system

Abstract

We provide the system. [Solution] A first means of receiving and analyzing user input, A second method for obtaining relevant information from a database based on the analysis results, A third method involves generating a response based on the acquired information and sending it to the user's terminal. A fourth means of supporting the user in proceeding with the next procedure based on the response displayed on the user terminal, A system that includes this.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] will be described accordingly.

[0005] The procedures in base station construction are complex and require a lot of documents and procedure steps, which is a great burden for the relevant parties. Especially when the details of the documents and procedures required in each step are unclear, the progress of the procedures is likely to be delayed. As a result, users are forced to spend time and effort. In the conventional method, it is difficult to obtain appropriate information quickly, and the efficiency of the whole procedure decreases. Therefore, a system for solving this problem is required.

Means for Solving the Problems

[0006] The present invention provides a system comprising: a first means for receiving and analyzing user input; a second means for obtaining relevant information from a database based on the analysis results; a third means for generating a response based on the acquired information and sending it to the user terminal; and a fourth means for supporting the user in proceeding with the next procedure based on the response displayed on the user terminal. This allows the user to quickly obtain the necessary information and proceed smoothly with the procedure. Furthermore, by analyzing the user's input using a natural language processing (NLP) engine, more accurate information can be provided.

[0007] A "user" is an individual or organization that uses the system to obtain procedural information and receive support for the progress of base station construction.

[0008] "Input" refers to text data that represents questions or information provided by the user to the system.

[0009] "Analysis" is the process of extracting and understanding intent and keywords from user input text using natural language processing.

[0010] The "first means" refers to a system component for receiving and analyzing user input.

[0011] The "second method" refers to a system component for retrieving relevant information from a database based on the analysis results.

[0012] The "third method" refers to a system component for generating a response based on the acquired information and sending it to the user's terminal.

[0013] The "fourth method" is a system component that supports the user in proceeding with the next procedure based on the response displayed on the user terminal.

[0014] A "response" is the answer or guidance that a server generates in response to user input.

[0015] A "user terminal" is a device that a user uses to interact with a system, such as a personal computer or a smartphone.

[0016] A "natural language processing (NLP) engine" refers to algorithms and technologies for analyzing and understanding text data.

[0017] A "database" is a repository within a system where information and data related to base station construction are stored.

[0018] "Information" refers to data and knowledge related to documents and procedures required for base station construction.

Brief Description of Drawings

[0019] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0023] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0024] 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.

[0025] 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).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] 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).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] ---

[0041] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. Specifically, it provides users with rapid and accurate procedural support by combining an interactive interface using a chatbot with natural language processing technology.

[0042] System Configuration

[0043] This system consists of the following components:

[0044] 1. User terminal

[0045] A chatbot is a device that allows users to input questions and obtain information. These devices include personal computers and smartphones.

[0046] 2. Server

[0047] It is a central computer that receives, analyzes, and provides appropriate information from users. The server can include a natural language processing (NLP) engine, a database, and a response generation module.

[0048] 3. Database

[0049] This is a repository for storing information on documents and procedures necessary for base station construction.

[0050] Operation details

[0051] The user terminal first receives input from the user via an interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0052] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[0053] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[0054] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[0055] The server analyzes the request again, and this time "design drawing" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna layout plan (3) Power wiring plan," and sends it to the user terminal.

[0056] The user terminal displays this information to the user and guides them through the next steps. For example, if the user asks, "How do I proceed to the next step?" after gathering the necessary documents, the same process will be repeated to provide the information.

[0057] This allows users to proceed with procedures efficiently and obtain necessary information in a timely manner. The above is an overview of the embodiments of the present invention, which provide users with a series of procedural support services.

[0058] The following describes the processing flow.

[0059] ---

[0060] Step 1:

[0061] The user enters a question into the chatbot using their device. For example, they might enter, "What documents are required for base station construction?"

[0062] Step 2:

[0063] The terminal receives user input and sends it to the server. The data sent includes the user's question text.

[0064] Step 3:

[0065] The server receives a request from the user. The received request is first sent to the Natural Language Processing (NLP) engine.

[0066] Step 4:

[0067] The server uses an NLP engine to analyze the user's input text. Specifically, it divides the text and extracts keywords and intent. For example, it extracts the keywords "base station construction" and "necessary documents" from the user's text.

[0068] Step 5:

[0069] The server queries the database based on the analyzed keywords. The query includes the extracted keywords, and the server retrieves the corresponding information from the database.

[0070] Step 6:

[0071] The server generates a response based on information retrieved from the database. For example, if the retrieved information includes a list of necessary documents such as "license application form," "design drawings," and "environmental assessment report," the server formats this information and generates a response statement.

[0072] Step 7:

[0073] The server sends the generated response to the user's terminal. The response includes the generated answer text.

[0074] Step 8:

[0075] The terminal displays the response received from the server in the user interface. This allows the user to visually confirm the necessary information.

[0076] Step 9:

[0077] Users ask additional questions or inquire about the next steps. For example, after reviewing the information above, they might type "Please tell me about the blueprint format" into the chatbot.

[0078] Step 10:

[0079] The terminal receives user input again and sends it to the server. The data sent to the server includes new question text.

[0080] Step 11:

[0081] The server receives a new request and analyzes it again using the NLP engine. For example, it extracts the keywords "blueprint" and "format" from the new text.

[0082] Step 12:

[0083] The server queries the database again to retrieve the relevant information. For example, it retrieves detailed information about the blueprint format from the database.

[0084] Step 13:

[0085] The server generates a new response based on the information it has obtained. For example, it might generate a statement like, "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna placement plan (3) Power wiring plan."

[0086] Step 14:

[0087] The server then sends the generated response back to the user's terminal.

[0088] Step 15:

[0089] The terminal displays the response it receives to the user, allowing the user to proceed with the process based on the provided information.

[0090] The above outlines the specific processing steps of the system that enable users to efficiently proceed with procedures related to base station construction.

[0091] (Example 1)

[0092] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] The current procedures associated with base station construction are extremely complex, making it difficult for users to obtain the necessary information quickly and accurately. This causes delays in the process and hinders efficient progress. Furthermore, there is a lack of systems that provide immediate and appropriate responses to user inquiries.

[0094] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0095] In this invention, the server includes means for receiving user input via a chatbot interface and analyzing it using a natural language processing engine, means for obtaining relevant information from a database based on the analysis results, and means for formatting the obtained information to generate a response and sending it to the user terminal. This enables the user to efficiently proceed with complex base station construction procedures and obtain necessary information quickly and accurately.

[0096] "User input" refers to the information and questions that users provide to the system via the chatbot interface.

[0097] A "chatbot interface" refers to a means of human-computer interaction that allows users to communicate with a system through natural language dialogue.

[0098] A "natural language processing engine" refers to a software module that analyzes user input, understands its meaning, and extracts necessary keywords.

[0099] "Analysis" refers to the process of breaking down user input and identifying its meaning and important keywords.

[0100] "Related information" refers to data about documents and procedures necessary for base station construction that the system retrieves from its database.

[0101] A "database" refers to an information repository used to store documents and procedural information related to the construction of base stations.

[0102] "Acquisition" refers to the process of extracting necessary information from a database based on the analysis results.

[0103] "Formatting" refers to the process of arranging acquired information into a form that is easy for users to understand.

[0104] "Response" refers to output data that includes answers and information that the system provides to the user.

[0105] A "user terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access a system and input / receive information.

[0106] "Transmission" refers to the process of delivering information from the server to the user's terminal.

[0107] "Display" refers to the process by which the user terminal shows the response received from the server to the user.

[0108] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. Specifically, it provides users with rapid and accurate procedural support by combining an interactive interface using a chatbot with natural language processing technology.

[0109] System Configuration

[0110] This system consists of the following components:

[0111] 1. User terminal: A device used by users to input questions and obtain information through a chatbot. This includes personal computers, smartphones, and other devices.

[0112] 2. Server: A central computer that receives, analyzes, and provides appropriate information from users. The server can include a natural language processing (NLP) engine, a database, and a response generation module. Specific software options for the natural language processing engine include SpaCy and Google® Cloud NLP API.

[0113] 3. Database: This is a repository for storing information on documents and procedures necessary for base station construction.

[0114] Operation details

[0115] The user terminal first receives input from the user via an interface. For example, the user might input the question, "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0116] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[0117] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[0118] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[0119] The server analyzes the request again, and this time "design drawing" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna layout plan (3) Power wiring plan," and sends it to the user terminal.

[0120] The user terminal displays this information to the user and guides them through the next steps. For example, if the user asks, "How do I proceed to the next step?" after gathering the necessary documents, the same process will be repeated to provide the information.

[0121] Examples of specific actions and prompts

[0122] As a concrete example of the process, we will show the user's process of researching the documents required for base station construction.

[0123] The user types "What documents are required for base station construction?" into the chatbot.

[0124] The system (server) uses a natural language processing engine to analyze the question and performs a database search using "base station construction" and "necessary documents" as keywords.

[0125] The server generates a response such as "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report" and sends it to the user terminal.

[0126] Based on this information, the user then enters, "Please tell me about the blueprint format."

[0127] The server analyzes the request again, extracts the keywords "blueprint" and "format," retrieves the appropriate information from the database, and generates a response.

[0128] The server sends information to the user terminal such as, "The design drawing format must be written on A3 size paper and include the following items: (1) an overall map of the construction site, (2) a diagram of the antenna layout, and (3) a power wiring plan."

[0129] Examples of prompt statements include the following:

[0130] 1. "What documents are required for base station construction?"

[0131] 2. "What documents are required for the procedure to construct a base station?"

[0132] 3. "Could you please tell me the format for the design drawings related to base station construction?"

[0133] 4. "Please tell me how to write an environmental assessment report for base station construction."

[0134] This series of processes allows users to proceed efficiently and obtain necessary information in a timely manner. The above is a specific description of the embodiment for carrying out the invention.

[0135] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0136] Step 1:

[0137] The user enters a question through the chatbot interface. For example, the user enters the question, "What documents are required for base station construction?" The input is received from the user as text information. At this point, the input is "What documents are required for base station construction?"

[0138] Step 2:

[0139] The terminal sends user input to the server as an HTTP request. This request contains the text data entered by the user. Specifically, the data is sent to an endpoint such as POST / api / v1 / queries. Here, the input data is the user's text question, and the request is sent to the server as output.

[0140] Step 3:

[0141] The server passes the received data to a natural language processing (NLP) engine (such as SpaCy or Google Cloud NLP API) to analyze the meaning of the question. The NLP engine analyzes the input text and extracts important keywords. In this case, "base station construction" and "necessary documents" are extracted as keywords. The input is the user's text question, and the output is the extracted keywords.

[0142] Step 4:

[0143] The server uses the extracted keywords to generate an SQL query against the database and executes the query. For example, the following SQL query is generated: "SELECT FROM required_documents WHERE category='Base station construction';" The database returns information corresponding to the query. In this step, the input is keywords, and the output is the relevant information obtained from the database.

[0144] Step 5:

[0145] The server generates a response based on information retrieved from the database. Specifically, it formats the data into an appropriate format and creates a response such as, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." The input is relevant information from the database, and the output is a formatted response.

[0146] Step 6:

[0147] The server sends the generated response to the user's terminal as an HTTP response. The output includes the response data that was just generated.

[0148] Step 7:

[0149] The terminal displays the received response to the user. Specifically, it displays in the browser window: "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." The input is response data from the server, and the output is the screen display to the user.

[0150] Step 8:

[0151] To proceed to the next step, the user enters a new question based on the displayed information. For example, they might enter, "Please tell me about the blueprint format." Subsequent processes are repeated following the flow from step 1 to step 7 described above.

[0152] This series of processes allows users to efficiently obtain the necessary information and proceed with the procedures related to base station construction.

[0153] (Application Example 1)

[0154] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0155] A lack of information support systems to efficiently and timely carry out various procedures is a problem in logistics centers. Furthermore, the insufficient methods for quickly providing necessary information regarding procedures are leading to decreased operational efficiency. In particular, information regarding required documents and procedures is scattered, and a system that can provide appropriate support is needed.

[0156] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0157] In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from a database based on the analysis results, and means for generating a response based on the obtained information and transmitting it to the user terminal. This makes it possible for the user to quickly obtain the information necessary to efficiently proceed with the procedure.

[0158] "User input" refers to questions or instructions that a user sends to the system through their device.

[0159] "Means of analysis" refers to technologies or devices that understand user input and extract important keywords and intentions.

[0160] "Means of obtaining from a database" refers to a technology or device that searches for and retrieves relevant information from data storage based on analyzed keywords and intent.

[0161] "Means for generating a response" refers to a technology or device that generates answers or instructions for the user based on acquired information.

[0162] A "user terminal" is a device used by a user to interact with the system, and includes smartphones, personal computers, smart glasses, and head-mounted displays.

[0163] A "natural language processing engine" is software or technology that analyzes sentences input in natural language to extract keywords and understand intent.

[0164] A "target object" is the information or content identified based on user input.

[0165] "Means of formatting information" refers to technologies or devices that reshape acquired information into a form that is easily understandable to the user.

[0166] This invention relates to a procedural support system for logistics centers. Users input procedural questions via user terminals such as smartphones, smart glasses, and head-mounted displays. The system uses natural language processing technology to analyze these questions and provide necessary information quickly and accurately. The system is implemented with the following configuration.

[0167] System components

[0168] 1. User terminal

[0169] These are devices that users use to input questions about procedures and obtain information. Examples include smartphones, smart glasses, and head-mounted displays.

[0170] 2. Server

[0171] This is the central computer that receives and analyzes requests from user terminals. The server includes a natural language processing engine, database, and response generation module.

[0172] 3. Database

[0173] This is a repository that stores information regarding various procedures and required documents for logistics centers.

[0174] Operation details

[0175] The user terminal receives user questions through the interface. For example, the user might type, "What is required for the ordering process?" The entered question is then sent from the user terminal to the server.

[0176] When the server receives a request, it uses a natural language processing engine to analyze the user's question. The analysis extracts the keywords "ordering procedure" and "necessary." Based on these keywords, a query is executed against the database. The information retrieved from the database is formatted on the server side, and an appropriate response is generated. This response is sent to the user's terminal in the form of "The following documents are required for the ordering procedure: purchase order, inventory confirmation form, and approval stamp."

[0177] The user proceeds to the next step based on the response displayed on their terminal. For example, they might then enter, "Please tell me about the format of the inventory confirmation form." At this point, the server analyzes the request again, and "inventory confirmation form" and "format" are extracted as keywords. The server retrieves the appropriate information from the database and generates a response such as, "The inventory confirmation form format must include the following items: product name, quantity, and inventory location," and sends it to the user's terminal.

[0178] Specific example

[0179] This system will enable logistics center employees to process procedures more efficiently. For example, if a new employee asks, "Please tell me about the procedure for receiving packages," the system will provide a response such as, "The following documents are required for package receiving: ID verification document, receipt confirmation," allowing them to quickly obtain the necessary information.

[0180] Example of a prompt:

[0181] Question: What documents are required for package delivery?

[0182] Context: Package receiving procedures, ordering procedures, and other logistics center procedures

[0183] Keywords: Package receiving procedure

[0184] Answer: The following documents are required: ID verification document, receipt confirmation.

[0185] Thus, the present invention improves the operational efficiency of logistics centers by streamlining user procedures and providing necessary information quickly.

[0186] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0187] Processing steps of the logistics procedure support assistant system

[0188] Step 1: Receiving user input

[0189] The user terminal receives questions and instructions from the user regarding the procedure. This input is in string format, for example, "What is required for the ordering procedure?". The user terminal sends this input as a string to the server. The input is a raw question, and the output is a request to the server.

[0190] Step 2: Server-based input analysis

[0191] The server passes the request received from the user terminal to the natural language processing engine for analysis. Here, the input question is analyzed, and key keywords (e.g., "ordering procedure," "necessary") are extracted. The input is the user's question, and the output is a list of extracted keywords.

[0192] Step 3: Database search for related information

[0193] The server queries the database based on the extracted keywords. Specifically, it retrieves relevant information (for example, a list of required documents) from the database. The input is a list of keywords, and the output is a list of relevant information.

[0194] Step 4: Generating the response

[0195] The server generates a response to the user based on the information it has retrieved. This response is formatted in a user-friendly format. For example, it might be in the format of, "The following documents are required for the ordering process: Purchase order, inventory confirmation form, and approval stamp." The input is a list of relevant information, and the output is a formatted response statement.

[0196] Step 5: Sending the Response

[0197] The server sends the generated response message to the user terminal. The user terminal receives this response and displays it to the user. Here, the display is performed using appropriate hardware (such as a display or smart glasses). The input is a formatted response message, and the output is the information displayed on the user terminal's screen.

[0198] Step 6: Acceptance of additional questions and re-analysis

[0199] Based on the displayed information, the user can request further questions or procedures. The user terminal sends this re-entry to the server, where it is analyzed again using the natural language processing engine. This repeats the cycle. The input is a new question, and the output is a newly extracted list of keywords.

[0200] The specific data processing and calculations performed at each step are as follows:

[0201] 1. Step 1: Send the user's question as a string to the server.

[0202] 2. Step 2: Analyze the question using a natural language processing engine and extract keywords.

[0203] 3. Step 3: Perform a database search based on the extracted keywords to retrieve relevant information.

[0204] 4. Step 4: Generate and format the response based on the acquired relevant information.

[0205] 5. Step 5: Send the formatted response message to the user's terminal and display it.

[0206] 6. Step 6: Accept the input again, analyze it again with the natural language processing engine, and repeat the cycle.

[0207] The above are the specific processing steps for carrying out this invention.

[0208] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0209] ---

[0210] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. It provides users with rapid and accurate procedural support by combining a chatbot-based interactive interface, natural language processing technology, and an emotion engine.

[0211] System Configuration

[0212] This system consists of the following components:

[0213] 1. User terminal

[0214] A chatbot is a device that allows users to input questions and obtain information. These devices include personal computers and smartphones.

[0215] 2. Server

[0216] It is a central computer that receives, analyzes, and provides appropriate information from users. The server may include a natural language processing (NLP) engine, a database, a response generation module, and an emotion engine.

[0217] 3. Database

[0218] This is a repository for storing information on documents and procedures necessary for base station construction.

[0219] 4. Emotional Engine

[0220] This module analyzes user sentiment from input text and generates appropriate responses based on that sentiment.

[0221] Operation details

[0222] The user terminal first receives input from the user via an interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0223] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[0224] Furthermore, the emotion engine analyzes the user's emotions from their text and adjusts the tone and content of its response based on that emotional state. For example, if it detects that the user is anxious, the emotion engine will explain things in a calmer tone.

[0225] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[0226] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[0227] The server analyzes the request again, and this time "blueprint" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as, "The blueprint format must be written on A3 size paper and include the following items: (1) overall plan of the construction site (2) antenna layout plan (3) power wiring plan," and then adjusts the tone again using the emotion engine, taking into account the user's emotional state.

[0228] For example, if the user's emotions indicate fatigue or frustration, the server can add more gentle and encouraging words. The response will be generated with additional sentences such as, "Please feel free to contact us anytime if you have any questions."

[0229] The user terminal displays this information to the user, providing guidance to help them proceed with the next steps. This allows the user to efficiently complete the process and obtain necessary information in a timely manner. Furthermore, the emotion engine adjusts the response according to the user's emotions, resulting in a better user experience.

[0230] The above is an overview of the embodiments of the present invention, which provide a series of procedural support to the user. The introduction of an emotion engine can further improve the user experience.

[0231] The following describes the processing flow.

[0232] ---

[0233] Step 1:

[0234] The user enters a question into the chatbot using their device. For example, they might type, "What documents are required for base station construction?"

[0235] Step 2:

[0236] The terminal receives user input and sends it to the server. The data sent includes the user's question text.

[0237] Step 3:

[0238] The server receives a request from the user and sends that request to the Natural Language Processing (NLP) engine.

[0239] Step 4:

[0240] The server uses an NLP engine to analyze the user's input text and extract keywords and intent. For example, keywords such as "base station construction" and "necessary documents" might be extracted.

[0241] Step 5:

[0242] The server queries the database based on the analysis results to retrieve relevant information. In this case, a list of necessary documents is retrieved from the database.

[0243] Step 6:

[0244] The server generates a response based on the information it has acquired. For example, it might generate a response message such as, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report."

[0245] Step 7:

[0246] The server uses an emotion engine to analyze the user's input text to determine their emotions. The emotion engine extracts emotional states (e.g., anxiety, frustration, relief, etc.).

[0247] Step 8:

[0248] The server adjusts the tone and content of its response based on the user's emotional state. For example, if the user is anxious, the response might include additional information such as, "Please rest assured. Detailed instructions are provided below."

[0249] Step 9:

[0250] The server sends a refined response to the user's terminal. The response sent includes emotionally sensitive content.

[0251] Step 10:

[0252] The terminal displays the response received from the server in the user interface. This allows the user to visually confirm the necessary information.

[0253] Step 11:

[0254] Users ask additional questions or inquire about the next steps. For example, after reviewing the information above, they might type "Please tell me about the blueprint format" into the chatbot.

[0255] Step 12:

[0256] The terminal receives user input again and sends it to the server. The data sent includes new question text.

[0257] Step 13:

[0258] The server receives a new request and analyzes it again using the NLP engine. For example, the keywords "blueprint" and "format" are extracted from the new text.

[0259] Step 14:

[0260] The server queries the database again to retrieve the relevant information. Detailed information about the blueprint format is retrieved from the database.

[0261] Step 15:

[0262] The server generates a new response based on the information it has acquired. For example, it might generate a statement like, "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna placement plan (3) Power wiring plan."

[0263] Step 16:

[0264] The server then uses the emotion engine to analyze the user's emotional state again and adjust the tone. If the user's emotions indicate fatigue or frustration, the server adds a gentle, encouraging message such as, "Please feel free to contact us anytime if you have any questions."

[0265] Step 17:

[0266] The server sends a new response to the user's terminal again. The response includes the adjusted tone and content.

[0267] Step 18:

[0268] The device displays a new response to the user. The user can then proceed with the process based on the information provided.

[0269] The above outlines the specific processing steps of the system to enable users to efficiently proceed with procedures related to base station construction. The introduction of an emotion engine will further enhance the user experience.

[0270] (Example 2)

[0271] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0272] Conventional support systems for base station construction procedures often provided insufficient answers to user-submitted questions and had overly complex procedural flows. Furthermore, they lacked appropriate support tailored to the user's emotional state, hindering a positive user experience. As a result, users frequently experienced stress during the procedural process.

[0273] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from data storage based on the analysis results, means for generating a response based on the acquired information and transmitting it to the user device, means for supporting the user in proceeding with the next procedure based on the response displayed on the user device, and means for analyzing emotions from the user's input text and adjusting the response based on those emotions. As a result, the user can quickly obtain appropriate information and proceed smoothly with the next procedure, and furthermore, the user experience is improved because the emotion engine can respond according to the user's emotions.

[0274] A "user" is an entity that uses the system to obtain information necessary to proceed with procedures related to base station construction.

[0275] "Input" refers to questions or instructions that a user sends to a system using an interface such as a chatbot.

[0276] "Analysis" is a process for understanding the user's input and grasping its meaning.

[0277] A "natural language processing engine" is software for analyzing the text of natural language input by the user and accurately understanding its meaning and intention.

[0278] "Relevant information" is the data necessary to provide an appropriate answer to the user's question or instruction.

[0279] "Data storage" refers to an information repository that stores documents and procedural information required for base station construction.

[0280] A "response" is a response message generated by the system in response to the user's question or instruction. [[ID=

[18] ]

[0281] [[ID=

[21] ]"User equipment" refers to terminal equipment as a device used by the user to utilize the system.

[0282] An "emotion engine" is a software module for analyzing the user's emotional state from the user's input text and appropriately adjusting the tone and content of the response based on the analysis result.

[0283] "Procedure" refers to a series of processes and operations for the user to proceed with various necessary matters related to base station construction.

[0284] System Configuration

[0285] This system is a system for the user to efficiently proceed with the procedures necessary for base station construction. This system consists of the following components. [[ID=

[39] ]

[0286] 1. User terminal: A device for the user to input questions and obtain information through the chatbot. The terminal includes a personal computer, smartphone, etc.

[0287] Note: There seems to be a formatting issue with ID 21 in the original where it's written as "ユーザ機器」とは" but the translation template might expect a more standard format. I've translated it as best as possible while keeping the overall structure intact. Also, ID 39 seems to be an incomplete number in the original, but I've left it as is in the translation. 2. Server: A central computer that receives, analyzes, and provides appropriate information from users. This server includes a natural language processing (NLP) engine, data storage, response generation modules, and a sentiment engine.

[0288] 3. Data Storage: This is a repository for storing information on documents and procedures necessary for base station construction.

[0289] 4. Emotion Engine: This module analyzes emotions from user input text and generates appropriate responses based on those emotions.

[0290] Operation details

[0291] First, the user terminal receives input from the user via the interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0292] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. For example, using Google's BERT, keywords such as "base station construction" and "required documents" are extracted. Based on this, a query is executed against the data storage. Based on the "required documents" information retrieved from the data storage, the server formats it and generates an appropriate response.

[0293] Furthermore, the emotion engine analyzes the user's emotions from their text. For example, by combining an emotion analysis module with OpenAI's GPT-3, it can generate a calm response if the user is feeling anxious.

[0294] The response might say something like, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." This response is sent to the user terminal.

[0295] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server. The server parses the request again and this time extracts the keywords "design drawing" and "format." It retrieves the appropriate information from data storage and generates a response stating, "The design drawing format should be written on A3 size paper and include the following items: (1) an overall map of the construction site (2) an antenna layout diagram (3) a power wiring plan diagram." The emotion engine then adjusts the tone again, taking into account the user's emotional state.

[0296] For example, if the user's emotions indicate fatigue or frustration, the server can add more gentle words of encouragement. The response may also include an additional sentence such as, "Please feel free to contact us anytime if you have any questions." The user terminal displays this information to the user, allowing them to receive guidance on how to proceed.

[0297] This type of system allows users to efficiently obtain the information necessary for base station construction, streamlining the process. Furthermore, the emotion engine adjusts responses according to the user's emotions, providing a better user experience.

[0298] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0299] Step 1:

[0300] The user enters a question via their device. For example, they might enter, "What documents are required for base station construction?" This input is then sent to the server via the chatbot.

[0301] Specific operation: The user uses a keyboard or touchscreen to enter a question into the chatbot's input field.

[0302] Step 2:

[0303] The server receives the user's input and analyzes it using a natural language processing (NLP) engine. It analyzes the input question "What documents are required for base station construction?" and extracts keywords such as "base station construction" and "required documents".

[0304] Specific operation: The server sends the input data to the NLP engine (e.g., BERT) and obtains the keywords extracted as the analysis result.

[0305] Step 3:

[0306] Based on the extracted keywords, the server executes a query on the data storage. It retrieves information corresponding to the keywords "base station construction" and "required documents" from the data storage.

[0307] Specific operation: The server executes a database query (e.g., SQL query) to obtain information about the required documents.

[0308] Step 4:

[0309] The server organizes the information obtained from the data storage and generates a response. At the same time, it uses an emotion engine to analyze the user's emotional state. If the user is perceived to be anxious, it generates a response in a calm tone.

[0310] Specific operation: The server formats the obtained data and generates a response such as "The following documents are required for base station construction: (1) Application for approval, (2) Design drawings, (3) Environmental assessment report". Also, it uses an emotion engine (e.g., GPT-3) to analyze the user's emotion and adjust the tone.

[0311] Step 5:

[0312] The server sends the generated response to the user terminal. The response displayed on the user terminal will read: "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report."

[0313] Specific operation: The server sends the generated response data to the user's terminal. The user's terminal renders this data for display and displays it on the chatbot screen.

[0314] Step 6:

[0315] The user checks the response displayed on their terminal and proceeds to the next step. For example, they might enter an additional question such as, "Please tell me about the design drawing format." This input is then sent back to the server from the user's terminal.

[0316] Specific operation: The user reviews the displayed information and, if they need more information, enters additional questions into the chatbot and sends them.

[0317] This series of steps allows users to efficiently obtain the necessary information and proceed to the next step. Furthermore, sentiment analysis by the emotion engine improves the user experience.

[0318] (Application Example 2)

[0319] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0320] Conventional procedural support systems provide a uniform response without considering the user's emotional state, resulting in insufficient support, especially for users experiencing stress or anxiety. Such systems are unsatisfactory in environments where quick and accurate support is required, such as for workers in logistics centers.

[0321] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from a database based on the analysis results, means for generating a response based on the acquired information and transmitting it to the user terminal, and an emotion engine that analyzes the emotions from the user's input text and adjusts the tone and content of the response based on those emotions. As a result, a response is provided that corresponds to the user's emotional state, enabling the user to proceed with procedures efficiently, especially in busy situations.

[0322] "User input" refers to text or commands that a user enters into the system via a terminal.

[0323] "Analysis" refers to the process of understanding user input and extracting appropriate keywords and context.

[0324] A "database" is a repository for storing and managing related information.

[0325] A "response" is the system's answer or response to user input.

[0326] A "user terminal" is a device used by a user to interface with a system, and examples include smartphones and personal computers.

[0327] A "natural language processing engine" is a collection of technologies and algorithms that analyze user-input text and understand its content.

[0328] An "emotion engine" is a module that analyzes the emotional state of a user from their input text and generates an appropriate response based on the results.

[0329] A "server" is a central computing system that analyzes information from user input, retrieves necessary information from a database, and generates and sends a final response.

[0330] This invention relates to a system that supports worker procedures in a logistics center, and provides an interactive interface that combines a natural language processing engine and an emotion engine.

[0331] System Configuration

[0332] This system consists of the following elements:

[0333] 1. User terminal:

[0334] This is a device used by workers to confirm procedures and enter questions. This includes smartphones.

[0335] 2. Server:

[0336] It is a central computing system that analyzes user input, extracts relevant information, and generates appropriate responses. The server includes a natural language processing engine, an emotion engine, and a database.

[0337] 3. Database:

[0338] This is a repository that stores information regarding procedures and required documents at logistics centers.

[0339] 4. Natural Language Processing Engine:

[0340] This refers to a set of algorithms that analyze user text input, understand its content, and extract keywords.

[0341] 5. Emotional Engine:

[0342] This refers to a module that analyzes the user's emotions from their text and adjusts the tone and content of the response based on those emotions.

[0343] Operation details

[0344] The user terminal first receives the worker's question via the interface. For example, if the worker enters the question "What documents are needed?", this text is sent from the user terminal to the server.

[0345] The server first parses the input using a natural language processing engine (e.g., the Hugging Face Transformers library). Specifically, keywords such as "necessary" and "documents" are extracted from the text. Based on this analysis, queries are executed on the database to retrieve relevant information (e.g., a list of documents required for a logistics center).

[0346] Based on the information obtained, the server generates a response. For example, it might say, "The following documents are required for the logistics center: packing list, shipping instructions, and customs documents."

[0347] Next, the emotion engine analyzes the user's emotional state from their input text. For example, if the input text is short and some words are emphasized, the user may be anxious. Based on the emotion analysis, the tone of the response is adjusted. For anxious users, a calmer tone is used to add supplementary explanations such as, "Please rest assured. We will provide you with the information you need shortly."

[0348] Finally, the generated response is sent to the user's terminal and displayed to the worker.

[0349] Specific example

[0350] For example, if a worker at a logistics center asks, "How do I follow the new shipping procedure?", the system will respond as follows:

[0351] "Once all necessary documents are complete, please obtain approval from your supervisor. After that, you can proceed with preparing for shipment. If you have any further questions, please feel free to ask."

[0352] Example of a prompt

[0353] How do I proceed with the new shipping procedure?

[0354] In this way, by providing accurate and adaptive support tailored to the user's emotional state, it becomes possible to improve the operational efficiency of the logistics center.

[0355] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0356] Program processing flow

[0357] Step 1:

[0358] The user enters a question using their smartphone. This input is specifically in text format and is sent to the server via the device's interface. For example, the user might enter, "How do I proceed with the new shipping procedure?"

[0359] Step 2:

[0360] The server receives user input. Next, a natural language processing engine (e.g., Hugging Face Transformers) is used to analyze this input text and extract keywords. The input is text data, and the output is a list of extracted keywords. For example, "shipping procedure" might be extracted as a keyword from the input text.

[0361] Step 3:

[0362] The server queries the database based on the extracted keywords. The input is a list of keywords, and it retrieves relevant information from the database. The output is the relevant information (e.g., a list of required documents or procedural steps). For example, the database might retrieve information such as, "Once all required documents are in place, obtain approval from your supervisor. Then proceed with shipping preparations."

[0363] Step 4:

[0364] The server generates a response based on the information it has retrieved. The input is information from the database, and the output is text to be sent to the user. For example, a response such as "The new shipping procedure is as follows: After all necessary documents are in place, please obtain approval from your supervisor. Then proceed with shipping preparation." might be generated.

[0365] Step 5:

[0366] Next, the emotion engine analyzes the user's input text to determine their emotions. The input is the user's text data, and the output is their emotional state (e.g., anxiety, stress). For example, if the user is anxious, the emotion engine will recognize it as "anxiety."

[0367] Step 6:

[0368] The server adjusts the tone and content of the response based on the output of the emotion engine. The input is the response text and the output of the emotion engine, and the adjusted response is generated. For example, if the user is determined to be "anxious," a supplementary explanation such as "Please rest assured" will be added to the response.

[0369] Step 7:

[0370] The server sends the generated response to the user's terminal. The input is the text of the adjusted response, which is displayed on the user's smartphone. The user can then receive specific guidance on how to proceed to the next step.

[0371] The above outlines the specific processing steps of this system. This will allow users to efficiently proceed with procedures at the logistics center.

[0372] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0373] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0374] 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.

[0375] [Second Embodiment]

[0376] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0377] 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.

[0378] 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).

[0379] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0388] ---

[0389] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. Specifically, it provides users with rapid and accurate procedural support by combining an interactive interface using a chatbot with natural language processing technology.

[0390] System Configuration

[0391] This system consists of the following components:

[0392] 1. User terminal

[0393] A chatbot is a device that allows users to input questions and obtain information. These devices include personal computers and smartphones.

[0394] 2. Server

[0395] It is a central computer that receives, analyzes, and provides appropriate information from users. The server can include a natural language processing (NLP) engine, a database, and a response generation module.

[0396] 3. Database

[0397] This is a repository for storing information on documents and procedures necessary for base station construction.

[0398] Operation details

[0399] The user terminal first receives input from the user via an interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0400] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[0401] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[0402] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[0403] The server analyzes the request again, and this time "design drawing" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna layout plan (3) Power wiring plan," and sends it to the user terminal.

[0404] The user terminal displays this information to the user and guides them through the next steps. For example, if the user asks, "How do I proceed to the next step?" after gathering the necessary documents, the same process will be repeated to provide the information.

[0405] This allows users to proceed with procedures efficiently and obtain necessary information in a timely manner. The above is an overview of the embodiments of the present invention, which provide users with a series of procedural support services.

[0406] The following describes the processing flow.

[0407] ---

[0408] Step 1:

[0409] The user enters a question into the chatbot using their device. For example, they might enter, "What documents are required for base station construction?"

[0410] Step 2:

[0411] The terminal receives user input and sends it to the server. The data sent includes the user's question text.

[0412] Step 3:

[0413] The server receives a request from the user. The received request is first sent to the Natural Language Processing (NLP) engine.

[0414] Step 4:

[0415] The server uses an NLP engine to analyze the user's input text. Specifically, it divides the text and extracts keywords and intent. For example, it extracts the keywords "base station construction" and "necessary documents" from the user's text.

[0416] Step 5:

[0417] The server queries the database based on the analyzed keywords. The query includes the extracted keywords, and the server retrieves the corresponding information from the database.

[0418] Step 6:

[0419] The server generates a response based on information retrieved from the database. For example, if the retrieved information includes a list of necessary documents such as "license application form," "design drawings," and "environmental assessment report," the server formats this information and generates a response statement.

[0420] Step 7:

[0421] The server sends the generated response to the user's terminal. The response includes the generated answer text.

[0422] Step 8:

[0423] The terminal displays the response received from the server in the user interface. This allows the user to visually confirm the necessary information.

[0424] Step 9:

[0425] Users ask additional questions or inquire about the next steps. For example, after reviewing the information above, they might type "Please tell me about the blueprint format" into the chatbot.

[0426] Step 10:

[0427] The terminal receives user input again and sends it to the server. The data sent to the server includes new question text.

[0428] Step 11:

[0429] The server receives a new request and analyzes it again using the NLP engine. For example, it extracts the keywords "blueprint" and "format" from the new text.

[0430] Step 12:

[0431] The server queries the database again to retrieve the relevant information. For example, it retrieves detailed information about the blueprint format from the database.

[0432] Step 13:

[0433] The server generates a new response based on the information it has obtained. For example, it might generate a statement like, "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna placement plan (3) Power wiring plan."

[0434] Step 14:

[0435] The server then sends the generated response back to the user's terminal.

[0436] Step 15:

[0437] The terminal displays the response it receives to the user, allowing the user to proceed with the process based on the provided information.

[0438] The above outlines the specific processing steps of the system that enable users to efficiently proceed with procedures related to base station construction.

[0439] (Example 1)

[0440] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0441] The current procedures associated with base station construction are extremely complex, making it difficult for users to obtain the necessary information quickly and accurately. This causes delays in the process and hinders efficient progress. Furthermore, there is a lack of systems that provide immediate and appropriate responses to user inquiries.

[0442] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0443] In this invention, the server includes means for receiving user input via a chatbot interface and analyzing it using a natural language processing engine, means for obtaining relevant information from a database based on the analysis results, and means for formatting the obtained information to generate a response and sending it to the user terminal. This enables the user to efficiently proceed with complex base station construction procedures and obtain necessary information quickly and accurately.

[0444] "User input" refers to the information and questions that users provide to the system via the chatbot interface.

[0445] A "chatbot interface" refers to a means of human-computer interaction that allows users to communicate with a system through natural language dialogue.

[0446] A "natural language processing engine" refers to a software module that analyzes user input, understands its meaning, and extracts necessary keywords.

[0447] "Analysis" refers to the process of breaking down user input and identifying its meaning and important keywords.

[0448] "Related information" refers to data about documents and procedures necessary for base station construction that the system retrieves from its database.

[0449] A "database" refers to an information repository used to store documents and procedural information related to the construction of base stations.

[0450] "Acquisition" refers to the process of extracting necessary information from a database based on the analysis results.

[0451] "Formatting" refers to the process of arranging acquired information into a form that is easy for users to understand.

[0452] "Response" refers to output data that includes answers and information that the system provides to the user.

[0453] A "user terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access a system and input / receive information.

[0454] "Transmission" refers to the process of delivering information from the server to the user's terminal.

[0455] "Display" refers to the process by which the user terminal shows the response received from the server to the user.

[0456] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. Specifically, it provides users with rapid and accurate procedural support by combining an interactive interface using a chatbot with natural language processing technology.

[0457] System Configuration

[0458] This system consists of the following components:

[0459] 1. User terminal: A device used by users to input questions and obtain information through a chatbot. This includes personal computers, smartphones, and other devices.

[0460] 2. Server: A central computer that receives, analyzes, and provides appropriate information from users. The server can include a natural language processing (NLP) engine, a database, and response generation modules. Specific software options for the natural language processing engine include SpaCy and the Google Cloud NLP API.

[0461] 3. Database: This is a repository for storing information on documents and procedures necessary for base station construction.

[0462] Operation details

[0463] The user terminal first receives input from the user via an interface. For example, the user might input the question, "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0464] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[0465] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[0466] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[0467] The server analyzes the request again, and this time "design drawing" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna layout plan (3) Power wiring plan," and sends it to the user terminal.

[0468] The user terminal displays this information to the user and guides them through the next steps. For example, if the user asks, "How do I proceed to the next step?" after gathering the necessary documents, the same process will be repeated to provide the information.

[0469] Examples of specific actions and prompts

[0470] As a concrete example of the process, we will show the user's process of researching the documents required for base station construction.

[0471] The user types "What documents are required for base station construction?" into the chatbot.

[0472] The system (server) uses a natural language processing engine to analyze the question and performs a database search using "base station construction" and "necessary documents" as keywords.

[0473] The server generates a response such as "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report" and sends it to the user terminal.

[0474] Based on this information, the user then enters, "Please tell me about the blueprint format."

[0475] The server analyzes the request again, extracts the keywords "blueprint" and "format," retrieves the appropriate information from the database, and generates a response.

[0476] The server sends information to the user terminal such as, "The design drawing format must be written on A3 size paper and include the following items: (1) an overall map of the construction site, (2) a diagram of the antenna layout, and (3) a power wiring plan."

[0477] Examples of prompt statements include the following:

[0478] 1. "What documents are required for base station construction?"

[0479] 2. "What documents are required for the procedure to construct a base station?"

[0480] 3. "Could you please tell me the format for the design drawings related to base station construction?"

[0481] 4. "Please tell me how to write an environmental assessment report for base station construction."

[0482] This series of processes allows users to proceed efficiently and obtain necessary information in a timely manner. The above is a specific description of the embodiment for carrying out the invention.

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] The user enters a question through the chatbot interface. For example, the user enters the question, "What documents are required for base station construction?" The input is received from the user as text information. At this point, the input is "What documents are required for base station construction?"

[0486] Step 2:

[0487] The terminal sends user input to the server as an HTTP request. This request contains the text data entered by the user. Specifically, the data is sent to an endpoint such as POST / api / v1 / queries. Here, the input data is the user's text question, and the request is sent to the server as output.

[0488] Step 3:

[0489] The server passes the received data to a natural language processing (NLP) engine (such as SpaCy or Google Cloud NLP API) to analyze the meaning of the question. The NLP engine analyzes the input text and extracts important keywords. In this case, "base station construction" and "necessary documents" are extracted as keywords. The input is the user's text question, and the output is the extracted keywords.

[0490] Step 4:

[0491] The server uses the extracted keywords to generate an SQL query against the database and executes the query. For example, the following SQL query is generated: "SELECT FROM required_documents WHERE category='Base station construction';" The database returns information corresponding to the query. In this step, the input is keywords, and the output is the relevant information obtained from the database.

[0492] Step 5:

[0493] The server generates a response based on information retrieved from the database. Specifically, it formats the data into an appropriate format and creates a response such as, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." The input is relevant information from the database, and the output is a formatted response.

[0494] Step 6:

[0495] The server sends the generated response to the user's terminal as an HTTP response. The output includes the response data that was just generated.

[0496] Step 7:

[0497] The terminal displays the received response to the user. Specifically, it displays in the browser window: "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." The input is response data from the server, and the output is the screen display to the user.

[0498] Step 8:

[0499] To proceed to the next step, the user enters a new question based on the displayed information. For example, they might enter, "Please tell me about the blueprint format." Subsequent processes are repeated following the flow from step 1 to step 7 described above.

[0500] This series of processes allows users to efficiently obtain the necessary information and proceed with the procedures related to base station construction.

[0501] (Application Example 1)

[0502] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0503] A lack of information support systems to efficiently and timely carry out various procedures is a problem in logistics centers. Furthermore, the insufficient methods for quickly providing necessary information regarding procedures are leading to decreased operational efficiency. In particular, information regarding required documents and procedures is scattered, and a system that can provide appropriate support is needed.

[0504] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0505] In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from a database based on the analysis results, and means for generating a response based on the obtained information and transmitting it to the user terminal. This makes it possible for the user to quickly obtain the information necessary to efficiently proceed with the procedure.

[0506] "User input" refers to questions or instructions that a user sends to the system through their device.

[0507] "Means of analysis" refers to technologies or devices that understand user input and extract important keywords and intentions.

[0508] "Means of obtaining from a database" refers to a technology or device that searches for and retrieves relevant information from data storage based on analyzed keywords and intent.

[0509] "Means for generating a response" refers to a technology or device that generates answers or instructions for the user based on acquired information.

[0510] A "user terminal" is a device used by a user to interact with the system, and includes smartphones, personal computers, smart glasses, and head-mounted displays.

[0511] A "natural language processing engine" is software or technology that analyzes sentences input in natural language to extract keywords and understand intent.

[0512] A "target object" is the information or content identified based on user input.

[0513] "Means of formatting information" refers to technologies or devices that reshape acquired information into a form that is easily understandable to the user.

[0514] This invention relates to a procedural support system for logistics centers. Users input procedural questions via user terminals such as smartphones, smart glasses, and head-mounted displays. The system uses natural language processing technology to analyze these questions and provide necessary information quickly and accurately. The system is implemented with the following configuration.

[0515] System components

[0516] 1. User terminal

[0517] These are devices that users use to input questions about procedures and obtain information. Examples include smartphones, smart glasses, and head-mounted displays.

[0518] 2. Server

[0519] This is the central computer that receives and analyzes requests from user terminals. The server includes a natural language processing engine, database, and response generation module.

[0520] 3. Database

[0521] This is a repository that stores information regarding various procedures and required documents for logistics centers.

[0522] Operation details

[0523] The user terminal receives user questions through the interface. For example, the user might type, "What is required for the ordering process?" The entered question is then sent from the user terminal to the server.

[0524] When the server receives a request, it uses a natural language processing engine to analyze the user's question. The analysis extracts the keywords "ordering procedure" and "necessary." Based on these keywords, a query is executed against the database. The information retrieved from the database is formatted on the server side, and an appropriate response is generated. This response is sent to the user's terminal in the form of "The following documents are required for the ordering procedure: purchase order, inventory confirmation form, and approval stamp."

[0525] The user proceeds to the next step based on the response displayed on their terminal. For example, they might then enter, "Please tell me about the format of the inventory confirmation form." At this point, the server analyzes the request again, and "inventory confirmation form" and "format" are extracted as keywords. The server retrieves the appropriate information from the database and generates a response such as, "The inventory confirmation form format must include the following items: product name, quantity, and inventory location," and sends it to the user's terminal.

[0526] Specific example

[0527] This system will enable logistics center employees to process procedures more efficiently. For example, if a new employee asks, "Please tell me about the procedure for receiving packages," the system will provide a response such as, "The following documents are required for package receiving: ID verification document, receipt confirmation," allowing them to quickly obtain the necessary information.

[0528] Example of a prompt:

[0529] Question: What documents are required for package delivery?

[0530] Context: Package receiving procedures, ordering procedures, and other logistics center procedures

[0531] Keywords: Package receiving procedure

[0532] Answer: The following documents are required: ID verification document, receipt confirmation.

[0533] Thus, the present invention improves the operational efficiency of logistics centers by streamlining user procedures and providing necessary information quickly.

[0534] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0535] Processing steps of the logistics procedure support assistant system

[0536] Step 1: Receiving user input

[0537] The user terminal receives questions and instructions from the user regarding the procedure. This input is in string format, for example, "What is required for the ordering procedure?". The user terminal sends this input as a string to the server. The input is a raw question, and the output is a request to the server.

[0538] Step 2: Server-based input analysis

[0539] The server passes the request received from the user terminal to the natural language processing engine for analysis. Here, the input question is analyzed, and key keywords (e.g., "ordering procedure," "necessary") are extracted. The input is the user's question, and the output is a list of extracted keywords.

[0540] Step 3: Database search for related information

[0541] The server queries the database based on the extracted keywords. Specifically, it retrieves relevant information (for example, a list of required documents) from the database. The input is a list of keywords, and the output is a list of relevant information.

[0542] Step 4: Generating the response

[0543] The server generates a response to the user based on the information it has retrieved. This response is formatted in a user-friendly format. For example, it might be in the format of, "The following documents are required for the ordering process: Purchase order, inventory confirmation form, and approval stamp." The input is a list of relevant information, and the output is a formatted response statement.

[0544] Step 5: Sending the Response

[0545] The server sends the generated response message to the user terminal. The user terminal receives this response and displays it to the user. Here, the display is performed using appropriate hardware (such as a display or smart glasses). The input is a formatted response message, and the output is the information displayed on the user terminal's screen.

[0546] Step 6: Acceptance of additional questions and re-analysis

[0547] Based on the displayed information, the user can request further questions or procedures. The user terminal sends this re-entry to the server, where it is analyzed again using the natural language processing engine. This repeats the cycle. The input is a new question, and the output is a newly extracted list of keywords.

[0548] The specific data processing and calculations performed at each step are as follows:

[0549] 1. Step 1: Send the user's question as a string to the server.

[0550] 2. Step 2: Analyze the question using a natural language processing engine and extract keywords.

[0551] 3. Step 3: Perform a database search based on the extracted keywords to retrieve relevant information.

[0552] 4. Step 4: Generate and format the response based on the acquired relevant information.

[0553] 5. Step 5: Send the formatted response message to the user's terminal and display it.

[0554] 6. Step 6: Accept the input again, analyze it again with the natural language processing engine, and repeat the cycle.

[0555] The above are the specific processing steps for carrying out this invention.

[0556] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0557] ---

[0558] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. It provides users with rapid and accurate procedural support by combining a chatbot-based interactive interface, natural language processing technology, and an emotion engine.

[0559] System Configuration

[0560] This system consists of the following components:

[0561] 1. User terminal

[0562] A chatbot is a device that allows users to input questions and obtain information. These devices include personal computers and smartphones.

[0563] 2. Server

[0564] It is a central computer that receives, analyzes, and provides appropriate information from users. The server may include a natural language processing (NLP) engine, a database, a response generation module, and an emotion engine.

[0565] 3. Database

[0566] This is a repository for storing information on documents and procedures necessary for base station construction.

[0567] 4. Emotional Engine

[0568] This module analyzes user sentiment from input text and generates appropriate responses based on that sentiment.

[0569] Operation details

[0570] The user terminal first receives input from the user via an interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0571] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[0572] Furthermore, the emotion engine analyzes the user's emotions from their text and adjusts the tone and content of its response based on that emotional state. For example, if it detects that the user is anxious, the emotion engine will explain things in a calmer tone.

[0573] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[0574] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[0575] The server analyzes the request again, and this time "blueprint" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as, "The blueprint format must be written on A3 size paper and include the following items: (1) overall plan of the construction site (2) antenna layout plan (3) power wiring plan," and then adjusts the tone again using the emotion engine, taking into account the user's emotional state.

[0576] For example, if the user's emotions indicate fatigue or frustration, the server can add more gentle and encouraging words. The response will be generated with additional sentences such as, "Please feel free to contact us anytime if you have any questions."

[0577] The user terminal displays this information to the user, providing guidance to help them proceed with the next steps. This allows the user to efficiently complete the process and obtain necessary information in a timely manner. Furthermore, the emotion engine adjusts the response according to the user's emotions, resulting in a better user experience.

[0578] The above is an overview of the embodiments of the present invention, which provide a series of procedural support to the user. The introduction of an emotion engine can further improve the user experience.

[0579] The following describes the processing flow.

[0580] ---

[0581] Step 1:

[0582] The user enters a question into the chatbot using their device. For example, they might type, "What documents are required for base station construction?"

[0583] Step 2:

[0584] The terminal receives user input and sends it to the server. The data sent includes the user's question text.

[0585] Step 3:

[0586] The server receives a request from the user and sends that request to the Natural Language Processing (NLP) engine.

[0587] Step 4:

[0588] The server uses an NLP engine to analyze the user's input text and extract keywords and intent. For example, keywords such as "base station construction" and "necessary documents" might be extracted.

[0589] Step 5:

[0590] The server queries the database based on the analysis results to retrieve relevant information. In this case, a list of necessary documents is retrieved from the database.

[0591] Step 6:

[0592] The server generates a response based on the information it has acquired. For example, it might generate a response message such as, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report."

[0593] Step 7:

[0594] The server uses an emotion engine to analyze the user's input text to determine their emotions. The emotion engine extracts emotional states (e.g., anxiety, frustration, relief, etc.).

[0595] Step 8:

[0596] The server adjusts the tone and content of its response based on the user's emotional state. For example, if the user is anxious, the response might include additional information such as, "Please rest assured. Detailed instructions are provided below."

[0597] Step 9:

[0598] The server sends a refined response to the user's terminal. The response sent includes emotionally sensitive content.

[0599] Step 10:

[0600] The terminal displays the response received from the server in the user interface. This allows the user to visually confirm the necessary information.

[0601] Step 11:

[0602] Users ask additional questions or inquire about the next steps. For example, after reviewing the information above, they might type "Please tell me about the blueprint format" into the chatbot.

[0603] Step 12:

[0604] The terminal receives user input again and sends it to the server. The data sent includes new question text.

[0605] Step 13:

[0606] The server receives a new request and analyzes it again using the NLP engine. For example, the keywords "blueprint" and "format" are extracted from the new text.

[0607] Step 14:

[0608] The server queries the database again to retrieve the relevant information. Detailed information about the blueprint format is retrieved from the database.

[0609] Step 15:

[0610] The server generates a new response based on the information it has acquired. For example, it might generate a statement like, "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna placement plan (3) Power wiring plan."

[0611] Step 16:

[0612] The server then uses the emotion engine to analyze the user's emotional state again and adjust the tone. If the user's emotions indicate fatigue or frustration, the server adds a gentle, encouraging message such as, "Please feel free to contact us anytime if you have any questions."

[0613] Step 17:

[0614] The server sends a new response to the user's terminal again. The response includes the adjusted tone and content.

[0615] Step 18:

[0616] The device displays a new response to the user. The user can then proceed with the process based on the information provided.

[0617] The above outlines the specific processing steps of the system to enable users to efficiently proceed with procedures related to base station construction. The introduction of an emotion engine will further enhance the user experience.

[0618] (Example 2)

[0619] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0620] Conventional support systems for base station construction procedures often provided insufficient answers to user-submitted questions and had overly complex procedural flows. Furthermore, they lacked appropriate support tailored to the user's emotional state, hindering a positive user experience. As a result, users frequently experienced stress during the procedural process.

[0621] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from data storage based on the analysis results, means for generating a response based on the acquired information and transmitting it to the user device, means for supporting the user in proceeding with the next procedure based on the response displayed on the user device, and means for analyzing emotions from the user's input text and adjusting the response based on those emotions. As a result, the user can quickly obtain appropriate information and proceed smoothly with the next procedure, and furthermore, the user experience is improved because the emotion engine can respond according to the user's emotions.

[0622] A "user" is an entity that uses the system to obtain information necessary to proceed with procedures related to base station construction.

[0623] "Input" refers to questions or instructions that a user sends to a system using an interface such as a chatbot.

[0624] "Analysis" is the process of understanding user input and grasping its meaning.

[0625] A "natural language processing engine" is software that analyzes natural language text entered by a user and accurately understands its meaning and intent.

[0626] "Related information" refers to data necessary to provide appropriate answers to user questions or instructions.

[0627] "Data storage" refers to an information repository that stores documents and procedural information necessary for the construction of base stations.

[0628] A "response" is a message that a system generates in response to a user's question or instruction.

[0629] "User equipment" refers to terminal devices used by users to access the system.

[0630] An "emotion engine" is a software module that analyzes the user's emotional state from the user's input text and adjusts the tone and content of the response appropriately based on the analysis results.

[0631] "Procedure" refers to a series of processes and operations that users perform to carry out various necessary tasks related to base station construction.

[0632] System Configuration

[0633] This system is designed to help users efficiently carry out the procedures necessary for base station construction. This system consists of the following components:

[0634] 1. User terminal: A device used by users to input questions and obtain information through a chatbot. This includes personal computers, smartphones, and other devices.

[0635] 2. Server: A central computer that receives, analyzes, and provides appropriate information from users. This server includes a natural language processing (NLP) engine, data storage, response generation modules, and a sentiment engine.

[0636] 3. Data Storage: This is a repository for storing information on documents and procedures necessary for base station construction.

[0637] 4. Emotion Engine: This module analyzes emotions from user input text and generates appropriate responses based on those emotions.

[0638] Operation details

[0639] First, the user terminal receives input from the user via the interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0640] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. For example, using Google's BERT, keywords such as "base station construction" and "required documents" are extracted. Based on this, a query is executed against the data storage. Based on the "required documents" information retrieved from the data storage, the server formats it and generates an appropriate response.

[0641] Furthermore, the emotion engine analyzes the user's emotions from their text. For example, by combining OpenAI's GPT-3 with an emotion analysis module, it can generate a calm response if the user is feeling anxious.

[0642] The response might say something like, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." This response is sent to the user terminal.

[0643] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server. The server parses the request again and this time extracts the keywords "design drawing" and "format." It retrieves the appropriate information from data storage and generates a response stating, "The design drawing format should be written on A3 size paper and include the following items: (1) an overall map of the construction site (2) an antenna layout diagram (3) a power wiring plan diagram." The emotion engine then adjusts the tone again, taking into account the user's emotional state.

[0644] For example, if the user's emotions indicate fatigue or frustration, the server can add more gentle words of encouragement. The response may also include an additional sentence such as, "Please feel free to contact us anytime if you have any questions." The user terminal displays this information to the user, allowing them to receive guidance on how to proceed.

[0645] This type of system allows users to efficiently obtain the information necessary for base station construction, ensuring a smoother process. Furthermore, the emotion engine adjusts responses based on the user's emotions, providing a better user experience.

[0646] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0647] Step 1:

[0648] The user enters a question via their device. For example, they might enter, "What documents are required for base station construction?" This input is then sent to the server via the chatbot.

[0649] Specific operation: The user uses a keyboard or touchscreen to enter a question into the chatbot's input field.

[0650] Step 2:

[0651] The server receives user input and performs analysis using a natural language processing (NLP) engine. It analyzes the input question, "What documents are needed for base station construction?", and extracts the keywords "base station construction" and "necessary documents".

[0652] Specific operation: The server sends input data to an NLP engine (e.g., BERT) and retrieves keywords extracted as a result of the analysis.

[0653] Step 3:

[0654] The server queries the data storage based on the extracted keywords. It retrieves information from the data storage corresponding to the keywords "base station construction" and "required documents".

[0655] Specific operation: The server executes database queries (e.g., SQL queries) to retrieve information about the required documents.

[0656] Step 4:

[0657] The server organizes the information retrieved from data storage and generates a response. Simultaneously, it uses an emotion engine to analyze the user's emotional state. If it detects that the user is anxious, it generates a response in a calm tone.

[0658] Specific operation: The server formats the acquired data and generates a response stating, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." It also uses an emotion engine (e.g., GPT-3) to analyze the user's emotions and adjust the tone accordingly.

[0659] Step 5:

[0660] The server sends the generated response to the user terminal. The response displayed on the user terminal will read: "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report."

[0661] Specific operation: The server sends the generated response data to the user's terminal. The user's terminal renders this data for display and displays it on the chatbot screen.

[0662] Step 6:

[0663] The user checks the response displayed on their terminal and proceeds to the next step. For example, they might enter an additional question such as, "Please tell me about the design drawing format." This input is then sent back to the server from the user's terminal.

[0664] Specific operation: The user reviews the displayed information and, if they need more information, enters additional questions into the chatbot and sends them.

[0665] This series of steps allows users to efficiently obtain the necessary information and proceed to the next step. Furthermore, sentiment analysis by the emotion engine improves the user experience.

[0666] (Application Example 2)

[0667] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0668] Conventional procedural support systems provide a uniform response without considering the user's emotional state, resulting in insufficient support, especially for users experiencing stress or anxiety. Such systems are unsatisfactory in environments where quick and accurate support is required, such as for workers in logistics centers.

[0669] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from a database based on the analysis results, means for generating a response based on the acquired information and transmitting it to the user terminal, and an emotion engine that analyzes the emotions from the user's input text and adjusts the tone and content of the response based on those emotions. As a result, a response is provided that corresponds to the user's emotional state, enabling the user to proceed with procedures efficiently, especially in busy situations.

[0670] "User input" refers to text or commands that a user enters into the system via a terminal.

[0671] "Analysis" refers to the process of understanding user input and extracting appropriate keywords and context.

[0672] A "database" is a repository for storing and managing related information.

[0673] A "response" is the system's answer or response to user input.

[0674] A "user terminal" is a device used by a user to interface with a system, and examples include smartphones and personal computers.

[0675] A "natural language processing engine" is a collection of technologies and algorithms that analyze user-input text and understand its content.

[0676] An "emotion engine" is a module that analyzes the emotional state of a user from their input text and generates an appropriate response based on the results.

[0677] A "server" is a central computing system that analyzes information from user input, retrieves necessary information from a database, and generates and sends a final response.

[0678] This invention relates to a system that supports worker procedures in a logistics center, and provides an interactive interface that combines a natural language processing engine and an emotion engine.

[0679] System Configuration

[0680] This system consists of the following elements:

[0681] 1. User terminal:

[0682] This is a device used by workers to confirm procedures and enter questions. This includes smartphones.

[0683] 2. Server:

[0684] It is a central computing system that analyzes user input, extracts relevant information, and generates appropriate responses. The server includes a natural language processing engine, an emotion engine, and a database.

[0685] 3. Database:

[0686] This is a repository that stores information regarding procedures and required documents at logistics centers.

[0687] 4. Natural Language Processing Engine:

[0688] This refers to a set of algorithms that analyze user text input, understand its content, and extract keywords.

[0689] 5. Emotional Engine:

[0690] This refers to a module that analyzes the user's emotions from their text and adjusts the tone and content of the response based on those emotions.

[0691] Operation details

[0692] The user terminal first receives the worker's question via the interface. For example, if the worker enters the question "What documents are needed?", this text is sent from the user terminal to the server.

[0693] The server first parses the input using a natural language processing engine (e.g., the Hugging Face Transformers library). Specifically, keywords such as "necessary" and "documents" are extracted from the text. Based on this analysis, queries are executed on the database to retrieve relevant information (e.g., a list of documents required for a logistics center).

[0694] Based on the information obtained, the server generates a response. For example, it might say, "The following documents are required for the logistics center: packing list, shipping instructions, and customs documents."

[0695] Next, the emotion engine analyzes the user's emotional state from their input text. For example, if the input text is short and some words are emphasized, the user may be anxious. Based on the emotion analysis, the tone of the response is adjusted. For anxious users, a calmer tone is used to add supplementary explanations such as, "Please rest assured. We will provide you with the information you need shortly."

[0696] Finally, the generated response is sent to the user's terminal and displayed to the worker.

[0697] Specific example

[0698] For example, if a worker at a logistics center asks, "How do I follow the new shipping procedure?", the system will respond as follows:

[0699] "Once all necessary documents are complete, please obtain approval from your supervisor. After that, you can proceed with preparing for shipment. If you have any further questions, please feel free to ask."

[0700] Example of a prompt

[0701] How do I proceed with the new shipping procedure?

[0702] In this way, by providing accurate and adaptive support tailored to the user's emotional state, it becomes possible to improve the operational efficiency of the logistics center.

[0703] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0704] Program processing flow

[0705] Step 1:

[0706] The user enters a question using their smartphone. This input is specifically in text format and is sent to the server via the device's interface. For example, the user might enter, "How do I proceed with the new shipping procedure?"

[0707] Step 2:

[0708] The server receives user input. Next, a natural language processing engine (e.g., Hugging Face Transformers) is used to analyze this input text and extract keywords. The input is text data, and the output is a list of extracted keywords. For example, "shipping procedure" might be extracted as a keyword from the input text.

[0709] Step 3:

[0710] The server queries the database based on the extracted keywords. The input is a list of keywords, and it retrieves relevant information from the database. The output is the relevant information (e.g., a list of required documents or procedural steps). For example, the database might retrieve information such as, "Once all required documents are in place, obtain approval from your supervisor. Then proceed with shipping preparations."

[0711] Step 4:

[0712] The server generates a response based on the information it has retrieved. The input is information from the database, and the output is text to be sent to the user. For example, a response such as "The new shipping procedure is as follows: After all necessary documents are in place, please obtain approval from your supervisor. Then proceed with shipping preparation." might be generated.

[0713] Step 5:

[0714] Next, the emotion engine analyzes the user's input text to determine their emotions. The input is the user's text data, and the output is their emotional state (e.g., anxiety, stress). For example, if the user is anxious, the emotion engine will recognize it as "anxiety."

[0715] Step 6:

[0716] The server adjusts the tone and content of the response based on the output of the emotion engine. The input is the response text and the output of the emotion engine, and the adjusted response is generated. For example, if the user is determined to be "anxious," a supplementary explanation such as "Please rest assured" will be added to the response.

[0717] Step 7:

[0718] The server sends the generated response to the user's terminal. The input is the text of the adjusted response, which is displayed on the user's smartphone. The user can then receive specific guidance on how to proceed to the next step.

[0719] The above outlines the specific processing steps of this system. This will allow users to efficiently proceed with procedures at the logistics center.

[0720] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0721] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0722] 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.

[0723] [Third Embodiment]

[0724] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0725] 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.

[0726] 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).

[0727] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0728] 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.

[0729] 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).

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0736] ---

[0737] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. Specifically, it provides users with rapid and accurate procedural support by combining an interactive interface using a chatbot with natural language processing technology.

[0738] System Configuration

[0739] This system consists of the following components:

[0740] 1. User terminal

[0741] A chatbot is a device that allows users to input questions and obtain information. These devices include personal computers and smartphones.

[0742] 2. Server

[0743] It is a central computer that receives, analyzes, and provides appropriate information from users. The server can include a natural language processing (NLP) engine, a database, and a response generation module.

[0744] 3. Database

[0745] This is a repository for storing information on documents and procedures necessary for base station construction.

[0746] Operation details

[0747] The user terminal first receives input from the user via an interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0748] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[0749] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[0750] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[0751] The server analyzes the request again, and this time "design drawing" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna layout plan (3) Power wiring plan," and sends it to the user terminal.

[0752] The user terminal displays this information to the user and guides them through the next steps. For example, if the user asks, "How do I proceed to the next step?" after gathering the necessary documents, the same process will be repeated to provide the information.

[0753] This allows users to proceed with procedures efficiently and obtain necessary information in a timely manner. The above is an overview of the embodiments of the present invention, which provide users with a series of procedural support services.

[0754] The following describes the processing flow.

[0755] ---

[0756] Step 1:

[0757] The user enters a question into the chatbot using their device. For example, they might enter, "What documents are required for base station construction?"

[0758] Step 2:

[0759] The terminal receives user input and sends it to the server. The data sent includes the user's question text.

[0760] Step 3:

[0761] The server receives a request from the user. The received request is first sent to the Natural Language Processing (NLP) engine.

[0762] Step 4:

[0763] The server uses an NLP engine to analyze the user's input text. Specifically, it divides the text and extracts keywords and intent. For example, it extracts the keywords "base station construction" and "necessary documents" from the user's text.

[0764] Step 5:

[0765] The server queries the database based on the analyzed keywords. The query includes the extracted keywords, and the server retrieves the corresponding information from the database.

[0766] Step 6:

[0767] The server generates a response based on information retrieved from the database. For example, if the retrieved information includes a list of necessary documents such as "license application form," "design drawings," and "environmental assessment report," the server formats this information and generates a response statement.

[0768] Step 7:

[0769] The server sends the generated response to the user's terminal. The response includes the generated answer text.

[0770] Step 8:

[0771] The terminal displays the response received from the server in the user interface. This allows the user to visually confirm the necessary information.

[0772] Step 9:

[0773] Users ask additional questions or inquire about the next steps. For example, after reviewing the information above, they might type "Please tell me about the blueprint format" into the chatbot.

[0774] Step 10:

[0775] The terminal receives user input again and sends it to the server. The data sent to the server includes new question text.

[0776] Step 11:

[0777] The server receives a new request and analyzes it again using the NLP engine. For example, it extracts the keywords "blueprint" and "format" from the new text.

[0778] Step 12:

[0779] The server queries the database again to retrieve the relevant information. For example, it retrieves detailed information about the blueprint format from the database.

[0780] Step 13:

[0781] The server generates a new response based on the information it has obtained. For example, it might generate a statement like, "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna placement plan (3) Power wiring plan."

[0782] Step 14:

[0783] The server then sends the generated response back to the user's terminal.

[0784] Step 15:

[0785] The terminal displays the response it receives to the user, allowing the user to proceed with the process based on the provided information.

[0786] The above outlines the specific processing steps of the system that enable users to efficiently proceed with procedures related to base station construction.

[0787] (Example 1)

[0788] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0789] The current procedures associated with base station construction are extremely complex, making it difficult for users to obtain the necessary information quickly and accurately. This causes delays in the process and hinders efficient progress. Furthermore, there is a lack of systems that provide immediate and appropriate responses to user inquiries.

[0790] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0791] In this invention, the server includes means for receiving user input via a chatbot interface and analyzing it using a natural language processing engine, means for obtaining relevant information from a database based on the analysis results, and means for formatting the obtained information to generate a response and sending it to the user terminal. This enables the user to efficiently proceed with complex base station construction procedures and obtain necessary information quickly and accurately.

[0792] "User input" refers to the information and questions that users provide to the system via the chatbot interface.

[0793] A "chatbot interface" refers to a means of human-computer interaction that allows users to communicate with a system through natural language dialogue.

[0794] A "natural language processing engine" refers to a software module that analyzes user input, understands its meaning, and extracts necessary keywords.

[0795] "Analysis" refers to the process of breaking down user input and identifying its meaning and important keywords.

[0796] "Related information" refers to data about documents and procedures necessary for base station construction that the system retrieves from its database.

[0797] A "database" refers to an information repository used to store documents and procedural information related to the construction of base stations.

[0798] "Acquisition" refers to the process of extracting necessary information from a database based on the analysis results.

[0799] "Formatting" refers to the process of arranging acquired information into a form that is easy for users to understand.

[0800] "Response" refers to output data that includes answers and information that the system provides to the user.

[0801] A "user terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access a system and input / receive information.

[0802] "Transmission" refers to the process of delivering information from the server to the user's terminal.

[0803] "Display" refers to the process by which the user terminal shows the response received from the server to the user.

[0804] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. Specifically, it provides users with rapid and accurate procedural support by combining an interactive interface using a chatbot with natural language processing technology.

[0805] System Configuration

[0806] This system consists of the following components:

[0807] 1. User terminal: A device used by users to input questions and obtain information through a chatbot. This includes personal computers, smartphones, and other devices.

[0808] 2. Server: A central computer that receives, analyzes, and provides appropriate information from users. The server can include a natural language processing (NLP) engine, a database, and response generation modules. Specific software options for the natural language processing engine include SpaCy and the Google Cloud NLP API.

[0809] 3. Database: This is a repository for storing information on documents and procedures necessary for base station construction.

[0810] Operation details

[0811] The user terminal first receives input from the user via an interface. For example, the user might input the question, "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0812] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[0813] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[0814] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[0815] The server analyzes the request again, and this time "design drawing" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna layout plan (3) Power wiring plan," and sends it to the user terminal.

[0816] The user terminal displays this information to the user and guides them through the next steps. For example, if the user asks, "How do I proceed to the next step?" after gathering the necessary documents, the same process will be repeated to provide the information.

[0817] Examples of specific actions and prompts

[0818] As a concrete example of the process, we will show the user's process of researching the documents required for base station construction.

[0819] The user types "What documents are required for base station construction?" into the chatbot.

[0820] The system (server) uses a natural language processing engine to analyze the question and performs a database search using "base station construction" and "necessary documents" as keywords.

[0821] The server generates a response such as "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report" and sends it to the user terminal.

[0822] Based on this information, the user then enters, "Please tell me about the blueprint format."

[0823] The server analyzes the request again, extracts the keywords "blueprint" and "format," retrieves the appropriate information from the database, and generates a response.

[0824] The server sends information to the user terminal such as, "The design drawing format must be written on A3 size paper and include the following items: (1) an overall map of the construction site, (2) a diagram of the antenna layout, and (3) a power wiring plan."

[0825] Examples of prompt statements include the following:

[0826] 1. "What documents are required for base station construction?"

[0827] 2. "What documents are required for the procedure to construct a base station?"

[0828] 3. "Could you please tell me the format for the design drawings related to base station construction?"

[0829] 4. "Please tell me how to write an environmental assessment report for base station construction."

[0830] This series of processes allows users to proceed efficiently and obtain necessary information in a timely manner. The above is a specific description of the embodiment for carrying out the invention.

[0831] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0832] Step 1:

[0833] The user enters a question through the chatbot interface. For example, the user enters the question, "What documents are required for base station construction?" The input is received from the user as text information. At this point, the input is "What documents are required for base station construction?"

[0834] Step 2:

[0835] The terminal sends user input to the server as an HTTP request. This request contains the text data entered by the user. Specifically, the data is sent to an endpoint such as POST / api / v1 / queries. Here, the input data is the user's text question, and the request is sent to the server as output.

[0836] Step 3:

[0837] The server passes the received data to a natural language processing (NLP) engine (such as SpaCy or Google Cloud NLP API) to analyze the meaning of the question. The NLP engine analyzes the input text and extracts important keywords. In this case, "base station construction" and "necessary documents" are extracted as keywords. The input is the user's text question, and the output is the extracted keywords.

[0838] Step 4:

[0839] The server uses the extracted keywords to generate an SQL query against the database and executes the query. For example, the following SQL query is generated: "SELECT FROM required_documents WHERE category='Base station construction';" The database returns information corresponding to the query. In this step, the input is keywords, and the output is the relevant information obtained from the database.

[0840] Step 5:

[0841] The server generates a response based on information retrieved from the database. Specifically, it formats the data into an appropriate format and creates a response such as, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." The input is relevant information from the database, and the output is a formatted response.

[0842] Step 6:

[0843] The server sends the generated response to the user's terminal as an HTTP response. The output includes the response data that was just generated.

[0844] Step 7:

[0845] The terminal displays the received response to the user. Specifically, it displays in the browser window: "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." The input is response data from the server, and the output is the screen display to the user.

[0846] Step 8:

[0847] To proceed to the next step, the user enters a new question based on the displayed information. For example, they might enter, "Please tell me about the blueprint format." Subsequent processes are repeated following the flow from step 1 to step 7 described above.

[0848] This series of processes allows users to efficiently obtain the necessary information and proceed with the procedures related to base station construction.

[0849] (Application Example 1)

[0850] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0851] A lack of information support systems to efficiently and timely carry out various procedures is a problem in logistics centers. Furthermore, the insufficient methods for quickly providing necessary information regarding procedures are leading to decreased operational efficiency. In particular, information regarding required documents and procedures is scattered, and a system that can provide appropriate support is needed.

[0852] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0853] In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from a database based on the analysis results, and means for generating a response based on the obtained information and transmitting it to the user terminal. This makes it possible for the user to quickly obtain the information necessary to efficiently proceed with the procedure.

[0854] "User input" refers to questions or instructions that a user sends to the system through their device.

[0855] "Means of analysis" refers to technologies or devices that understand user input and extract important keywords and intentions.

[0856] "Means of obtaining from a database" refers to a technology or device that searches for and retrieves relevant information from data storage based on analyzed keywords and intent.

[0857] "Means for generating a response" refers to a technology or device that generates answers or instructions for the user based on acquired information.

[0858] A "user terminal" is a device used by a user to interact with the system, and includes smartphones, personal computers, smart glasses, and head-mounted displays.

[0859] A "natural language processing engine" is software or technology that analyzes sentences input in natural language to extract keywords and understand intent.

[0860] A "target object" is the information or content identified based on user input.

[0861] "Means of formatting information" refers to technologies or devices that reshape acquired information into a form that is easily understandable to the user.

[0862] This invention relates to a procedural support system for logistics centers. Users input procedural questions via user terminals such as smartphones, smart glasses, and head-mounted displays. The system uses natural language processing technology to analyze these questions and provide necessary information quickly and accurately. The system is implemented with the following configuration.

[0863] System components

[0864] 1. User terminal

[0865] These are devices that users use to input questions about procedures and obtain information. Examples include smartphones, smart glasses, and head-mounted displays.

[0866] 2. Server

[0867] This is the central computer that receives and analyzes requests from user terminals. The server includes a natural language processing engine, database, and response generation module.

[0868] 3. Database

[0869] This is a repository that stores information regarding various procedures and required documents for logistics centers.

[0870] Operation details

[0871] The user terminal receives user questions through the interface. For example, the user might type, "What is required for the ordering process?" The entered question is then sent from the user terminal to the server.

[0872] When the server receives a request, it uses a natural language processing engine to analyze the user's question. The analysis extracts the keywords "ordering procedure" and "necessary." Based on these keywords, a query is executed against the database. The information retrieved from the database is formatted on the server side, and an appropriate response is generated. This response is sent to the user's terminal in the form of "The following documents are required for the ordering procedure: purchase order, inventory confirmation form, and approval stamp."

[0873] The user proceeds to the next step based on the response displayed on their terminal. For example, they might then enter, "Please tell me about the format of the inventory confirmation form." At this point, the server analyzes the request again, and "inventory confirmation form" and "format" are extracted as keywords. The server retrieves the appropriate information from the database and generates a response such as, "The inventory confirmation form format must include the following items: product name, quantity, and inventory location," and sends it to the user's terminal.

[0874] Specific example

[0875] This system will enable logistics center employees to process procedures more efficiently. For example, if a new employee asks, "Please tell me about the procedure for receiving packages," the system will provide a response such as, "The following documents are required for package receiving: ID verification document, receipt confirmation," allowing them to quickly obtain the necessary information.

[0876] Example of a prompt:

[0877] Question: What documents are required for package delivery?

[0878] Context: Package receiving procedures, ordering procedures, and other logistics center procedures

[0879] Keywords: Package receiving procedure

[0880] Answer: The following documents are required: ID verification document, receipt confirmation.

[0881] Thus, the present invention improves the operational efficiency of logistics centers by streamlining user procedures and providing necessary information quickly.

[0882] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0883] Processing steps of the logistics procedure support assistant system

[0884] Step 1: Receiving user input

[0885] The user terminal receives questions and instructions from the user regarding the procedure. This input is in string format, for example, "What is required for the ordering procedure?". The user terminal sends this input as a string to the server. The input is a raw question, and the output is a request to the server.

[0886] Step 2: Server-based input analysis

[0887] The server passes the request received from the user terminal to the natural language processing engine for analysis. Here, the input question is analyzed, and key keywords (e.g., "ordering procedure," "necessary") are extracted. The input is the user's question, and the output is a list of extracted keywords.

[0888] Step 3: Database search for related information

[0889] The server queries the database based on the extracted keywords. Specifically, it retrieves relevant information (for example, a list of required documents) from the database. The input is a list of keywords, and the output is a list of relevant information.

[0890] Step 4: Generating the response

[0891] The server generates a response to the user based on the information it has retrieved. This response is formatted in a user-friendly format. For example, it might be in the format of, "The following documents are required for the ordering process: Purchase order, inventory confirmation form, and approval stamp." The input is a list of relevant information, and the output is a formatted response statement.

[0892] Step 5: Sending the Response

[0893] The server sends the generated response message to the user terminal. The user terminal receives this response and displays it to the user. Here, the display is performed using appropriate hardware (such as a display or smart glasses). The input is a formatted response message, and the output is the information displayed on the user terminal's screen.

[0894] Step 6: Acceptance of additional questions and re-analysis

[0895] Based on the displayed information, the user can request further questions or procedures. The user terminal sends this re-entry to the server, where it is analyzed again using the natural language processing engine. This repeats the cycle. The input is a new question, and the output is a newly extracted list of keywords.

[0896] The specific data processing and calculations performed at each step are as follows:

[0897] 1. Step 1: Send the user's question as a string to the server.

[0898] 2. Step 2: Analyze the question using a natural language processing engine and extract keywords.

[0899] 3. Step 3: Perform a database search based on the extracted keywords to retrieve relevant information.

[0900] 4. Step 4: Generate and format the response based on the acquired relevant information.

[0901] 5. Step 5: Send the formatted response message to the user's terminal and display it.

[0902] 6. Step 6: Accept the input again, analyze it again with the natural language processing engine, and repeat the cycle.

[0903] The above are the specific processing steps for carrying out this invention.

[0904] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0905] ---

[0906] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. It provides users with rapid and accurate procedural support by combining a chatbot-based interactive interface, natural language processing technology, and an emotion engine.

[0907] System Configuration

[0908] This system consists of the following components:

[0909] 1. User terminal

[0910] A chatbot is a device that allows users to input questions and obtain information. These devices include personal computers and smartphones.

[0911] 2. Server

[0912] It is a central computer that receives, analyzes, and provides appropriate information from users. The server may include a natural language processing (NLP) engine, a database, a response generation module, and an emotion engine.

[0913] 3. Database

[0914] This is a repository for storing information on documents and procedures necessary for base station construction.

[0915] 4. Emotional Engine

[0916] This module analyzes user sentiment from input text and generates appropriate responses based on that sentiment.

[0917] Operation details

[0918] The user terminal first receives input from the user via an interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0919] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[0920] Furthermore, the emotion engine analyzes the user's emotions from their text and adjusts the tone and content of its response based on that emotional state. For example, if it detects that the user is anxious, the emotion engine will explain things in a calmer tone.

[0921] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[0922] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[0923] The server analyzes the request again, and this time "blueprint" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as, "The blueprint format must be written on A3 size paper and include the following items: (1) overall plan of the construction site (2) antenna layout plan (3) power wiring plan," and then adjusts the tone again using the emotion engine, taking into account the user's emotional state.

[0924] For example, if the user's emotions indicate fatigue or frustration, the server can add more gentle and encouraging words. The response will be generated with additional sentences such as, "Please feel free to contact us anytime if you have any questions."

[0925] The user terminal displays this information to the user, providing guidance to help them proceed with the next steps. This allows the user to efficiently complete the process and obtain necessary information in a timely manner. Furthermore, the emotion engine adjusts the response according to the user's emotions, resulting in a better user experience.

[0926] The above is an overview of the embodiments of the present invention, which provide a series of procedural support to the user. The introduction of an emotion engine can further improve the user experience.

[0927] The following describes the processing flow.

[0928] ---

[0929] Step 1:

[0930] The user enters a question into the chatbot using their device. For example, they might type, "What documents are required for base station construction?"

[0931] Step 2:

[0932] The terminal receives user input and sends it to the server. The data sent includes the user's question text.

[0933] Step 3:

[0934] The server receives a request from the user and sends that request to the Natural Language Processing (NLP) engine.

[0935] Step 4:

[0936] The server uses an NLP engine to analyze the user's input text and extract keywords and intent. For example, keywords such as "base station construction" and "necessary documents" might be extracted.

[0937] Step 5:

[0938] The server queries the database based on the analysis results to retrieve relevant information. In this case, a list of necessary documents is retrieved from the database.

[0939] Step 6:

[0940] The server generates a response based on the information it has acquired. For example, it might generate a response message such as, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report."

[0941] Step 7:

[0942] The server uses an emotion engine to analyze the user's input text to determine their emotions. The emotion engine extracts emotional states (e.g., anxiety, frustration, relief, etc.).

[0943] Step 8:

[0944] The server adjusts the tone and content of its response based on the user's emotional state. For example, if the user is anxious, the response might include additional information such as, "Please rest assured. Detailed instructions are provided below."

[0945] Step 9:

[0946] The server sends a refined response to the user's terminal. The response sent includes emotionally sensitive content.

[0947] Step 10:

[0948] The terminal displays the response received from the server in the user interface. This allows the user to visually confirm the necessary information.

[0949] Step 11:

[0950] Users ask additional questions or inquire about the next steps. For example, after reviewing the information above, they might type "Please tell me about the blueprint format" into the chatbot.

[0951] Step 12:

[0952] The terminal receives user input again and sends it to the server. The data sent includes new question text.

[0953] Step 13:

[0954] The server receives a new request and analyzes it again using the NLP engine. For example, the keywords "blueprint" and "format" are extracted from the new text.

[0955] Step 14:

[0956] The server queries the database again to retrieve the relevant information. Detailed information about the blueprint format is retrieved from the database.

[0957] Step 15:

[0958] The server generates a new response based on the information it has acquired. For example, it might generate a statement like, "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna placement plan (3) Power wiring plan."

[0959] Step 16:

[0960] The server then uses the emotion engine to analyze the user's emotional state again and adjust the tone. If the user's emotions indicate fatigue or frustration, the server adds a gentle, encouraging message such as, "Please feel free to contact us anytime if you have any questions."

[0961] Step 17:

[0962] The server sends a new response to the user's terminal again. The response includes the adjusted tone and content.

[0963] Step 18:

[0964] The device displays a new response to the user. The user can then proceed with the process based on the information provided.

[0965] The above outlines the specific processing steps of the system to enable users to efficiently proceed with procedures related to base station construction. The introduction of an emotion engine will further enhance the user experience.

[0966] (Example 2)

[0967] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0968] Conventional support systems for base station construction procedures often provided insufficient answers to user-submitted questions and had overly complex procedural flows. Furthermore, they lacked appropriate support tailored to the user's emotional state, hindering a positive user experience. As a result, users frequently experienced stress during the procedural process.

[0969] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from data storage based on the analysis results, means for generating a response based on the acquired information and transmitting it to the user device, means for supporting the user in proceeding with the next procedure based on the response displayed on the user device, and means for analyzing emotions from the user's input text and adjusting the response based on those emotions. As a result, the user can quickly obtain appropriate information and proceed smoothly with the next procedure, and furthermore, the user experience is improved because the emotion engine can respond according to the user's emotions.

[0970] A "user" is an entity that uses the system to obtain information necessary to proceed with procedures related to base station construction.

[0971] "Input" refers to questions or instructions that a user sends to a system using an interface such as a chatbot.

[0972] "Analysis" is the process of understanding user input and grasping its meaning.

[0973] A "natural language processing engine" is software that analyzes natural language text entered by a user and accurately understands its meaning and intent.

[0974] "Related information" refers to data necessary to provide appropriate answers to user questions or instructions.

[0975] "Data storage" refers to an information repository that stores documents and procedural information necessary for the construction of base stations.

[0976] A "response" is a message that a system generates in response to a user's question or instruction.

[0977] "User equipment" refers to terminal devices used by users to access the system.

[0978] An "emotion engine" is a software module that analyzes the user's emotional state from the user's input text and adjusts the tone and content of the response appropriately based on the analysis results.

[0979] "Procedure" refers to a series of processes and operations that users perform to carry out various necessary tasks related to base station construction.

[0980] System Configuration

[0981] This system is designed to help users efficiently carry out the procedures necessary for base station construction. This system consists of the following components:

[0982] 1. User terminal: A device used by users to input questions and obtain information through a chatbot. This includes personal computers, smartphones, and other devices.

[0983] 2. Server: A central computer that receives, analyzes, and provides appropriate information from users. This server includes a natural language processing (NLP) engine, data storage, response generation modules, and a sentiment engine.

[0984] 3. Data Storage: This is a repository for storing information on documents and procedures necessary for base station construction.

[0985] 4. Emotion Engine: This module analyzes emotions from user input text and generates appropriate responses based on those emotions.

[0986] Operation details

[0987] First, the user terminal receives input from the user via the interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[0988] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. For example, using Google's BERT, keywords such as "base station construction" and "required documents" are extracted. Based on this, a query is executed against the data storage. Based on the "required documents" information retrieved from the data storage, the server formats it and generates an appropriate response.

[0989] Furthermore, the emotion engine analyzes the user's emotions from their text. For example, by combining OpenAI's GPT-3 with an emotion analysis module, it can generate a calm response if the user is feeling anxious.

[0990] The response might say something like, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." This response is sent to the user terminal.

[0991] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server. The server parses the request again and this time extracts the keywords "design drawing" and "format." It retrieves the appropriate information from data storage and generates a response stating, "The design drawing format should be written on A3 size paper and include the following items: (1) an overall map of the construction site (2) an antenna layout diagram (3) a power wiring plan diagram." The emotion engine then adjusts the tone again, taking into account the user's emotional state.

[0992] For example, if the user's emotions indicate fatigue or frustration, the server can add more gentle words of encouragement. The response may also include an additional sentence such as, "Please feel free to contact us anytime if you have any questions." The user terminal displays this information to the user, allowing them to receive guidance on how to proceed.

[0993] This type of system allows users to efficiently obtain the information necessary for base station construction, ensuring a smoother process. Furthermore, the emotion engine adjusts responses based on the user's emotions, providing a better user experience.

[0994] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0995] Step 1:

[0996] The user enters a question via their device. For example, they might enter, "What documents are required for base station construction?" This input is then sent to the server via the chatbot.

[0997] Specific operation: The user uses a keyboard or touchscreen to enter a question into the chatbot's input field.

[0998] Step 2:

[0999] The server receives user input and performs analysis using a natural language processing (NLP) engine. It analyzes the input question, "What documents are needed for base station construction?", and extracts the keywords "base station construction" and "necessary documents".

[1000] Specific operation: The server sends input data to an NLP engine (e.g., BERT) and retrieves keywords extracted as a result of the analysis.

[1001] Step 3:

[1002] The server queries the data storage based on the extracted keywords. It retrieves information from the data storage corresponding to the keywords "base station construction" and "required documents".

[1003] Specific operation: The server executes database queries (e.g., SQL queries) to retrieve information about the required documents.

[1004] Step 4:

[1005] The server organizes the information retrieved from data storage and generates a response. Simultaneously, it uses an emotion engine to analyze the user's emotional state. If it detects that the user is anxious, it generates a response in a calm tone.

[1006] Specific operation: The server formats the acquired data and generates a response stating, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." It also uses an emotion engine (e.g., GPT-3) to analyze the user's emotions and adjust the tone accordingly.

[1007] Step 5:

[1008] The server sends the generated response to the user terminal. The response displayed on the user terminal will read: "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report."

[1009] Specific operation: The server sends the generated response data to the user's terminal. The user's terminal renders this data for display and displays it on the chatbot screen.

[1010] Step 6:

[1011] The user checks the response displayed on their terminal and proceeds to the next step. For example, they might enter an additional question such as, "Please tell me about the design drawing format." This input is then sent back to the server from the user's terminal.

[1012] Specific operation: The user reviews the displayed information and, if they need more information, enters additional questions into the chatbot and sends them.

[1013] This series of steps allows users to efficiently obtain the necessary information and proceed to the next step. Furthermore, sentiment analysis by the emotion engine improves the user experience.

[1014] (Application Example 2)

[1015] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1016] Conventional procedural support systems provide a uniform response without considering the user's emotional state, resulting in insufficient support, especially for users experiencing stress or anxiety. Such systems are unsatisfactory in environments where quick and accurate support is required, such as for workers in logistics centers.

[1017] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from a database based on the analysis results, means for generating a response based on the acquired information and transmitting it to the user terminal, and an emotion engine that analyzes the emotions from the user's input text and adjusts the tone and content of the response based on those emotions. As a result, a response is provided that corresponds to the user's emotional state, enabling the user to proceed with procedures efficiently, especially in busy situations.

[1018] "User input" refers to text or commands that a user enters into the system via a terminal.

[1019] "Analysis" refers to the process of understanding user input and extracting appropriate keywords and context.

[1020] A "database" is a repository for storing and managing related information.

[1021] A "response" is the system's answer or response to user input.

[1022] A "user terminal" is a device used by a user to interface with a system, and examples include smartphones and personal computers.

[1023] A "natural language processing engine" is a collection of technologies and algorithms that analyze user-input text and understand its content.

[1024] An "emotion engine" is a module that analyzes the emotional state of a user from their input text and generates an appropriate response based on the results.

[1025] A "server" is a central computing system that analyzes information from user input, retrieves necessary information from a database, and generates and sends a final response.

[1026] This invention relates to a system that supports worker procedures in a logistics center, and provides an interactive interface that combines a natural language processing engine and an emotion engine.

[1027] System Configuration

[1028] This system consists of the following elements:

[1029] 1. User terminal:

[1030] This is a device used by workers to confirm procedures and enter questions. This includes smartphones.

[1031] 2. Server:

[1032] It is a central computing system that analyzes user input, extracts relevant information, and generates appropriate responses. The server includes a natural language processing engine, an emotion engine, and a database.

[1033] 3. Database:

[1034] This is a repository that stores information regarding procedures and required documents at logistics centers.

[1035] 4. Natural Language Processing Engine:

[1036] This refers to a set of algorithms that analyze user text input, understand its content, and extract keywords.

[1037] 5. Emotional Engine:

[1038] This refers to a module that analyzes the user's emotions from their text and adjusts the tone and content of the response based on those emotions.

[1039] Operation details

[1040] The user terminal first receives the worker's question via the interface. For example, if the worker enters the question "What documents are needed?", this text is sent from the user terminal to the server.

[1041] The server first parses the input using a natural language processing engine (e.g., the Hugging Face Transformers library). Specifically, keywords such as "necessary" and "documents" are extracted from the text. Based on this analysis, queries are executed on the database to retrieve relevant information (e.g., a list of documents required for a logistics center).

[1042] Based on the information obtained, the server generates a response. For example, it might say, "The following documents are required for the logistics center: packing list, shipping instructions, and customs documents."

[1043] Next, the emotion engine analyzes the user's emotional state from their input text. For example, if the input text is short and some words are emphasized, the user may be anxious. Based on the emotion analysis, the tone of the response is adjusted. For anxious users, a calmer tone is used to add supplementary explanations such as, "Please rest assured. We will provide you with the information you need shortly."

[1044] Finally, the generated response is sent to the user's terminal and displayed to the worker.

[1045] Specific example

[1046] For example, if a worker at a logistics center asks, "How do I follow the new shipping procedure?", the system will respond as follows:

[1047] "Once all necessary documents are complete, please obtain approval from your supervisor. After that, you can proceed with preparing for shipment. If you have any further questions, please feel free to ask."

[1048] Example of a prompt

[1049] How do I proceed with the new shipping procedure?

[1050] In this way, by providing accurate and adaptive support tailored to the user's emotional state, it becomes possible to improve the operational efficiency of the logistics center.

[1051] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1052] Program processing flow

[1053] Step 1:

[1054] The user enters a question using their smartphone. This input is specifically in text format and is sent to the server via the device's interface. For example, the user might enter, "How do I proceed with the new shipping procedure?"

[1055] Step 2:

[1056] The server receives user input. Next, a natural language processing engine (e.g., Hugging Face Transformers) is used to analyze this input text and extract keywords. The input is text data, and the output is a list of extracted keywords. For example, "shipping procedure" might be extracted as a keyword from the input text.

[1057] Step 3:

[1058] The server queries the database based on the extracted keywords. The input is a list of keywords, and it retrieves relevant information from the database. The output is the relevant information (e.g., a list of required documents or procedural steps). For example, the database might retrieve information such as, "Once all required documents are in place, obtain approval from your supervisor. Then proceed with shipping preparations."

[1059] Step 4:

[1060] The server generates a response based on the information it has retrieved. The input is information from the database, and the output is text to be sent to the user. For example, a response such as "The new shipping procedure is as follows: After all necessary documents are in place, please obtain approval from your supervisor. Then proceed with shipping preparation." might be generated.

[1061] Step 5:

[1062] Next, the emotion engine analyzes the user's input text to determine their emotions. The input is the user's text data, and the output is their emotional state (e.g., anxiety, stress). For example, if the user is anxious, the emotion engine will recognize it as "anxiety."

[1063] Step 6:

[1064] The server adjusts the tone and content of the response based on the output of the emotion engine. The input is the response text and the output of the emotion engine, and the adjusted response is generated. For example, if the user is determined to be "anxious," a supplementary explanation such as "Please rest assured" will be added to the response.

[1065] Step 7:

[1066] The server sends the generated response to the user's terminal. The input is the text of the adjusted response, which is displayed on the user's smartphone. The user can then receive specific guidance on how to proceed to the next step.

[1067] The above outlines the specific processing steps of this system. This will allow users to efficiently proceed with procedures at the logistics center.

[1068] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1069] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1070] 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.

[1071] [Fourth Embodiment]

[1072] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1073] 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.

[1074] 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).

[1075] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1076] 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.

[1077] 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).

[1078] 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.

[1079] 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.

[1080] 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.

[1081] 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.

[1082] 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.

[1083] 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.

[1084] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1085] ---

[1086] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. Specifically, it provides users with rapid and accurate procedural support by combining an interactive interface using a chatbot with natural language processing technology.

[1087] System Configuration

[1088] This system consists of the following components:

[1089] 1. User terminal

[1090] A chatbot is a device that allows users to input questions and obtain information. These devices include personal computers and smartphones.

[1091] 2. Server

[1092] It is a central computer that receives, analyzes, and provides appropriate information from users. The server can include a natural language processing (NLP) engine, a database, and a response generation module.

[1093] 3. Database

[1094] This is a repository for storing information on documents and procedures necessary for base station construction.

[1095] Operation details

[1096] The user terminal first receives input from the user via an interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[1097] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[1098] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[1099] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[1100] The server analyzes the request again, and this time "design drawing" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna layout plan (3) Power wiring plan," and sends it to the user terminal.

[1101] The user terminal displays this information to the user and guides them through the next steps. For example, if the user asks, "How do I proceed to the next step?" after gathering the necessary documents, the same process will be repeated to provide the information.

[1102] This allows users to proceed with procedures efficiently and obtain necessary information in a timely manner. The above is an overview of the embodiments of the present invention, which provide users with a series of procedural support services.

[1103] The following describes the processing flow.

[1104] ---

[1105] Step 1:

[1106] The user enters a question into the chatbot using their device. For example, they might enter, "What documents are required for base station construction?"

[1107] Step 2:

[1108] The terminal receives user input and sends it to the server. The data sent includes the user's question text.

[1109] Step 3:

[1110] The server receives a request from the user. The received request is first sent to the Natural Language Processing (NLP) engine.

[1111] Step 4:

[1112] The server uses an NLP engine to analyze the user's input text. Specifically, it divides the text and extracts keywords and intent. For example, it extracts the keywords "base station construction" and "necessary documents" from the user's text.

[1113] Step 5:

[1114] The server queries the database based on the analyzed keywords. The query includes the extracted keywords, and the server retrieves the corresponding information from the database.

[1115] Step 6:

[1116] The server generates a response based on information retrieved from the database. For example, if the retrieved information includes a list of necessary documents such as "license application form," "design drawings," and "environmental assessment report," the server formats this information and generates a response statement.

[1117] Step 7:

[1118] The server sends the generated response to the user's terminal. The response includes the generated answer text.

[1119] Step 8:

[1120] The terminal displays the response received from the server in the user interface. This allows the user to visually confirm the necessary information.

[1121] Step 9:

[1122] Users ask additional questions or inquire about the next steps. For example, after reviewing the information above, they might type "Please tell me about the blueprint format" into the chatbot.

[1123] Step 10:

[1124] The terminal receives user input again and sends it to the server. The data sent to the server includes new question text.

[1125] Step 11:

[1126] The server receives a new request and analyzes it again using the NLP engine. For example, it extracts the keywords "blueprint" and "format" from the new text.

[1127] Step 12:

[1128] The server queries the database again to retrieve the relevant information. For example, it retrieves detailed information about the blueprint format from the database.

[1129] Step 13:

[1130] The server generates a new response based on the information it has obtained. For example, it might generate a statement like, "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna placement plan (3) Power wiring plan."

[1131] Step 14:

[1132] The server then sends the generated response back to the user's terminal.

[1133] Step 15:

[1134] The terminal displays the response it receives to the user, allowing the user to proceed with the process based on the provided information.

[1135] The above outlines the specific processing steps of the system that enable users to efficiently proceed with procedures related to base station construction.

[1136] (Example 1)

[1137] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1138] The current procedures associated with base station construction are extremely complex, making it difficult for users to obtain the necessary information quickly and accurately. This causes delays in the process and hinders efficient progress. Furthermore, there is a lack of systems that provide immediate and appropriate responses to user inquiries.

[1139] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1140] In this invention, the server includes means for receiving user input via a chatbot interface and analyzing it using a natural language processing engine, means for obtaining relevant information from a database based on the analysis results, and means for formatting the obtained information to generate a response and sending it to the user terminal. This enables the user to efficiently proceed with complex base station construction procedures and obtain necessary information quickly and accurately.

[1141] "User input" refers to the information and questions that users provide to the system via the chatbot interface.

[1142] A "chatbot interface" refers to a means of human-computer interaction that allows users to communicate with a system through natural language dialogue.

[1143] A "natural language processing engine" refers to a software module that analyzes user input, understands its meaning, and extracts necessary keywords.

[1144] "Analysis" refers to the process of breaking down user input and identifying its meaning and important keywords.

[1145] "Related information" refers to data about documents and procedures necessary for base station construction that the system retrieves from its database.

[1146] A "database" refers to an information repository used to store documents and procedural information related to the construction of base stations.

[1147] "Acquisition" refers to the process of extracting necessary information from a database based on the analysis results.

[1148] "Formatting" refers to the process of arranging acquired information into a form that is easy for users to understand.

[1149] "Response" refers to output data that includes answers and information that the system provides to the user.

[1150] A "user terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access a system and input / receive information.

[1151] "Transmission" refers to the process of delivering information from the server to the user's terminal.

[1152] "Display" refers to the process by which the user terminal shows the response received from the server to the user.

[1153] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. Specifically, it provides users with rapid and accurate procedural support by combining an interactive interface using a chatbot with natural language processing technology.

[1154] System Configuration

[1155] This system consists of the following components:

[1156] 1. User terminal: A device used by users to input questions and obtain information through a chatbot. This includes personal computers, smartphones, and other devices.

[1157] 2. Server: A central computer that receives, analyzes, and provides appropriate information from users. The server can include a natural language processing (NLP) engine, a database, and response generation modules. Specific software options for the natural language processing engine include SpaCy and the Google Cloud NLP API.

[1158] 3. Database: This is a repository for storing information on documents and procedures necessary for base station construction.

[1159] Operation details

[1160] The user terminal first receives input from the user via an interface. For example, the user might input the question, "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[1161] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[1162] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[1163] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[1164] The server analyzes the request again, and this time "design drawing" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna layout plan (3) Power wiring plan," and sends it to the user terminal.

[1165] The user terminal displays this information to the user and guides them through the next steps. For example, if the user asks, "How do I proceed to the next step?" after gathering the necessary documents, the same process will be repeated to provide the information.

[1166] Examples of specific actions and prompts

[1167] As a concrete example of the process, we will show the user's process of researching the documents required for base station construction.

[1168] The user types "What documents are required for base station construction?" into the chatbot.

[1169] The system (server) uses a natural language processing engine to analyze the question and performs a database search using "base station construction" and "necessary documents" as keywords.

[1170] The server generates a response such as "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report" and sends it to the user terminal.

[1171] Based on this information, the user then enters, "Please tell me about the blueprint format."

[1172] The server analyzes the request again, extracts the keywords "blueprint" and "format," retrieves the appropriate information from the database, and generates a response.

[1173] The server sends information to the user terminal such as, "The design drawing format must be written on A3 size paper and include the following items: (1) an overall map of the construction site, (2) a diagram of the antenna layout, and (3) a power wiring plan."

[1174] Examples of prompt statements include the following:

[1175] 1. "What documents are required for base station construction?"

[1176] 2. "What documents are required for the procedure to construct a base station?"

[1177] 3. "Could you please tell me the format for the design drawings related to base station construction?"

[1178] 4. "Please tell me how to write an environmental assessment report for base station construction."

[1179] This series of processes allows users to proceed efficiently and obtain necessary information in a timely manner. The above is a specific description of the embodiment for carrying out the invention.

[1180] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1181] Step 1:

[1182] The user enters a question through the chatbot interface. For example, the user enters the question, "What documents are required for base station construction?" The input is received from the user as text information. At this point, the input is "What documents are required for base station construction?"

[1183] Step 2:

[1184] The terminal sends user input to the server as an HTTP request. This request contains the text data entered by the user. Specifically, the data is sent to an endpoint such as POST / api / v1 / queries. Here, the input data is the user's text question, and the request is sent to the server as output.

[1185] Step 3:

[1186] The server passes the received data to a natural language processing (NLP) engine (such as SpaCy or Google Cloud NLP API) to analyze the meaning of the question. The NLP engine analyzes the input text and extracts important keywords. In this case, "base station construction" and "necessary documents" are extracted as keywords. The input is the user's text question, and the output is the extracted keywords.

[1187] Step 4:

[1188] The server uses the extracted keywords to generate an SQL query against the database and executes the query. For example, the following SQL query is generated: "SELECT FROM required_documents WHERE category='Base station construction';" The database returns information corresponding to the query. In this step, the input is keywords, and the output is the relevant information obtained from the database.

[1189] Step 5:

[1190] The server generates a response based on information retrieved from the database. Specifically, it formats the data into an appropriate format and creates a response such as, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." The input is relevant information from the database, and the output is a formatted response.

[1191] Step 6:

[1192] The server sends the generated response to the user's terminal as an HTTP response. The output includes the response data that was just generated.

[1193] Step 7:

[1194] The terminal displays the received response to the user. Specifically, it displays in the browser window: "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." The input is response data from the server, and the output is the screen display to the user.

[1195] Step 8:

[1196] To proceed to the next step, the user enters a new question based on the displayed information. For example, they might enter, "Please tell me about the blueprint format." Subsequent processes are repeated following the flow from step 1 to step 7 described above.

[1197] This series of processes allows users to efficiently obtain the necessary information and proceed with the procedures related to base station construction.

[1198] (Application Example 1)

[1199] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1200] A lack of information support systems to efficiently and timely carry out various procedures is a problem in logistics centers. Furthermore, the insufficient methods for quickly providing necessary information regarding procedures are leading to decreased operational efficiency. In particular, information regarding required documents and procedures is scattered, and a system that can provide appropriate support is needed.

[1201] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1202] In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from a database based on the analysis results, and means for generating a response based on the obtained information and transmitting it to the user terminal. This makes it possible for the user to quickly obtain the information necessary to efficiently proceed with the procedure.

[1203] "User input" refers to questions or instructions that a user sends to the system through their device.

[1204] "Means of analysis" refers to technologies or devices that understand user input and extract important keywords and intentions.

[1205] "Means of obtaining from a database" refers to a technology or device that searches for and retrieves relevant information from data storage based on analyzed keywords and intent.

[1206] "Means for generating a response" refers to a technology or device that generates answers or instructions for the user based on acquired information.

[1207] A "user terminal" is a device used by a user to interact with the system, and includes smartphones, personal computers, smart glasses, and head-mounted displays.

[1208] A "natural language processing engine" is software or technology that analyzes sentences input in natural language to extract keywords and understand intent.

[1209] A "target object" is the information or content identified based on user input.

[1210] "Means of formatting information" refers to technologies or devices that reshape acquired information into a form that is easily understandable to the user.

[1211] This invention relates to a procedural support system for logistics centers. Users input procedural questions via user terminals such as smartphones, smart glasses, and head-mounted displays. The system uses natural language processing technology to analyze these questions and provide necessary information quickly and accurately. The system is implemented with the following configuration.

[1212] System components

[1213] 1. User terminal

[1214] These are devices that users use to input questions about procedures and obtain information. Examples include smartphones, smart glasses, and head-mounted displays.

[1215] 2. Server

[1216] This is the central computer that receives and analyzes requests from user terminals. The server includes a natural language processing engine, database, and response generation module.

[1217] 3. Database

[1218] This is a repository that stores information regarding various procedures and required documents for logistics centers.

[1219] Operation details

[1220] The user terminal receives user questions through the interface. For example, the user might type, "What is required for the ordering process?" The entered question is then sent from the user terminal to the server.

[1221] When the server receives a request, it uses a natural language processing engine to analyze the user's question. The analysis extracts the keywords "ordering procedure" and "necessary." Based on these keywords, a query is executed against the database. The information retrieved from the database is formatted on the server side, and an appropriate response is generated. This response is sent to the user's terminal in the form of "The following documents are required for the ordering procedure: purchase order, inventory confirmation form, and approval stamp."

[1222] The user proceeds to the next step based on the response displayed on their terminal. For example, they might then enter, "Please tell me about the format of the inventory confirmation form." At this point, the server analyzes the request again, and "inventory confirmation form" and "format" are extracted as keywords. The server retrieves the appropriate information from the database and generates a response such as, "The inventory confirmation form format must include the following items: product name, quantity, and inventory location," and sends it to the user's terminal.

[1223] Specific example

[1224] This system will enable logistics center employees to process procedures more efficiently. For example, if a new employee asks, "Please tell me about the procedure for receiving packages," the system will provide a response such as, "The following documents are required for package receiving: ID verification document, receipt confirmation," allowing them to quickly obtain the necessary information.

[1225] Example of a prompt:

[1226] Question: What documents are required for package delivery?

[1227] Context: Package receiving procedures, ordering procedures, and other logistics center procedures

[1228] Keywords: Package receiving procedure

[1229] Answer: The following documents are required: ID verification document, receipt confirmation.

[1230] Thus, the present invention improves the operational efficiency of logistics centers by streamlining user procedures and providing necessary information quickly.

[1231] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1232] Processing steps of the logistics procedure support assistant system

[1233] Step 1: Receiving user input

[1234] The user terminal receives questions and instructions from the user regarding the procedure. This input is in string format, for example, "What is required for the ordering procedure?". The user terminal sends this input as a string to the server. The input is a raw question, and the output is a request to the server.

[1235] Step 2: Server-based input analysis

[1236] The server passes the request received from the user terminal to the natural language processing engine for analysis. Here, the input question is analyzed, and key keywords (e.g., "ordering procedure," "necessary") are extracted. The input is the user's question, and the output is a list of extracted keywords.

[1237] Step 3: Database search for related information

[1238] The server queries the database based on the extracted keywords. Specifically, it retrieves relevant information (for example, a list of required documents) from the database. The input is a list of keywords, and the output is a list of relevant information.

[1239] Step 4: Generating the response

[1240] The server generates a response to the user based on the information it has retrieved. This response is formatted in a user-friendly format. For example, it might be in the format of, "The following documents are required for the ordering process: Purchase order, inventory confirmation form, and approval stamp." The input is a list of relevant information, and the output is a formatted response statement.

[1241] Step 5: Sending the Response

[1242] The server sends the generated response message to the user terminal. The user terminal receives this response and displays it to the user. Here, the display is performed using appropriate hardware (such as a display or smart glasses). The input is a formatted response message, and the output is the information displayed on the user terminal's screen.

[1243] Step 6: Acceptance of additional questions and re-analysis

[1244] Based on the displayed information, the user can request further questions or procedures. The user terminal sends this re-entry to the server, where it is analyzed again using the natural language processing engine. This repeats the cycle. The input is a new question, and the output is a newly extracted list of keywords.

[1245] The specific data processing and calculations performed at each step are as follows:

[1246] 1. Step 1: Send the user's question as a string to the server.

[1247] 2. Step 2: Analyze the question using a natural language processing engine and extract keywords.

[1248] 3. Step 3: Perform a database search based on the extracted keywords to retrieve relevant information.

[1249] 4. Step 4: Generate and format the response based on the acquired relevant information.

[1250] 5. Step 5: Send the formatted response message to the user's terminal and display it.

[1251] 6. Step 6: Accept the input again, analyze it again with the natural language processing engine, and repeat the cycle.

[1252] The above are the specific processing steps for carrying out this invention.

[1253] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1254] ---

[1255] This invention relates to a system that enables users to efficiently carry out the procedures necessary for base station construction. It provides users with rapid and accurate procedural support by combining a chatbot-based interactive interface, natural language processing technology, and an emotion engine.

[1256] System Configuration

[1257] This system consists of the following components:

[1258] 1. User terminal

[1259] A chatbot is a device that allows users to input questions and obtain information. These devices include personal computers and smartphones.

[1260] 2. Server

[1261] It is a central computer that receives, analyzes, and provides appropriate information from users. The server may include a natural language processing (NLP) engine, a database, a response generation module, and an emotion engine.

[1262] 3. Database

[1263] This is a repository for storing information on documents and procedures necessary for base station construction.

[1264] 4. Emotional Engine

[1265] This module analyzes user sentiment from input text and generates appropriate responses based on that sentiment.

[1266] Operation details

[1267] The user terminal first receives input from the user via an interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[1268] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. The analysis extracts "base station construction" and "required documents" as keywords. Based on these, a query is executed against the database. The server then formats the "required documents" information retrieved from the database and generates an appropriate response.

[1269] Furthermore, the emotion engine analyzes the user's emotions from their text and adjusts the tone and content of its response based on that emotional state. For example, if it detects that the user is anxious, the emotion engine will explain things in a calmer tone.

[1270] This response is generated in the form of, for example, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report," and sent to the user terminal.

[1271] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server.

[1272] The server analyzes the request again, and this time "blueprint" and "format" are extracted as keywords. It retrieves the appropriate information from the database and generates a response such as, "The blueprint format must be written on A3 size paper and include the following items: (1) overall plan of the construction site (2) antenna layout plan (3) power wiring plan," and then adjusts the tone again using the emotion engine, taking into account the user's emotional state.

[1273] For example, if the user's emotions indicate fatigue or frustration, the server can add more gentle and encouraging words. The response will be generated with additional sentences such as, "Please feel free to contact us anytime if you have any questions."

[1274] The user terminal displays this information to the user, providing guidance to help them proceed with the next steps. This allows the user to efficiently complete the process and obtain necessary information in a timely manner. Furthermore, the emotion engine adjusts the response according to the user's emotions, resulting in a better user experience.

[1275] The above is an overview of the embodiments of the present invention, which provide a series of procedural support to the user. The introduction of an emotion engine can further improve the user experience.

[1276] The following describes the processing flow.

[1277] ---

[1278] Step 1:

[1279] The user enters a question into the chatbot using their device. For example, they might type, "What documents are required for base station construction?"

[1280] Step 2:

[1281] The terminal receives user input and sends it to the server. The data sent includes the user's question text.

[1282] Step 3:

[1283] The server receives a request from the user and sends that request to the Natural Language Processing (NLP) engine.

[1284] Step 4:

[1285] The server uses an NLP engine to analyze the user's input text and extract keywords and intent. For example, keywords such as "base station construction" and "necessary documents" might be extracted.

[1286] Step 5:

[1287] The server queries the database based on the analysis results to retrieve relevant information. In this case, a list of necessary documents is retrieved from the database.

[1288] Step 6:

[1289] The server generates a response based on the information it has acquired. For example, it might generate a response message such as, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report."

[1290] Step 7:

[1291] The server uses an emotion engine to analyze the user's input text to determine their emotions. The emotion engine extracts emotional states (e.g., anxiety, frustration, relief, etc.).

[1292] Step 8:

[1293] The server adjusts the tone and content of its response based on the user's emotional state. For example, if the user is anxious, the response might include additional information such as, "Please rest assured. Detailed instructions are provided below."

[1294] Step 9:

[1295] The server sends a refined response to the user's terminal. The response sent includes emotionally sensitive content.

[1296] Step 10:

[1297] The terminal displays the response received from the server in the user interface. This allows the user to visually confirm the necessary information.

[1298] Step 11:

[1299] Users ask additional questions or inquire about the next steps. For example, after reviewing the information above, they might type "Please tell me about the blueprint format" into the chatbot.

[1300] Step 12:

[1301] The terminal receives user input again and sends it to the server. The data sent includes new question text.

[1302] Step 13:

[1303] The server receives a new request and analyzes it again using the NLP engine. For example, the keywords "blueprint" and "format" are extracted from the new text.

[1304] Step 14:

[1305] The server queries the database again to retrieve the relevant information. Detailed information about the blueprint format is retrieved from the database.

[1306] Step 15:

[1307] The server generates a new response based on the information it has acquired. For example, it might generate a statement like, "The design drawing format must be written on A3 size paper and include the following items: (1) Overall plan of the construction site (2) Antenna placement plan (3) Power wiring plan."

[1308] Step 16:

[1309] The server then uses the emotion engine to analyze the user's emotional state again and adjust the tone. If the user's emotions indicate fatigue or frustration, the server adds a gentle, encouraging message such as, "Please feel free to contact us anytime if you have any questions."

[1310] Step 17:

[1311] The server sends a new response to the user's terminal again. The response includes the adjusted tone and content.

[1312] Step 18:

[1313] The device displays a new response to the user. The user can then proceed with the process based on the information provided.

[1314] The above outlines the specific processing steps of the system to enable users to efficiently proceed with procedures related to base station construction. The introduction of an emotion engine will further enhance the user experience.

[1315] (Example 2)

[1316] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1317] Conventional support systems for base station construction procedures often provided insufficient answers to user-submitted questions and had overly complex procedural flows. Furthermore, they lacked appropriate support tailored to the user's emotional state, hindering a positive user experience. As a result, users frequently experienced stress during the procedural process.

[1318] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from data storage based on the analysis results, means for generating a response based on the acquired information and transmitting it to the user device, means for supporting the user in proceeding with the next procedure based on the response displayed on the user device, and means for analyzing emotions from the user's input text and adjusting the response based on those emotions. As a result, the user can quickly obtain appropriate information and proceed smoothly with the next procedure, and furthermore, the user experience is improved because the emotion engine can respond according to the user's emotions.

[1319] A "user" is an entity that uses the system to obtain information necessary to proceed with procedures related to base station construction.

[1320] "Input" refers to questions or instructions that a user sends to a system using an interface such as a chatbot.

[1321] "Analysis" is the process of understanding user input and grasping its meaning.

[1322] A "natural language processing engine" is software that analyzes natural language text entered by a user and accurately understands its meaning and intent.

[1323] "Related information" refers to data necessary to provide appropriate answers to user questions or instructions.

[1324] "Data storage" refers to an information repository that stores documents and procedural information necessary for the construction of base stations.

[1325] A "response" is a message that a system generates in response to a user's question or instruction.

[1326] "User equipment" refers to terminal devices used by users to access the system.

[1327] An "emotion engine" is a software module that analyzes the user's emotional state from the user's input text and adjusts the tone and content of the response appropriately based on the analysis results.

[1328] "Procedure" refers to a series of processes and operations that users perform to carry out various necessary tasks related to base station construction.

[1329] System Configuration

[1330] This system is designed to help users efficiently carry out the procedures necessary for base station construction. This system consists of the following components:

[1331] 1. User terminal: A device used by users to input questions and obtain information through a chatbot. This includes personal computers, smartphones, and other devices.

[1332] 2. Server: A central computer that receives, analyzes, and provides appropriate information from users. This server includes a natural language processing (NLP) engine, data storage, response generation modules, and a sentiment engine.

[1333] 3. Data Storage: This is a repository for storing information on documents and procedures necessary for base station construction.

[1334] 4. Emotion Engine: This module analyzes emotions from user input text and generates appropriate responses based on those emotions.

[1335] Operation details

[1336] First, the user terminal receives input from the user via the interface. For example, the user enters the question "What documents are needed for base station construction?" into the chatbot. This question is then sent from the user terminal to the server.

[1337] When the server receives a request, it first uses a natural language processing (NLP) engine to analyze the user's question. For example, using Google's BERT, keywords such as "base station construction" and "required documents" are extracted. Based on this, a query is executed against the data storage. Based on the "required documents" information retrieved from the data storage, the server formats it and generates an appropriate response.

[1338] Furthermore, the emotion engine analyzes the user's emotions from their text. For example, by combining OpenAI's GPT-3 with an emotion analysis module, it can generate a calm response if the user is feeling anxious.

[1339] The response might say something like, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." This response is sent to the user terminal.

[1340] The user proceeds to the next step based on the response displayed on their terminal. If they then input "Please tell me about the design drawing format," the user terminal similarly sends this input to the server. The server parses the request again and this time extracts the keywords "design drawing" and "format." It retrieves the appropriate information from data storage and generates a response stating, "The design drawing format should be written on A3 size paper and include the following items: (1) an overall map of the construction site (2) an antenna layout diagram (3) a power wiring plan diagram." The emotion engine then adjusts the tone again, taking into account the user's emotional state.

[1341] For example, if the user's emotions indicate fatigue or frustration, the server can add more gentle words of encouragement. The response may also include an additional sentence such as, "Please feel free to contact us anytime if you have any questions." The user terminal displays this information to the user, allowing them to receive guidance on how to proceed.

[1342] This type of system allows users to efficiently obtain the information necessary for base station construction, streamlining the process. Furthermore, the emotion engine adjusts responses according to the user's emotions, providing a better user experience.

[1343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1344] Step 1:

[1345] The user enters a question via their device. For example, they might enter, "What documents are required for base station construction?" This input is then sent to the server via the chatbot.

[1346] Specific operation: The user uses a keyboard or touchscreen to enter a question into the chatbot's input field.

[1347] Step 2:

[1348] The server receives user input and performs analysis using a natural language processing (NLP) engine. It analyzes the input question, "What documents are needed for base station construction?", and extracts the keywords "base station construction" and "necessary documents".

[1349] Specific operation: The server sends input data to an NLP engine (e.g., BERT) and retrieves keywords extracted as a result of the analysis.

[1350] Step 3:

[1351] The server queries the data storage based on the extracted keywords. It retrieves information from the data storage corresponding to the keywords "base station construction" and "required documents".

[1352] Specific operation: The server executes database queries (e.g., SQL queries) to retrieve information about the required documents.

[1353] Step 4:

[1354] The server organizes the information retrieved from data storage and generates a response. Simultaneously, it uses an emotion engine to analyze the user's emotional state. If it detects that the user is anxious, it generates a response in a calm tone.

[1355] Specific operation: The server formats the acquired data and generates a response stating, "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report." It also uses an emotion engine (e.g., GPT-3) to analyze the user's emotions and adjust the tone accordingly.

[1356] Step 5:

[1357] The server sends the generated response to the user terminal. The response displayed on the user terminal will read: "The following documents are required for base station construction: (1) Permit application form (2) Design drawings (3) Environmental assessment report."

[1358] Specific operation: The server sends the generated response data to the user's terminal. The user's terminal renders this data for display and displays it on the chatbot screen.

[1359] Step 6:

[1360] The user checks the response displayed on their terminal and proceeds to the next step. For example, they might enter an additional question such as, "Please tell me about the design drawing format." This input is then sent back to the server from the user's terminal.

[1361] Specific operation: The user reviews the displayed information and, if they need more information, enters additional questions into the chatbot and sends them.

[1362] This series of steps allows users to efficiently obtain the necessary information and proceed to the next step. Furthermore, sentiment analysis by the emotion engine improves the user experience.

[1363] (Application Example 2)

[1364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1365] Conventional procedural support systems provide a uniform response without considering the user's emotional state, resulting in insufficient support, especially for users experiencing stress or anxiety. Such systems are unsatisfactory in environments where quick and accurate support is required, such as for workers in logistics centers.

[1366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and analyzing user input, means for obtaining relevant information from a database based on the analysis results, means for generating a response based on the acquired information and transmitting it to the user terminal, and an emotion engine that analyzes the emotions from the user's input text and adjusts the tone and content of the response based on those emotions. As a result, a response is provided that corresponds to the user's emotional state, enabling the user to proceed with procedures efficiently, especially in busy situations.

[1367] "User input" refers to text or commands that a user enters into the system via a terminal.

[1368] "Analysis" refers to the process of understanding user input and extracting appropriate keywords and context.

[1369] A "database" is a repository for storing and managing related information.

[1370] A "response" is the system's answer or response to user input.

[1371] A "user terminal" is a device used by a user to interface with a system, and examples include smartphones and personal computers.

[1372] A "natural language processing engine" is a collection of technologies and algorithms that analyze user-input text and understand its content.

[1373] An "emotion engine" is a module that analyzes the emotional state of a user from their input text and generates an appropriate response based on the results.

[1374] A "server" is a central computing system that analyzes information from user input, retrieves necessary information from a database, and generates and sends a final response.

[1375] This invention relates to a system that supports worker procedures in a logistics center, and provides an interactive interface that combines a natural language processing engine and an emotion engine.

[1376] System Configuration

[1377] This system consists of the following elements:

[1378] 1. User terminal:

[1379] This is a device used by workers to confirm procedures and enter questions. This includes smartphones.

[1380] 2. Server:

[1381] It is a central computing system that analyzes user input, extracts relevant information, and generates appropriate responses. The server includes a natural language processing engine, an emotion engine, and a database.

[1382] 3. Database:

[1383] This is a repository that stores information regarding procedures and required documents at logistics centers.

[1384] 4. Natural Language Processing Engine:

[1385] This refers to a set of algorithms that analyze user text input, understand its content, and extract keywords.

[1386] 5. Emotional Engine:

[1387] This refers to a module that analyzes the user's emotions from their text and adjusts the tone and content of the response based on those emotions.

[1388] Operation details

[1389] The user terminal first receives the worker's question via the interface. For example, if the worker enters the question "What documents are needed?", this text is sent from the user terminal to the server.

[1390] The server first parses the input using a natural language processing engine (e.g., the Hugging Face Transformers library). Specifically, keywords such as "necessary" and "documents" are extracted from the text. Based on this analysis, queries are executed on the database to retrieve relevant information (e.g., a list of documents required for a logistics center).

[1391] Based on the information obtained, the server generates a response. For example, it might say, "The following documents are required for the logistics center: packing list, shipping instructions, and customs documents."

[1392] Next, the emotion engine analyzes the user's emotional state from their input text. For example, if the input text is short and some words are emphasized, the user may be anxious. Based on the emotion analysis, the tone of the response is adjusted. For anxious users, a calmer tone is used to add supplementary explanations such as, "Please rest assured. We will provide you with the information you need shortly."

[1393] Finally, the generated response is sent to the user's terminal and displayed to the worker.

[1394] Specific example

[1395] For example, if a worker at a logistics center asks, "How do I follow the new shipping procedure?", the system will respond as follows:

[1396] "Once all necessary documents are complete, please obtain approval from your supervisor. After that, you can proceed with preparing for shipment. If you have any further questions, please feel free to ask."

[1397] Example of a prompt

[1398] How do I proceed with the new shipping procedure?

[1399] In this way, by providing accurate and adaptive support tailored to the user's emotional state, it becomes possible to improve the operational efficiency of the logistics center.

[1400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1401] Program processing flow

[1402] Step 1:

[1403] The user enters a question using their smartphone. This input is specifically in text format and is sent to the server via the device's interface. For example, the user might enter, "How do I proceed with the new shipping procedure?"

[1404] Step 2:

[1405] The server receives user input. Next, a natural language processing engine (e.g., Hugging Face Transformers) is used to analyze this input text and extract keywords. The input is text data, and the output is a list of extracted keywords. For example, "shipping procedure" might be extracted as a keyword from the input text.

[1406] Step 3:

[1407] The server queries the database based on the extracted keywords. The input is a list of keywords, and it retrieves relevant information from the database. The output is the relevant information (e.g., a list of required documents or procedural steps). For example, the database might retrieve information such as, "Once all required documents are in place, obtain approval from your supervisor. Then proceed with shipping preparations."

[1408] Step 4:

[1409] The server generates a response based on the information it has retrieved. The input is information from the database, and the output is text to be sent to the user. For example, a response such as "The new shipping procedure is as follows: After all necessary documents are in place, please obtain approval from your supervisor. Then proceed with shipping preparation." might be generated.

[1410] Step 5:

[1411] Next, the emotion engine analyzes the user's input text to determine their emotions. The input is the user's text data, and the output is their emotional state (e.g., anxiety, stress). For example, if the user is anxious, the emotion engine will recognize it as "anxiety."

[1412] Step 6:

[1413] The server adjusts the tone and content of the response based on the output of the emotion engine. The input is the response text and the output of the emotion engine, and the adjusted response is generated. For example, if the user is determined to be "anxious," a supplementary explanation such as "Please rest assured" will be added to the response.

[1414] Step 7:

[1415] The server sends the generated response to the user's terminal. The input is the text of the adjusted response, which is displayed on the user's smartphone. The user can then receive specific guidance on how to proceed to the next step.

[1416] The above outlines the specific processing steps of this system. This will allow users to efficiently proceed with procedures at the logistics center.

[1417] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1418] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1419] 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.

[1420] 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.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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."

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] 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.

[1438] The following is further disclosed regarding the embodiments described above.

[1439] (Claim 1)

[1440] A first means of receiving and analyzing user input,

[1441] A second method for obtaining relevant information from a database based on the analysis results,

[1442] A third method involves generating a response based on the acquired information and sending it to the user's terminal.

[1443] A fourth means of supporting the user in proceeding with the next procedure based on the response displayed on the user terminal,

[1444] A system that includes this.

[1445] (Claim 2)

[1446] The system according to claim 1, which re-analyzes additional questions or inputs from the user, re-obtains the corresponding information, and re-sends a response to the user terminal.

[1447] (Claim 3)

[1448] The system according to claim 1, which analyzes user input using a natural language processing (NLP) engine.

[1449] "Example 1"

[1450] (Claim 1)

[1451] A means of receiving user input via a chatbot interface and analyzing it using a natural language processing engine,

[1452] A means of obtaining relevant information from a database based on the analysis results,

[1453] A means of formatting the acquired information, generating a response, and sending it to the user terminal,

[1454] A means to support the user in proceeding with the next procedure based on the response displayed on the user terminal,

[1455] A system that includes this.

[1456] (Claim 2)

[1457] The system according to claim 1, which re-analyzes additional questions or inputs from the user, re-obtains the corresponding information, and re-sends a response to the user terminal.

[1458] (Claim 3)

[1459] The system according to claim 1, which analyzes user input using a natural language processing engine and performs a database search based on the analysis results.

[1460] "Application Example 1"

[1461] (Claim 1)

[1462] A first means of receiving and analyzing user input,

[1463] A second method for obtaining relevant information from a database based on the analysis results,

[1464] A third method involves generating a response based on the acquired information and sending it to the user's terminal.

[1465] A fourth means of supporting the user in proceeding with the next procedure based on the response displayed on the user terminal,

[1466] The analysis means includes a natural language processing engine and a fifth means for identifying a target object based on a question,

[1467] A sixth method for retrieving information from the database based on the target object and formatting the information,

[1468] A system that includes this.

[1469] (Claim 2)

[1470] The system according to claim 1, which re-analyzes additional questions or inputs from the user, re-obtains the corresponding information, and re-sends a response to the user terminal.

[1471] (Claim 3)

[1472] The system according to claim 1, which uses a natural language processing engine to analyze user input and identify necessary documents or procedures.

[1473] "Example 2 of combining an emotion engine"

[1474] (Claim 1)

[1475] A first means of receiving and analyzing user input,

[1476] A second method for obtaining relevant information from data storage based on the analysis results,

[1477] A third means of generating a response based on the acquired information and sending it to the user's device,

[1478] A fourth means of supporting the user in proceeding with the next procedure based on the response displayed on the user's device,

[1479] A fifth method for analyzing emotions from user input text and adjusting responses based on those emotions,

[1480] A system that includes this.

[1481] (Claim 2)

[1482] The system according to claim 1, which re-analyzes additional questions or inputs from the user, re-acquires the corresponding information, and re-sends a response to the user's device.

[1483] (Claim 3)

[1484] The system according to claim 1, which analyzes user input using a natural language processing engine.

[1485] "Application example 2 when combining with an emotional engine"

[1486] 2. Extract the novel aspects of the technology from the explanation of the application examples.

[1487] One novel application example involves using an emotion engine in a smartphone app for workers at a logistics center to provide responses tailored to the user's emotions.

[1488] The new invention incorporates an emotion engine and a system that adjusts responses according to the user's emotional state. It also features an interactive interface that operates on smart devices.

[1489] (Claim 1)

[1490] A first means of receiving and analyzing user input,

[1491] A second method for obtaining relevant information from a database based on the analysis results,

[1492] A third method involves generating a response based on the acquired information and sending it to the user's terminal.

[1493] A fourth means of supporting the user in proceeding with the next procedure based on the response displayed on the user terminal,

[1494] A fifth means includes an emotion engine that analyzes emotions from the user's input text and adjusts the tone and content of the response based on those emotions,

[1495] A system that includes this.

[1496] (Claim 2)

[1497] The system according to claim 1, which re-analyzes additional questions or inputs from the user, re-obtains the corresponding information, and re-sends a response to the user terminal.

[1498] (Claim 3)

[1499] The system according to claim 1, which analyzes user input using a natural language processing engine.

[1500] That's all. [Explanation of Symbols]

[1501] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A first means of receiving and analyzing user input, A second method for obtaining relevant information from a database based on the analysis results, A third method involves generating a response based on the acquired information and sending it to the user's terminal. A fourth means of supporting the user in proceeding with the next procedure based on the response displayed on the user terminal, A system that includes this.

2. The system according to claim 1, which re-analyzes additional questions or inputs from the user, re-obtains the corresponding information, and re-sends a response to the user terminal.

3. The system according to claim 1, which analyzes user input using a natural language processing (NLP) engine.

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