System

Abstract

An object of a system according to an embodiment is to quickly and flexibly respond to fluctuations in supply and demand and to propose and adjust an optimum inventory level.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects past sales data and demand prediction data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an inventory level based on the analysis result obtained by the analysis unit. The adjustment unit adjusts the stock level proposed by the proposal unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to manage inventory in a way that responds quickly and flexibly to fluctuations in supply and demand, and there is room for improvement in inventory optimization.

[0005] The system according to the embodiment aims to respond quickly and flexibly to fluctuations in supply and demand, and to propose and adjust optimal inventory levels. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects past sales data and demand forecast data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes inventory levels based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the inventory levels proposed by the proposal unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and flexibly respond to fluctuations in supply and demand, and propose and adjust optimal inventory levels. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An inventory optimization system according to an embodiment of the present invention collects past sales data and demand forecast data, and a generation AI analyzes the data to propose optimal inventory levels and adjust them in real time. The inventory optimization system collects past sales data and demand forecast data, and a generation AI analyzes the data to propose optimal inventory levels. Furthermore, the inventory optimization system analyzes data in real time to respond quickly and flexibly to fluctuations in supply and demand. For example, the inventory optimization system collects each company's sales history and market demand forecast data. For example, it collects sales data from the past year and seasonal demand forecast data. Next, the inventory optimization system uses a generation AI to analyze the collected data. The generation AI proposes optimal inventory levels based on the collected data. For example, it analyzes past sales data and demand forecast data to calculate the optimal inventory amount for each product. Furthermore, the inventory optimization system analyzes data in real time to respond quickly and flexibly to fluctuations in supply and demand. For example, if sales conditions or market demand suddenly change, the generation AI analyzes the data and adjusts inventory levels in real time. This allows the inventory optimization system to provide efficient and accurate inventory management for all companies, improving business efficiency and reducing costs. As a result, inventory optimization systems can provide efficient and accurate inventory management, improving business efficiency and reducing costs. For example, in the retail industry, it can optimize inventory levels at each store and prevent product shortages and excess inventory. In the wholesale industry, it can manage inventory according to customer demand, achieving efficient supply. In the manufacturing industry, it can optimize inventory of raw materials and parts, improving production efficiency. For businesses using e-commerce platforms, it can manage inventory according to online sales demand, achieving faster delivery.

[0029] An inventory optimization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects past sales data and demand forecast data. The collection unit collects, for example, each company's sales history and market demand forecast data. For example, the collection unit can collect sales data for the past year and seasonal demand forecast data. The collection unit can also collect data in real time via a database or API. The analysis unit uses a generation AI to analyze the data collected by the collection unit. For example, the analysis unit analyzes past sales data and demand forecast data to calculate optimal inventory levels for each product. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI predicts future demand based on past sales data and proposes optimal inventory levels. The proposal unit proposes optimal inventory levels based on the analysis results obtained by the analysis unit. For example, the proposal unit calculates optimal inventory levels for each product and proposes inventory levels. The proposal unit can propose optimal inventory levels for each company using the generation AI. The adjustment unit adjusts the inventory level proposed by the proposal unit in real time. For example, the adjustment unit adjusts the inventory level in real time when there is a sudden change in sales conditions or market demand. The adjustment unit can respond quickly and flexibly to fluctuations in supply and demand using generative AI. For example, when there is a sudden change in sales conditions or market demand, the adjustment unit analyzes data in real time and adjusts the inventory level. As a result, the inventory optimization system according to the embodiment can provide efficient and accurate inventory management, thereby improving business efficiency and reducing costs.

[0030] The collection unit can collect company sales history and market demand forecast data. The collection unit, for example, collects company sales history. For example, the collection unit can collect sales history for the past year or sales history of a specific product. The collection unit also collects market demand forecast data. For example, the collection unit can collect demand forecasts for the entire industry or demand forecasts for a specific region. By collecting sales history and market demand forecast data for each company, the collection unit can perform more accurate inventory management. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input company sales history and market demand forecast data into the generation AI and have the generation AI collect the data.

[0031] The analysis unit can propose optimal inventory levels based on the collected data. The analysis unit, for example, proposes optimal inventory levels based on the collected data. For example, the analysis unit can analyze past sales data and demand forecast data and calculate optimal inventory levels for each product. The analysis unit can also analyze the collected data using a generation AI. For example, the analysis unit can use a generation AI to predict future demand based on past sales data and propose optimal inventory levels. This allows the analysis unit to propose optimal inventory levels based on the collected data, preventing inventory surpluses and shortages and enabling efficient inventory management. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input collected data into a generation AI and have the generation AI propose optimal inventory levels.

[0032] The adjustment unit can adjust inventory levels in real time when sales conditions or market demand fluctuate. The adjustment unit can adjust inventory levels in real time when sales conditions or market demand fluctuate, for example. For example, the adjustment unit can analyze data in real time and adjust inventory levels when sales conditions or market demand suddenly change. The adjustment unit can also use a generation AI to quickly and flexibly respond to fluctuations in supply and demand. For example, the adjustment unit can use a generation AI to analyze data in real time and adjust inventory levels when sales conditions or market demand suddenly change. This allows the adjustment unit to quickly respond to fluctuations in supply and demand and maintain inventory optimization by adjusting inventory levels in real time when sales conditions or market demand suddenly change. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, when sales conditions or market demand suddenly change, the adjustment unit can input data into the generation AI and cause the generation AI to adjust the inventory levels.

[0033] The proposal unit can propose optimal inventory levels to companies in the retail, wholesale, manufacturing, and e-commerce platform industries. The proposal unit proposes optimal inventory levels to companies in the retail, wholesale, manufacturing, and e-commerce platform industries, for example. For example, the proposal unit can propose to retailers how to optimize inventory levels at each store and prevent product stockouts and excess inventory. The proposal unit can also propose inventory management in accordance with customer demand to wholesalers. The proposal unit can also propose to manufacturers how to optimize inventory of raw materials and parts and improve production efficiency. The proposal unit can also propose inventory management in accordance with online sales demand to businesses using e-commerce platforms. By doing so, the proposal unit can propose optimal inventory levels to companies in the retail, wholesale, manufacturing, and e-commerce platform industries, enabling efficient inventory management tailored to each industry. Some or all of the above-described processing by the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data tailored to each industry into the generation AI and have the generation AI execute a proposal for optimal inventory levels.

[0034] The adjustment unit can adjust inventory levels based on application examples for each industry. The adjustment unit adjusts inventory levels based on application examples for each industry, for example. For example, in inventory management in the food industry, the adjustment unit can adjust inventory in response to seasonal demand fluctuations. Furthermore, in inventory management in the apparel industry, the adjustment unit can adjust inventory based on trends and seasonal factors. Furthermore, in the electronics manufacturing industry, the adjustment unit can adjust inventory based on component supply conditions and production plans. As a result, the adjustment unit adjusts inventory levels based on specific application examples for each industry, enabling optimal inventory management for each industry. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input data for each industry into the generation AI and have the generation AI adjust the inventory levels.

[0035] The collection unit can analyze each company's past sales history and select the optimal data collection method. For example, the collection unit can analyze each company's past sales history and select the optimal data collection method. For example, the collection unit can identify products whose demand increases during a particular season from the past sales history and focus on collecting data during that period. The collection unit can also identify products whose demand is concentrated on a particular day of the week or time period based on the sales history and collect data at that time. Furthermore, the collection unit can analyze the sales history and identify products whose demand increases during a particular campaign period and focus on collecting data during that period. This allows the collection unit to analyze each company's past sales history and select the optimal data collection method, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past sales history data into a generation AI and have the generation AI select the optimal data collection method.

[0036] The collection unit can filter data based on the company's current market conditions and seasonal factors when collecting data. For example, the collection unit can filter data based on the company's current market conditions and seasonal factors when collecting data. For example, the collection unit can analyze current market conditions and prioritize collecting data for products with increasing demand. The collection unit can also consider seasonal factors and collect data for products with high demand in specific seasons. Furthermore, the collection unit can analyze market trends and collect data for popular products. This allows the collection unit to collect more relevant data by filtering data based on the company's current market conditions and seasonal factors. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the current market conditions and seasonal factors into the generation AI and have the generation AI perform data filtering.

[0037] The collection unit can select the optimal collection means depending on the company's input method when collecting data. For example, when collecting data, the collection unit selects the optimal collection means depending on the company's input method. For example, if the company uses voice input, the collection unit can collect voice data and use it for analysis. Furthermore, if the company uses text input, the collection unit can collect text data and use it for analysis. Furthermore, if the company uses image input, the collection unit can collect image data and use it for analysis. This enables the collection unit to select the optimal collection means depending on the company's input method, thereby enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's input data into a generation AI and have the generation AI select the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company when collecting data. For example, the collection unit can collect region-specific demand data based on the location of the company. The collection unit can also collect sales data of nearby competitors based on the geographical location information of the company. Furthermore, the collection unit can collect data related to local events and seasonal factors by taking into account the geographical location information of the company. This enables the collection unit to prioritize collecting highly relevant data by taking into account the geographical location information of the company, thereby enabling efficient data collection. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the company to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the company's social media activities and collect related data when collecting data. For example, the collection unit can analyze the company's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of the company's social media posts and collect related demand data. The collection unit can also analyze the reactions of the company's social media followers and collect data useful for demand forecasting. Furthermore, the collection unit can analyze the company's social media campaigns and promotional activities and collect related data. In this way, the collection unit can analyze the company's social media activities and collect related data, thereby enabling more accurate demand forecasting. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the company's social media data into the generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the company's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the company's past feedback when collecting data. For example, the collection unit can adjust the frequency and timing of data collection based on the company's past feedback. The collection unit can also customize the type and scope of data to be collected by reflecting the company's past feedback. Furthermore, the collection unit can improve the collection method by referring to the company's past feedback and achieve efficient data collection. In this way, the collection unit can customize the collection method by reflecting the company's past feedback, thereby enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data with high importance to improve accuracy. The analysis unit can also perform a concise analysis on data with low importance to emphasize efficiency. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a time series analysis algorithm to sales data. The analysis unit can also apply a regression analysis algorithm to demand forecast data. Furthermore, the analysis unit can apply a clustering algorithm to market data. This allows the analysis unit to apply different analysis algorithms depending on the category of data, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input the category of data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results during the analysis. For example, the analysis unit can adjust the parameters of the analysis algorithm based on the company's past analysis results. The analysis unit can also improve the analysis method by referring to the company's past analysis results. Furthermore, the analysis unit can verify and improve the accuracy of the analysis by using the company's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results, thereby enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the company's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit can, for example, determine the analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize analysis of the most recent data and perform real-time demand forecasting. The analysis unit can also analyze long-term trends based on past data. Furthermore, the analysis unit can focus on analyzing data from a specific period and perform demand forecasting taking seasonal factors into account. This enables the analysis unit to determine the analysis priority based on the time of data collection, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data collection into the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data to improve accuracy. The analysis unit can also prioritize analysis of less relevant data to prioritize efficiency. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This enables the analysis unit to adjust the order of analysis based on the relevance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of analysis.

[0046] The analysis unit can adjust the use of analytical terminology during analysis according to the company's level of expertise. For example, the analysis unit can adjust the use of analytical terminology during analysis according to the company's level of expertise. For example, the analysis unit can provide analysis results using detailed terminology for companies with high levels of expertise. For example, the analysis unit can provide analysis results using concise and easy-to-understand terminology for companies with low levels of expertise. Furthermore, the analysis unit can customize the way the analysis results are presented according to the company's level of expertise. This allows the analysis unit to provide more understandable analysis results by adjusting the use of analytical terminology according to the company's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the company's level of expertise into the generation AI and have the generation AI use analytical terminology.

[0047] The proposal unit can adjust the level of detail of the proposal based on the importance of the inventory level when making a proposal. For example, the proposal unit can adjust the level of detail of the proposal based on the importance of the inventory level when making a proposal. For example, the proposal unit can make a detailed proposal for inventory with high importance to improve accuracy. The proposal unit can also make a concise proposal for inventory with low importance to emphasize efficiency. Furthermore, the proposal unit can optimally allocate proposal resources according to the importance of the inventory level. As a result, the proposal unit can adjust the level of detail of the proposal based on the importance of the inventory level, enabling efficient proposals. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the importance of the inventory level into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] The proposal unit can apply different proposal algorithms to each industry when making a proposal. For example, the proposal unit can apply different proposal algorithms to each industry when making a proposal. For example, the proposal unit can apply a proposal algorithm based on sales data to the retail industry. The proposal unit can apply a proposal algorithm based on production data to the manufacturing industry. The proposal unit can also apply a proposal algorithm based on online sales data to businesses that use e-commerce platforms. This allows the proposal unit to apply different proposal algorithms to each industry, thereby enabling optimal proposals for each industry. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input data for each industry into the generation AI and cause the generation AI to apply different proposal algorithms.

[0049] The proposal unit can improve the accuracy of the proposal by referring to the company's past proposal results when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by referring to the company's past proposal results when making a proposal. For example, the proposal unit can adjust the parameters of the proposal algorithm based on the company's past proposal results. The proposal unit can also improve the proposal method by referring to the company's past proposal results. Furthermore, the proposal unit can verify and improve the accuracy of the proposal by using the company's past proposal results. In this way, the proposal unit can improve the accuracy of the proposal by referring to the company's past proposal results, thereby enabling more accurate proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the company's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] The proposal unit can determine the priority of proposals based on the submission timing of inventory levels when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission timing of inventory levels when making proposals. For example, the proposal unit can prioritize proposals for inventory with high urgency. The proposal unit can also quickly make proposals for inventory whose submission timing is approaching. Furthermore, the proposal unit can optimally allocate proposal resources based on the submission timing. This enables the proposal unit to prioritize proposals based on the submission timing of inventory levels, thereby enabling efficient proposals. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the submission timing of inventory levels into the generation AI and have the generation AI determine the priority of proposals.

[0051] The proposal unit can adjust the order of proposals based on the relevance of inventory levels when making a proposal. The proposal unit, for example, adjusts the order of proposals based on the relevance of inventory levels when making a proposal. For example, the proposal unit can prioritize proposals for highly relevant inventory. The proposal unit can also postpone proposals for less relevant inventory. Furthermore, the proposal unit can optimally allocate proposal resources according to the relevance of inventory levels. This enables the proposal unit to make efficient proposals by adjusting the order of proposals based on the relevance of inventory levels. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the relevance of inventory levels into the generation AI and cause the generation AI to adjust the order of proposals.

[0052] The suggestion unit can adjust the use of technical terminology in the proposal according to the company's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the company's level of expertise when making a proposal. For example, the suggestion unit can provide a proposal using detailed technical terminology for companies with high levels of expertise. The suggestion unit can provide a proposal using concise and easy-to-understand terminology for companies with low levels of expertise. Furthermore, the suggestion unit can customize the way the proposal is expressed according to the company's level of expertise. This allows the suggestion unit to adjust the use of technical terminology in the proposal according to the company's level of expertise, thereby enabling a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the company's level of expertise into the generation AI and cause the generation AI to use technical terminology in the proposal.

[0053] The adjustment unit can analyze the company's past inventory adjustment history and select the optimal adjustment method during adjustment. For example, the adjustment unit can analyze the company's past inventory adjustment history and select the optimal adjustment method during adjustment. For example, the adjustment unit can select the optimal adjustment method based on the company's past inventory adjustment history. The adjustment unit can also analyze the company's past inventory adjustment history and improve the adjustment method. Furthermore, the adjustment unit can improve the accuracy of adjustments by referring to the company's past inventory adjustment history. This enables efficient inventory adjustment by the adjustment unit analyzing the company's past inventory adjustment history and selecting the optimal adjustment method. Some or all of the above-described processing in the adjustment unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the adjustment unit can input the company's past inventory adjustment history into the generation AI and have the generation AI select the optimal adjustment method.

[0054] The adjustment unit can customize the adjustment means based on the company's current market conditions during adjustment. For example, the adjustment unit can analyze the current market conditions and prioritize inventory adjustment for products with increasing demand. The adjustment unit can also analyze market trends and adjust inventory for popular products. Furthermore, the adjustment unit can customize the inventory adjustment means based on the current market conditions. This enables efficient inventory adjustment by customizing the adjustment means based on the company's current market conditions. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input current market condition data into the generation AI and have the generation AI customize the adjustment means.

[0055] The adjustment unit can improve the adjustment method by reflecting the company's feedback during adjustment. For example, the adjustment unit can improve the adjustment method by reflecting the company's feedback during adjustment. For example, the adjustment unit can adjust the frequency and timing of inventory adjustments based on the company's feedback. The adjustment unit can also customize the type and range of inventory to be adjusted by reflecting the company's feedback. Furthermore, the adjustment unit can improve the adjustment method by referring to the company's feedback and achieve efficient inventory adjustment. As a result, the adjustment unit can improve the adjustment method by reflecting the company's feedback, thereby enabling efficient inventory adjustment. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, the generation AI. For example, the adjustment unit can input company feedback data into the generation AI and have the generation AI improve the adjustment method.

[0056] The adjustment unit can select the optimal adjustment method by taking into account the geographical location information of the company during adjustment. For example, the adjustment unit can select the optimal adjustment method by taking into account the geographical location information of the company during adjustment. For example, the adjustment unit can adjust inventory in accordance with local demand based on the company's location. The adjustment unit can also adjust inventory by taking into account the inventory status of nearby competitors based on the company's geographical location information. Furthermore, the adjustment unit can adjust inventory in accordance with local events and seasonal factors by taking into account the company's geographical location information. This enables efficient inventory adjustment by the adjustment unit selecting the optimal adjustment method by taking into account the company's geographical location information. Some or all of the above-described processing in the adjustment unit can be performed using, or without, a generation AI. For example, the adjustment unit can input the company's geographical location information into the generation AI and have the generation AI select the optimal adjustment method.

[0057] The adjustment unit can analyze the company's social media activities and propose adjustment measures during adjustment. For example, the adjustment unit can analyze the company's social media activities and propose adjustment measures during adjustment. For example, the adjustment unit can analyze the company's social media posts and propose relevant inventory adjustments. The adjustment unit can also analyze the responses of the company's social media followers and propose inventory adjustments based on demand forecasts. Furthermore, the adjustment unit can analyze the company's social media campaigns and promotional activities and propose relevant inventory adjustments. This allows the adjustment unit to analyze the company's social media activities and propose adjustment measures, thereby enabling more accurate inventory adjustments. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the company's social media data into the generation AI and have the generation AI execute the proposed adjustment measures.

[0058] The adjustment unit can customize the adjustment method by reflecting the company's past feedback when making an adjustment. For example, the adjustment unit can customize the adjustment method by reflecting the company's past feedback when making an adjustment. For example, the adjustment unit can adjust the frequency and timing of inventory adjustments based on the company's past feedback. The adjustment unit can also customize the type and range of inventory to be adjusted by reflecting the company's past feedback. Furthermore, the adjustment unit can improve the adjustment method by referring to the company's past feedback and achieve efficient inventory adjustment. As a result, the adjustment unit can customize the adjustment method by reflecting the company's past feedback, thereby enabling efficient inventory adjustment. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the company's past feedback data into the generation AI and have the generation AI customize the adjustment method.

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

[0060] When collecting data, the collection unit can identify products whose demand will increase during a specific campaign period based on the company's past sales history and collect data intensively during that period. For example, the collection unit can identify products whose demand will increase during a specific season from the past sales history and collect data intensively during that period. The collection unit can also identify products whose demand will be concentrated on specific days of the week or time periods based on the sales history and collect data at those times. This allows the collection unit to analyze each company's past sales history and select the optimal data collection method, enabling efficient data collection.

[0061] When making adjustments, the adjustment unit can select the optimal adjustment method by taking into account the geographic location information of the company. For example, the adjustment unit can make inventory adjustments in response to local demand based on the company's location. The adjustment unit can also make adjustments by taking into account the inventory status of nearby competitors based on the company's geographic location information. Furthermore, the adjustment unit can make inventory adjustments in response to local events or seasonal factors by taking into account the company's geographic location information. This allows the adjustment unit to select the optimal adjustment method by taking into account the company's geographic location information, thereby enabling efficient inventory adjustments.

[0062] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit can prioritize analysis of the most recent data and perform real-time demand forecasting. The analysis unit can also analyze long-term trends based on past data. Furthermore, the analysis unit can focus on analyzing data from a specific period and perform demand forecasting that takes seasonal factors into account. This allows the analysis unit to determine the priority of analysis based on the time when the data was collected, enabling efficient analysis.

[0063] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission timing of the inventory level. For example, the proposal unit can give priority to proposals for inventory with a high degree of urgency. Also, the proposal unit can quickly make proposals for inventory whose submission timing is approaching. Furthermore, the proposal unit can optimally allocate proposal resources based on the submission timing. This allows the proposal unit to determine the priority of proposals based on the submission timing of the inventory level, thereby enabling efficient proposals.

[0064] When collecting data, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit can analyze the content of the company's social media posts and collect related demand data. The collection unit can also analyze the reactions of the company's social media followers and collect data that is useful for demand forecasting. Furthermore, the collection unit can analyze the company's social media campaigns and promotional activities and collect related data. In this way, the collection unit can analyze the company's social media activities and collect related data, thereby enabling more accurate demand forecasting.

[0065] When making a proposal, the proposal unit can apply a different proposal algorithm to each industry. For example, the proposal unit can apply a proposal algorithm based on sales data to the retail industry. For the manufacturing industry, the proposal unit can apply a proposal algorithm based on production data. Furthermore, the proposal unit can apply a proposal algorithm based on online sales data to businesses that use an e-commerce platform. In this way, the proposal unit can apply a different proposal algorithm to each industry, thereby making it possible to make optimal proposals for each industry.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects past sales data and demand forecast data. For example, it can collect each company's sales history and market demand forecast data, as well as sales data for the past year and seasonal demand forecast data. It can also collect data in real time via databases and APIs. Step 2: The analysis unit uses generation AI to analyze the data collected by the collection unit. For example, it analyzes past sales data and demand forecast data and calculates the optimal inventory level for each product. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI to predict future demand. Step 3: The proposal unit proposes optimal inventory levels based on the analysis results obtained by the analysis unit. For example, it calculates the optimal inventory amount for each product and uses generation AI to propose optimal inventory levels for each company. Step 4: The adjustment unit adjusts the inventory levels proposed by the proposal unit in real time. For example, if there is a sudden change in sales conditions or market demand, the adjustment unit analyzes data in real time and adjusts inventory levels. Using generative AI, it is possible to respond quickly and flexibly to fluctuations in supply and demand.

[0068] (Example 2) An inventory optimization system according to an embodiment of the present invention collects past sales data and demand forecast data, and a generation AI analyzes the data to propose optimal inventory levels and adjust them in real time. The inventory optimization system collects past sales data and demand forecast data, and a generation AI analyzes the data to propose optimal inventory levels. Furthermore, the inventory optimization system analyzes data in real time to respond quickly and flexibly to fluctuations in supply and demand. For example, the inventory optimization system collects each company's sales history and market demand forecast data. For example, it collects sales data from the past year and seasonal demand forecast data. Next, the inventory optimization system uses a generation AI to analyze the collected data. The generation AI proposes optimal inventory levels based on the collected data. For example, it analyzes past sales data and demand forecast data to calculate the optimal inventory amount for each product. Furthermore, the inventory optimization system analyzes data in real time to respond quickly and flexibly to fluctuations in supply and demand. For example, if sales conditions or market demand suddenly change, the generation AI analyzes the data and adjusts inventory levels in real time. This allows the inventory optimization system to provide efficient and accurate inventory management for all companies, improving business efficiency and reducing costs. As a result, inventory optimization systems can provide efficient and accurate inventory management, improving business efficiency and reducing costs. For example, in the retail industry, it can optimize inventory levels at each store and prevent product shortages and excess inventory. In the wholesale industry, it can manage inventory according to customer demand, achieving efficient supply. In the manufacturing industry, it can optimize inventory of raw materials and parts, improving production efficiency. For businesses using e-commerce platforms, it can manage inventory according to online sales demand, achieving faster delivery.

[0069] An inventory optimization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects past sales data and demand forecast data. The collection unit collects, for example, each company's sales history and market demand forecast data. For example, the collection unit can collect sales data for the past year and seasonal demand forecast data. The collection unit can also collect data in real time via a database or API. The analysis unit uses a generation AI to analyze the data collected by the collection unit. For example, the analysis unit analyzes past sales data and demand forecast data to calculate optimal inventory levels for each product. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI predicts future demand based on past sales data and proposes optimal inventory levels. The proposal unit proposes optimal inventory levels based on the analysis results obtained by the analysis unit. For example, the proposal unit calculates optimal inventory levels for each product and proposes inventory levels. The proposal unit can propose optimal inventory levels for each company using the generation AI. The adjustment unit adjusts the inventory level proposed by the proposal unit in real time. For example, the adjustment unit adjusts the inventory level in real time when there is a sudden change in sales conditions or market demand. The adjustment unit can respond quickly and flexibly to fluctuations in supply and demand using generative AI. For example, when there is a sudden change in sales conditions or market demand, the adjustment unit analyzes data in real time and adjusts the inventory level. As a result, the inventory optimization system according to the embodiment can provide efficient and accurate inventory management, thereby improving business efficiency and reducing costs.

[0070] The collection unit can collect company sales history and market demand forecast data. The collection unit, for example, collects company sales history. For example, the collection unit can collect sales history for the past year or sales history of a specific product. The collection unit also collects market demand forecast data. For example, the collection unit can collect demand forecasts for the entire industry or demand forecasts for a specific region. By collecting sales history and market demand forecast data for each company, the collection unit can perform more accurate inventory management. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input company sales history and market demand forecast data into the generation AI and have the generation AI collect the data.

[0071] The analysis unit can propose optimal inventory levels based on the collected data. The analysis unit, for example, proposes optimal inventory levels based on the collected data. For example, the analysis unit can analyze past sales data and demand forecast data and calculate optimal inventory levels for each product. The analysis unit can also analyze the collected data using a generation AI. For example, the analysis unit can use a generation AI to predict future demand based on past sales data and propose optimal inventory levels. This allows the analysis unit to propose optimal inventory levels based on the collected data, preventing inventory surpluses and shortages and enabling efficient inventory management. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input collected data into a generation AI and have the generation AI propose optimal inventory levels.

[0072] The adjustment unit can adjust inventory levels in real time when sales conditions or market demand fluctuate. The adjustment unit can adjust inventory levels in real time when sales conditions or market demand fluctuate, for example. For example, the adjustment unit can analyze data in real time and adjust inventory levels when sales conditions or market demand suddenly change. The adjustment unit can also use a generation AI to quickly and flexibly respond to fluctuations in supply and demand. For example, the adjustment unit can use a generation AI to analyze data in real time and adjust inventory levels when sales conditions or market demand suddenly change. This allows the adjustment unit to quickly respond to fluctuations in supply and demand and maintain inventory optimization by adjusting inventory levels in real time when sales conditions or market demand suddenly change. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, when sales conditions or market demand suddenly change, the adjustment unit can input data into the generation AI and cause the generation AI to adjust the inventory levels.

[0073] The proposal unit can propose optimal inventory levels to companies in the retail, wholesale, manufacturing, and e-commerce platform industries. The proposal unit proposes optimal inventory levels to companies in the retail, wholesale, manufacturing, and e-commerce platform industries, for example. For example, the proposal unit can propose to retailers how to optimize inventory levels at each store and prevent product stockouts and excess inventory. The proposal unit can also propose inventory management in accordance with customer demand to wholesalers. The proposal unit can also propose to manufacturers how to optimize inventory of raw materials and parts and improve production efficiency. The proposal unit can also propose inventory management in accordance with online sales demand to businesses using e-commerce platforms. By doing so, the proposal unit can propose optimal inventory levels to companies in the retail, wholesale, manufacturing, and e-commerce platform industries, enabling efficient inventory management tailored to each industry. Some or all of the above-described processing by the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data tailored to each industry into the generation AI and have the generation AI execute a proposal for optimal inventory levels.

[0074] The adjustment unit can adjust inventory levels based on application examples for each industry. The adjustment unit adjusts inventory levels based on application examples for each industry, for example. For example, in inventory management in the food industry, the adjustment unit can adjust inventory in response to seasonal demand fluctuations. Furthermore, in inventory management in the apparel industry, the adjustment unit can adjust inventory based on trends and seasonal factors. Furthermore, in the electronics manufacturing industry, the adjustment unit can adjust inventory based on component supply conditions and production plans. As a result, the adjustment unit adjusts inventory levels based on specific application examples for each industry, enabling optimal inventory management for each industry. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input data for each industry into the generation AI and have the generation AI adjust the inventory levels.

[0075] The collection unit can analyze the user's emotions and adjust the timing of data collection based on the analyzed user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Furthermore, if the user is relaxed, the collection unit can collect detailed data to improve accuracy. Furthermore, if the user is in a hurry, the collection unit can quickly collect necessary data and immediately send it for analysis. This allows the collection unit to adjust the timing of data collection based on the user's emotions, thereby reducing the burden on the user and enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0076] The collection unit can analyze each company's past sales history and select the optimal data collection method. For example, the collection unit can analyze each company's past sales history and select the optimal data collection method. For example, the collection unit can identify products whose demand increases during a particular season from the past sales history and focus on collecting data during that period. The collection unit can also identify products whose demand is concentrated on a particular day of the week or time period based on the sales history and collect data at that time. Furthermore, the collection unit can analyze the sales history and identify products whose demand increases during a particular campaign period and focus on collecting data during that period. This allows the collection unit to analyze each company's past sales history and select the optimal data collection method, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past sales history data into a generation AI and have the generation AI select the optimal data collection method.

[0077] The collection unit can filter data based on the company's current market conditions and seasonal factors when collecting data. For example, the collection unit can filter data based on the company's current market conditions and seasonal factors when collecting data. For example, the collection unit can analyze current market conditions and prioritize collecting data for products with increasing demand. The collection unit can also consider seasonal factors and collect data for products with high demand in specific seasons. Furthermore, the collection unit can analyze market trends and collect data for popular products. This allows the collection unit to collect more relevant data by filtering data based on the company's current market conditions and seasonal factors. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the current market conditions and seasonal factors into the generation AI and have the generation AI perform data filtering.

[0078] The collection unit can select the optimal collection means depending on the company's input method when collecting data. For example, when collecting data, the collection unit selects the optimal collection means depending on the company's input method. For example, if the company uses voice input, the collection unit can collect voice data and use it for analysis. Furthermore, if the company uses text input, the collection unit can collect text data and use it for analysis. Furthermore, if the company uses image input, the collection unit can collect image data and use it for analysis. This enables the collection unit to select the optimal collection means depending on the company's input method, thereby enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's input data into a generation AI and have the generation AI select the optimal collection means.

[0079] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting important data to reduce the burden on the user. Furthermore, when the user is relaxed, the collection unit can collect detailed data to improve accuracy. Furthermore, when the user is in a hurry, the collection unit can quickly collect necessary data and immediately send it for analysis. This reduces the burden on the user by determining the priority of data to be collected based on the user's emotions, enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0080] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company when collecting data. For example, the collection unit can collect region-specific demand data based on the location of the company. The collection unit can also collect sales data of nearby competitors based on the geographical location information of the company. Furthermore, the collection unit can collect data related to local events and seasonal factors by taking into account the geographical location information of the company. This enables the collection unit to prioritize collecting highly relevant data by taking into account the geographical location information of the company, thereby enabling efficient data collection. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the company to the generation AI and cause the generation AI to collect highly relevant data.

[0081] The collection unit can analyze the company's social media activities and collect related data when collecting data. For example, the collection unit can analyze the company's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of the company's social media posts and collect related demand data. The collection unit can also analyze the reactions of the company's social media followers and collect data useful for demand forecasting. Furthermore, the collection unit can analyze the company's social media campaigns and promotional activities and collect related data. In this way, the collection unit can analyze the company's social media activities and collect related data, thereby enabling more accurate demand forecasting. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the company's social media data into the generation AI and cause the generation AI to collect related data.

[0082] The collection unit can customize the collection method by reflecting the company's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the company's past feedback when collecting data. For example, the collection unit can adjust the frequency and timing of data collection based on the company's past feedback. The collection unit can also customize the type and scope of data to be collected by reflecting the company's past feedback. Furthermore, the collection unit can improve the collection method by referring to the company's past feedback and achieve efficient data collection. In this way, the collection unit can customize the collection method by reflecting the company's past feedback, thereby enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can provide analysis results using visually stimulating graphs or charts. This allows the analysis unit to deepen understanding by adjusting the presentation method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data with high importance to improve accuracy. The analysis unit can also perform a concise analysis on data with low importance to emphasize efficiency. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a time series analysis algorithm to sales data. The analysis unit can also apply a regression analysis algorithm to demand forecast data. Furthermore, the analysis unit can apply a clustering algorithm to market data. This allows the analysis unit to apply different analysis algorithms depending on the category of data, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input the category of data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0086] The analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results during the analysis. For example, the analysis unit can adjust the parameters of the analysis algorithm based on the company's past analysis results. The analysis unit can also improve the analysis method by referring to the company's past analysis results. Furthermore, the analysis unit can verify and improve the accuracy of the analysis by using the company's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results, thereby enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the company's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the analysis unit to adjust the length of the analysis based on the user's emotions, thereby deepening the user's understanding. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0088] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit can, for example, determine the analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize analysis of the most recent data and perform real-time demand forecasting. The analysis unit can also analyze long-term trends based on past data. Furthermore, the analysis unit can focus on analyzing data from a specific period and perform demand forecasting taking seasonal factors into account. This enables the analysis unit to determine the analysis priority based on the time of data collection, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data collection into the generation AI and have the generation AI determine the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data to improve accuracy. The analysis unit can also prioritize analysis of less relevant data to prioritize efficiency. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This enables the analysis unit to adjust the order of analysis based on the relevance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of analysis.

[0090] The analysis unit can adjust the use of analytical terminology during analysis according to the company's level of expertise. For example, the analysis unit can adjust the use of analytical terminology during analysis according to the company's level of expertise. For example, the analysis unit can provide analysis results using detailed terminology for companies with high levels of expertise. For example, the analysis unit can provide analysis results using concise and easy-to-understand terminology for companies with low levels of expertise. Furthermore, the analysis unit can customize the way the analysis results are presented according to the company's level of expertise. This allows the analysis unit to provide more understandable analysis results by adjusting the use of analytical terminology according to the company's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the company's level of expertise into the generation AI and have the generation AI use analytical terminology.

[0091] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions to deepen understanding. If the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. If the user is excited, the suggestion unit can provide suggestions using visually stimulating graphs or charts. This allows the suggestion unit to deepen understanding by adjusting the way the suggestions are presented based on the user'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 suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are presented.

[0092] The proposal unit can adjust the level of detail of the proposal based on the importance of the inventory level when making a proposal. For example, the proposal unit can adjust the level of detail of the proposal based on the importance of the inventory level when making a proposal. For example, the proposal unit can make a detailed proposal for inventory with high importance to improve accuracy. The proposal unit can also make a concise proposal for inventory with low importance to emphasize efficiency. Furthermore, the proposal unit can optimally allocate proposal resources according to the importance of the inventory level. As a result, the proposal unit can adjust the level of detail of the proposal based on the importance of the inventory level, enabling efficient proposals. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the importance of the inventory level into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0093] The proposal unit can apply different proposal algorithms to each industry when making a proposal. For example, the proposal unit can apply different proposal algorithms to each industry when making a proposal. For example, the proposal unit can apply a proposal algorithm based on sales data to the retail industry. The proposal unit can apply a proposal algorithm based on production data to the manufacturing industry. The proposal unit can also apply a proposal algorithm based on online sales data to businesses that use e-commerce platforms. This allows the proposal unit to apply different proposal algorithms to each industry, thereby enabling optimal proposals for each industry. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input data for each industry into the generation AI and cause the generation AI to apply different proposal algorithms.

[0094] The proposal unit can improve the accuracy of the proposal by referring to the company's past proposal results when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by referring to the company's past proposal results when making a proposal. For example, the proposal unit can adjust the parameters of the proposal algorithm based on the company's past proposal results. The proposal unit can also improve the proposal method by referring to the company's past proposal results. Furthermore, the proposal unit can verify and improve the accuracy of the proposal by using the company's past proposal results. In this way, the proposal unit can improve the accuracy of the proposal by referring to the company's past proposal results, thereby enabling more accurate proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the company's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0095] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows the suggestion unit to deepen the user's understanding by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0096] The proposal unit can determine the priority of proposals based on the submission timing of inventory levels when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission timing of inventory levels when making proposals. For example, the proposal unit can prioritize proposals for inventory with high urgency. The proposal unit can also quickly make proposals for inventory whose submission timing is approaching. Furthermore, the proposal unit can optimally allocate proposal resources based on the submission timing. This enables the proposal unit to prioritize proposals based on the submission timing of inventory levels, thereby enabling efficient proposals. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the submission timing of inventory levels into the generation AI and have the generation AI determine the priority of proposals.

[0097] The proposal unit can adjust the order of proposals based on the relevance of inventory levels when making a proposal. The proposal unit, for example, adjusts the order of proposals based on the relevance of inventory levels when making a proposal. For example, the proposal unit can prioritize proposals for highly relevant inventory. The proposal unit can also postpone proposals for less relevant inventory. Furthermore, the proposal unit can optimally allocate proposal resources according to the relevance of inventory levels. This enables the proposal unit to make efficient proposals by adjusting the order of proposals based on the relevance of inventory levels. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the relevance of inventory levels into the generation AI and cause the generation AI to adjust the order of proposals.

[0098] The suggestion unit can adjust the use of technical terminology in the proposal according to the company's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the company's level of expertise when making a proposal. For example, the suggestion unit can provide a proposal using detailed technical terminology for companies with high levels of expertise. The suggestion unit can provide a proposal using concise and easy-to-understand terminology for companies with low levels of expertise. Furthermore, the suggestion unit can customize the way the proposal is expressed according to the company's level of expertise. This allows the suggestion unit to adjust the use of technical terminology in the proposal according to the company's level of expertise, thereby enabling a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the company's level of expertise into the generation AI and cause the generation AI to use technical terminology in the proposal.

[0099] The adjustment unit can estimate a user's emotion and adjust the inventory level adjustment method based on the estimated user emotion. The adjustment unit, for example, estimates a user's emotion and adjusts the inventory level adjustment method based on the estimated user emotion. For example, if the user is stressed, the adjustment unit can provide a simple adjustment method to reduce the burden on the user. Furthermore, if the user is relaxed, the adjustment unit can provide a detailed adjustment method to improve accuracy. Furthermore, if the user is in a hurry, the adjustment unit can provide a method to quickly adjust the inventory level. This allows the adjustment unit to adjust the inventory level adjustment method based on the user's emotion, thereby reducing the user's burden and enabling efficient inventory adjustment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input user emotion data into the generation AI and cause the generation AI to adjust the inventory level adjustment method.

[0100] The adjustment unit can analyze the company's past inventory adjustment history and select the optimal adjustment method during adjustment. For example, the adjustment unit can analyze the company's past inventory adjustment history and select the optimal adjustment method during adjustment. For example, the adjustment unit can select the optimal adjustment method based on the company's past inventory adjustment history. The adjustment unit can also analyze the company's past inventory adjustment history and improve the adjustment method. Furthermore, the adjustment unit can improve the accuracy of adjustments by referring to the company's past inventory adjustment history. This enables efficient inventory adjustment by the adjustment unit analyzing the company's past inventory adjustment history and selecting the optimal adjustment method. Some or all of the above-described processing in the adjustment unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the adjustment unit can input the company's past inventory adjustment history into the generation AI and have the generation AI select the optimal adjustment method.

[0101] The adjustment unit can customize the adjustment means based on the company's current market conditions during adjustment. For example, the adjustment unit can analyze the current market conditions and prioritize inventory adjustment for products with increasing demand. The adjustment unit can also analyze market trends and adjust inventory for popular products. Furthermore, the adjustment unit can customize the inventory adjustment means based on the current market conditions. This enables efficient inventory adjustment by customizing the adjustment means based on the company's current market conditions. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input current market condition data into the generation AI and have the generation AI customize the adjustment means.

[0102] The adjustment unit can improve the adjustment method by reflecting the company's feedback during adjustment. For example, the adjustment unit can improve the adjustment method by reflecting the company's feedback during adjustment. For example, the adjustment unit can adjust the frequency and timing of inventory adjustments based on the company's feedback. The adjustment unit can also customize the type and range of inventory to be adjusted by reflecting the company's feedback. Furthermore, the adjustment unit can improve the adjustment method by referring to the company's feedback and achieve efficient inventory adjustment. As a result, the adjustment unit can improve the adjustment method by reflecting the company's feedback, thereby enabling efficient inventory adjustment. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, the generation AI. For example, the adjustment unit can input company feedback data into the generation AI and have the generation AI improve the adjustment method.

[0103] The adjustment unit can estimate the user's emotions and prioritize inventory levels based on the estimated user emotions. The adjustment unit, for example, estimates the user's emotions and prioritizes inventory levels based on the estimated user emotions. For example, when the user is feeling stressed, the adjustment unit can prioritize and adjust inventory of high importance, thereby reducing the user's burden. Furthermore, when the user is relaxed, the adjustment unit can perform detailed inventory adjustments to improve accuracy. Furthermore, when the user is in a hurry, the adjustment unit can quickly adjust the necessary inventory and respond immediately. This allows the adjustment unit to prioritize inventory levels based on the user's emotions, thereby reducing the user's burden and enabling efficient inventory adjustment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input user emotion data into the generation AI and have the generation AI determine the priority of inventory levels.

[0104] The adjustment unit can select the optimal adjustment method by taking into account the geographical location information of the company during adjustment. For example, the adjustment unit can select the optimal adjustment method by taking into account the geographical location information of the company during adjustment. For example, the adjustment unit can adjust inventory in accordance with local demand based on the company's location. The adjustment unit can also adjust inventory by taking into account the inventory status of nearby competitors based on the company's geographical location information. Furthermore, the adjustment unit can adjust inventory in accordance with local events and seasonal factors by taking into account the company's geographical location information. This enables efficient inventory adjustment by the adjustment unit selecting the optimal adjustment method by taking into account the company's geographical location information. Some or all of the above-described processing in the adjustment unit can be performed using, or without, a generation AI. For example, the adjustment unit can input the company's geographical location information into the generation AI and have the generation AI select the optimal adjustment method.

[0105] The adjustment unit can analyze the company's social media activities and propose adjustment measures during adjustment. For example, the adjustment unit can analyze the company's social media activities and propose adjustment measures during adjustment. For example, the adjustment unit can analyze the company's social media posts and propose relevant inventory adjustments. The adjustment unit can also analyze the responses of the company's social media followers and propose inventory adjustments based on demand forecasts. Furthermore, the adjustment unit can analyze the company's social media campaigns and promotional activities and propose relevant inventory adjustments. This allows the adjustment unit to analyze the company's social media activities and propose adjustment measures, thereby enabling more accurate inventory adjustments. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the company's social media data into the generation AI and have the generation AI execute the proposed adjustment measures.

[0106] The adjustment unit can customize the adjustment method by reflecting the company's past feedback when making an adjustment. For example, the adjustment unit can customize the adjustment method by reflecting the company's past feedback when making an adjustment. For example, the adjustment unit can adjust the frequency and timing of inventory adjustments based on the company's past feedback. The adjustment unit can also customize the type and range of inventory to be adjusted by reflecting the company's past feedback. Furthermore, the adjustment unit can improve the adjustment method by referring to the company's past feedback and achieve efficient inventory adjustment. As a result, the adjustment unit can customize the adjustment method by reflecting the company's past feedback, thereby enabling efficient inventory adjustment. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the company's past feedback data into the generation AI and have the generation AI customize the adjustment method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and adjustment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect past sales data and demand forecast data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI. The proposal unit is realized, for example, by the control unit 46A of the smart device 14 and proposes optimal inventory levels based on the analysis results. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts inventory levels in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect past sales data and demand forecast data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI. The proposal unit is realized, for example, by the control unit 46A of the smart glasses 214 and proposes optimal inventory levels based on the analysis results. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts inventory levels in real time. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and adjustment unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect past sales data and demand forecast data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI. The proposal unit is realized, for example, by the control unit 46A of the headset terminal 314 and proposes optimal inventory levels based on the analysis results. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts inventory levels in real time. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect past sales data and demand forecast data using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI. The proposal unit is realized, for example, by the control unit 46A of the robot 414 and proposes optimal inventory levels based on the analysis results. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts inventory levels in real time.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analysis of important data, reducing the burden on the user. Also, if the user is relaxed, it can perform detailed analysis and improve accuracy. Furthermore, if the user is in a hurry, it can quickly analyze the necessary data and respond immediately. In this way, the analysis unit can determine the priority of analysis based on the user's emotions, reducing the burden on the user and enabling efficient analysis.

[0109] When collecting data, the collection unit can identify products whose demand will increase during a specific campaign period based on the company's past sales history and collect data intensively during that period. For example, the collection unit can identify products whose demand will increase during a specific season from the past sales history and collect data intensively during that period. The collection unit can also identify products whose demand will be concentrated on specific days of the week or time periods based on the sales history and collect data at those times. This allows the collection unit to analyze each company's past sales history and select the optimal data collection method, enabling efficient data collection.

[0110] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be provided to deepen understanding. If the user is in a hurry, concise suggestions that focus on the main points can be provided. If the user is excited, suggestions can be provided using visually stimulating graphs and charts. In this way, the suggestion unit can deepen understanding of the user by adjusting the way suggestions are presented based on the user's emotions.

[0111] When making adjustments, the adjustment unit can select the optimal adjustment method by taking into account the geographic location information of the company. For example, the adjustment unit can make inventory adjustments in response to local demand based on the company's location. The adjustment unit can also make adjustments by taking into account the inventory status of nearby competitors based on the company's geographic location information. Furthermore, the adjustment unit can make inventory adjustments in response to local events or seasonal factors by taking into account the company's geographic location information. This allows the adjustment unit to select the optimal adjustment method by taking into account the company's geographic location information, thereby enabling efficient inventory adjustments.

[0112] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit can prioritize analysis of the most recent data and perform real-time demand forecasting. The analysis unit can also analyze long-term trends based on past data. Furthermore, the analysis unit can focus on analyzing data from a specific period and perform demand forecasting that takes seasonal factors into account. This allows the analysis unit to determine the priority of analysis based on the time when the data was collected, enabling efficient analysis.

[0113] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission timing of the inventory level. For example, the proposal unit can give priority to proposals for inventory with a high degree of urgency. Also, the proposal unit can quickly make proposals for inventory whose submission timing is approaching. Furthermore, the proposal unit can optimally allocate proposal resources based on the submission timing. This allows the proposal unit to determine the priority of proposals based on the submission timing of the inventory level, thereby enabling efficient proposals.

[0114] The adjustment unit can estimate the user's emotions and adjust the inventory level adjustment method based on the estimated user's emotions. For example, if the user is feeling stressed, the adjustment unit can provide a simple adjustment method to reduce the burden. Alternatively, if the user is relaxed, the adjustment unit can provide a detailed adjustment method to improve accuracy. Alternatively, if the user is in a hurry, the adjustment unit can provide a method to quickly adjust the inventory level. Thus, by adjusting the inventory level adjustment method based on the user's emotions, the adjustment unit can reduce the burden on the user and enable efficient inventory adjustment.

[0115] When collecting data, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit can analyze the content of the company's social media posts and collect related demand data. The collection unit can also analyze the reactions of the company's social media followers and collect data that is useful for demand forecasting. Furthermore, the collection unit can analyze the company's social media campaigns and promotional activities and collect related data. In this way, the collection unit can analyze the company's social media activities and collect related data, thereby enabling more accurate demand forecasting.

[0116] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can provide analysis results using visually stimulating graphs and charts. In this way, the analysis unit can deepen understanding by adjusting the way the analysis is presented based on the user's emotions.

[0117] When making a proposal, the proposal unit can apply a different proposal algorithm to each industry. For example, the proposal unit can apply a proposal algorithm based on sales data to the retail industry. For the manufacturing industry, the proposal unit can apply a proposal algorithm based on production data. Furthermore, the proposal unit can apply a proposal algorithm based on online sales data to businesses that use an e-commerce platform. In this way, the proposal unit can apply a different proposal algorithm to each industry, thereby making it possible to make optimal proposals for each industry.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects past sales data and demand forecast data. For example, it can collect each company's sales history and market demand forecast data, as well as sales data for the past year and seasonal demand forecast data. It can also collect data in real time via databases and APIs. Step 2: The analysis unit uses generation AI to analyze the data collected by the collection unit. For example, it analyzes past sales data and demand forecast data and calculates the optimal inventory level for each product. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI to predict future demand. Step 3: The proposal unit proposes optimal inventory levels based on the analysis results obtained by the analysis unit. For example, it calculates the optimal inventory amount for each product and uses generation AI to propose optimal inventory levels for each company. Step 4: The adjustment unit adjusts the inventory levels proposed by the proposal unit in real time. For example, if there is a sudden change in sales conditions or market demand, the adjustment unit analyzes data in real time and adjusts inventory levels. Using generative AI, it is possible to respond quickly and flexibly to fluctuations in supply and demand.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects past sales data and demand forecast data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes an inventory level based on the analysis result obtained by the analysis unit; an adjusting unit that adjusts the inventory level proposed by the proposing unit in real time. A system characterized by:

2. The collecting unit Collect company sales history and market demand forecast data 2. The system of claim 1.

3. The analysis unit Recommend optimal inventory levels based on collected data 2. The system of claim 1.

4. The adjustment unit Adjust inventory levels in real time as sales and market demand fluctuate 2. The system of claim 1.

5. The proposal unit Propose optimal inventory levels to retailers, wholesalers, manufacturers, and companies using e-commerce platforms 2. The system of claim 1.

6. The adjustment unit Adjust inventory levels based on each industry application 2. The system of claim 1.

7. The collecting unit Analyze user sentiment and adjust the timing of data collection based on the analyzed user sentiment.

2. The system of claim 1.

8. The collecting unit Analyze each company's past sales history and select the most appropriate data collection method 2. The system of claim 1.

9. The collecting unit When collecting data, filter it based on the company's current market conditions and seasonal factors.

2. The system of claim 1.

10. The collecting unit When collecting data, choose the most appropriate collection method depending on the company's input method.

2. The system of claim 1.

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