system

Abstract

The system according to the embodiment aims to identify the causes of lost sales and propose effective solutions. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, an evaluation unit, and a storage unit. The reception unit inputs the items that are bottlenecks in lost orders. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes a solution based on the information analyzed by the analysis unit. The evaluation unit evaluates the effectiveness of the solution proposed by the proposal unit. The storage unit stores the data evaluated by the evaluation unit.

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] Conventional techniques make it difficult to identify the causes of lost orders and find effective solutions, leaving room for improvement.

[0005] The system according to the embodiment aims to identify the causes of lost sales and propose effective solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, an evaluation unit, and a storage unit. The reception unit inputs the items that are bottlenecks in lost orders. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes a solution based on the information analyzed by the analysis unit. The evaluation unit evaluates the effectiveness of the solution proposed by the proposal unit. The storage unit stores the data evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the causes of lost sales and propose effective solutions. [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) In an embodiment of the present invention, a pre-loss consultation system is a system in which a crew inputs items that are bottlenecks to losing a deal, and a generation AI analyzes, proposes, evaluates, and accumulates the results. In the pre-loss consultation system, a crew inputs items that are bottlenecks to losing a deal, and a generation AI analyzes and proposes solutions. For example, if the price is high, a price adjustment is proposed, and if the delivery date is late, a delivery date improvement is proposed. The generation AI derives the optimal solution based on past data. Furthermore, the generation AI evaluates the effectiveness of the proposed solution and accumulates the data. This enables more accurate proposals in subsequent pre-loss consultations. For example, if a price adjustment was effective in the past, it can be proposed again in similar situations. This allows the pre-loss consultation system to reduce the risk of losing a deal and improve customer satisfaction. Furthermore, the solutions proposed by the generation AI enable quick and effective responses. For example, the crew can immediately propose a price adjustment, resolving customer dissatisfaction and concluding the contract. This allows the pre-loss consultation system to reduce the risk of losing a deal and improve customer satisfaction. Furthermore, the solutions proposed by the generation AI enable quick and effective responses. For example, a crew member can instantly propose a price adjustment to resolve a customer complaint and close the deal.

[0029] A pre-loss consultation system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, an evaluation unit, and a storage unit. The reception unit inputs items that are bottlenecks in losing a sale. Items that are bottlenecks in losing a sale include, but are not limited to, price, delivery time, and quality. The reception unit receives, for example, information that a price is high input by a crew member. The reception unit can also receive information that a delivery time is late. The reception unit can also receive information that a quality is uncertain. The analysis unit uses a generation AI to analyze the information input by the reception unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning, or other methods, but is not limited to, these. For example, the analysis unit analyzes information that a price is high and determines that a price adjustment is necessary. The analysis unit can also analyze information that a delivery time is late and determine that a delivery time improvement is necessary. The analysis unit can also analyze information that a quality is uncertain and determine that a quality improvement is necessary. The proposal unit uses the generation AI to propose solutions based on the information analyzed by the analysis unit. Solutions include, but are not limited to, price adjustments, delivery time reductions, and quality improvements. For example, the proposal unit proposes price adjustments when the price is high. The proposal unit can also propose delivery time improvements when the delivery time is late. Furthermore, the proposal unit can also propose quality improvements when there are concerns about quality. The evaluation unit evaluates the effectiveness of the solutions proposed by the proposal unit. The evaluation is performed by, but is not limited to, methods such as effect measurement, feedback collection, and quantitative evaluation. For example, the evaluation unit evaluates the effectiveness of price adjustments. The evaluation unit can also evaluate the effectiveness of delivery time improvements. Furthermore, the evaluation unit can also evaluate the effectiveness of quality improvements. The storage unit stores the data evaluated by the evaluation unit. The storage is performed by, but is not limited to, methods such as saving in a database or using cloud storage. For example, the storage unit stores effect data of price adjustments. The storage unit can also store effect data of delivery time improvements. Furthermore, the storage unit can also store effect data of quality improvements.As a result, the pre-loss consultation system of the embodiment can improve the accuracy of pre-loss consultation by inputting the items that are bottlenecks in losing a contract, and analyzing, proposing, evaluating, and storing the information.

[0030] The reception unit can analyze past unsuccessful sales data and select an appropriate input method. The reception unit analyzes past unsuccessful sales data and selects an appropriate input method. Past unsuccessful sales data includes, but is not limited to, reasons for unsuccessful sales, customer feedback, and competitive information. For example, if the past unsuccessful sales data indicates that voice input is effective, the reception unit can recommend voice input. Furthermore, if the past unsuccessful sales data indicates that text input is effective, the reception unit can recommend text input. Furthermore, if the past unsuccessful sales data indicates that image input is effective, the reception unit can recommend image input. This improves the efficiency and accuracy of input by selecting the optimal input method based on past data. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input past unsuccessful sales data into a generation AI and have the generation AI select the optimal input method.

[0031] When inputting the bottleneck items for lost sales, the reception unit can filter them based on the crew's current project and areas of interest. When inputting the bottleneck items for lost sales, the reception unit can filter them based on the crew's current project and areas of interest. The crew's current project includes, but is not limited to, the project's progress and areas of interest. For example, the reception unit can prioritize input of bottleneck items for lost sales related to the project the crew is currently working on. The reception unit can also prioritize input of bottleneck items for lost sales related to the crew's areas of interest. Furthermore, the reception unit can refer to the crew's past project history to input related bottleneck items for lost sales. In this way, by filtering based on the crew's current project and areas of interest, highly relevant items can be prioritized and input. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the crew's project data to the generation AI and have the generation AI perform filtering.

[0032] The reception unit can select an appropriate input means according to the crew's input method when inputting the bottleneck items of lost orders. The reception unit selects an appropriate input means according to the crew's input method when inputting the bottleneck items of lost orders. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the crew prefers voice input, the reception unit can prioritize voice input. Also, if the crew prefers text input, the reception unit can prioritize text input. Furthermore, if the crew prefers image input, the reception unit can prioritize image input. This improves input efficiency by selecting the optimal input means according to the crew's preferred input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the crew's input method data into the generation AI and have the generation AI select the optimal input means.

[0033] When inputting bottleneck items for lost sales, the reception unit can prioritize inputting highly relevant items taking into account the crew's geographical location information. When inputting bottleneck items for lost sales, the reception unit prioritizes inputting highly relevant items taking into account the crew's geographical location information. Geographical location information can be obtained, for example, using GPS data, location information services, etc., but is not limited to these examples. For example, when the crew is in a specific area, the reception unit can prioritize inputting bottleneck items for lost sales related to that area. Furthermore, when the crew is traveling, the reception unit can prioritize inputting bottleneck items for lost sales related to their destination. Furthermore, when the crew is at a specific customer's location, the reception unit can prioritize inputting bottleneck items for lost sales related to that customer. In this way, by taking the crew's geographical location information into account, highly relevant items can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the crew's location information data to the generation AI and cause the generation AI to select highly relevant items.

[0034] The reception unit can analyze the crew's social media activities and input related items when inputting bottleneck items for lost sales. The reception unit can analyze the crew's social media activities and input related items when inputting bottleneck items for lost sales. Social media activities can be analyzed, for example, by analyzing post content, analyzing followers, etc., but are not limited to these examples. For example, the reception unit can input issues mentioned by the crew on social media as bottleneck items for lost sales. The reception unit can also extract and input related bottleneck items for lost sales from the crew's social media activities. Furthermore, the reception unit can input related bottleneck items for lost sales based on the activities of the crew's friends on social media. This allows for efficient input of related items by analyzing the crew's social media activities. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the crew's social media data into the generation AI and cause the generation AI to extract related items.

[0035] The reception unit can customize the input method by reflecting the crew's past feedback when inputting bottleneck items for lost sales. The reception unit customizes the input method by reflecting the crew's past feedback when inputting bottleneck items for lost sales. Past feedback includes, but is not limited to, customer opinions and crew evaluations. For example, the reception unit preferentially provides the input method that the crew has previously preferred. The reception unit can also customize the input interface based on the crew's past feedback. Furthermore, the reception unit can optimize the input procedure by referring to the crew's past feedback. In this way, the input method can be optimized by reflecting the crew's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the crew's feedback data into the generation AI and have the generation AI customize the input method.

[0036] The analysis unit can adjust the level of detail of the analysis based on the importance of the bottleneck item causing the lost sale during the analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the bottleneck item causing the lost sale during the analysis. The importance of the bottleneck item causing the lost sale is evaluated based on, for example, the impact on sales, customer feedback, etc., but is not limited to these examples. For example, the analysis unit performs a detailed analysis of bottleneck items causing the lost sale with high importance. The analysis unit can also perform a concise analysis of bottleneck items causing the lost sale with low importance. Furthermore, the analysis unit can adjust the depth of the analysis depending on the importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the bottleneck item causing the lost sale. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input importance data of bottleneck items causing the lost sale into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] The analysis unit can apply different analysis algorithms depending on the category of the bottleneck item causing the lost order during analysis. The analysis unit can apply different analysis algorithms depending on the category of the bottleneck item causing the lost order during analysis. The categories of bottleneck items causing the lost order are classified by criteria such as, but not limited to, price, delivery time, and quality. For example, the analysis unit can apply a price adjustment algorithm to bottleneck items causing the lost order related to price. The analysis unit can also apply a delivery time improvement algorithm to bottleneck items causing the lost order related to delivery time. Furthermore, the analysis unit can apply a quality improvement algorithm to bottleneck items causing the lost order related to quality. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the bottleneck item causing the lost order. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of bottleneck items causing the lost order into the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the crew's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the crew's past analysis results during analysis. Past analysis results include, but are not limited to, past success stories and failure stories. For example, the analysis unit can optimize the analysis algorithm based on the crew's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the crew's past analysis results. Furthermore, the analysis unit can analyze the crew's past analysis results and identify areas for improvement in the analysis. As a result, the accuracy of the analysis is improved by referring to the crew's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the crew's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making the proposal. The proposal unit adjusts the level of detail of the proposal based on the importance of the solution when making the proposal. The importance of the solution is evaluated based on criteria such as, but not limited to, the impact on sales and customer feedback. For example, the proposal unit makes a detailed proposal for a solution with high importance. The proposal unit can also make a concise proposal for a solution with low importance. Furthermore, the proposal unit can adjust the level of detail of the proposal according to the importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the solution. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input solution importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0040] The suggestion unit can apply different proposal algorithms depending on the category of the solution when proposing. The suggestion unit applies different proposal algorithms depending on the category of the solution when proposing. Solution categories are classified by criteria such as, but not limited to, price adjustment, delivery time shortening, and quality improvement. For example, the suggestion unit applies a price adjustment algorithm to solutions related to price. The suggestion unit can also apply a delivery time improvement algorithm to solutions related to delivery time. Furthermore, the suggestion unit can also apply a quality improvement algorithm to solutions related to quality. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the category of the solution. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input solution category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0041] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the crew's past proposal results. When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the crew's past proposal results. Past proposal results include, but are not limited to, past success cases and failure cases. For example, the suggestion unit optimizes the proposal algorithm based on the crew's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the crew's past proposal results. Furthermore, the suggestion unit can analyze the crew's past proposal results and identify areas for improvement in the proposal. As a result, the accuracy of the proposal is improved by referring to the crew's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the crew's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0042] The evaluation unit can set an index for quantitatively evaluating the effectiveness of the solution during evaluation. The evaluation unit sets an index for quantitatively evaluating the effectiveness of the solution during evaluation. The index for quantitative evaluation is set based on criteria such as, for example, KPI, ROI, customer satisfaction, etc., but is not limited to these examples. For example, the evaluation unit evaluates the effectiveness of the solution by the sales increase rate. The evaluation unit can also evaluate the effectiveness of the solution by the improvement rate of customer satisfaction. Furthermore, the evaluation unit can evaluate the effectiveness of the solution by the cost reduction rate. In this way, quantitative evaluation of the effectiveness of the solution improves the objectivity of the evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input solution effect data into the generation AI and cause the generation AI to perform a quantitative evaluation.

[0043] The evaluation unit can improve the accuracy of the evaluation by referring to the crew's past evaluation results during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the crew's past evaluation results during the evaluation. Past evaluation results include, but are not limited to, past success stories and failure stories. For example, the evaluation unit optimizes the evaluation algorithm based on the crew's past evaluation results. The evaluation unit can also improve the accuracy of the evaluation by referring to the crew's past evaluation results. Furthermore, the evaluation unit can analyze the crew's past evaluation results and identify areas for improvement in the evaluation. In this way, the accuracy of the evaluation is improved by referring to the crew's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the crew's past evaluation result data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0044] The evaluation unit can apply different evaluation methods depending on the category of the solution during evaluation. The evaluation unit applies different evaluation methods depending on the category of the solution during evaluation. Solution categories are classified by criteria such as, but not limited to, price adjustment, delivery time reduction, and quality improvement. For example, the evaluation unit applies a method to evaluate the effectiveness of price adjustment to solutions related to price. The evaluation unit can also apply a method to evaluate the effectiveness of delivery time improvement to solutions related to delivery time. Furthermore, the evaluation unit can also apply a method to evaluate the effectiveness of quality improvement to solutions related to quality. In this way, by applying an appropriate evaluation method depending on the category of the solution, the accuracy of the evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input solution category data into the generation AI and cause the generation AI to apply the evaluation method.

[0045] The evaluation unit can adjust the order of evaluation based on the submission time of the solutions during evaluation. The evaluation unit adjusts the order of evaluation based on the submission time of the solutions during evaluation. The submission time of the solutions is evaluated based on criteria such as, but not limited to, the submission deadline and the progress of the project. For example, the evaluation unit prioritizes evaluation of solutions submitted earlier. The evaluation unit can also postpone solutions submitted later. Furthermore, the evaluation unit can adjust the order of evaluation depending on the submission time. This enables efficient evaluation by adjusting the order of evaluation based on the submission time of the solutions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input solution submission time data into the generation AI and cause the generation AI to adjust the order of evaluation.

[0046] The evaluation unit can weight the evaluation based on the relevance of the solution during the evaluation. The evaluation unit weights the evaluation based on the relevance of the solution during the evaluation. The relevance of the solution is evaluated based on criteria such as, but not limited to, the degree of agreement with the project goal and the suitability with the customer needs. For example, the evaluation unit may emphasize highly relevant solutions in the evaluation. The evaluation unit may also lightly emphasize less relevant solutions in the evaluation. Furthermore, the evaluation unit can adjust the evaluation weight according to the relevance. Thus, weighting the evaluation based on the relevance of the solution improves the accuracy of the evaluation. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input relevance data of the solution to the generation AI and cause the generation AI to weight the evaluation.

[0047] The evaluation unit can adjust the evaluation criteria according to the crew's expertise level during evaluation. The evaluation unit can adjust the evaluation criteria according to the crew's expertise level during evaluation. The crew's expertise level is evaluated based on criteria such as, but not limited to, qualifications, years of experience, and past project performance. For example, the evaluation unit can apply strict evaluation criteria when the crew's expertise level is high. The evaluation unit can also apply lenient evaluation criteria when the crew's expertise level is low. Furthermore, the evaluation unit can adjust the evaluation criteria according to the crew's expertise level. This enables appropriate evaluation by adjusting the evaluation criteria according to the crew's expertise level. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the crew's expertise level data into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0048] The storage unit can optimize the storage algorithm by referring to past data during storage. The storage unit optimizes the storage algorithm by referring to past data during storage. Past data includes, but is not limited to, past project data, customer feedback, etc. For example, the storage unit optimizes the storage algorithm based on past data. The storage unit can also improve the accuracy of storage by referring to past data. Furthermore, the storage unit can analyze past data and identify areas for improvement in storage. This makes it possible to optimize the storage algorithm by referring to past data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past data into a generation AI and have the generation AI optimize the storage algorithm.

[0049] The storage unit can update the stored data by reflecting crew feedback during storage. The storage unit updates the stored data by reflecting crew feedback during storage. Crew feedback includes, for example, survey results, direct opinions, etc., but is not limited to these examples. For example, the storage unit updates the stored data based on crew feedback. The storage unit can also improve the accuracy of the stored data by referring to the crew feedback. Furthermore, the storage unit can analyze the crew feedback and identify areas for improvement in the stored data. As a result, the accuracy of the stored data is improved by reflecting the crew feedback. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input crew feedback data to a generation AI and have the generation AI update the stored data.

[0050] The accumulation unit can apply different accumulation methods depending on the data category during accumulation. The accumulation unit applies different accumulation methods depending on the data category during accumulation. Data categories are classified based on criteria such as, but not limited to, customer data, project data, and feedback data. For example, the accumulation unit applies a price adjustment accumulation method to data related to price. The accumulation unit can also apply a delivery time improvement accumulation method to data related to delivery time. Furthermore, the accumulation unit can also apply a quality improvement accumulation method to data related to quality. In this way, the accuracy of the accumulated data is improved by applying an appropriate accumulation method depending on the data category. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input data category data to a generation AI and cause the generation AI to apply an accumulation method.

[0051] The accumulation unit can weight the accumulated data based on the time of data submission during accumulation. The accumulation unit weights the accumulated data based on the time of data submission during accumulation. The time of data submission is evaluated based on criteria such as, but not limited to, a submission deadline and project progress. For example, the accumulation unit prioritizes and accumulates data submitted early. The accumulation unit can also prioritize and accumulate data submitted late. Furthermore, the accumulation unit can adjust the weighting of the accumulated data depending on the time of submission. Thus, weighting the accumulated data based on the time of data submission enables efficient data accumulation. Some or all of the above-described processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input data on the time of data submission to a generation AI and have the generation AI perform weighting of the accumulated data.

[0052] The storage unit can integrate information from different data sources to enrich the stored data during storage. The storage unit integrates information from different data sources to enrich the stored data during storage. Examples of different data sources include, but are not limited to, internal data, external data, and social media data. For example, the storage unit integrates information from different data sources to enrich the stored data. The storage unit can also improve the accuracy of the stored data by referring to information from different data sources. Furthermore, the storage unit can analyze information from different data sources and identify areas for improvement in the stored data. In this way, the accuracy of the stored data is improved by integrating information from different data sources. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input information from different data sources into a generation AI and have the generation AI integrate the information.

[0053] The storage unit can adjust the format of the stored data according to the crew's expertise level during storage. The storage unit adjusts the format of the stored data according to the crew's expertise level during storage. The crew's expertise level is evaluated based on criteria such as, but not limited to, qualifications, years of experience, and past project performance. For example, if the crew's expertise level is high, the storage unit stores the data in a detailed data format. Alternatively, if the crew's expertise level is low, the storage unit can store the data in a concise data format. Furthermore, the storage unit can adjust the format of the stored data according to the crew's expertise level. This enables appropriate data storage by adjusting the format of the stored data according to the crew's expertise level. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the crew's expertise level data to a generation AI and have the generation AI adjust the format of the stored data.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can analyze the crew's past input history and customize it to improve input efficiency. For example, by prioritizing the display of items that the crew has frequently input in the past, it is possible to reduce the effort required for input. It can also display a message urging the crew to input items where they have made input errors in the past. It can also analyze the crew's input speed and patterns and provide an optimal input interface. This makes it possible to utilize the crew's past input history to improve input efficiency and accuracy.

[0056] The reception unit can analyze the crew's past project data and automatically complete related project information when inputting data. For example, by automatically inputting data related to the current project based on information about projects the crew has worked on in the past, the amount of work required for input can be reduced. Input items can also be automatically generated based on templates and formats used by the crew in the past. Furthermore, the reception unit can analyze the crew's past project data and provide reference information when inputting data. This improves the efficiency and accuracy of input by utilizing the crew's past project data.

[0057] The reception unit can monitor the crew's current work status in real time and suggest the optimal timing for input. For example, if the crew is performing other important tasks, input can be postponed until that task is completed. Also, if the crew is on break, input can be prompted after the break, enabling efficient input. Furthermore, the reception unit can analyze the crew's workload and prompt input when the workload has been reduced. This improves the efficiency and accuracy of input by suggesting the optimal input timing according to the crew's work status.

[0058] The analysis unit can dynamically adjust the analysis algorithm based on the crew's past analysis results. For example, by preferentially applying analysis methods that have been successful in the past, the accuracy of the analysis can be improved. Also, by avoiding analysis methods that have failed in the past, it is possible to reduce unnecessary analysis. Furthermore, it is possible to analyze the crew's past analysis results and propose new analysis methods. In this way, the efficiency and accuracy of analysis can be improved by utilizing the crew's past analysis results.

[0059] The proposal unit can analyze the crew's past proposal history and customize the proposals to improve the accuracy of the proposals. For example, by preferentially displaying proposals that the crew has adopted in the past, it is possible to make effective proposals. It can also reduce useless proposals by avoiding proposals that the crew has rejected in the past. Furthermore, it can analyze the crew's past proposal history and generate new proposals. In this way, by utilizing the crew's past proposal history, the efficiency and accuracy of proposals can be improved.

[0060] The evaluation unit can dynamically adjust the evaluation criteria based on the crew's past evaluation results. For example, by preferentially applying evaluation criteria that have been successful in the past, the accuracy of the evaluation can be improved. Also, by avoiding evaluation criteria that have failed in the past, it can reduce unnecessary evaluations. Furthermore, it can analyze the crew's past evaluation results and propose new evaluation criteria. In this way, by utilizing the crew's past evaluation results, the efficiency and accuracy of the evaluation can be improved.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception department inputs the items that are causing the lost order. Items that are causing the lost order include, for example, price, delivery time, and quality. The reception department receives information input by the crew that the price is high, the delivery time is late, or the quality is uncertain. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit may analyze information that the price is high and determine that a price adjustment is necessary. It may also analyze information that the delivery date is late and determine that the delivery date needs to be improved. It may also analyze information that there are concerns about the quality and determine that quality needs to be improved. Step 3: The proposal department uses the generative AI to propose solutions based on the information analyzed by the analysis department. Solutions include price adjustments, shortened delivery times, and quality improvements. For example, the proposal department will propose price adjustments if the price is high, shortened delivery times if the delivery time is long, and quality improvements if there are concerns about quality. Step 4: The evaluation department evaluates the effectiveness of the solutions proposed by the proposal department. Evaluation is carried out using methods such as effect measurement, feedback collection, and quantitative evaluation. For example, the evaluation department evaluates the effectiveness of price adjustments, delivery time improvements, and quality improvements. Step 5: The storage unit stores the data evaluated by the evaluation unit. The data is stored in a database, using cloud storage, or other methods. For example, the storage unit stores data on the effects of price adjustments, delivery time improvements, and quality improvements.

[0063] (Example 2) In an embodiment of the present invention, a pre-loss consultation system is a system in which a crew inputs items that are bottlenecks to losing a deal, and a generation AI analyzes, proposes, evaluates, and accumulates the results. In the pre-loss consultation system, a crew inputs items that are bottlenecks to losing a deal, and a generation AI analyzes and proposes solutions. For example, if the price is high, a price adjustment is proposed, and if the delivery date is late, a delivery date improvement is proposed. The generation AI derives the optimal solution based on past data. Furthermore, the generation AI evaluates the effectiveness of the proposed solution and accumulates the data. This enables more accurate proposals in subsequent pre-loss consultations. For example, if a price adjustment was effective in the past, it can be proposed again in similar situations. This allows the pre-loss consultation system to reduce the risk of losing a deal and improve customer satisfaction. Furthermore, the solutions proposed by the generation AI enable quick and effective responses. For example, the crew can immediately propose a price adjustment, resolving customer dissatisfaction and concluding the contract. This allows the pre-loss consultation system to reduce the risk of losing a deal and improve customer satisfaction. Furthermore, the solutions proposed by the generation AI enable quick and effective responses. For example, a crew member can instantly propose a price adjustment to resolve a customer complaint and close the deal.

[0064] A pre-loss consultation system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, an evaluation unit, and a storage unit. The reception unit inputs items that are bottlenecks in losing a sale. Items that are bottlenecks in losing a sale include, but are not limited to, price, delivery time, and quality. The reception unit receives, for example, information that a price is high input by a crew member. The reception unit can also receive information that a delivery time is late. The reception unit can also receive information that a quality is uncertain. The analysis unit uses a generation AI to analyze the information input by the reception unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning, or other methods, but is not limited to, these. For example, the analysis unit analyzes information that a price is high and determines that a price adjustment is necessary. The analysis unit can also analyze information that a delivery time is late and determine that a delivery time improvement is necessary. The analysis unit can also analyze information that a quality is uncertain and determine that a quality improvement is necessary. The proposal unit uses the generation AI to propose solutions based on the information analyzed by the analysis unit. Solutions include, but are not limited to, price adjustments, delivery time reductions, and quality improvements. For example, the proposal unit proposes price adjustments when the price is high. The proposal unit can also propose delivery time improvements when the delivery time is late. Furthermore, the proposal unit can also propose quality improvements when there are concerns about quality. The evaluation unit evaluates the effectiveness of the solutions proposed by the proposal unit. The evaluation is performed by, but is not limited to, methods such as effect measurement, feedback collection, and quantitative evaluation. For example, the evaluation unit evaluates the effectiveness of price adjustments. The evaluation unit can also evaluate the effectiveness of delivery time improvements. Furthermore, the evaluation unit can also evaluate the effectiveness of quality improvements. The storage unit stores the data evaluated by the evaluation unit. The storage is performed by, but is not limited to, methods such as saving in a database or using cloud storage. For example, the storage unit stores effect data of price adjustments. The storage unit can also store effect data of delivery time improvements. Furthermore, the storage unit can also store effect data of quality improvements.As a result, the pre-loss consultation system of the embodiment can improve the accuracy of pre-loss consultation by inputting the items that are bottlenecks in losing a contract, and analyzing, proposing, evaluating, and storing the information.

[0065] The pre-loss consultation system includes a reception unit that estimates the crew's emotions and adjusts the timing of inputting the bottleneck items for lost sales based on the estimated crew's emotions. The reception unit estimates the crew's emotions and adjusts the timing of inputting the bottleneck items for lost sales based on the estimated crew's emotions. The crew's emotions are estimated using, for example, facial expression recognition, voice analysis, questionnaire surveys, and other methods, but are not limited to these examples. For example, if the crew is feeling stressed, the reception unit may prompt the crew to input the bottleneck items for lost sales at a time when they are able to relax. Furthermore, if the crew is concentrating, the reception unit may encourage the crew to quickly input the bottleneck items for lost sales by taking advantage of their concentration. Furthermore, if the crew is tired, the reception unit may prompt the crew to input the bottleneck items for lost sales after a break. This allows for efficient input by adjusting the input timing according to the crew's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input facial expression data of the crew members into the generation AI and have the generation AI estimate their emotions.

[0066] The reception unit can analyze past unsuccessful sales data and select an appropriate input method. The reception unit analyzes past unsuccessful sales data and selects an appropriate input method. Past unsuccessful sales data includes, but is not limited to, reasons for unsuccessful sales, customer feedback, and competitive information. For example, if the past unsuccessful sales data indicates that voice input is effective, the reception unit can recommend voice input. Furthermore, if the past unsuccessful sales data indicates that text input is effective, the reception unit can recommend text input. Furthermore, if the past unsuccessful sales data indicates that image input is effective, the reception unit can recommend image input. This improves the efficiency and accuracy of input by selecting the optimal input method based on past data. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input past unsuccessful sales data into a generation AI and have the generation AI select the optimal input method.

[0067] When inputting the bottleneck items for lost sales, the reception unit can filter them based on the crew's current project and areas of interest. When inputting the bottleneck items for lost sales, the reception unit can filter them based on the crew's current project and areas of interest. The crew's current project includes, but is not limited to, the project's progress and areas of interest. For example, the reception unit can prioritize input of bottleneck items for lost sales related to the project the crew is currently working on. The reception unit can also prioritize input of bottleneck items for lost sales related to the crew's areas of interest. Furthermore, the reception unit can refer to the crew's past project history to input related bottleneck items for lost sales. In this way, by filtering based on the crew's current project and areas of interest, highly relevant items can be prioritized and input. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the crew's project data to the generation AI and have the generation AI perform filtering.

[0068] The reception unit can select an appropriate input means according to the crew's input method when inputting the bottleneck items of lost orders. The reception unit selects an appropriate input means according to the crew's input method when inputting the bottleneck items of lost orders. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the crew prefers voice input, the reception unit can prioritize voice input. Also, if the crew prefers text input, the reception unit can prioritize text input. Furthermore, if the crew prefers image input, the reception unit can prioritize image input. This improves input efficiency by selecting the optimal input means according to the crew's preferred input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the crew's input method data into the generation AI and have the generation AI select the optimal input means.

[0069] The reception unit can estimate the crew's emotions and determine the priority of bottleneck items to be input based on the estimated crew's emotions. The reception unit can estimate the crew's emotions and determine the priority of bottleneck items to be input based on the estimated crew's emotions. The crew's emotions can be estimated using, for example, facial expression recognition, voice analysis, questionnaire surveys, etc., but are not limited to these examples. For example, if the crew is feeling stressed, the reception unit can have the crew input simple items first. Also, if the crew is relaxed, the reception unit can have the crew input important items first. Furthermore, if the crew is concentrating, the reception unit can have the crew input complex items first. This enables efficient input by determining the priority of input items according to the crew'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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception department can input facial expression data of the crew into the generation AI and have the generation AI estimate the emotions.

[0070] When inputting bottleneck items for lost sales, the reception unit can prioritize inputting highly relevant items taking into account the crew's geographical location information. When inputting bottleneck items for lost sales, the reception unit prioritizes inputting highly relevant items taking into account the crew's geographical location information. Geographical location information can be obtained, for example, using GPS data, location information services, etc., but is not limited to these examples. For example, when the crew is in a specific area, the reception unit can prioritize inputting bottleneck items for lost sales related to that area. Furthermore, when the crew is traveling, the reception unit can prioritize inputting bottleneck items for lost sales related to their destination. Furthermore, when the crew is at a specific customer's location, the reception unit can prioritize inputting bottleneck items for lost sales related to that customer. In this way, by taking the crew's geographical location information into account, highly relevant items can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the crew's location information data to the generation AI and cause the generation AI to select highly relevant items.

[0071] The reception unit can analyze the crew's social media activities and input related items when inputting bottleneck items for lost sales. The reception unit can analyze the crew's social media activities and input related items when inputting bottleneck items for lost sales. Social media activities can be analyzed, for example, by analyzing post content, analyzing followers, etc., but are not limited to these examples. For example, the reception unit can input issues mentioned by the crew on social media as bottleneck items for lost sales. The reception unit can also extract and input related bottleneck items for lost sales from the crew's social media activities. Furthermore, the reception unit can input related bottleneck items for lost sales based on the activities of the crew's friends on social media. This allows for efficient input of related items by analyzing the crew's social media activities. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the crew's social media data into the generation AI and cause the generation AI to extract related items.

[0072] The reception unit can customize the input method by reflecting the crew's past feedback when inputting bottleneck items for lost sales. The reception unit customizes the input method by reflecting the crew's past feedback when inputting bottleneck items for lost sales. Past feedback includes, but is not limited to, customer opinions and crew evaluations. For example, the reception unit preferentially provides the input method that the crew has previously preferred. The reception unit can also customize the input interface based on the crew's past feedback. Furthermore, the reception unit can optimize the input procedure by referring to the crew's past feedback. In this way, the input method can be optimized by reflecting the crew's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the crew's feedback data into the generation AI and have the generation AI customize the input method.

[0073] The analysis unit can estimate the crew member's emotions and adjust the analysis presentation method based on the estimated crew member's emotions. The analysis unit can estimate the crew member's emotions and adjust the analysis presentation method based on the estimated crew member's emotions. The crew member's emotions can be estimated using, for example, facial expression recognition, voice analysis, questionnaire surveys, etc., but are not limited to these examples. For example, the analysis unit can provide detailed analysis results if the crew member is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points if the crew member is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results if the crew member is excited. Thus, by adjusting the analysis presentation method according to the crew member's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but are not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input facial expression data of the crew into the generation AI and have the generation AI estimate their emotions.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the bottleneck item causing the lost sale during the analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the bottleneck item causing the lost sale during the analysis. The importance of the bottleneck item causing the lost sale is evaluated based on, for example, the impact on sales, customer feedback, etc., but is not limited to these examples. For example, the analysis unit performs a detailed analysis of bottleneck items causing the lost sale with high importance. The analysis unit can also perform a concise analysis of bottleneck items causing the lost sale with low importance. Furthermore, the analysis unit can adjust the depth of the analysis depending on the importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the bottleneck item causing the lost sale. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input importance data of bottleneck items causing the lost sale into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the category of the bottleneck item causing the lost order during analysis. The analysis unit can apply different analysis algorithms depending on the category of the bottleneck item causing the lost order during analysis. The categories of bottleneck items causing the lost order are classified by criteria such as, but not limited to, price, delivery time, and quality. For example, the analysis unit can apply a price adjustment algorithm to bottleneck items causing the lost order related to price. The analysis unit can also apply a delivery time improvement algorithm to bottleneck items causing the lost order related to delivery time. Furthermore, the analysis unit can apply a quality improvement algorithm to bottleneck items causing the lost order related to quality. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the bottleneck item causing the lost order. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of bottleneck items causing the lost order into the generation AI and cause the generation AI to apply the analysis algorithm.

[0076] The analysis unit can improve the accuracy of the analysis by referring to the crew's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the crew's past analysis results during analysis. Past analysis results include, but are not limited to, past success stories and failure stories. For example, the analysis unit can optimize the analysis algorithm based on the crew's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the crew's past analysis results. Furthermore, the analysis unit can analyze the crew's past analysis results and identify areas for improvement in the analysis. As a result, the accuracy of the analysis is improved by referring to the crew's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the crew's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0077] The suggestion unit can estimate the crew member's emotions and adjust the way in which the suggestion is expressed based on the estimated crew member's emotions. The suggestion unit can estimate the crew member's emotions and adjust the way in which the suggestion is expressed based on the estimated crew member's emotions. The crew member's emotions can be estimated using, for example, facial expression recognition, voice analysis, questionnaire surveys, etc., but are not limited to these examples. For example, the suggestion unit can provide detailed suggestions when the crew member is relaxed. The suggestion unit can also provide concise suggestions that focus on the main points when the crew member is in a hurry. Furthermore, the suggestion unit can provide visually stimulating suggestions when the crew member is excited. In this way, appropriate suggestions can be provided by adjusting the way in which the suggestion is expressed based on the crew member's emotions. Emotion estimation is realized 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 are not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without using an AI. For example, the suggestion unit can input facial expression data of the crew into the generation AI and have the generation AI estimate the emotions.

[0078] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making the proposal. The proposal unit adjusts the level of detail of the proposal based on the importance of the solution when making the proposal. The importance of the solution is evaluated based on criteria such as, but not limited to, the impact on sales and customer feedback. For example, the proposal unit makes a detailed proposal for a solution with high importance. The proposal unit can also make a concise proposal for a solution with low importance. Furthermore, the proposal unit can adjust the level of detail of the proposal according to the importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the solution. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input solution importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0079] The suggestion unit can apply different proposal algorithms depending on the category of the solution when proposing. The suggestion unit applies different proposal algorithms depending on the category of the solution when proposing. Solution categories are classified by criteria such as, but not limited to, price adjustment, delivery time shortening, and quality improvement. For example, the suggestion unit applies a price adjustment algorithm to solutions related to price. The suggestion unit can also apply a delivery time improvement algorithm to solutions related to delivery time. Furthermore, the suggestion unit can also apply a quality improvement algorithm to solutions related to quality. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the category of the solution. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input solution category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0080] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the crew's past proposal results. When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the crew's past proposal results. Past proposal results include, but are not limited to, past success cases and failure cases. For example, the suggestion unit optimizes the proposal algorithm based on the crew's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the crew's past proposal results. Furthermore, the suggestion unit can analyze the crew's past proposal results and identify areas for improvement in the proposal. As a result, the accuracy of the proposal is improved by referring to the crew's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the crew's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0081] The evaluation unit can estimate the crew member's emotions and adjust the evaluation method based on the estimated crew member's emotions. The evaluation unit can estimate the crew member's emotions and adjust the evaluation method based on the estimated crew member's emotions. The crew member's emotions can be estimated using, for example, facial expression recognition, voice analysis, questionnaire surveys, etc., but are not limited to these examples. For example, the evaluation unit can perform a detailed evaluation if the crew member is relaxed. Furthermore, the evaluation unit can perform a concise evaluation that focuses on the main points if the crew member is in a hurry. Furthermore, the evaluation unit can perform a visually stimulating evaluation if the crew member is excited. This allows for appropriate evaluation by adjusting the evaluation method according to the crew member's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input facial expression data of the crew into the generation AI and have the generation AI estimate the emotions.

[0082] The evaluation unit can set an index for quantitatively evaluating the effectiveness of the solution during evaluation. The evaluation unit sets an index for quantitatively evaluating the effectiveness of the solution during evaluation. The index for quantitative evaluation is set based on criteria such as, for example, KPI, ROI, customer satisfaction, etc., but is not limited to these examples. For example, the evaluation unit evaluates the effectiveness of the solution by the sales increase rate. The evaluation unit can also evaluate the effectiveness of the solution by the improvement rate of customer satisfaction. Furthermore, the evaluation unit can evaluate the effectiveness of the solution by the cost reduction rate. In this way, quantitative evaluation of the effectiveness of the solution improves the objectivity of the evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input solution effect data into the generation AI and cause the generation AI to perform a quantitative evaluation.

[0083] The evaluation unit can improve the accuracy of the evaluation by referring to the crew's past evaluation results during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the crew's past evaluation results during the evaluation. Past evaluation results include, but are not limited to, past success stories and failure stories. For example, the evaluation unit optimizes the evaluation algorithm based on the crew's past evaluation results. The evaluation unit can also improve the accuracy of the evaluation by referring to the crew's past evaluation results. Furthermore, the evaluation unit can analyze the crew's past evaluation results and identify areas for improvement in the evaluation. In this way, the accuracy of the evaluation is improved by referring to the crew's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the crew's past evaluation result data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0084] The evaluation unit can apply different evaluation methods depending on the category of the solution during evaluation. The evaluation unit applies different evaluation methods depending on the category of the solution during evaluation. Solution categories are classified by criteria such as, but not limited to, price adjustment, delivery time reduction, and quality improvement. For example, the evaluation unit applies a method to evaluate the effectiveness of price adjustment to solutions related to price. The evaluation unit can also apply a method to evaluate the effectiveness of delivery time improvement to solutions related to delivery time. Furthermore, the evaluation unit can also apply a method to evaluate the effectiveness of quality improvement to solutions related to quality. In this way, by applying an appropriate evaluation method depending on the category of the solution, the accuracy of the evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input solution category data into the generation AI and cause the generation AI to apply the evaluation method.

[0085] The evaluation unit can estimate the crew member's emotions and determine the priority of the evaluations based on the estimated crew member's emotions. The evaluation unit can estimate the crew member's emotions and determine the priority of the evaluations based on the estimated crew member's emotions. The crew member's emotions can be estimated using, for example, facial expression recognition, voice analysis, questionnaire surveys, etc., but are not limited to these examples. For example, the evaluation unit can start with a simple evaluation if the crew member is stressed. Alternatively, the evaluation unit can start with a critical evaluation if the crew member is relaxed. Furthermore, the evaluation unit can start with a complex evaluation if the crew member is focused. This enables efficient evaluation by determining the priority of the evaluations based on the crew member's emotions. The emotion estimation can be achieved using an emotion estimation function, for example, 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. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the crew member's facial expression data into the generation AI and cause the generation AI to estimate the emotions.

[0086] The evaluation unit can adjust the order of evaluation based on the submission time of the solutions during evaluation. The evaluation unit adjusts the order of evaluation based on the submission time of the solutions during evaluation. The submission time of the solutions is evaluated based on criteria such as, but not limited to, the submission deadline and the progress of the project. For example, the evaluation unit prioritizes evaluation of solutions submitted earlier. The evaluation unit can also postpone solutions submitted later. Furthermore, the evaluation unit can adjust the order of evaluation depending on the submission time. This enables efficient evaluation by adjusting the order of evaluation based on the submission time of the solutions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input solution submission time data into the generation AI and cause the generation AI to adjust the order of evaluation.

[0087] The evaluation unit can weight the evaluation based on the relevance of the solution during the evaluation. The evaluation unit weights the evaluation based on the relevance of the solution during the evaluation. The relevance of the solution is evaluated based on criteria such as, but not limited to, the degree of agreement with the project goal and the suitability with the customer needs. For example, the evaluation unit may emphasize highly relevant solutions in the evaluation. The evaluation unit may also lightly emphasize less relevant solutions in the evaluation. Furthermore, the evaluation unit can adjust the evaluation weight according to the relevance. Thus, weighting the evaluation based on the relevance of the solution improves the accuracy of the evaluation. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input relevance data of the solution to the generation AI and cause the generation AI to weight the evaluation.

[0088] The evaluation unit can adjust the evaluation criteria according to the crew's expertise level during evaluation. The evaluation unit can adjust the evaluation criteria according to the crew's expertise level during evaluation. The crew's expertise level is evaluated based on criteria such as, but not limited to, qualifications, years of experience, and past project performance. For example, the evaluation unit can apply strict evaluation criteria when the crew's expertise level is high. The evaluation unit can also apply lenient evaluation criteria when the crew's expertise level is low. Furthermore, the evaluation unit can adjust the evaluation criteria according to the crew's expertise level. This enables appropriate evaluation by adjusting the evaluation criteria according to the crew's expertise level. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input the crew's expertise level data into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0089] The storage unit can estimate the crew member's emotions and select data to be stored based on the estimated crew member's emotions. The storage unit can estimate the crew member's emotions and select data to be stored based on the estimated crew member's emotions. The crew member's emotions can be estimated using, for example, facial expression recognition, voice analysis, questionnaire surveys, etc., but are not limited to these examples. For example, if the crew member is relaxed, the storage unit can store detailed data. If the crew member is in a hurry, the storage unit can store concise data that focuses on the main points. Furthermore, if the crew member is excited, the storage unit can store visually stimulating data. This enables efficient data storage by selecting data to be stored based on the crew member's emotions. 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. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, an AI, or without using an AI. For example, the storage unit can input facial expression data of crew members into the generation AI and have the generation AI estimate their emotions.

[0090] The storage unit can optimize the storage algorithm by referring to past data during storage. The storage unit optimizes the storage algorithm by referring to past data during storage. Past data includes, but is not limited to, past project data, customer feedback, etc. For example, the storage unit optimizes the storage algorithm based on past data. The storage unit can also improve the accuracy of storage by referring to past data. Furthermore, the storage unit can analyze past data and identify areas for improvement in storage. This makes it possible to optimize the storage algorithm by referring to past data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past data into a generation AI and have the generation AI optimize the storage algorithm.

[0091] The storage unit can update the stored data by reflecting crew feedback during storage. The storage unit updates the stored data by reflecting crew feedback during storage. Crew feedback includes, for example, survey results, direct opinions, etc., but is not limited to these examples. For example, the storage unit updates the stored data based on crew feedback. The storage unit can also improve the accuracy of the stored data by referring to the crew feedback. Furthermore, the storage unit can analyze the crew feedback and identify areas for improvement in the stored data. As a result, the accuracy of the stored data is improved by reflecting the crew feedback. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input crew feedback data to a generation AI and have the generation AI update the stored data.

[0092] The accumulation unit can apply different accumulation methods depending on the data category during accumulation. The accumulation unit applies different accumulation methods depending on the data category during accumulation. Data categories are classified based on criteria such as, but not limited to, customer data, project data, and feedback data. For example, the accumulation unit applies a price adjustment accumulation method to data related to price. The accumulation unit can also apply a delivery time improvement accumulation method to data related to delivery time. Furthermore, the accumulation unit can also apply a quality improvement accumulation method to data related to quality. In this way, the accuracy of the accumulated data is improved by applying an appropriate accumulation method depending on the data category. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input data category data to a generation AI and cause the generation AI to apply an accumulation method.

[0093] The accumulation unit can estimate the crew member's emotions and adjust the accumulation frequency based on the estimated crew member's emotions. The accumulation unit can estimate the crew member's emotions and adjust the accumulation frequency based on the estimated crew member's emotions. The crew member's emotions can be estimated using, for example, facial expression recognition, voice analysis, questionnaire surveys, etc., but are not limited to these examples. For example, the accumulation unit can accumulate data frequently when the crew member is relaxed. The accumulation unit can also reduce the accumulation frequency when the crew member is in a hurry. Furthermore, the accumulation unit can also frequently accumulate visually stimulating data when the crew member is excited. This enables efficient data accumulation by adjusting the accumulation frequency according to the crew member's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-mentioned processing in the accumulation unit can be performed using, for example, AI, or without AI. For example, the storage unit can input facial expression data of crew members into the generation AI and have the generation AI estimate their emotions.

[0094] The accumulation unit can weight the accumulated data based on the time of data submission during accumulation. The accumulation unit weights the accumulated data based on the time of data submission during accumulation. The time of data submission is evaluated based on criteria such as, but not limited to, a submission deadline and project progress. For example, the accumulation unit prioritizes and accumulates data submitted early. The accumulation unit can also prioritize and accumulate data submitted late. Furthermore, the accumulation unit can adjust the weighting of the accumulated data depending on the time of submission. Thus, weighting the accumulated data based on the time of data submission enables efficient data accumulation. Some or all of the above-described processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input data on the time of data submission to a generation AI and have the generation AI perform weighting of the accumulated data.

[0095] The storage unit can integrate information from different data sources to enrich the stored data during storage. The storage unit integrates information from different data sources to enrich the stored data during storage. Examples of different data sources include, but are not limited to, internal data, external data, and social media data. For example, the storage unit integrates information from different data sources to enrich the stored data. The storage unit can also improve the accuracy of the stored data by referring to information from different data sources. Furthermore, the storage unit can analyze information from different data sources and identify areas for improvement in the stored data. In this way, the accuracy of the stored data is improved by integrating information from different data sources. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input information from different data sources into a generation AI and have the generation AI integrate the information.

[0096] The storage unit can adjust the format of the stored data according to the crew's expertise level during storage. The storage unit adjusts the format of the stored data according to the crew's expertise level during storage. The crew's expertise level is evaluated based on criteria such as, but not limited to, qualifications, years of experience, and past project performance. For example, if the crew's expertise level is high, the storage unit stores the data in a detailed data format. Alternatively, if the crew's expertise level is low, the storage unit can store the data in a concise data format. Furthermore, the storage unit can adjust the format of the stored data according to the crew's expertise level. This enables appropriate data storage by adjusting the format of the stored data according to the crew's expertise level. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the crew's expertise level data to a generation AI and have the generation AI adjust the format of the stored data. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, evaluation unit, and storage 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 reception unit is realized by the control unit 46A of the smart device 14 and receives the items that are bottlenecks in lost sales entered by the crew. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the entered information using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a solution based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the proposed solution. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores the evaluated data. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, evaluation unit, and storage 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 reception unit is realized by the control unit 46A of the smart glasses 214 and receives the items that are bottlenecks in lost sales entered by the crew. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the entered information using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a solution based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the proposed solution. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores the evaluated data. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, evaluation unit, and storage unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives the items that are bottlenecks in lost sales entered by the crew. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the entered information using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a solution based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the proposed solution. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores the evaluated data. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, evaluation unit, and storage unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the items that are bottlenecks in lost sales entered by the crew. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the entered information using a generative AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a solution based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the proposed solution. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores the evaluated data.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can analyze the crew's past input history and customize it to improve input efficiency. For example, by prioritizing the display of items that the crew has frequently input in the past, it is possible to reduce the effort required for input. It can also display a message urging the crew to input items where they have made input errors in the past. It can also analyze the crew's input speed and patterns and provide an optimal input interface. This makes it possible to utilize the crew's past input history to improve input efficiency and accuracy.

[0099] The reception unit can estimate the crew's emotions and adjust the design of the input interface based on the estimated crew's emotions. For example, if the crew is feeling stressed, a simple and intuitive interface can be provided to reduce the burden of input. Alternatively, if the crew is relaxed, an interface that allows detailed information to be input can be provided. Furthermore, if the crew is concentrating, multiple input items can be displayed at once to encourage efficient input. In this way, adjusting the input interface according to the crew's emotions improves input efficiency and satisfaction.

[0100] The reception unit can analyze the crew's past project data and automatically complete related project information when inputting data. For example, by automatically inputting data related to the current project based on information about projects the crew has worked on in the past, the amount of work required for input can be reduced. Input items can also be automatically generated based on templates and formats used by the crew in the past. Furthermore, the reception unit can analyze the crew's past project data and provide reference information when inputting data. This improves the efficiency and accuracy of input by utilizing the crew's past project data.

[0101] The reception unit can monitor the crew's current work status in real time and suggest the optimal timing for input. For example, if the crew is performing other important tasks, input can be postponed until that task is completed. Also, if the crew is on break, input can be prompted after the break, enabling efficient input. Furthermore, the reception unit can analyze the crew's workload and prompt input when the workload has been reduced. This improves the efficiency and accuracy of input by suggesting the optimal input timing according to the crew's work status.

[0102] The reception unit can estimate the crew's emotions and adjust the feedback at the time of input based on the estimated crew's emotions. For example, if the crew is feeling stressed, providing positive feedback can improve their motivation. Also, if the crew is relaxed, providing detailed feedback can improve the accuracy of input. Furthermore, if the crew is concentrating, providing quick feedback can encourage efficient input. In this way, adjusting the feedback according to the crew's emotions improves input efficiency and satisfaction.

[0103] The analysis unit can dynamically adjust the analysis algorithm based on the crew's past analysis results. For example, by preferentially applying analysis methods that have been successful in the past, the accuracy of the analysis can be improved. Also, by avoiding analysis methods that have failed in the past, it is possible to reduce unnecessary analysis. Furthermore, it is possible to analyze the crew's past analysis results and propose new analysis methods. In this way, the efficiency and accuracy of analysis can be improved by utilizing the crew's past analysis results.

[0104] The analysis unit can estimate the crew's emotions and adjust the display method of the analysis results based on the estimated crew's emotions. For example, if the crew is feeling stressed, a simple and intuitive display can make the analysis results easier to understand. If the crew is relaxed, detailed analysis results can be provided to encourage deeper understanding. Furthermore, if the crew is concentrating, multiple analysis results can be displayed at once to encourage efficient analysis. In this way, the efficiency and accuracy of analysis can be improved by adjusting the display method of the analysis results according to the crew's emotions.

[0105] The proposal unit can analyze the crew's past proposal history and customize the proposals to improve the accuracy of the proposals. For example, by preferentially displaying proposals that the crew has adopted in the past, it is possible to make effective proposals. It can also reduce useless proposals by avoiding proposals that the crew has rejected in the past. Furthermore, it can analyze the crew's past proposal history and generate new proposals. In this way, by utilizing the crew's past proposal history, the efficiency and accuracy of proposals can be improved.

[0106] The suggestion unit can estimate the crew's emotions and adjust the timing of suggestions based on the estimated crew's emotions. For example, if the crew is feeling stressed, the suggestion unit can make suggestions at a time when the crew is relaxed, thereby improving the acceptance rate of the suggestions. Also, if the crew is relaxed, detailed suggestions can be made to encourage deeper understanding. Furthermore, if the crew is concentrating, quick suggestions can be made to encourage efficient suggestions. In this way, the efficiency and accuracy of suggestions can be improved by adjusting the timing of suggestions according to the crew's emotions.

[0107] The evaluation unit can dynamically adjust the evaluation criteria based on the crew's past evaluation results. For example, by preferentially applying evaluation criteria that have been successful in the past, the accuracy of the evaluation can be improved. Also, by avoiding evaluation criteria that have failed in the past, it can reduce unnecessary evaluations. Furthermore, it can analyze the crew's past evaluation results and propose new evaluation criteria. In this way, by utilizing the crew's past evaluation results, the efficiency and accuracy of the evaluation can be improved.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception department inputs the items that are causing the lost order. Items that are causing the lost order include, for example, price, delivery time, and quality. The reception department receives information input by the crew that the price is high, the delivery time is late, or the quality is uncertain. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit may analyze information that the price is high and determine that a price adjustment is necessary. It may also analyze information that the delivery date is late and determine that the delivery date needs to be improved. It may also analyze information that there are concerns about the quality and determine that quality needs to be improved. Step 3: The proposal department uses the generative AI to propose solutions based on the information analyzed by the analysis department. Solutions include price adjustments, shortened delivery times, and quality improvements. For example, the proposal department will propose price adjustments if the price is high, shortened delivery times if the delivery time is long, and quality improvements if there are concerns about quality. Step 4: The evaluation department evaluates the effectiveness of the solutions proposed by the proposal department. Evaluation is carried out using methods such as effect measurement, feedback collection, and quantitative evaluation. For example, the evaluation department evaluates the effectiveness of price adjustments, delivery time improvements, and quality improvements. Step 5: The storage unit stores the data evaluated by the evaluation unit. The data is stored in a database, using cloud storage, or other methods. For example, the storage unit stores data on the effects of price adjustments, delivery time improvements, and quality improvements.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 reception section for inputting items that are causing lost orders; an analysis unit that analyzes the information input by the reception unit; a proposal unit that proposes a solution based on the information analyzed by the analysis unit; an evaluation unit that evaluates the effectiveness of the solution proposed by the proposal unit; a storage unit that stores the data evaluated by the evaluation unit; A system characterized by:

2. The reception unit Estimate the crew's emotions and adjust the timing of inputting bottleneck items for lost sales based on the estimated crew's emotions 2. The system of claim 1.

3. The reception unit Analyze past lost sales data and select the appropriate input method 2. The system of claim 1.

4. The reception unit Filter lost sales bottlenecks based on crew members' current projects and areas of interest 2. The system of claim 1.

5. The reception unit When entering bottleneck items for lost orders, select the appropriate input method depending on the crew's input method.

2. The system of claim 1.

6. The reception unit Estimate crew sentiment and prioritize bottleneck items to be entered based on the estimated crew sentiment 2. The system of claim 1.

7. The reception unit When entering bottleneck items for lost sales, prioritize the most relevant items by taking into account the crew's geographic location information.

2. The system of claim 1.

8. The reception unit When entering bottleneck items for lost sales, analyze the crew's social media activity and enter related items.

2. The system of claim 1.

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