system

Abstract

The system according to the embodiment aims to reduce the burden on managers by automatically approving small-scale cases. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and an approval unit. The collection unit collects information about a case. The analysis unit analyzes the information collected by the collection unit. The determination unit determines the size of the case based on the information analyzed by the analysis unit. The approval unit automatically approves a case if the determination unit determines that the case is small.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, approval procedures for small projects were time-consuming and placed a heavy burden on managers.

[0005] The system according to the embodiment aims to reduce the burden on managers by automatically approving small-scale cases. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and an approval unit. The collection unit collects information about the case. The analysis unit analyzes the information collected by the collection unit. The determination unit determines the scale of the case based on the information analyzed by the analysis unit. The approval unit automatically approves the case when the determination unit determines that the case is small. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on managers by automatically approving small-scale cases. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An approval system according to an embodiment of the present invention automatically collects and analyzes project information, determines project size, and automatically approves small projects. The approval system collects and analyzes project information, determines project size, and automatically approves projects that are determined to be small. This reduces the burden of daily approval tasks on managers, allowing them to allocate their time and effort to other important tasks. Furthermore, the approval system utilizes the data collection capabilities and text comprehension capabilities of generative AI to process projects that require careful judgment without overlooking them. For example, the approval system collects and analyzes detailed project information to determine project size. For example, it collects and analyzes information such as the project's content, size, and importance. Next, the approval system determines the project size based on the collected information. For example, the project size is determined taking into account the project's amount, scope of impact, risk, and other factors. Furthermore, the approval system automatically approves projects that are determined to be small. For example, it automatically approves projects with amounts below a certain level or low risk. This reduces the burden of approval tasks on managers, allowing them to allocate their time and effort to other important tasks. This allows the approval system to reduce the burden of approval work on managers, allowing them to allocate their time and effort to other important tasks. For example, they can focus on strategic decision-making and team management. The approval system also utilizes the generative AI's data collection capabilities and text comprehension skills to process cases that require careful judgment without overlooking them. This allows the system to focus on checking things that humans tend to skip, minimizing mistakes and errors.

[0029] The approval system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and an approval unit. The collection unit collects information about a case. The case information includes, for example, the content, scale, and importance of the case, but is not limited to these examples. The collection unit can collect information manually or using an automated collection method. The collection unit can also automatically collect the case information using a generation AI. For example, the collection unit inputs the content of the case into the generation AI, and the generation AI automatically collects the information. The analysis unit analyzes the information collected by the collection unit. The analysis is performed based on, for example, a data analysis method or an algorithm used, but is not limited to these examples. For example, the analysis unit analyzes the collected information using a text analysis algorithm. The analysis unit can also analyze the collected information using the generation AI. For example, the analysis unit inputs the collected information into the generation AI, and the generation AI automatically analyzes it. The determination unit determines the scale of the case based on the information analyzed by the analysis unit. The determination is performed taking into account, for example, the amount of the case, the scope of impact, and the risk, but is not limited to these examples. For example, the determination unit determines the size of the project based on the amount of the project. The determination unit can also use the generation AI to determine the size of the project. For example, the determination unit inputs analyzed information into the generation AI, and the generation AI automatically determines the size. The approval unit automatically approves the project if the determination unit determines that the project is small. Approval is performed, for example, for projects with an amount below a certain level or with low risk, but is not limited to these examples. For example, the approval unit automatically approves projects with an amount below a certain level. The approval unit can also automatically approve the project using the generation AI. For example, the approval unit inputs the determination result into the generation AI, and the generation AI automatically approves the project. As a result, the approval system according to the embodiment can reduce the burden of approval work on managers by collecting, analyzing, and determining project information and automatically approving small projects.

[0030] The collection unit can collect information on the content, scale, and importance of the case. The content of the case includes, for example, the purpose, background, and detailed description of the case, but is not limited to these examples. The collection unit, for example, collects the purpose of the case. The collection unit can also collect the background of the case. The collection unit can also collect a detailed description of the case. The scale of the case includes, for example, the amount, scope of impact, and amount of resource input, but is not limited to these examples. The collection unit, for example, collects the amount of the case. The collection unit can also collect the scope of impact of the case. The collection unit can also collect the amount of resource input of the case. The importance of the case includes, for example, the impact on business and urgency, but is not limited to these examples. The collection unit, for example, collects the impact of the case on business. The collection unit can also collect the urgency of the case. The collection unit can also collect criteria for evaluating the importance of the case. By collecting detailed information about the case, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input information on the content, scale, and importance of the case into the generation AI, which may then automatically collect the information.

[0031] The analysis unit can determine the scale of the case based on the collected information. The collected information includes, for example, text data, numerical data, image data, etc., but is not limited to these examples. The analysis unit, for example, analyzes the collected text data. The analysis unit can also analyze the collected numerical data. The analysis unit can also analyze the collected image data. The determination of the scale is performed, for example, based on a data analysis method or an algorithm used, but is not limited to these examples. The analysis unit can determine the scale using, for example, a text analysis algorithm. The analysis unit can also determine the scale using a numerical analysis algorithm. The analysis unit can also determine the scale using an image analysis algorithm. This allows the scale of the case to be accurately determined based on the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information to a generation AI, and the generation AI can automatically determine the scale.

[0032] The determination unit can determine the scale of a project by considering the amount, scope of impact, and risk of the project. The amount includes, for example, budget, cost, revenue, etc., but is not limited to these examples. The determination unit can determine the scale by considering, for example, the budget of the project. The determination unit can also determine the scale by considering the cost of the project. The determination unit can also determine the scale by considering the revenue of the project. The scope of impact includes, for example, affected departments, regions, customers, etc., but is not limited to these examples. The determination unit can determine the scale by considering, for example, affected departments. The determination unit can also determine the scale by considering the affected regions. The determination unit can also determine the scale by considering the affected customers. The risk includes, for example, type of risk, risk assessment method, etc., but is not limited to these examples. The determination unit can determine the scale by considering, for example, type of risk. The determination unit can also determine the scale by considering the risk assessment method. The determination unit can also determine the scale by considering risk assessment criteria. This enables more accurate size determination by considering the amount, scope of impact, risk, etc. of the project. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input information on the amount, scope of impact, and risk of the project into the generation AI, which may then automatically determine the scale.

[0033] The approval unit can automatically approve cases with amounts below a certain level or cases with low risk. An amount below a certain level includes, but is not limited to, a specific upper limit of the amount. For example, the approval unit automatically approves cases with amounts below 1 million yen. The approval unit can also automatically approve cases with amounts below 500,000 yen. The approval unit can also automatically approve cases with amounts below 100,000 yen. Low risk includes, but is not limited to, risk assessment scores and types of risk. For example, the approval unit automatically approves cases with low risk assessment scores. The approval unit can also automatically approve cases with few types of risk. The approval unit can also automatically approve cases with low risk impact. This improves the efficiency of approval work by automatically approving cases with amounts below a certain level or cases with low risk. Some or all of the above-described processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval department can input information about projects with amounts below a certain level or low risk into the generation AI, which can then automatically approve the projects.

[0034] The collection unit can analyze past case information and select the optimal collection method. For example, the collection unit analyzes past case information and selects the most efficient collection method. The collection unit can also find specific patterns from past case information and select a collection method based on the patterns. The collection unit can also customize a collection method based on past case information and select the optimal method. In this way, the optimal collection method can be selected by analyzing past case information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past case information into a generation AI, which then automatically analyzes and selects the optimal collection method.

[0035] When collecting project information, the collection unit can filter the project information based on the user's current project or area of ​​interest. For example, the collection unit collects only project information related to the user's current project. The collection unit can also prioritize collecting related project information based on the user's area of ​​interest. The collection unit can also filter and collect necessary project information according to the user's project progress. This makes it possible to collect highly relevant project information by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input information on the user's current project or area of ​​interest to the generation AI, and the generation AI can automatically perform filtering.

[0036] When collecting case information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect case information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect case information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect case information using image recognition technology. This allows case information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information about the user's input method into a generation AI, which can then automatically select the optimal collection means.

[0037] When collecting case information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting case information close to the user's current location. The collection unit can also filter highly relevant case information based on the user's geographical location information. The collection unit can also prioritize collecting region-specific case information based on the user's location information. In this way, highly relevant case information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI, and the generation AI can automatically prioritize collecting highly relevant information.

[0038] When collecting case information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content of the user's social media posts and collect related case information. The collection unit can also collect related case information by referring to the activities of the user's friends on social media. The collection unit can also collect related case information based on the user's social media check-in information. In this way, related case information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into a generation AI, which can then automatically analyze and collect related information.

[0039] When collecting case information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, improves the collection method based on the user's past feedback. The collection unit can also adjust the type of case information to be collected by reflecting the user's feedback. The collection unit can also optimize the collection timing by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of the user's past feedback into the generation AI, which then automatically analyzes and customizes the collection method.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the case during analysis. For example, the analysis unit performs a detailed analysis on a case with a high level of importance. The analysis unit can also perform a concise analysis on a case with a low level of importance. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the case. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the importance of the case to the generation AI, and the generation AI can automatically adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the case category. For example, the analysis unit can apply a financial data analysis algorithm to a financial case. The analysis unit can also apply a legal data analysis algorithm to a legal case. The analysis unit can also apply a technical data analysis algorithm to a technical case. In this way, by applying different analysis algorithms depending on the case category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input case category information into the generation AI, and the generation AI can automatically apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also learn the user's past analysis results and continuously improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI, and the generation AI can automatically improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the submission date of the case. For example, the analysis unit prioritizes analysis of cases that were submitted earlier. The analysis unit can also postpone cases that were submitted later. The analysis unit can also gradually adjust the analysis priority according to the submission date. This enables efficient analysis by determining the analysis priority based on the submission date of the case. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the submission date of the case into the generation AI, and the generation AI can automatically determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of cases during analysis. For example, the analysis unit prioritizes analysis of highly relevant cases. The analysis unit can also postpone less relevant cases. The analysis unit can also gradually adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of cases. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the relevance of cases to the generation AI, and the generation AI can automatically adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit can use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can avoid technical terms and use concise expressions. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the user's level of expertise into the generation AI, which can then automatically adjust the use of technical terms in the analysis.

[0046] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships between cases when making a judgment. The judgment unit, for example, analyzes the interrelationships between cases and makes a judgment on related cases collectively. The judgment unit can also improve the accuracy of the judgment by taking into account the interrelationships between cases. The judgment unit can also adjust the judgment criteria based on the interrelationships between cases. In this way, the accuracy of the judgment is improved by taking into account the interrelationships between cases. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input information on the interrelationships between cases into the generation AI, and the generation AI can automatically improve the accuracy of the judgment.

[0047] When making a judgment, the judgment unit can make a judgment taking into account the attribute information of the submitter of the case. The judgment unit can make a judgment taking into account, for example, the submitter's past performance. The judgment unit can also make a judgment taking into account the submitter's level of expertise. The judgment unit can also adjust the judgment criteria based on the submitter's attribute information. This makes it possible to make a more appropriate judgment by taking into account the attribute information of the submitter of the case. Some or all of the above-mentioned processing in the judgment unit can be performed using, for example, AI, or can be performed without using AI. For example, the judgment unit can input the submitter's attribute information into the generation AI, and the generation AI can automatically make a judgment.

[0048] The judgment unit can weight the judgment based on the frequency of submission of the case when making the judgment. For example, the judgment unit makes a judgment based on strict criteria for cases that are submitted frequently. The judgment unit can also make a judgment based on flexible criteria for cases that are submitted infrequently. The judgment unit can also adjust the weight of the judgment in stages according to the submission frequency. This enables efficient judgment by weighting the judgment based on the frequency of submission of the case. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input information on the frequency of submission of the case to the generation AI, and the generation AI can automatically weight the judgment.

[0049] The determination unit can make a determination taking into account the geographical distribution of cases. For example, the determination unit gives priority to determining geographically close cases. The determination unit can also postpone geographically distant cases. The determination unit can also adjust the priority of determination according to the geographical distribution. This allows for more appropriate determination by taking the geographical distribution of cases into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input information on the geographical distribution of cases into the generation AI, and the generation AI can automatically make a determination.

[0050] The judgment unit can improve the accuracy of the judgment by referring to related literature of the case when making a judgment. The judgment unit, for example, refers to related literature to improve the accuracy of the judgment. The judgment unit can also adjust the judgment criteria based on the related literature. The judgment unit can also analyze related literature to continuously improve the accuracy of the judgment. In this way, the accuracy of the judgment is improved by referring to related literature of the case. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input information on related literature into the generation AI, and the generation AI can automatically improve the accuracy of the judgment.

[0051] The judgment unit can make a judgment taking into account the market value of the project. For example, the judgment unit makes a judgment using strict criteria for projects with high market value. The judgment unit can also make a judgment using flexible criteria for projects with low market value. The judgment unit can also gradually adjust the judgment criteria depending on the market value. This allows for a more appropriate judgment by taking the market value of the project into consideration. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input information on the market value of the project into the generation AI, and the generation AI can automatically make a judgment.

[0052] At the time of approval, the approval unit can select the optimal approval method by analyzing the user's past approval behavior. The approval unit selects the optimal approval method based on, for example, the user's past approval behavior. The approval unit can also analyze the user's past approval behavior and customize the approval method. The approval unit can also improve the approval method by referring to the user's past approval behavior. In this way, the optimal approval method can be selected by analyzing the user's past approval behavior. Some or all of the above-mentioned processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input data on the user's past approval behavior into a generation AI, which can automatically select the optimal approval method.

[0053] The approval unit can customize the approval means based on the user's current living situation at the time of approval. For example, if the user is busy, the approval unit provides a means for quick approval. The approval unit can also provide a detailed approval means if the user is relaxed. The approval unit can also customize the approval means according to the user's living situation. This enables more appropriate approval by customizing the approval means based on the user's current living situation. Some or all of the above-mentioned processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input information about the user's living situation into the generation AI, which can then automatically customize the approval means.

[0054] The approval unit can improve the approval method by reflecting user feedback at the time of approval. The approval unit, for example, improves the approval method based on user feedback. The approval unit can also customize the approval means by reflecting user feedback. The approval unit can also continuously improve the approval method by referring to user feedback. In this way, the approval method can be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the approval unit may be performed using AI, for example, or may be performed without using AI. For example, the approval unit can input user feedback data into a generation AI, which can then automatically improve the approval method.

[0055] At the time of approval, the approval unit can select the optimal approval method by taking into account the user's geographical location information. The approval unit selects the optimal approval method based on, for example, the user's current location. The approval unit can also customize the approval means by taking into account the user's geographical location information. The approval unit can also provide a region-specific approval method based on the user's location information. This makes it possible to select the optimal approval method by taking into account the user's geographical location information. Some or all of the above-described processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input the user's geographical location information data into the generation AI, and the generation AI can automatically select the optimal approval method.

[0056] At the time of approval, the approval unit can analyze the user's social media activity and suggest a means of approval. For example, the approval unit can analyze the content of the user's social media posts and suggest the optimal means of approval. The approval unit can also suggest a means of approval by referring to the activities of the user's friends on social media. The approval unit can also suggest a means of approval based on the user's check-in information on social media. In this way, the optimal means of approval can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input data on the user's social media activity into a generation AI, which then automatically analyzes the data and suggests the optimal means of approval.

[0057] The approval unit can customize the approval method by reflecting the user's past feedback at the time of approval. The approval unit can improve the approval method based on the user's past feedback, for example. The approval unit can also customize the approval means by reflecting the user's feedback. The approval unit can also continuously improve the approval method by referring to the user's past feedback. In this way, the approval method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input data of the user's past feedback into the generation AI, and the generation AI can automatically customize the approval method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The approval system may further include a notification unit. The notification unit has the function of notifying the relevant parties of the approval results of a case. For example, the notification unit may notify the relevant parties by email about cases for which approval has been completed. The notification unit may also send alerts to the relevant parties about cases for which approval is on hold. Furthermore, the notification unit may send notifications to the relevant parties, including detailed reasons for denial of approval, about cases for which approval has been denied. This allows the relevant parties to grasp the progress of the case in real time and respond quickly.

[0060] When analyzing project information, the analysis unit can improve the accuracy of the analysis by linking with external databases. For example, the analysis unit obtains the latest market trends from an external market database and evaluates the market value of the project. The analysis unit can also obtain risk information from an external risk database and perform project risk evaluation. Furthermore, the analysis unit can obtain technical information from an external technology database and perform technical evaluation. By linking with external databases, the accuracy of the analysis can be improved, enabling more accurate evaluation.

[0061] When approving a project, the approval department can improve the accuracy of approval by referring to the approval history of the project. For example, the approval department can refer to the history of similar projects that have been approved in the past and adjust the approval criteria. The approval department can also refer to the history of projects that have been rejected in the past and perform risk assessment. Furthermore, the approval department can optimize the approval process based on the past approval history. In this way, by referring to the past approval history, the accuracy of approval can be improved and more appropriate approval can be achieved.

[0062] When collecting case information, the collection unit can analyze the user's past behavioral patterns to determine the optimal collection timing. For example, the collection unit analyzes the time periods in which the user collected information in the past and collects information during those time periods. The collection unit can also find the most efficient collection timing from the user's past behavioral patterns and collect information at that timing. Furthermore, the collection unit can customize the collection timing based on the user's past behavioral patterns. This makes it possible to determine the optimal collection timing by analyzing the user's past behavioral patterns.

[0063] When collecting project information, the collection unit can adjust the information to be collected taking into account the user's current project progress. For example, if the user's project is in the early stages, the collection unit can collect basic information. If the project is approaching the middle stage, the collection unit can also collect detailed information. Furthermore, if the project is in the final stages, the collection unit can prioritize the collection of the most important information. In this way, by adjusting the information to be collected according to the user's project progress, the project can be smoothly progressed.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects case information. Case information includes, for example, the content, scale, and importance of the case. The collection unit can collect information manually, using automated collection methods, or using generation AI. For example, the content of the case is input into the generation AI, which then automatically collects information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data analysis techniques and text analysis algorithms. The analysis unit can also automatically analyze the collected information using the generation AI. For example, the collected information is input into the generation AI, which then automatically performs the analysis. Step 3: The determination unit determines the scale of the project based on the information analyzed by the analysis unit. The determination is made taking into account the project's amount, scope of impact, risk, etc. The determination unit can also use the generation AI to automatically determine the scale of the project. For example, the analyzed information is input into the generation AI, which then automatically determines the scale. Step 4: The approval department automatically approves projects that are judged to be small-scale by the judgment department. Approval is given to projects with amounts below a certain level or low risk. The approval department can also use the generation AI to automatically approve projects. For example, the judgment results are input into the generation AI, which then automatically approves.

[0066] (Example 2) An approval system according to an embodiment of the present invention automatically collects and analyzes project information, determines project size, and automatically approves small projects. The approval system collects and analyzes project information, determines project size, and automatically approves projects that are determined to be small. This reduces the burden of daily approval tasks on managers, allowing them to allocate their time and effort to other important tasks. Furthermore, the approval system utilizes the data collection capabilities and text comprehension capabilities of generative AI to process projects that require careful judgment without overlooking them. For example, the approval system collects and analyzes detailed project information to determine project size. For example, it collects and analyzes information such as the project's content, size, and importance. Next, the approval system determines the project size based on the collected information. For example, the project size is determined taking into account the project's amount, scope of impact, risk, and other factors. Furthermore, the approval system automatically approves projects that are determined to be small. For example, it automatically approves projects with amounts below a certain level or low risk. This reduces the burden of approval tasks on managers, allowing them to allocate their time and effort to other important tasks. This allows the approval system to reduce the burden of approval work on managers, allowing them to allocate their time and effort to other important tasks. For example, they can focus on strategic decision-making and team management. The approval system also utilizes the generative AI's data collection capabilities and text comprehension skills to process cases that require careful judgment without overlooking them. This allows the system to focus on checking things that humans tend to skip, minimizing mistakes and errors.

[0067] The approval system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and an approval unit. The collection unit collects information about a case. The case information includes, for example, the content, scale, and importance of the case, but is not limited to these examples. The collection unit can collect information manually or using an automated collection method. The collection unit can also automatically collect the case information using a generation AI. For example, the collection unit inputs the content of the case into the generation AI, and the generation AI automatically collects the information. The analysis unit analyzes the information collected by the collection unit. The analysis is performed based on, for example, a data analysis method or an algorithm used, but is not limited to these examples. For example, the analysis unit analyzes the collected information using a text analysis algorithm. The analysis unit can also analyze the collected information using the generation AI. For example, the analysis unit inputs the collected information into the generation AI, and the generation AI automatically analyzes it. The determination unit determines the scale of the case based on the information analyzed by the analysis unit. The determination is performed taking into account, for example, the amount of the case, the scope of impact, and the risk, but is not limited to these examples. For example, the determination unit determines the size of the project based on the amount of the project. The determination unit can also use the generation AI to determine the size of the project. For example, the determination unit inputs analyzed information into the generation AI, and the generation AI automatically determines the size. The approval unit automatically approves the project if the determination unit determines that the project is small. Approval is performed, for example, for projects with an amount below a certain level or with low risk, but is not limited to these examples. For example, the approval unit automatically approves projects with an amount below a certain level. The approval unit can also automatically approve the project using the generation AI. For example, the approval unit inputs the determination result into the generation AI, and the generation AI automatically approves the project. As a result, the approval system according to the embodiment can reduce the burden of approval work on managers by collecting, analyzing, and determining project information and automatically approving small projects.

[0068] The collection unit can collect information on the content, scale, and importance of the case. The content of the case includes, for example, the purpose, background, and detailed description of the case, but is not limited to these examples. The collection unit, for example, collects the purpose of the case. The collection unit can also collect the background of the case. The collection unit can also collect a detailed description of the case. The scale of the case includes, for example, the amount, scope of impact, and amount of resource input, but is not limited to these examples. The collection unit, for example, collects the amount of the case. The collection unit can also collect the scope of impact of the case. The collection unit can also collect the amount of resource input of the case. The importance of the case includes, for example, the impact on business and urgency, but is not limited to these examples. The collection unit, for example, collects the impact of the case on business. The collection unit can also collect the urgency of the case. The collection unit can also collect criteria for evaluating the importance of the case. By collecting detailed information about the case, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input information on the content, scale, and importance of the case into the generation AI, which may then automatically collect the information.

[0069] The analysis unit can determine the scale of the case based on the collected information. The collected information includes, for example, text data, numerical data, image data, etc., but is not limited to these examples. The analysis unit, for example, analyzes the collected text data. The analysis unit can also analyze the collected numerical data. The analysis unit can also analyze the collected image data. The determination of the scale is performed, for example, based on a data analysis method or an algorithm used, but is not limited to these examples. The analysis unit can determine the scale using, for example, a text analysis algorithm. The analysis unit can also determine the scale using a numerical analysis algorithm. The analysis unit can also determine the scale using an image analysis algorithm. This allows the scale of the case to be accurately determined based on the collected information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information to a generation AI, and the generation AI can automatically determine the scale.

[0070] The determination unit can determine the scale of a project by considering the amount, scope of impact, and risk of the project. The amount includes, for example, budget, cost, revenue, etc., but is not limited to these examples. The determination unit can determine the scale by considering, for example, the budget of the project. The determination unit can also determine the scale by considering the cost of the project. The determination unit can also determine the scale by considering the revenue of the project. The scope of impact includes, for example, affected departments, regions, customers, etc., but is not limited to these examples. The determination unit can determine the scale by considering, for example, affected departments. The determination unit can also determine the scale by considering the affected regions. The determination unit can also determine the scale by considering the affected customers. The risk includes, for example, type of risk, risk assessment method, etc., but is not limited to these examples. The determination unit can determine the scale by considering, for example, type of risk. The determination unit can also determine the scale by considering the risk assessment method. The determination unit can also determine the scale by considering risk assessment criteria. This enables more accurate size determination by considering the amount, scope of impact, risk, etc. of the project. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input information on the amount, scope of impact, and risk of the project into the generation AI, which may then automatically determine the scale.

[0071] The approval unit can automatically approve cases with amounts below a certain level or cases with low risk. An amount below a certain level includes, but is not limited to, a specific upper limit of the amount. For example, the approval unit automatically approves cases with amounts below 1 million yen. The approval unit can also automatically approve cases with amounts below 500,000 yen. The approval unit can also automatically approve cases with amounts below 100,000 yen. Low risk includes, but is not limited to, risk assessment scores and types of risk. For example, the approval unit automatically approves cases with low risk assessment scores. The approval unit can also automatically approve cases with few types of risk. The approval unit can also automatically approve cases with low risk impact. This improves the efficiency of approval work by automatically approving cases with amounts below a certain level or cases with low risk. Some or all of the above-described processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval department can input information about projects with amounts below a certain level or low risk into the generation AI, which can then automatically approve the projects.

[0072] The collection unit can estimate the user's emotions and adjust the timing of collecting case information according to the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect the case information when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately collect the case information and quickly start processing. Furthermore, if the user is in a hurry, the collection unit can advance the collection timing and quickly collect the case information. This allows the case information to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI, which then automatically estimates the emotion, and adjust the collection timing based on the result.

[0073] The collection unit can analyze past case information and select the optimal collection method. For example, the collection unit analyzes past case information and selects the most efficient collection method. The collection unit can also find specific patterns from past case information and select a collection method based on the patterns. The collection unit can also customize a collection method based on past case information and select the optimal method. In this way, the optimal collection method can be selected by analyzing past case information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past case information into a generation AI, which then automatically analyzes and selects the optimal collection method.

[0074] When collecting project information, the collection unit can filter the project information based on the user's current project or area of ​​interest. For example, the collection unit collects only project information related to the user's current project. The collection unit can also prioritize collecting related project information based on the user's area of ​​interest. The collection unit can also filter and collect necessary project information according to the user's project progress. This makes it possible to collect highly relevant project information by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input information on the user's current project or area of ​​interest to the generation AI, and the generation AI can automatically perform filtering.

[0075] When collecting case information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect case information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect case information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect case information using image recognition technology. This allows case information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information about the user's input method into a generation AI, which can then automatically select the optimal collection means.

[0076] The collection unit can estimate the user's emotions and determine the priority of the case information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones collecting less important case information. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting more important case information. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting case information that requires immediate processing. Thus, by determining the priority of the case information according to the user's emotions, important case information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI, which then automatically estimates the emotion and determines the priority of the case information based on the result.

[0077] When collecting case information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting case information close to the user's current location. The collection unit can also filter highly relevant case information based on the user's geographical location information. The collection unit can also prioritize collecting region-specific case information based on the user's location information. In this way, highly relevant case information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI, and the generation AI can automatically prioritize collecting highly relevant information.

[0078] When collecting case information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content of the user's social media posts and collect related case information. The collection unit can also collect related case information by referring to the activities of the user's friends on social media. The collection unit can also collect related case information based on the user's social media check-in information. In this way, related case information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into a generation AI, which can then automatically analyze and collect related information.

[0079] When collecting case information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, improves the collection method based on the user's past feedback. The collection unit can also adjust the type of case information to be collected by reflecting the user's feedback. The collection unit can also optimize the collection timing by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of the user's past feedback into the generation AI, which then automatically analyzes and customizes the collection method.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the presentation method of the analysis based on the result.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the case during analysis. For example, the analysis unit performs a detailed analysis on a case with a high level of importance. The analysis unit can also perform a concise analysis on a case with a low level of importance. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the case. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the importance of the case to the generation AI, and the generation AI can automatically adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the case category. For example, the analysis unit can apply a financial data analysis algorithm to a financial case. The analysis unit can also apply a legal data analysis algorithm to a legal case. The analysis unit can also apply a technical data analysis algorithm to a technical case. In this way, by applying different analysis algorithms depending on the case category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input case category information into the generation AI, and the generation AI can automatically apply an appropriate analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also learn the user's past analysis results and continuously improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI, and the generation AI can automatically improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the length of the analysis based on the result.

[0085] During analysis, the analysis unit can determine the analysis priority based on the submission date of the case. For example, the analysis unit prioritizes analysis of cases that were submitted earlier. The analysis unit can also postpone cases that were submitted later. The analysis unit can also gradually adjust the analysis priority according to the submission date. This enables efficient analysis by determining the analysis priority based on the submission date of the case. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the submission date of the case into the generation AI, and the generation AI can automatically determine the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of cases during analysis. For example, the analysis unit prioritizes analysis of highly relevant cases. The analysis unit can also postpone less relevant cases. The analysis unit can also gradually adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of cases. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the relevance of cases to the generation AI, and the generation AI can automatically adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit can use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can avoid technical terms and use concise expressions. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the user's level of expertise into the generation AI, which can then automatically adjust the use of technical terms in the analysis.

[0088] The determination unit can estimate the user's emotions and adjust the criteria for determination based on the estimated user emotions. For example, if the user is nervous, the determination unit can make a determination using strict criteria. Furthermore, if the user is relaxed, the determination unit can also make a determination using flexible criteria. Furthermore, if the user is in a hurry, the determination unit can set criteria for making a quick determination. This allows for more appropriate determination by adjusting the criteria for determination according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI, which then automatically estimates the emotion and adjusts the criteria for determination based on the result.

[0089] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships between cases when making a judgment. The judgment unit, for example, analyzes the interrelationships between cases and makes a judgment on related cases collectively. The judgment unit can also improve the accuracy of the judgment by taking into account the interrelationships between cases. The judgment unit can also adjust the judgment criteria based on the interrelationships between cases. In this way, the accuracy of the judgment is improved by taking into account the interrelationships between cases. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input information on the interrelationships between cases into the generation AI, and the generation AI can automatically improve the accuracy of the judgment.

[0090] When making a judgment, the judgment unit can make a judgment taking into account the attribute information of the submitter of the case. The judgment unit can make a judgment taking into account, for example, the submitter's past performance. The judgment unit can also make a judgment taking into account the submitter's level of expertise. The judgment unit can also adjust the judgment criteria based on the submitter's attribute information. This makes it possible to make a more appropriate judgment by taking into account the attribute information of the submitter of the case. Some or all of the above-mentioned processing in the judgment unit can be performed using, for example, AI, or can be performed without using AI. For example, the judgment unit can input the submitter's attribute information into the generation AI, and the generation AI can automatically make a judgment.

[0091] The judgment unit can weight the judgment based on the frequency of submission of the case when making the judgment. For example, the judgment unit makes a judgment based on strict criteria for cases that are submitted frequently. The judgment unit can also make a judgment based on flexible criteria for cases that are submitted infrequently. The judgment unit can also adjust the weight of the judgment in stages according to the submission frequency. This enables efficient judgment by weighting the judgment based on the frequency of submission of the case. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input information on the frequency of submission of the case to the generation AI, and the generation AI can automatically weight the judgment.

[0092] The determination unit can estimate the user's emotions and adjust the order in which the determination results are displayed based on the estimated user emotions. For example, if the user is nervous, the determination unit can display results of higher importance first. Furthermore, if the user is relaxed, the determination unit can sequentially display detailed results. Furthermore, if the user is in a hurry, the determination unit can display results that highlight the main points first. This allows for more appropriate results to be displayed by adjusting the order in which the determination results are displayed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit can be performed using, for example, an AI, or without an AI. For example, the determination unit can input the user's emotion data into the generation AI, have the generation AI automatically estimate the emotion, and adjust the order in which the determination results are displayed based on the estimation result.

[0093] The determination unit can make a determination taking into account the geographical distribution of cases. For example, the determination unit gives priority to determining geographically close cases. The determination unit can also postpone geographically distant cases. The determination unit can also adjust the priority of determination according to the geographical distribution. This allows for more appropriate determination by taking the geographical distribution of cases into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input information on the geographical distribution of cases into the generation AI, and the generation AI can automatically make a determination.

[0094] The judgment unit can improve the accuracy of the judgment by referring to related literature of the case when making a judgment. The judgment unit, for example, refers to related literature to improve the accuracy of the judgment. The judgment unit can also adjust the judgment criteria based on the related literature. The judgment unit can also analyze related literature to continuously improve the accuracy of the judgment. In this way, the accuracy of the judgment is improved by referring to related literature of the case. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input information on related literature into the generation AI, and the generation AI can automatically improve the accuracy of the judgment.

[0095] The judgment unit can make a judgment taking into account the market value of the project. For example, the judgment unit makes a judgment using strict criteria for projects with high market value. The judgment unit can also make a judgment using flexible criteria for projects with low market value. The judgment unit can also gradually adjust the judgment criteria depending on the market value. This allows for a more appropriate judgment by taking the market value of the project into consideration. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input information on the market value of the project into the generation AI, and the generation AI can automatically make a judgment.

[0096] The approval unit can estimate the user's emotions and adjust the approval method based on the estimated user emotions. For example, if the user is nervous, the approval unit can provide a simple and highly visible approval method. If the user is relaxed, the approval unit can also provide a detailed approval method. If the user is in a hurry, the approval unit can also provide a method for quickly approving. This enables more appropriate approval by adjusting the approval method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the approval unit can be performed using AI, or without AI. For example, the approval unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and adjust the approval method based on the result.

[0097] At the time of approval, the approval unit can select the optimal approval method by analyzing the user's past approval behavior. The approval unit selects the optimal approval method based on, for example, the user's past approval behavior. The approval unit can also analyze the user's past approval behavior and customize the approval method. The approval unit can also improve the approval method by referring to the user's past approval behavior. In this way, the optimal approval method can be selected by analyzing the user's past approval behavior. Some or all of the above-mentioned processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input data on the user's past approval behavior into a generation AI, which can automatically select the optimal approval method.

[0098] The approval unit can customize the approval means based on the user's current living situation at the time of approval. For example, if the user is busy, the approval unit provides a means for quick approval. The approval unit can also provide a detailed approval means if the user is relaxed. The approval unit can also customize the approval means according to the user's living situation. This enables more appropriate approval by customizing the approval means based on the user's current living situation. Some or all of the above-mentioned processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input information about the user's living situation into the generation AI, which can then automatically customize the approval means.

[0099] The approval unit can improve the approval method by reflecting user feedback at the time of approval. The approval unit, for example, improves the approval method based on user feedback. The approval unit can also customize the approval means by reflecting user feedback. The approval unit can also continuously improve the approval method by referring to user feedback. In this way, the approval method can be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the approval unit may be performed using AI, for example, or may be performed without using AI. For example, the approval unit can input user feedback data into a generation AI, which can then automatically improve the approval method.

[0100] The approval unit can estimate the user's emotions and determine the priority of approvals based on the estimated user emotions. For example, if the user is nervous, the approval unit can prioritize approvals of higher importance. Furthermore, if the user is relaxed, the approval unit can also sequentially perform detailed approvals. Furthermore, if the user is in a hurry, the approval unit can set priorities for quick approvals. By determining the priority of approvals according to the user's emotions, important matters can be prioritized. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the approval unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the approval unit can input the user's emotion data into the generation AI, which can automatically estimate the emotion and determine the priority of approvals based on the results.

[0101] At the time of approval, the approval unit can select the optimal approval method by taking into account the user's geographical location information. The approval unit selects the optimal approval method based on, for example, the user's current location. The approval unit can also customize the approval means by taking into account the user's geographical location information. The approval unit can also provide a region-specific approval method based on the user's location information. This makes it possible to select the optimal approval method by taking into account the user's geographical location information. Some or all of the above-described processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input the user's geographical location information data into the generation AI, and the generation AI can automatically select the optimal approval method.

[0102] At the time of approval, the approval unit can analyze the user's social media activity and suggest a means of approval. For example, the approval unit can analyze the content of the user's social media posts and suggest the optimal means of approval. The approval unit can also suggest a means of approval by referring to the activities of the user's friends on social media. The approval unit can also suggest a means of approval based on the user's check-in information on social media. In this way, the optimal means of approval can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input data on the user's social media activity into a generation AI, which then automatically analyzes the data and suggests the optimal means of approval.

[0103] The approval unit can customize the approval method by reflecting the user's past feedback at the time of approval. The approval unit can improve the approval method based on the user's past feedback, for example. The approval unit can also customize the approval means by reflecting the user's feedback. The approval unit can also continuously improve the approval method by referring to the user's past feedback. In this way, the approval method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the approval unit may be performed using, for example, AI, or may be performed without using AI. For example, the approval unit can input data of the user's past feedback into the generation AI, and the generation AI can automatically customize the approval method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and approval unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects job information using the camera 42 and microphone 38B of the smart device 14, and collects the information using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the size of the job based on the analyzed information. The approval unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically approves small jobs. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and approval unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects job information using the camera 42 and microphone 238 of the smart glasses 214, and collects the information by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the size of the job based on the analyzed information. The approval unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically approves small jobs. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and approval unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects job information using the camera 42 and microphone 238 of the headset-type terminal 314, and collects the information by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the size of the job based on the analyzed information. The approval unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically approves small jobs. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and approval unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects job information using the camera 42 and microphone 238 of the robot 414, and collects the information by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the size of the job based on the analyzed information. The approval unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically approves small jobs.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The approval system may further include a notification unit. The notification unit has the function of notifying the relevant parties of the approval results of a case. For example, the notification unit may notify the relevant parties by email about cases for which approval has been completed. The notification unit may also send alerts to the relevant parties about cases for which approval is on hold. Furthermore, the notification unit may send notifications to the relevant parties, including detailed reasons for denial of approval, about cases for which approval has been denied. This allows the relevant parties to grasp the progress of the case in real time and respond quickly.

[0106] The collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can collect concise information. If the user is relaxed, the collection unit can also collect detailed information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. In this way, adjusting the level of detail of the information to be collected according to the user's emotions reduces the burden on the user and enables efficient information collection.

[0107] When analyzing project information, the analysis unit can improve the accuracy of the analysis by linking with external databases. For example, the analysis unit obtains the latest market trends from an external market database and evaluates the market value of the project. The analysis unit can also obtain risk information from an external risk database and perform project risk evaluation. Furthermore, the analysis unit can obtain technical information from an external technology database and perform technical evaluation. By linking with external databases, the accuracy of the analysis can be improved, enabling more accurate evaluation.

[0108] The determination unit can estimate the user's emotions and adjust the priority of determination based on the estimated user's emotions. For example, if the user is nervous, it can prioritize determining important cases. Also, if the user is relaxed, it can perform detailed determination. Furthermore, if the user is in a hurry, it can prioritize determining cases that require a quick determination. In this way, by adjusting the priority of determination according to the user's emotions, it is possible to quickly process important cases.

[0109] When approving a project, the approval department can improve the accuracy of approval by referring to the approval history of the project. For example, the approval department can refer to the history of similar projects that have been approved in the past and adjust the approval criteria. The approval department can also refer to the history of projects that have been rejected in the past and perform risk assessment. Furthermore, the approval department can optimize the approval process based on the past approval history. In this way, by referring to the past approval history, the accuracy of approval can be improved and more appropriate approval can be achieved.

[0110] The collection unit can estimate the user's emotions and adjust the format of the information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can collect information in a visually easy-to-understand format. If the user is relaxed, the collection unit can collect information in a detailed text format. Furthermore, if the user is in a hurry, the collection unit can collect information in a concise summary format. In this way, adjusting the format of information according to the user's emotions reduces the burden on the user and enables efficient information collection.

[0111] When collecting case information, the collection unit can analyze the user's past behavioral patterns to determine the optimal collection timing. For example, the collection unit analyzes the time periods in which the user collected information in the past and collects information during those time periods. The collection unit can also find the most efficient collection timing from the user's past behavioral patterns and collect information at that timing. Furthermore, the collection unit can customize the collection timing based on the user's past behavioral patterns. This makes it possible to determine the optimal collection timing by analyzing the user's past behavioral patterns.

[0112] The collection unit can estimate the user's emotions when collecting case information and determine the priority of the information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, less important information can be put off. Also, if the user is relaxed, it is possible to prioritize collecting more important information. Furthermore, if the user is in a hurry, it is possible to prioritize collecting information that requires quick processing. In this way, by determining the priority of information according to the user's emotions, it is possible to quickly collect important information.

[0113] When collecting project information, the collection unit can adjust the information to be collected taking into account the user's current project progress. For example, if the user's project is in the early stages, the collection unit can collect basic information. If the project is approaching the middle stage, the collection unit can also collect detailed information. Furthermore, if the project is in the final stages, the collection unit can prioritize the collection of the most important information. In this way, by adjusting the information to be collected according to the user's project progress, the project can be smoothly progressed.

[0114] The collection unit can estimate the user's emotions when collecting case information and adjust the categories of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, concise, to-the-point information can be collected. If the user is relaxed, detailed categories of information can be collected. Furthermore, if the user is in a hurry, information in categories that require quick processing can be collected preferentially. This allows for efficient information collection by adjusting the categories of information according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects case information. Case information includes, for example, the content, scale, and importance of the case. The collection unit can collect information manually, using automated collection methods, or using generation AI. For example, the content of the case is input into the generation AI, which then automatically collects information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data analysis techniques and text analysis algorithms. The analysis unit can also automatically analyze the collected information using the generation AI. For example, the collected information is input into the generation AI, which then automatically performs the analysis. Step 3: The determination unit determines the scale of the project based on the information analyzed by the analysis unit. The determination is made taking into account the project's amount, scope of impact, risk, etc. The determination unit can also use the generation AI to automatically determine the scale of the project. For example, the analyzed information is input into the generation AI, which then automatically determines the scale. Step 4: The approval department automatically approves projects that are judged to be small-scale by the judgment department. Approval is given to projects with amounts below a certain level or low risk. The approval department can also use the generation AI to automatically approve projects. For example, the judgment results are input into the generation AI, which then automatically approves.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

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

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0161] 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.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] 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.

[0173] 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).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] 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.

[0184] 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.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects case information; an analysis unit that analyzes the information collected by the collection unit; a determination unit that determines the scale of a case based on the information analyzed by the analysis unit; an approval unit that automatically approves a case that is determined to be a small-scale case by the determination unit; A system characterized by:

2. The collecting unit Collect information on the content, scale, and importance of the project 2. The system of claim 1.

3. The analysis unit Determine the size of the case based on the collected information 2. The system of claim 1.

4. The determination unit Determine the scale of the project by considering the amount, scope of impact, and risk.

2. The system of claim 1.

5. The approval unit Automatically approve projects below a certain amount or with low risk 2. The system of claim 1.

6. The collecting unit To estimate a user's emotions and adjust the timing of collecting project information according to the estimated user's emotions.

2. The system of claim 1.

7. The collecting unit Analyze past project information and select the optimal collection method 2. The system of claim 1.

8. The collecting unit When collecting job information, filter it based on the user's current projects or areas of interest 2. The system of claim 1.

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