system

Abstract

The system according to the embodiment aims to reduce the amount of work required to arrange for congratulatory telegrams and flowers, and to prevent any omissions. [Solution] The system according to the embodiment comprises a collection unit, a search unit, an identification unit, a judgment unit, a transmission unit, and an ordering unit. The collection unit collects promotion information. The search unit searches for addresses based on the information collected by the collection unit. The identification unit identifies a sales representative based on the information searched by the search unit. The judgment unit determines the amount based on the information identified by the identification unit. The transmission unit collectively transmits the information judged by the judgment unit. The ordering unit submits an order request based on the information transmitted by the transmission unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, arranging for congratulatory telegrams and flowers takes a lot of time and effort, and there is a risk of oversight.

[0005] The system according to the embodiment aims to reduce the amount of work required to arrange for congratulatory telegrams and flowers, and to prevent any omissions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a search unit, an identification unit, a judgment unit, a transmission unit, and an ordering unit. The collection unit collects promotion information. The search unit searches for an address based on the information collected by the collection unit. The identification unit identifies a sales representative based on the information searched by the search unit. The judgment unit determines the amount based on the information identified by the identification unit. The transmission unit collectively transmits the information determined by the judgment unit. The ordering unit submits an order request based on the information transmitted by the transmission unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the amount of work required to arrange for congratulatory telegrams and flowers, and prevent omissions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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.

[0018] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The congratulatory telegram and flower arrangement system of an embodiment of the present invention reduces the labor required to arrange congratulatory telegrams and flowers and prevents oversight. This system automatically selects online promotion information, searches for the company's headquarters address on the company's official website, searches for sales representatives in the company's internal system, and uses AI to determine the appropriate amount of flowers and telegrams. It then compiles all of this information and requests the sales representative to make the final decision on whether to send it. The sales representative then clicks a button to place an order. For example, when automatically selecting online promotion information, a web crawler is used to collect promotion information from official company announcements and news sites. Articles containing specific keywords (e.g., "promotion" or "advancement") are automatically detected and the relevant information is extracted. Next, when searching for a company's headquarters address on the company's official website, address information is extracted from the company's official website using web scraping technology. For example, address information is obtained from the company's "Company Profile" page or "Contact Us" page. Furthermore, when searching for sales representatives in the internal system, the system uses the company's customer relationship management (CRM) system to search for the relevant company's sales representative. By entering the company name, the system obtains information about the sales representative in charge. Next, when the AI ​​determines the appropriate amount of flowers or telegrams, it calculates the appropriate amount based on past data and industry practices. By learning from past data on the prices of flowers and telegrams, the AI ​​automatically suggests the appropriate amount. When the sales representative makes a final decision on whether to send all of this information, the collected information is compiled into a single report and sent to the sales representative. The sales representative reviews the report and approves it if there are no problems. Finally, when the sales representative presses a button to submit an order, the order is automatically submitted based on the approved information. The necessary information is entered into the ordering system, and the order is completed with a single button. This system significantly reduces the labor required to arrange flowers and telegrams and prevents oversights. For example, manual information gathering and confirmation is no longer necessary, allowing for efficient arrangements. Furthermore, by using AI, the system can suggest the appropriate amount of flowers and telegrams, ensuring accurate arrangements. This allows the system to significantly reduce the labor required to arrange flowers and telegrams and prevent oversights.

[0029] A congratulatory telegram and flower arrangement system according to an embodiment includes a collection unit, a search unit, an identification unit, a determination unit, a transmission unit, and an ordering unit. The collection unit collects promotion information. The collection unit, for example, uses a web crawler to collect the promotion information. The web crawler automatically detects articles containing specific keywords (such as "promotion" or "advancement") and extracts relevant information. For example, the web crawler collects promotion information from official company announcements and news sites. The search unit searches for addresses based on the information collected by the collection unit. For example, the search unit uses web scraping technology to search for address information from official company websites. Web scraping technology acquires address information from the company's "company profile" page or "contact us" page. For example, the search unit extracts address information from official company websites. The identification unit identifies a sales representative based on the information retrieved by the search unit. For example, the identification unit searches for a sales representative using an internal customer relationship management (CRM) system. The internal customer relationship management system acquires information about sales representatives by inputting the company name. For example, the identification unit searches the in-house system for a sales representative of the relevant company. The determination unit determines the amount based on the information identified by the identification unit. The determination unit determines the amount based on, for example, past data and industry practices. The past data includes data on the amounts of past congratulatory flowers and telegrams. For example, the determination unit calculates an appropriate amount by learning data on the amounts of past congratulatory flowers and telegrams. The transmission unit compiles and transmits the information determined by the determination unit. For example, the transmission unit compiles and transmits the collected information in a single report. The report includes the collected information. For example, the transmission unit transmits the collected information to a sales representative. The ordering unit submits an order request based on the information transmitted by the transmission unit. For example, the ordering unit inputs necessary information into an ordering system and submits an order request with the press of a button. The ordering system automatically submits an order request based on the approved information. For example, the ordering unit completes the request with the press of a button. As a result, the congratulatory flower and telegram arrangement system according to the embodiment can significantly reduce the amount of work required to arrange congratulatory flowers and telegrams and prevent omissions.

[0030] The collection unit can collect promotion information using a web crawler. The collection unit, for example, collects promotion information using a web crawler. The web crawler automatically detects articles containing specific keywords (such as "promotion" or "advancement") and extracts the relevant information. For example, the web crawler collects promotion information from official company announcements and news sites. In this way, the use of the web crawler automates the collection of promotion information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the promotion information collected by the web crawler into a generation AI and have the generation AI analyze the promotion information.

[0031] The search unit can use web scraping technology to search for address information from a company's official website. For example, the search unit uses web scraping technology to search for address information from a company's official website. Web scraping technology acquires address information from a company's "Company Profile" page or "Contact Us" page. For example, the search unit extracts address information from a company's official website. In this way, the search for company address information is automated using web scraping technology. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the address information acquired by web scraping technology into a generation AI and have the generation AI analyze the address information.

[0032] The search unit can search for a sales representative using an internal customer management system. The search unit searches for a sales representative, for example, using an internal customer relationship management system (CRM). The internal customer management system acquires information on the sales representative in charge by inputting the name of a company. For example, the search unit searches for the sales representative of the relevant company from the internal system. This makes the search for a sales representative more efficient by using the internal customer management system. Some or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input information on the sales representative acquired by the internal customer management system into the generation AI and have the generation AI identify the sales representative.

[0033] The judgment unit can determine the amount based on past data and industry practices. The judgment unit determines the amount based on, for example, past data and industry practices. Past data includes data on the amounts of past flowers and congratulatory telegrams. For example, the judgment unit calculates an appropriate amount by learning data on the amounts of past flowers and congratulatory telegrams. This makes it possible to determine an appropriate amount based on past data and industry practices. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit may have the generation AI determine the amount based on past data and industry practices.

[0034] The transmission unit can compile the collected information into a single report and transmit it. The transmission unit, for example, compiles the collected information into a single report and transmits it. The report includes the collected information. For example, the transmission unit transmits the collected information to a sales representative. By compiling the information into a single report, transmission becomes more efficient. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the collected information into a generation AI and have the generation AI create a report.

[0035] The ordering unit can input the necessary information into the ordering system and submit an order request with one button. The ordering unit, for example, inputs the necessary information into the ordering system and submits an order request with one button. The ordering system automatically submits an order request based on the approved information. For example, the ordering unit completes the request with one button. This allows for quick ordering by submitting an order request with one button. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the necessary information for the ordering system into a generation AI and have the generation AI execute the order request.

[0036] When collecting promotion information, the collection unit can prioritize collecting promotion information from specific industries or companies. For example, the collection unit may prioritize collecting promotion information from the IT industry and postpone collecting information from other industries. For example, the collection unit may prioritize collecting promotion information from large companies and postpone collecting information from small and medium-sized companies. For example, the collection unit may prioritize collecting promotion information from companies located in a specific region and postpone collecting information from other regions. In this way, by collecting information from specific industries or companies preferentially, important information can be obtained quickly. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit may input promotion information from specific industries or companies into the generation AI and have the generation AI determine the priorities.

[0037] When collecting promotion information, the collection unit can optimize the collection targets by referring to past collection history. For example, the collection unit prioritizes collecting information on companies where promotions occur frequently based on the history of promotion information collected in the past. For example, the collection unit prioritizes collecting information on companies where promotions occur frequently during a specific period from the past collection history. For example, the collection unit analyzes the past collection history and selects optimal collection targets to maximize collection efficiency. In this way, collection efficiency is improved by referring to the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past collection history into a generation AI and cause the generation AI to optimize the collection targets.

[0038] When collecting promotion information, the collection unit can prioritize collecting promotion information for a specific region or country. For example, the collection unit prioritizes collecting promotion information for Japan and postpones information for overseas. For example, the collection unit prioritizes collecting promotion information for a specific city (e.g., Tokyo, Osaka). For example, the collection unit prioritizes collecting promotion information for a specific country (e.g., the United States, the United Kingdom). In this way, by collecting information for a specific region or country preferentially, information specific to the region or country can be quickly obtained. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input promotion information for a specific region or country to the generation AI and have the generation AI determine the priorities.

[0039] When collecting promotion information, the collection unit may combine and collect information from social media and news sites. For example, the collection unit may collect promotion information from social media (e.g., X (formerly Twitter®), LinkedIn®) and combine it with official announcements. For example, the collection unit may collect promotion information from news sites (e.g., Nikkei Shimbun, Wall Street Journal) and combine it with other information sources. For example, the collection unit may integrate information from social media and news sites to collect comprehensive promotion information. In this way, comprehensive promotion information can be collected by combining information from social media and news sites. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit may input information collected from social media and news sites into the generation AI and have the generation AI integrate the information.

[0040] When searching for address information, the search unit can refer to a reliable database other than the company's official website. For example, the search unit acquires address information from an industry association database in addition to the company's official website. For example, the search unit acquires address information from a government business registration database in addition to the company's official website. For example, the search unit acquires address information from a commercial database (e.g., D&B, Hoover's) in addition to the company's official website. This allows accurate address information to be acquired by referencing a reliable database. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input address information acquired from a reliable database into the generation AI and cause the generation AI to analyze the address information.

[0041] When searching for address information, the search unit can improve search accuracy by referring to past search history. For example, the search unit preferentially displays address information of similar companies based on address information searched in the past. For example, the search unit preferentially displays address information of companies that are frequently searched for from the past search history. For example, the search unit analyzes the past search history and suggests an optimal search method for improving search accuracy. In this way, search accuracy is improved by referring to the past search history. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the past search history into the generation AI and cause the generation AI to improve search accuracy.

[0042] When searching for address information, the search unit can simultaneously search for address information of a company's affiliates and subsidiaries. For example, the search unit simultaneously searches for address information of affiliates in addition to the company's head office address. For example, the search unit simultaneously searches for address information of subsidiaries in addition to the company's head office address. For example, the search unit integrates and displays address information of affiliates and subsidiaries in addition to the company's head office address. This makes it possible to obtain comprehensive address information by simultaneously searching for information on affiliates and subsidiaries. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input address information of affiliates and subsidiaries into the generation AI and have the generation AI integrate the address information.

[0043] The search unit can reflect updated information from the company's official website in real time when searching for address information. For example, the search unit reflects the address information in real time when the company's official website is updated. For example, the search unit periodically checks for updated information from the company's official website to obtain the latest address information. For example, the search unit automatically obtains updated information from the company's official website to keep the address information up to date. This allows the latest address information to be obtained by reflecting updated information from the official website in real time. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input updated information from the company's official website into the generation AI and cause the generation AI to update the address information.

[0044] When determining the amount, the judgment unit can calculate the optimal amount by referring to the past sending history of congratulatory flowers and telegrams. The judgment unit calculates the optimal amount, for example, based on the amount data of congratulatory flowers and telegrams sent in the past. The judgment unit calculates the appropriate amount for a specific company or industry, for example, from the past sending history. The judgment unit analyzes the past sending history, for example, and proposes the most appropriate amount. In this way, the optimal amount can be calculated by referring to the past sending history. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the past sending history into the generation AI and have the generation AI calculate the amount.

[0045] When determining the amount, the determination unit can customize the determination criteria by taking into account the customs of a specific industry or company. The determination unit determines an appropriate amount by taking into account the customs of the IT industry, for example. The determination unit determines an appropriate amount by taking into account the customs of a major company, for example. The determination unit determines an appropriate amount by taking into account the customs of a company located in a specific region, for example. In this way, a more appropriate amount can be determined by taking into account the customs of a specific industry or company. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the customs of a specific industry or company into the generation AI and have the generation AI customize the determination criteria.

[0046] When determining the amount, the determination unit can adjust the amount according to a specific event or season. For example, the determination unit adjusts the amount according to a year-end / New Year event. For example, the determination unit adjusts the amount according to a specific season (e.g., spring, summer). For example, the determination unit adjusts the amount according to a specific event (e.g., founding anniversary, anniversary). In this way, by adjusting the amount according to a specific event or season, an appropriate amount can be suggested. Some or all of the above-described processing in the determination unit may be performed using, or without, AI, for example. For example, the determination unit can input information about a specific event or season into the generation AI and have the generation AI adjust the amount.

[0047] When determining the amount, the judgment unit can propose an appropriate amount taking into consideration the company's financial situation and performance. The judgment unit, for example, proposes an appropriate amount based on the company's latest financial report. The judgment unit, for example, analyzes the company's performance data and proposes an appropriate amount. The judgment unit, for example, proposes an amount that is neither excessive nor insufficient, taking into consideration the company's financial situation. In this way, an amount that is neither excessive nor insufficient can be proposed by taking into consideration the company's financial situation and performance. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input data on the company's financial situation and performance into the generation AI and have the generation AI propose an amount.

[0048] At the time of transmission, the transmission unit can determine the optimal transmission timing by referring to past transmission history. The transmission unit, for example, determines the most effective transmission timing based on the past transmission history. For example, the transmission unit confirms from the past transmission history that transmission during a specific time period is highly effective, and transmits during that time period. For example, the transmission unit analyzes the past transmission history and proposes the optimal transmission timing. In this way, the optimal transmission timing can be determined by referring to the past transmission history. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the past transmission history into a generation AI and have the generation AI optimize the transmission timing.

[0049] The sending unit can customize the content of the message at the time of sending by taking into consideration the attribute information of the recipient's company and the person in charge. The sending unit customizes the content of the message, for example, according to the industry and size of the recipient's company. The sending unit customizes the content of the message, for example, according to the position and job responsibilities of the recipient's person in charge. The sending unit customizes the content of the message by taking into consideration the culture and customs of the recipient's company. This makes it possible to provide more appropriate content by taking into consideration the recipient's attribute information. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input the recipient's attribute information into a generation AI and have the generation AI customize the content of the message.

[0050] At the time of transmission, the transmission unit can select the optimal transmission method by taking into account the geographical information of the destination. For example, if the destination is domestic, the transmission unit uses a domestic delivery company for transmission. For example, if the destination is overseas, the transmission unit uses an international delivery company for transmission. For example, the transmission unit selects the optimal delivery method based on the geographical information of the destination. In this way, the optimal transmission method can be selected by taking into account the geographical information of the destination. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the geographical information of the destination into the generation AI and have the generation AI select the transmission method.

[0051] The transmitting unit can attach literature and reference materials related to the content of transmission when transmitting. The transmitting unit, for example, attaches literature related to the content of transmission and provides it to the recipient. The transmitting unit, for example, attaches reference materials related to the content of transmission and provides it to the recipient. The transmitting unit, for example, attaches data and statistical information related to the content of transmission and provides it to the recipient. By attaching related literature and reference materials, the content of transmission can be better understood. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input literature and reference materials related to the content of transmission into a generating AI and have the generating AI select the attached materials.

[0052] When placing an order, the ordering unit can select the optimal ordering method by referring to past order history. The ordering unit, for example, selects the most effective ordering method based on past order history. The ordering unit, for example, preferentially selects a specific vendor or service provider from past order history. The ordering unit, for example, analyzes past order history and proposes the optimal ordering method. In this way, the optimal ordering method can be selected by referring to past order history. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can input past order history into a generation AI and have the generation AI select an ordering method.

[0053] When placing an order, the ordering unit can place an order taking into consideration evaluation information of the contractor and service provider. The ordering unit, for example, selects the most suitable contractor based on evaluation information of the contractor. The ordering unit, for example, selects the most suitable provider based on evaluation information of the service provider. The ordering unit, for example, analyzes the evaluation information of the contractor and proposes the most suitable contractor. This enables highly reliable ordering by taking the evaluation information into consideration. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the evaluation information of the contractor into the generation AI and have the generation AI select the contractor.

[0054] When placing an order, the ordering unit can select the optimal ordering method by taking into account the geographical information of the supplier. For example, if the supplier is domestic, the ordering unit places the order using a domestic supplier. For example, if the supplier is overseas, the ordering unit places the order using an international supplier. The ordering unit selects the optimal ordering method based on the geographical information of the supplier. This makes it possible to select the optimal ordering method by taking into account the geographical information of the supplier. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the geographical information of the supplier into the generation AI and have the generation AI select the ordering method.

[0055] When placing an order, the ordering department can attach literature and reference materials related to the order contents. The ordering department, for example, attaches literature related to the order contents and provides it to the client. The ordering department, for example, attaches reference materials related to the order contents and provides it to the client. The ordering department, for example, attaches data and statistical information related to the order contents and provides it to the client. By attaching the related literature and reference materials, the order contents can be better understood. Some or all of the above-mentioned processing in the ordering department may be performed using, for example, AI, or may be performed without using AI. For example, the ordering department can input literature and reference materials related to the order contents into a generation AI and have the generation AI select the attached materials.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] When searching for address information, the search unit can refer to a reliable database other than the company's official website. For example, in addition to the company's official website, address information can be obtained from an industry association database. In addition to the company's official website, address information can also be obtained from a government business registration database. Furthermore, address information can also be obtained from commercial databases (e.g., D&B, Hoover's). This allows accurate address information to be obtained by referencing a reliable database. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input address information obtained from a reliable database into the generation AI and have the generation AI analyze the address information.

[0058] When transmitting, the transmitting unit can determine the optimal transmission timing by referring to past transmission history. For example, the most effective transmission timing can be determined based on the past transmission history. From the past transmission history, it can be confirmed that transmitting during a specific time period is more effective, and the message can be transmitted during that time period. Furthermore, it is also possible to analyze the past transmission history and propose the optimal transmission timing. In this way, the optimal transmission timing can be determined by referring to the past transmission history. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the past transmission history into a generating AI and have the generating AI optimize the transmission timing.

[0059] When collecting promotion information, the collection unit can prioritize collecting promotion information from specific industries or companies. For example, it can prioritize collecting promotion information from the IT industry and postpone collecting information from other industries. It is also possible to prioritize collecting promotion information from large companies and postpone collecting information from small and medium-sized companies. Furthermore, it is also possible to prioritize collecting promotion information from companies located in a specific region and postpone collecting information from other regions. In this way, by collecting information from specific industries or companies preferentially, important information can be obtained quickly. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input promotion information from specific industries or companies into the generation AI and have the generation AI determine the priorities.

[0060] When searching for address information, the search unit can improve search accuracy by referring to past search history. For example, it can prioritize displaying address information of similar companies based on address information searched in the past. It is also possible to prioritize displaying address information of frequently searched companies based on past search history. Furthermore, it is possible to analyze past search history and propose an optimal search method to improve search accuracy. In this way, by referring to past search history, search accuracy is improved. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input past search history into a generation AI and have the generation AI improve search accuracy.

[0061] When determining the amount, the determination unit can adjust the amount according to a specific event or season. For example, the amount can be adjusted according to a year-end or New Year's event. The amount can also be adjusted according to a specific season (e.g., spring, summer). Furthermore, the amount can also be adjusted according to a specific event (e.g., founding anniversary, anniversary). In this way, by adjusting the amount according to a specific event or season, an appropriate amount can be suggested. Some or all of the above-described processing in the determination unit may be performed using, or without, AI, for example. For example, the determination unit can input information about a specific event or season into the generation AI and have the generation AI adjust the amount.

[0062] When placing an order, the ordering unit can place an order taking into consideration evaluation information of the contractor and service provider. For example, the ordering unit can select the most suitable contractor based on the evaluation information of the contractor. It is also possible to select the most suitable provider based on the evaluation information of the contractor's service provider. Furthermore, it is possible to analyze the contractor's evaluation information and propose the most suitable contractor. This makes it possible to place a highly reliable order by taking the evaluation information into consideration. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the contractor's evaluation information into the generation AI and have the generation AI select a contractor.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects promotion information. The collection unit collects promotion information using, for example, a web crawler. The web crawler automatically detects articles containing specific keywords (such as "promotion" or "advancement") and extracts the relevant information. For example, the web crawler collects promotion information from official company announcements and news sites. Step 2: The search unit searches for addresses based on the information collected by the collection unit. For example, the search unit uses web scraping technology to search for address information from the company's official website. Web scraping technology obtains address information from the company's "About Us" page or "Contact Us" page. For example, the search unit extracts address information from the company's official website. Step 3: The identification unit identifies a sales representative based on the information searched by the search unit. The identification unit searches for a sales representative, for example, using an internal customer relationship management system (CRM). The internal customer relationship management system obtains information about the sales representative in charge by inputting the company name. For example, the identification unit searches the internal system for the sales representative of the relevant company. Step 4: The determination unit determines the amount based on the information identified by the identification unit. The determination unit determines the amount based on, for example, past data and industry practices. Past data includes data on the amounts of past congratulatory flowers and telegrams. For example, the determination unit calculates an appropriate amount by learning data on the amounts of past congratulatory flowers and telegrams. Step 5: The transmitting unit compiles and transmits the information determined by the determining unit. For example, the transmitting unit compiles and transmits the collected information in a single report. The report includes the collected information. For example, the transmitting unit transmits the collected information to a sales representative. Step 6: The ordering unit submits an order application based on the information sent by the sending unit. For example, the ordering unit inputs the necessary information into the ordering system and submits an order application with the press of a button. The ordering system automatically submits an order application based on the approved information. For example, the ordering unit completes the application with the press of a button.

[0065] (Example 2) The congratulatory telegram and flower arrangement system of an embodiment of the present invention reduces the labor required to arrange congratulatory telegrams and flowers and prevents oversight. This system automatically selects online promotion information, searches for the company's headquarters address on the company's official website, searches for sales representatives in the company's internal system, and uses AI to determine the appropriate amount of flowers and telegrams. It then compiles all of this information and requests the sales representative to make the final decision on whether to send it. The sales representative then clicks a button to place an order. For example, when automatically selecting online promotion information, a web crawler is used to collect promotion information from official company announcements and news sites. Articles containing specific keywords (e.g., "promotion" or "advancement") are automatically detected and the relevant information is extracted. Next, when searching for a company's headquarters address on the company's official website, address information is extracted from the company's official website using web scraping technology. For example, address information is obtained from the company's "Company Profile" page or "Contact Us" page. Furthermore, when searching for sales representatives in the internal system, the system uses the company's customer relationship management (CRM) system to search for the relevant company's sales representative. By entering the company name, the system obtains information about the sales representative in charge. Next, when the AI ​​determines the appropriate amount of flowers or telegrams, it calculates the appropriate amount based on past data and industry practices. By learning from past data on the prices of flowers and telegrams, the AI ​​automatically suggests the appropriate amount. When the sales representative makes a final decision on whether to send all of this information, the collected information is compiled into a single report and sent to the sales representative. The sales representative reviews the report and approves it if there are no problems. Finally, when the sales representative presses a button to submit an order, the order is automatically submitted based on the approved information. The necessary information is entered into the ordering system, and the order is completed with a single button. This system significantly reduces the labor required to arrange flowers and telegrams and prevents oversights. For example, manual information gathering and confirmation is no longer necessary, allowing for efficient arrangements. Furthermore, by using AI, the system can suggest the appropriate amount of flowers and telegrams, ensuring accurate arrangements. This allows the system to significantly reduce the labor required to arrange flowers and telegrams and prevent oversights.

[0066] A congratulatory telegram and flower arrangement system according to an embodiment includes a collection unit, a search unit, an identification unit, a determination unit, a transmission unit, and an ordering unit. The collection unit collects promotion information. The collection unit, for example, uses a web crawler to collect the promotion information. The web crawler automatically detects articles containing specific keywords (such as "promotion" or "advancement") and extracts relevant information. For example, the web crawler collects promotion information from official company announcements and news sites. The search unit searches for addresses based on the information collected by the collection unit. For example, the search unit uses web scraping technology to search for address information from official company websites. Web scraping technology acquires address information from the company's "company profile" page or "contact us" page. For example, the search unit extracts address information from official company websites. The identification unit identifies a sales representative based on the information retrieved by the search unit. For example, the identification unit searches for a sales representative using an internal customer relationship management (CRM) system. The internal customer relationship management system acquires information about sales representatives by inputting the company name. For example, the identification unit searches the in-house system for a sales representative of the relevant company. The determination unit determines the amount based on the information identified by the identification unit. The determination unit determines the amount based on, for example, past data and industry practices. The past data includes data on the amounts of past congratulatory flowers and telegrams. For example, the determination unit calculates an appropriate amount by learning data on the amounts of past congratulatory flowers and telegrams. The transmission unit compiles and transmits the information determined by the determination unit. For example, the transmission unit compiles and transmits the collected information in a single report. The report includes the collected information. For example, the transmission unit transmits the collected information to a sales representative. The ordering unit submits an order request based on the information transmitted by the transmission unit. For example, the ordering unit inputs necessary information into an ordering system and submits an order request with the press of a button. The ordering system automatically submits an order request based on the approved information. For example, the ordering unit completes the request with the press of a button. As a result, the congratulatory flower and telegram arrangement system according to the embodiment can significantly reduce the amount of work required to arrange congratulatory flowers and telegrams and prevent omissions.

[0067] The collection unit can collect promotion information using a web crawler. The collection unit, for example, collects promotion information using a web crawler. The web crawler automatically detects articles containing specific keywords (such as "promotion" or "advancement") and extracts the relevant information. For example, the web crawler collects promotion information from official company announcements and news sites. In this way, the use of the web crawler automates the collection of promotion information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the promotion information collected by the web crawler into a generation AI and have the generation AI analyze the promotion information.

[0068] The search unit can use web scraping technology to search for address information from a company's official website. For example, the search unit uses web scraping technology to search for address information from a company's official website. Web scraping technology acquires address information from a company's "Company Profile" page or "Contact Us" page. For example, the search unit extracts address information from a company's official website. In this way, the search for company address information is automated using web scraping technology. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the address information acquired by web scraping technology into a generation AI and have the generation AI analyze the address information.

[0069] The search unit can search for a sales representative using an internal customer management system. The search unit searches for a sales representative, for example, using an internal customer relationship management system (CRM). The internal customer management system acquires information on the sales representative in charge by inputting the name of a company. For example, the search unit searches for the sales representative of the relevant company from the internal system. This makes the search for a sales representative more efficient by using the internal customer management system. Some or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input information on the sales representative acquired by the internal customer management system into the generation AI and have the generation AI identify the sales representative.

[0070] The judgment unit can determine the amount based on past data and industry practices. The judgment unit determines the amount based on, for example, past data and industry practices. Past data includes data on the amounts of past flowers and congratulatory telegrams. For example, the judgment unit calculates an appropriate amount by learning data on the amounts of past flowers and congratulatory telegrams. This makes it possible to determine an appropriate amount based on past data and industry practices. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit may have the generation AI determine the amount based on past data and industry practices.

[0071] The transmission unit can compile the collected information into a single report and transmit it. The transmission unit, for example, compiles the collected information into a single report and transmits it. The report includes the collected information. For example, the transmission unit transmits the collected information to a sales representative. By compiling the information into a single report, transmission becomes more efficient. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the collected information into a generation AI and have the generation AI create a report.

[0072] The ordering unit can input the necessary information into the ordering system and submit an order request with one button. The ordering unit, for example, inputs the necessary information into the ordering system and submits an order request with one button. The ordering system automatically submits an order request based on the approved information. For example, the ordering unit completes the request with one button. This allows for quick ordering by submitting an order request with one button. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the necessary information for the ordering system into a generation AI and have the generation AI execute the order request.

[0073] The collection unit can estimate the user's emotions and adjust the timing of collecting promotion information based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects the information when the user is relaxed. For example, if the user is relaxed, the collection unit immediately collects promotion information and quickly proceeds with the processing. For example, if the user is in a hurry, the collection unit advances the collection timing and quickly collects promotion information. This allows information to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0074] When collecting promotion information, the collection unit can prioritize collecting promotion information from specific industries or companies. For example, the collection unit may prioritize collecting promotion information from the IT industry and postpone collecting information from other industries. For example, the collection unit may prioritize collecting promotion information from large companies and postpone collecting information from small and medium-sized companies. For example, the collection unit may prioritize collecting promotion information from companies located in a specific region and postpone collecting information from other regions. In this way, by collecting information from specific industries or companies preferentially, important information can be obtained quickly. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit may input promotion information from specific industries or companies into the generation AI and have the generation AI determine the priorities.

[0075] When collecting promotion information, the collection unit can optimize the collection targets by referring to past collection history. For example, the collection unit prioritizes collecting information on companies where promotions occur frequently based on the history of promotion information collected in the past. For example, the collection unit prioritizes collecting information on companies where promotions occur frequently during a specific period from the past collection history. For example, the collection unit analyzes the past collection history and selects optimal collection targets to maximize collection efficiency. In this way, collection efficiency is improved by referring to the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past collection history into a generation AI and cause the generation AI to optimize the collection targets.

[0076] The collection unit can estimate the user's emotions and determine the priority of promotion information to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit postpones less important promotion information and prioritizes collecting more important information. For example, when the user is relaxed, the collection unit collects all promotion information equally. For example, when the user is in a hurry, the collection unit prioritizes collecting the most important promotion information and postpones other information. This allows important information to be collected preferentially by determining the priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0077] When collecting promotion information, the collection unit can prioritize collecting promotion information for a specific region or country. For example, the collection unit prioritizes collecting promotion information for Japan and postpones information for overseas. For example, the collection unit prioritizes collecting promotion information for a specific city (e.g., Tokyo, Osaka). For example, the collection unit prioritizes collecting promotion information for a specific country (e.g., the United States, the United Kingdom). In this way, by collecting information for a specific region or country preferentially, information specific to the region or country can be quickly obtained. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input promotion information for a specific region or country to the generation AI and have the generation AI determine the priorities.

[0078] When collecting promotion information, the collection unit may combine and collect information from social media and news sites. For example, the collection unit may collect promotion information from social media (e.g., X (formerly Twitter), LinkedIn) and combine it with official announcements. For example, the collection unit may collect promotion information from news sites (e.g., Nikkei Shimbun, Wall Street Journal) and combine it with other information sources. For example, the collection unit may integrate information from social media and news sites to collect comprehensive promotion information. In this way, comprehensive promotion information can be collected by combining information from social media and news sites. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input information collected from social media and news sites into the generation AI and have the generation AI integrate the information.

[0079] The search unit can estimate the user's emotions and adjust the address information search method based on the estimated user emotions. For example, if the user is stressed, the search unit provides a simple search method and minimizes input steps. For example, if the user is relaxed, the search unit provides detailed search options and suggests a customizable search method. For example, if the user is in a hurry, the search unit prioritizes voice input to enable a quick search for address information. This enables a more appropriate search by adjusting the search method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the search unit can be performed using AI, for example, or without AI. For example, the search unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0080] When searching for address information, the search unit can refer to a reliable database other than the company's official website. For example, the search unit acquires address information from an industry association database in addition to the company's official website. For example, the search unit acquires address information from a government business registration database in addition to the company's official website. For example, the search unit acquires address information from a commercial database (e.g., D&B, Hoover's) in addition to the company's official website. This allows accurate address information to be acquired by referencing a reliable database. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input address information acquired from a reliable database into the generation AI and cause the generation AI to analyze the address information.

[0081] When searching for address information, the search unit can improve search accuracy by referring to past search history. For example, the search unit preferentially displays address information of similar companies based on address information searched in the past. For example, the search unit preferentially displays address information of companies that are frequently searched for from the past search history. For example, the search unit analyzes the past search history and suggests an optimal search method for improving search accuracy. In this way, search accuracy is improved by referring to the past search history. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the past search history into the generation AI and cause the generation AI to improve search accuracy.

[0082] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. For example, if the user is feeling stressed, the search unit prioritizes displaying address information with high importance. For example, if the user is relaxed, the search unit evenly displays all address information. For example, if the user is in a hurry, the search unit prioritizes displaying the most important address information. This allows important information to be displayed preferentially by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the search unit may be performed using an AI, for example, or without an AI. For example, the search unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0083] When searching for address information, the search unit can simultaneously search for address information of a company's affiliates and subsidiaries. For example, the search unit simultaneously searches for address information of affiliates in addition to the company's head office address. For example, the search unit simultaneously searches for address information of subsidiaries in addition to the company's head office address. For example, the search unit integrates and displays address information of affiliates and subsidiaries in addition to the company's head office address. This makes it possible to obtain comprehensive address information by simultaneously searching for information on affiliates and subsidiaries. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input address information of affiliates and subsidiaries into the generation AI and have the generation AI integrate the address information.

[0084] The search unit can reflect updated information from the company's official website in real time when searching for address information. For example, the search unit reflects the address information in real time when the company's official website is updated. For example, the search unit periodically checks for updated information from the company's official website to obtain the latest address information. For example, the search unit automatically obtains updated information from the company's official website to keep the address information up to date. This allows the latest address information to be obtained by reflecting updated information from the official website in real time. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input updated information from the company's official website into the generation AI and cause the generation AI to update the address information.

[0085] The judgment unit can estimate the user's emotions and adjust the criteria for determining the amount based on the estimated user emotions. For example, if the user is feeling stressed, the judgment unit provides simple criteria and quickly determines the amount. For example, if the user is relaxed, the judgment unit provides detailed criteria and suggests a customizable amount. For example, if the user is in a hurry, the judgment unit quickly determines the amount based on past data. This allows for a more appropriate amount to be determined by adjusting the criteria for determining the amount according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] When determining the amount, the judgment unit can calculate the optimal amount by referring to the past sending history of congratulatory flowers and telegrams. The judgment unit calculates the optimal amount, for example, based on the amount data of congratulatory flowers and telegrams sent in the past. The judgment unit calculates the appropriate amount for a specific company or industry, for example, from the past sending history. The judgment unit analyzes the past sending history, for example, and proposes the most appropriate amount. In this way, the optimal amount can be calculated by referring to the past sending history. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the past sending history into the generation AI and have the generation AI calculate the amount.

[0087] When determining the amount, the determination unit can customize the determination criteria by taking into account the customs of a specific industry or company. The determination unit determines an appropriate amount by taking into account the customs of the IT industry, for example. The determination unit determines an appropriate amount by taking into account the customs of a major company, for example. The determination unit determines an appropriate amount by taking into account the customs of a company located in a specific region, for example. In this way, a more appropriate amount can be determined by taking into account the customs of a specific industry or company. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the customs of a specific industry or company into the generation AI and have the generation AI customize the determination criteria.

[0088] The determination unit can estimate the user's emotions and prioritize amounts based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit prioritizes amounts with a high degree of importance. For example, if the user is relaxed, the determination unit assigns all amounts equally. For example, if the user is in a hurry, the determination unit prioritizes amounts with a high degree of importance. In this way, by prioritizing amounts according to the user's emotions, important amounts can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0089] When determining the amount, the determination unit can adjust the amount according to a specific event or season. For example, the determination unit adjusts the amount according to a year-end / New Year event. For example, the determination unit adjusts the amount according to a specific season (e.g., spring, summer). For example, the determination unit adjusts the amount according to a specific event (e.g., founding anniversary, anniversary). In this way, by adjusting the amount according to a specific event or season, an appropriate amount can be suggested. Some or all of the above-described processing in the determination unit may be performed using, or without, AI, for example. For example, the determination unit can input information about a specific event or season into the generation AI and have the generation AI adjust the amount.

[0090] When determining the amount, the judgment unit can propose an appropriate amount taking into consideration the company's financial situation and performance. The judgment unit, for example, proposes an appropriate amount based on the company's latest financial report. The judgment unit, for example, analyzes the company's performance data and proposes an appropriate amount. The judgment unit, for example, proposes an amount that is neither excessive nor insufficient, taking into consideration the company's financial situation. In this way, an amount that is neither excessive nor insufficient can be proposed by taking into consideration the company's financial situation and performance. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input data on the company's financial situation and performance into the generation AI and have the generation AI propose an amount.

[0091] The transmission unit can estimate the user's emotions and adjust the way the transmitted content is expressed based on the estimated user's emotions. For example, if the user is stressed, the transmission unit provides a simple and clear way of expression. For example, if the user is relaxed, the transmission unit provides a way of expression that includes detailed information. For example, if the user is in a hurry, the transmission unit provides a way of expression that focuses on the main points. This allows for more appropriate expression by adjusting the way the transmitted content is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0092] At the time of transmission, the transmission unit can determine the optimal transmission timing by referring to past transmission history. The transmission unit, for example, determines the most effective transmission timing based on the past transmission history. For example, the transmission unit confirms from the past transmission history that transmission during a specific time period is highly effective, and transmits during that time period. For example, the transmission unit analyzes the past transmission history and proposes the optimal transmission timing. In this way, the optimal transmission timing can be determined by referring to the past transmission history. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the past transmission history into a generation AI and have the generation AI optimize the transmission timing.

[0093] The sending unit can customize the content of the message at the time of sending by taking into consideration the attribute information of the recipient's company and the person in charge. The sending unit customizes the content of the message, for example, according to the industry and size of the recipient's company. The sending unit customizes the content of the message, for example, according to the position and job responsibilities of the recipient's person in charge. The sending unit customizes the content of the message by taking into consideration the culture and customs of the recipient's company. This makes it possible to provide more appropriate content by taking into consideration the recipient's attribute information. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input the recipient's attribute information into a generation AI and have the generation AI customize the content of the message.

[0094] The transmission unit can estimate the user's emotions and determine the priority of the transmission content based on the estimated user emotions. For example, when the user is feeling stressed, the transmission unit prioritizes transmission of the most important transmission content. For example, when the user is relaxed, the transmission unit transmits all transmission content equally. For example, when the user is in a hurry, the transmission unit prioritizes transmission of the most important transmission content. In this way, by determining the priority of the transmission content according to the user's emotions, important content can be transmitted preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0095] At the time of transmission, the transmission unit can select the optimal transmission method by taking into account the geographical information of the destination. For example, if the destination is domestic, the transmission unit uses a domestic delivery company for transmission. For example, if the destination is overseas, the transmission unit uses an international delivery company for transmission. For example, the transmission unit selects the optimal delivery method based on the geographical information of the destination. In this way, the optimal transmission method can be selected by taking into account the geographical information of the destination. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the geographical information of the destination into the generation AI and have the generation AI select the transmission method.

[0096] The transmitting unit can attach literature and reference materials related to the content of transmission when transmitting. The transmitting unit, for example, attaches literature related to the content of transmission and provides it to the recipient. The transmitting unit, for example, attaches reference materials related to the content of transmission and provides it to the recipient. The transmitting unit, for example, attaches data and statistical information related to the content of transmission and provides it to the recipient. By attaching related literature and reference materials, the content of transmission can be better understood. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input literature and reference materials related to the content of transmission into a generating AI and have the generating AI select the attached materials.

[0097] The order unit can estimate the user's emotions and adjust the order confirmation method based on the estimated user emotions. For example, if the user is stressed, the order unit provides a simple confirmation method and quickly confirms the order details. For example, if the user is relaxed, the order unit provides a detailed confirmation method and suggests customizable order details. For example, if the user is in a hurry, the order unit quickly confirms the order details based on past data. This allows for more appropriate confirmation by adjusting the confirmation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the order unit may be performed using AI, for example, or without AI. For example, the order unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0098] When placing an order, the ordering unit can select the optimal ordering method by referring to past order history. The ordering unit, for example, selects the most effective ordering method based on past order history. The ordering unit, for example, preferentially selects a specific vendor or service provider from past order history. The ordering unit, for example, analyzes past order history and proposes the optimal ordering method. In this way, the optimal ordering method can be selected by referring to past order history. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI. For example, the ordering unit can input past order history into a generation AI and have the generation AI select an ordering method.

[0099] When placing an order, the ordering unit can place an order taking into consideration evaluation information of the contractor and service provider. The ordering unit, for example, selects the most suitable contractor based on evaluation information of the contractor. The ordering unit, for example, selects the most suitable provider based on evaluation information of the service provider. The ordering unit, for example, analyzes the evaluation information of the contractor and proposes the most suitable contractor. This enables highly reliable ordering by taking the evaluation information into consideration. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the evaluation information of the contractor into the generation AI and have the generation AI select the contractor.

[0100] The ordering unit can estimate the user's emotions and prioritize order contents based on the estimated user emotions. For example, if the user is feeling stressed, the ordering unit prioritizes order contents with high importance. For example, if the user is relaxed, the ordering unit equally prioritizes all order contents. For example, if the user is in a hurry, the ordering unit prioritizes order contents with high importance. This allows important contents to be ordered preferentially by prioritizing order contents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the ordering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the ordering unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] When placing an order, the ordering unit can select the optimal ordering method by taking into account the geographical information of the supplier. For example, if the supplier is domestic, the ordering unit places the order using a domestic supplier. For example, if the supplier is overseas, the ordering unit places the order using an international supplier. The ordering unit selects the optimal ordering method based on the geographical information of the supplier. This makes it possible to select the optimal ordering method by taking into account the geographical information of the supplier. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the geographical information of the supplier into the generation AI and have the generation AI select the ordering method.

[0102] When placing an order, the ordering department can attach literature and reference materials related to the order contents. The ordering department, for example, attaches literature related to the order contents and provides it to the client. The ordering department, for example, attaches reference materials related to the order contents and provides it to the client. The ordering department, for example, attaches data and statistical information related to the order contents and provides it to the client. By attaching the related literature and reference materials, the order contents can be better understood. Some or all of the above-mentioned processing in the ordering department may be performed using, for example, AI, or may be performed without using AI. For example, the ordering department can input literature and reference materials related to the order contents into a generation AI and have the generation AI select the attached materials. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, search unit, identification unit, determination unit, transmission unit, and order unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses a web crawler in the smart device 14 to collect promotion information, which is then processed by the identification processing unit 290 in the data processing device 12. The search unit uses web scraping technology in the smart device 14 to search for address information from a company's official website. The identification unit uses the identification processing unit 290 in the data processing device 12 to identify a sales representative. The determination unit determines the price based on past data and industry practices using the identification processing unit 290 in the data processing device 12. The transmission unit compiles and transmits the information using the control unit 46A of the smart device 14. The order unit submits an order request with the push of a button using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, search unit, identification unit, determination unit, transmission unit, and order unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses a web crawler in the smart glasses 214 to collect promotion information, which is processed by the identification processing unit 290 in the data processing device 12. The search unit uses web scraping technology in the smart glasses 214 to search for address information from a company's official website. The identification unit uses the identification processing unit 290 in the data processing device 12 to identify a sales representative. The determination unit determines the price based on past data and industry practices using the identification processing unit 290 in the data processing device 12. The transmission unit compiles and transmits the information using the control unit 46A of the smart glasses 214. The order unit submits an order request with one button using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, search unit, identification unit, determination unit, transmission unit, and order unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit uses a web crawler in the headset terminal 314 to collect promotion information, which is then processed by the identification processing unit 290 in the data processing device 12. The search unit uses web scraping technology in the headset terminal 314 to search for address information from a company's official website. The identification unit uses the identification processing unit 290 in the data processing device 12 to identify a sales representative. The determination unit determines the price based on past data and industry practices using the identification processing unit 290 in the data processing device 12. The transmission unit compiles and transmits the information using the control unit 46A in the headset terminal 314. The order unit allows the control unit 46A in the headset terminal 314 to issue an order request with the press of a button. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, search unit, identification unit, determination unit, transmission unit, and order unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the robot 414's web crawler to collect promotion information, which is then processed by the identification processing unit 290 of the data processing device 12. The search unit uses the robot 414's web scraping technology to search for address information from a company's official website. The identification unit uses the identification processing unit 290 of the data processing device 12 to identify a sales representative. The determination unit determines the price based on past data and industry practices using the identification processing unit 290 of the data processing device 12. The transmission unit compiles and transmits the information using the control unit 46A of the robot 414. The order unit submits an order request with the push of a button using the control unit 46A of the robot 414.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The collection unit can estimate the user's emotions and determine the priority of promotion information to be collected based on the estimated user emotions. For example, if the user is stressed, less important promotion information can be postponed and more important information can be collected first. If the user is relaxed, all promotion information can be collected equally. If the user is in a hurry, the most important promotion information can be collected first and other information can be postponed. This allows important information to be collected first by determining the priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0105] When searching for address information, the search unit can refer to a reliable database other than the company's official website. For example, in addition to the company's official website, address information can be obtained from an industry association database. In addition to the company's official website, address information can also be obtained from a government business registration database. Furthermore, address information can also be obtained from commercial databases (e.g., D&B, Hoover's). This allows accurate address information to be obtained by referencing a reliable database. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input address information obtained from a reliable database into the generation AI and have the generation AI analyze the address information.

[0106] The determination unit can estimate the user's emotions and adjust the criteria for determining the amount based on the estimated user emotions. For example, if the user is stressed, simple criteria can be provided to quickly determine the amount. If the user is relaxed, detailed criteria can be provided to quickly suggest a customizable amount. If the user is in a hurry, the amount can be quickly determined based on past data. This allows for a more appropriate amount to be determined by adjusting the criteria for determining the amount according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the determination unit can be performed using AI, for example, or without AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0107] When transmitting, the transmitting unit can determine the optimal transmission timing by referring to past transmission history. For example, the most effective transmission timing can be determined based on the past transmission history. From the past transmission history, it can be confirmed that transmitting during a specific time period is more effective, and the message can be transmitted during that time period. Furthermore, it is also possible to analyze the past transmission history and propose the optimal transmission timing. In this way, the optimal transmission timing can be determined by referring to the past transmission history. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the past transmission history into a generating AI and have the generating AI optimize the transmission timing.

[0108] The order unit can estimate the user's emotions and adjust the order confirmation method based on the estimated user emotions. For example, if the user is stressed, a simple confirmation method can be provided to quickly confirm the order details. If the user is relaxed, a detailed confirmation method can be provided to quickly suggest customizable order details. If the user is in a hurry, the order details can be quickly confirmed based on past data. This allows for more appropriate confirmation by adjusting the confirmation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the order unit may be performed using AI, for example, or without AI. For example, the order unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0109] When collecting promotion information, the collection unit can prioritize collecting promotion information from specific industries or companies. For example, it can prioritize collecting promotion information from the IT industry and postpone collecting information from other industries. It is also possible to prioritize collecting promotion information from large companies and postpone collecting information from small and medium-sized companies. Furthermore, it is also possible to prioritize collecting promotion information from companies located in a specific region and postpone collecting information from other regions. In this way, by collecting information from specific industries or companies preferentially, important information can be obtained quickly. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input promotion information from specific industries or companies into the generation AI and have the generation AI determine the priorities.

[0110] When searching for address information, the search unit can improve search accuracy by referring to past search history. For example, it can prioritize displaying address information of similar companies based on address information searched in the past. It is also possible to prioritize displaying address information of frequently searched companies based on past search history. Furthermore, it is possible to analyze past search history and propose an optimal search method to improve search accuracy. In this way, by referring to past search history, search accuracy is improved. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input past search history into a generation AI and have the generation AI improve search accuracy.

[0111] When determining the amount, the determination unit can adjust the amount according to a specific event or season. For example, the amount can be adjusted according to a year-end or New Year's event. The amount can also be adjusted according to a specific season (e.g., spring, summer). Furthermore, the amount can also be adjusted according to a specific event (e.g., founding anniversary, anniversary). In this way, by adjusting the amount according to a specific event or season, an appropriate amount can be suggested. Some or all of the above-described processing in the determination unit may be performed using, or without, AI, for example. For example, the determination unit can input information about a specific event or season into the generation AI and have the generation AI adjust the amount.

[0112] The transmission unit can estimate the user's emotions and adjust the way the transmitted content is expressed based on the estimated user emotions. For example, if the user is stressed, a simple and clear way of expression can be provided. If the user is relaxed, a way of expression including detailed information can be provided. If the user is in a hurry, a way of expression that focuses on the main points can be provided. This allows for more appropriate expression by adjusting the way the transmitted content is expressed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit can be performed using, for example, AI, or without AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0113] When placing an order, the ordering unit can place an order taking into consideration evaluation information of the contractor and service provider. For example, the ordering unit can select the most suitable contractor based on the evaluation information of the contractor. It is also possible to select the most suitable provider based on the evaluation information of the contractor's service provider. Furthermore, it is possible to analyze the contractor's evaluation information and propose the most suitable contractor. This makes it possible to place a highly reliable order by taking the evaluation information into consideration. Some or all of the above-mentioned processing in the ordering unit may be performed using, for example, AI, or may be performed without using AI. For example, the ordering unit can input the contractor's evaluation information into the generation AI and have the generation AI select a contractor.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The collection unit collects promotion information. The collection unit collects promotion information using, for example, a web crawler. The web crawler automatically detects articles containing specific keywords (such as "promotion" or "advancement") and extracts the relevant information. For example, the web crawler collects promotion information from official company announcements and news sites. Step 2: The search unit searches for addresses based on the information collected by the collection unit. For example, the search unit uses web scraping technology to search for address information from the company's official website. Web scraping technology obtains address information from the company's "About Us" page or "Contact Us" page. For example, the search unit extracts address information from the company's official website. Step 3: The identification unit identifies a sales representative based on the information searched by the search unit. The identification unit searches for a sales representative, for example, using an internal customer relationship management system (CRM). The internal customer relationship management system obtains information about the sales representative in charge by inputting the company name. For example, the identification unit searches the internal system for the sales representative of the relevant company. Step 4: The determination unit determines the amount based on the information identified by the identification unit. The determination unit determines the amount based on, for example, past data and industry practices. Past data includes data on the amounts of past congratulatory flowers and telegrams. For example, the determination unit calculates an appropriate amount by learning data on the amounts of past congratulatory flowers and telegrams. Step 5: The transmitting unit compiles and transmits the information determined by the determining unit. For example, the transmitting unit compiles and transmits the collected information in a single report. The report includes the collected information. For example, the transmitting unit transmits the collected information to a sales representative. Step 6: The ordering unit submits an order application based on the information sent by the sending unit. For example, the ordering unit inputs the necessary information into the ordering system and submits an order application with the press of a button. The ordering system automatically submits an order application based on the approved information. For example, the ordering unit completes the application with the press of a button.

[0116] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0118] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 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 the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department for collecting promotion information; a search unit that searches for an address based on the information collected by the collection unit; an identification unit that identifies a sales representative based on the information searched by the search unit; a determination unit that determines an amount based on the information identified by the identification unit; a transmitting unit that collectively transmits the information determined by the determining unit; an order placing unit that places an order based on the information transmitted by the transmission unit; A system characterized by:

2. The collecting unit Use a web crawler to collect promotion information The system of claim 1 .

3. The search unit Use web scraping technology to retrieve address information from official company websites The system of claim 1 .

4. The search unit Search for sales representatives using the company's customer management system The system of claim 1 .

5. The determination unit Determine the amount based on past data and industry practices The system of claim 1 .

6. The transmission unit Collect all collected information and send it in a single report The system of claim 1 .

7. The ordering unit Enter the necessary information into the ordering system and submit an order with one button. The system of claim 1 .

8. The collecting unit To estimate a user's feelings and adjust a timing of collecting promotion information based on the estimated user's feelings. The system of claim 1 .

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