system

Abstract

We provide the system. [Solution] The present invention relates to a system for automating and standardizing business processes from contract to invoicing, comprising a contract information management unit, a product operation optimization unit, and an invoice processing automation unit. Using generative AI, it realizes automatic extraction and validity determination of contract contents, automatic generation of optimal operation flows for each product, and automatic creation and consistency check of invoices. As a result, even when adding new products or changing operations, it is possible to reduce human resources and costs while simultaneously achieving operational standardization, stabilization of work quality, and improvement of work efficiency, thereby preventing reliance on individuals, errors, and operational complexity.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method including the steps of: receiving a user utterance; adding the user utterance to a prompt that includes an instruction sentence related to a description of a chatbot character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problem that this invention aims to solve is that the business process from contract to billing has become more complex each time a new product is added or operations are changed. Conventionally, this has required new operational flows, tools, and personnel to be assigned each time, resulting in increased human resources and costs, reliance on individual expertise, inconsistencies in work quality, difficulty in standardizing operations, and decreased operational efficiency. In particular, it has been difficult to ensure consistency with existing operational flows and to rapidly optimize operations when adding new products. Furthermore, human errors and inconsistencies are prone to occur in verifying the validity of contract content and invoicing processing, resulting in insufficient overall operational stability and quality assurance. To solve these problems, this invention aims to consistently achieve contract information management, automatic optimization of operational flows for each product, and automation of invoicing processing by utilizing generative AI, thereby simultaneously achieving the resolution of labor shortages, cost reduction, operational standardization, stabilization of work quality, and improvement of operational efficiency. [Means for solving the problem]

[0005] The means for solving the problems according to the present invention is to configure a system comprising a contract information management unit, a product operation optimization unit, and an automated billing process unit. The contract information management unit accepts inputs such as customer information, product information, and contract terms, and uses generative AI to automatically determine the validity and consistency of the contract content, thereby eliminating human errors and inconsistencies by centrally managing the contract status. The product operation optimization unit uses generative AI to automatically generate the optimal operation flow for a new product when it is added, while ensuring consistency and standardization with existing operation flows, thereby optimizing operation procedures and stabilizing quality. The automated billing process unit uses generative AI to automatically create invoices in accordance with contract terms based on information provided by the contract information management unit and the product operation optimization unit, manages billing information, billing amounts, and billing timing, and executes billing processing periodically or at predetermined times, thereby reducing the human resources and costs that were previously required, and simultaneously achieving operational standardization, stabilization of business quality, and improvement of business efficiency. [Brief explanation of the drawing]

[0006] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0007] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0008] First, let's explain the terminology used in the following explanation.

[0009] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0010] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0011] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0012] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be A alone, B alone, or a combination of A and B. In addition, in this specification, when three or more matters are expressed in a connected manner using "and / or", the same concept as that for "A and / or B" applies.

[0014] [First Embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] As shown in FIG. 1, the data processing system 10 includes a data processing apparatus 12 and a smart device 14. An example of the data processing apparatus 12 is a server.

[0017] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of the "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0018] The smart device 14 includes a computer 36, a reception apparatus 38, an output apparatus 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 apparatus 38, the output apparatus 40, the camera 42, and the communication I / F 44 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A receives user input by contact with an indicator (for example, a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives voice-based user input by detecting the user's voice. The control unit 46A in the processor 46 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 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A, a speaker 40B, and the like, and presents data to the user 20 by outputting the data in a representation form (for example, voice and / or text) perceivable by the user 20. The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs voice in accordance with instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to the network 54. The communication I / Fs 44 and 26 manage the exchange of various types of information between the processor 46 and the processor 28 via the network 54.

[0022] Figure 2 illustrates an example of the main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0024] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0026] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0027] (Example 1)

[0028] The flow of a specific process in Example 1 will now be described. Each part of the system described below is implemented by a data processing device 12 and a smart device 14. The data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0029] (Embodiments for carrying out the present invention)

[0030] As an embodiment for carrying out the present invention, a system consisting of a server and a terminal is provided as an example. The server has multiple functional modules such as a contract information management unit, a product operation optimization unit, an automated billing processing unit, a database management unit, a notification management unit, a log management unit, and a security management unit, while the terminal is equipped with input and output interfaces used by various users such as employees, administrators, accounting personnel, and sales personnel. The server and terminal communicate via a network to send and receive various data and notify processing results in real time.

[0031] The contract information management department receives customer information (e.g., customer name, address, contact information, contact person name), product information (e.g., product name, product code, delivery method, pricing structure, start date, end date), and contract terms (e.g., contract period, payment terms, cancellation terms, special notes) entered from terminals and registers them in a database on the server. The generative AI, for example, if an employee enters in natural language "I would like to enter into a contract with customer B to provide product Y for 20,000 yen per month for one year," automatically extracts information such as the customer name, product name, amount, and period from the text and stores it in the database as structured data. Furthermore, the generative AI automatically determines the validity and consistency of the contract content, automatically detecting, for example, if the contract amount is abnormally high or low compared to similar past contracts, if the contract period contradicts the product's availability period, or if the payment terms violate internal regulations, and notifies the terminal with a warning or correction suggestion. For example, it outputs specific feedback such as, "This contract amount is set 30% higher than the average of past contracts. Please check the reason," or "The contract period exceeds the product's end date. Corrections are needed."

[0032] The Product Management Optimization Department automatically generates optimal operational flows using generative AI when new products are added or the operational conditions of existing products change. For example, if an employee inputs "I want to add a new product Z. Initial cost: 50,000 yen, monthly usage fee: 10,000 yen, minimum usage period: 6 months, delivery method: cloud-based" from a terminal, the generative AI will refer to the operational history and work procedures of similar products in the past, internal and external regulations, relevant laws and regulations, and customer inquiry history to automatically design the necessary operational procedures, such as "When introducing the new product Z, initial setup support for customers, identity verification before use, obtaining agreement to the terms of service, automatic distribution of monthly usage reports, and data deletion procedures upon cancellation." Furthermore, the generative AI is also responsible for standardizing and optimizing operational flows, for example, extracting and unifying procedures common to multiple products (e.g., identity verification, invoice issuance, support contact guidance), and automatically generating and distributing documents such as procedure manuals, checklists, and FAQs to terminals. For example, notifications such as "We have automatically generated an operational procedure manual for new product Z. Please download it from your terminal" will be sent. If there are changes to the operational flow, or if there are legal revisions or updates to internal regulations, the generation AI will automatically extract the changes, create a proposed update to the operational procedure, and notify the terminal and prompt for approval. For example, specific operational procedures such as "We have added an identity verification procedure to comply with the new personal information protection regulations. Please approve it" are possible.

[0033] The automated billing department uses generative AI to automatically create invoices in accordance with contract terms, based on information provided by the contract information management department and the product operation optimization department. For example, if the contract period or fee structure is complex (e.g., initial fee + monthly usage fee + usage-based billing + discount application + consumption tax calculation), the generative AI analyzes the contract details, accurately calculates the billing amount, billing information, and billing timing (e.g., end of each month, quarterly, at contract renewal), and automatically generates the invoice. For example, it can perform specific processing such as, "An invoice for July 2024 (initial fee 50,000 yen + monthly usage fee 10,000 yen + usage-based billing 3,000 yen - discount 2,000 yen + consumption tax) has been automatically generated for customer B." Furthermore, if there are deficiencies or inconsistencies in the invoice (e.g., calculation errors in the billing amount, incorrect billing information, inconsistency with contract terms), the generative AI automatically detects them and notifies the terminal with a suggested correction. For example, feedback such as "The billing amount does not match the contract terms. Please recalculate" may be output. Billing is processed automatically at regular or predetermined intervals; for example, invoices are automatically issued at the end of each month or upon contract renewal. After the invoice is issued, notifications are sent to the relevant personnel and customers via the terminal, and distributed via email or messaging services as needed. For example, notifications such as "We have emailed an invoice to customer B" or "You can download the invoice PDF from your terminal" may be sent.

[0034] Furthermore, when a new product is added, for example, the Product Operation Optimization Department and the Billing Process Automation Department collaborate, and the Generative AI can simultaneously set up an operational flow and billing process that corresponds to the new product. For example, even if a new product is added that has a different pricing structure or terms of service than existing products, the Generative AI can automatically generate the optimal operational procedure and billing logic by referring to past data and similar cases, and prompt the person in charge to confirm and approve it on their terminal. Moreover, even if there are multiple contracts simultaneously, or if different contract terms are set for each customer, the Generative AI can individually analyze the contents of each contract and automatically execute the optimal billing process. For example, it can flexibly handle different billing patterns for each contract, such as "Customer C is only charged on a pay-as-you-go basis, Customer D is a fixed monthly fee plus pay-as-you-go, and Customer E is eligible for a free initial setup campaign."

[0035] Furthermore, the notification management department delivers various processing results, warnings, correction suggestions, approval requests, and invoice issuance notifications from the generative AI to terminals in real time. For example, notifications such as "The operational flow for new product Z has been automatically generated. Please approve it," "There are deficiencies in the contract details. Please correct them," and "An invoice has been issued. Do you want to send it to the customer?" are displayed on the terminal. The log management department records the history of various processes, the basis for AI decisions, correction history, and approval history in chronological order, which can be used later for audits and troubleshooting. For example, a detailed history such as "On June 1, 2024, the AI ​​detected deficiencies in the contract details, and the person in charge corrected them" is recorded. The security management department encrypts confidential data such as contract information and billing information to prevent unauthorized access and information leakage. For example, it is possible to operate in a way such as "customer information is encrypted and stored on the server, and only the minimum necessary information is displayed on the terminal."

[0036] In this way, the system of the present invention realizes the functions of each part using generative AI on the server, and the terminal acts as a user interface, handling roles such as input, confirmation, and notification. This automates and standardizes the entire business process from contract to billing, simultaneously achieving reductions in human resources, cost reductions, operational standardization, stabilization of work quality, and improvement of operational efficiency. For example, when an employee inputs information on a new product from the terminal, the generative AI on the server immediately designs the optimal operational flow and billing process and notifies the person in charge, eliminating the need for individual handling of operational flow design and billing processes that was previously required, and significantly streamlining the overall business process. Furthermore, even if changes to contract details or revisions to the operational flow are necessary, for example, the generative AI automatically proposes the optimal countermeasures and prompts notification and approval from relevant parties via the terminal, enabling quick and accurate business operations. For example, in the event of legal revisions or changes to internal regulations, the generative AI automatically identifies the scope of impact, creates proposed revisions to necessary operational procedures and contract terms, and notifies the terminal, contributing to legal compliance and strengthening internal controls. Furthermore, by utilizing AI-powered automatic learning functions, for example, and continuously learning from past operational performance, trouble cases, and customer feedback, it is possible to continuously improve the accuracy and efficiency of operational flows and billing processes.

[0037] (System Configuration)

[0038] The system according to this embodiment comprises a contract information management unit, a product operation optimization unit, an automated billing processing unit, a database management unit, a notification management unit, a log management unit, a security management unit, and a user interface unit. The contract information management unit has the function of receiving information such as customer information, product information, and contract terms when an employee inputs them from a terminal, sending this information to the server, and using generative AI to automatically extract elements such as customer name, product name, contract amount, contract period, payment terms, and special notes from the contract content written in natural language, and registering them in the database as structured data. For example, if the input is "We would like to conclude a contract to provide product X to customer A for 12 months at a monthly fee of 10,000 yen," the generative AI will automatically extract information such as "customer A," "product X," "10,000 yen per month," and "12 months." Furthermore, it automatically determines the validity and consistency of the contract content, detecting cases such as when the contract amount is abnormally high or low compared to past similar contracts, when the contract period contradicts the product's availability period, or when the payment terms violate internal regulations, and notifying the terminal with a warning or correction suggestion. For example, it outputs specific feedback such as, "This contract amount is set 30% higher than the average of past contracts. Please check the reason," or "The contract period exceeds the product's end date. Corrections are required." Furthermore, if there are any changes or updates to the contract, the generation AI automatically extracts the changes and notifies the device of any necessary corrections or requests for approval.

[0039] The Product Management Optimization Department has a function that automatically generates the optimal operational flow using generative AI when a new product is added or the operational conditions of an existing product are changed. For example, if the input is "We want to add a new product Y. Initial cost: 30,000 yen, monthly usage fee: 5,000 yen, minimum usage period: 6 months, delivery method: cloud-based," the generative AI will refer to the operational history and work procedures of similar products in the past, internal and external regulations, relevant laws and regulations, customer inquiry history, etc., and automatically design the necessary operational procedures, such as "When introducing the new product Y, initial setup support for customers, identity verification before use, obtaining agreement to the terms of service, automatic distribution of monthly usage reports, and data deletion procedures upon cancellation." Furthermore, it is also responsible for standardizing and optimizing the operational flow, for example, extracting and unifying procedures common to multiple products (identity verification, invoice issuance, support contact guidance), and automatically generating documents such as procedure manuals, checklists, and FAQs, and distributing them to terminals. For example, a notification such as "The operational procedure manual for the new product Y has been automatically generated. Please download it from your terminal" will be sent. Even when there are changes to the operational flow, legal revisions, or updates to internal regulations, the generative AI automatically extracts the changes, creates proposed updates to the operational procedures, and notifies and prompts approval on the terminal. For example, it can implement specific operational procedures such as, "We have added an identity verification procedure to comply with the new personal information protection regulations. Please approve." Furthermore, to prevent operational flow from becoming dependent on individual employees, the AI ​​can automatically detect different procedures and decision criteria for each person in charge and encourage unification to standardized operational procedures.

[0040] The automated billing department has the function of automatically creating invoices in accordance with contract terms using generative AI based on information provided by the contract information management department and the product operation optimization department. For example, if the contract period or fee structure is complex (initial fee + monthly usage fee + usage-based charges + discount application + consumption tax calculation), the generative AI analyzes the contract details, accurately calculates the billing amount, billing information, and billing timing (end of each month, quarterly, at contract renewal), and automatically generates the invoice. For example, it can perform specific processing such as "An invoice for July 2024 (initial fee 50,000 yen + monthly usage fee 10,000 yen + usage-based charges 3,000 yen - discount 2,000 yen + consumption tax) has been automatically generated for customer B." Furthermore, if there are deficiencies or inconsistencies in the invoice content (calculation errors in the billing amount, incorrect billing information, inconsistencies with contract terms), the generative AI automatically detects them and notifies the terminal with suggested corrections. For example, feedback such as "The billing amount does not match the contract terms. Please recalculate" will be output. Billing is processed automatically at regular or predetermined intervals, for example, by automatically issuing invoices at the end of each month or upon contract renewal. After invoice issuance, notifications are sent to the relevant personnel and customers via the terminal, and distributed via email or messaging services as needed. For example, notifications such as "We have emailed an invoice to customer B" or "You can download the invoice PDF from your terminal" are sent. Furthermore, even when multiple contracts exist simultaneously or when different contract terms are set for each customer, the generative AI can individually analyze the contents of each contract and automatically execute the optimal billing process. For example, it can flexibly handle different billing patterns for each contract, such as "Customer C is billed only on a pay-as-you-go basis, Customer D is billed monthly with a fixed monthly fee plus pay-as-you-go charges, and Customer E is eligible for a free initial setup campaign."

[0041] The Database Management Department centrally manages all data handled by the entire system, including contract information, product information, customer information, operational flow, billing information, and historical information, and provides functions such as search, update, delete, and backup. For example, it is possible to search past contract history to extract conditions for similar contracts, refer to the change history of operational flow for each product, and display a list of invoice issuance history. Furthermore, the Database Management Department also functions as a dataset for generative AI to learn from, accumulating past operational results, trouble cases, and customer feedback to contribute to improving the accuracy of the AI.

[0042] The notification management unit has the function of delivering various processing results, warnings, correction suggestions, approval requests, and invoice issuance notifications to terminals in real time, generated by AI. For example, notifications such as "The operational flow for new product Y has been automatically generated. Please approve it," "There are deficiencies in the contract details. Please correct them," and "An invoice has been issued. Do you want to send it to the customer?" will be displayed on the terminal. Furthermore, the notification management unit can also manage whether notifications have been read or unread, customize the content of notifications, and set notification recipients (e.g., sales staff, administrators, accounting staff). For example, it is possible to set it up so that "only invoice issuance notifications are sent to accounting staff, and only contract change notifications are sent to sales staff."

[0043] The log management department has the functionality to record the history of various processes, the basis for AI decisions, correction history, and approval history in chronological order, which can then be used for audits and troubleshooting. For example, it records detailed history such as "On June 1, 2024, the AI ​​detected a deficiency in the contract, and the person in charge corrected it." Furthermore, the log management department also records the AI's decision-making process and learning history, functioning as evidence to ensure the AI's accountability and transparency.

[0044] The Security Management Department has the functionality to encrypt confidential data such as contract information and billing information to prevent unauthorized access and information leaks. For example, it can implement operations such as "encrypting and storing customer information on the server and displaying only the minimum necessary information on terminals" and "restricting the displayed content according to access rights." Furthermore, the Security Management Department also implements security measures such as monitoring access logs, detecting fraudulent operations, password management, and multi-factor authentication.

[0045] The user interface unit provides input and output interfaces for various users, including employees, administrators, accounting staff, and sales representatives. Users can input contract and product information, check operational flows and invoices, receive notifications, and perform approval operations on their terminals. For example, it offers a variety of screens, such as a contract details input screen, a product addition application screen, an operational procedure manual download screen, an invoice confirmation and sending screen, a notification list screen, and an approval request screen. Furthermore, the user interface unit includes features such as automated input assistance using generative AI, real-time checking of input content, and natural language support for inquiries.

[0046] Specific examples of prompts to be fed into the generative AI when implementing the present invention include, for example, "Extract the customer name, product name, contract amount, contract period, payment terms, and special notes from the following contract details written in natural language, and output them as structured data. Contract details: We would like to enter into a contract with customer A to provide product X for 12 months at a monthly fee of 10,000 yen," or "Automatically generate the operational flow for new product Y. Product information: Initial fee of 30,000 yen, monthly usage fee of 5,000 yen, minimum usage period of 6 months, delivery method is cloud-based. Please refer to the operational history of similar products in the past, business procedures, internal regulations, and relevant laws and regulations," or "Automatically generate an invoice based on the following contract terms. Contract terms: Initial fee of 50,000 yen, monthly usage fee of 10,000 yen, usage-based charge of 3,000 yen, discount of 2,000 yen, consumption tax of 10%, billing timing is the end of each month," or "Automatically detect any deficiencies or inconsistencies in the contract details or invoice details and output a revised version." Furthermore, prompts such as "Standardize and optimize operational flows, and automatically generate procedure manuals, checklists, and FAQs," and "If there are changes in laws or regulations or internal rules, identify the scope of impact and create proposed revisions to necessary operational procedures and contract terms," ​​can also be utilized.

[0047] (Implementation Steps)

[0048] Step 1: Enter and register customer information, product information, and contract terms. Employees and sales representatives use the user interface of a terminal to input customer information (customer name, address, contact information, contact person name, etc.), product information (product name, product code, delivery method, pricing structure, start date, end date, etc.), and contract terms (contract period, payment terms, cancellation terms, special notes, etc.), and send this information to the server. This information is centrally stored by the database management department and serves as the basis for subsequent processing. For example, when inputting a contract to provide product X to customer A for 12 months at a monthly cost of 10,000 yen, all items are entered via the terminal.

[0049] Step 2: Automatic extraction and structuring of contract details using generation AI. By utilizing generative AI, elements such as customer name, product name, contract amount, contract period, payment terms, and special notes are automatically extracted from contract details and product descriptions entered in natural language, and registered as structured data in the database. For example, if an employee enters "I would like to enter into a contract with customer B to provide product Y for 20,000 yen per month for one year," the generative AI will automatically extract "customer B," "product Y," "20,000 yen per month," and "one year." A concrete example of a prompt is "Please extract the customer name, product name, contract amount, contract period, payment terms, and special notes from the following contract details written in natural language, and output them as structured data. Contract details: I would like to enter into a contract with customer B to provide product Y for 20,000 yen per month for one year."

[0050] Step 3: Check the validity and consistency of the contract content using generated AI. Using a generation AI, the system automatically determines the validity and consistency of registered contract details. For example, it can detect cases where the contract amount is abnormally high or low compared to similar past contracts, where the contract period contradicts the product's availability period, or where payment terms violate internal regulations, and then notify the terminal with a warning or a suggested correction. An example of a prompt message is: "Please compare the following contract details with past contract history and internal regulations to determine if there are any problems with validity or consistency, and output a warning or suggested correction if there are problems. Contract details: A contract to provide product Y to customer B at a monthly fee of 20,000 yen for one year."

[0051] Step 4: Automatic generation of new product operation flow using generation AI When a new product is added or the operating conditions of an existing product change, the generating AI automatically generates the optimal operating flow for the new product by referring to the operating history of similar products in the past, business procedures, internal and external regulations, relevant laws and regulations, and customer inquiry history. For example, it automatically designs procedures such as initial setup support, identity verification, obtaining agreement to terms of service, monthly report distribution, and data deletion upon cancellation. An example of a prompt message is: "Please automatically generate the operating flow for new product Z. Product information: Initial fee 50,000 yen, monthly usage fee 10,000 yen, minimum usage period 6 months, delivery method is cloud-based. Please refer to the operating history, business procedures, internal regulations, and relevant laws and regulations of similar products in the past."

[0052] Step 5: Standardization, optimization, and automated document generation of operational flows using AI. The generation AI extracts common procedures across multiple products, standardizes and optimizes operational flows, and automatically generates and distributes documents such as procedure manuals, checklists, and FAQs to terminals. For example, it unifies common procedures such as identity verification and invoice issuance and distributes them to the responsible personnel. A specific example of a prompt message is: "Standardize and optimize the following operational flow and automatically generate procedure manuals, checklists, and FAQs. Operational flow: All procedures from introduction to cancellation of new product Z."

[0053] Step 6: Automatic correction of operational flow when laws and regulations change using generated AI. When laws and regulations are amended or internal company rules are changed, the AI ​​generates the data to identify the scope of impact, create proposed revisions to necessary operational procedures and contract terms, and prompt the terminal for notification and approval. For example, when personal information protection regulations are amended, actions such as adding identity verification procedures are automatically taken. An example of a prompt message is: "Please create a proposed revision to the existing operational flow to reflect the changes in new laws and internal regulations. Changes: Amendment to personal information protection regulations."

[0054] Step 7: Automated invoice generation and processing using AI. Based on information provided by the Contract Information Management Department and the Product Management Optimization Department, the AI ​​generates invoices automatically according to contract terms, accurately calculating the billing amount, billing recipient information, and billing timing, and automatically generating the invoices. For example, it automatically calculates initial fees, monthly usage fees, usage-based charges, discounts, and consumption tax, and generates an invoice PDF. A specific example of a prompt message is: "Please automatically generate an invoice based on the following contract terms. Contract terms: Initial fee 50,000 yen, monthly usage fee 10,000 yen, usage-based charges 3,000 yen, discount 2,000 yen, consumption tax 10%, billing timing at the end of each month."

[0055] Step 8: Check the validity and consistency of the invoice using the generated AI. The generating AI automatically detects any errors or inconsistencies in the invoice content, outputs a suggested correction if problems are found, and notifies the terminal. For example, it automatically detects calculation errors in the invoice amount or discrepancies with the contract terms. An example of a prompt message is: "Please compare the following invoice details with the contract terms and past invoice history to determine if there are any problems with validity or consistency, and output a suggested correction if there are any problems. Invoice details: Customer B, monthly usage fee 20,000 yen, usage-based charge 5,000 yen, discount 1,000 yen, consumption tax 10%."

[0056] Step 9: Notifications, Approvals, and History Management The notification management department delivers various processing results, warnings, correction suggestions, approval requests, and invoice issuance notifications generated by the AI ​​to terminals in real time. Users can check the content on their terminals and approve or correct as needed. The log management department records all processing history, the basis for the AI's decisions, correction history, and approval history in chronological order, which can be used later for audits and troubleshooting. For example, a detailed history such as "On June 1, 2024, the AI ​​detected a deficiency in the contract, and the person in charge corrected it" is recorded.

[0057] Step 10: Security Management and Access Control The security management department encrypts confidential data such as contract information and billing information to prevent unauthorized access and data leaks. They restrict the displayed content according to access permissions and implement security measures such as monitoring access logs, detecting fraudulent activity, and multi-factor authentication. For example, customer information is encrypted and stored on the server, and only the minimum necessary information is displayed on the terminal.

[0058] (Specific use cases)

[0059] For example, consider a scenario in the information and communication sector where a sales representative concludes a contract for cloud-based business support services with a new corporate client. The sales representative uses the terminal's user interface to input information such as the client's name, address, contact person's name, contact details, product name, product code, delivery method (cloud-based), initial cost, monthly usage fee, minimum usage period, contract period, payment terms, cancellation terms, and special notes. The input may also include natural language descriptions such as, "For corporate client C, we will provide cloud-based business support services with an initial cost of 100,000 yen, a monthly usage fee of 20,000 yen, a minimum usage period of 12 months, a contract period of 24 months, payment terms of monthly closing at the end of the following month, 30 days' notice required for cancellation, and a special note of a 10% discount for the first year only."

[0060] This input is sent to the server and received by the Contract Information Management Department. The generating AI automatically extracts elements such as customer name, product name, initial cost, monthly usage fee, minimum usage period, contract period, payment terms, cancellation terms, and special notes from the contract details written in natural language, and registers them in the database as structured data. An example of a prompt message for the generating AI is: "From the following contract details written in natural language, please extract the customer name, product name, initial cost, monthly usage fee, minimum usage period, contract period, payment terms, cancellation terms, and special notes, and output them as structured data. Contract details: For corporate customer C, a cloud-based business support service is provided with an initial cost of 100,000 yen, a monthly usage fee of 20,000 yen, a minimum usage period of 12 months, a contract period of 24 months, payment terms are due at the end of the following month for billing at the end of each month, 30 days' notice is required for cancellation, and a 10% discount is applied only for the first year as a special note."

[0061] The extracted data is centrally managed by the Contract Information Management Department, and the generating AI compares it with past similar contracts and internal regulations to automatically determine whether the contract amount deviates significantly from the past average, whether the contract period is consistent with the product's availability period, and whether the payment and termination conditions comply with internal regulations. For example, warnings and correction suggestions such as "The contract amount is set 20% higher than the past average. Please check the reason" or "The contract period exceeds the product's availability period. Correction is required" are notified to the terminal. A concrete example of a prompt message for the generating AI is: "Please compare the following contract details with past contract history and internal regulations to determine if there are any problems with validity or consistency, and output warnings or correction suggestions if there are problems. Contract details: For corporate customer C, a cloud-based business support service is provided with an initial fee of 100,000 yen, a monthly usage fee of 20,000 yen, a minimum usage period of 12 months, a contract period of 24 months, payment terms are the end of each month, payment due at the end of the following month, 30 days' notice required for termination, and a 10% discount applied only for the first year."

[0062] When a new product is added, the Product Operation Optimization Department uses a generation AI to automatically design operational procedures such as "initial setup support," "identity verification before use," "obtaining agreement to terms of service," "automatic distribution of monthly usage reports," and "data deletion procedures upon cancellation," referencing the operational history, work procedures, internal and external regulations, relevant laws, and customer inquiry history of similar products. An example of a prompt message to the generation AI is: "Please automatically generate the operational flow for the new product. Product information: Cloud-based business support service, initial fee 100,000 yen, monthly usage fee 20,000 yen, minimum usage period 12 months, delivery method is cloud-based. Please refer to the operational history, work procedures, internal regulations, and relevant laws of similar products from the past."

[0063] Furthermore, the system extracts common procedures across multiple products, standardizes and optimizes operational flows, and automatically generates and distributes documents such as procedure manuals, checklists, and FAQs to terminals. For example, common procedures such as "identity verification," "invoice issuance," and "support contact information" are unified and distributed to the responsible personnel. A concrete example of a prompt message for the generating AI is: "Standardize and optimize the following operational flow and automatically generate procedure manuals, checklists, and FAQs. Operational flow: All procedures from introduction to cancellation of the cloud-based business support service."

[0064] Based on information provided by the Contract Information Management Department and the Product Management Optimization Department, the Billing Processing Automation Department uses a generation AI to automatically create invoices according to contract terms, accurately calculating the billing amount, billing recipient information, and billing timing, and automatically generating the invoices. For example, it automatically calculates conditions such as "initial fee of 100,000 yen," "monthly usage fee of 20,000 yen," "10% discount for the first year," "10% consumption tax," and "billing timing at the end of each month," and generates an invoice PDF. A concrete example of a prompt message to the generation AI is: "Please automatically generate an invoice based on the following contract terms. Contract terms: initial fee of 100,000 yen, monthly usage fee of 20,000 yen, 10% discount for the first year, 10% consumption tax, billing timing at the end of each month."

[0065] The AI ​​automatically detects any errors or inconsistencies in the invoice content, outputs a suggested correction, and notifies the terminal if problems are found. For example, it automatically detects "calculation errors in the invoice amount" and "inconsistencies with contract terms." An example of a prompt message to the AI ​​is: "Please compare the following invoice content with the contract terms and past invoice history to determine if there are any problems with its validity or consistency, and output a suggested correction if there are any problems. Invoice content: Corporate customer C, monthly usage fee 20,000 yen, 10% discount for the first year, 10% consumption tax."

[0066] The Notification Management Department delivers various processing results, warnings, correction suggestions, approval requests, and invoice issuance notifications generated by AI to terminals in real time. Users can review the content on their terminals and approve or correct as needed. The Log Management Department records all processing history, AI decision basis, correction history, and approval history in chronological order, which can be used later for audits and troubleshooting. The Security Management Department encrypts confidential data such as contract information and billing information to prevent unauthorized access and information leaks. They restrict the content displayed according to access rights and implement security measures such as monitoring access logs, detecting fraudulent operations, and multi-factor authentication.

[0067] (Application Example 1) The flow of a specific process in Application Example 1 will be explained below. Each part of the system described below is implemented by a data processing device 12 and a smart device 14. The data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0068] (Embodiments for carrying out the present invention)

[0069] As an embodiment for carrying out the present invention, a system consisting of a server and multiple terminals is provided as an example. The server is equipped with a product information management unit, an order processing optimization unit, an automated billing, shipping, and return unit, a database management unit, a notification management unit, a log management unit, a security management unit, and a user interface unit, while the terminals are used by a variety of users such as product managers, administrators, customer support staff, and customers. The server and terminals communicate via a network to send and receive various data and notify processing results in real time.

[0070] The product information management department, for example, uses a generative AI to analyze the input of new product descriptions, specifications, prices, stock levels, features, categories, and campaign information in natural language from a terminal. The AI ​​automatically extracts and structures the information for each item, such as category, price, stock level, features, product attributes, and campaign application conditions, and registers it in the product database. For example, if the input is "New wireless earphones, 12 hours of continuous playback, waterproof, price 12,000 yen, 100 units in stock, 10% discount campaign for first-time purchases," the generative AI automatically organizes and registers information such as "wireless earphones," "12 hours of continuous playback," "waterproof," "12,000 yen," "100 units," and "10% discount for first-time purchases." Furthermore, when inputting product descriptions, specifications, features, and campaign information, the generative AI automatically detects deficiencies and inconsistencies in the product information, warning of errors such as "price not entered," "stock level is a negative value," "campaign period is past date," and "product description and specifications do not match," and outputs correction suggestions. For example, automated checks are performed to improve the quality of input, such as "automatically determining that a product is unsaleable if the stock quantity is 0," "displaying a warning if the campaign application conditions contradict the product attributes," and "suggesting corrections if the features described in the product description are not reflected in the specifications." When products are added, specifications are changed, or campaigns start and end, the generative AI automatically generates and manages optimal product information, keeping the product database always up-to-date and accurate. For example, the AI ​​consistently manages the entire product information lifecycle, including adding new products seasonally, changing the specifications of existing products, automatically reflecting limited-time campaigns, automatically updating stock quantities, and automatically archiving discontinued products.

[0071] The order processing optimization unit analyzes the order details, quantity, desired delivery date, and special conditions when a customer selects products from a terminal and enters the quantity, desired delivery date, gift wrapping, message card, simultaneous order of multiple items, and coupon code input, using a generative AI to comprehensively determine the validity of the order, including inventory status, campaign application conditions, discount conditions, delivery conditions, and gift wrapping eligibility. For example, it performs processes such as "order not possible if inventory is insufficient," "do not display gift wrapping options for items that cannot be gift-wrapped," "automatically detect expired coupon codes," "automatically select the optimal delivery method when multiple items are ordered simultaneously," "automatically calculate special shipping fees if the delivery address is an outlying island," and "immediately warn if there are inconsistencies in the order details." Once the order is confirmed, it automatically subtracts the inventory quantity and flexibly handles simultaneous orders of multiple items and special conditions. For example, the AI ​​can automatically select the optimal processing for complex orders, such as "ordering two wireless earphones and one smartwatch at the same time, with gift wrapping only for the earphones and standard shipping for the smartwatch," "automatically suggesting combined shipping when the same customer places multiple orders," "automatically applying discounts when the order meets the campaign eligibility requirements," and "the AI ​​automatically selecting the most suitable shipping carrier and delivery date and time based on the order content."

[0072] The automated billing, shipping, and returns department automatically creates invoices using a generation AI after an order is confirmed, automatically filling in customer information, product name, quantity, unit price, total amount, discount applied, consumption tax, shipping fee, and campaign applicable amount. For example, an invoice such as "Customer E, 2 wireless earphones, unit price 12,000 yen, total 24,000 yen, 10% discount for first-time purchase applied, 10% consumption tax, shipping fee 500 yen" is automatically generated. At the same time, it comprehensively assesses the order details, delivery address, desired date and time, inventory status, and the operating status of the delivery company to automatically determine the optimal shipping schedule. For example, it performs processes such as "shipping on the next business day, automatically selecting the fastest delivery company," "automatically determining whether multiple products can be shipped together," "arranging special delivery if the delivery address is on an outlying island," "automatically evaluating the risk of delivery delays considering weather and traffic conditions," "automatically generating shipping instructions and delivery slips," and "automatically coordinating with delivery companies." Furthermore, when returns or refunds occur, customers or customer support staff input the reason and circumstances of the return via a terminal, and the generative AI analyzes the information, automating the entire process from determining whether a return is possible, the refund amount, automatically updating inventory, and executing the refund process. For example, the AI ​​standardizes and streamlines all stages of the return and refund process, including features such as "full refund if the product has an initial defect," "customer is responsible for shipping costs for returns due to customer reasons," "automatically increasing inventory after a return is accepted," "automatically updating the invoice status after the refund is processed," "automatically detecting products with frequent return reasons and notifying the quality control department," and "automatically issuing return shipping labels when a return is accepted."

[0073] The Database Management Department centrally manages all data handled by the entire system, including product information, order information, customer information, billing information, shipping information, return / refund information, campaign history, and inquiry history, and provides functions such as search, update, delete, and backup. For example, it is possible to search past order history to extract conditions for similar orders, refer to inventory trends for each product, display a list of invoice issuance history, and automatically perform sales analysis and return rate analysis for each campaign. Furthermore, the Database Management Department also functions as a dataset for generative AI to learn from, accumulating past operational results, trouble cases, and customer feedback to contribute to improving the accuracy of the AI. For example, the scope of data utilization expands, such as "AI automatically suggesting improvements to product descriptions and specifications by analyzing past return reasons," "AI automatically suggesting orders based on sales data and inventory data," and "AI automatically generating personalized product recommendations based on customer purchase history."

[0074] The notification management unit has the function of delivering various processing results, warnings, correction suggestions, approval requests, invoice issuance notifications, shipment completion notifications, return acceptance notifications, campaign start / end notifications, and stock shortage warnings to terminals in real time, generated by a generation AI. For example, notifications such as "There is an error in the registration details of the new product. Please correct it," "There is an inconsistency in the order details. Please check again," "An invoice has been issued. Do you want to send it to the customer?", "Shipment is complete. Please check the tracking number from the shipping company," "A return has been accepted. Do you want to proceed with the refund process?", "Stock is below 10 units. Please consider placing an additional order," and "The campaign ends today. We will send a final notice to the customer" will be displayed on the terminal. The notification management unit can also manage read / unread status of notifications, customize notification content, and set notification recipients (e.g., staff, administrators, customer support staff).

[0075] The log management department has the functionality to record various processing histories, AI decision-making rationale, correction history, approval history, shipping history, return history, campaign history, and notification history in chronological order, which can then be used for audits and troubleshooting. For example, it records detailed histories such as "July 1, 2024: AI detected a deficiency in product information, and the person in charge corrected it," "July 2, 2024: AI detected an inconsistency in the order details, and the customer corrected it," "July 3, 2024: AI analyzed the reason for the return, and a full refund was processed," and "July 4, 2024: AI issued a stock shortage warning, and an additional order was placed." The log management department also records the AI's decision-making process and learning history, functioning as evidence to ensure the AI's accountability and transparency.

[0076] The Security Management Department has the function of encrypting confidential data such as product information, order information, customer information, billing information, shipping information, and return / refund information to prevent unauthorized access and information leakage. For example, it implements security measures such as "encrypting and storing customer information on the server and displaying only the minimum necessary information on terminals," "restricting displayed content according to access rights," "monitoring access logs and detecting unauthorized operations," "password management and multi-factor authentication," "automatic defense against cyberattacks from external sources," and "detection and automatic recovery of unauthorized data tampering."

[0077] The user interface section provides input and output interfaces for various users, including product managers, administrators, customer support staff, and customers. Users can input product and order information, check invoices and shipping information, submit return and refund requests, receive notifications, and perform approval operations on their terminals. For example, it offers a variety of screens, including product registration, order application, invoice confirmation, shipping status confirmation, return request, notification list, approval request, campaign management, inventory management, and sales analysis. The user interface section also includes features such as automated input assistance using generative AI, real-time checking of input content, natural language support for inquiries, automated FAQ responses, and chatbot support.

[0078] As described above, the system of the present invention enables standardized, high-quality operation of complex tasks that previously required different judgments and manual work by each person in charge, by using generative AI to automatically perform tasks such as analyzing natural language input, structuring data, determining conditions, optimizing, notifying, managing history, and security monitoring in all business processes from product registration to order acceptance, billing, shipping, return and refund processing, campaign management, inventory management, sales analysis, notification and history management, and security management. For example, in cases where individual handling was previously required, such as special discounts during campaign periods, simultaneous orders of multiple products, gift handling, special delivery conditions, diverse reasons for returns, automatic ordering when inventory is low, sales strategy proposals based on sales analysis, and automatic response to security incidents, the generative AI automatically selects the optimal processing by referring to past history and rules, so a significant improvement in operational efficiency and customer satisfaction can be expected.

[0079] (System Configuration)

[0080] The system according to this embodiment comprises a product information management unit, an order processing optimization unit, an automated billing, shipping, and return unit, a database management unit, a notification management unit, a log management unit, a security management unit, and a user interface unit. The product information management unit has the function of analyzing the content of new product descriptions, specifications, prices, stock levels, features, categories, and campaign information entered by a product manager from a terminal using natural language, and automatically extracting and structuring the information for each item such as category, price, stock level, features, product attributes, and campaign application conditions, and registering it in the product database. For example, if the input for a new wireless earphone product is "12 hours of continuous playback, waterproof, price 12,000 yen, 100 units in stock, 10% discount campaign for first-time purchases," the generating AI will automatically organize and register information such as "wireless earphones," "12 hours of continuous playback," "waterproof," "12,000 yen," "100 units," and "10% discount for first-time purchases." Furthermore, when entering product descriptions, specifications, features, and campaign information, the generating AI automatically detects deficiencies and inconsistencies in the product information, warning of errors such as "price not entered," "stock quantity is negative," "campaign period is past date," and "product description and specifications do not match," and outputting correction suggestions. In addition, when adding products, changing specifications, or starting and ending campaigns, the generating AI automatically generates and manages optimal product information, consistently managing the entire product information lifecycle, including adding new products seasonally, changing specifications of existing products, automatically reflecting limited-time campaigns, automatically updating stock quantities, and automatically archiving discontinued products.

[0081] The order processing optimization unit analyzes the order details, quantity, desired delivery date, and special conditions when a customer selects products from a terminal and enters the quantity, desired delivery date, gift wrapping, message card, simultaneous order of multiple items, and coupon code input, using generated AI to comprehensively determine the validity of the order based on inventory status, campaign application conditions, discount conditions, delivery conditions, and gift wrapping eligibility. For example, it performs processes such as "order not possible if inventory is insufficient," "do not display gift wrapping options for items that cannot be gift-wrapped," "automatically detect expired coupon codes," "automatically select the optimal delivery method when multiple items are ordered simultaneously," "automatically calculate special shipping fees if the delivery address is an outlying island," and "immediately warn if there are inconsistencies in the order details." Once the order is confirmed, it automatically subtracts the inventory quantity and flexibly handles simultaneous orders of multiple items and special conditions. For example, the AI ​​generates orders that automatically select the optimal processing method, such as "ordering two wireless earphones and one smartwatch at the same time, with gift wrapping only for the earphones and standard shipping for the smartwatch," "automatically suggesting combined shipping when the same customer places multiple orders," "automatically applying discounts when the order content meets the campaign eligibility requirements," and "automatically selecting the most suitable shipping carrier and delivery date and time based on the order content."

[0082] The automated billing, shipping, and returns department automatically creates invoices using AI after an order is confirmed, automatically filling in customer information, product name, quantity, unit price, total amount, discount applied, consumption tax, shipping fee, and campaign applicable amount. For example, an invoice such as "Customer E, 2 wireless earphones, unit price 12,000 yen, total 24,000 yen, 10% discount for first-time purchase applied, 10% consumption tax, shipping fee 500 yen" is automatically generated. At the same time, it comprehensively assesses the order details, delivery address, desired date and time, inventory status, and the operating status of the delivery company to automatically determine the optimal shipping schedule. For example, it performs processes such as "shipping on the next business day, automatically selecting the fastest delivery company," "automatically determining whether multiple products can be shipped together," "arranging special delivery if the delivery address is on an outlying island," "automatically evaluating the risk of delivery delays considering weather and traffic conditions," "automatically generating shipping instructions and delivery slips," and "automatically coordinating with delivery companies." Furthermore, when returns or refunds occur, customers or customer support staff input the reason and circumstances of the return via a terminal, and the generated AI analyzes the information, automating the entire process from determining whether a return is possible, the refund amount, automatically updating inventory, and executing the refund process. For example, the AI ​​standardizes and streamlines all stages of the return and refund process, including features such as "full refund if the product has an initial defect," "customer is responsible for shipping costs for returns due to customer reasons," "automatically increasing inventory after a return is accepted," "automatically updating the invoice status after the refund is processed," "automatically detecting products with frequent return reasons and notifying the quality control department," and "automatically issuing return shipping labels when a return is accepted."

[0083] The Database Management Department centrally manages all data handled by the entire system, including product information, order information, customer information, billing information, shipping information, return / refund information, campaign history, and inquiry history, and provides functions such as search, update, delete, and backup. For example, it is possible to search past order history to extract conditions for similar orders, refer to inventory trends for each product, display a list of invoice issuance history, and automatically perform sales analysis and return rate analysis for each campaign. Furthermore, the Database Management Department also functions as a dataset for the generating AI to learn from, accumulating past operational results, trouble cases, and customer feedback, contributing to the improvement of AI accuracy. For example, the scope of data utilization expands, such as "AI automatically suggesting improvements to product descriptions and specifications by analyzing past return reasons," "AI automatically suggesting orders based on sales data and inventory data," and "AI automatically generating personalized product recommendations based on customer purchase history."

[0084] The notification management unit has the function of delivering various processing results generated by AI, warnings, correction suggestions, approval requests, invoice issuance notifications, shipment completion notifications, return acceptance notifications, campaign start / end notifications, and stock shortage warnings to terminals in real time. For example, notifications such as "There is an error in the registration details of the new product. Please correct it," "There is an inconsistency in the order details. Please check again," "An invoice has been issued. Do you want to send it to the customer?", "Shipment is complete. Please check the tracking number from the shipping company," "A return has been accepted. Do you want to proceed with the refund process?", "Stock is below 10 units. Please consider placing an additional order," and "The campaign ends today. We will send a final notice to the customer" will be displayed on the terminal. The notification management unit can also manage read / unread status of notifications, customize notification content, and set notification destinations.

[0085] The log management department has the functionality to record various processing histories, AI decision-making rationale, correction history, approval history, shipping history, return history, campaign history, and notification history in chronological order, which can then be used for audits and troubleshooting. For example, it records detailed histories such as "July 1, 2024: AI detected a deficiency in product information, and the person in charge corrected it," "July 2, 2024: AI detected an inconsistency in the order details, and the customer corrected it," "July 3, 2024: AI analyzed the reason for the return, and a full refund was processed," and "July 4, 2024: AI issued a stock shortage warning, and an additional order was placed." The log management department also records the AI's decision-making process and learning history, functioning as evidence to ensure the AI's accountability and transparency.

[0086] The Security Management Department has the function of encrypting confidential data such as product information, order information, customer information, billing information, shipping information, and return / refund information to prevent unauthorized access and information leakage. For example, it implements security measures such as "encrypting and storing customer information on the server and displaying only the minimum necessary information on terminals," "restricting displayed content according to access rights," "monitoring access logs and detecting unauthorized operations," "password management and multi-factor authentication," "automatic defense against cyberattacks from external sources," and "detection and automatic recovery of unauthorized data tampering."

[0087] The user interface section provides input and output interfaces for various users, including product managers, administrators, customer support staff, and customers. Users can input product and order information, check invoices and shipping information, submit return and refund requests, receive notifications, and perform approval operations on their devices. For example, it offers a variety of screens, including product registration, order application, invoice confirmation, shipping status confirmation, return request, notification list, approval request, campaign management, inventory management, and sales analysis. The user interface section also includes features such as automated input assistance using AI generation, real-time checking of input content, natural language support for inquiries, automated FAQ responses, and chatbot support.

[0088] Specific examples of prompts to be fed into the generating AI when implementing the present invention include: "Extract the category, price, stock quantity, features, and campaign application conditions from the following product description written in natural language and output them as structured data. Product description: New wireless earphones, 12 hours of continuous playback, waterproof function, price 12,000 yen, 100 units in stock, 10% discount campaign applied for first-time purchases."; "Determine the stock status, campaign application conditions, delivery conditions, and gift wrapping eligibility for the following order and propose the optimal order processing. Order details: 2 wireless earphones, gift wrapping requested, coupon code applied, delivery destination is an outlying island."; and "Determine the return eligibility, refund amount, stock update, and whether a refund is necessary for the following return request and output the optimal response. Return request: Request for return due to initial defect, purchased product is a smartwatch, within 7 days of purchase."

[0089] (Implementation Steps)

[0090] Step 1: Enter and register product information. Product managers use the user interface of a terminal to input information such as product descriptions, specifications, prices, stock levels, features, categories, and campaign information for new products in natural language and send it to the server. For example, input might include: "New wireless earphones, 12 hours of continuous playback, waterproof, price 12,000 yen, 100 units in stock, 10% discount campaign for first-time purchases."

[0091] Step 2: Automatic extraction and structuring of product information using generation AI. The generating AI analyzes natural language data such as product descriptions, specifications, features, and campaign information, automatically extracting and structuring it for each item such as category, price, stock quantity, features, product attributes, and campaign eligibility conditions, and registering it in the product database. An example of a prompt is: "From the following natural language product description, please extract the category, price, stock quantity, features, and campaign eligibility conditions and output them as structured data. Product description: New wireless earphones, 12 hours of continuous playback, waterproof, price 12,000 yen, 100 units in stock, 10% discount campaign for first-time purchases."

[0092] Step 3: Checking for inconsistencies and contradictions in product information using AI generation. The generating AI automatically detects any errors or inconsistencies in the input content, such as product descriptions, specifications, features, and campaign information. It warns of errors such as "price not entered," "stock quantity is a negative value," "campaign period is past date," and "product description and specifications do not match," and outputs correction suggestions. An example of a prompt message is: "Please check the following product information for any errors or inconsistencies, and output a warning and correction suggestion if there are any problems. Product information: New wireless earphones, 12 hours of continuous playback, waterproof, price 12,000 yen, stock -5 units, 10% discount campaign applied for first-time purchases."

[0093] Step 4: Order acceptance and special conditions entry Customers select products from a terminal and enter special conditions such as quantity, desired delivery date, gift wrapping, message card, simultaneous order of multiple items, and coupon code before submitting their order. For example, an order might be entered such as "2 wireless earphones, gift wrapping requested, coupon code applied, delivery to an outlying island."

[0094] Step 5: Validation and optimization of order content by generating AI The generating AI analyzes the order details, quantity, desired delivery date, and special conditions, comprehensively determining inventory status, campaign eligibility, discount conditions, delivery conditions, and gift wrapping eligibility to automatically assess the order's validity and execute the optimal order processing. An example of a prompt message is: "For the following order details, please determine inventory status, campaign eligibility, delivery conditions, and gift wrapping eligibility, and propose the optimal order processing. Order details: 2 wireless earphones, gift wrapping requested, coupon code applied, delivery destination is an outlying island."

[0095] Step 6: Order Confirmation & Automatic Inventory Update If an order is deemed valid, it is confirmed and the inventory count is automatically reduced. The system flexibly handles simultaneous orders of multiple products and special conditions, and also optimizes processes such as automatically consolidating multiple orders from the same customer into a single shipment.

[0096] Step 7: Automatic invoice generation using AI After the order is confirmed, the AI ​​automatically calculates customer information, product name, quantity, unit price, total amount, discount amount, consumption tax, shipping fee, and campaign discount amount, and automatically generates an invoice. An example of a prompt message is: "Please automatically generate an invoice based on the following order details. Order details: Customer E, 2 wireless earphones, unit price 12,000 yen, 10% discount for first-time purchase, 10% consumption tax, 500 yen shipping fee."

[0097] Step 8: Automatic determination of shipping schedule and delivery arrangement by generating AI. The AI ​​generates the optimal shipping schedule by comprehensively analyzing the order details, delivery destination, desired date and time, inventory status, and the availability of the shipping company. It then issues shipping instructions to the shipping company and automatically generates shipping labels. An example of a prompt message is: "Please output the optimal shipping schedule and delivery arrangement proposal for the following order details. Order details: 2 wireless earphones, delivery destination is an outlying island, desired arrival date is 3 days from now."

[0098] Step 9: Return / Refund Request Acceptance Customers and customer support staff can enter the reason and circumstances of a return via a terminal to submit a return and refund request. For example, a request might read, "Requesting a return due to initial defect, purchased item is a smartwatch, within 7 days of purchase."

[0099] Step 10: Automate return and refund processing using generated AI. The generating AI analyzes the reason and circumstances of a return, determines whether the return is possible, the refund amount, whether inventory is automatically updated, and whether a refund is necessary, and automatically executes or proposes the optimal course of action. An example of a prompt message is: "For the following return request, please determine whether the return is possible, the refund amount, whether inventory is updated, and whether a refund is necessary, and output the optimal course of action. Return request: Request for return due to initial defect, purchased item is a smartwatch, within 7 days of purchase."

[0100] Step 11: Notifications, History Management, and Security Management The Notification Management Department delivers various processing results, warnings, correction suggestions, approval requests, invoice issuance notifications, shipment completion notifications, and return acceptance notifications to terminals in real time using AI generation. The Log Management Department records all processing history, AI decision basis, correction history, approval history, shipment history, return history, and notification history in chronological order. The Security Management Department encrypts confidential data such as product information, order information, customer information, billing information, shipment information, and return / refund information to prevent unauthorized access and information leaks.

[0101] (Specific use cases)

[0102] For example, when adding a new electronic device accessory to the product lineup on an e-commerce site, the product manager enters a product description in natural language on the terminal's input screen, such as "New wireless earphones, 12 hours of continuous playback, waterproof, price 12,000 yen, 100 units in stock, 10% discount campaign for first-time purchases." This input information is sent to the server, where the product information management department's generating AI automatically extracts items such as "wireless earphones," "12 hours of continuous playback," "waterproof," "12,000 yen," "100 units," and "10% discount for first-time purchases," and registers them in the product database in a structured format. A concrete example of a prompt sentence to be fed into the generating AI is: "From the following product description written in natural language, please extract the category, price, number of units in stock, features, and campaign application conditions, and output them as structured data. Product description: New wireless earphones, 12 hours of continuous playback, waterproof, price 12,000 yen, 100 units in stock, 10% discount campaign for first-time purchases."

[0103] Next, if a customer selects two wireless earphones on the e-commerce site, requests gift wrapping, enters a coupon code, and specifies an outlying island as the delivery address, the AI ​​generated by the order processing optimization unit analyzes the order details, quantity, desired delivery date, gift wrapping request, coupon application, and delivery address information to comprehensively determine the inventory status, campaign application conditions, discount conditions, delivery conditions, and gift eligibility. For example, it may make decisions such as "Order can be accepted as there is sufficient stock," "Gift wrapping is available for this product, so the option will be applied," "The coupon code is valid, so the discount will be applied," or "Special shipping charges will be automatically calculated because the delivery address is an outlying island," then confirms the order and automatically deducts the stock quantity. A concrete example of a prompt message to be fed into the generating AI is: "For the following order details, please determine the inventory status, campaign application conditions, delivery conditions, and gift eligibility, and propose the optimal order processing. Order details: 2 wireless earphones, gift wrapping requested, coupon code applied, delivery address is an outlying island."

[0104] After an order is confirmed, the automated billing, shipping, and returns department uses AI to automatically calculate customer information, product name, quantity, unit price, total amount, discount amount, consumption tax, shipping fee, and campaign discount amount, and automatically generate an invoice. For example, an invoice such as "Customer X, 2 wireless earphones, unit price 12,000 yen, total 24,000 yen, 10% first-time purchase discount applied, coupon discount 1,000 yen, consumption tax 10%, shipping fee 1,000 yen" will be automatically generated. An example of a prompt message to be fed into the generation AI is: "Please automatically generate an invoice based on the following order details. Order details: Customer X, 2 wireless earphones, unit price 12,000 yen, 10% first-time purchase discount, coupon discount 1,000 yen, consumption tax 10%, shipping fee 1,000 yen."

[0105] Furthermore, the generation AI comprehensively assesses order details, delivery destination, desired date and time, inventory status, and delivery carrier availability to automatically determine the optimal shipping schedule and issue shipping instructions to the delivery carrier and automatically generate shipping labels. For example, it may process orders such as "ship on the next business day, automatically select the shortest delivery carrier, and specify a special route for delivery to an outlying island." A concrete example of a prompt message to be fed into the generation AI is: "Please output the optimal shipping schedule and delivery arrangement proposal for the following order details. Order details: 2 wireless earphones, delivery destination: outlying island, desired arrival date: 3 days from now."

[0106] Furthermore, if a customer submits a return request after receiving a product, such as "I would like to return this item due to a manufacturing defect, the purchased item is wireless earphones, and it is within 5 days of purchase," the generating AI will analyze the reason and circumstances of the return, determine whether the return is possible, the refund amount, whether inventory will be automatically updated, and whether a refund process is necessary, and then automatically execute or propose the most suitable course of action. For example, a series of processes such as "full refund due to manufacturing defect, automatically increase inventory after return acceptance, automatically update invoice status after refund processing, and automatically issue return shipping label" will be automated. A concrete example of a prompt message to be fed into the generating AI is: "For the following return request, please determine whether the return is possible, the refund amount, whether inventory will be updated, and whether a refund process is necessary, and output the most suitable course of action. Return request: I would like to return this item due to a manufacturing defect, the purchased item is wireless earphones, and it is within 5 days of purchase."

[0107] Thus, the system of the present invention enables standardized, high-quality operation of complex tasks by having the generating AI automatically perform tasks such as natural language input analysis, data structuring, conditional judgment, optimization, notification, and history management in all business processes from product registration to order acceptance, billing, shipping, and return / refund processing.

[0108] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] For example, the data collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart device 14 and processes it with the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0112] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0113] [Second Embodiment]

[0114] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and communication interface 44 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the identification processing unit 290 using these models.

[0125] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is realized by the data processing device 12 and the smart glasses 214. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0126] (Example 1)

[0127] Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0128] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0129] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Furthermore, the processing by the data processing system 10 described above is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart glasses 214 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0132] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0133] [Third Embodiment]

[0134] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, display 343, and communication interface 44 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0144] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0145] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the headset terminal 314. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0146] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0147] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0148] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the headset terminal 314 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the speaker 240 and display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0151] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0152] [Fourth Embodiment]

[0153] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, controlled object 443, and communication interface 44 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0164] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0165] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the robot 414. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0166] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0167] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0168] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0170] For example, the data collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the robot 414 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using a generation AI. For example, the serving unit is implemented by the speaker 240 and controlled object 443 of the robot 414 and the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0171] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] 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.

[0183] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] Furthermore, the following additional information is disclosed regarding the above explanation.

[0191] (Claim 1)

[0192] The system comprises a Product Information Management Department, an Order Processing Optimization Department, and an Automated Billing, Shipping, and Return Department. The Product Information Management Department uses generative AI to analyze product descriptions, specifications, prices, inventory levels, features, categories, etc., entered in natural language from terminals. It automatically extracts and structures this information for each item such as category, price, inventory level, features, and product attributes, registers it in the product database, and also has the function to automatically generate and manage optimal product information when products are added, campaigns are launched, or specifications are changed. The Order Processing Optimization Department uses generative AI to analyze order details, quantities, desired delivery dates, gift wrapping, and other special conditions when receiving orders from customers, and comprehensively determines inventory status, campaign application conditions, discount conditions, delivery conditions, etc. The system automatically determines the validity of orders and executes the optimal order processing, automatically updates inventory levels upon order confirmation, and has the functionality to flexibly handle simultaneous orders of multiple products and special conditions. The automated billing, shipping, and return department automatically creates invoices using a generative AI after order confirmation, automatically filling in customer information, product name, quantity, unit price, total amount, applicable discounts, consumption tax, shipping costs, etc. At the same time, it comprehensively determines the optimal shipping schedule by considering order details, delivery address, desired date and time, inventory status, and the operational status of the delivery company. Furthermore, in the event of returns or refunds, the generative AI analyzes the reason and circumstances of the return, and the system automates the entire process from determining whether the return is possible, the refund amount, automatic inventory updates, and the execution of the refund process. (Claim 2)

[0193] The system according to claim 1, characterized in that the product information management department is equipped with a generative AI-based natural language processing function, and when product descriptions, specifications, features, campaign information, etc. are entered, it analyzes the entered natural language data to automatically extract categories, prices, inventory, features, product attributes, campaign application conditions, etc., registers them in the product database as structured data, and further automatically outputs warnings or correction suggestions if it detects any deficiencies or inconsistencies in the product information. (Claim 3)

[0194] The system described in paragraph 1 is characterized in that, when the billing, shipping, and return automation department uses a generative AI to automatically create invoices, automatically determine shipping schedules, and automate return and refund processing after an order is confirmed, it learns from past order history, shipping records, return reasons, customer inquiry history, delivery company operating status, campaign history, etc., and automatically selects and executes the optimal billing process, shipping arrangements, and return / refund response for each order and return, thereby simultaneously improving operational efficiency and customer satisfaction. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system comprising a contract information management unit, a product operation optimization unit, and an automated billing process unit, wherein the contract information management unit receives customer information, product information, and contract terms and registers them in a database, automatically determines the validity and consistency of contract content using generative AI, and has the function of managing the contract status; the product operation optimization unit automatically generates an operation flow suitable for the product using generative AI when a new product is added, ensures consistency and standardization with existing operation flows, and has the function of optimizing operation procedures and stabilizing quality; and the automated billing process unit automatically creates invoices in accordance with contract terms using generative AI based on information provided by the contract information management unit and the product operation optimization unit, manages billing information, billing amount, and billing timing, and has the function of executing billing processing periodically or at predetermined intervals.

2. The system according to claim 1, characterized in that it is equipped with a natural language processing function using generative AI, and when contract details and product information are entered in the contract information management department, it analyzes the entered natural language data, automatically extracts contract conditions and product attributes, structures and registers them in the database, and further automatically outputs warnings or correction suggestions if it detects any deficiencies or inconsistencies in the contract details.

3. The system according to claim 1, characterized in that when the Product Operation Optimization Department receives new product information, it uses a generative AI to learn past operation history and operation patterns of existing products, automatically generates an optimal operation flow for the added product, automatically corrects operation procedures and notifies operation personnel as necessary, and further works in cooperation with the Billing Processing Automation Department to automatically set up billing processes corresponding to the new product.

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