system

Abstract

The system according to the embodiment aims to efficiently collect and analyze the business data of each person in charge, and generate and distribute appropriate reports. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects business data for each person in charge. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a report based on the data analyzed by the analysis unit. The distribution unit distributes the report generated by the generation unit to each person in charge.

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, it was difficult to efficiently collect and analyze each employee's work data and generate and distribute appropriate reports.

[0005] The system according to the embodiment aims to efficiently collect and analyze the business data of each person in charge, and generate and distribute appropriate reports. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects business data of each person in charge. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a report based on the data analyzed by the analysis unit. The distribution unit distributes the report generated by the generation unit to each person in charge. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze the business data of each person in charge, and generate and distribute appropriate reports. [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 AI tool according to an embodiment of the present invention is a system for automating daily tasks. This system collects work data from each employee, analyzes it using AI, automatically generates reports, and distributes them to each employee. For example, the AI ​​tool collects and analyzes performance data and distribution-related data for each employee's assigned area. The AI ​​tool then generates reports based on the analysis results and distributes them to each employee. This allows employees to easily understand the progress and performance of their work. This reduces their workload and man-hours. This allows employees to spend time on more important tasks and improve their productivity. For example, this tool allows employees to analyze the performance of their assigned area and secure time to identify areas for improvement. Furthermore, this tool generates individually optimized reports based on each employee's work data, thereby maximizing each employee's productivity. For example, if a specific employee is achieving high performance in a specific area, it can generate a report that includes suggestions for expanding that performance to other areas. In this way, the AI ​​tool automates daily tasks, reduces work burden and man-hours, and improves each employee's productivity.

[0029] The AI ​​tool according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects business data of each employee. The business data includes, but is not limited to, sales data, customer data, and work time data. The collection unit automatically collects data using, for example, an API. The collection unit can also collect data using a sensor. For example, the collection unit collects performance data for the area of ​​responsibility and distribution-related data. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit analyzes the collected data and generates an optimal report for each employee. The generation unit generates a report based on the data analyzed by the analysis unit. The report includes, for example, information displayed in a graph or table format, but is not limited to, examples. For example, the generation unit generates a report based on the analysis results. The distribution unit distributes the report generated by the generation unit to each employee. The distribution is performed, for example, via email or a dashboard, but is not limited to, examples. For example, the distribution unit distributes the generated report to each person in charge, thereby enabling the AI ​​tool according to the embodiment to efficiently collect, analyze, generate, and distribute business data of each person in charge.

[0030] The collection unit can collect performance data or distribution-related data for the area of ​​responsibility. Performance data includes, for example, sales performance and business performance, but is not limited to these examples. Distribution-related data includes, for example, distribution history and distribution success rate, but is not limited to these examples. The collection unit collects performance data for the area of ​​responsibility using, for example, an API. The collection unit can also collect distribution-related data using a sensor. For example, the collection unit automatically collects performance data for the area of ​​responsibility and analyzes the distribution-related data. This allows performance data and distribution-related data for the area of ​​responsibility to be collected efficiently.

[0031] The analysis unit can analyze the collected data and generate a report for each employee. Examples of optimal reports include, but are not limited to, customization to meet the needs of each employee and highlighting important information. The analysis unit can analyze the collected data using, for example, a statistical analysis algorithm. The analysis unit can also generate optimal reports for each employee using a machine learning algorithm. For example, the analysis unit can analyze the collected data and generate optimal reports for each employee. This improves work efficiency by generating optimal reports for each employee.

[0032] The generation unit can generate a report based on the analysis results. The analysis results include, but are not limited to, for example, statistical data, graphs, and trend analysis. For example, the generation unit generates a report including statistical data based on the analysis results. The generation unit can also generate a report including graphs and trend analysis based on the analysis results. For example, the generation unit generates a report based on the analysis results. In this way, accurate information can be provided by generating a report based on the analysis results.

[0033] The distribution unit can distribute the generated report to each person in charge. Rapid distribution includes, but is not limited to, for example, a distribution delay time and real-time distribution means. For example, the distribution unit distributes the generated report to each person in charge by email. The distribution unit can also distribute the generated report to each person in charge via a dashboard. For example, the distribution unit distributes the generated report to each person in charge quickly. This allows the generated report to be distributed quickly to each person in charge.

[0034] If a specific employee has achieved excellent results in a specific area, the generation unit can generate a report including suggestions for expanding that performance to other areas. Examples of excellent performance include, but are not limited to, achieving sales targets and improving customer satisfaction. Examples of reports including suggestions include, but are not limited to, improvement measures and proposals for new strategies. For example, if a specific employee has achieved sales targets in a specific area, the generation unit can generate a report including suggestions for expanding that performance to other areas. Furthermore, if a specific employee has improved customer satisfaction in a specific area, the generation unit can generate a report including suggestions for expanding that performance to other areas. For example, if a specific employee has achieved excellent results in a specific area, the generation unit generates a report including suggestions for expanding that performance to other areas. By generating a report including suggestions for expanding excellent performance to other areas, overall performance can be expected to improve.

[0035] The collection unit can analyze each person's past work data and select the optimal data collection method. The optimal data collection method includes, but is not limited to, optimization of collection means and collection processes, for example. The collection unit, for example, selects the most efficient data collection method based on each person's past work data. The collection unit can also analyze each person's past work data and optimize the timing of data collection. For example, the collection unit selects a data collection means (audio, text, etc.) based on each person's past work data. This enables efficient data collection by selecting the optimal data collection method based on past work data.

[0036] When collecting data, the collection unit can perform filtering based on the current work situation and areas of interest of the person in charge. Filtering includes, for example, filtering conditions, algorithms to be used, and the like, but is not limited to these examples. For example, the collection unit collects only highly relevant data, taking into account the current work situation of the person in charge. The collection unit can also prioritize the collection of necessary data based on the area of ​​interest of the person in charge. For example, the collection unit filters and collects unnecessary data to reduce the work burden on the person in charge. In this way, highly relevant data can be collected by filtering data based on the work situation and areas of interest of the person in charge.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the input method of the person in charge. Input methods include, but are not limited to, for example, voice input, text input, and image input. The optimal collection means includes, but is not limited to, for example, the device to be used and optimization of the collection process. For example, if the person in charge prefers voice input, the collection unit may preferentially collect voice data. Also, if the person in charge prefers text input, the collection unit may preferentially collect text data. For example, if the person in charge prefers image input, the collection unit may preferentially collect image data. This enables efficient data collection by selecting the optimal collection means depending on the person in charge's input method.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the person in charge. Examples of geographical location information include, but are not limited to, GPS data and location information services. The collection unit prioritizes collecting highly relevant data, for example, based on the current location of the person in charge. The collection unit can also prioritize collecting highly relevant data by taking into account the person in charge's movement history. For example, the collection unit selects the optimal data collection point based on the geographical location information of the person in charge. This allows highly relevant data to be collected efficiently by taking into account the geographical location information of the person in charge.

[0039] When collecting data, the collection unit can analyze the social media activity of the person in charge and collect related data. Social media activity includes, but is not limited to, for example, the content of posts and engagement rates. For example, the collection unit analyzes the content of posts made by the person in charge on social media and collects related data. The collection unit can also collect related data by referring to the activities of the person in charge's friends on social media. For example, the collection unit collects related data based on the check-in information of the person in charge on social media. In this way, highly relevant data can be collected by analyzing social media activity.

[0040] The collection unit can customize the collection method by reflecting the person in charge's past feedback when collecting data. Feedback includes, but is not limited to, for example, survey results and user comments. The collection unit selects the optimal data collection method, for example, based on the person in charge's past feedback. The collection unit can also adjust the timing of data collection by reflecting the person in charge's past feedback. For example, the collection unit customizes the means of data collection by referring to the person in charge's past feedback. In this way, the optimal data collection method can be selected by reflecting the past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Examples of the importance of the data include, but are not limited to, business impact and urgency. Examples of the level of detail of the analysis include, but are not limited to, the depth of the analysis and the complexity of the algorithm used. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit dynamically adjusts the level of detail of the analysis depending on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the data.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, sales data, customer data, and marketing data. Analysis algorithms include, but are not limited to, regression analysis, clustering, and classification. The analysis unit applies a statistical analysis algorithm to performance data, for example. The analysis unit can also apply a time series analysis algorithm to distribution-related data. For example, the analysis unit selects the optimal analysis algorithm depending on each data category. This enables highly accurate analysis by applying the optimal analysis algorithm depending on the data category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the person in charge's past analysis results. Past analysis results include, but are not limited to, past trends and success stories. For example, the analysis unit optimizes the analysis algorithm based on the person in charge's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the person in charge's past analysis results. For example, the analysis unit analyzes the person in charge's past analysis results and finds areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. Examples of the time when the data was collected include, but are not limited to, the collection date and collection frequency. Examples of the analysis priority include, but are not limited to, importance and urgency. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also determine the analysis priority by referring to past data. For example, the analysis unit dynamically adjusts the analysis priority depending on the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed with priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, correlation and causal relationship. Examples of analysis order include, but are not limited to, priority and dependency. For example, the analysis unit prioritizes analysis of highly related data. The analysis unit can also analyze less related data later. For example, the analysis unit dynamically adjusts the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the level of expertise of the person in charge. Examples of the level of expertise include, but are not limited to, years of experience, qualifications, and past achievements. Examples of technical terminology include, but are not limited to, technical terms and industry jargon. For example, if the level of expertise of the person in charge is high, the analysis unit provides analysis results that use a lot of technical terminology. Furthermore, if the level of expertise of the person in charge is low, the analysis unit can also provide analysis results that avoid technical terminology. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the level of expertise of the person in charge. In this way, by adjusting the use of technical terminology in the analysis according to the level of expertise of the person in charge, it is possible to provide analysis results that are easy to understand.

[0047] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the analysis results. The level of detail of the report includes, but is not limited to, for example, the depth of the report and the range of data used. For example, the generation unit generates a detailed report for analysis results with high importance. The generation unit can also generate a simplified report for analysis results with low importance. For example, the generation unit dynamically adjusts the level of detail of the report depending on the importance of the analysis results. This enables efficient report generation by adjusting the level of detail of the report depending on the importance of the analysis results.

[0048] When generating a report, the generation unit can apply different report generation algorithms depending on the category of the analysis results. Examples of report generation algorithms include, but are not limited to, template-based and data-driven algorithms. For example, the generation unit applies a statistical report generation algorithm to performance data. The generation unit can also apply a time-series report generation algorithm to distribution-related data. For example, the generation unit selects the optimal report generation algorithm depending on the category of each analysis result. This enables highly accurate report generation by applying the optimal report generation algorithm depending on the category of the analysis result.

[0049] When generating a report, the generation unit can improve the accuracy of the report by referring to the person in charge's past report results. Past report results include, but are not limited to, past trends, success stories, etc. The generation unit, for example, optimizes the report generation algorithm based on the person in charge's past report results. The generation unit can also improve the accuracy of the report by referring to the person in charge's past report results. For example, the generation unit analyzes the person in charge's past report results and finds areas for improvement in the report. In this way, the accuracy of the report is improved by referring to the person in charge's past report results.

[0050] When generating a report, the generation unit can determine the priority of the report based on the time when the analysis results were collected. Examples of report priority include, but are not limited to, importance and urgency. For example, the generation unit preferentially reflects the latest analysis results in the report. The generation unit can also determine the priority of the report by referring to past analysis results. For example, the generation unit dynamically adjusts the priority of the report depending on the time when the analysis results were collected. In this way, by determining the priority of the report based on the time when the analysis results were collected, the latest information can be provided preferentially.

[0051] When generating a report, the generation unit can adjust the order of the reports based on the relevance of the analysis results. The order of the reports includes, but is not limited to, priorities, dependencies, and the like. For example, the generation unit preferentially reflects highly relevant analysis results in the report. The generation unit can also reflect less relevant analysis results in the report later. For example, the generation unit dynamically adjusts the order of the reports according to the relevance of the analysis results. This enables efficient report generation by adjusting the order of the reports based on the relevance of the analysis results.

[0052] When generating a report, the generation unit can adjust the use of technical terms in the report according to the level of expertise of the person in charge. Technical terms include, but are not limited to, technical terms, industry jargon, etc. For example, if the level of expertise of the person in charge is high, the generation unit generates a report that uses a lot of technical terms. Furthermore, if the level of expertise of the person in charge is low, the generation unit can also generate a report that avoids technical terms. For example, the generation unit adjusts the way the report is expressed according to the level of expertise of the person in charge. In this way, by adjusting the use of technical terms in the report according to the level of expertise of the person in charge, it is possible to provide a report that is easy to understand.

[0053] When distributing a report, the distribution unit can select the optimal distribution method by referring to the person in charge's past distribution history. The distribution history includes, for example, past distribution dates and times, delivery success rates, etc., but is not limited to these examples. The distribution unit selects the optimal distribution method based on, for example, the person in charge's past distribution history. The distribution unit can also adjust the timing of distribution by referring to the person in charge's past distribution history. For example, the distribution unit analyzes the person in charge's past distribution history and finds areas for improvement in the distribution method. In this way, the optimal distribution method can be selected by referring to the past distribution history.

[0054] When distributing a report, the distribution unit can customize the distribution content based on the current work situation of the person in charge. The distribution content includes, for example, the type of report and the range of information included, but is not limited to these examples. For example, the distribution unit takes into account the current work situation of the person in charge and prioritizes the distribution of highly relevant content. In addition, the distribution unit can also filter out unnecessary content to reduce the work burden on the person in charge. For example, the distribution unit dynamically customizes the distribution content based on the current work situation of the person in charge. In this way, highly relevant information can be provided by customizing the distribution content based on the current work situation.

[0055] The distribution unit can improve the distribution method by reflecting the feedback from the person in charge when distributing the report. Examples of feedback include, but are not limited to, survey results and user comments. For example, the distribution unit selects the optimal distribution method based on the feedback from the person in charge. The distribution unit can also adjust the timing of distribution by reflecting the feedback from the person in charge. For example, the distribution unit can refer to the feedback from the person in charge to find areas for improvement in the distribution method. This makes it possible to improve the distribution method by reflecting the feedback.

[0056] When distributing a report, the distribution unit can select the optimal distribution method by taking into account the geographical location information of the person in charge. Examples of geographical location information include, but are not limited to, GPS data and location information services. The distribution unit can select the optimal distribution method, for example, based on the current location of the person in charge. The distribution unit can also select the optimal distribution method by taking into account the movement history of the person in charge. For example, the distribution unit selects the optimal distribution timing based on the geographical location information of the person in charge. This allows the optimal distribution method to be selected by taking into account the geographical location information.

[0057] When delivering a report, the distribution unit can analyze the social media activity of the person in charge to customize the content of the report. Social media activity includes, but is not limited to, for example, the content of posts and engagement rates. For example, the distribution unit analyzes the content of posts made by the person in charge on social media and delivers related reports. The distribution unit can also deliver related reports based on the activities of the person in charge's friends on social media. For example, the distribution unit delivers related reports based on the check-in information of the person in charge on social media. In this way, highly relevant information can be provided by analyzing social media activity.

[0058] When distributing a report, the distribution unit can customize the distribution method by reflecting the person in charge's past feedback. Feedback includes, but is not limited to, for example, survey results and user comments. The distribution unit selects the optimal distribution method, for example, based on the person in charge's past feedback. The distribution unit can also adjust the timing of distribution by reflecting the person in charge's past feedback. For example, the distribution unit can refer to the person in charge's past feedback to find areas for improvement in the distribution method. In this way, the optimal distribution method can be selected by reflecting the past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting data, the collection unit can select the optimal data collection method based on the person in charge's past work data. For example, if the person in charge has frequently used voice input in the past, voice data can be collected with priority. Also, if the person in charge prefers text input, text data can be collected with priority. Furthermore, if the person in charge has frequently used image input, image data can be collected with priority. This allows for efficient data collection by selecting the optimal data collection method based on the person in charge's past work data.

[0061] When distributing a report, the distribution department can customize the content based on the person in charge's current work situation. For example, if the person in charge is busy, a simplified report containing only important information can be distributed. On the other hand, if the person in charge has time, a report containing detailed information can be distributed. Furthermore, if the person in charge is concentrating on a specific project, information related to that project can be distributed preferentially. In this way, by customizing the content based on the person in charge's current work situation, highly relevant information can be provided.

[0062] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a regression analysis algorithm can be applied to sales data, and a clustering algorithm can be applied to customer data. A classification algorithm can also be applied to marketing data. Furthermore, a time series analysis algorithm can be applied to distribution-related data. This allows for highly accurate analysis by applying the optimal analysis algorithm depending on the data category.

[0063] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the analysis results. For example, a detailed report can be generated for analysis results with high importance, and a simplified report can be generated for analysis results with low importance. It is also possible to quickly generate a report for analysis results with high urgency, and generate a report at a regular time for analysis results with low urgency. Furthermore, it is also possible to generate a report including an in-depth analysis for analysis results with a large business impact. This allows for efficient report generation by adjusting the level of detail of the report according to the importance of the analysis results.

[0064] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the person in charge. For example, if the person in charge is in a specific area, data related to that area can be collected preferentially. It can also prioritize collecting highly relevant data by taking into account the person in charge's movement history. Furthermore, it can select the optimal data collection point based on the person in charge's current location. In this way, highly relevant data can be collected efficiently by taking into account the person in charge's geographical location information.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects the business data of each employee. Business data includes sales data, customer data, and work time data. The collection unit can automatically collect data using APIs, and can also collect data using sensors. For example, it can collect performance data for the area of ​​responsibility and data related to distribution. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit analyzes the collected data and provides the basic data for generating optimal reports for each person in charge. Step 3: The generator generates a report based on the data analyzed by the analyzer. The report may include information displayed in a graph or table format. For example, the generator generates a report based on the analysis results. Step 4: The distribution unit distributes the reports generated by the generation unit to each person in charge. The distribution is performed via email or dashboard. For example, the distribution unit distributes the generated reports to each person in charge.

[0067] (Example 2) An AI tool according to an embodiment of the present invention is a system for automating daily tasks. This system collects work data from each employee, analyzes it using AI, automatically generates reports, and distributes them to each employee. For example, the AI ​​tool collects and analyzes performance data and distribution-related data for each employee's assigned area. The AI ​​tool then generates reports based on the analysis results and distributes them to each employee. This allows employees to easily understand the progress and performance of their work. This reduces their workload and man-hours. This allows employees to spend time on more important tasks and improve their productivity. For example, this tool allows employees to analyze the performance of their assigned area and secure time to identify areas for improvement. Furthermore, this tool generates individually optimized reports based on each employee's work data, thereby maximizing each employee's productivity. For example, if a specific employee is achieving high performance in a specific area, it can generate a report that includes suggestions for expanding that performance to other areas. In this way, the AI ​​tool automates daily tasks, reduces work burden and man-hours, and improves each employee's productivity.

[0068] The AI ​​tool according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects business data of each employee. The business data includes, but is not limited to, sales data, customer data, and work time data. The collection unit automatically collects data using, for example, an API. The collection unit can also collect data using a sensor. For example, the collection unit collects performance data for the area of ​​responsibility and distribution-related data. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit analyzes the collected data and generates an optimal report for each employee. The generation unit generates a report based on the data analyzed by the analysis unit. The report includes, for example, information displayed in a graph or table format, but is not limited to, examples. For example, the generation unit generates a report based on the analysis results. The distribution unit distributes the report generated by the generation unit to each employee. The distribution is performed, for example, via email or a dashboard, but is not limited to, examples. For example, the distribution unit distributes the generated report to each person in charge, thereby enabling the AI ​​tool according to the embodiment to efficiently collect, analyze, generate, and distribute business data of each person in charge.

[0069] The collection unit can collect performance data or distribution-related data for the area of ​​responsibility. Performance data includes, for example, sales performance and business performance, but is not limited to these examples. Distribution-related data includes, for example, distribution history and distribution success rate, but is not limited to these examples. The collection unit collects performance data for the area of ​​responsibility using, for example, an API. The collection unit can also collect distribution-related data using a sensor. For example, the collection unit automatically collects performance data for the area of ​​responsibility and analyzes the distribution-related data. This allows performance data and distribution-related data for the area of ​​responsibility to be collected efficiently.

[0070] The analysis unit can analyze the collected data and generate a report for each employee. Examples of optimal reports include, but are not limited to, customization to meet the needs of each employee and highlighting important information. The analysis unit can analyze the collected data using, for example, a statistical analysis algorithm. The analysis unit can also generate optimal reports for each employee using a machine learning algorithm. For example, the analysis unit can analyze the collected data and generate optimal reports for each employee. This improves work efficiency by generating optimal reports for each employee.

[0071] The generation unit can generate a report based on the analysis results. The analysis results include, but are not limited to, for example, statistical data, graphs, and trend analysis. For example, the generation unit generates a report including statistical data based on the analysis results. The generation unit can also generate a report including graphs and trend analysis based on the analysis results. For example, the generation unit generates a report based on the analysis results. In this way, accurate information can be provided by generating a report based on the analysis results.

[0072] The distribution unit can distribute the generated report to each person in charge. Rapid distribution includes, but is not limited to, for example, a distribution delay time and real-time distribution means. For example, the distribution unit distributes the generated report to each person in charge by email. The distribution unit can also distribute the generated report to each person in charge via a dashboard. For example, the distribution unit distributes the generated report to each person in charge quickly. This allows the generated report to be distributed quickly to each person in charge.

[0073] If a specific employee has achieved excellent results in a specific area, the generation unit can generate a report including suggestions for expanding that performance to other areas. Examples of excellent performance include, but are not limited to, achieving sales targets and improving customer satisfaction. Examples of reports including suggestions include, but are not limited to, improvement measures and proposals for new strategies. For example, if a specific employee has achieved sales targets in a specific area, the generation unit can generate a report including suggestions for expanding that performance to other areas. Furthermore, if a specific employee has improved customer satisfaction in a specific area, the generation unit can generate a report including suggestions for expanding that performance to other areas. For example, if a specific employee has achieved excellent results in a specific area, the generation unit generates a report including suggestions for expanding that performance to other areas. By generating a report including suggestions for expanding excellent performance to other areas, overall performance can be expected to improve.

[0074] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. Examples of user emotions include, but are not limited to, joy, sadness, anger, etc. The timing of data collection can include, but are not limited to, collection frequency and collection trigger conditions. For example, if the user is stressed, the collection unit can reduce the frequency of data collection to reduce workload. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the collection unit can adjust the timing of data collection to quickly collect necessary data. This can reduce workload by adjusting the timing of data collection 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The collection unit can analyze each person's past work data and select the optimal data collection method. The optimal data collection method includes, but is not limited to, optimization of collection means and collection processes, for example. The collection unit, for example, selects the most efficient data collection method based on each person's past work data. The collection unit can also analyze each person's past work data and optimize the timing of data collection. For example, the collection unit selects a data collection means (audio, text, etc.) based on each person's past work data. This enables efficient data collection by selecting the optimal data collection method based on past work data.

[0076] When collecting data, the collection unit can perform filtering based on the current work situation and areas of interest of the person in charge. Filtering includes, for example, filtering conditions, algorithms to be used, and the like, but is not limited to these examples. For example, the collection unit collects only highly relevant data, taking into account the current work situation of the person in charge. The collection unit can also prioritize the collection of necessary data based on the area of ​​interest of the person in charge. For example, the collection unit filters and collects unnecessary data to reduce the work burden on the person in charge. In this way, highly relevant data can be collected by filtering data based on the work situation and areas of interest of the person in charge.

[0077] When collecting data, the collection unit can select the optimal collection means depending on the input method of the person in charge. Input methods include, but are not limited to, for example, voice input, text input, and image input. The optimal collection means includes, but is not limited to, for example, the device to be used and optimization of the collection process. For example, if the person in charge prefers voice input, the collection unit may preferentially collect voice data. Also, if the person in charge prefers text input, the collection unit may preferentially collect text data. For example, if the person in charge prefers image input, the collection unit may preferentially collect image data. This enables efficient data collection by selecting the optimal collection means depending on the person in charge's input method.

[0078] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. Data priorities include, but are not limited to, importance and urgency. For example, when the user is stressed, the collection unit prioritizes collecting data of high importance. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed data. For example, when the user is in a hurry, the collection unit prioritizes collecting data that can be collected quickly. Thus, by determining the priority of data according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, using 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.

[0079] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the person in charge. Examples of geographical location information include, but are not limited to, GPS data and location information services. The collection unit prioritizes collecting highly relevant data, for example, based on the current location of the person in charge. The collection unit can also prioritize collecting highly relevant data by taking into account the person in charge's movement history. For example, the collection unit selects the optimal data collection point based on the geographical location information of the person in charge. This allows highly relevant data to be collected efficiently by taking into account the geographical location information of the person in charge.

[0080] When collecting data, the collection unit can analyze the social media activity of the person in charge and collect related data. Social media activity includes, but is not limited to, for example, the content of posts and engagement rates. For example, the collection unit analyzes the content of posts made by the person in charge on social media and collects related data. The collection unit can also collect related data by referring to the activities of the person in charge's friends on social media. For example, the collection unit collects related data based on the check-in information of the person in charge on social media. In this way, highly relevant data can be collected by analyzing social media activity.

[0081] The collection unit can customize the collection method by reflecting the person in charge's past feedback when collecting data. Feedback includes, but is not limited to, for example, survey results and user comments. The collection unit selects the optimal data collection method, for example, based on the person in charge's past feedback. The collection unit can also adjust the timing of data collection by reflecting the person in charge's past feedback. For example, the collection unit customizes the means of data collection by referring to the person in charge's past feedback. In this way, the optimal data collection method can be selected by reflecting the past feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Examples of presentation methods of the analysis include, but are not limited to, graphs, charts, and text reports. For example, when the user is stressed, the analysis unit provides simple, highly visible analysis results. Furthermore, when the user is relaxed, the analysis unit can provide detailed analysis results. For example, when the user is in a hurry, the analysis unit provides analysis results that focus on the main points. By adjusting the presentation method of the analysis according to the user's emotions, highly visible analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Examples of the importance of the data include, but are not limited to, business impact and urgency. Examples of the level of detail of the analysis include, but are not limited to, the depth of the analysis and the complexity of the algorithm used. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit dynamically adjusts the level of detail of the analysis depending on the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis depending on the importance of the data.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, sales data, customer data, and marketing data. Analysis algorithms include, but are not limited to, regression analysis, clustering, and classification. The analysis unit applies a statistical analysis algorithm to performance data, for example. The analysis unit can also apply a time series analysis algorithm to distribution-related data. For example, the analysis unit selects the optimal analysis algorithm depending on each data category. This enables highly accurate analysis by applying the optimal analysis algorithm depending on the data category.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the person in charge's past analysis results. Past analysis results include, but are not limited to, past trends and success stories. For example, the analysis unit optimizes the analysis algorithm based on the person in charge's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the person in charge's past analysis results. For example, the analysis unit analyzes the person in charge's past analysis results and finds areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Examples of the length of the analysis include, but are not limited to, the time required for the analysis and the length of the report. For example, if the user is stressed, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is in a hurry, the analysis unit can quickly provide the analysis result. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. Examples of the time when the data was collected include, but are not limited to, the collection date and collection frequency. Examples of the analysis priority include, but are not limited to, importance and urgency. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also determine the analysis priority by referring to past data. For example, the analysis unit dynamically adjusts the analysis priority depending on the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed with priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, correlation and causal relationship. Examples of analysis order include, but are not limited to, priority and dependency. For example, the analysis unit prioritizes analysis of highly related data. The analysis unit can also analyze less related data later. For example, the analysis unit dynamically adjusts the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0089] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the level of expertise of the person in charge. Examples of the level of expertise include, but are not limited to, years of experience, qualifications, and past achievements. Examples of technical terminology include, but are not limited to, technical terms and industry jargon. For example, if the level of expertise of the person in charge is high, the analysis unit provides analysis results that use a lot of technical terminology. Furthermore, if the level of expertise of the person in charge is low, the analysis unit can also provide analysis results that avoid technical terminology. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the level of expertise of the person in charge. In this way, by adjusting the use of technical terminology in the analysis according to the level of expertise of the person in charge, it is possible to provide analysis results that are easy to understand.

[0090] The generation unit can estimate the user's emotions and adjust the presentation method of the report based on the estimated user's emotions. Examples of report presentation methods include, but are not limited to, graphs, charts, and text reports. For example, when the user is stressed, the generation unit generates a simple, highly visible report. Furthermore, when the user is relaxed, the generation unit can also generate a detailed report. For example, when the user is in a hurry, the generation unit generates a report that focuses on the main points. This allows the report presentation method to be adjusted according to the user's emotions, resulting in a highly visible report. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the analysis results. The level of detail of the report includes, but is not limited to, for example, the depth of the report and the range of data used. For example, the generation unit generates a detailed report for analysis results with high importance. The generation unit can also generate a simplified report for analysis results with low importance. For example, the generation unit dynamically adjusts the level of detail of the report depending on the importance of the analysis results. This enables efficient report generation by adjusting the level of detail of the report depending on the importance of the analysis results.

[0092] When generating a report, the generation unit can apply different report generation algorithms depending on the category of the analysis results. Examples of report generation algorithms include, but are not limited to, template-based and data-driven algorithms. For example, the generation unit applies a statistical report generation algorithm to performance data. The generation unit can also apply a time-series report generation algorithm to distribution-related data. For example, the generation unit selects the optimal report generation algorithm depending on the category of each analysis result. This enables highly accurate report generation by applying the optimal report generation algorithm depending on the category of the analysis result.

[0093] When generating a report, the generation unit can improve the accuracy of the report by referring to the person in charge's past report results. Past report results include, but are not limited to, past trends, success stories, etc. The generation unit, for example, optimizes the report generation algorithm based on the person in charge's past report results. The generation unit can also improve the accuracy of the report by referring to the person in charge's past report results. For example, the generation unit analyzes the person in charge's past report results and finds areas for improvement in the report. In this way, the accuracy of the report is improved by referring to the person in charge's past report results.

[0094] The generation unit can estimate the user's emotions and adjust the length of the report based on the estimated user's emotions. Examples of report length include, but are not limited to, the number of pages in the report and the level of detail. For example, if the user is stressed, the generation unit can generate a short, concise report. Alternatively, if the user is relaxed, the generation unit can generate a longer report with detailed explanations. For example, if the user is in a hurry, the generation unit can generate a report that summarizes the main points so that it can be read quickly. This allows the report length to be adjusted according to the user's emotions, thereby providing an appropriate report. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] When generating a report, the generation unit can determine the priority of the report based on the time when the analysis results were collected. Examples of report priority include, but are not limited to, importance and urgency. For example, the generation unit preferentially reflects the latest analysis results in the report. The generation unit can also determine the priority of the report by referring to past analysis results. For example, the generation unit dynamically adjusts the priority of the report depending on the time when the analysis results were collected. In this way, by determining the priority of the report based on the time when the analysis results were collected, the latest information can be provided preferentially.

[0096] When generating a report, the generation unit can adjust the order of the reports based on the relevance of the analysis results. The order of the reports includes, but is not limited to, priorities, dependencies, and the like. For example, the generation unit preferentially reflects highly relevant analysis results in the report. The generation unit can also reflect less relevant analysis results in the report later. For example, the generation unit dynamically adjusts the order of the reports according to the relevance of the analysis results. This enables efficient report generation by adjusting the order of the reports based on the relevance of the analysis results.

[0097] When generating a report, the generation unit can adjust the use of technical terms in the report according to the level of expertise of the person in charge. Technical terms include, but are not limited to, technical terms, industry jargon, etc. For example, if the level of expertise of the person in charge is high, the generation unit generates a report that uses a lot of technical terms. Furthermore, if the level of expertise of the person in charge is low, the generation unit can also generate a report that avoids technical terms. For example, the generation unit adjusts the way the report is expressed according to the level of expertise of the person in charge. In this way, by adjusting the use of technical terms in the report according to the level of expertise of the person in charge, it is possible to provide a report that is easy to understand.

[0098] The delivery unit can estimate the user's emotions and adjust the report delivery method based on the estimated user's emotions. Examples of report delivery methods include, but are not limited to, email, dashboard, and notification. For example, if the user is feeling stressed, the delivery unit can select a simple delivery method. Alternatively, if the user is relaxed, the delivery unit can select a detailed delivery method. For example, if the user is in a hurry, the delivery unit can select a method that allows for quick delivery. This allows for appropriate delivery by adjusting the report delivery method according to 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.

[0099] When distributing a report, the distribution unit can select the optimal distribution method by referring to the person in charge's past distribution history. The distribution history includes, for example, past distribution dates and times, delivery success rates, etc., but is not limited to these examples. The distribution unit selects the optimal distribution method based on, for example, the person in charge's past distribution history. The distribution unit can also adjust the timing of distribution by referring to the person in charge's past distribution history. For example, the distribution unit analyzes the person in charge's past distribution history and finds areas for improvement in the distribution method. In this way, the optimal distribution method can be selected by referring to the past distribution history.

[0100] When distributing a report, the distribution unit can customize the distribution content based on the current work situation of the person in charge. The distribution content includes, for example, the type of report and the range of information included, but is not limited to these examples. For example, the distribution unit takes into account the current work situation of the person in charge and prioritizes the distribution of highly relevant content. In addition, the distribution unit can also filter out unnecessary content to reduce the work burden on the person in charge. For example, the distribution unit dynamically customizes the distribution content based on the current work situation of the person in charge. In this way, highly relevant information can be provided by customizing the distribution content based on the current work situation.

[0101] The distribution unit can improve the distribution method by reflecting the feedback from the person in charge when distributing the report. Examples of feedback include, but are not limited to, survey results and user comments. For example, the distribution unit selects the optimal distribution method based on the feedback from the person in charge. The distribution unit can also adjust the timing of distribution by reflecting the feedback from the person in charge. For example, the distribution unit can refer to the feedback from the person in charge to find areas for improvement in the distribution method. This makes it possible to improve the distribution method by reflecting the feedback.

[0102] The delivery unit can estimate the user's emotions and determine the delivery priority of reports based on the estimated user emotions. Delivery priorities include, but are not limited to, importance and urgency. For example, if the user is feeling stressed, the delivery unit can prioritize delivery of reports with high importance. Furthermore, if the user is relaxed, the delivery unit can prioritize delivery of detailed reports. For example, if the user is in a hurry, the delivery unit prioritizes delivery of reports that can be delivered quickly. This allows important information to be provided preferentially by determining the delivery priority of reports according to the user's emotions. The emotion estimation is realized 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.

[0103] When distributing a report, the distribution unit can select the optimal distribution method by taking into account the geographical location information of the person in charge. Examples of geographical location information include, but are not limited to, GPS data and location information services. The distribution unit can select the optimal distribution method, for example, based on the current location of the person in charge. The distribution unit can also select the optimal distribution method by taking into account the movement history of the person in charge. For example, the distribution unit selects the optimal distribution timing based on the geographical location information of the person in charge. This allows the optimal distribution method to be selected by taking into account the geographical location information.

[0104] When delivering a report, the distribution unit can analyze the social media activity of the person in charge to customize the content of the report. Social media activity includes, but is not limited to, for example, the content of posts and engagement rates. For example, the distribution unit analyzes the content of posts made by the person in charge on social media and delivers related reports. The distribution unit can also deliver related reports based on the activities of the person in charge's friends on social media. For example, the distribution unit delivers related reports based on the check-in information of the person in charge on social media. In this way, highly relevant information can be provided by analyzing social media activity.

[0105] When distributing a report, the distribution unit can customize the distribution method by reflecting the person in charge's past feedback. Feedback includes, but is not limited to, for example, survey results and user comments. The distribution unit selects the optimal distribution method, for example, based on the person in charge's past feedback. The distribution unit can also adjust the timing of distribution by reflecting the person in charge's past feedback. For example, the distribution unit can refer to the person in charge's past feedback to find areas for improvement in the distribution method. In this way, the optimal distribution method can be selected by reflecting the past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and distribution 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 business data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via 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 data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a report based on the analysis results. The distribution unit is realized, for example, by the control unit 46A of the smart device 14 and distributes the generated report to each person in charge. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and distribution 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 business data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via 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 data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a report based on the analysis results. The distribution unit is realized, for example, by the control unit 46A of the smart glasses 214 and distributes the generated report to each responsible person. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and distribution unit 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 business data using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the data to the data processing device 12 via 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 data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a report based on the analysis results. The distribution unit is realized, for example, by the control unit 46A of the headset type terminal 314 and distributes the generated report to each person in charge. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and distribution 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 business data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via 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 data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a report based on the analysis results. The distribution unit is realized, for example, by the control unit 46A of the robot 414 and distributes the generated report to each person in charge.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analysis of important data. Also, if the user is relaxed, it can prioritize analysis of detailed data. Furthermore, if the user is in a hurry, it can perform a simplified analysis to provide analysis results quickly. This allows for efficient analysis by adjusting the analysis priority according to the user's emotions.

[0108] When collecting data, the collection unit can select the optimal data collection method based on the person in charge's past work data. For example, if the person in charge has frequently used voice input in the past, voice data can be collected with priority. Also, if the person in charge prefers text input, text data can be collected with priority. Furthermore, if the person in charge has frequently used image input, image data can be collected with priority. This allows for efficient data collection by selecting the optimal data collection method based on the person in charge's past work data.

[0109] The generation unit can estimate the user's emotions and adjust the presentation method of the report based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible report can be generated. If the user is relaxed, a detailed report can be generated. Furthermore, if the user is in a hurry, a report that focuses on the main points can be generated. In this way, by adjusting the presentation method of the report according to the user's emotions, a highly visible report can be provided.

[0110] When distributing a report, the distribution department can customize the content based on the person in charge's current work situation. For example, if the person in charge is busy, a simplified report containing only important information can be distributed. On the other hand, if the person in charge has time, a report containing detailed information can be distributed. Furthermore, if the person in charge is concentrating on a specific project, information related to that project can be distributed preferentially. In this way, by customizing the content based on the person in charge's current work situation, highly relevant information can be provided.

[0111] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a regression analysis algorithm can be applied to sales data, and a clustering algorithm can be applied to customer data. A classification algorithm can also be applied to marketing data. Furthermore, a time series analysis algorithm can be applied to distribution-related data. This allows for highly accurate analysis by applying the optimal analysis algorithm depending on the data category.

[0112] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the workload. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, the timing of data collection can be adjusted to quickly collect the necessary data. In this way, the workload can be reduced by adjusting the timing of data collection according to the user's emotions.

[0113] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the analysis results. For example, a detailed report can be generated for analysis results with high importance, and a simplified report can be generated for analysis results with low importance. It is also possible to quickly generate a report for analysis results with high urgency, and generate a report at a regular time for analysis results with low urgency. Furthermore, it is also possible to generate a report including an in-depth analysis for analysis results with a large business impact. This allows for efficient report generation by adjusting the level of detail of the report according to the importance of the analysis results.

[0114] The delivery unit can estimate the user's emotions and adjust the report delivery method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple delivery method can be selected. Alternatively, if the user is relaxed, a detailed delivery method can be selected. Furthermore, if the user is in a hurry, a method that allows for quick delivery can be selected. In this way, by adjusting the report delivery method according to the user's emotions, appropriate delivery is possible.

[0115] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the person in charge. For example, if the person in charge is in a specific area, data related to that area can be collected preferentially. It can also prioritize collecting highly relevant data by taking into account the person in charge's movement history. Furthermore, it can select the optimal data collection point based on the person in charge's current location. In this way, highly relevant data can be collected efficiently by taking into account the person in charge's geographical location information.

[0116] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a summary analysis result can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, highly visible analysis results can be provided.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects the business data of each employee. Business data includes sales data, customer data, and work time data. The collection unit can automatically collect data using APIs, and can also collect data using sensors. For example, it can collect performance data for the area of ​​responsibility and data related to distribution. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit analyzes the collected data and provides the basic data for generating optimal reports for each person in charge. Step 3: The generator generates a report based on the data analyzed by the analyzer. The report may include information displayed in a graph or table format. For example, the generator generates a report based on the analysis results. Step 4: The distribution unit distributes the reports generated by the generation unit to each person in charge. The distribution is performed via email or dashboard. For example, the distribution unit distributes the generated reports to each person in charge.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 business data from each employee; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a report based on the data analyzed by the analysis unit; a distribution unit that distributes the report generated by the generation unit to each person in charge. A system characterized by:

2. The collecting unit Collect performance data or delivery-related data for assigned area 2. The system of claim 1.

3. The analysis unit Analyze the collected data and generate reports for each person in charge 2. The system of claim 1.

4. The generation unit Generate a report based on the analysis results 2. The system of claim 1.

5. The distribution unit Distribute the generated report to each person in charge 2. The system of claim 1.

6. The generation unit If a specific person is performing well in a particular area, generate a report with suggestions for expanding that performance to other areas.

2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze each employee's past work data and select the optimal data collection method 2. The system of claim 1.

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