location of the ordering company when proposing partners. For example, it can prioritize suggesting partners located close to the ordering company's location. It can also prioritize suggesting partners that are geographically close. Furthermore, it can prioritize suggesting partners that can address region-specific business needs. In this way, by suggesting geographically relevant partners, it can address region-specific needs.

[0094] The personalization function can estimate the user's emotions and adjust the tone and style of the content displayed based on those emotions. For example, if the user is stressed, the content can be displayed in a relaxing tone. If the user is excited, the content can be displayed in an energetic tone. Furthermore, if the user is sad, the content can be displayed in a comforting tone. This allows for more effective information delivery by displaying content that matches the user's emotions.

[0095] The personalization feature can prioritize displaying highly relevant content by considering the user's geographical location. For example, it can display content based on local news and events related to the user's location. It can also prioritize displaying business information that is geographically close. Furthermore, it can display content tailored to the specific culture and customs of a region. This allows the display of geographically relevant content to address region-specific needs.

[0096] The personalization function can estimate the user's emotions and determine the priority of content to display based on those emotions. For example, if the user is excited, energetic content will be prioritized. If the user is calm, calming content will be prioritized. Furthermore, if the user is stressed, relaxing content will be prioritized. This allows for more effective information delivery by prioritizing content according to the user's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The AI ​​order-taking agent automatically generates and posts content for the client company. For example, it automatically generates content about products and services, and creates SEO articles to ensure they rank highly in search engine results. It also analyzes the client company's past content generation history and selects the optimal content generation method. Step 2: The AI ​​ordering agent supports the ordering company from information gathering to placing an order. For example, it proposes the most suitable partner based on the ordering company's needs and selects the best partner proposal method by analyzing past transaction history. It also supports the process from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing an order. Step 3: The personalization section provides personalized content to all users. For example, it provides relevant business information based on the user's interests and selects the optimal content display method by analyzing past browsing history. It also estimates the user's emotions and adjusts the tone and style of the content displayed based on those emotions.

[0099] 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the order-taking AI agent, the purchase-ordering AI agent, and the personalization unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the order-taking AI agent is implemented by the control unit 46A of the smart device 14, and automatically generates and posts content related to products and services. The purchase-ordering AI agent is implemented by the specific processing unit 290 of the data processing unit 12, and proposes the optimal partner based on the needs of the ordering company. The personalization unit is implemented, for example, by the control unit 46A of the smart device 14, and provides relevant business information based on the user's interests and preferences. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0106] 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.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0108] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] 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.

[0110] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the order-taking AI agent, the purchase-ordering AI agent, and the personalization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the order-taking AI agent is implemented by the control unit 46A of the smart glasses 214, which automatically generates and posts content related to products and services. The purchase-ordering AI agent is implemented by the identification processing unit 290 of the data processing unit 12, which proposes the optimal partner based on the needs of the ordering company. The personalization unit is implemented by the control unit 46A of the smart glasses 214, which provides relevant business information based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0122] 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.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0124] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] 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.

[0126] 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.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the order-taking AI agent, the purchase-ordering AI agent, and the personalization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the order-taking AI agent is implemented by the control unit 46A of the headset terminal 314, which automatically generates and posts content related to products and services. The purchase-ordering AI agent is implemented by the identification processing unit 290 of the data processing unit 12, which proposes the optimal partner based on the needs of the ordering company. The personalization unit is implemented by the control unit 46A of the headset terminal 314, which provides relevant business information based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0138] 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.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0140] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] 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.

[0142] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] 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.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the order-taking AI agent, the purchase-ordering AI agent, and the personalization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the order-taking AI agent is implemented by the control unit 46A of the robot 414 and automatically generates and posts content related to products and services. The purchase-ordering AI agent is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal partner based on the needs of the ordering company. The personalization unit is implemented by the control unit 46A of the robot 414 and provides relevant business information based on the user's interests. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0152] 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.

[0153] Figure 9 shows the 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.

[0154] 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.

[0155] 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.

[0156] 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, and motorcycles, 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 based, for example, 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.

[0157] 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."

[0158] 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.

[0159] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] 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 other things 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.

[0169] 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.

[0170] (Note 1) An AI agent for order fulfillment that automatically generates and posts content for client companies, An AI ordering agent that supports everything from gathering information on client companies to placing orders, It includes a personalization unit that provides personalized content to all users. A system characterized by the following features. (Note 2) The aforementioned AI order-taking agent, Generate SEO articles and ensure they rank highly in search engine results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned ordering AI agent, We propose the most suitable partner based on the needs of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 4) The personalization unit described above is Provide relevant business information based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned AI order-taking agent, Automatically generate and post content related to products and services. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned ordering AI agent, We support the entire process, from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned AI order-taking agent, It estimates the user's emotions and adjusts the tone and style of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned AI order-taking agent, We analyze the past content generation history of the contracting company and select the optimal content generation method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned AI order-taking agent, When generating content, filtering is performed based on the client company's current marketing campaigns and promotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned AI order-taking agent, It estimates user sentiment and determines the priority of content to generate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned AI order-taking agent, When generating content, the system prioritizes generating highly relevant content by considering the geographical location information of the contracting company. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned AI order-taking agent, When generating content, the system analyzes the social media activities of the client company and generates relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned ordering AI agent, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned ordering AI agent, We analyze the client company's past transaction history and select the most suitable partner proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned ordering AI agent, When proposing partners, filtering is performed based on the client company's current business needs and projects. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned ordering AI agent, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned ordering AI agent, When proposing partners, we prioritize suggesting highly relevant partners by considering the geographical location of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned ordering AI agent, When proposing partners, we analyze the client company's social media activities and suggest relevant partners. The system described in Appendix 1, characterized by the features described herein. (Note 19) The personalization unit described above is It estimates the user's emotions and adjusts the tone and style of the content displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The personalization unit described above is Analyze the user's past browsing history to select the optimal content display method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The personalization unit described above is When displaying content, filtering is performed based on the user's current business needs and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The personalization unit described above is It estimates the user's emotions and determines the priority of content to display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The personalization unit described above is When displaying content, the system prioritizes showing relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The personalization unit described above is When displaying content, the system analyzes the user's social media activity and displays relevant content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An AI agent for order fulfillment that automatically generates and posts content for client companies, An AI ordering agent that supports everything from gathering information on client companies to placing orders, It includes a personalization unit that provides personalized content to all users. A system characterized by the following features.

2. The aforementioned AI order-taking agent, Generate SEO articles and ensure they rank highly in search engine results. The system according to feature 1.

3. The aforementioned ordering AI agent, We propose the most suitable partner based on the needs of the client company. The system according to feature 1.

4. The personalization unit described above is Provide relevant business information based on the user's interests and preferences. The system according to feature 1.

5. The aforementioned AI order-taking agent, Automatically generate and post content related to products and services. The system according to feature 1.

6. The aforementioned ordering AI agent, We support the entire process, from information gathering and document inquiries to Q&A with sales representatives, meeting scheduling, and placing orders. The system according to feature 1.

7. The aforementioned AI order-taking agent, It estimates the user's emotions and adjusts the tone and style of the generated content based on those estimated emotions. The system according to feature 1.

8. The aforementioned AI order-taking agent, We analyze the past content generation history of the contracting company and select the optimal content generation method. The system according to feature 1.

Images