System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving and storing information from a user in an internal data structure; means for performing cost optimization based on the stored information; and means for providing analysis results and improvement suggestions to the user in real-time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, many companies have sought to reduce costs and increase efficiency, but analyzing data and generating improvement proposals typically requires a great deal of time and effort. Furthermore, manual data analysis is prone to errors and bias, making it difficult to perform highly accurate analysis. Furthermore, due to a lack of adaptability across industries, there is a lack of universally applicable systems. For this reason, there is a strong demand for the development of a system that can quickly and accurately optimize costs, detect waste, and provide efficient improvement proposals. [Means for solving the problem]

[0005] The present invention aims to solve the above problems by providing a system that includes a means for receiving information from users and storing it in an internal data structure, a means for optimizing costs based on the stored information, and a means for providing analysis results and improvement suggestions to users in real time. This system analyzes data provided by companies with high accuracy, compares revenues and expenses to detect waste, and generates specific improvement suggestions based on the detection results and provides them to users in real time, supporting rapid decision-making and action. This eliminates errors and biases that occur during manual data analysis, resulting in a highly versatile cost optimization system that can be applied regardless of industry.

[0006] "User" refers to any individual or legal entity that uses the System.

[0007] "Means for receiving and storing information in internal data structures" refers to the functions or processes for receiving user-provided data and maintaining it internally.

[0008] "Cost optimization tools" refers to methods and algorithms that analyze expenses and revenues based on received data, detect waste, and improve cost efficiency.

[0009] "Means for providing analysis results and improvement proposals in real time" refers to functions and systems for quickly communicating the results of cost optimization and specific improvement methods based on them to users.

[0010] "Company financial data" refers to information about a company's financial status, such as revenues, expenditures, budgets, and expenses.

[0011] "Waste detection" refers to the process of identifying excessive spending and unnecessary costs relative to revenue. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] The present invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial data provided by users and provides improvement proposals in real time.

[0034] The server is responsible for receiving data provided by the company and storing it in an internal data structure. The data is typically provided in a structured format, such as JSON. This data includes the company's financial data and other relevant business data. Once the server receives the data, it is immediately stored in an internal database.

[0035] The device performs analysis to optimize costs based on the stored data. Specifically, the device compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "waste."

[0036] For example, if a manufacturing company provides monthly expenditure and revenue data, the device analyzes the data and generates a result that reads, "Waste detected: Costs exceed 80% of revenues" if expenditures exceed 80% of revenues.

[0037] The device not only detects waste but also generates specific improvement suggestions, such as suggesting ways to automate manufacturing processes and improve efficiency, giving companies concrete ways to reduce costs and increase efficiency.

[0038] Users can receive these analysis results and improvement suggestions provided by the server and terminals in real time, which dramatically improves the speed of decision-making and enables them to take action immediately. For example, when a user logs into the system and uploads the latest data, the analysis results and improvement suggestions are displayed immediately. This process is carried out in near real time, minimizing user waiting time.

[0039] Through this series of processes, the present invention enables companies to efficiently optimize their costs and can be applied to a wide range of industries, particularly in addition to the manufacturing industry, such as the IT industry and the service industry.

[0040] The processing flow will be explained below.

[0041] Step 1:

[0042] The server receives the data provided by the company. The data is usually sent in JSON format, and the server temporarily stores it in an internal database. This receiving process prepares the data necessary for subsequent analysis.

[0043] Step 2:

[0044] The server stores the received data in internal data structures, which ensures the system maintains data consistency and ensures data integrity in subsequent processing. This step also checks the input data to filter out incomplete or invalid data.

[0045] Step 3:

[0046] The device retrieves the stored data and prepares it for analysis by examining its format and content and extracting cost and revenue information, which forms the basis for the analysis.

[0047] Step 4:

[0048] The device then starts a cost optimization analysis based on the data. It compares expenditures with revenues, and if expenditures are excessive compared to revenues, it detects them as wasteful costs. For example, if expenditures exceed 80% of revenues, it is deemed wasteful. The results of this analysis influence subsequent improvement proposals.

[0049] Step 5:

[0050] The device generates specific improvement suggestions when waste is detected, such as suggestions for improving efficiency by automating manufacturing processes or reallocating resources. This step may also take into account historical data and examples from other companies.

[0051] Step 6:

[0052] Users can access the system, upload the latest data, and receive analysis results and improvement suggestions in real time, allowing them to make quick decisions and develop action plans on-site.

[0053] Step 7:

[0054] The server stores the analysis results and improvement suggestions provided to the user and keeps them for future reference. This data can be reused at a later date to help continuously improve business performance.

[0055] These steps enable companies to quickly and accurately optimize costs, eliminate waste, and achieve efficient operations.

[0056] Example 1

[0057] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0058] It is difficult for companies to efficiently and quickly grasp their financial situation and propose specific improvement measures to reduce wasteful spending. With conventional systems, it takes time to analyze financial data, making it difficult to make decisions in real time, preventing efficient cost management. Furthermore, the improvement suggestions provided by conventional systems are limited to general instructions and lack specific implementation measures.

[0059] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0060] In this invention, the server includes means for receiving financial information in a structured format from a user and storing it in an internal database, means for analyzing expenses and revenues from the stored financial information and detecting wasteful expenses, means for generating specific improvement proposals when wasteful expenses are detected, and means for providing the analysis results and improvement proposals to the user in real time, thereby enabling a company to quickly grasp its financial situation and receive specific and feasible improvement measures in real time.

[0061] A "user" is an entity that provides information to the system and receives analysis results and improvement suggestions for that information.

[0062] "Structured" means that the data is organized according to certain rules and has a specific format, such as JSON.

[0063] "Financial information" refers to data that shows a company's financial status, such as its revenues and expenses.

[0064] An "internal database" is a data storage system that resides within the server and is used to store and manage received data.

[0065] "Storage" means storing the received data in an internal database so that it can be retrieved when needed.

[0066] "Analysis" is the process of performing calculations and evaluations based on stored data to arrive at a specific result.

[0067] "Wasteful spending" refers to a company's financial data where expenses exceed 80% of revenue and are deemed to be wasteful costs.

[0068] "Improvement proposals" refer to specific and feasible measures to reduce wasteful expenditures when they are detected.

[0069] "Real-time" means that information is processed and provided immediately, without delay.

[0070] The present invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial information provided by users and provides improvement proposals in real time.

[0071] Server Processing

[0072] The server receives financial information provided by the company. The information is usually provided in a structured format, such as JSON. This data includes the company's revenue and expenses. After receiving this data, the server quickly stores it in an internal database. Suitable database management systems are MySQL or PostgreSQL.

[0073] Terminal handling

[0074] The device performs the following analysis based on the financial information stored on the server. First, the device reads the latest financial data from the server and compares revenue with expenses. Next, if expenses exceed 80% of revenue, it detects this as "wasteful spending."

[0075] If the analysis identifies wasteful spending, the device will generate specific improvement suggestions, such as "Suggest automating the manufacturing process," giving companies concrete ways to reduce costs and improve efficiency.

[0076] User Action

[0077] Users can receive analysis results and improvement suggestions provided by the server and terminals in real time, which improves the speed of decision-making and enables them to take action immediately. Specifically, when a user logs into the system and uploads their latest financial information, the analysis results and improvement suggestions are displayed immediately.

[0078] Specific examples

[0079] For example, a manufacturing company provides the following monthly financial data:

[0080] Revenue: 1 million

[0081] Expenditure: 850000

[0082] The server receives and stores the data. The device reads the data and detects that expenses exceed 80% of income. As a result, a message like this is generated:

[0083] Waste detection: Costs exceed 80% of revenues

[0084] Improvement suggestion: Please introduce automation into the manufacturing process.

[0085] Users can log in to the system and receive the results in real time, allowing them to take appropriate measures immediately.

[0086] Prompt Sentence Examples

[0087] Examples of prompts for use with generative AI models include:

[0088] Given a company's monthly financial data (revenues, expenses), write a program to analyze it and generate an alert if the following conditions are met:

[0089] 1. If expenses exceed 80% of revenue, report them as "waste."

[0090] 2. Generate concrete improvement suggestions

[0091] Example: Proposing automation of a manufacturing process

[0092] Examples of data provided:

[0093] Revenue: 1 million

[0094] Expenditure: 850000

[0095] This system allows companies to quickly understand their financial situation and take concrete actions to optimize costs in real time.

[0096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0097] Step 1:

[0098] The server receives financial information from the user in a structured format (e.g. JSON format). As input, financial data is provided. The server parses this data to ensure it is in the correct format. After validation, it stores the data in an internal database. With this operation, the server securely stores the company's revenue and expenditure information. As output, it gets the data stored in the internal database.

[0099] Step 2:

[0100] The terminal reads the financial data stored in the internal database from the server. As input, it has the financial data retrieved from the internal database. Based on this data, the terminal analyzes expenses and revenues. It proceeds with the data reading process and stores the read data in memory. As output, it obtains a dataset of revenues and expenses that will serve as the basis for analysis.

[0101] Step 3:

[0102] The terminal compares the revenue and expenses it reads to determine whether expenses exceed 80% of revenue. The revenue and expense figures are used as input. The terminal performs an arithmetic operation and generates a warning if expenses exceed 80% of revenue. Specifically, it performs an operation to compare the revenue x 0.8 value with expenses. The output is a warning message: "Waste detected: costs exceed 80% of revenue."

[0103] Step 4:

[0104] If wasteful spending is detected, the device generates a specific improvement suggestion. The results of detecting wasteful spending are used as input. The device lists improvement suggestions and selects the most appropriate one. For example, it may suggest ways to consider automating the manufacturing process. An AI model could be used to generate the improvement suggestion. The output is a specific improvement suggestion such as "Introduce automation into the manufacturing process."

[0105] Step 5:

[0106] A user logs into the system and receives analysis results and improvement suggestions. User authentication information and the latest financial data are used as input. The server authenticates the user and provides the latest analysis results and improvement suggestions. Specific operations include a process where the results are displayed in real time on the user interface. As output, the user receives the analysis results and improvement suggestions displayed in real time.

[0107] This series of steps enables companies to quickly and accurately analyze their financial situation and receive specific improvement measures in real time.

[0108] (Application example 1)

[0109] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0110] In modern manufacturing factories, the operating efficiency of each robot and the state of material usage have a significant impact on a company's overall costs, but there is a lack of effective ways to monitor and optimize them in real time. As a result, many companies overlook wasteful spending and are unable to properly utilize efficient improvement proposals. There is also a need for a system that integrates financial data and on-site data to reduce costs. Therefore, there is a need for an effective system that can detect wasteful costs based on factory robot operating data and financial data and provide improvement proposals in real time.

[0111] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0112] In this invention, the server includes means for receiving information from a user and storing it in an internal data structure, means for optimizing costs based on the stored information, means for collecting and analyzing operational data of each robot in the factory and related financial data in real time, and means for providing the user with analysis results and improvement suggestions in real time, thereby enabling cost optimization that combines operational data and financial data of the factory.

[0113] A "user" is a user of the system, who provides information and receives results.

[0114] "Information" refers to data including company financial data and robot operation data.

[0115] "Internal data structures" refers to the databases and file systems used by the server to store and manage the information it receives.

[0116] "Cost optimization" is the process of reducing wasteful spending and maximizing profits.

[0117] "Stored Information" refers to information stored by the Server in internal data structures.

[0118] The "analysis results" are reports that are generated when cost optimization is performed, including recommendations for wasteful spending and efficiency improvements.

[0119] "Improvement proposals" are specific methods and action plans for reducing wasteful spending and improving work efficiency.

[0120] "Operation data for each robot in the factory" refers to data including the working time, input resources, output efficiency, etc. of robots in the manufacturing factory.

[0121] "Real-time" refers to the processing speed, which allows users to receive analysis results and improvement suggestions immediately after uploading data.

[0122] "Financial data" includes economic data such as a company's revenues, expenses, and profits.

[0123] This invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial data provided by users and operational data from each robot in a factory, and provides improvement proposals in real time.

[0124] System configuration

[0125] This system is mainly composed of a server and a terminal. The server receives and stores information, and the terminal is responsible for optimizing costs based on that information.

[0126] Server Roles

[0127] The server receives the information provided by the user and stores it in an internal data structure. The received information is structured data, such as JSON format, and includes financial data for the company and operational data for each robot in the factory. Once the server receives the data, it is immediately stored in an internal database.

[0128] Device Role

[0129] The device optimizes costs based on the information stored on the server. Specifically, the device compares revenue with expenditures to determine whether there is any waste. For example, if expenditures exceed 80% of revenue, it will detect this as "waste." In addition, the device generates specific improvement suggestions and provides them to the user. This allows companies to know specifically how to reduce costs and increase efficiency.

[0130] User operations

[0131] Users can receive analysis results and improvement suggestions provided by the server and terminal in real time. When a user logs in to the system and uploads the latest data, the analysis results and improvement suggestions are displayed immediately. This process is carried out in near real time, minimizing user waiting time.

[0132] Hardware and software used

[0133] Hardware: Servers, devices (smartphones, tablets), factory robots

[0134] Software: Python, requests library, JSON format data

[0135] The server is a Python program that uses the requests library to receive and store information from users, while the device also uses Python to analyze the stored data and generate analysis results and improvement suggestions.

[0136] Specific examples

[0137] For example, if a manufacturing plant provides monthly expenditure and revenue data, as well as robot operation data, the server receives this data and stores it in an internal data structure. Based on this data, if expenditures exceed 80% of revenue, the terminal generates a result such as "Waste detected: Costs exceed 80% of revenue" and presents specific improvement suggestions such as "Automation suggestion: Improve the operating efficiency of robots."

[0138] Prompt Sentence Examples

[0139] Based on the following financial and operational data, detect waste when expenses exceed 80% of revenue and generate improvement suggestions.

[0140] Financial data: Expenses 90000, Revenues 100000

[0141] Operation data: Robot 1 operation time 50 hours, Robot 2 operation time 60 hours

[0142] This allows users to take concrete actions in real time to reduce wasteful spending and improve efficiency.

[0143] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0144] Step 1:

[0145] A user logs in to the system.

[0146] Input: User credentials (e.g. username and password)

[0147] How it works: The server receives the user's authentication information and checks the credentials against a database.

[0148] Output: Notification of authentication success or failure

[0149] Step 2:

[0150] Users upload financial data and robot operation data.

[0151] Input: Financial data and robot operation data (e.g., JSON format)

[0152] How it works: The server receives these data and stores them in an internal data structure (database).

[0153] Output: Notification of successful data reception and storage

[0154] Step 3:

[0155] The server transmits the information stored on the device.

[0156] Input: Financial data and robot operation data stored in internal data structures

[0157] Operation: The server extracts data from the database and sends it to the device.

[0158] Output: Notification of completion of sending financial data and operational data

[0159] Step 4:

[0160] Cost optimization is performed based on the data received by the terminal.

[0161] Input: Received financial data and robot operation data

[0162] How it works: The device compares expenses and revenues and performs data calculations to detect wasteful spending (e.g., calculating the expense / revenue ratio).

[0163] Output: Wasteful spending detection results

[0164] Step 5:

[0165] The device generates specific improvement suggestions for wasteful spending.

[0166] Input: Wasteful spending detection results

[0167] How it works: The device uses the generative AI model to generate specific suggestions for improving efficiency (e.g., suggestions for improving the operating efficiency of a robot).

[0168] Output: Improvement suggestions

[0169] Step 6:

[0170] The generated analysis results and improvement suggestions are provided to the user in real time.

[0171] Input: Analysis results and improvement suggestions

[0172] How it works: The device displays the analysis results and recommendations to the user by sending a notification to the user's device and displaying the results on the application screen.

[0173] Output: User notification and results display

[0174] Step 7:

[0175] The user checks the analysis results and improvement suggestions and implements the countermeasures.

[0176] Input: Notify user and display results

[0177] Action: The user reviews the proposal and takes appropriate action (e.g., adjusting the manufacturing process or changing the operating parameters of a robot).

[0178] Output: Confirmation and feedback of the results

[0179] In this way, the system can generate and quickly provide cost optimization analysis results and improvement proposals in real time based on data input from the user.

[0180] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0181] The present invention relates to a system aimed at optimizing corporate costs, and in particular, a system that quickly and accurately detects cost waste based on financial data provided by users and provides improvement proposals in real time. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the present invention aims to adjust the method of providing analysis results and improvement proposals based on the user's emotional state, and further to use emotion data to improve the accuracy of future analyses.

[0182] The server is responsible for receiving financial data provided by the company and storing it in an internal data structure. The data is usually provided in a structured format such as JSON, and the server stores it in an internal database, preparing the data for subsequent analytical processing.

[0183] The device performs analysis for cost optimization based on the stored data. Specifically, the device compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "wasteful." The results of this analysis are presented to the user.

[0184] The device also uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes emotions from the user's facial expressions, tone of voice, input content, etc. and stores the results in a database. For example, if the user is angry, the emotion engine recognizes the emotion as "anger" and stores that state in the database.

[0185] The device then adjusts the way it presents analysis results and improvement suggestions based on the user's emotional state. For example, if the user is tired, it provides information in a concise and easy-to-understand format. On the other hand, if the user is interested, it provides detailed analysis results and multiple improvement suggestions. In this way, it provides information optimized for the user's emotional state.

[0186] Furthermore, emotional data can be used to improve the accuracy of future analyses and recommendations. For example, based on a user's emotional history, the system can learn what information should be provided at what timing and in what format to provide it most effectively, and provide this information for future sessions.

[0187] Users can receive analysis results and improvement suggestions in real time in a format optimized by the emotion engine, which reduces their stress level and improves the speed of decision-making.In addition, when users log in to the system and upload their latest data, they can receive optimal advice based on that data.

[0188] For example, if a manufacturing company provides monthly expenditure and revenue data, the system analyzes the data and generates a result such as "Waste detected: Costs exceed 80% of revenue" if expenditures exceed 80% of revenue. Furthermore, if the emotion engine recognizes that the user has previously experienced stress, concise and specific improvement suggestions are presented. For example, suggestions such as "Consider automating the manufacturing process to improve efficiency" are provided in real time.

[0189] Through this series of processes, the present invention enables efficient cost optimization for companies and flexible and effective information provision according to the user's emotional state, making it applicable to a wide range of industries, particularly in the manufacturing, IT, and service industries.

[0190] The processing flow will be explained below.

[0191] Step 1:

[0192] The server receives financial data provided by the company. The data is usually sent in JSON format, and the server temporarily stores it in an internal database. This receiving process prepares the data necessary for subsequent analysis.

[0193] Step 2:

[0194] The server stores the received data in internal data structures, which ensures the system maintains data consistency and ensures data integrity in subsequent processing. This step also checks the input data to filter out incomplete or invalid data.

[0195] Step 3:

[0196] The device retrieves the stored data and prepares it for analysis by examining its format and content and extracting cost and revenue information, which forms the basis for the analysis.

[0197] Step 4:

[0198] The device then begins a cost optimization analysis based on the data. It compares expenditures with revenues, and if expenditures are excessive compared to revenues, it detects them as wasteful costs. For example, if expenditures exceed 80% of revenues, it detects this as "waste." The results of this analysis influence subsequent improvement proposals.

[0199] Step 5:

[0200] The device generates specific improvement suggestions when waste is detected, such as suggestions for improving efficiency by automating manufacturing processes or reallocating resources. This step may also take into account historical data and examples from other companies.

[0201] Step 6:

[0202] The device activates an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, input content, etc. The results of this analysis are stored in a database.

[0203] Step 7:

[0204] The emotion engine recognizes the user's emotional state and adjusts how the device presents analysis results and improvement suggestions based on that state. For example, if the user is tired, it will provide information in a concise and easy-to-understand format.

[0205] Step 8:

[0206] Users access the system, upload the latest data, and receive analysis results and improvement suggestions optimized by the emotion engine in real time, which helps users make quick decisions.

[0207] Step 9:

[0208] The server stores the analysis results and improvement suggestions provided to the user and keeps them for future reference. This data can be reused at a later date to help continuously improve business performance.

[0209] These steps enable companies to quickly and accurately optimize costs, eliminate waste, and achieve efficient business operations. Furthermore, by providing flexible information according to the user's emotional state, the system reduces user stress and supports efficient decision-making.

[0210] Example 2

[0211] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0212] Traditional cost optimization systems focus on analyzing financial data and detecting wasteful spending, but do not take the user's emotional state into account, which can lead to lower user stress levels and reduced decision-making efficiency. Furthermore, they lack the ability to utilize user emotional data to improve analytical accuracy, limiting their long-term effectiveness.

[0213] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information from a user and storing it in an internal data structure, means for optimizing costs based on the stored information, means for recognizing the user's emotional state and adjusting the method for providing analysis results and improvement suggestions based on that state, means for analyzing data and detecting wasteful expenditures, means for storing the recognized emotional state in a database, means for improving the accuracy of future analyses using the accumulated emotional data, and means for providing the user with analysis results and improvement suggestions in real time in a format optimized according to their emotional state. This not only enables efficient cost optimization for companies but also enables flexible and effective information provision according to the user's emotional state.

[0214] "User" refers to a company or individual that uses this system.

[0215] "Information" refers generally to data provided by users, including but not limited to financial data.

[0216] "Internal data structure" refers to the database or storage system that stores and manages data efficiently and systematically within the server.

[0217] "Cost optimization" is the process of analyzing financial data and providing analysis and recommendations to reduce wasteful spending and maximize profits.

[0218] "Emotional state" refers to the user's mental and emotional state, including anger, stress, interest, and the like.

[0219] "Emotion Engine" refers to the combination of analytical algorithms and hardware / software for recognizing a user's emotional state.

[0220] "Analysis results" refers to results obtained based on the analysis of financial data, including identifying wasteful expenditures and proposing cost reductions.

[0221] "Improvement recommendations" refer to specific actions and strategies provided to improve a company's cost efficiency based on the analysis results.

[0222] "Real-time" refers to a process in which a response is returned immediately after the user provides information, without delay.

[0223] A "database" is a system for managing accumulated data, and includes formats such as SQL and NoSQL.

[0224] "Analysis" refers to the process of processing data to extract useful information.

[0225] "Expenditure" refers to the economic resources consumed by a business to produce and provide goods and services.

[0226] "Revenue" refers to the total income a company receives from selling goods and services.

[0227] "Waste" refers to expenses that are incurred but are not necessarily necessary.

[0228] "Accuracy" refers to an indicator of the accuracy of the analysis and suggestions.

[0229] "Optimized format" refers to a format of information presentation that is tailored based on the user's emotional state.

[0230] This invention relates to a system aimed at optimizing corporate costs. In particular, it quickly and accurately detects cost waste based on financial data provided by users and provides improvement suggestions in real time. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it aims to adjust the method of providing analysis results and improvement suggestions based on the user's emotional state, and furthermore, to use emotion data to improve the accuracy of future analyses.

[0231] The server receives financial data provided by the company and stores it in an internal data structure. Specific examples of databases used include MySQL and PostgreSQL. The data is usually provided in a structured format such as JSON and is then stored in the database. This prepares the data required for subsequent analytical processing.

[0232] The device then performs an analysis process for cost optimization based on the stored data. Specifically, it compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "waste." The results of this analysis are presented to the user. For example, if monthly expenditures reach 85% of revenue, the device generates the result "Waste detected: costs exceed 80% of revenue."

[0233] The device also uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes emotions from the user's facial expressions, tone of voice, input content, etc., and stores the results in a database. Specific examples of the technologies used include facial expression recognition algorithms and voice analysis algorithms. For example, if the user has a tired expression, the device recognizes the user's emotional state as "tired" and stores that information in the database.

[0234] The device then adjusts how it presents analysis results and improvement suggestions based on the user's perceived emotional state. If the user is tired, it will provide information in a concise, easy-to-understand format. If the user is interested, it will provide detailed analysis results and multiple improvement suggestions. For example, if the user is recognized as "tired," the device will display a concise suggestion: "Consider automating your manufacturing process to improve efficiency."

[0235] Additionally, the server accumulates emotional data and uses it to improve the accuracy of future analysis and recommendations. This is achieved by training generative AI models (e.g., neural networks for emotion recognition and recommendation generation) based on the accumulated emotional data, which allows for more accurate results in subsequent sessions.

[0236] Ultimately, users can log in to the system and upload their latest financial data to receive analysis results and improvement suggestions in real time. For example, when a user logs in and uploads a new month's financial data, the device immediately begins analysis and displays suggestions tailored to the user's emotional state (e.g., "Waste detection: Costs exceed 80% of revenue. Consider automating the manufacturing process.").

[0237] An example prompt is, "Detect waste in a company's financial data and present the results. Also, provide improvement suggestions in an appropriate format based on the user's current emotional state (e.g., tired, interested, etc.)."

[0238] Through this series of processes, the present invention efficiently optimizes corporate costs and enables flexible and effective information provision according to the user's emotional state, which is expected to reduce the user's stress level and improve the speed of decision-making.

[0239] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0240] Step 1:

[0241] The server receives financial data from the user. As input, a financial data file in JSON format is used. The server parses this data and stores it in a structured format (e.g., a MySQL database). Specifically, the server receives the provided "financial_data.json" file and stores the income and expense data in the database. As output, the server obtains the financial data stored in the database.

[0242] Step 2:

[0243] The terminal begins an analysis to optimize costs based on the stored financial data. Revenue and expenditure data retrieved from the database is used as input. The terminal compares this data and determines whether expenditure exceeds 80% of revenue. Specifically, the terminal retrieves the data "Revenue: 5 million yen, Expenses: 4.5 million yen" and calculates that expenditures are 90% of revenue. The output is the analysis result "Waste detected: Costs exceed 80% of revenue."

[0244] Step 3:

[0245] The device uses an emotion engine to recognize the user's emotional state. The inputs include the user's facial expressions, tone of voice, and input content. The emotion engine analyzes this data to determine the user's emotional state. Specifically, the device uses a camera and microphone to capture the user's video and audio, and the facial expression recognition algorithm determines that the user's face looks tired, and the voice analysis algorithm determines that the user's voice lacks vitality. The output, "User's emotional state: tired," is saved in the database.

[0246] Step 4:

[0247] The device adjusts the way it presents analysis results and improvement suggestions based on the recognized emotional state of the user. The analysis results and the user's emotional state data are used as input. Specifically, the device generates concise and easy-to-understand suggestions based on the data "User's emotional state: Fatigue." Suggestions such as "Waste detected: Costs exceed 80% of revenue. Consider automating the manufacturing process to improve efficiency" are generated. The adjusted analysis results and improvement suggestions are displayed to the user as output.

[0248] Step 5:

[0249] The server uses the accumulated emotion data to improve future analysis accuracy. Past emotion data and analysis results are used as input. The server learns from these data and updates the generative AI model. Specifically, the server uses a neural network to train the model based on the emotion data and analysis results, enabling it to make more accurate suggestions in the next session. The output is an updated generative AI model.

[0250] Step 6:

[0251] Users log in to the system and upload their latest financial data to receive real-time adjusted analysis results and improvement suggestions. A new financial data file is used as input. Specifically, when a user uploads a new "financial_data.json", the device immediately starts analysis and restarts the emotion engine. As output, the user is provided with real-time optimized analysis results and improvement suggestions.

[0252] (Application example 2)

[0253] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0254] Modern business activities require the rapid detection and optimization of cost waste. Furthermore, flexible responses based on the user's emotional state are also required for on-site workers and managers to receive appropriate improvement suggestions in real time. However, existing systems struggle to provide effective improvement suggestions that take the user's emotional state into account, and the suggestions often do not fit the user's situation. Therefore, there is a need for a system that supports decision-making while reducing user stress and providing effective cost optimization.

[0255] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0256] In this invention, the server includes a means for receiving information from a user and storing it in an internal data structure, a means for optimizing costs based on the stored information, a means for recognizing the user's emotional state, a means for adjusting the method for providing analysis results and improvement suggestions based on the stored emotional state, and a means for providing the analysis results and improvement suggestions to the user in real time. This enables the system to analyze a company's financial data to detect waste and provide information optimized according to the user's emotional state. Furthermore, by using prompt sentences based on a generative AI model, it is possible to provide effective improvement suggestions in real time and support decision-making that meets on-site requirements.

[0257] "User" refers to an individual or company that uses the system.

[0258] "Information" generally refers to data provided by users, including, specifically, company financial data and emotional state data.

[0259] "Internal data structure" refers to the format in which received information is stored and the data is organized and managed within the system for use in subsequent processing.

[0260] "Means" refers to methods or techniques for achieving a specific purpose.

[0261] "Cost optimization" refers to methods and techniques for optimizing a company's expenditures and reducing wasteful spending.

[0262] "User's emotional state" refers to the psychological state of the user using the system, and mainly includes states such as stress, interest, and fatigue.

[0263] "To recognize" refers to accurately grasping a specific piece of information or situation.

[0264] "Analysis results" refers to the conclusions and indicators obtained after data analysis.

[0265] "Improvement proposal" refers to proposing specific improvement methods or countermeasures for detected problems.

[0266] "Real-time" refers to processing or responding to information immediately after it is received or generated.

[0267] "Based on the stored emotional state" means that the next processing or response is based on the emotional data.

[0268] A "generative AI model" refers to algorithms or techniques that use artificial intelligence to generate data for a specific purpose.

[0269] A "prompt" refers to text in the form of instructions or questions that are input to a generative AI model.

[0270] The present invention is a system that analyzes financial data, detects wasteful spending, and makes improvement suggestions based on the results, with the aim of optimizing corporate costs.The present invention also recognizes the user's emotional state and provides improvement suggestions based on that emotion, thereby reducing the user's stress and supporting decision-making.

[0271] The system includes the following major components:

[0272] 1. Server:

[0273] The server receives the company's financial data from the user and stores it in an internal data structure. The data is usually provided in a structured format such as JSON, and the server stores it in an internal database (e.g., SQLite) and makes the stored data available to other components.

[0274] 2. Data analysis engine:

[0275] Cost optimization is performed based on the stored financial data. Specifically, expenses are compared with revenues to detect wasteful spending. This analysis engine is implemented using software such as Cost Analysis Engine. If expenses exceed the threshold for waste, they are detected as "waste."

[0276] 3. Emotion Recognition Engine:

[0277] Recognize the user's emotional state. An emotion recognition engine analyzes the user's facial expressions and tone of voice and stores the results in a database. Using software such as EmotionEngine, it can determine whether the user is stressed, calm, interested, etc. This emotional data is used to tailor how subsequent suggestions are presented.

[0278] 4. Proposal Generation Engine:

[0279] The engine adjusts the way it presents analysis results and improvement suggestions based on the user's perceived emotional state. For example, if the user is tired, it will provide information in a concise and easy-to-understand format. On the other hand, if the user is interested, it will provide detailed analysis results and multiple improvement suggestions. This suggestion generation engine works by using prompt sentences based on a generative AI model.

[0280] Specific use cases:

[0281] When a manufacturing company provides monthly expenditure and revenue data to the system, the system analyzes the data and generates a result saying, "Waste detected: Costs exceed 80% of revenue" if expenditures exceed 80% of revenue. The system also analyzes the user's emotional state, and if it recognizes that the user is prone to stress, for example, it will present a concise and specific improvement suggestion such as, "Consider automating the manufacturing process to improve efficiency."

[0282] Example prompt sentence:

[0283] If the user's facial expression and tone of voice indicate stress, generate concise and specific suggestions for cost reduction.

[0284] This allows users to receive effective improvement suggestions at the right time, reducing stress and enabling quick decision-making. In addition, the accumulation of emotional data will contribute to improving the accuracy of future suggestions.

[0285] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0286] Step 1:

[0287] The server receives the company's financial data from the user. The user provides the financial data (e.g., expenditure and revenue data) to the system, and the server receives it in a structured format, such as JSON. The entered data is stored in an internal data structure, which prepares the data needed for subsequent data analysis.

[0288] Step 2:

[0289] The server stores the received financial data in an internal database. Specifically, it parses the received JSON format data and stores it in a database such as SQLite, ensuring data persistence.

[0290] Step 3:

[0291] The data analysis engine optimizes costs based on stored financial data. In other words, it compares expenses with revenue and detects wasteful spending. For example, if expenses exceed 80% of revenue, it detects this as waste and saves it as the analysis result. The input is financial data, and the output is the waste detection results.

[0292] Step 4:

[0293] An emotion recognition engine recognizes the user's emotional state. The facial expressions and tone of voice input by the user to the system are analyzed by emotion recognition software such as EmotionEngine. The input is the user's facial expressions and voice data, and the output is the recognized emotional state. This emotional data is stored in a database.

[0294] Step 5:

[0295] The server generates improvement suggestions through a suggestion generation engine based on the recognized emotional state. Using a generative AI model, it generates suggestion sentences based on the prompt sentences and corresponding to the user's emotional state. The input is the emotional state and analysis results, and the output is individually tailored improvement suggestions.

[0296] Step 6:

[0297] The proposal generation engine provides the user with analysis results and improvement suggestions in real time. Specifically, it displays proposals in a format that corresponds to the user's emotional state. For example, if the user is feeling stressed, it displays concise and specific suggestions. The input is the adjusted improvement suggestions, and the output is the suggestions that are displayed to the user.

[0298] Step 7:

[0299] The user decides on the next action based on the provided improvement suggestions. By receiving feedback from the system, the user can make efficient cost optimization decisions.

[0300] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0301] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0302] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0303] [Second embodiment]

[0304] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0305] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0306] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0307] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0308] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0309] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0310] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0311] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0312] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0313] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0314] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0315] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0316] The present invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial data provided by users and provides improvement proposals in real time.

[0317] The server is responsible for receiving data provided by the company and storing it in an internal data structure. The data is typically provided in a structured format, such as JSON. This data includes the company's financial data and other relevant business data. Once the server receives the data, it is immediately stored in an internal database.

[0318] The device performs analysis to optimize costs based on the stored data. Specifically, the device compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "waste."

[0319] For example, if a manufacturing company provides monthly expenditure and revenue data, the device analyzes the data and generates a result that reads, "Waste detected: Costs exceed 80% of revenues" if expenditures exceed 80% of revenues.

[0320] The device not only detects waste but also generates specific improvement suggestions, such as suggesting ways to automate manufacturing processes and improve efficiency, giving companies concrete ways to reduce costs and increase efficiency.

[0321] Users can receive these analysis results and improvement suggestions provided by the server and terminals in real time, which dramatically improves the speed of decision-making and enables them to take action immediately. For example, when a user logs into the system and uploads the latest data, the analysis results and improvement suggestions are displayed immediately. This process is carried out in near real time, minimizing user waiting time.

[0322] Through this series of processes, the present invention enables companies to efficiently optimize their costs and can be applied to a wide range of industries, particularly in addition to the manufacturing industry, such as the IT industry and the service industry.

[0323] The processing flow will be explained below.

[0324] Step 1:

[0325] The server receives the data provided by the company. The data is usually sent in JSON format, and the server temporarily stores it in an internal database. This receiving process prepares the data necessary for subsequent analysis.

[0326] Step 2:

[0327] The server stores the received data in internal data structures, which ensures the system maintains data consistency and ensures data integrity in subsequent processing. This step also checks the input data to filter out incomplete or invalid data.

[0328] Step 3:

[0329] The device retrieves the stored data and prepares it for analysis by examining its format and content and extracting cost and revenue information, which forms the basis for the analysis.

[0330] Step 4:

[0331] The device then starts a cost optimization analysis based on the data. It compares expenditures with revenues, and if expenditures are excessive compared to revenues, it detects them as wasteful costs. For example, if expenditures exceed 80% of revenues, it is deemed wasteful. The results of this analysis influence subsequent improvement proposals.

[0332] Step 5:

[0333] The device generates specific improvement suggestions when waste is detected, such as suggestions for improving efficiency by automating manufacturing processes or reallocating resources. This step may also take into account historical data and examples from other companies.

[0334] Step 6:

[0335] Users can access the system, upload the latest data, and receive analysis results and improvement suggestions in real time, allowing them to make quick decisions and develop action plans on-site.

[0336] Step 7:

[0337] The server stores the analysis results and improvement suggestions provided to the user and keeps them for future reference. This data can be reused at a later date to help continuously improve business performance.

[0338] These steps enable companies to quickly and accurately optimize costs, eliminate waste, and achieve efficient operations.

[0339] Example 1

[0340] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0341] It is difficult for companies to efficiently and quickly grasp their financial situation and propose specific improvement measures to reduce wasteful spending. With conventional systems, it takes time to analyze financial data, making it difficult to make decisions in real time, preventing efficient cost management. Furthermore, the improvement suggestions provided by conventional systems are limited to general instructions and lack specific implementation measures.

[0342] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0343] In this invention, the server includes means for receiving financial information in a structured format from a user and storing it in an internal database, means for analyzing expenses and revenues from the stored financial information and detecting wasteful expenses, means for generating specific improvement proposals when wasteful expenses are detected, and means for providing the analysis results and improvement proposals to the user in real time, thereby enabling a company to quickly grasp its financial situation and receive specific and feasible improvement measures in real time.

[0344] A "user" is an entity that provides information to the system and receives analysis results and improvement suggestions for that information.

[0345] "Structured" means that the data is organized according to certain rules and has a specific format, such as JSON.

[0346] "Financial information" refers to data that shows a company's financial status, such as its revenues and expenses.

[0347] An "internal database" is a data storage system that resides within the server and is used to store and manage received data.

[0348] "Storage" means storing the received data in an internal database so that it can be retrieved when needed.

[0349] "Analysis" is the process of performing calculations and evaluations based on stored data to arrive at a specific result.

[0350] "Wasteful spending" refers to a company's financial data where expenses exceed 80% of revenue and are deemed to be wasteful costs.

[0351] "Improvement proposals" refer to specific and feasible measures to reduce wasteful expenditures when they are detected.

[0352] "Real-time" means that information is processed and provided immediately, without delay.

[0353] The present invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial information provided by users and provides improvement proposals in real time.

[0354] Server Processing

[0355] The server receives financial information provided by the company. The information is usually provided in a structured format, such as JSON. This data includes the company's revenue and expenses. After receiving this data, the server quickly stores it in an internal database. Suitable database management systems are MySQL or PostgreSQL.

[0356] Terminal handling

[0357] The device performs the following analysis based on the financial information stored on the server. First, the device reads the latest financial data from the server and compares revenue with expenses. Next, if expenses exceed 80% of revenue, it detects this as "wasteful spending."

[0358] If the analysis identifies wasteful spending, the device will generate specific improvement suggestions, such as "Suggest automating the manufacturing process," giving companies concrete ways to reduce costs and improve efficiency.

[0359] User Action

[0360] Users can receive analysis results and improvement suggestions provided by the server and terminals in real time, which improves the speed of decision-making and enables them to take action immediately. Specifically, when a user logs into the system and uploads their latest financial information, the analysis results and improvement suggestions are displayed immediately.

[0361] Specific examples

[0362] For example, a manufacturing company provides the following monthly financial data:

[0363] Revenue: 1 million

[0364] Expenditure: 850000

[0365] The server receives and stores the data. The device reads the data and detects that expenses exceed 80% of income. As a result, a message like this is generated:

[0366] Waste detection: Costs exceed 80% of revenues

[0367] Improvement suggestion: Please introduce automation into the manufacturing process.

[0368] Users can log in to the system and receive the results in real time, allowing them to take appropriate measures immediately.

[0369] Prompt Sentence Examples

[0370] Examples of prompts for use with generative AI models include:

[0371] Given a company's monthly financial data (revenues, expenses), write a program to analyze it and generate an alert if the following conditions are met:

[0372] 1. If expenses exceed 80% of revenue, report them as "waste."

[0373] 2. Generate concrete improvement suggestions

[0374] Example: Proposing automation of a manufacturing process

[0375] Examples of data provided:

[0376] Revenue: 1 million

[0377] Expenditure: 850000

[0378] This system allows companies to quickly understand their financial situation and take concrete actions to optimize costs in real time.

[0379] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0380] Step 1:

[0381] The server receives financial information from the user in a structured format (e.g. JSON format). As input, financial data is provided. The server parses this data to ensure it is in the correct format. After validation, it stores the data in an internal database. With this operation, the server securely stores the company's revenue and expenditure information. As output, it gets the data stored in the internal database.

[0382] Step 2:

[0383] The terminal reads the financial data stored in the internal database from the server. As input, it has the financial data retrieved from the internal database. Based on this data, the terminal analyzes expenses and revenues. It proceeds with the data reading process and stores the read data in memory. As output, it obtains a dataset of revenues and expenses that will serve as the basis for analysis.

[0384] Step 3:

[0385] The terminal compares the revenue and expenses it reads to determine whether expenses exceed 80% of revenue. The revenue and expense figures are used as input. The terminal performs an arithmetic operation and generates a warning if expenses exceed 80% of revenue. Specifically, it performs an operation to compare the revenue x 0.8 value with expenses. The output is a warning message: "Waste detected: costs exceed 80% of revenue."

[0386] Step 4:

[0387] If wasteful spending is detected, the device generates a specific improvement suggestion. The results of detecting wasteful spending are used as input. The device lists improvement suggestions and selects the most appropriate one. For example, it may suggest ways to consider automating the manufacturing process. An AI model could be used to generate the improvement suggestion. The output is a specific improvement suggestion such as "Introduce automation into the manufacturing process."

[0388] Step 5:

[0389] A user logs into the system and receives analysis results and improvement suggestions. User authentication information and the latest financial data are used as input. The server authenticates the user and provides the latest analysis results and improvement suggestions. Specific operations include a process where the results are displayed in real time on the user interface. As output, the user receives the analysis results and improvement suggestions displayed in real time.

[0390] This series of steps enables companies to quickly and accurately analyze their financial situation and receive specific improvement measures in real time.

[0391] (Application example 1)

[0392] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0393] In modern manufacturing factories, the operating efficiency of each robot and the state of material usage have a significant impact on a company's overall costs, but there is a lack of effective ways to monitor and optimize them in real time. As a result, many companies overlook wasteful spending and are unable to properly utilize efficient improvement proposals. There is also a need for a system that integrates financial data and on-site data to reduce costs. Therefore, there is a need for an effective system that can detect wasteful costs based on factory robot operating data and financial data and provide improvement proposals in real time.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0395] In this invention, the server includes means for receiving information from a user and storing it in an internal data structure, means for optimizing costs based on the stored information, means for collecting and analyzing operational data of each robot in the factory and related financial data in real time, and means for providing the user with analysis results and improvement suggestions in real time, thereby enabling cost optimization that combines operational data and financial data of the factory.

[0396] A "user" is a user of the system, who provides information and receives results.

[0397] "Information" refers to data including company financial data and robot operation data.

[0398] "Internal data structures" refers to the databases and file systems used by the server to store and manage the information it receives.

[0399] "Cost optimization" is the process of reducing wasteful spending and maximizing profits.

[0400] "Stored Information" refers to information stored by the Server in internal data structures.

[0401] The "analysis results" are reports that are generated when cost optimization is performed, including recommendations for wasteful spending and efficiency improvements.

[0402] "Improvement proposals" are specific methods and action plans for reducing wasteful spending and improving work efficiency.

[0403] "Operation data for each robot in the factory" refers to data including the working time, input resources, output efficiency, etc. of robots in the manufacturing factory.

[0404] "Real-time" refers to the processing speed, which allows users to receive analysis results and improvement suggestions immediately after uploading data.

[0405] "Financial data" includes economic data such as a company's revenues, expenses, and profits.

[0406] This invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial data provided by users and operational data from each robot in a factory, and provides improvement proposals in real time.

[0407] System configuration

[0408] This system is mainly composed of a server and a terminal. The server receives and stores information, and the terminal is responsible for optimizing costs based on that information.

[0409] Server Roles

[0410] The server receives the information provided by the user and stores it in an internal data structure. The received information is structured data, such as JSON format, and includes financial data for the company and operational data for each robot in the factory. Once the server receives the data, it is immediately stored in an internal database.

[0411] Device Role

[0412] The device optimizes costs based on the information stored on the server. Specifically, the device compares revenue with expenditures to determine whether there is any waste. For example, if expenditures exceed 80% of revenue, it will detect this as "waste." In addition, the device generates specific improvement suggestions and provides them to the user. This allows companies to know specifically how to reduce costs and increase efficiency.

[0413] User operations

[0414] Users can receive analysis results and improvement suggestions provided by the server and terminal in real time. When a user logs in to the system and uploads the latest data, the analysis results and improvement suggestions are displayed immediately. This process is carried out in near real time, minimizing user waiting time.

[0415] Hardware and software used

[0416] Hardware: Servers, devices (smartphones, tablets), factory robots

[0417] Software: Python, requests library, JSON format data

[0418] The server is a Python program that uses the requests library to receive and store information from users, while the device also uses Python to analyze the stored data and generate analysis results and improvement suggestions.

[0419] Specific examples

[0420] For example, if a manufacturing plant provides monthly expenditure and revenue data, as well as robot operation data, the server receives this data and stores it in an internal data structure. Based on this data, if expenditures exceed 80% of revenue, the terminal generates a result such as "Waste detected: Costs exceed 80% of revenue" and presents specific improvement suggestions such as "Automation suggestion: Improve the operating efficiency of robots."

[0421] Prompt Sentence Examples

[0422] Based on the following financial and operational data, detect waste when expenses exceed 80% of revenue and generate improvement suggestions.

[0423] Financial data: Expenses 90000, Revenues 100000

[0424] Operation data: Robot 1 operation time 50 hours, Robot 2 operation time 60 hours

[0425] This allows users to take concrete actions in real time to reduce wasteful spending and improve efficiency.

[0426] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0427] Step 1:

[0428] A user logs in to the system.

[0429] Input: User credentials (e.g. username and password)

[0430] How it works: The server receives the user's authentication information and checks the credentials against a database.

[0431] Output: Notification of authentication success or failure

[0432] Step 2:

[0433] Users upload financial data and robot operation data.

[0434] Input: Financial data and robot operation data (e.g., JSON format)

[0435] How it works: The server receives these data and stores them in an internal data structure (database).

[0436] Output: Notification of successful data reception and storage

[0437] Step 3:

[0438] The server transmits the information stored on the device.

[0439] Input: Financial data and robot operation data stored in internal data structures

[0440] Operation: The server extracts data from the database and sends it to the device.

[0441] Output: Notification of completion of sending financial data and operational data

[0442] Step 4:

[0443] Cost optimization is performed based on the data received by the terminal.

[0444] Input: Received financial data and robot operation data

[0445] How it works: The device compares expenses and revenues and performs data calculations to detect wasteful spending (e.g., calculating the expense / revenue ratio).

[0446] Output: Wasteful spending detection results

[0447] Step 5:

[0448] The device generates specific improvement suggestions for wasteful spending.

[0449] Input: Wasteful spending detection results

[0450] How it works: The device uses the generative AI model to generate specific suggestions for improving efficiency (e.g., suggestions for improving the operating efficiency of a robot).

[0451] Output: Improvement suggestions

[0452] Step 6:

[0453] The generated analysis results and improvement suggestions are provided to the user in real time.

[0454] Input: Analysis results and improvement suggestions

[0455] How it works: The device displays the analysis results and recommendations to the user by sending a notification to the user's device and displaying the results on the application screen.

[0456] Output: User notification and results display

[0457] Step 7:

[0458] The user checks the analysis results and improvement suggestions and implements the countermeasures.

[0459] Input: Notify user and display results

[0460] Action: The user reviews the proposal and takes appropriate action (e.g., adjusting the manufacturing process or changing the operating parameters of a robot).

[0461] Output: Confirmation and feedback of the results

[0462] In this way, the system can generate and quickly provide cost optimization analysis results and improvement proposals in real time based on data input from the user.

[0463] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0464] The present invention relates to a system aimed at optimizing corporate costs, and in particular, a system that quickly and accurately detects cost waste based on financial data provided by users and provides improvement proposals in real time. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the present invention aims to adjust the method of providing analysis results and improvement proposals based on the user's emotional state, and further to use emotion data to improve the accuracy of future analyses.

[0465] The server is responsible for receiving financial data provided by the company and storing it in an internal data structure. The data is usually provided in a structured format such as JSON, and the server stores it in an internal database, preparing the data for subsequent analytical processing.

[0466] The device performs analysis for cost optimization based on the stored data. Specifically, the device compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "wasteful." The results of this analysis are presented to the user.

[0467] The device also uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes emotions from the user's facial expressions, tone of voice, input content, etc. and stores the results in a database. For example, if the user is angry, the emotion engine recognizes the emotion as "anger" and stores that state in the database.

[0468] The device then adjusts the way it presents analysis results and improvement suggestions based on the user's emotional state. For example, if the user is tired, it provides information in a concise and easy-to-understand format. On the other hand, if the user is interested, it provides detailed analysis results and multiple improvement suggestions. In this way, it provides information optimized for the user's emotional state.

[0469] Furthermore, emotional data can be used to improve the accuracy of future analyses and recommendations. For example, based on a user's emotional history, the system can learn what information should be provided at what timing and in what format to provide it most effectively, and provide this information for future sessions.

[0470] Users can receive analysis results and improvement suggestions in real time in a format optimized by the emotion engine, which reduces their stress level and improves the speed of decision-making.In addition, when users log in to the system and upload their latest data, they can receive optimal advice based on that data.

[0471] For example, if a manufacturing company provides monthly expenditure and revenue data, the system analyzes the data and generates a result such as "Waste detected: Costs exceed 80% of revenue" if expenditures exceed 80% of revenue. Furthermore, if the emotion engine recognizes that the user has previously experienced stress, concise and specific improvement suggestions are presented. For example, suggestions such as "Consider automating the manufacturing process to improve efficiency" are provided in real time.

[0472] Through this series of processes, the present invention enables efficient cost optimization for companies and flexible and effective information provision according to the user's emotional state, making it applicable to a wide range of industries, particularly in the manufacturing, IT, and service industries.

[0473] The processing flow will be explained below.

[0474] Step 1:

[0475] The server receives financial data provided by the company. The data is usually sent in JSON format, and the server temporarily stores it in an internal database. This receiving process prepares the data necessary for subsequent analysis.

[0476] Step 2:

[0477] The server stores the received data in internal data structures, which ensures the system maintains data consistency and ensures data integrity in subsequent processing. This step also checks the input data to filter out incomplete or invalid data.

[0478] Step 3:

[0479] The device retrieves the stored data and prepares it for analysis by examining its format and content and extracting cost and revenue information, which forms the basis for the analysis.

[0480] Step 4:

[0481] The device then begins a cost optimization analysis based on the data. It compares expenditures with revenues, and if expenditures are excessive compared to revenues, it detects them as wasteful costs. For example, if expenditures exceed 80% of revenues, it detects this as "waste." The results of this analysis influence subsequent improvement proposals.

[0482] Step 5:

[0483] The device generates specific improvement suggestions when waste is detected, such as suggestions for improving efficiency by automating manufacturing processes or reallocating resources. This step may also take into account historical data and examples from other companies.

[0484] Step 6:

[0485] The device activates an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, input content, etc. The results of this analysis are stored in a database.

[0486] Step 7:

[0487] The emotion engine recognizes the user's emotional state and adjusts how the device presents analysis results and improvement suggestions based on that state. For example, if the user is tired, it will provide information in a concise and easy-to-understand format.

[0488] Step 8:

[0489] Users access the system, upload the latest data, and receive analysis results and improvement suggestions optimized by the emotion engine in real time, which helps users make quick decisions.

[0490] Step 9:

[0491] The server stores the analysis results and improvement suggestions provided to the user and keeps them for future reference. This data can be reused at a later date to help continuously improve business performance.

[0492] These steps enable companies to quickly and accurately optimize costs, eliminate waste, and achieve efficient business operations. Furthermore, by providing flexible information according to the user's emotional state, the system reduces user stress and supports efficient decision-making.

[0493] Example 2

[0494] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0495] Traditional cost optimization systems focus on analyzing financial data and detecting wasteful spending, but do not take the user's emotional state into account, which can lead to lower user stress levels and reduced decision-making efficiency. Furthermore, they lack the ability to utilize user emotional data to improve analytical accuracy, limiting their long-term effectiveness.

[0496] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information from a user and storing it in an internal data structure, means for optimizing costs based on the stored information, means for recognizing the user's emotional state and adjusting the method for providing analysis results and improvement suggestions based on that state, means for analyzing data and detecting wasteful expenditures, means for storing the recognized emotional state in a database, means for improving the accuracy of future analyses using the accumulated emotional data, and means for providing the user with analysis results and improvement suggestions in real time in a format optimized according to their emotional state. This not only enables efficient cost optimization for companies but also enables flexible and effective information provision according to the user's emotional state.

[0497] "User" refers to a company or individual that uses this system.

[0498] "Information" refers generally to data provided by users, including but not limited to financial data.

[0499] "Internal data structure" refers to the database or storage system that stores and manages data efficiently and systematically within the server.

[0500] "Cost optimization" is the process of analyzing financial data and providing analysis and recommendations to reduce wasteful spending and maximize profits.

[0501] "Emotional state" refers to the user's mental and emotional state, including anger, stress, interest, and the like.

[0502] "Emotion Engine" refers to the combination of analytical algorithms and hardware / software for recognizing a user's emotional state.

[0503] "Analysis results" refers to results obtained based on the analysis of financial data, including identifying wasteful expenditures and proposing cost reductions.

[0504] "Improvement recommendations" refer to specific actions and strategies provided to improve a company's cost efficiency based on the analysis results.

[0505] "Real-time" refers to a process in which a response is returned immediately after the user provides information, without delay.

[0506] A "database" is a system for managing accumulated data, and includes formats such as SQL and NoSQL.

[0507] "Analysis" refers to the process of processing data to extract useful information.

[0508] "Expenditure" refers to the economic resources consumed by a business to produce and provide goods and services.

[0509] "Revenue" refers to the total income a company receives from selling goods and services.

[0510] "Waste" refers to expenses that are incurred but are not necessarily necessary.

[0511] "Accuracy" refers to an indicator of the accuracy of the analysis and suggestions.

[0512] "Optimized format" refers to a format of information presentation that is tailored based on the user's emotional state.

[0513] This invention relates to a system aimed at optimizing corporate costs. In particular, it quickly and accurately detects cost waste based on financial data provided by users and provides improvement suggestions in real time. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it aims to adjust the method of providing analysis results and improvement suggestions based on the user's emotional state, and furthermore, to use emotion data to improve the accuracy of future analyses.

[0514] The server receives financial data provided by the company and stores it in an internal data structure. Specific examples of databases used include MySQL and PostgreSQL. The data is usually provided in a structured format such as JSON and is then stored in the database. This prepares the data required for subsequent analytical processing.

[0515] The device then performs an analysis process for cost optimization based on the stored data. Specifically, it compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "waste." The results of this analysis are presented to the user. For example, if monthly expenditures reach 85% of revenue, the device generates the result "Waste detected: costs exceed 80% of revenue."

[0516] The device also uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes emotions from the user's facial expressions, tone of voice, input content, etc., and stores the results in a database. Specific examples of the technologies used include facial expression recognition algorithms and voice analysis algorithms. For example, if the user has a tired expression, the device recognizes the user's emotional state as "tired" and stores that information in the database.

[0517] The device then adjusts how it presents analysis results and improvement suggestions based on the user's perceived emotional state. If the user is tired, it will provide information in a concise, easy-to-understand format. If the user is interested, it will provide detailed analysis results and multiple improvement suggestions. For example, if the user is recognized as "tired," the device will display a concise suggestion: "Consider automating your manufacturing process to improve efficiency."

[0518] Additionally, the server accumulates emotional data and uses it to improve the accuracy of future analysis and recommendations. This is achieved by training generative AI models (e.g., neural networks for emotion recognition and recommendation generation) based on the accumulated emotional data, which allows for more accurate results in subsequent sessions.

[0519] Ultimately, users can log in to the system and upload their latest financial data to receive analysis results and improvement suggestions in real time. For example, when a user logs in and uploads a new month's financial data, the device immediately begins analysis and displays suggestions tailored to the user's emotional state (e.g., "Waste detection: Costs exceed 80% of revenue. Consider automating the manufacturing process.").

[0520] An example prompt is, "Detect waste in a company's financial data and present the results. Also, provide improvement suggestions in an appropriate format based on the user's current emotional state (e.g., tired, interested, etc.)."

[0521] Through this series of processes, the present invention efficiently optimizes corporate costs and enables flexible and effective information provision according to the user's emotional state, which is expected to reduce the user's stress level and improve the speed of decision-making.

[0522] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0523] Step 1:

[0524] The server receives financial data from the user. As input, a financial data file in JSON format is used. The server parses this data and stores it in a structured format (e.g., a MySQL database). Specifically, the server receives the provided "financial_data.json" file and stores the income and expense data in the database. As output, the server obtains the financial data stored in the database.

[0525] Step 2:

[0526] The terminal begins an analysis to optimize costs based on the stored financial data. Revenue and expenditure data retrieved from the database is used as input. The terminal compares this data and determines whether expenditure exceeds 80% of revenue. Specifically, the terminal retrieves the data "Revenue: 5 million yen, Expenses: 4.5 million yen" and calculates that expenditures are 90% of revenue. The output is the analysis result "Waste detected: Costs exceed 80% of revenue."

[0527] Step 3:

[0528] The device uses an emotion engine to recognize the user's emotional state. The inputs include the user's facial expressions, tone of voice, and input content. The emotion engine analyzes this data to determine the user's emotional state. Specifically, the device uses a camera and microphone to capture the user's video and audio, and the facial expression recognition algorithm determines that the user's face looks tired, and the voice analysis algorithm determines that the user's voice lacks vitality. The output, "User's emotional state: tired," is saved in the database.

[0529] Step 4:

[0530] The device adjusts the way it presents analysis results and improvement suggestions based on the recognized emotional state of the user. The analysis results and the user's emotional state data are used as input. Specifically, the device generates concise and easy-to-understand suggestions based on the data "User's emotional state: Fatigue." Suggestions such as "Waste detected: Costs exceed 80% of revenue. Consider automating the manufacturing process to improve efficiency" are generated. The adjusted analysis results and improvement suggestions are displayed to the user as output.

[0531] Step 5:

[0532] The server uses the accumulated emotion data to improve future analysis accuracy. Past emotion data and analysis results are used as input. The server learns from these data and updates the generative AI model. Specifically, the server uses a neural network to train the model based on the emotion data and analysis results, enabling it to make more accurate suggestions in the next session. The output is an updated generative AI model.

[0533] Step 6:

[0534] Users log in to the system and upload their latest financial data to receive real-time adjusted analysis results and improvement suggestions. A new financial data file is used as input. Specifically, when a user uploads a new "financial_data.json", the device immediately starts analysis and restarts the emotion engine. As output, the user is provided with real-time optimized analysis results and improvement suggestions.

[0535] (Application example 2)

[0536] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0537] Modern business activities require the rapid detection and optimization of cost waste. Furthermore, flexible responses based on the user's emotional state are also required for on-site workers and managers to receive appropriate improvement suggestions in real time. However, existing systems struggle to provide effective improvement suggestions that take the user's emotional state into account, and the suggestions often do not fit the user's situation. Therefore, there is a need for a system that supports decision-making while reducing user stress and providing effective cost optimization.

[0538] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0539] In this invention, the server includes a means for receiving information from a user and storing it in an internal data structure, a means for optimizing costs based on the stored information, a means for recognizing the user's emotional state, a means for adjusting the method for providing analysis results and improvement suggestions based on the stored emotional state, and a means for providing the analysis results and improvement suggestions to the user in real time. This enables the system to analyze a company's financial data to detect waste and provide information optimized according to the user's emotional state. Furthermore, by using prompt sentences based on a generative AI model, it is possible to provide effective improvement suggestions in real time and support decision-making that meets on-site requirements.

[0540] "User" refers to an individual or company that uses the system.

[0541] "Information" generally refers to data provided by users, including, specifically, company financial data and emotional state data.

[0542] "Internal data structure" refers to the format in which received information is stored and the data is organized and managed within the system for use in subsequent processing.

[0543] "Means" refers to methods or techniques for achieving a specific purpose.

[0544] "Cost optimization" refers to methods and techniques for optimizing a company's expenditures and reducing wasteful spending.

[0545] "User's emotional state" refers to the psychological state of the user using the system, and mainly includes states such as stress, interest, and fatigue.

[0546] "To recognize" refers to accurately grasping a specific piece of information or situation.

[0547] "Analysis results" refers to the conclusions and indicators obtained after data analysis.

[0548] "Improvement proposal" refers to proposing specific improvement methods or countermeasures for detected problems.

[0549] "Real-time" refers to processing or responding to information immediately after it is received or generated.

[0550] "Based on the stored emotional state" means that the next processing or response is based on the emotional data.

[0551] A "generative AI model" refers to algorithms or techniques that use artificial intelligence to generate data for a specific purpose.

[0552] A "prompt" refers to text in the form of instructions or questions that are input to a generative AI model.

[0553] The present invention is a system that analyzes financial data, detects wasteful spending, and makes improvement suggestions based on the results, with the aim of optimizing corporate costs.The present invention also recognizes the user's emotional state and provides improvement suggestions based on that emotion, thereby reducing the user's stress and supporting decision-making.

[0554] The system includes the following major components:

[0555] 1. Server:

[0556] The server receives the company's financial data from the user and stores it in an internal data structure. The data is usually provided in a structured format such as JSON, and the server stores it in an internal database (e.g., SQLite) and makes the stored data available to other components.

[0557] 2. Data analysis engine:

[0558] Cost optimization is performed based on the stored financial data. Specifically, expenses are compared with revenues to detect wasteful spending. This analysis engine is implemented using software such as Cost Analysis Engine. If expenses exceed the threshold for waste, they are detected as "waste."

[0559] 3. Emotion Recognition Engine:

[0560] Recognize the user's emotional state. An emotion recognition engine analyzes the user's facial expressions and tone of voice and stores the results in a database. Using software such as EmotionEngine, it can determine whether the user is stressed, calm, interested, etc. This emotional data is used to tailor how subsequent suggestions are presented.

[0561] 4. Proposal Generation Engine:

[0562] The engine adjusts the way it presents analysis results and improvement suggestions based on the user's perceived emotional state. For example, if the user is tired, it will provide information in a concise and easy-to-understand format. On the other hand, if the user is interested, it will provide detailed analysis results and multiple improvement suggestions. This suggestion generation engine works by using prompt sentences based on a generative AI model.

[0563] Specific use cases:

[0564] When a manufacturing company provides monthly expenditure and revenue data to the system, the system analyzes the data and generates a result saying, "Waste detected: Costs exceed 80% of revenue" if expenditures exceed 80% of revenue. The system also analyzes the user's emotional state, and if it recognizes that the user is prone to stress, for example, it will present a concise and specific improvement suggestion such as, "Consider automating the manufacturing process to improve efficiency."

[0565] Example prompt sentence:

[0566] If the user's facial expression and tone of voice indicate stress, generate concise and specific suggestions for cost reduction.

[0567] This allows users to receive effective improvement suggestions at the right time, reducing stress and enabling quick decision-making. In addition, the accumulation of emotional data will contribute to improving the accuracy of future suggestions.

[0568] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0569] Step 1:

[0570] The server receives the company's financial data from the user. The user provides the financial data (e.g., expenditure and revenue data) to the system, and the server receives it in a structured format, such as JSON. The entered data is stored in an internal data structure, which prepares the data needed for subsequent data analysis.

[0571] Step 2:

[0572] The server stores the received financial data in an internal database. Specifically, it parses the received JSON format data and stores it in a database such as SQLite, ensuring data persistence.

[0573] Step 3:

[0574] The data analysis engine optimizes costs based on stored financial data. In other words, it compares expenses with revenue and detects wasteful spending. For example, if expenses exceed 80% of revenue, it detects this as waste and saves it as the analysis result. The input is financial data, and the output is the waste detection results.

[0575] Step 4:

[0576] An emotion recognition engine recognizes the user's emotional state. The facial expressions and tone of voice input by the user to the system are analyzed by emotion recognition software such as EmotionEngine. The input is the user's facial expressions and voice data, and the output is the recognized emotional state. This emotional data is stored in a database.

[0577] Step 5:

[0578] The server generates improvement suggestions through a suggestion generation engine based on the recognized emotional state. Using a generative AI model, it generates suggestion sentences based on the prompt sentences and corresponding to the user's emotional state. The input is the emotional state and analysis results, and the output is individually tailored improvement suggestions.

[0579] Step 6:

[0580] The proposal generation engine provides the user with analysis results and improvement suggestions in real time. Specifically, it displays proposals in a format that corresponds to the user's emotional state. For example, if the user is feeling stressed, it displays concise and specific suggestions. The input is the adjusted improvement suggestions, and the output is the suggestions that are displayed to the user.

[0581] Step 7:

[0582] The user decides on the next action based on the provided improvement suggestions. By receiving feedback from the system, the user can make efficient cost optimization decisions.

[0583] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0584] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0585] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0586] [Third embodiment]

[0587] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0588] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0589] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0590] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0591] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0592] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0593] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0594] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0595] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0596] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0597] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0598] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0599] The present invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial data provided by users and provides improvement proposals in real time.

[0600] The server is responsible for receiving data provided by the company and storing it in an internal data structure. The data is typically provided in a structured format, such as JSON. This data includes the company's financial data and other relevant business data. Once the server receives the data, it is immediately stored in an internal database.

[0601] The device performs analysis to optimize costs based on the stored data. Specifically, the device compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "waste."

[0602] For example, if a manufacturing company provides monthly expenditure and revenue data, the device analyzes the data and generates a result that reads, "Waste detected: Costs exceed 80% of revenues" if expenditures exceed 80% of revenues.

[0603] The device not only detects waste but also generates specific improvement suggestions, such as suggesting ways to automate manufacturing processes and improve efficiency, giving companies concrete ways to reduce costs and increase efficiency.

[0604] Users can receive these analysis results and improvement suggestions provided by the server and terminals in real time, which dramatically improves the speed of decision-making and enables them to take action immediately. For example, when a user logs into the system and uploads the latest data, the analysis results and improvement suggestions are displayed immediately. This process is carried out in near real time, minimizing user waiting time.

[0605] Through this series of processes, the present invention enables companies to efficiently optimize their costs and can be applied to a wide range of industries, particularly in addition to the manufacturing industry, such as the IT industry and the service industry.

[0606] The processing flow will be explained below.

[0607] Step 1:

[0608] The server receives the data provided by the company. The data is usually sent in JSON format, and the server temporarily stores it in an internal database. This receiving process prepares the data necessary for subsequent analysis.

[0609] Step 2:

[0610] The server stores the received data in internal data structures, which ensures the system maintains data consistency and ensures data integrity in subsequent processing. This step also checks the input data to filter out incomplete or invalid data.

[0611] Step 3:

[0612] The device retrieves the stored data and prepares it for analysis by examining its format and content and extracting cost and revenue information, which forms the basis for the analysis.

[0613] Step 4:

[0614] The device then starts a cost optimization analysis based on the data. It compares expenditures with revenues, and if expenditures are excessive compared to revenues, it detects them as wasteful costs. For example, if expenditures exceed 80% of revenues, it is deemed wasteful. The results of this analysis influence subsequent improvement proposals.

[0615] Step 5:

[0616] The device generates specific improvement suggestions when waste is detected, such as suggestions for improving efficiency by automating manufacturing processes or reallocating resources. This step may also take into account historical data and examples from other companies.

[0617] Step 6:

[0618] Users can access the system, upload the latest data, and receive analysis results and improvement suggestions in real time, allowing them to make quick decisions and develop action plans on-site.

[0619] Step 7:

[0620] The server stores the analysis results and improvement suggestions provided to the user and keeps them for future reference. This data can be reused at a later date to help continuously improve business performance.

[0621] These steps enable companies to quickly and accurately optimize costs, eliminate waste, and achieve efficient operations.

[0622] Example 1

[0623] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0624] It is difficult for companies to efficiently and quickly grasp their financial situation and propose specific improvement measures to reduce wasteful spending. With conventional systems, it takes time to analyze financial data, making it difficult to make decisions in real time, preventing efficient cost management. Furthermore, the improvement suggestions provided by conventional systems are limited to general instructions and lack specific implementation measures.

[0625] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0626] In this invention, the server includes means for receiving financial information in a structured format from a user and storing it in an internal database, means for analyzing expenses and revenues from the stored financial information and detecting wasteful expenses, means for generating specific improvement proposals when wasteful expenses are detected, and means for providing the analysis results and improvement proposals to the user in real time, thereby enabling a company to quickly grasp its financial situation and receive specific and feasible improvement measures in real time.

[0627] A "user" is an entity that provides information to the system and receives analysis results and improvement suggestions for that information.

[0628] "Structured" means that the data is organized according to certain rules and has a specific format, such as JSON.

[0629] "Financial information" refers to data that shows a company's financial status, such as its revenues and expenses.

[0630] An "internal database" is a data storage system that resides within the server and is used to store and manage received data.

[0631] "Storage" means storing the received data in an internal database so that it can be retrieved when needed.

[0632] "Analysis" is the process of performing calculations and evaluations based on stored data to arrive at a specific result.

[0633] "Wasteful spending" refers to a company's financial data where expenses exceed 80% of revenue and are deemed to be wasteful costs.

[0634] "Improvement proposals" refer to specific and feasible measures to reduce wasteful expenditures when they are detected.

[0635] "Real-time" means that information is processed and provided immediately, without delay.

[0636] The present invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial information provided by users and provides improvement proposals in real time.

[0637] Server Processing

[0638] The server receives financial information provided by the company. The information is usually provided in a structured format, such as JSON. This data includes the company's revenue and expenses. After receiving this data, the server quickly stores it in an internal database. Suitable database management systems are MySQL or PostgreSQL.

[0639] Terminal handling

[0640] The device performs the following analysis based on the financial information stored on the server. First, the device reads the latest financial data from the server and compares revenue with expenses. Next, if expenses exceed 80% of revenue, it detects this as "wasteful spending."

[0641] If the analysis identifies wasteful spending, the device will generate specific improvement suggestions, such as "Suggest automating the manufacturing process," giving companies concrete ways to reduce costs and improve efficiency.

[0642] User Action

[0643] Users can receive analysis results and improvement suggestions provided by the server and terminals in real time, which improves the speed of decision-making and enables them to take action immediately. Specifically, when a user logs into the system and uploads their latest financial information, the analysis results and improvement suggestions are displayed immediately.

[0644] Specific examples

[0645] For example, a manufacturing company provides the following monthly financial data:

[0646] Revenue: 1 million

[0647] Expenditure: 850000

[0648] The server receives and stores the data. The device reads the data and detects that expenses exceed 80% of income. As a result, a message like this is generated:

[0649] Waste detection: Costs exceed 80% of revenues

[0650] Improvement suggestion: Please introduce automation into the manufacturing process.

[0651] Users can log in to the system and receive the results in real time, allowing them to take appropriate measures immediately.

[0652] Prompt Sentence Examples

[0653] Examples of prompts for use with generative AI models include:

[0654] Given a company's monthly financial data (revenues, expenses), write a program to analyze it and generate an alert if the following conditions are met:

[0655] 1. If expenses exceed 80% of revenue, report them as "waste."

[0656] 2. Generate concrete improvement suggestions

[0657] Example: Proposing automation of a manufacturing process

[0658] Examples of data provided:

[0659] Revenue: 1 million

[0660] Expenditure: 850000

[0661] This system allows companies to quickly understand their financial situation and take concrete actions to optimize costs in real time.

[0662] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0663] Step 1:

[0664] The server receives financial information from the user in a structured format (e.g. JSON format). As input, financial data is provided. The server parses this data to ensure it is in the correct format. After validation, it stores the data in an internal database. With this operation, the server securely stores the company's revenue and expenditure information. As output, it gets the data stored in the internal database.

[0665] Step 2:

[0666] The terminal reads the financial data stored in the internal database from the server. As input, it has the financial data retrieved from the internal database. Based on this data, the terminal analyzes expenses and revenues. It proceeds with the data reading process and stores the read data in memory. As output, it obtains a dataset of revenues and expenses that will serve as the basis for analysis.

[0667] Step 3:

[0668] The terminal compares the revenue and expenses it reads to determine whether expenses exceed 80% of revenue. The revenue and expense figures are used as input. The terminal performs an arithmetic operation and generates a warning if expenses exceed 80% of revenue. Specifically, it performs an operation to compare the revenue x 0.8 value with expenses. The output is a warning message: "Waste detected: costs exceed 80% of revenue."

[0669] Step 4:

[0670] If wasteful spending is detected, the device generates a specific improvement suggestion. The results of detecting wasteful spending are used as input. The device lists improvement suggestions and selects the most appropriate one. For example, it may suggest ways to consider automating the manufacturing process. An AI model could be used to generate the improvement suggestion. The output is a specific improvement suggestion such as "Introduce automation into the manufacturing process."

[0671] Step 5:

[0672] A user logs into the system and receives analysis results and improvement suggestions. User authentication information and the latest financial data are used as input. The server authenticates the user and provides the latest analysis results and improvement suggestions. Specific operations include a process where the results are displayed in real time on the user interface. As output, the user receives the analysis results and improvement suggestions displayed in real time.

[0673] This series of steps enables companies to quickly and accurately analyze their financial situation and receive specific improvement measures in real time.

[0674] (Application example 1)

[0675] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0676] In modern manufacturing factories, the operating efficiency of each robot and the state of material usage have a significant impact on a company's overall costs, but there is a lack of effective ways to monitor and optimize them in real time. As a result, many companies overlook wasteful spending and are unable to properly utilize efficient improvement proposals. There is also a need for a system that integrates financial data and on-site data to reduce costs. Therefore, there is a need for an effective system that can detect wasteful costs based on factory robot operating data and financial data and provide improvement proposals in real time.

[0677] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0678] In this invention, the server includes means for receiving information from a user and storing it in an internal data structure, means for optimizing costs based on the stored information, means for collecting and analyzing operational data of each robot in the factory and related financial data in real time, and means for providing the user with analysis results and improvement suggestions in real time, thereby enabling cost optimization that combines operational data and financial data of the factory.

[0679] A "user" is a user of the system, who provides information and receives results.

[0680] "Information" refers to data including company financial data and robot operation data.

[0681] "Internal data structures" refers to the databases and file systems used by the server to store and manage the information it receives.

[0682] "Cost optimization" is the process of reducing wasteful spending and maximizing profits.

[0683] "Stored Information" refers to information stored by the Server in internal data structures.

[0684] The "analysis results" are reports that are generated when cost optimization is performed, including recommendations for wasteful spending and efficiency improvements.

[0685] "Improvement proposals" are specific methods and action plans for reducing wasteful spending and improving work efficiency.

[0686] "Operation data for each robot in the factory" refers to data including the working time, input resources, output efficiency, etc. of robots in the manufacturing factory.

[0687] "Real-time" refers to the processing speed, which allows users to receive analysis results and improvement suggestions immediately after uploading data.

[0688] "Financial data" includes economic data such as a company's revenues, expenses, and profits.

[0689] This invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial data provided by users and operational data from each robot in a factory, and provides improvement proposals in real time.

[0690] System configuration

[0691] This system is mainly composed of a server and a terminal. The server receives and stores information, and the terminal is responsible for optimizing costs based on that information.

[0692] Server Roles

[0693] The server receives the information provided by the user and stores it in an internal data structure. The received information is structured data, such as JSON format, and includes financial data for the company and operational data for each robot in the factory. Once the server receives the data, it is immediately stored in an internal database.

[0694] Device Role

[0695] The device optimizes costs based on the information stored on the server. Specifically, the device compares revenue with expenditures to determine whether there is any waste. For example, if expenditures exceed 80% of revenue, it will detect this as "waste." In addition, the device generates specific improvement suggestions and provides them to the user. This allows companies to know specifically how to reduce costs and increase efficiency.

[0696] User operations

[0697] Users can receive analysis results and improvement suggestions provided by the server and terminal in real time. When a user logs in to the system and uploads the latest data, the analysis results and improvement suggestions are displayed immediately. This process is carried out in near real time, minimizing user waiting time.

[0698] Hardware and software used

[0699] Hardware: Servers, devices (smartphones, tablets), factory robots

[0700] Software: Python, requests library, JSON format data

[0701] The server is a Python program that uses the requests library to receive and store information from users, while the device also uses Python to analyze the stored data and generate analysis results and improvement suggestions.

[0702] Specific examples

[0703] For example, if a manufacturing plant provides monthly expenditure and revenue data, as well as robot operation data, the server receives this data and stores it in an internal data structure. Based on this data, if expenditures exceed 80% of revenue, the terminal generates a result such as "Waste detected: Costs exceed 80% of revenue" and presents specific improvement suggestions such as "Automation suggestion: Improve the operating efficiency of robots."

[0704] Prompt Sentence Examples

[0705] Based on the following financial and operational data, detect waste when expenses exceed 80% of revenue and generate improvement suggestions.

[0706] Financial data: Expenses 90000, Revenues 100000

[0707] Operation data: Robot 1 operation time 50 hours, Robot 2 operation time 60 hours

[0708] This allows users to take concrete actions in real time to reduce wasteful spending and improve efficiency.

[0709] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0710] Step 1:

[0711] A user logs in to the system.

[0712] Input: User credentials (e.g. username and password)

[0713] How it works: The server receives the user's authentication information and checks the credentials against a database.

[0714] Output: Notification of authentication success or failure

[0715] Step 2:

[0716] Users upload financial data and robot operation data.

[0717] Input: Financial data and robot operation data (e.g., JSON format)

[0718] How it works: The server receives these data and stores them in an internal data structure (database).

[0719] Output: Notification of successful data reception and storage

[0720] Step 3:

[0721] The server transmits the information stored on the device.

[0722] Input: Financial data and robot operation data stored in internal data structures

[0723] Operation: The server extracts data from the database and sends it to the device.

[0724] Output: Notification of completion of sending financial data and operational data

[0725] Step 4:

[0726] Cost optimization is performed based on the data received by the terminal.

[0727] Input: Received financial data and robot operation data

[0728] How it works: The device compares expenses and revenues and performs data calculations to detect wasteful spending (e.g., calculating the expense / revenue ratio).

[0729] Output: Wasteful spending detection results

[0730] Step 5:

[0731] The device generates specific improvement suggestions for wasteful spending.

[0732] Input: Wasteful spending detection results

[0733] How it works: The device uses the generative AI model to generate specific suggestions for improving efficiency (e.g., suggestions for improving the operating efficiency of a robot).

[0734] Output: Improvement suggestions

[0735] Step 6:

[0736] The generated analysis results and improvement suggestions are provided to the user in real time.

[0737] Input: Analysis results and improvement suggestions

[0738] How it works: The device displays the analysis results and recommendations to the user by sending a notification to the user's device and displaying the results on the application screen.

[0739] Output: User notification and results display

[0740] Step 7:

[0741] The user checks the analysis results and improvement suggestions and implements the countermeasures.

[0742] Input: Notify user and display results

[0743] Action: The user reviews the proposal and takes appropriate action (e.g., adjusting the manufacturing process or changing the operating parameters of a robot).

[0744] Output: Confirmation and feedback of the results

[0745] In this way, the system can generate and quickly provide cost optimization analysis results and improvement proposals in real time based on data input from the user.

[0746] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0747] The present invention relates to a system aimed at optimizing corporate costs, and in particular, a system that quickly and accurately detects cost waste based on financial data provided by users and provides improvement proposals in real time. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the present invention aims to adjust the method of providing analysis results and improvement proposals based on the user's emotional state, and further to use emotion data to improve the accuracy of future analyses.

[0748] The server is responsible for receiving financial data provided by the company and storing it in an internal data structure. The data is usually provided in a structured format such as JSON, and the server stores it in an internal database, preparing the data for subsequent analytical processing.

[0749] The device performs analysis for cost optimization based on the stored data. Specifically, the device compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "wasteful." The results of this analysis are presented to the user.

[0750] The device also uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes emotions from the user's facial expressions, tone of voice, input content, etc. and stores the results in a database. For example, if the user is angry, the emotion engine recognizes the emotion as "anger" and stores that state in the database.

[0751] The device then adjusts the way it presents analysis results and improvement suggestions based on the user's emotional state. For example, if the user is tired, it provides information in a concise and easy-to-understand format. On the other hand, if the user is interested, it provides detailed analysis results and multiple improvement suggestions. In this way, it provides information optimized for the user's emotional state.

[0752] Furthermore, emotional data can be used to improve the accuracy of future analyses and recommendations. For example, based on a user's emotional history, the system can learn what information should be provided at what timing and in what format to provide it most effectively, and provide this information for future sessions.

[0753] Users can receive analysis results and improvement suggestions in real time in a format optimized by the emotion engine, which reduces their stress level and improves the speed of decision-making.In addition, when users log in to the system and upload their latest data, they can receive optimal advice based on that data.

[0754] For example, if a manufacturing company provides monthly expenditure and revenue data, the system analyzes the data and generates a result such as "Waste detected: Costs exceed 80% of revenue" if expenditures exceed 80% of revenue. Furthermore, if the emotion engine recognizes that the user has previously experienced stress, concise and specific improvement suggestions are presented. For example, suggestions such as "Consider automating the manufacturing process to improve efficiency" are provided in real time.

[0755] Through this series of processes, the present invention enables efficient cost optimization for companies and flexible and effective information provision according to the user's emotional state, making it applicable to a wide range of industries, particularly in the manufacturing, IT, and service industries.

[0756] The processing flow will be explained below.

[0757] Step 1:

[0758] The server receives financial data provided by the company. The data is usually sent in JSON format, and the server temporarily stores it in an internal database. This receiving process prepares the data necessary for subsequent analysis.

[0759] Step 2:

[0760] The server stores the received data in internal data structures, which ensures the system maintains data consistency and ensures data integrity in subsequent processing. This step also checks the input data to filter out incomplete or invalid data.

[0761] Step 3:

[0762] The device retrieves the stored data and prepares it for analysis by examining its format and content and extracting cost and revenue information, which forms the basis for the analysis.

[0763] Step 4:

[0764] The device then begins a cost optimization analysis based on the data. It compares expenditures with revenues, and if expenditures are excessive compared to revenues, it detects them as wasteful costs. For example, if expenditures exceed 80% of revenues, it detects this as "waste." The results of this analysis influence subsequent improvement proposals.

[0765] Step 5:

[0766] The device generates specific improvement suggestions when waste is detected, such as suggestions for improving efficiency by automating manufacturing processes or reallocating resources. This step may also take into account historical data and examples from other companies.

[0767] Step 6:

[0768] The device activates an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, input content, etc. The results of this analysis are stored in a database.

[0769] Step 7:

[0770] The emotion engine recognizes the user's emotional state and adjusts how the device presents analysis results and improvement suggestions based on that state. For example, if the user is tired, it will provide information in a concise and easy-to-understand format.

[0771] Step 8:

[0772] Users access the system, upload the latest data, and receive analysis results and improvement suggestions optimized by the emotion engine in real time, which helps users make quick decisions.

[0773] Step 9:

[0774] The server stores the analysis results and improvement suggestions provided to the user and keeps them for future reference. This data can be reused at a later date to help continuously improve business performance.

[0775] These steps enable companies to quickly and accurately optimize costs, eliminate waste, and achieve efficient business operations. Furthermore, by providing flexible information according to the user's emotional state, the system reduces user stress and supports efficient decision-making.

[0776] Example 2

[0777] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0778] Traditional cost optimization systems focus on analyzing financial data and detecting wasteful spending, but do not take the user's emotional state into account, which can lead to lower user stress levels and reduced decision-making efficiency. Furthermore, they lack the ability to utilize user emotional data to improve analytical accuracy, limiting their long-term effectiveness.

[0779] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information from a user and storing it in an internal data structure, means for optimizing costs based on the stored information, means for recognizing the user's emotional state and adjusting the method for providing analysis results and improvement suggestions based on that state, means for analyzing data and detecting wasteful expenditures, means for storing the recognized emotional state in a database, means for improving the accuracy of future analyses using the accumulated emotional data, and means for providing the user with analysis results and improvement suggestions in real time in a format optimized according to their emotional state. This not only enables efficient cost optimization for companies but also enables flexible and effective information provision according to the user's emotional state.

[0780] "User" refers to a company or individual that uses this system.

[0781] "Information" refers generally to data provided by users, including but not limited to financial data.

[0782] "Internal data structure" refers to the database or storage system that stores and manages data efficiently and systematically within the server.

[0783] "Cost optimization" is the process of analyzing financial data and providing analysis and recommendations to reduce wasteful spending and maximize profits.

[0784] "Emotional state" refers to the user's mental and emotional state, including anger, stress, interest, and the like.

[0785] "Emotion Engine" refers to the combination of analytical algorithms and hardware / software for recognizing a user's emotional state.

[0786] "Analysis results" refers to results obtained based on the analysis of financial data, including identifying wasteful expenditures and proposing cost reductions.

[0787] "Improvement recommendations" refer to specific actions and strategies provided to improve a company's cost efficiency based on the analysis results.

[0788] "Real-time" refers to a process in which a response is returned immediately after the user provides information, without delay.

[0789] A "database" is a system for managing accumulated data, and includes formats such as SQL and NoSQL.

[0790] "Analysis" refers to the process of processing data to extract useful information.

[0791] "Expenditure" refers to the economic resources consumed by a business to produce and provide goods and services.

[0792] "Revenue" refers to the total income a company receives from selling goods and services.

[0793] "Waste" refers to expenses that are incurred but are not necessarily necessary.

[0794] "Accuracy" refers to an indicator of the accuracy of the analysis and suggestions.

[0795] "Optimized format" refers to a format of information presentation that is tailored based on the user's emotional state.

[0796] This invention relates to a system aimed at optimizing corporate costs. In particular, it quickly and accurately detects cost waste based on financial data provided by users and provides improvement suggestions in real time. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it aims to adjust the method of providing analysis results and improvement suggestions based on the user's emotional state, and furthermore, to use emotion data to improve the accuracy of future analyses.

[0797] The server receives financial data provided by the company and stores it in an internal data structure. Specific examples of databases used include MySQL and PostgreSQL. The data is usually provided in a structured format such as JSON and is then stored in the database. This prepares the data required for subsequent analytical processing.

[0798] The device then performs an analysis process for cost optimization based on the stored data. Specifically, it compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "waste." The results of this analysis are presented to the user. For example, if monthly expenditures reach 85% of revenue, the device generates the result "Waste detected: costs exceed 80% of revenue."

[0799] The device also uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes emotions from the user's facial expressions, tone of voice, input content, etc., and stores the results in a database. Specific examples of the technologies used include facial expression recognition algorithms and voice analysis algorithms. For example, if the user has a tired expression, the device recognizes the user's emotional state as "tired" and stores that information in the database.

[0800] The device then adjusts how it presents analysis results and improvement suggestions based on the user's perceived emotional state. If the user is tired, it will provide information in a concise, easy-to-understand format. If the user is interested, it will provide detailed analysis results and multiple improvement suggestions. For example, if the user is recognized as "tired," the device will display a concise suggestion: "Consider automating your manufacturing process to improve efficiency."

[0801] Additionally, the server accumulates emotional data and uses it to improve the accuracy of future analysis and recommendations. This is achieved by training generative AI models (e.g., neural networks for emotion recognition and recommendation generation) based on the accumulated emotional data, which allows for more accurate results in subsequent sessions.

[0802] Ultimately, users can log in to the system and upload their latest financial data to receive analysis results and improvement suggestions in real time. For example, when a user logs in and uploads a new month's financial data, the device immediately begins analysis and displays suggestions tailored to the user's emotional state (e.g., "Waste detection: Costs exceed 80% of revenue. Consider automating the manufacturing process.").

[0803] An example prompt is, "Detect waste in a company's financial data and present the results. Also, provide improvement suggestions in an appropriate format based on the user's current emotional state (e.g., tired, interested, etc.)."

[0804] Through this series of processes, the present invention efficiently optimizes corporate costs and enables flexible and effective information provision according to the user's emotional state, which is expected to reduce the user's stress level and improve the speed of decision-making.

[0805] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0806] Step 1:

[0807] The server receives financial data from the user. As input, a financial data file in JSON format is used. The server parses this data and stores it in a structured format (e.g., a MySQL database). Specifically, the server receives the provided "financial_data.json" file and stores the income and expense data in the database. As output, the server obtains the financial data stored in the database.

[0808] Step 2:

[0809] The terminal begins an analysis to optimize costs based on the stored financial data. Revenue and expenditure data retrieved from the database is used as input. The terminal compares this data and determines whether expenditure exceeds 80% of revenue. Specifically, the terminal retrieves the data "Revenue: 5 million yen, Expenses: 4.5 million yen" and calculates that expenditures are 90% of revenue. The output is the analysis result "Waste detected: Costs exceed 80% of revenue."

[0810] Step 3:

[0811] The device uses an emotion engine to recognize the user's emotional state. The inputs include the user's facial expressions, tone of voice, and input content. The emotion engine analyzes this data to determine the user's emotional state. Specifically, the device uses a camera and microphone to capture the user's video and audio, and the facial expression recognition algorithm determines that the user's face looks tired, and the voice analysis algorithm determines that the user's voice lacks vitality. The output, "User's emotional state: tired," is saved in the database.

[0812] Step 4:

[0813] The device adjusts the way it presents analysis results and improvement suggestions based on the recognized emotional state of the user. The analysis results and the user's emotional state data are used as input. Specifically, the device generates concise and easy-to-understand suggestions based on the data "User's emotional state: Fatigue." Suggestions such as "Waste detected: Costs exceed 80% of revenue. Consider automating the manufacturing process to improve efficiency" are generated. The adjusted analysis results and improvement suggestions are displayed to the user as output.

[0814] Step 5:

[0815] The server uses the accumulated emotion data to improve future analysis accuracy. Past emotion data and analysis results are used as input. The server learns from these data and updates the generative AI model. Specifically, the server uses a neural network to train the model based on the emotion data and analysis results, enabling it to make more accurate suggestions in the next session. The output is an updated generative AI model.

[0816] Step 6:

[0817] Users log in to the system and upload their latest financial data to receive real-time adjusted analysis results and improvement suggestions. A new financial data file is used as input. Specifically, when a user uploads a new "financial_data.json", the device immediately starts analysis and restarts the emotion engine. As output, the user is provided with real-time optimized analysis results and improvement suggestions.

[0818] (Application example 2)

[0819] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0820] Modern business activities require the rapid detection and optimization of cost waste. Furthermore, flexible responses based on the user's emotional state are also required for on-site workers and managers to receive appropriate improvement suggestions in real time. However, existing systems struggle to provide effective improvement suggestions that take the user's emotional state into account, and the suggestions often do not fit the user's situation. Therefore, there is a need for a system that supports decision-making while reducing user stress and providing effective cost optimization.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0822] In this invention, the server includes a means for receiving information from a user and storing it in an internal data structure, a means for optimizing costs based on the stored information, a means for recognizing the user's emotional state, a means for adjusting the method for providing analysis results and improvement suggestions based on the stored emotional state, and a means for providing the analysis results and improvement suggestions to the user in real time. This enables the system to analyze a company's financial data to detect waste and provide information optimized according to the user's emotional state. Furthermore, by using prompt sentences based on a generative AI model, it is possible to provide effective improvement suggestions in real time and support decision-making that meets on-site requirements.

[0823] "User" refers to an individual or company that uses the system.

[0824] "Information" generally refers to data provided by users, including, specifically, company financial data and emotional state data.

[0825] "Internal data structure" refers to the format in which received information is stored and the data is organized and managed within the system for use in subsequent processing.

[0826] "Means" refers to methods or techniques for achieving a specific purpose.

[0827] "Cost optimization" refers to methods and techniques for optimizing a company's expenditures and reducing wasteful spending.

[0828] "User's emotional state" refers to the psychological state of the user using the system, and mainly includes states such as stress, interest, and fatigue.

[0829] "To recognize" refers to accurately grasping a specific piece of information or situation.

[0830] "Analysis results" refers to the conclusions and indicators obtained after data analysis.

[0831] "Improvement proposal" refers to proposing specific improvement methods or countermeasures for detected problems.

[0832] "Real-time" refers to processing or responding to information immediately after it is received or generated.

[0833] "Based on the stored emotional state" means that the next processing or response is based on the emotional data.

[0834] A "generative AI model" refers to algorithms or techniques that use artificial intelligence to generate data for a specific purpose.

[0835] A "prompt" refers to text in the form of instructions or questions that are input to a generative AI model.

[0836] The present invention is a system that analyzes financial data, detects wasteful spending, and makes improvement suggestions based on the results, with the aim of optimizing corporate costs.The present invention also recognizes the user's emotional state and provides improvement suggestions based on that emotion, thereby reducing the user's stress and supporting decision-making.

[0837] The system includes the following major components:

[0838] 1. Server:

[0839] The server receives the company's financial data from the user and stores it in an internal data structure. The data is usually provided in a structured format such as JSON, and the server stores it in an internal database (e.g., SQLite) and makes the stored data available to other components.

[0840] 2. Data analysis engine:

[0841] Cost optimization is performed based on the stored financial data. Specifically, expenses are compared with revenues to detect wasteful spending. This analysis engine is implemented using software such as Cost Analysis Engine. If expenses exceed the threshold for waste, they are detected as "waste."

[0842] 3. Emotion Recognition Engine:

[0843] Recognize the user's emotional state. An emotion recognition engine analyzes the user's facial expressions and tone of voice and stores the results in a database. Using software such as EmotionEngine, it can determine whether the user is stressed, calm, interested, etc. This emotional data is used to tailor how subsequent suggestions are presented.

[0844] 4. Proposal Generation Engine:

[0845] The engine adjusts the way it presents analysis results and improvement suggestions based on the user's perceived emotional state. For example, if the user is tired, it will provide information in a concise and easy-to-understand format. On the other hand, if the user is interested, it will provide detailed analysis results and multiple improvement suggestions. This suggestion generation engine works by using prompt sentences based on a generative AI model.

[0846] Specific use cases:

[0847] When a manufacturing company provides monthly expenditure and revenue data to the system, the system analyzes the data and generates a result saying, "Waste detected: Costs exceed 80% of revenue" if expenditures exceed 80% of revenue. The system also analyzes the user's emotional state, and if it recognizes that the user is prone to stress, for example, it will present a concise and specific improvement suggestion such as, "Consider automating the manufacturing process to improve efficiency."

[0848] Example prompt sentence:

[0849] If the user's facial expression and tone of voice indicate stress, generate concise and specific suggestions for cost reduction.

[0850] This allows users to receive effective improvement suggestions at the right time, reducing stress and enabling quick decision-making. In addition, the accumulation of emotional data will contribute to improving the accuracy of future suggestions.

[0851] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0852] Step 1:

[0853] The server receives the company's financial data from the user. The user provides the financial data (e.g., expenditure and revenue data) to the system, and the server receives it in a structured format, such as JSON. The entered data is stored in an internal data structure, which prepares the data needed for subsequent data analysis.

[0854] Step 2:

[0855] The server stores the received financial data in an internal database. Specifically, it parses the received JSON format data and stores it in a database such as SQLite, ensuring data persistence.

[0856] Step 3:

[0857] The data analysis engine optimizes costs based on stored financial data. In other words, it compares expenses with revenue and detects wasteful spending. For example, if expenses exceed 80% of revenue, it detects this as waste and saves it as the analysis result. The input is financial data, and the output is the waste detection results.

[0858] Step 4:

[0859] An emotion recognition engine recognizes the user's emotional state. The facial expressions and tone of voice input by the user to the system are analyzed by emotion recognition software such as EmotionEngine. The input is the user's facial expressions and voice data, and the output is the recognized emotional state. This emotional data is stored in a database.

[0860] Step 5:

[0861] The server generates improvement suggestions through a suggestion generation engine based on the recognized emotional state. Using a generative AI model, it generates suggestion sentences based on the prompt sentences and corresponding to the user's emotional state. The input is the emotional state and analysis results, and the output is individually tailored improvement suggestions.

[0862] Step 6:

[0863] The proposal generation engine provides the user with analysis results and improvement suggestions in real time. Specifically, it displays proposals in a format that corresponds to the user's emotional state. For example, if the user is feeling stressed, it displays concise and specific suggestions. The input is the adjusted improvement suggestions, and the output is the suggestions that are displayed to the user.

[0864] Step 7:

[0865] The user decides on the next action based on the provided improvement suggestions. By receiving feedback from the system, the user can make efficient cost optimization decisions.

[0866] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0867] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0868] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0869] [Fourth embodiment]

[0870] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0871] 7, a 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.

[0872] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0873] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0874] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0875] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0876] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0877] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0878] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0879] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0880] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0881] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0882] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0883] The present invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial data provided by users and provides improvement proposals in real time.

[0884] The server is responsible for receiving data provided by the company and storing it in an internal data structure. The data is typically provided in a structured format, such as JSON. This data includes the company's financial data and other relevant business data. Once the server receives the data, it is immediately stored in an internal database.

[0885] The device performs analysis to optimize costs based on the stored data. Specifically, the device compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "waste."

[0886] For example, if a manufacturing company provides monthly expenditure and revenue data, the device analyzes the data and generates a result that reads, "Waste detected: Costs exceed 80% of revenues" if expenditures exceed 80% of revenues.

[0887] The device not only detects waste but also generates specific improvement suggestions, such as suggesting ways to automate manufacturing processes and improve efficiency, giving companies concrete ways to reduce costs and increase efficiency.

[0888] Users can receive these analysis results and improvement suggestions provided by the server and terminals in real time, which dramatically improves the speed of decision-making and enables them to take action immediately. For example, when a user logs into the system and uploads the latest data, the analysis results and improvement suggestions are displayed immediately. This process is carried out in near real time, minimizing user waiting time.

[0889] Through this series of processes, the present invention enables companies to efficiently optimize their costs and can be applied to a wide range of industries, particularly in addition to the manufacturing industry, such as the IT industry and the service industry.

[0890] The processing flow will be explained below.

[0891] Step 1:

[0892] The server receives the data provided by the company. The data is usually sent in JSON format, and the server temporarily stores it in an internal database. This receiving process prepares the data necessary for subsequent analysis.

[0893] Step 2:

[0894] The server stores the received data in internal data structures, which ensures the system maintains data consistency and ensures data integrity in subsequent processing. This step also checks the input data to filter out incomplete or invalid data.

[0895] Step 3:

[0896] The device retrieves the stored data and prepares it for analysis by examining its format and content and extracting cost and revenue information, which forms the basis for the analysis.

[0897] Step 4:

[0898] The device then starts a cost optimization analysis based on the data. It compares expenditures with revenues, and if expenditures are excessive compared to revenues, it detects them as wasteful costs. For example, if expenditures exceed 80% of revenues, it is deemed wasteful. The results of this analysis influence subsequent improvement proposals.

[0899] Step 5:

[0900] The device generates specific improvement suggestions when waste is detected, such as suggestions for improving efficiency by automating manufacturing processes or reallocating resources. This step may also take into account historical data and examples from other companies.

[0901] Step 6:

[0902] Users can access the system, upload the latest data, and receive analysis results and improvement suggestions in real time, allowing them to make quick decisions and develop action plans on-site.

[0903] Step 7:

[0904] The server stores the analysis results and improvement suggestions provided to the user and keeps them for future reference. This data can be reused at a later date to help continuously improve business performance.

[0905] These steps enable companies to quickly and accurately optimize costs, eliminate waste, and achieve efficient operations.

[0906] Example 1

[0907] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0908] It is difficult for companies to efficiently and quickly grasp their financial situation and propose specific improvement measures to reduce wasteful spending. With conventional systems, it takes time to analyze financial data, making it difficult to make decisions in real time, preventing efficient cost management. Furthermore, the improvement suggestions provided by conventional systems are limited to general instructions and lack specific implementation measures.

[0909] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0910] In this invention, the server includes means for receiving financial information in a structured format from a user and storing it in an internal database, means for analyzing expenses and revenues from the stored financial information and detecting wasteful expenses, means for generating specific improvement proposals when wasteful expenses are detected, and means for providing the analysis results and improvement proposals to the user in real time, thereby enabling a company to quickly grasp its financial situation and receive specific and feasible improvement measures in real time.

[0911] A "user" is an entity that provides information to the system and receives analysis results and improvement suggestions for that information.

[0912] "Structured" means that the data is organized according to certain rules and has a specific format, such as JSON.

[0913] "Financial information" refers to data that shows a company's financial status, such as its revenues and expenses.

[0914] An "internal database" is a data storage system that resides within the server and is used to store and manage received data.

[0915] "Storage" means storing the received data in an internal database so that it can be retrieved when needed.

[0916] "Analysis" is the process of performing calculations and evaluations based on stored data to arrive at a specific result.

[0917] "Wasteful spending" refers to a company's financial data where expenses exceed 80% of revenue and are deemed to be wasteful costs.

[0918] "Improvement proposals" refer to specific and feasible measures to reduce wasteful expenditures when they are detected.

[0919] "Real-time" means that information is processed and provided immediately, without delay.

[0920] The present invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial information provided by users and provides improvement proposals in real time.

[0921] Server Processing

[0922] The server receives financial information provided by the company. The information is usually provided in a structured format, such as JSON. This data includes the company's revenue and expenses. After receiving this data, the server quickly stores it in an internal database. Suitable database management systems are MySQL or PostgreSQL.

[0923] Terminal handling

[0924] The device performs the following analysis based on the financial information stored on the server. First, the device reads the latest financial data from the server and compares revenue with expenses. Next, if expenses exceed 80% of revenue, it detects this as "wasteful spending."

[0925] If the analysis identifies wasteful spending, the device will generate specific improvement suggestions, such as "Suggest automating the manufacturing process," giving companies concrete ways to reduce costs and improve efficiency.

[0926] User Action

[0927] Users can receive analysis results and improvement suggestions provided by the server and terminals in real time, which improves the speed of decision-making and enables them to take action immediately. Specifically, when a user logs into the system and uploads their latest financial information, the analysis results and improvement suggestions are displayed immediately.

[0928] Specific examples

[0929] For example, a manufacturing company provides the following monthly financial data:

[0930] Revenue: 1 million

[0931] Expenditure: 850000

[0932] The server receives and stores the data. The device reads the data and detects that expenses exceed 80% of income. As a result, a message like this is generated:

[0933] Waste detection: Costs exceed 80% of revenues

[0934] Improvement suggestion: Please introduce automation into the manufacturing process.

[0935] Users can log in to the system and receive the results in real time, allowing them to take appropriate measures immediately.

[0936] Prompt Sentence Examples

[0937] Examples of prompts for use with generative AI models include:

[0938] Given a company's monthly financial data (revenues, expenses), write a program to analyze it and generate an alert if the following conditions are met:

[0939] 1. If expenses exceed 80% of revenue, report them as "waste."

[0940] 2. Generate concrete improvement suggestions

[0941] Example: Proposing automation of a manufacturing process

[0942] Examples of data provided:

[0943] Revenue: 1 million

[0944] Expenditure: 850000

[0945] This system allows companies to quickly understand their financial situation and take concrete actions to optimize costs in real time.

[0946] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0947] Step 1:

[0948] The server receives financial information from the user in a structured format (e.g. JSON format). As input, financial data is provided. The server parses this data to ensure it is in the correct format. After validation, it stores the data in an internal database. With this operation, the server securely stores the company's revenue and expenditure information. As output, it gets the data stored in the internal database.

[0949] Step 2:

[0950] The terminal reads the financial data stored in the internal database from the server. As input, it has the financial data retrieved from the internal database. Based on this data, the terminal analyzes expenses and revenues. It proceeds with the data reading process and stores the read data in memory. As output, it obtains a dataset of revenues and expenses that will serve as the basis for analysis.

[0951] Step 3:

[0952] The terminal compares the revenue and expenses it reads to determine whether expenses exceed 80% of revenue. The revenue and expense figures are used as input. The terminal performs an arithmetic operation and generates a warning if expenses exceed 80% of revenue. Specifically, it performs an operation to compare the revenue x 0.8 value with expenses. The output is a warning message: "Waste detected: costs exceed 80% of revenue."

[0953] Step 4:

[0954] If wasteful spending is detected, the device generates a specific improvement suggestion. The results of detecting wasteful spending are used as input. The device lists improvement suggestions and selects the most appropriate one. For example, it may suggest ways to consider automating the manufacturing process. An AI model could be used to generate the improvement suggestion. The output is a specific improvement suggestion such as "Introduce automation into the manufacturing process."

[0955] Step 5:

[0956] A user logs into the system and receives analysis results and improvement suggestions. User authentication information and the latest financial data are used as input. The server authenticates the user and provides the latest analysis results and improvement suggestions. Specific operations include a process where the results are displayed in real time on the user interface. As output, the user receives the analysis results and improvement suggestions displayed in real time.

[0957] This series of steps enables companies to quickly and accurately analyze their financial situation and receive specific improvement measures in real time.

[0958] (Application example 1)

[0959] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0960] In modern manufacturing factories, the operating efficiency of each robot and the state of material usage have a significant impact on a company's overall costs, but there is a lack of effective ways to monitor and optimize them in real time. As a result, many companies overlook wasteful spending and are unable to properly utilize efficient improvement proposals. There is also a need for a system that integrates financial data and on-site data to reduce costs. Therefore, there is a need for an effective system that can detect wasteful costs based on factory robot operating data and financial data and provide improvement proposals in real time.

[0961] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0962] In this invention, the server includes means for receiving information from a user and storing it in an internal data structure, means for optimizing costs based on the stored information, means for collecting and analyzing operational data of each robot in the factory and related financial data in real time, and means for providing the user with analysis results and improvement suggestions in real time, thereby enabling cost optimization that combines operational data and financial data of the factory.

[0963] A "user" is a user of the system, who provides information and receives results.

[0964] "Information" refers to data including company financial data and robot operation data.

[0965] "Internal data structures" refers to the databases and file systems used by the server to store and manage the information it receives.

[0966] "Cost optimization" is the process of reducing wasteful spending and maximizing profits.

[0967] "Stored Information" refers to information stored by the Server in internal data structures.

[0968] The "analysis results" are reports that are generated when cost optimization is performed, including recommendations for wasteful spending and efficiency improvements.

[0969] "Improvement proposals" are specific methods and action plans for reducing wasteful spending and improving work efficiency.

[0970] "Operation data for each robot in the factory" refers to data including the working time, input resources, output efficiency, etc. of robots in the manufacturing factory.

[0971] "Real-time" refers to the processing speed, which allows users to receive analysis results and improvement suggestions immediately after uploading data.

[0972] "Financial data" includes economic data such as a company's revenues, expenses, and profits.

[0973] This invention relates to a system aimed at optimizing corporate costs, and in particular to a system that quickly and accurately detects cost waste based on financial data provided by users and operational data from each robot in a factory, and provides improvement proposals in real time.

[0974] System configuration

[0975] This system is mainly composed of a server and a terminal. The server receives and stores information, and the terminal is responsible for optimizing costs based on that information.

[0976] Server Roles

[0977] The server receives the information provided by the user and stores it in an internal data structure. The received information is structured data, such as JSON format, and includes financial data for the company and operational data for each robot in the factory. Once the server receives the data, it is immediately stored in an internal database.

[0978] Device Role

[0979] The device optimizes costs based on the information stored on the server. Specifically, the device compares revenue with expenditures to determine whether there is any waste. For example, if expenditures exceed 80% of revenue, it will detect this as "waste." In addition, the device generates specific improvement suggestions and provides them to the user. This allows companies to know specifically how to reduce costs and increase efficiency.

[0980] User operations

[0981] Users can receive analysis results and improvement suggestions provided by the server and terminal in real time. When a user logs in to the system and uploads the latest data, the analysis results and improvement suggestions are displayed immediately. This process is carried out in near real time, minimizing user waiting time.

[0982] Hardware and software used

[0983] Hardware: Servers, devices (smartphones, tablets), factory robots

[0984] Software: Python, requests library, JSON format data

[0985] The server is a Python program that uses the requests library to receive and store information from users, while the device also uses Python to analyze the stored data and generate analysis results and improvement suggestions.

[0986] Specific examples

[0987] For example, if a manufacturing plant provides monthly expenditure and revenue data, as well as robot operation data, the server receives this data and stores it in an internal data structure. Based on this data, if expenditures exceed 80% of revenue, the terminal generates a result such as "Waste detected: Costs exceed 80% of revenue" and presents specific improvement suggestions such as "Automation suggestion: Improve the operating efficiency of robots."

[0988] Prompt Sentence Examples

[0989] Based on the following financial and operational data, detect waste when expenses exceed 80% of revenue and generate improvement suggestions.

[0990] Financial data: Expenses 90000, Revenues 100000

[0991] Operation data: Robot 1 operation time 50 hours, Robot 2 operation time 60 hours

[0992] This allows users to take concrete actions in real time to reduce wasteful spending and improve efficiency.

[0993] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0994] Step 1:

[0995] A user logs in to the system.

[0996] Input: User credentials (e.g. username and password)

[0997] How it works: The server receives the user's authentication information and checks the credentials against a database.

[0998] Output: Notification of authentication success or failure

[0999] Step 2:

[1000] Users upload financial data and robot operation data.

[1001] Input: Financial data and robot operation data (e.g., JSON format)

[1002] How it works: The server receives these data and stores them in an internal data structure (database).

[1003] Output: Notification of successful data reception and storage

[1004] Step 3:

[1005] The server transmits the information stored on the device.

[1006] Input: Financial data and robot operation data stored in internal data structures

[1007] Operation: The server extracts data from the database and sends it to the device.

[1008] Output: Notification of completion of sending financial data and operational data

[1009] Step 4:

[1010] Cost optimization is performed based on the data received by the terminal.

[1011] Input: Received financial data and robot operation data

[1012] How it works: The device compares expenses and revenues and performs data calculations to detect wasteful spending (e.g., calculating the expense / revenue ratio).

[1013] Output: Wasteful spending detection results

[1014] Step 5:

[1015] The device generates specific improvement suggestions for wasteful spending.

[1016] Input: Wasteful spending detection results

[1017] How it works: The device uses the generative AI model to generate specific suggestions for improving efficiency (e.g., suggestions for improving the operating efficiency of a robot).

[1018] Output: Improvement suggestions

[1019] Step 6:

[1020] The generated analysis results and improvement suggestions are provided to the user in real time.

[1021] Input: Analysis results and improvement suggestions

[1022] How it works: The device displays the analysis results and recommendations to the user by sending a notification to the user's device and displaying the results on the application screen.

[1023] Output: User notification and results display

[1024] Step 7:

[1025] The user checks the analysis results and improvement suggestions and implements the countermeasures.

[1026] Input: Notify user and display results

[1027] Action: The user reviews the proposal and takes appropriate action (e.g., adjusting the manufacturing process or changing the operating parameters of a robot).

[1028] Output: Confirmation and feedback of the results

[1029] In this way, the system can generate and quickly provide cost optimization analysis results and improvement proposals in real time based on data input from the user.

[1030] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1031] The present invention relates to a system aimed at optimizing corporate costs, and in particular, a system that quickly and accurately detects cost waste based on financial data provided by users and provides improvement proposals in real time. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the present invention aims to adjust the method of providing analysis results and improvement proposals based on the user's emotional state, and further to use emotion data to improve the accuracy of future analyses.

[1032] The server is responsible for receiving financial data provided by the company and storing it in an internal data structure. The data is usually provided in a structured format such as JSON, and the server stores it in an internal database, preparing the data for subsequent analytical processing.

[1033] The device performs analysis for cost optimization based on the stored data. Specifically, the device compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "wasteful." The results of this analysis are presented to the user.

[1034] The device also uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes emotions from the user's facial expressions, tone of voice, input content, etc. and stores the results in a database. For example, if the user is angry, the emotion engine recognizes the emotion as "anger" and stores that state in the database.

[1035] The device then adjusts the way it presents analysis results and improvement suggestions based on the user's emotional state. For example, if the user is tired, it provides information in a concise and easy-to-understand format. On the other hand, if the user is interested, it provides detailed analysis results and multiple improvement suggestions. In this way, it provides information optimized for the user's emotional state.

[1036] Furthermore, emotional data can be used to improve the accuracy of future analyses and recommendations. For example, based on a user's emotional history, the system can learn what information should be provided at what timing and in what format to provide it most effectively, and provide this information for future sessions.

[1037] Users can receive analysis results and improvement suggestions in real time in a format optimized by the emotion engine, which reduces their stress level and improves the speed of decision-making.In addition, when users log in to the system and upload their latest data, they can receive optimal advice based on that data.

[1038] For example, if a manufacturing company provides monthly expenditure and revenue data, the system analyzes the data and generates a result such as "Waste detected: Costs exceed 80% of revenue" if expenditures exceed 80% of revenue. Furthermore, if the emotion engine recognizes that the user has previously experienced stress, concise and specific improvement suggestions are presented. For example, suggestions such as "Consider automating the manufacturing process to improve efficiency" are provided in real time.

[1039] Through this series of processes, the present invention enables efficient cost optimization for companies and flexible and effective information provision according to the user's emotional state, making it applicable to a wide range of industries, particularly in the manufacturing, IT, and service industries.

[1040] The processing flow will be explained below.

[1041] Step 1:

[1042] The server receives financial data provided by the company. The data is usually sent in JSON format, and the server temporarily stores it in an internal database. This receiving process prepares the data necessary for subsequent analysis.

[1043] Step 2:

[1044] The server stores the received data in internal data structures, which ensures the system maintains data consistency and ensures data integrity in subsequent processing. This step also checks the input data to filter out incomplete or invalid data.

[1045] Step 3:

[1046] The device retrieves the stored data and prepares it for analysis by examining its format and content and extracting cost and revenue information, which forms the basis for the analysis.

[1047] Step 4:

[1048] The device then begins a cost optimization analysis based on the data. It compares expenditures with revenues, and if expenditures are excessive compared to revenues, it detects them as wasteful costs. For example, if expenditures exceed 80% of revenues, it detects this as "waste." The results of this analysis influence subsequent improvement proposals.

[1049] Step 5:

[1050] The device generates specific improvement suggestions when waste is detected, such as suggestions for improving efficiency by automating manufacturing processes or reallocating resources. This step may also take into account historical data and examples from other companies.

[1051] Step 6:

[1052] The device activates an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's emotions from facial expressions, tone of voice, input content, etc. The results of this analysis are stored in a database.

[1053] Step 7:

[1054] The emotion engine recognizes the user's emotional state and adjusts how the device presents analysis results and improvement suggestions based on that state. For example, if the user is tired, it will provide information in a concise and easy-to-understand format.

[1055] Step 8:

[1056] Users access the system, upload the latest data, and receive analysis results and improvement suggestions optimized by the emotion engine in real time, which helps users make quick decisions.

[1057] Step 9:

[1058] The server stores the analysis results and improvement suggestions provided to the user and keeps them for future reference. This data can be reused at a later date to help continuously improve business performance.

[1059] These steps enable companies to quickly and accurately optimize costs, eliminate waste, and achieve efficient business operations. Furthermore, by providing flexible information according to the user's emotional state, the system reduces user stress and supports efficient decision-making.

[1060] Example 2

[1061] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1062] Traditional cost optimization systems focus on analyzing financial data and detecting wasteful spending, but do not take the user's emotional state into account, which can lead to lower user stress levels and reduced decision-making efficiency. Furthermore, they lack the ability to utilize user emotional data to improve analytical accuracy, limiting their long-term effectiveness.

[1063] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information from a user and storing it in an internal data structure, means for optimizing costs based on the stored information, means for recognizing the user's emotional state and adjusting the method for providing analysis results and improvement suggestions based on that state, means for analyzing data and detecting wasteful expenditures, means for storing the recognized emotional state in a database, means for improving the accuracy of future analyses using the accumulated emotional data, and means for providing the user with analysis results and improvement suggestions in real time in a format optimized according to their emotional state. This not only enables efficient cost optimization for companies but also enables flexible and effective information provision according to the user's emotional state.

[1064] "User" refers to a company or individual that uses this system.

[1065] "Information" refers generally to data provided by users, including but not limited to financial data.

[1066] "Internal data structure" refers to the database or storage system that stores and manages data efficiently and systematically within the server.

[1067] "Cost optimization" is the process of analyzing financial data and providing analysis and recommendations to reduce wasteful spending and maximize profits.

[1068] "Emotional state" refers to the user's mental and emotional state, including anger, stress, interest, and the like.

[1069] "Emotion Engine" refers to the combination of analytical algorithms and hardware / software for recognizing a user's emotional state.

[1070] "Analysis results" refers to results obtained based on the analysis of financial data, including identifying wasteful expenditures and proposing cost reductions.

[1071] "Improvement recommendations" refer to specific actions and strategies provided to improve a company's cost efficiency based on the analysis results.

[1072] "Real-time" refers to a process in which a response is returned immediately after the user provides information, without delay.

[1073] A "database" is a system for managing accumulated data, and includes formats such as SQL and NoSQL.

[1074] "Analysis" refers to the process of processing data to extract useful information.

[1075] "Expenditure" refers to the economic resources consumed by a business to produce and provide goods and services.

[1076] "Revenue" refers to the total income a company receives from selling goods and services.

[1077] "Waste" refers to expenses that are incurred but are not necessarily necessary.

[1078] "Accuracy" refers to an indicator of the accuracy of the analysis and suggestions.

[1079] "Optimized format" refers to a format of information presentation that is tailored based on the user's emotional state.

[1080] This invention relates to a system aimed at optimizing corporate costs. In particular, it quickly and accurately detects cost waste based on financial data provided by users and provides improvement suggestions in real time. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it aims to adjust the method of providing analysis results and improvement suggestions based on the user's emotional state, and furthermore, to use emotion data to improve the accuracy of future analyses.

[1081] The server receives financial data provided by the company and stores it in an internal data structure. Specific examples of databases used include MySQL and PostgreSQL. The data is usually provided in a structured format such as JSON and is then stored in the database. This prepares the data required for subsequent analytical processing.

[1082] The device then performs an analysis process for cost optimization based on the stored data. Specifically, it compares expenditures with revenue to determine whether there is any wasteful spending. If expenditures exceed a certain threshold (for example, 80% of revenue), they are detected as "waste." The results of this analysis are presented to the user. For example, if monthly expenditures reach 85% of revenue, the device generates the result "Waste detected: costs exceed 80% of revenue."

[1083] The device also uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes emotions from the user's facial expressions, tone of voice, input content, etc., and stores the results in a database. Specific examples of the technologies used include facial expression recognition algorithms and voice analysis algorithms. For example, if the user has a tired expression, the device recognizes the user's emotional state as "tired" and stores that information in the database.

[1084] The device then adjusts how it presents analysis results and improvement suggestions based on the user's perceived emotional state. If the user is tired, it will provide information in a concise, easy-to-understand format. If the user is interested, it will provide detailed analysis results and multiple improvement suggestions. For example, if the user is recognized as "tired," the device will display a concise suggestion: "Consider automating your manufacturing process to improve efficiency."

[1085] Additionally, the server accumulates emotional data and uses it to improve the accuracy of future analysis and recommendations. This is achieved by training generative AI models (e.g., neural networks for emotion recognition and recommendation generation) based on the accumulated emotional data, which allows for more accurate results in subsequent sessions.

[1086] Ultimately, users can log in to the system and upload their latest financial data to receive analysis results and improvement suggestions in real time. For example, when a user logs in and uploads a new month's financial data, the device immediately begins analysis and displays suggestions tailored to the user's emotional state (e.g., "Waste detection: Costs exceed 80% of revenue. Consider automating the manufacturing process.").

[1087] An example prompt is, "Detect waste in a company's financial data and present the results. Also, provide improvement suggestions in an appropriate format based on the user's current emotional state (e.g., tired, interested, etc.)."

[1088] Through this series of processes, the present invention efficiently optimizes corporate costs and enables flexible and effective information provision according to the user's emotional state, which is expected to reduce the user's stress level and improve the speed of decision-making.

[1089] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1090] Step 1:

[1091] The server receives financial data from the user. As input, a financial data file in JSON format is used. The server parses this data and stores it in a structured format (e.g., a MySQL database). Specifically, the server receives the provided "financial_data.json" file and stores the income and expense data in the database. As output, the server obtains the financial data stored in the database.

[1092] Step 2:

[1093] The terminal begins an analysis to optimize costs based on the stored financial data. Revenue and expenditure data retrieved from the database is used as input. The terminal compares this data and determines whether expenditure exceeds 80% of revenue. Specifically, the terminal retrieves the data "Revenue: 5 million yen, Expenses: 4.5 million yen" and calculates that expenditures are 90% of revenue. The output is the analysis result "Waste detected: Costs exceed 80% of revenue."

[1094] Step 3:

[1095] The device uses an emotion engine to recognize the user's emotional state. The inputs include the user's facial expressions, tone of voice, and input content. The emotion engine analyzes this data to determine the user's emotional state. Specifically, the device uses a camera and microphone to capture the user's video and audio, and the facial expression recognition algorithm determines that the user's face looks tired, and the voice analysis algorithm determines that the user's voice lacks vitality. The output, "User's emotional state: tired," is saved in the database.

[1096] Step 4:

[1097] The device adjusts the way it presents analysis results and improvement suggestions based on the recognized emotional state of the user. The analysis results and the user's emotional state data are used as input. Specifically, the device generates concise and easy-to-understand suggestions based on the data "User's emotional state: Fatigue." Suggestions such as "Waste detected: Costs exceed 80% of revenue. Consider automating the manufacturing process to improve efficiency" are generated. The adjusted analysis results and improvement suggestions are displayed to the user as output.

[1098] Step 5:

[1099] The server uses the accumulated emotion data to improve future analysis accuracy. Past emotion data and analysis results are used as input. The server learns from these data and updates the generative AI model. Specifically, the server uses a neural network to train the model based on the emotion data and analysis results, enabling it to make more accurate suggestions in the next session. The output is an updated generative AI model.

[1100] Step 6:

[1101] Users log in to the system and upload their latest financial data to receive real-time adjusted analysis results and improvement suggestions. A new financial data file is used as input. Specifically, when a user uploads a new "financial_data.json", the device immediately starts analysis and restarts the emotion engine. As output, the user is provided with real-time optimized analysis results and improvement suggestions.

[1102] (Application example 2)

[1103] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1104] Modern business activities require the rapid detection and optimization of cost waste. Furthermore, flexible responses based on the user's emotional state are also required for on-site workers and managers to receive appropriate improvement suggestions in real time. However, existing systems struggle to provide effective improvement suggestions that take the user's emotional state into account, and the suggestions often do not fit the user's situation. Therefore, there is a need for a system that supports decision-making while reducing user stress and providing effective cost optimization.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1106] In this invention, the server includes a means for receiving information from a user and storing it in an internal data structure, a means for optimizing costs based on the stored information, a means for recognizing the user's emotional state, a means for adjusting the method for providing analysis results and improvement suggestions based on the stored emotional state, and a means for providing the analysis results and improvement suggestions to the user in real time. This enables the system to analyze a company's financial data to detect waste and provide information optimized according to the user's emotional state. Furthermore, by using prompt sentences based on a generative AI model, it is possible to provide effective improvement suggestions in real time and support decision-making that meets on-site requirements.

[1107] "User" refers to an individual or company that uses the system.

[1108] "Information" generally refers to data provided by users, including, specifically, company financial data and emotional state data.

[1109] "Internal data structure" refers to the format in which received information is stored and the data is organized and managed within the system for use in subsequent processing.

[1110] "Means" refers to methods or techniques for achieving a specific purpose.

[1111] "Cost optimization" refers to methods and techniques for optimizing a company's expenditures and reducing wasteful spending.

[1112] "User's emotional state" refers to the psychological state of the user using the system, and mainly includes states such as stress, interest, and fatigue.

[1113] "To recognize" refers to accurately grasping a specific piece of information or situation.

[1114] "Analysis results" refers to the conclusions and indicators obtained after data analysis.

[1115] "Improvement proposal" refers to proposing specific improvement methods or countermeasures for detected problems.

[1116] "Real-time" refers to processing or responding to information immediately after it is received or generated.

[1117] "Based on the stored emotional state" means that the next processing or response is based on the emotional data.

[1118] A "generative AI model" refers to algorithms or techniques that use artificial intelligence to generate data for a specific purpose.

[1119] A "prompt" refers to text in the form of instructions or questions that are input to a generative AI model.

[1120] The present invention is a system that analyzes financial data, detects wasteful spending, and makes improvement suggestions based on the results, with the aim of optimizing corporate costs.The present invention also recognizes the user's emotional state and provides improvement suggestions based on that emotion, thereby reducing the user's stress and supporting decision-making.

[1121] The system includes the following major components:

[1122] 1. Server:

[1123] The server receives the company's financial data from the user and stores it in an internal data structure. The data is usually provided in a structured format such as JSON, and the server stores it in an internal database (e.g., SQLite) and makes the stored data available to other components.

[1124] 2. Data analysis engine:

[1125] Cost optimization is performed based on the stored financial data. Specifically, expenses are compared with revenues to detect wasteful spending. This analysis engine is implemented using software such as Cost Analysis Engine. If expenses exceed the threshold for waste, they are detected as "waste."

[1126] 3. Emotion Recognition Engine:

[1127] Recognize the user's emotional state. An emotion recognition engine analyzes the user's facial expressions and tone of voice and stores the results in a database. Using software such as EmotionEngine, it can determine whether the user is stressed, calm, interested, etc. This emotional data is used to tailor how subsequent suggestions are presented.

[1128] 4. Proposal Generation Engine:

[1129] The engine adjusts the way it presents analysis results and improvement suggestions based on the user's perceived emotional state. For example, if the user is tired, it will provide information in a concise and easy-to-understand format. On the other hand, if the user is interested, it will provide detailed analysis results and multiple improvement suggestions. This suggestion generation engine works by using prompt sentences based on a generative AI model.

[1130] Specific use cases:

[1131] When a manufacturing company provides monthly expenditure and revenue data to the system, the system analyzes the data and generates a result saying, "Waste detected: Costs exceed 80% of revenue" if expenditures exceed 80% of revenue. The system also analyzes the user's emotional state, and if it recognizes that the user is prone to stress, for example, it will present a concise and specific improvement suggestion such as, "Consider automating the manufacturing process to improve efficiency."

[1132] Example prompt sentence:

[1133] If the user's facial expression and tone of voice indicate stress, generate concise and specific suggestions for cost reduction.

[1134] This allows users to receive effective improvement suggestions at the right time, reducing stress and enabling quick decision-making. In addition, the accumulation of emotional data will contribute to improving the accuracy of future suggestions.

[1135] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1136] Step 1:

[1137] The server receives the company's financial data from the user. The user provides the financial data (e.g., expenditure and revenue data) to the system, and the server receives it in a structured format, such as JSON. The entered data is stored in an internal data structure, which prepares the data needed for subsequent data analysis.

[1138] Step 2:

[1139] The server stores the received financial data in an internal database. Specifically, it parses the received JSON format data and stores it in a database such as SQLite, ensuring data persistence.

[1140] Step 3:

[1141] The data analysis engine optimizes costs based on stored financial data. In other words, it compares expenses with revenue and detects wasteful spending. For example, if expenses exceed 80% of revenue, it detects this as waste and saves it as the analysis result. The input is financial data, and the output is the waste detection results.

[1142] Step 4:

[1143] An emotion recognition engine recognizes the user's emotional state. The facial expressions and tone of voice input by the user to the system are analyzed by emotion recognition software such as EmotionEngine. The input is the user's facial expressions and voice data, and the output is the recognized emotional state. This emotional data is stored in a database.

[1144] Step 5:

[1145] The server generates improvement suggestions through a suggestion generation engine based on the recognized emotional state. Using a generative AI model, it generates suggestion sentences based on the prompt sentences and corresponding to the user's emotional state. The input is the emotional state and analysis results, and the output is individually tailored improvement suggestions.

[1146] Step 6:

[1147] The proposal generation engine provides the user with analysis results and improvement suggestions in real time. Specifically, it displays proposals in a format that corresponds to the user's emotional state. For example, if the user is feeling stressed, it displays concise and specific suggestions. The input is the adjusted improvement suggestions, and the output is the suggestions that are displayed to the user.

[1148] Step 7:

[1149] The user decides on the next action based on the provided improvement suggestions. By receiving feedback from the system, the user can make efficient cost optimization decisions.

[1150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1151] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1152] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1154] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1160] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1161] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1163] 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.

[1164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1165] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1166] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1171] The following is further disclosed regarding the above embodiment.

[1172] (Claim 1)

[1173] means for receiving information from a user and storing it in an internal data structure;

[1174] A means for cost optimization based on the stored information;

[1175] A means for providing users with analysis results and improvement suggestions in real time;

[1176] A system including:

[1177] (Claim 2)

[1178] 10. The system of claim 1, wherein the received information includes financial data of the company.

[1179] (Claim 3)

[1180] 10. The system of claim 1, wherein the cost optimization means compares expenditures with revenues to detect waste.

[1181] "Example 1"

[1182] (Claim 1)

[1183] means for receiving financial information from users in a structured format and storing the information in an internal database;

[1184] A means for analyzing expenses and revenues from stored financial information and detecting wasteful expenses;

[1185] A means of generating specific improvement suggestions when wasteful spending is detected;

[1186] A means of providing users with analysis results and improvement suggestions in real time;

[1187] A system including:

[1188] (Claim 2)

[1189] 10. The system of claim 1, wherein the received information includes financial data of the company.

[1190] (Claim 3)

[1191] 2. The system of claim 1, wherein the cost optimization suggestions include instructions for specific efficiency improvements.

[1192] "Application Example 1"

[1193] (Claim 1)

[1194] means for receiving information from a user and storing it in an internal data structure;

[1195] A means for cost optimization based on the stored information;

[1196] A means to collect and analyze operational data of each robot in the factory and related financial data in real time,

[1197] A means for providing users with analysis results and improvement suggestions in real time;

[1198] A system including:

[1199] (Claim 2)

[1200] 10. The system of claim 1, wherein the received information includes company financial data and robot operational data.

[1201] (Claim 3)

[1202] 2. The system of claim 1, wherein the cost optimization means compares expenditures with revenues to detect waste and makes suggestions to improve the operating efficiency of the robots.

[1203] "Example 2: Combining Emotion Engines"

[1204] (Claim 1)

[1205] means for receiving information from a user and storing it in an internal data structure;

[1206] A means for cost optimization based on the stored information;

[1207] means for recognizing a user's emotional state and tailoring the delivery of analysis results and improvement suggestions based on that state;

[1208] A means of analyzing data and detecting wasteful spending;

[1209] means for storing the recognized emotional state in a database;

[1210] A means of using accumulated emotion data to improve the accuracy of future analysis;

[1211] a means for providing the user with analysis results and improvement suggestions in real time in an optimized format according to the user's emotional state;

[1212] A system including:

[1213] (Claim 2)

[1214] 10. The system of claim 1, wherein the received information includes financial data of the company.

[1215] (Claim 3)

[1216] 10. The system of claim 1, wherein the cost optimization means compares expenditures with revenues to detect waste.

[1217] "Application example 2 when combining emotion engines"

[1218] (Claim 1)

[1219] means for receiving information from a user and storing it in an internal data structure;

[1220] A means for cost optimization based on the stored information;

[1221] means for recognizing the emotional state of a user;

[1222] means for tailoring the delivery of analysis results and improvement suggestions based on the stored emotional state;

[1223] A means for providing users with analysis results and improvement suggestions in real time;

[1224] A system including:

[1225] (Claim 2)

[1226] 10. The system of claim 1, wherein the received information includes financial data of the company.

[1227] (Claim 3)

[1228] 10. The system of claim 1, wherein the cost optimization means compares expenditures with revenues to detect waste.

[1229] (Claim 4)

[1230] 2. The system of claim 1, wherein the suggestions are provided using prompt sentences based on a generative AI model. [Explanation of symbols]

[1231] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving information from a user and storing it in an internal data structure; A means for cost optimization based on the stored information; A means for providing users with analysis results and improvement suggestions in real time; A system including:

2. 10. The system of claim 1, wherein the received information includes financial data of the company.

3. 10. The system of claim 1, wherein the cost optimization means compares expenses to revenues to detect waste.

Images