system

Abstract

We provide the system. [Solution] An information processing means that receives operational requests, analyzes their contents, and classifies the type of request, A notification system that reviews the request based on criteria and requests additional information if any is missing, A means of accessing the resource management system to check the current resource status and determine the feasibility of the request, A generation means that generates an optimal resource allocation plan for insufficient resources and notifies the proposal, An information processing system that includes a registration mechanism for registering confirmed requests in an internal management system.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] The operation request reception work in a data center or the like is often performed manually, and there are risks of copying errors in information and inappropriate request handling. Also, the processing of operation requests takes a lot of time, and it may be difficult to respond promptly to customers. Such operational complexity and risk of errors lead to a decrease in business efficiency.

Means for Solving the Problems

[0005] This invention provides an information processing means that automatically receives operational requests and analyzes their content. This allows the system to verify the request content according to established criteria and, if necessary information is missing, automatically requests the user to provide additional information using a notification means. Furthermore, by linking with a resource management system and checking the current resource status, the system automatically determines the feasibility of the request. It also enables efficient resource utilization by predicting and generating an optimal allocation plan for insufficient resources. Confirmed requests are automatically registered in the internal management system, promoting automation of tasks, while work instructions are automatically generated, ensuring rapid communication to workers. After the work is completed, accuracy is ensured by cross-referencing with reports, improving overall operational efficiency.

[0006] An "information processing means" is an element that has the function of receiving operational requests, analyzing their content, and classifying them.

[0007] A "notification method" is an element that has the function of automatically requesting missing information from the user based on the request content.

[0008] A "verification mechanism" is an element that has the function of accessing a resource management system to check the current resource status and determine the feasibility of the request.

[0009] A "generation means" is an element that has the function of generating an optimal resource allocation plan for insufficient resources and notifying the proposed plan.

[0010] A "registration method" is an element that has the function of automatically registering confirmed requests in an internal management system.

[0011] A "work instruction sheet" is a document that is automatically generated to provide instructions to workers based on the work content.

[0012] "Verification" refers to the act of comparing the contents of the work completion report with the instruction sheet to confirm whether they match or not. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that automates the process from receiving operational requests to completing tasks, primarily in data centers. To achieve this, the following operations are performed between the server, terminal, and user.

[0035] Request reception and analysis

[0036] First, the user sends an operational request through their terminal. Upon receiving this request, the server uses its built-in text analysis engine to analyze its content and classify the type of request. This analysis result forms the basis for information processing in subsequent processing steps.

[0037] Validation and Notification

[0038] Next, the server verifies the request based on pre-configured criteria. Specifically, it checks whether all the necessary requirements and information are present. If any information is missing, the server automatically sends a notification to the user requesting additional input. This notification is typically sent via email or as a pop-up message on the device.

[0039] Resource status check and plan proposal

[0040] After confirming the request, the server accesses the resource management system and automatically checks the current resource status. If the request cannot be executed directly, the server generates an optimal resource allocation plan. For example, if additional memory is needed, it calculates how much resource should be allocated from which server, optimizing the overall system. This generated plan is proposed to the user, and after approval, the process proceeds to the next step.

[0041] System registration and work order creation

[0042] Approved requests are automatically registered by the server in the internal management system. Based on this registration information, a work order, including the necessary work procedures and resources, is automatically generated. The generated order is distributed to the worker via their terminal, ensuring that the actual work can begin quickly and reliably.

[0043] Work completion and report confirmation

[0044] Once the work is completed, the worker submits a completion report via their terminal. The server automatically analyzes this report and compares it with the original instructions to verify that the work was performed correctly. This process prevents errors and provides status updates to users and workers as needed.

[0045] By implementing this system, errors in information conversion will be significantly reduced, operational efficiency will be improved, and requests will be handled more quickly.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] Users enter and submit operational requests using their devices. These requests contain information about the necessary operations and resources.

[0049] Step 2:

[0050] The server receives a request from a user. To analyze the request's content, a natural language processing algorithm is used to classify the request type. This provides the basis for determining the appropriate processing route.

[0051] Step 3:

[0052] The server verifies the validity and completeness of the information based on the parsed request. If necessary information is missing based on the verification criteria, the server automatically sends a notification to the user requesting the missing information.

[0053] Step 4:

[0054] The server queries the data center's resource management system to check the current availability of the requested resource. This information is then used to determine whether the request can be executed.

[0055] Step 5:

[0056] If resources are insufficient, the server uses an algorithm to generate a resource allocation plan. The generated plan is sent to the user as a proposal for approval.

[0057] Step 6:

[0058] Once the request is confirmed and the plan is approved, the server automatically registers the request with its internal management system. This ensures that the work is recorded as a formal procedure.

[0059] Step 7:

[0060] The server automatically generates work instructions based on the work content. The generated instructions are distributed to workers via terminals, leading to the next action.

[0061] Step 8:

[0062] After completing the work, the worker submits a completion report to the system. The server compares this report with the instructions to verify that the work was performed as instructed. If a match is not found, the server requests the user and worker to reconfirm.

[0063] (Example 1)

[0064] 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."

[0065] Traditional data center operational request processing relied heavily on manual processes from information reception to implementation, resulting in inefficiencies. Furthermore, optimal resource allocation and accurate monitoring of work progress were difficult, potentially leading to information gaps and inconsistencies. There was a need to automate and optimize these processes to achieve rapid and accurate operational management.

[0066] 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.

[0067] In this invention, the server includes processing means for receiving and analyzing operational requests, communication means for verifying the content of the requests and requesting additional information, and verification means for checking resource status and determining feasibility. This enables efficient classification and management of requirements, optimized resource allocation, and rapid provision of work instructions.

[0068] An "operational request" is a request that includes instructions for operations or tasks that a user wants to perform in an information processing system.

[0069] A "text processing device" is a device that uses natural language processing technology to analyze and classify the content and intent of received information.

[0070] "Communication means" refers to a means of providing electronic notifications to inform users of the lack of information.

[0071] A "resource management device" is a device that monitors the status of computing resources used in an information processing system and ensures their efficient allocation.

[0072] "Verification methods" refer to means of verifying the information necessary to determine the feasibility of fulfilling a request.

[0073] A "generation method" is a means of automatically creating optimal plans and instructions based on received requests and circumstances.

[0074] "Verification means" refers to a method of comparing the information in the work completion report with the contents of the instruction sheet to confirm whether the work was performed accurately and to identify any problems.

[0075] "Natural language processing technology" refers to the technology used by computers to understand and appropriately process human language.

[0076] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate the optimal output based on user input.

[0077] A "prompt statement" is an instruction given to a generative AI model, and it is a specific input that leads to the desired output result.

[0078] This invention is a system that automates the process from receiving to executing operational requests in a data center. This system operates collaboratively among servers, terminals, and users.

[0079] First, the user sends an operational request to the server using a terminal. An example of a prompt message would be an instruction such as, "Optimize the database using a generative AI model." Upon receiving this request, the server uses a text processing unit and its built-in natural language processing technology to analyze the content of the request and classify it as a request type.

[0080] Next, the server accesses the resource management device based on the analysis results to check the current status of operational resources and determine feasibility. If necessary resources are insufficient, a generation mechanism for optimizing resource allocation is used, and an optimal resource allocation plan is automatically generated.

[0081] Furthermore, if the server needs to request any missing information from the user, it will send an email or a notification on the user's device using communication methods. After the user adds the necessary information to the request, the server will verify this information and register the request with the system's management device.

[0082] Furthermore, the server utilizes a generation mechanism to automatically generate work instructions based on requests and distribute them to workers via terminals. This allows workers to start their work quickly and efficiently.

[0083] After completing a task, the worker submits a completion report to the server via their terminal. The server compares this report with the instruction sheet using a verification mechanism to confirm that the work was completed accurately. This system significantly reduces information conversion errors, improves the efficiency of operational tasks, and enables faster response times to requests.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user sends operational requests from the terminal to the server using prompt messages. For example, the user might input the instruction, "Optimize the database using a generative AI model." The server receives the prompt message and activates a text processing device. This device uses natural language processing technology to analyze the intent of the request and classify it as a database-related operation. Based on the input prompt message, the type of analyzed request is output.

[0087] Step 2:

[0088] The server verifies the request based on the analysis results. During the verification process, it checks whether the data contains the necessary information and determines that additional information is needed if any is missing. It receives the analysis results as input and outputs a list of missing information. The server then generates a message to notify the user of the missing information using a communication method based on this list. This message is sent to the user via email or as a notification on the terminal screen.

[0089] Step 3:

[0090] The server accesses the resource management device to check the current resource status. Here, it verifies whether there is sufficient memory and CPU space to execute the request. It receives resource information requiring verification as input and outputs a feasibility assessment result. If resources are insufficient, the server uses a generation tool to create an optimal resource allocation plan. This plan calculates how much resource should be borrowed from which server and presents it to the user as a proposal.

[0091] Step 4:

[0092] The user reviews and approves the resource allocation plan proposed by the server. Based on the approved plan, the server formally registers the request with the management device. This registration is used as information necessary for creating subsequent work orders. The server receives the proposed resource allocation plan as input and outputs a status indicating that registration with the management device is complete.

[0093] Step 5:

[0094] The server generates detailed work instructions using a generation mechanism based on approved requests. These instructions detail the work procedures and the resources to be used. The generated instructions are provided to the worker via a terminal. The server receives approved request information as input and outputs work instructions.

[0095] Step 6:

[0096] Workers complete tasks according to the instructions. After completion, they submit a work completion report to the server via their terminal. The server reads the report and performs intelligent analysis to compare it with the initial work instructions. It receives the work completion report data as input and outputs the results of the work completion confirmation. If necessary, the results are notified to the user and the worker.

[0097] (Application Example 1)

[0098] 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."

[0099] Operating automated machinery in factories is complex and often requires a large workforce. Furthermore, errors in information transmission and decreased work efficiency can occur during the process from receiving, analyzing, and executing various work instructions. There is a need to solve these problems and achieve faster and more accurate work processes.

[0100] 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.

[0101] In this invention, the server includes processing means for receiving operational requests, analyzing their content, and classifying the type of request; notification means for verifying the request content based on criteria and requesting additional information if necessary; and verification means for accessing a resource management entrustment mechanism to check the current resource status and determine the feasibility of the request. This enables centralized management of commands to automated work machines within the factory and rapid execution of work commands.

[0102] "Operational requirements" refer to specific instructions or requests related to operations at a factory or facility.

[0103] "Processing means" refers to devices or software that have the function of receiving, analyzing, and classifying input information.

[0104] "Notification means" refers to devices or methods that have the function of quickly transmitting missing information or necessary instructions to relevant parties.

[0105] A "resource management entrustment mechanism" refers to a system for understanding the current status of resources and equipment necessary for operations and for ensuring appropriate management.

[0106] "Verification means" refers to equipment or processes that have the function of determining whether a request is feasible.

[0107] "Generating means" refers to a system or method that has the function of automatically creating optimal plans or instructions based on specified conditions.

[0108] An "internal control system" refers to a system for properly storing and managing data related to business operations and resources.

[0109] An "automated work machine" refers to a machine or device that can perform specific tasks automatically in a factory or similar environment.

[0110] A "work request" refers to an instruction or request that specifies the details of the tasks or work that need to be performed.

[0111] "Operation commands" refer to specific instructions necessary for an automated machine to perform a designated task.

[0112] This invention is a system that centralizes information management and promotes automation in order to achieve efficient management and operation of automated machinery within a factory. The system mainly consists of a server, terminals, and users, and operates as follows.

[0113] The server receives operational requests from users and analyzes their content using a text analysis engine such as spaCy. The analyzed data is then linked with a resource management system, where it checks the current status of resources using tools like pandas and generates an appropriate plan. After confirming that the generated plan is appropriate, it requests approval from the user and transmits the instructions. The server also forwards the generated instructions to automated work machines, receives work completion reports, and verifies them against the instructions.

[0114] The terminal functions as an interface with the server, supporting user request input and notification reception. Using the terminal, users can send necessary additional information and receive notifications from the server in real time.

[0115] Users input and send requests via smartphones or tablets. For example, if a user sends a request from their smartphone to repair a stepper motor in a factory, the server analyzes it, determines the appropriate tools and necessary parts, and sends a work command to the most suitable robot. An example of a prompt message would be: "Request: Stepper motor replacement. What tools are needed? Generate an appropriate work plan while checking the current robot availability. After completing the work, please send a report to the server."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] Users input operational requests into their devices via smartphones or tablets and send the requests. The information entered includes the type of request and the specific details of the request, which then becomes the data input to the server.

[0119] Step 2:

[0120] The server receives requests sent by users and analyzes their content using a text analysis engine such as spaCy. This analysis classifies the type of request and extracts its content. The results of this analysis become the output for proceeding to the next processing step.

[0121] Step 3:

[0122] Based on the analyzed request, the server accesses the resource management system and checks the current status of resources using pandas. This input data includes current equipment usage and inventory information for necessary parts, and the server uses this data to determine the feasibility of the request. As a result, it outputs which resources are available.

[0123] Step 4:

[0124] The server compares resource availability with the request details and generates an optimal resource allocation plan if any resources are lacking. It uses pandas to process existing data and generate proposed resource reallocations. The generated plan is then output for user approval.

[0125] Step 5:

[0126] The server transfers the generated resource allocation plan to the user's terminal and requests approval. The user can review the proposed plan on their terminal and request approval or modifications.

[0127] Step 6:

[0128] The server automatically generates and sends instructions to the automated work machine based on the resource allocation plan approved by the user. These instructions include the schedule and necessary operating procedures, and are then sent to the automated work machine.

[0129] Step 7:

[0130] When the automated work machine completes the specified task, the server receives a notification of completion. The server then compares the contents of the received completion report with the previously sent instruction sheet. A match or mismatch is then confirmed, and the results are notified to both the user and the automated work machine as needed.

[0131] 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.

[0132] This invention provides a system for automatically processing operational requests in data centers and online services, as well as recognizing the user's emotional state and adjusting responses accordingly. This system is implemented through the following process involving a server, a terminal, and a user.

[0133] Request and emotion recognition

[0134] The user sends an operational request via their terminal. Upon receiving the request, the server analyzes the text data using an emotion engine to identify the user's emotions. This emotion recognition is performed using natural language processing techniques to identify emotional states such as positive, negative, or neutral.

[0135] Emotion-based response regulation

[0136] When a response is required based on an emotional state, for example, if negative emotions are detected, the server provides pre-configured response patterns and additional support options to generate a response that is sensitive to the user's emotions. This information is then communicated to the user as a customized message.

[0137] Specific example

[0138] Suppose a user submits a request stating, "I am dissatisfied with the slowness of this service." The server not only processes the content of the request, but also recognizes the negative emotion of dissatisfaction through its sentiment engine. As a result, in addition to its normal processing routine, the server takes actions to encourage human intervention, such as sending a message promising a quick response and, if necessary, sending alerts to the appropriate personnel.

[0139] Alert to the person in charge

[0140] When the server detects a certain level of negative emotion using its emotion engine, it sends an alert to the relevant personnel. This feature enables a system in place to respond to situations requiring immediate human intervention. This approach contributes to improved customer satisfaction and the rapid resolution of complaints.

[0141] In this way, the present invention enhances the efficiency of automated processing of operational requests and provides a flexible system that can appropriately adjust responses based on user sentiment. This is expected to improve the user experience and, as a result, enhance the quality of service.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] Users enter and submit operational requests using their devices. These requests include specific information about the operations and resources required.

[0145] Step 2:

[0146] The server receives a request from the user. Simultaneously, it uses an emotion engine to analyze the text data of the request and identify the emotional state as positive, negative, or neutral.

[0147] Step 3:

[0148] The server analyzes the request content and classifies the request type using standard information processing methods. This determines the appropriate processing flow for each request.

[0149] Step 4:

[0150] The server verifies the validity of the request based on certain criteria. If necessary information is missing, the server automatically sends a notification to the user requesting that the missing information be provided.

[0151] Step 5:

[0152] The server accesses the data center's resource management system to check the availability of the requested resource. It verifies the resource status to determine the feasibility of the request.

[0153] Step 6:

[0154] If resources are insufficient, the server generates an optimal resource allocation plan and proposes it to the user. After user approval, the process proceeds to the next step.

[0155] Step 7:

[0156] If the emotion engine detects a negative emotion, the server generates a customized response message and notifies the user of a promise of prompt action.

[0157] Step 8:

[0158] If a certain level of negative emotion is detected, the server sends an alert to the relevant person, prompting immediate human intervention.

[0159] Step 9:

[0160] Once the request is confirmed and the necessary plan is approved, the server registers the request in its internal management system. This ensures that the work is recorded as a formal procedure.

[0161] Step 10:

[0162] The server automatically generates work instructions based on the work content. The generated instructions are distributed to workers via terminals, prompting them to take the next action.

[0163] Step 11:

[0164] After completing the work, the worker submits a completion report to the system. The server compares this report with the instructions, checking for matches and detecting any discrepancies to ensure the accuracy of the work.

[0165] (Example 2)

[0166] 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".

[0167] Modern information processing systems are required not only to efficiently process user operational requests but also to appropriately understand and respond to user emotions. Conventional systems often provide uniform responses without considering user emotional states, potentially leading to decreased service satisfaction. Furthermore, further improvements are needed in resource management, request classification, and processing efficiency.

[0168] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0169] In this invention, the server includes information processing means for receiving and classifying operational requests, notification means for evaluating the content of requests and supplementing incomplete information, and confirmation means for checking the resource management status. This enables efficient resource management and rapid processing of requests while improving service satisfaction by analyzing the user's emotional state and providing an optimal response.

[0170] "Operational requests" refer to instructions from users requesting the execution of operations or services on the system.

[0171] "Information processing means" refers to a device or program that has the function of acquiring, analyzing, and classifying digital data.

[0172] "Notification means" refers to a device or program that has the function of generating and sending messages, alerts, or notifications to provide information to a user.

[0173] A "resource management device" is a device or program that has the function of checking the status of available hardware and software resources within a system and adjusting their use as needed.

[0174] A "verification tool" is a device or program that has the function of examining the status of resources and the details of requests, and determining their suitability and feasibility.

[0175] A "generation means" is a device or program for creating new information or responses and providing them to a user or system.

[0176] A "registration means" is a device or program that has the function of saving processed data or requests to a database or recording device within the system, in preparation for later reference or processing.

[0177] "Emotion recognition processing" is a technology that analyzes and identifies emotions from a user's text or voice.

[0178] "Service satisfaction" is an indicator that shows the level of satisfaction and evaluation users have of the services they have received.

[0179] This invention relates to an information processing system that efficiently processes operational requests from users while recognizing and appropriately responding to the user's emotional state. The entire system operates in cooperation with three parties: a server, a terminal, and the user.

[0180] The system's hardware configuration includes terminals for receiving user input and servers for processing operational requests. The servers are located in data centers or the cloud and contain an emotion recognition engine. This engine utilizes a generative AI model and employs natural language processing techniques to analyze text data from users. This analysis allows for the accurate identification of emotional states such as positive, negative, and neutral.

[0181] The software used includes a natural language processing library and sentiment analysis algorithms that leverage generative AI models. This allows the server to analyze text sent by the user in real time and generate responses tailored to the user's emotions. The generated responses are then sent to the user's device as customized messages.

[0182] As a concrete example, consider a case where a user submits feedback stating, "I am dissatisfied with the slowness of this service." In this case, the server recognizes the negative emotion of dissatisfaction using an emotion recognition engine and constructs a message promising a prompt response. An example of a prompt in this case would be, "Design a system that receives user feedback, automatically analyzes the emotion, and generates an appropriate response."

[0183] This system aims to improve customer satisfaction and optimize service quality by enabling responses that respond to user emotions.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] Users create and send operational requests using their terminals. These requests include instructions and feedback for the system and are sent from the terminal to the server as text data. The input at this time is the user's request text, which is sent to the server as output without any conversion.

[0187] Step 2:

[0188] The server parses the requests received from users. In this process, the received text data is used as input, and the content and type of request are initially determined. The data is then classified using information processing tools, and preparations are made for requests requiring processing to proceed to the next step. The output is the parsed data.

[0189] Step 3:

[0190] The server performs emotion recognition processing using a generative AI model. It takes analyzed text data as input and uses natural language processing techniques to classify the user's emotions as positive, negative, neutral, etc. During this process, data processing is performed to extract and evaluate keywords and contextual information. The output is data representing the user's emotional state.

[0191] Step 4:

[0192] The server generates a response based on the user's emotional state. Emotional state data is used as input, and a generative AI model and response generation algorithm are used to create the optimal response. This step utilizes emotion-appropriate template messages and, if necessary, presents additional support options. The output is the generated custom message.

[0193] Step 5:

[0194] The server sends the generated response to the user's terminal. The generated custom message is used as input, and the message is delivered to the user in real time using a notification method. Specifically, it is received on the user's terminal as email or message notification. The output is the response message received by the user.

[0195] (Application Example 2)

[0196] 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".

[0197] Information processing systems are required to efficiently and appropriately handle operational requests from users, but the lack of response adjustments that take into account user emotions makes improving the user experience a challenge. Furthermore, there is a need to increase resource utilization efficiency while also providing flexible responses that consider emotional states.

[0198] 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.

[0199] In this invention, the server includes information processing means for receiving operational requests, analyzing their content, and classifying the type of request; emotion processing means for recognizing the user's emotional state using an information processing device and adjusting the response based on the emotion analysis results; and verification means for accessing a resource management system to check the current resource status and determine the feasibility of the request. This makes it possible to provide appropriate responses according to the user's emotions, enabling efficient use of resources and improved user satisfaction.

[0200] "Operation request" is a term that refers to the content of operations or requests that users make to the system.

[0201] "Information processing means" refers to a device or program that has the function of analyzing received data and classifying and processing it based on specific criteria.

[0202] A "notification means" is a device or program that has the function of informing the user of missing information or additional information that is needed.

[0203] "Emotional processing means" refers to a device or program that analyzes user input data to identify their emotional state and generates a response corresponding to that emotion.

[0204] A "resource management system" is a system that monitors the resource status within a system and manages appropriate resource allocation.

[0205] "Verification means" refers to a device or program that has the function of checking whether a requested operation can be performed based on specified criteria.

[0206] A "generation means" is a device or program that has the function of creating an optimal resource allocation and plan based on specific conditions and outputting a proposal.

[0207] "Registration means" refers to a device or program that has the function of saving confirmed data or requests as records within the system.

[0208] This invention is a system for automating operational requests and adjusting responses based on the user's emotional state. The system primarily consists of three components: a server, a terminal, and a user. The specific functions of each component are described below.

[0209] The server uses information processing tools to analyze and classify operational requests received from users. Natural language processing techniques are utilized during the analysis to precisely determine the content and nuances of the text data. Furthermore, as an emotion processing tool, it is equipped with an emotion recognition engine to understand the user's emotional state, identifying it as positive, negative, or neutral. This process can utilize open-source natural language processing libraries or custom AI models.

[0210] The terminal acts as the interface for the user, forwarding user-inputted requests to the server. This interface could be a smartphone application, a web browser, or a custom-built device.

[0211] If a user makes a request expressing dissatisfaction, such as feedback like "The recent payment system is extremely slow," the server immediately detects the negative emotion using emotion processing mechanisms and provides a swift and appropriate response. Specifically, it generates a response promising a quick resolution and sends an alert to the appropriate person if necessary.

[0212] An example of a prompt is: "Analyze the sentiment of the following user feedback and suggest a response: 'Recent payment services have been very slow.'" This prompt is an example input when using a generative AI model to create an appropriate response scenario.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The terminal receives an operational request from the user. The data entered is text information entered by the user into the terminal. This data may include requests and feedback regarding operations.

[0216] Step 2:

[0217] The terminal sends the operational request it receives to the server. The data sent is text information entered by the user. The server receives this data and parses it in the next step.

[0218] Step 3:

[0219] The server uses information processing tools to analyze the received request. The input is text information, and the server uses a natural language processing engine to classify the content and identify the type of request. The output is category information for the classified request.

[0220] Step 4:

[0221] The server uses emotion processing tools to analyze the emotional state of the received request. The input is the previously analyzed text information, and the emotion recognition engine identifies one of three emotional states: positive, negative, or neutral. The output is the result of the emotion analysis.

[0222] Step 5:

[0223] The server generates an appropriate response based on the sentiment analysis results. The input consists of emotional state and request category data, and a generative AI model is used to create a response scenario that harmonizes with the emotion. The output is the adjusted response message.

[0224] Step 6:

[0225] The server sends a response message to the terminal. The terminal displays the received response message to the user, completing the notification to the user. The final output is the response message displayed to the user.

[0226] Step 7:

[0227] The server detects negative emotions and, if necessary, sends an alert to the appropriate person if immediate action is required. The input is the result of emotion analysis, and negative emotions exceeding a certain threshold trigger an alert notification as output. The person receiving this alert can then intervene quickly.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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".

[0244] This invention is a system that automates the process from receiving operational requests to completing tasks, primarily in data centers. To achieve this, the following operations are performed between servers, terminals, and users.

[0245] Request reception and analysis

[0246] First, the user sends an operational request through their terminal. Upon receiving this request, the server uses its built-in text analysis engine to analyze its content and classify the type of request. This analysis result forms the basis for information processing in subsequent processing steps.

[0247] Validation and Notification

[0248] Next, the server verifies the request based on pre-configured criteria. Specifically, it checks whether all the necessary requirements and information are present. If any information is missing, the server automatically sends a notification to the user requesting additional input. This notification is typically sent via email or as a pop-up message on the device.

[0249] Resource status check and plan proposal

[0250] After confirming the request, the server accesses the resource management system and automatically checks the current resource status. If the request cannot be executed directly, the server generates an optimal resource allocation plan. For example, if additional memory is needed, it calculates how much resource should be allocated from which server, optimizing the overall system. This generated plan is proposed to the user, and after approval, the process proceeds to the next step.

[0251] System registration and work order creation

[0252] Approved requests are automatically registered by the server in the internal management system. Based on this registration information, work instructions, including necessary work procedures and resources, are automatically generated. The generated instructions are distributed to workers via terminals, ensuring that the actual work can begin quickly and reliably.

[0253] Work completion and report confirmation

[0254] Once the work is completed, the worker submits a completion report via their terminal. The server automatically analyzes this report and compares it with the original instructions to verify that the work was performed correctly. This process prevents errors and provides status updates to users and workers as needed.

[0255] By implementing this system, errors in information conversion will be significantly reduced, operational efficiency will be improved, and requests will be handled more quickly.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] Users enter and submit operational requests using their devices. These requests contain information about the necessary operations and resources.

[0259] Step 2:

[0260] The server receives a request from a user. To analyze the request's content, a natural language processing algorithm is used to classify the request type. This provides the basis for determining the appropriate processing route.

[0261] Step 3:

[0262] The server verifies the validity and completeness of the information based on the parsed request. If necessary information is missing based on the verification criteria, the server automatically sends a notification to the user requesting the missing information.

[0263] Step 4:

[0264] The server queries the data center's resource management system to check the current availability of the requested resource. This information is then used to determine whether the request can be executed.

[0265] Step 5:

[0266] If resources are insufficient, the server uses an algorithm to generate a resource allocation plan. The generated plan is sent to the user as a proposal for approval.

[0267] Step 6:

[0268] Once the request is confirmed and the plan is approved, the server automatically registers the request with its internal management system. This ensures that the work is recorded as a formal procedure.

[0269] Step 7:

[0270] The server automatically generates work instructions based on the work content. The generated instructions are distributed to workers via terminals, leading to the next action.

[0271] Step 8:

[0272] After completing the work, the worker submits a completion report to the system. The server compares this report with the instructions to verify that the work was performed as instructed. If a match is not found, the server requests the user and worker to reconfirm.

[0273] (Example 1)

[0274] 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."

[0275] Traditional data center operational request processing relied heavily on manual processes from information reception to implementation, resulting in inefficiencies. Furthermore, optimal resource allocation and accurate monitoring of work progress were difficult, potentially leading to information gaps and inconsistencies. There was a need to automate and optimize these processes to achieve rapid and accurate operational management.

[0276] 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.

[0277] In this invention, the server includes processing means for receiving and analyzing operation requests, communication means for verifying the request content and requesting additional information, and confirmation means for checking the resource status and determining feasibility. As a result, requirements can be efficiently classified and managed, enabling optimal allocation of resources and rapid provision of work instructions.

[0278] An "operation request" is a request including an operation or work instruction that a user desires to execute in an information processing system.

[0279] A "text processing device" is a device for analyzing and classifying the content and intention of received information using natural language processing technology.

[0280] "Communication means" is means for electronically notifying a user of a lack of information.

[0281] A "resource management device" is a device for monitoring the status of computing resources used in an information processing system and performing efficient allocation.

[0282] "Confirmation means" is means for verifying information necessary for determining the feasibility of a request.

[0283] "Generation means" is means for automatically creating an optimal plan or instruction sheet based on received requests and situations.

[0284] "Verification means" is means for comparing the information in the work completion report with the content of the instruction sheet, checking whether the work was accurately performed, and identifying problems.

[0285] "Natural language processing technology" is technology for a computer to understand and appropriately process human language.

[0286] A "generation AI model" is an artificial intelligence model that uses machine learning algorithms to generate an optimal output based on user input.

[0287] A "prompt statement" is an instruction given to a generative AI model, and it is a specific input that leads to the desired output result.

[0288] This invention is a system that automates the process from receiving to executing operational requests in a data center. This system operates collaboratively among servers, terminals, and users.

[0289] First, the user sends an operational request to the server using a terminal. An example of a prompt message would be an instruction such as, "Optimize the database using a generative AI model." Upon receiving this request, the server uses a text processing unit and its built-in natural language processing technology to analyze the content of the request and classify it as a request type.

[0290] Next, the server accesses the resource management device based on the analysis results to check the current status of operational resources and determine feasibility. If necessary resources are insufficient, a generation mechanism for optimizing resource allocation is used, and an optimal resource allocation plan is automatically generated.

[0291] Furthermore, if the server needs to request any missing information from the user, it will send an email or a notification on the user's device using communication methods. After the user adds the necessary information to the request, the server will verify this information and register the request with the system's management device.

[0292] Furthermore, the server utilizes a generation mechanism to automatically generate work instructions based on requests and distribute them to workers via terminals. This allows workers to start their work quickly and efficiently.

[0293] After completing a task, the worker submits a completion report to the server via their terminal. The server compares this report with the instruction sheet using a verification mechanism to confirm that the work was completed accurately. This system significantly reduces information conversion errors, improves the efficiency of operational tasks, and enables faster response times to requests.

[0294] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0295] Step 1:

[0296] The user sends operational requests from the terminal to the server using prompt messages. For example, the user might input the instruction, "Optimize the database using a generative AI model." The server receives the prompt message and activates a text processing device. This device uses natural language processing technology to analyze the intent of the request and classify it as a database-related operation. Based on the input prompt message, the type of analyzed request is output.

[0297] Step 2:

[0298] The server verifies the request based on the analysis results. During the verification process, it checks whether the data contains the necessary information and determines that additional information is needed if any is missing. It receives the analysis results as input and outputs a list of missing information. The server then generates a message to notify the user of the missing information using a communication method based on this list. This message is sent to the user via email or as a notification on the terminal screen.

[0299] Step 3:

[0300] The server accesses the resource management device to check the current resource status. Here, it verifies whether there is sufficient memory and CPU space to execute the request. It receives resource information requiring verification as input and outputs a feasibility assessment result. If resources are insufficient, the server uses a generation tool to create an optimal resource allocation plan. This plan calculates how much resource should be borrowed from which server and presents it to the user as a proposal.

[0301] Step 4:

[0302] The user checks the resource allocation plan proposed by the server and approves its content. Based on the approved plan, the server formally registers a request with the management device. This registration is used as information necessary for subsequent creation of work instructions. Receives the proposed resource allocation plan as input and outputs the registration completion status to the management device.

[0303] Step 5:

[0304] Based on the approved request, the server uses generation means to generate a detailed work instruction. The instruction details the work procedures and resources to be used. The generated instruction is provided to the worker through the terminal. Receives the approved request information as input and outputs the work instruction.

[0305] Step 6:

[0306] The worker completes the work based on the instruction. After completion, the worker submits a work completion report to the server through the terminal. The server reads the content of the report and performs intelligent analysis to match it with the initial work instruction. Receives the data of the work completion report as input and outputs the result of confirming the match of work completion. If necessary, the result is notified to the user and the worker.

[0307] (Application Example 1)

[0308] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] The operation of the working machines in the factory is complex and may require a lot of manpower. Furthermore, information transmission errors and a decrease in work efficiency may occur in the process from receiving, analyzing, and implementing various work instructions. It is required to solve such problems and achieve faster work and improved accuracy.

[0310] 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.

[0311] In this invention, the server includes processing means for receiving operational requests, analyzing their content, and classifying the type of request; notification means for verifying the request content based on criteria and requesting additional information if necessary; and verification means for accessing a resource management entrustment mechanism to check the current resource status and determine the feasibility of the request. This enables centralized management of commands to automated work machines within the factory and rapid execution of work commands.

[0312] "Operational requirements" refer to specific instructions or requests related to operations at a factory or facility.

[0313] "Processing means" refers to devices or software that have the function of receiving, analyzing, and classifying input information.

[0314] "Notification means" refers to devices or methods that have the function of quickly transmitting missing information or necessary instructions to relevant parties.

[0315] A "resource management entrustment mechanism" refers to a system for understanding the current status of resources and equipment necessary for operations and for ensuring appropriate management.

[0316] "Verification means" refers to equipment or processes that have the function of determining whether a request is feasible.

[0317] "Generating means" refers to a system or method that has the function of automatically creating optimal plans or instructions based on specified conditions.

[0318] An "internal control system" refers to a system for properly storing and managing data related to business operations and resources.

[0319] An "automated work machine" refers to a machine or device that can perform specific tasks automatically in a factory or similar environment.

[0320] A "work request" refers to an instruction or request that specifies the details of the tasks or work that need to be performed.

[0321] "Operation commands" refer to specific instructions necessary for an automated machine to perform a designated task.

[0322] This invention is a system that centralizes information management and promotes automation in order to achieve efficient management and operation of automated machinery within a factory. The system mainly consists of a server, terminals, and users, and operates as follows.

[0323] The server receives operational requests from users and analyzes their content using a text analysis engine such as spaCy. The analyzed data is then linked with a resource management system, where it checks the current status of resources using tools like pandas and generates an appropriate plan. After confirming that the generated plan is appropriate, it requests approval from the user and transmits the instructions. The server also forwards the generated instructions to automated work machines, receives work completion reports, and verifies them against the instructions.

[0324] The terminal functions as an interface with the server, supporting user request input and notification reception. Using the terminal, users can send necessary additional information and receive notifications from the server in real time.

[0325] Users input and send requests via smartphones or tablets. For example, if a user sends a request from their smartphone to repair a stepper motor in a factory, the server analyzes it, determines the appropriate tools and necessary parts, and sends a work command to the most suitable robot. An example of a prompt message would be: "Request: Stepper motor replacement. What tools are needed? Generate an appropriate work plan while checking the current robot availability. After completing the work, please send a report to the server."

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] Users input operational requests into their devices via smartphones or tablets and send the requests. The information entered includes the type of request and the specific details of the request, which then becomes the data input to the server.

[0329] Step 2:

[0330] The server receives requests sent by users and analyzes their content using a text analysis engine such as spaCy. This analysis classifies the type of request and extracts its content. The results of this analysis become the output for proceeding to the next processing step.

[0331] Step 3:

[0332] Based on the analyzed request, the server accesses the resource management system and checks the current status of resources using pandas. This input data includes current equipment usage and inventory information for necessary parts, and the server uses this data to determine the feasibility of the request. As a result, it outputs which resources are available.

[0333] Step 4:

[0334] The server compares resource availability with the request details and generates an optimal resource allocation plan if any resources are lacking. It uses pandas to process existing data and generate proposed resource reallocations. The generated plan is then output for user approval.

[0335] Step 5:

[0336] The server transfers the generated resource allocation plan to the user's terminal and requests approval. The user can review the proposed plan on their terminal and request approval or modifications.

[0337] Step 6:

[0338] The server automatically generates and sends instructions to the automated work machine based on the resource allocation plan approved by the user. These instructions include the schedule and necessary operating procedures, and are then sent to the automated work machine.

[0339] Step 7:

[0340] When the automated work machine completes the specified task, the server receives a notification of completion. The server then compares the contents of the received completion report with the previously sent instruction sheet. A match or mismatch is then confirmed, and the results are notified to both the user and the automated work machine as needed.

[0341] 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.

[0342] This invention provides a system for automatically processing operational requests in data centers and online services, as well as recognizing the user's emotional state and adjusting responses accordingly. This system is implemented through the following process involving a server, a terminal, and a user.

[0343] Request and emotion recognition

[0344] The user sends an operational request via their terminal. Upon receiving the request, the server analyzes the text data using an emotion engine to identify the user's emotions. This emotion recognition is performed using natural language processing techniques to identify emotional states such as positive, negative, or neutral.

[0345] Emotion-based response regulation

[0346] When a response is required based on an emotional state, for example, if negative emotions are detected, the server provides pre-configured response patterns and additional support options to generate a response that is sensitive to the user's emotions. This information is then communicated to the user as a customized message.

[0347] Specific example

[0348] Suppose a user submits a request stating, "I am dissatisfied with the slowness of this service." The server not only processes the content of the request, but also recognizes the negative emotion of dissatisfaction through its sentiment engine. As a result, in addition to its normal processing routine, the server takes actions to encourage human intervention, such as sending a message promising a quick response and, if necessary, sending alerts to the appropriate personnel.

[0349] Alert to the person in charge

[0350] When the server detects a certain level of negative emotion using its emotion engine, it sends an alert to the relevant personnel. This feature enables a system in place to respond to situations requiring immediate human intervention. This approach contributes to improved customer satisfaction and the rapid resolution of complaints.

[0351] In this way, the present invention enhances the efficiency of automated processing of operational requests and provides a flexible system that can appropriately adjust responses based on user sentiment. This is expected to improve the user experience and, as a result, enhance the quality of service.

[0352] The following describes the processing flow.

[0353] Step 1:

[0354] Users enter and submit operational requests using their devices. These requests include specific information about the operations and resources required.

[0355] Step 2:

[0356] The server receives a request from the user. Simultaneously, it uses an emotion engine to analyze the text data of the request and identify the emotional state as positive, negative, or neutral.

[0357] Step 3:

[0358] The server analyzes the request content and classifies the request type using standard information processing methods. This determines the appropriate processing flow for each request.

[0359] Step 4:

[0360] The server verifies the validity of the request based on certain criteria. If necessary information is missing, the server automatically sends a notification to the user requesting that the missing information be provided.

[0361] Step 5:

[0362] The server accesses the data center's resource management system to check the availability of the requested resource. It verifies the resource status to determine the feasibility of the request.

[0363] Step 6:

[0364] If resources are insufficient, the server generates an optimal resource allocation plan and proposes it to the user. After user approval, the process proceeds to the next step.

[0365] Step 7:

[0366] If the emotion engine detects a negative emotion, the server generates a customized response message and notifies the user of a promise of prompt action.

[0367] Step 8:

[0368] If a certain level of negative emotion is detected, the server sends an alert to the relevant person, prompting immediate human intervention.

[0369] Step 9:

[0370] Once the request is confirmed and the necessary plan is approved, the server registers the request in its internal management system. This ensures that the work is recorded as a formal procedure.

[0371] Step 10:

[0372] The server automatically generates work instructions based on the work content. The generated instructions are distributed to workers via terminals, prompting them to take the next action.

[0373] Step 11:

[0374] After completing the work, the worker submits a completion report to the system. The server compares this report with the instructions, checking for matches and detecting any discrepancies to ensure the accuracy of the work.

[0375] (Example 2)

[0376] 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".

[0377] Modern information processing systems are required not only to efficiently process user operational requests but also to appropriately understand and respond to user emotions. Conventional systems often provide uniform responses without considering user emotional states, potentially leading to decreased service satisfaction. Furthermore, further improvements are needed in resource management, request classification, and processing efficiency.

[0378] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0379] In this invention, the server includes information processing means for receiving and classifying operational requests, notification means for evaluating the content of requests and supplementing incomplete information, and confirmation means for checking the resource management status. This enables efficient resource management and rapid processing of requests while improving service satisfaction by analyzing the user's emotional state and providing an optimal response.

[0380] "Operational requests" refer to instructions from users requesting the execution of operations or services on the system.

[0381] "Information processing means" refers to a device or program that has the function of acquiring, analyzing, and classifying digital data.

[0382] "Notification means" refers to a device or program that has the function of generating and sending messages, alerts, or notifications to provide information to a user.

[0383] A "resource management device" is a device or program that has the function of checking the status of available hardware and software resources within a system and adjusting their use as needed.

[0384] A "verification tool" is a device or program that has the function of examining the status of resources and the details of requests, and determining their suitability and feasibility.

[0385] A "generation means" is a device or program for creating new information or responses and providing them to a user or system.

[0386] A "registration means" is a device or program that has the function of saving processed data or requests to a database or recording device within the system, in preparation for later reference or processing.

[0387] "Emotion recognition processing" is a technology that analyzes and identifies emotions from a user's text or voice.

[0388] "Service satisfaction" is an indicator that shows the level of satisfaction and evaluation users have of the services they have received.

[0389] This invention relates to an information processing system that efficiently processes operational requests from users while recognizing and appropriately responding to the user's emotional state. The entire system operates in cooperation with three parties: a server, a terminal, and the user.

[0390] The system's hardware configuration includes terminals for receiving user input and servers for processing operational requests. The servers are located in data centers or the cloud and contain an emotion recognition engine. This engine utilizes a generative AI model and employs natural language processing techniques to analyze text data from users. This analysis allows for the accurate identification of emotional states such as positive, negative, and neutral.

[0391] The software used includes a natural language processing library and sentiment analysis algorithms that leverage generative AI models. This allows the server to analyze text sent by the user in real time and generate responses tailored to the user's emotions. The generated responses are then sent to the user's device as customized messages.

[0392] As a concrete example, consider a case where a user submits feedback stating, "I am dissatisfied with the slowness of this service." In this case, the server recognizes the negative emotion of dissatisfaction using an emotion recognition engine and constructs a message promising a prompt response. An example of a prompt in this case would be, "Design a system that receives user feedback, automatically analyzes the emotion, and generates an appropriate response."

[0393] This system aims to improve customer satisfaction and optimize service quality by enabling responses that respond to user emotions.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] Users create and send operational requests using their terminals. These requests include instructions and feedback for the system and are sent from the terminal to the server as text data. The input at this time is the user's request text, which is sent to the server as output without any conversion.

[0397] Step 2:

[0398] The server parses the requests received from users. In this process, the received text data is used as input, and the content and type of request are initially determined. The data is then classified using information processing tools, and preparations are made for requests requiring processing to proceed to the next step. The output is the parsed data.

[0399] Step 3:

[0400] The server performs emotion recognition processing using a generative AI model. It takes analyzed text data as input and uses natural language processing techniques to classify the user's emotions as positive, negative, neutral, etc. During this process, data processing is performed to extract and evaluate keywords and contextual information. The output is data representing the user's emotional state.

[0401] Step 4:

[0402] The server generates a response based on the user's emotional state. Emotional state data is used as input, and a generative AI model and response generation algorithm are used to create the optimal response. In this step, emotion-appropriate template messages are utilized, and additional support options may be presented if necessary. The output is the generated custom message.

[0403] Step 5:

[0404] The server sends the generated response to the user's terminal. The generated custom message is used as input, and the message is delivered to the user in real time using a notification method. Specifically, it is received on the user's terminal as email or message notification. The output is the response message received by the user.

[0405] (Application Example 2)

[0406] 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."

[0407] Information processing systems are required to efficiently and appropriately handle operational requests from users, but the lack of response adjustments that take into account user emotions makes improving the user experience a challenge. Furthermore, there is a need to increase resource utilization efficiency while also providing flexible responses that consider emotional states.

[0408] 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.

[0409] In this invention, the server includes information processing means for receiving operational requests, analyzing their content, and classifying the type of request; emotion processing means for recognizing the user's emotional state using an information processing device and adjusting the response based on the emotion analysis results; and verification means for accessing a resource management system to check the current resource status and determine the feasibility of the request. This makes it possible to provide appropriate responses according to the user's emotions, enabling efficient use of resources and improved user satisfaction.

[0410] "Operational request" is a term that refers to the content of operations or requests that users make to the system.

[0411] "Information processing means" refers to a device or program that has the function of analyzing received data and classifying and processing it based on specific criteria.

[0412] A "notification means" is a device or program that has the function of informing the user of missing information or additional information that is needed.

[0413] "Emotional processing means" refers to a device or program that analyzes user input data to identify their emotional state and generates a response corresponding to that emotion.

[0414] A "resource management system" is a system that monitors the resource status within a system and manages appropriate resource allocation.

[0415] "Verification means" refers to a device or program that has the function of checking whether a requested operation is feasible based on specified criteria.

[0416] A "generation means" is a device or program that has the function of creating an optimal resource allocation and plan based on specific conditions and outputting a proposal.

[0417] "Registration means" refers to a device or program that has the function of saving confirmed data or requests as records within the system.

[0418] This invention is a system for automating operational requests and adjusting responses based on the user's emotional state. The system primarily consists of three components: a server, a terminal, and a user. The specific functions of each component are described below.

[0419] The server uses information processing tools to analyze and classify operational requests received from users. Natural language processing techniques are utilized during the analysis to precisely determine the content and nuances of the text data. Furthermore, as an emotion processing tool, it is equipped with an emotion recognition engine to understand the user's emotional state, identifying it as positive, negative, or neutral. This process can utilize open-source natural language processing libraries or custom AI models.

[0420] The terminal acts as the interface for the user, forwarding user-inputted requests to the server. This interface could be a smartphone application, a web browser, or a custom-built device.

[0421] If a user makes a request expressing dissatisfaction, such as feedback like "The recent payment system is extremely slow," the server immediately detects the negative emotion using emotion processing mechanisms and provides a swift and appropriate response. Specifically, it generates a response promising a quick resolution and sends an alert to the appropriate person if necessary.

[0422] An example of a prompt is: "Analyze the sentiment of the following user feedback and suggest a response: 'Recent payment services have been very slow.'" This prompt is an example input when using a generative AI model to create an appropriate response scenario.

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] The terminal receives an operational request from the user. The data entered is text information entered by the user into the terminal. This data may include requests and feedback regarding operations.

[0426] Step 2:

[0427] The terminal sends the operational request it receives to the server. The data sent is text information entered by the user. The server receives this data and parses it in the next step.

[0428] Step 3:

[0429] The server uses information processing tools to analyze the received request. The input is text information, and the server uses a natural language processing engine to classify the content and identify the type of request. The output is category information for the classified request.

[0430] Step 4:

[0431] The server uses emotion processing tools to analyze the emotional state of the received request. The input is the previously analyzed text information, and the emotion recognition engine identifies one of three emotional states: positive, negative, or neutral. The output is the result of the emotion analysis.

[0432] Step 5:

[0433] The server generates an appropriate response based on the sentiment analysis results. The input consists of emotional state and request category data, and a generative AI model is used to create a response scenario that harmonizes with the emotion. The output is the adjusted response message.

[0434] Step 6:

[0435] The server sends a response message to the terminal. The terminal displays the received response message to the user, completing the notification to the user. The final output is the response message displayed to the user.

[0436] Step 7:

[0437] The server detects negative emotions and, if necessary, sends an alert to the appropriate person if immediate action is required. The input is the result of emotion analysis, and negative emotions exceeding a certain threshold trigger an alert notification as output. The person receiving this alert can then intervene quickly.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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".

[0454] This invention is a system that automates the process from receiving operational requests to completing tasks, primarily in data centers. To achieve this, the following operations are performed between servers, terminals, and users.

[0455] Request reception and analysis

[0456] First, the user sends an operational request through their terminal. Upon receiving this request, the server uses its built-in text analysis engine to analyze its content and classify the type of request. This analysis result forms the basis for information processing in subsequent processing steps.

[0457] Validation and Notification

[0458] Next, the server verifies the request based on pre-configured criteria. Specifically, it checks whether all the necessary requirements and information are present. If any information is missing, the server automatically sends a notification to the user requesting additional input. This notification is typically sent via email or as a pop-up message on the device.

[0459] Resource status check and plan proposal

[0460] After confirming the request, the server accesses the resource management system and automatically checks the current resource status. If the request cannot be executed directly, the server generates an optimal resource allocation plan. For example, if additional memory is needed, it calculates how much resource should be allocated from which server, optimizing the overall system. This generated plan is proposed to the user, and after approval, the process proceeds to the next step.

[0461] System registration and work order creation

[0462] Approved requests are automatically registered by the server in the internal management system. Based on this registration information, work instructions, including necessary work procedures and resources, are automatically generated. The generated instructions are distributed to workers via terminals, ensuring that the actual work can begin quickly and reliably.

[0463] Work completion and report confirmation

[0464] Once the work is completed, the worker submits a completion report via their terminal. The server automatically analyzes this report and compares it with the original instructions to verify that the work was performed correctly. This process prevents errors and provides status updates to users and workers as needed.

[0465] By implementing this system, errors in information conversion will be significantly reduced, operational efficiency will be improved, and requests will be handled more quickly.

[0466] The following describes the processing flow.

[0467] Step 1:

[0468] Users enter and submit operational requests using their devices. These requests contain information about the necessary operations and resources.

[0469] Step 2:

[0470] The server receives a request from a user. To analyze the request's content, a natural language processing algorithm is used to classify the request type. This provides the basis for determining the appropriate processing route.

[0471] Step 3:

[0472] The server verifies the validity and completeness of the information based on the parsed request. If necessary information is missing based on the verification criteria, the server automatically sends a notification to the user requesting the missing information.

[0473] Step 4:

[0474] The server queries the data center's resource management system to check the current availability of the requested resource. This information is then used to determine whether the request can be executed.

[0475] Step 5:

[0476] If resources are insufficient, the server uses an algorithm to generate a resource allocation plan. The generated plan is sent to the user as a proposal for approval.

[0477] Step 6:

[0478] Once the request is confirmed and the plan is approved, the server automatically registers the request with its internal management system. This ensures that the work is recorded as a formal procedure.

[0479] Step 7:

[0480] The server automatically generates work instructions based on the work content. The generated instructions are distributed to workers via terminals, leading to the next action.

[0481] Step 8:

[0482] After completing the work, the worker submits a completion report to the system. The server compares this report with the instructions to verify that the work was performed as instructed. If a match is not found, the server requests the user and worker to reconfirm.

[0483] (Example 1)

[0484] 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."

[0485] Traditional data center operational request processing relied heavily on manual processes from information reception to implementation, resulting in inefficiencies. Furthermore, optimal resource allocation and accurate monitoring of work progress were difficult, potentially leading to information gaps and inconsistencies. There was a need to automate and optimize these processes to achieve rapid and accurate operational management.

[0486] 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.

[0487] In this invention, the server includes processing means for receiving and analyzing operational requests, communication means for verifying the content of the requests and requesting additional information, and verification means for checking resource status and determining feasibility. This enables efficient classification and management of requirements, optimized resource allocation, and rapid provision of work instructions.

[0488] An "operational request" is a request that includes instructions for operations or tasks that a user wants to perform in an information processing system.

[0489] A "text processing device" is a device that uses natural language processing technology to analyze and classify the content and intent of received information.

[0490] "Communication means" refers to a means of providing electronic notifications to inform users of the lack of information.

[0491] A "resource management device" is a device that monitors the status of computing resources used in an information processing system and ensures their efficient allocation.

[0492] "Verification methods" refer to means of verifying the information necessary to determine the feasibility of fulfilling a request.

[0493] A "generation method" is a means of automatically creating optimal plans and instructions based on received requests and circumstances.

[0494] "Verification means" refers to a method of comparing the information in the work completion report with the contents of the instruction sheet to confirm whether the work was performed accurately and to identify any problems.

[0495] "Natural language processing technology" refers to the technology used by computers to understand and appropriately process human language.

[0496] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate the optimal output based on user input.

[0497] A "prompt statement" is an instruction given to a generative AI model, and it is a specific input that leads to the desired output result.

[0498] This invention is a system that automates the process from receiving to executing operational requests in a data center. This system operates collaboratively among servers, terminals, and users.

[0499] First, the user sends an operational request to the server using a terminal. An example of a prompt message would be an instruction such as, "Optimize the database using a generative AI model." Upon receiving this request, the server uses a text processing unit and its built-in natural language processing technology to analyze the content of the request and classify it as a request type.

[0500] Next, the server accesses the resource management device based on the analysis results to check the current status of operational resources and determine feasibility. If necessary resources are insufficient, a generation mechanism for optimizing resource allocation is used, and an optimal resource allocation plan is automatically generated.

[0501] Furthermore, if the server needs to request any missing information from the user, it will send an email or a notification on the user's device using communication methods. After the user adds the necessary information to the request, the server will verify this information and register the request with the system's management device.

[0502] Furthermore, the server utilizes a generation mechanism to automatically generate work instructions based on requests and distribute them to workers via terminals. This allows workers to start their work quickly and efficiently.

[0503] After completing a task, the worker submits a completion report to the server via their terminal. The server compares this report with the instruction sheet using a verification mechanism to confirm that the work was completed accurately. This system significantly reduces information conversion errors, improves the efficiency of operational tasks, and enables faster response times to requests.

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] The user sends operational requests from the terminal to the server using prompt messages. For example, the user might input the instruction, "Optimize the database using a generative AI model." The server receives the prompt message and activates a text processing device. This device uses natural language processing technology to analyze the intent of the request and classify it as a database-related operation. Based on the input prompt message, the type of analyzed request is output.

[0507] Step 2:

[0508] The server verifies the request based on the analysis results. During the verification process, it checks whether the data contains the necessary information and determines that additional information is needed if any is missing. It receives the analysis results as input and outputs a list of missing information. The server then generates a message to notify the user of the missing information using a communication method based on this list. This message is sent to the user via email or as a notification on the terminal screen.

[0509] Step 3:

[0510] The server accesses the resource management device to check the current resource status. Here, it verifies whether there is sufficient memory and CPU space to execute the request. It receives resource information requiring verification as input and outputs a feasibility assessment result. If resources are insufficient, the server uses a generation tool to create an optimal resource allocation plan. This plan calculates how much resource should be borrowed from which server and presents it to the user as a proposal.

[0511] Step 4:

[0512] The user reviews and approves the resource allocation plan proposed by the server. Based on the approved plan, the server formally registers the request with the management device. This registration is used as information necessary for creating subsequent work orders. The server receives the proposed resource allocation plan as input and outputs a status indicating that registration with the management device is complete.

[0513] Step 5:

[0514] The server generates detailed work instructions using a generation mechanism based on approved requests. These instructions detail the work procedures and the resources to be used. The generated instructions are provided to the worker via a terminal. The server receives approved request information as input and outputs work instructions.

[0515] Step 6:

[0516] Workers complete tasks according to the instructions. After completion, they submit a work completion report to the server via their terminal. The server reads the report and performs intelligent analysis to compare it with the initial work instructions. It receives the work completion report data as input and outputs the results of the work completion confirmation. If necessary, the results are notified to the user and the worker.

[0517] (Application Example 1)

[0518] 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."

[0519] Operating automated machinery in factories is complex and often requires a large workforce. Furthermore, errors in information transmission and decreased work efficiency can occur during the process from receiving, analyzing, and executing various work instructions. There is a need to solve these problems and achieve faster and more accurate work processes.

[0520] 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.

[0521] In this invention, the server includes processing means for receiving operational requests, analyzing their content, and classifying the type of request; notification means for verifying the request content based on criteria and requesting additional information if necessary; and verification means for accessing a resource management entrustment mechanism to check the current resource status and determine the feasibility of the request. This enables centralized management of commands to automated work machines within the factory and rapid execution of work commands.

[0522] "Operational requirements" refer to specific instructions or requests related to operations at a factory or facility.

[0523] "Processing means" refers to devices or software that have the function of receiving, analyzing, and classifying input information.

[0524] "Notification means" refers to devices or methods that have the function of quickly transmitting missing information or necessary instructions to relevant parties.

[0525] A "resource management entrustment mechanism" refers to a system for understanding the current status of resources and equipment necessary for operations and for ensuring appropriate management.

[0526] "Verification means" refers to equipment or processes that have the function of determining whether a request is feasible.

[0527] "Generating means" refers to a system or method that has the function of automatically creating optimal plans or instructions based on specified conditions.

[0528] An "internal control system" refers to a system for properly storing and managing data related to business operations and resources.

[0529] An "automated work machine" refers to a machine or device that can perform specific tasks automatically in a factory or similar environment.

[0530] A "work request" refers to an instruction or request that specifies the details of the tasks or work that need to be performed.

[0531] "Operation commands" refer to specific instructions necessary for an automated machine to perform a designated task.

[0532] This invention is a system that centralizes information management and promotes automation in order to achieve efficient management and operation of automated machinery within a factory. The system mainly consists of a server, terminals, and users, and operates as follows.

[0533] The server receives operational requests from users and analyzes their content using a text analysis engine such as spaCy. The analyzed data is then linked with a resource management system, where it checks the current status of resources using tools like pandas and generates an appropriate plan. After confirming that the generated plan is appropriate, it requests approval from the user and transmits the instructions. The server also forwards the generated instructions to automated work machines, receives work completion reports, and verifies them against the instructions.

[0534] The terminal functions as an interface with the server, supporting user request input and notification reception. Using the terminal, users can send necessary additional information and receive notifications from the server in real time.

[0535] Users input and send requests via smartphones or tablets. For example, if a user sends a request from their smartphone to repair a stepper motor in a factory, the server analyzes it, determines the appropriate tools and necessary parts, and sends a work command to the most suitable robot. An example of a prompt message would be: "Request: Stepper motor replacement. What tools are needed? Generate an appropriate work plan while checking the current robot availability. After completing the work, please send a report to the server."

[0536] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0537] Step 1:

[0538] Users input operational requests into their devices via smartphones or tablets and send the requests. The information entered includes the type of request and the specific details of the request, which then becomes the data input to the server.

[0539] Step 2:

[0540] The server receives requests sent by users and analyzes their content using a text analysis engine such as spaCy. This analysis classifies the type of request and extracts its content. The results of this analysis become the output for proceeding to the next processing step.

[0541] Step 3:

[0542] Based on the analyzed request, the server accesses the resource management system and checks the current status of resources using pandas. This input data includes current equipment usage and inventory information for necessary parts, and the server uses this data to determine the feasibility of the request. As a result, it outputs which resources are available.

[0543] Step 4:

[0544] The server compares resource availability with the request details and generates an optimal resource allocation plan if any resources are lacking. It uses pandas to process existing data and generate proposed resource reallocations. The generated plan is then output for user approval.

[0545] Step 5:

[0546] The server transfers the generated resource allocation plan to the user's terminal and requests approval. The user can review the proposed plan on their terminal and request approval or modifications.

[0547] Step 6:

[0548] The server automatically generates and sends instructions to the automated work machine based on the resource allocation plan approved by the user. These instructions include the schedule and necessary operating procedures, and are then sent to the automated work machine.

[0549] Step 7:

[0550] When the automated work machine completes the specified task, the server receives a notification of completion. The server then compares the contents of the received completion report with the previously sent instruction sheet. A match or mismatch is then confirmed, and the results are notified to both the user and the automated work machine as needed.

[0551] 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.

[0552] This invention provides a system for automatically processing operational requests in data centers and online services, as well as recognizing the user's emotional state and adjusting responses accordingly. This system is implemented through the following process involving a server, a terminal, and a user.

[0553] Request and emotion recognition

[0554] The user sends an operational request via their terminal. Upon receiving the request, the server analyzes the text data using an emotion engine to identify the user's emotions. This emotion recognition is performed using natural language processing techniques to identify emotional states such as positive, negative, or neutral.

[0555] Emotion-based response regulation

[0556] When a response is required based on an emotional state, for example, if negative emotions are detected, the server provides pre-configured response patterns and additional support options to generate a response that is sensitive to the user's emotions. This information is then communicated to the user as a customized message.

[0557] Specific example

[0558] Suppose a user submits a request stating, "I am dissatisfied with the slowness of this service." The server not only processes the content of the request, but also recognizes the negative emotion of dissatisfaction through its sentiment engine. As a result, in addition to its normal processing routine, the server takes actions to encourage human intervention, such as sending a message promising a quick response and, if necessary, sending alerts to the appropriate personnel.

[0559] Alert to the person in charge

[0560] When the server detects a certain level of negative emotion using its emotion engine, it sends an alert to the relevant personnel. This feature enables a system in place to respond to situations requiring immediate human intervention. This approach contributes to improved customer satisfaction and the rapid resolution of complaints.

[0561] In this way, the present invention enhances the efficiency of automated processing of operational requests and provides a flexible system that can appropriately adjust responses based on user sentiment. This is expected to improve the user experience and, as a result, enhance the quality of service.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] Users enter and submit operational requests using their devices. These requests include specific information about the operations and resources required.

[0565] Step 2:

[0566] The server receives a request from the user. Simultaneously, it uses an emotion engine to analyze the text data of the request and identify the emotional state as positive, negative, or neutral.

[0567] Step 3:

[0568] The server analyzes the request content and classifies the request type using standard information processing methods. This determines the appropriate processing flow for each request.

[0569] Step 4:

[0570] The server verifies the validity of the request based on certain criteria. If necessary information is missing, the server automatically sends a notification to the user requesting that the missing information be provided.

[0571] Step 5:

[0572] The server accesses the data center's resource management system to check the availability of the requested resource. It verifies the resource status to determine the feasibility of the request.

[0573] Step 6:

[0574] If resources are insufficient, the server generates an optimal resource allocation plan and proposes it to the user. After user approval, the process proceeds to the next step.

[0575] Step 7:

[0576] If the emotion engine detects a negative emotion, the server generates a customized response message and notifies the user of a promise of prompt action.

[0577] Step 8:

[0578] If a certain level of negative emotion is detected, the server sends an alert to the relevant person, prompting immediate human intervention.

[0579] Step 9:

[0580] Once the request is confirmed and the necessary plan is approved, the server registers the request in its internal management system. This ensures that the work is recorded as a formal procedure.

[0581] Step 10:

[0582] The server automatically generates work instructions based on the work content. The generated instructions are distributed to workers via terminals, prompting them to take the next action.

[0583] Step 11:

[0584] After completing the work, the worker submits a completion report to the system. The server compares this report with the instructions, checking for matches and detecting any discrepancies to ensure the accuracy of the work.

[0585] (Example 2)

[0586] 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."

[0587] Modern information processing systems are required not only to efficiently process user operational requests but also to appropriately understand and respond to user emotions. Conventional systems often provide uniform responses without considering user emotional states, potentially leading to decreased service satisfaction. Furthermore, further improvements are needed in resource management, request classification, and processing efficiency.

[0588] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0589] In this invention, the server includes information processing means for receiving and classifying operational requests, notification means for evaluating the content of requests and supplementing incomplete information, and confirmation means for checking the resource management status. This enables efficient resource management and rapid processing of requests while improving service satisfaction by analyzing the user's emotional state and providing an optimal response.

[0590] "Operational requests" refer to instructions from users requesting the execution of operations or services on the system.

[0591] "Information processing means" refers to a device or program that has the function of acquiring, analyzing, and classifying digital data.

[0592] "Notification means" refers to a device or program that has the function of generating and sending messages, alerts, or notifications to provide information to a user.

[0593] A "resource management device" is a device or program that has the function of checking the status of available hardware and software resources within a system and adjusting their use as needed.

[0594] A "verification tool" is a device or program that has the function of examining the status of resources and the details of requests, and determining their suitability and feasibility.

[0595] A "generation means" is a device or program for creating new information or responses and providing them to a user or system.

[0596] A "registration means" is a device or program that has the function of saving processed data or requests to a database or recording device within the system, in preparation for later reference or processing.

[0597] "Emotion recognition processing" is a technology that analyzes and identifies emotions from a user's text or voice.

[0598] "Service satisfaction" is an indicator that shows the level of satisfaction and evaluation users have of the services they have received.

[0599] This invention relates to an information processing system that efficiently processes operational requests from users while recognizing and appropriately responding to the user's emotional state. The entire system operates in cooperation with three parties: a server, a terminal, and the user.

[0600] The system's hardware configuration includes terminals for receiving user input and servers for processing operational requests. The servers are located in data centers or the cloud and contain an emotion recognition engine. This engine utilizes a generative AI model and employs natural language processing techniques to analyze text data from users. This analysis allows for the accurate identification of emotional states such as positive, negative, and neutral.

[0601] The software used includes a natural language processing library and sentiment analysis algorithms that leverage generative AI models. This allows the server to analyze text sent by the user in real time and generate responses tailored to the user's emotions. The generated responses are then sent to the user's device as customized messages.

[0602] As a concrete example, consider a case where a user submits feedback stating, "I am dissatisfied with the slowness of this service." In this case, the server recognizes the negative emotion of dissatisfaction using an emotion recognition engine and constructs a message promising a prompt response. An example of a prompt in this case would be, "Design a system that receives user feedback, automatically analyzes the emotion, and generates an appropriate response."

[0603] This system aims to improve customer satisfaction and optimize service quality by enabling responses that respond to user emotions.

[0604] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0605] Step 1:

[0606] Users create and send operational requests using their terminals. These requests include instructions and feedback for the system and are sent from the terminal to the server as text data. The input at this time is the user's request text, which is sent to the server as output without any conversion.

[0607] Step 2:

[0608] The server parses the requests received from users. In this process, the received text data is used as input, and the content and type of request are initially determined. The data is then classified using information processing tools, and preparations are made for requests requiring processing to proceed to the next step. The output is the parsed data.

[0609] Step 3:

[0610] The server performs emotion recognition processing using a generative AI model. It takes analyzed text data as input and uses natural language processing techniques to classify the user's emotions as positive, negative, neutral, etc. During this process, data processing is performed to extract and evaluate keywords and contextual information. The output is data representing the user's emotional state.

[0611] Step 4:

[0612] The server generates a response based on the user's emotional state. Emotional state data is used as input, and a generative AI model and response generation algorithm are used to create the optimal response. In this step, emotion-appropriate template messages are utilized, and additional support options may be presented if necessary. The output is the generated custom message.

[0613] Step 5:

[0614] The server sends the generated response to the user's terminal. The generated custom message is used as input, and the message is delivered to the user in real time using a notification method. Specifically, it is received on the user's terminal as email or message notification. The output is the response message received by the user.

[0615] (Application Example 2)

[0616] 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."

[0617] Information processing systems are required to efficiently and appropriately handle operational requests from users, but the lack of response adjustments that take into account user emotions makes improving the user experience a challenge. Furthermore, there is a need to increase resource utilization efficiency while also providing flexible responses that consider emotional states.

[0618] 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.

[0619] In this invention, the server includes information processing means for receiving operational requests, analyzing their content, and classifying the type of request; emotion processing means for recognizing the user's emotional state using an information processing device and adjusting the response based on the emotion analysis results; and verification means for accessing a resource management system to check the current resource status and determine the feasibility of the request. This makes it possible to provide appropriate responses according to the user's emotions, enabling efficient use of resources and improved user satisfaction.

[0620] "Operational request" is a term that refers to the content of operations or requests that users make to the system.

[0621] "Information processing means" refers to a device or program that has the function of analyzing received data and classifying and processing it based on specific criteria.

[0622] A "notification means" is a device or program that has the function of informing the user of missing information or additional information that is needed.

[0623] "Emotional processing means" refers to a device or program that analyzes user input data to identify their emotional state and generates a response corresponding to that emotion.

[0624] A "resource management system" is a system that monitors the resource status within a system and manages appropriate resource allocation.

[0625] "Verification means" refers to a device or program that has the function of checking whether a requested operation is feasible based on specified criteria.

[0626] A "generation means" is a device or program that has the function of creating an optimal resource allocation and plan based on specific conditions and outputting a proposal.

[0627] "Registration means" refers to a device or program that has the function of saving confirmed data or requests as records within the system.

[0628] This invention is a system for automating operational requests and adjusting responses based on the user's emotional state. The system primarily consists of three components: a server, a terminal, and a user. The specific functions of each component are described below.

[0629] The server uses information processing tools to analyze and classify operational requests received from users. Natural language processing techniques are utilized during the analysis to precisely determine the content and nuances of the text data. Furthermore, as an emotion processing tool, it is equipped with an emotion recognition engine to understand the user's emotional state, identifying it as positive, negative, or neutral. This process can utilize open-source natural language processing libraries or custom AI models.

[0630] The terminal acts as the interface for the user, forwarding user-inputted requests to the server. This interface could be a smartphone application, a web browser, or a custom-built device.

[0631] If a user makes a request expressing dissatisfaction, such as feedback like "The recent payment system is extremely slow," the server immediately detects the negative emotion using emotion processing mechanisms and provides a swift and appropriate response. Specifically, it generates a response promising a quick resolution and sends an alert to the appropriate person if necessary.

[0632] An example of a prompt is: "Analyze the sentiment of the following user feedback and suggest a response: 'Recent payment services have been very slow.'" This prompt is an example input when using a generative AI model to create an appropriate response scenario.

[0633] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0634] Step 1:

[0635] The terminal receives an operational request from the user. The data entered is text information entered by the user into the terminal. This data may include requests and feedback regarding operations.

[0636] Step 2:

[0637] The terminal sends the operational request it receives to the server. The data sent is text information entered by the user. The server receives this data and parses it in the next step.

[0638] Step 3:

[0639] The server uses information processing tools to analyze the received request. The input is text information, and the server uses a natural language processing engine to classify the content and identify the type of request. The output is category information for the classified request.

[0640] Step 4:

[0641] The server uses emotion processing tools to analyze the emotional state of the received request. The input is the previously analyzed text information, and the emotion recognition engine identifies one of three emotional states: positive, negative, or neutral. The output is the result of the emotion analysis.

[0642] Step 5:

[0643] The server generates an appropriate response based on the sentiment analysis results. The input consists of emotional state and request category data, and a generative AI model is used to create a response scenario that harmonizes with the emotion. The output is the adjusted response message.

[0644] Step 6:

[0645] The server sends a response message to the terminal. The terminal displays the received response message to the user, completing the notification to the user. The final output is the response message displayed to the user.

[0646] Step 7:

[0647] The server detects negative emotions and, if necessary, sends an alert to the appropriate person if immediate action is required. The input is the result of emotion analysis, and negative emotions exceeding a certain threshold trigger an alert notification as output. The person receiving this alert can then intervene quickly.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] [Fourth Embodiment]

[0652] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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).

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] 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".

[0665] This invention is a system that automates the process from receiving operational requests to completing tasks, primarily in data centers. To achieve this, the following operations are performed between servers, terminals, and users.

[0666] Request reception and analysis

[0667] First, the user sends an operational request through their terminal. Upon receiving this request, the server uses its built-in text analysis engine to analyze its content and classify the type of request. This analysis result forms the basis for information processing in subsequent processing steps.

[0668] Validation and Notification

[0669] Next, the server verifies the request based on pre-configured criteria. Specifically, it checks whether all the necessary requirements and information are present. If any information is missing, the server automatically sends a notification to the user requesting additional input. This notification is typically sent via email or as a pop-up message on the device.

[0670] Resource status check and plan proposal

[0671] After confirming the request, the server accesses the resource management system and automatically checks the current resource status. If the request cannot be executed directly, the server generates an optimal resource allocation plan. For example, if additional memory is needed, it calculates how much resource should be allocated from which server, optimizing the overall system. This generated plan is proposed to the user, and after approval, the process proceeds to the next step.

[0672] System registration and work order creation

[0673] Approved requests are automatically registered by the server in the internal management system. Based on this registration information, work instructions, including necessary work procedures and resources, are automatically generated. The generated instructions are distributed to workers via terminals, ensuring that the actual work can begin quickly and reliably.

[0674] Work completion and report confirmation

[0675] Once the work is completed, the worker submits a completion report via their terminal. The server automatically analyzes this report and compares it with the original instructions to verify that the work was performed correctly. This process prevents errors and provides status updates to users and workers as needed.

[0676] By implementing this system, errors in information conversion will be significantly reduced, operational efficiency will be improved, and requests will be handled more quickly.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] Users enter and submit operational requests using their devices. These requests contain information about the necessary operations and resources.

[0680] Step 2:

[0681] The server receives a request from a user. To analyze the request's content, a natural language processing algorithm is used to classify the request type. This provides the basis for determining the appropriate processing route.

[0682] Step 3:

[0683] The server verifies the validity and completeness of the information based on the parsed request. If necessary information is missing based on the verification criteria, the server automatically sends a notification to the user requesting the missing information.

[0684] Step 4:

[0685] The server queries the data center's resource management system to check the current availability of the requested resource. This information is then used to determine whether the request can be executed.

[0686] Step 5:

[0687] If resources are insufficient, the server uses an algorithm to generate a resource allocation plan. The generated plan is sent to the user as a proposal for approval.

[0688] Step 6:

[0689] Once the request is confirmed and the plan is approved, the server automatically registers the request with its internal management system. This ensures that the work is recorded as a formal procedure.

[0690] Step 7:

[0691] The server automatically generates work instructions based on the work content. The generated instructions are distributed to workers via terminals, leading to the next action.

[0692] Step 8:

[0693] After completing the work, the worker submits a completion report to the system. The server compares this report with the instructions to verify that the work was performed as instructed. If a match is not found, the server requests the user and worker to reconfirm.

[0694] (Example 1)

[0695] 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".

[0696] Traditional data center operational request processing relied heavily on manual processes from information reception to implementation, resulting in inefficiencies. Furthermore, optimal resource allocation and accurate monitoring of work progress were difficult, potentially leading to information gaps and inconsistencies. There was a need to automate and optimize these processes to achieve rapid and accurate operational management.

[0697] 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.

[0698] In this invention, the server includes processing means for receiving and analyzing operational requests, communication means for verifying the content of the requests and requesting additional information, and verification means for checking resource status and determining feasibility. This enables efficient classification and management of requirements, optimized resource allocation, and rapid provision of work instructions.

[0699] An "operational request" is a request that includes instructions for operations or tasks that a user wants to perform in an information processing system.

[0700] A "text processing device" is a device that uses natural language processing technology to analyze and classify the content and intent of received information.

[0701] "Communication means" refers to a means of providing electronic notifications to inform users of the lack of information.

[0702] A "resource management device" is a device that monitors the status of computing resources used in an information processing system and ensures their efficient allocation.

[0703] "Verification methods" refer to means of verifying the information necessary to determine the feasibility of fulfilling a request.

[0704] A "generation method" is a means of automatically creating optimal plans and instructions based on received requests and circumstances.

[0705] "Verification means" refers to a method of comparing the information in the work completion report with the contents of the instruction sheet to confirm whether the work was performed accurately and to identify any problems.

[0706] "Natural language processing technology" refers to the technology used by computers to understand and appropriately process human language.

[0707] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate the optimal output based on user input.

[0708] A "prompt statement" is an instruction given to a generative AI model, and it is a specific input that leads to the desired output result.

[0709] This invention is a system that automates the process from receiving to executing operational requests in a data center. This system operates collaboratively among servers, terminals, and users.

[0710] First, the user sends an operational request to the server using a terminal. An example of a prompt message would be an instruction such as, "Optimize the database using a generative AI model." Upon receiving this request, the server uses a text processing unit and its built-in natural language processing technology to analyze the content of the request and classify it as a request type.

[0711] Next, the server accesses the resource management device based on the analysis results to check the current status of operational resources and determine feasibility. If necessary resources are insufficient, a generation mechanism for optimizing resource allocation is used, and an optimal resource allocation plan is automatically generated.

[0712] Furthermore, if the server needs to request any missing information from the user, it will send an email or a notification on the user's device using communication methods. After the user adds the necessary information to the request, the server will verify this information and register the request with the system's management device.

[0713] Furthermore, the server utilizes a generation mechanism to automatically generate work instructions based on requests and distribute them to workers via terminals. This allows workers to start their work quickly and efficiently.

[0714] After completing a task, the worker submits a completion report to the server via their terminal. The server compares this report with the instruction sheet using a verification mechanism to confirm that the work was completed accurately. This system significantly reduces information conversion errors, improves the efficiency of operational tasks, and enables faster response times to requests.

[0715] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0716] Step 1:

[0717] The user sends operational requests from the terminal to the server using prompt messages. For example, the user might input the instruction, "Optimize the database using a generative AI model." The server receives the prompt message and activates a text processing device. This device uses natural language processing technology to analyze the intent of the request and classify it as a database-related operation. Based on the input prompt message, the type of analyzed request is output.

[0718] Step 2:

[0719] The server verifies the request based on the analysis results. During the verification process, it checks whether the data contains the necessary information and determines that additional information is needed if any is missing. It receives the analysis results as input and outputs a list of missing information. The server then generates a message to notify the user of the missing information using a communication method based on this list. This message is sent to the user via email or as a notification on the terminal screen.

[0720] Step 3:

[0721] The server accesses the resource management device to check the current resource status. Here, it verifies whether there is sufficient memory and CPU space to execute the request. It receives resource information requiring verification as input and outputs a feasibility assessment result. If resources are insufficient, the server uses a generation tool to create an optimal resource allocation plan. This plan calculates how much resource should be borrowed from which server and presents it to the user as a proposal.

[0722] Step 4:

[0723] The user reviews and approves the resource allocation plan proposed by the server. Based on the approved plan, the server formally registers the request with the management device. This registration is used as information necessary for creating subsequent work orders. The server receives the proposed resource allocation plan as input and outputs a status indicating that registration with the management device is complete.

[0724] Step 5:

[0725] The server generates detailed work instructions using a generation mechanism based on approved requests. These instructions detail the work procedures and the resources to be used. The generated instructions are provided to the worker via a terminal. The server receives approved request information as input and outputs work instructions.

[0726] Step 6:

[0727] Workers complete tasks according to the instructions. After completion, they submit a work completion report to the server via their terminal. The server reads the report and performs intelligent analysis to compare it with the initial work instructions. It receives the work completion report data as input and outputs the results of the work completion confirmation. If necessary, the results are notified to the user and the worker.

[0728] (Application Example 1)

[0729] 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".

[0730] Operating automated machinery in factories is complex and often requires a large workforce. Furthermore, errors in information transmission and decreased work efficiency can occur during the process from receiving, analyzing, and executing various work instructions. There is a need to solve these problems and achieve faster and more accurate work processes.

[0731] 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.

[0732] In this invention, the server includes processing means for receiving operational requests, analyzing their content, and classifying the type of request; notification means for verifying the request content based on criteria and requesting additional information if necessary; and verification means for accessing a resource management entrustment mechanism to check the current resource status and determine the feasibility of the request. This enables centralized management of commands to automated work machines within the factory and rapid execution of work commands.

[0733] "Operational requirements" refer to specific instructions or requests related to operations at a factory or facility.

[0734] "Processing means" refers to devices or software that have the function of receiving, analyzing, and classifying input information.

[0735] "Notification means" refers to devices or methods that have the function of quickly transmitting missing information or necessary instructions to relevant parties.

[0736] A "resource management entrustment mechanism" refers to a system for understanding the current status of resources and equipment necessary for operations and for ensuring appropriate management.

[0737] "Verification means" refers to equipment or processes that have the function of determining whether a request is feasible.

[0738] "Generating means" refers to a system or method that has the function of automatically creating optimal plans or instructions based on specified conditions.

[0739] An "internal control system" refers to a system for properly storing and managing data related to business operations and resources.

[0740] An "automated work machine" refers to a machine or device that can perform specific tasks automatically in a factory or similar environment.

[0741] A "work request" refers to an instruction or request that specifies the details of the tasks or work that need to be performed.

[0742] "Operation commands" refer to specific instructions necessary for an automated machine to perform a designated task.

[0743] This invention is a system that centralizes information management and promotes automation in order to achieve efficient management and operation of automated machinery within a factory. The system mainly consists of a server, terminals, and users, and operates as follows.

[0744] The server receives operational requests from users and analyzes their content using a text analysis engine such as spaCy. The analyzed data is then linked with a resource management system, where it checks the current status of resources using tools like pandas and generates an appropriate plan. After confirming that the generated plan is appropriate, it requests approval from the user and transmits the instructions. The server also forwards the generated instructions to automated work machines, receives work completion reports, and verifies them against the instructions.

[0745] The terminal functions as an interface with the server, supporting user request input and notification reception. Using the terminal, users can send necessary additional information and receive notifications from the server in real time.

[0746] Users input and send requests via smartphones or tablets. For example, if a user sends a request from their smartphone to repair a stepper motor in a factory, the server analyzes it, determines the appropriate tools and necessary parts, and sends a work command to the most suitable robot. An example of a prompt message would be: "Request: Stepper motor replacement. What tools are needed? Generate an appropriate work plan while checking the current robot availability. After completing the work, please send a report to the server."

[0747] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0748] Step 1:

[0749] Users input operational requests into their devices via smartphones or tablets and send the requests. The information entered includes the type of request and the specific details of the request, which then becomes the data input to the server.

[0750] Step 2:

[0751] The server receives requests sent by users and analyzes their content using a text analysis engine such as spaCy. This analysis classifies the type of request and extracts its content. The results of this analysis become the output for proceeding to the next processing step.

[0752] Step 3:

[0753] Based on the analyzed request, the server accesses the resource management system and checks the current status of resources using pandas. This input data includes current equipment usage and inventory information for necessary parts, and the server uses this data to determine the feasibility of the request. As a result, it outputs which resources are available.

[0754] Step 4:

[0755] The server compares resource availability with the request details and generates an optimal resource allocation plan if any resources are lacking. It uses pandas to process existing data and generate proposed resource reallocations. The generated plan is then output for user approval.

[0756] Step 5:

[0757] The server transfers the generated resource allocation plan to the user's terminal and requests approval. The user can review the proposed plan on their terminal and request approval or modifications.

[0758] Step 6:

[0759] The server automatically generates and sends instructions to the automated work machine based on the resource allocation plan approved by the user. These instructions include the schedule and necessary operating procedures, and are then sent to the automated work machine.

[0760] Step 7:

[0761] When the automated work machine completes the specified task, the server receives a notification of completion. The server then compares the contents of the received completion report with the previously sent instruction sheet. A match or mismatch is then confirmed, and the results are notified to both the user and the automated work machine as needed.

[0762] 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.

[0763] This invention provides a system for automatically processing operational requests in data centers and online services, as well as recognizing the user's emotional state and adjusting responses accordingly. This system is implemented through the following process involving a server, a terminal, and a user.

[0764] Request and emotion recognition

[0765] The user sends an operational request via their terminal. Upon receiving the request, the server analyzes the text data using an emotion engine to identify the user's emotions. This emotion recognition is performed using natural language processing techniques to identify emotional states such as positive, negative, or neutral.

[0766] Emotion-based response regulation

[0767] When a response is required based on an emotional state, for example, if negative emotions are detected, the server provides pre-configured response patterns and additional support options to generate a response that is sensitive to the user's emotions. This information is then communicated to the user as a customized message.

[0768] Specific example

[0769] Suppose a user submits a request stating, "I am dissatisfied with the slowness of this service." The server not only processes the content of the request, but also recognizes the negative emotion of dissatisfaction through its sentiment engine. As a result, in addition to its normal processing routine, the server takes actions to encourage human intervention, such as sending a message promising a quick response and, if necessary, sending alerts to the appropriate personnel.

[0770] Alert to the person in charge

[0771] When the server detects a certain level of negative emotion using its emotion engine, it sends an alert to the relevant personnel. This feature enables a system in place to respond to situations requiring immediate human intervention. This approach contributes to improved customer satisfaction and the rapid resolution of complaints.

[0772] In this way, the present invention enhances the efficiency of automated processing of operational requests and provides a flexible system that can appropriately adjust responses based on user sentiment. This is expected to improve the user experience and, as a result, enhance the quality of service.

[0773] The following describes the processing flow.

[0774] Step 1:

[0775] Users enter and submit operational requests using their devices. These requests include specific information about the operations and resources required.

[0776] Step 2:

[0777] The server receives a request from the user. Simultaneously, it uses an emotion engine to analyze the text data of the request and identify the emotional state as positive, negative, or neutral.

[0778] Step 3:

[0779] The server analyzes the request content and classifies the request type using standard information processing methods. This determines the appropriate processing flow for each request.

[0780] Step 4:

[0781] The server verifies the validity of the request based on certain criteria. If necessary information is missing, the server automatically sends a notification to the user requesting that the missing information be provided.

[0782] Step 5:

[0783] The server accesses the data center's resource management system to check the availability of the requested resource. It verifies the resource status to determine the feasibility of the request.

[0784] Step 6:

[0785] If resources are insufficient, the server generates an optimal resource allocation plan and proposes it to the user. After user approval, the process proceeds to the next step.

[0786] Step 7:

[0787] If the emotion engine detects a negative emotion, the server generates a customized response message and notifies the user of a promise of prompt action.

[0788] Step 8:

[0789] If a certain level of negative emotion is detected, the server sends an alert to the relevant person, prompting immediate human intervention.

[0790] Step 9:

[0791] Once the request is confirmed and the necessary plan is approved, the server registers the request in its internal management system. This ensures that the work is recorded as a formal procedure.

[0792] Step 10:

[0793] The server automatically generates work instructions based on the work content. The generated instructions are distributed to workers via terminals, prompting them to take the next action.

[0794] Step 11:

[0795] After completing the work, the worker submits a completion report to the system. The server compares this report with the instructions, checking for matches and detecting any discrepancies to ensure the accuracy of the work.

[0796] (Example 2)

[0797] 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".

[0798] Modern information processing systems are required not only to efficiently process user operational requests but also to appropriately understand and respond to user emotions. Conventional systems often provide uniform responses without considering user emotional states, potentially leading to decreased service satisfaction. Furthermore, further improvements are needed in resource management, request classification, and processing efficiency.

[0799] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0800] In this invention, the server includes information processing means for receiving and classifying operational requests, notification means for evaluating the content of requests and supplementing incomplete information, and confirmation means for checking the resource management status. This enables efficient resource management and rapid processing of requests while improving service satisfaction by analyzing the user's emotional state and providing an optimal response.

[0801] "Operational requests" refer to instructions from users requesting the execution of operations or services on the system.

[0802] "Information processing means" refers to a device or program that has the function of acquiring, analyzing, and classifying digital data.

[0803] "Notification means" refers to a device or program that has the function of generating and sending messages, alerts, or notifications to provide information to a user.

[0804] A "resource management device" is a device or program that has the function of checking the status of available hardware and software resources within a system and adjusting their use as needed.

[0805] A "verification tool" is a device or program that has the function of examining the status of resources and the details of requests, and determining their suitability and feasibility.

[0806] A "generation means" is a device or program for creating new information or responses and providing them to a user or system.

[0807] A "registration means" is a device or program that has the function of saving processed data or requests to a database or recording device within the system, in preparation for later reference or processing.

[0808] "Emotion recognition processing" is a technology that analyzes and identifies emotions from a user's text or voice.

[0809] "Service satisfaction" is an indicator that shows the level of satisfaction and evaluation users have of the services they have received.

[0810] This invention relates to an information processing system that efficiently processes operational requests from users while recognizing and appropriately responding to the user's emotional state. The entire system operates in cooperation with three parties: a server, a terminal, and the user.

[0811] The system's hardware configuration includes terminals for receiving user input and servers for processing operational requests. The servers are located in data centers or the cloud and contain an emotion recognition engine. This engine utilizes a generative AI model and employs natural language processing techniques to analyze text data from users. This analysis allows for the accurate identification of emotional states such as positive, negative, and neutral.

[0812] The software used includes a natural language processing library and sentiment analysis algorithms that leverage generative AI models. This allows the server to analyze text sent by the user in real time and generate responses tailored to the user's emotions. The generated responses are then sent to the user's device as customized messages.

[0813] As a concrete example, consider a case where a user submits feedback stating, "I am dissatisfied with the slowness of this service." In this case, the server recognizes the negative emotion of dissatisfaction using an emotion recognition engine and constructs a message promising a prompt response. An example of a prompt in this case would be, "Design a system that receives user feedback, automatically analyzes the emotion, and generates an appropriate response."

[0814] This system aims to improve customer satisfaction and optimize service quality by enabling responses that respond to user emotions.

[0815] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0816] Step 1:

[0817] Users create and send operational requests using their terminals. These requests include instructions and feedback for the system and are sent from the terminal to the server as text data. The input at this time is the user's request text, which is sent to the server as output without any conversion.

[0818] Step 2:

[0819] The server parses the requests received from users. In this process, the received text data is used as input, and the content and type of request are initially determined. The data is then classified using information processing tools, and preparations are made for requests requiring processing to proceed to the next step. The output is the parsed data.

[0820] Step 3:

[0821] The server performs emotion recognition processing using a generative AI model. It takes analyzed text data as input and uses natural language processing techniques to classify the user's emotions as positive, negative, neutral, etc. During this process, data processing is performed to extract and evaluate keywords and contextual information. The output is data representing the user's emotional state.

[0822] Step 4:

[0823] The server generates a response based on the user's emotional state. Emotional state data is used as input, and a generative AI model and response generation algorithm are used to create the optimal response. In this step, emotion-appropriate template messages are utilized, and additional support options may be presented if necessary. The output is the generated custom message.

[0824] Step 5:

[0825] The server sends the generated response to the user's terminal. The generated custom message is used as input, and the message is delivered to the user in real time using a notification method. Specifically, it is received on the user's terminal as email or message notification. The output is the response message received by the user.

[0826] (Application Example 2)

[0827] 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".

[0828] Information processing systems are required to efficiently and appropriately handle operational requests from users, but the lack of response adjustments that take into account user emotions makes improving the user experience a challenge. Furthermore, there is a need to increase resource utilization efficiency while also providing flexible responses that consider emotional states.

[0829] 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.

[0830] In this invention, the server includes information processing means for receiving operational requests, analyzing their content, and classifying the type of request; emotion processing means for recognizing the user's emotional state using an information processing device and adjusting the response based on the emotion analysis results; and verification means for accessing a resource management system to check the current resource status and determine the feasibility of the request. This makes it possible to provide appropriate responses according to the user's emotions, enabling efficient use of resources and improved user satisfaction.

[0831] "Operational request" is a term that refers to the content of operations or requests that users make to the system.

[0832] "Information processing means" refers to a device or program that has the function of analyzing received data and classifying and processing it based on specific criteria.

[0833] A "notification means" is a device or program that has the function of informing the user of missing information or additional information that is needed.

[0834] "Emotional processing means" refers to a device or program that analyzes user input data to identify their emotional state and generates a response corresponding to that emotion.

[0835] A "resource management system" is a system that monitors the resource status within a system and manages appropriate resource allocation.

[0836] "Verification means" refers to a device or program that has the function of checking whether a requested operation is feasible based on specified criteria.

[0837] A "generation means" is a device or program that has the function of creating an optimal resource allocation and plan based on specific conditions and outputting a proposal.

[0838] "Registration means" refers to a device or program that has the function of saving confirmed data or requests as records within the system.

[0839] This invention is a system for automating operational requests and adjusting responses based on the user's emotional state. The system primarily consists of three components: a server, a terminal, and a user. The specific functions of each component are described below.

[0840] The server uses information processing tools to analyze and classify operational requests received from users. Natural language processing techniques are utilized during the analysis to precisely determine the content and nuances of the text data. Furthermore, as an emotion processing tool, it is equipped with an emotion recognition engine to understand the user's emotional state, identifying it as positive, negative, or neutral. This process can utilize open-source natural language processing libraries or custom AI models.

[0841] The terminal acts as the interface for the user, forwarding user-inputted requests to the server. This interface could be a smartphone application, a web browser, or a custom-built device.

[0842] If a user makes a request expressing dissatisfaction, such as feedback like "The recent payment system is extremely slow," the server immediately detects the negative emotion using emotion processing mechanisms and provides a swift and appropriate response. Specifically, it generates a response promising a quick resolution and sends an alert to the appropriate person if necessary.

[0843] An example of a prompt is: "Analyze the sentiment of the following user feedback and suggest a response: 'Recent payment services have been very slow.'" This prompt is an example input when using a generative AI model to create an appropriate response scenario.

[0844] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0845] Step 1:

[0846] The terminal receives an operational request from the user. The data entered is text information entered by the user into the terminal. This data may include requests and feedback regarding operations.

[0847] Step 2:

[0848] The terminal sends the operational request it receives to the server. The data sent is text information entered by the user. The server receives this data and parses it in the next step.

[0849] Step 3:

[0850] The server uses information processing tools to analyze the received request. The input is text information, and the server uses a natural language processing engine to classify the content and identify the type of request. The output is category information for the classified request.

[0851] Step 4:

[0852] The server uses emotion processing tools to analyze the emotional state of the received request. The input is the previously analyzed text information, and the emotion recognition engine identifies one of three emotional states: positive, negative, or neutral. The output is the result of the emotion analysis.

[0853] Step 5:

[0854] The server generates an appropriate response based on the sentiment analysis results. The input consists of emotional state and request category data, and a generative AI model is used to create a response scenario that harmonizes with the emotion. The output is the adjusted response message.

[0855] Step 6:

[0856] The server sends a response message to the terminal. The terminal displays the received response message to the user, completing the notification to the user. The final output is the response message displayed to the user.

[0857] Step 7:

[0858] The server detects negative emotions and, if necessary, sends an alert to the appropriate person if immediate action is required. The input is the result of emotion analysis, and negative emotions exceeding a certain threshold trigger an alert notification as output. The person receiving this alert can then intervene quickly.

[0859] 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.

[0860] 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.

[0861] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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."

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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 as being incorporated by reference.

[0880] The following is further disclosed regarding the embodiments described above.

[0881] (Claim 1)

[0882] An information processing means that receives operational requests, analyzes their contents, and classifies the type of request,

[0883] A notification system that reviews the request based on criteria and requests additional information if any is missing,

[0884] A means of accessing the resource management system to check the current resource status and determine the feasibility of the request,

[0885] A generation means that generates an optimal resource allocation plan for insufficient resources and notifies the proposal,

[0886] An information processing system that includes a registration mechanism for registering confirmed requests in an internal management system.

[0887] (Claim 2)

[0888] The system according to claim 1, comprising a generation means for automatically generating instruction sheets based on work content and distributing them to workers.

[0889] (Claim 3)

[0890] The system according to claim 1, comprising verification means for comparing the contents of a work completion report with instructions and detecting any discrepancies or problems.

[0891] "Example 1"

[0892] (Claim 1)

[0893] A processing means that receives an operational request, analyzes its content using a text processing device, and classifies its type,

[0894] A means of communication to verify the request based on criteria and request additional information if any is missing,

[0895] A means of verifying that the resource management device is accessed to check the current resource status and determine the feasibility of the request,

[0896] A generation means for generating an optimal resource allocation plan for deficient resources and notifying the proposal,

[0897] A registration means for registering confirmed requests with an internal management device,

[0898] A generation method for automatically generating and providing work instructions to workers,

[0899] A means for comparing information in the work completion report with the instruction sheet to confirm a match or identify a discrepancy,

[0900] A system including a text analysis device that uses natural language processing technology to analyze input and efficiently classify and manage requirements.

[0901] (Claim 2)

[0902] The system according to claim 1, which uses a generation AI model to automatically construct an optimal work plan based on requirements in the generation of work instructions.

[0903] (Claim 3)

[0904] The system according to claim 1, which inputs prompt statements into a generating AI model and proposes an optimized resource allocation in the generation of a resource allocation plan.

[0905] "Application Example 1"

[0906] (Claim 1)

[0907] A processing means that receives operational requests, analyzes their content, and classifies the type of request,

[0908] A notification mechanism to review the request based on criteria and request additional information if necessary,

[0909] A means of accessing the resource management entrustment mechanism to check the current resource status and determine the feasibility of the request,

[0910] A generation means for generating an optimal resource allocation plan for insufficient resources and notifying the proposal,

[0911] A registration means for registering confirmed requests with the internal management system,

[0912] A generation means for generating instruction documents based on work requests and transmitting them to an automated work machine,

[0913] A system that includes a verification mechanism to compare the contents of the work completion notice with the instruction document and to confirm whether they match or detect discrepancies.

[0914] (Claim 2)

[0915] The system according to claim 1, comprising processing means for centralized management of work requests.

[0916] (Claim 3)

[0917] The system according to claim 1, comprising means for comprehensively managing operation commands to an automated work machine.

[0918] "Example 2 of combining an emotion engine"

[0919] (Claim 1)

[0920] Information processing means that receives operational requests, analyzes their content, and classifies the type of request,

[0921] A notification mechanism to review the request based on criteria and request additional information if necessary,

[0922] A means of accessing the resource management device to check the current resource status and determine the feasibility of the request,

[0923] A generation means for generating an optimal supply plan for insufficient resources and notifying the proposal,

[0924] A registration means for registering confirmed requests with an internal management device,

[0925] A processing means that analyzes the received request and performs emotion recognition processing to identify the user's emotional state,

[0926] A generation means that adjusts the response according to the emotional state and notifies the user,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, comprising a generation means for automatically generating work instructions and distributing them to workers.

[0930] (Claim 3)

[0931] The system according to claim 1, comprising a verification means for comparing work result reports with work instructions and performing matching or mismatch detection.

[0932] "Application example 2 when combining with an emotional engine"

[0933] (Claim 1)

[0934] An information processing means that receives operational requests, analyzes their contents, and classifies the type of request,

[0935] A notification system that reviews the request based on criteria and requests additional information if any is missing,

[0936] An emotion processing means that recognizes the user's emotional state using an information processing device and adjusts the response based on the emotion analysis results,

[0937] A means of accessing the resource management system to check the current resource status and determine the feasibility of the request,

[0938] A generation means for generating an optimal resource allocation plan for insufficient resources and notifying the proposal,

[0939] A system that includes a registration mechanism for registering confirmed requests in an internal management system.

[0940] (Claim 2)

[0941] The system according to claim 1, comprising a generation means for automatically generating instruction sheets based on work content and distributing them to workers, and a notification means for analyzing emotions using user feedback and sending alerts to the person in charge as necessary.

[0942] (Claim 3)

[0943] The system according to claim 1, comprising a verification means for comparing the contents of a work completion report with an instruction sheet and detecting any discrepancies or problems, and a response means for adjusting the user response based on the sentiment analysis results. [Explanation of Symbols]

[0944] 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. An information processing means that receives operational requests, analyzes their contents, and classifies the type of request, A notification system that reviews the request based on criteria and requests additional information if any is missing, A means of accessing the resource management system to check the current resource status and determine the feasibility of the request, A generation means that generates an optimal resource allocation plan for insufficient resources and notifies the proposal, An information processing system that includes a registration mechanism for registering confirmed requests in an internal management system.

2. The system according to claim 1, comprising a generation means for automatically generating instruction sheets based on work content and distributing them to workers.

3. The system according to claim 1, comprising a verification means for comparing the contents of a work completion report with an instruction sheet and detecting any discrepancies or problems.

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