System

Abstract

An object of a system according to an embodiment is to efficiently order a product or create a promotional item corresponding to a regional event.SOLUTION: A system according to an embodiment includes an event information collection unit, a sales data acquisition unit, an order presentation unit, and a sales promotion item creation unit. The event information collection unit collects event information of an area. The sales data acquisition unit acquires sales and order data of a store. The order presentation unit presents a necessary commodity order based on the data acquired by the sales data acquisition unit. The sales promotion item creation unit creates a sales promotion item.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] Conventional technologies have not been able to efficiently order products or create promotional materials for local events, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently order products and create promotional materials for local events. [Means for solving the problem]

[0006] The system according to the embodiment includes an event information collection unit, a sales data acquisition unit, an order submission unit, and a promotional item creation unit. The event information collection unit collects local event information. The sales data acquisition unit acquires sales and order data for the store. The order submission unit submits necessary product orders based on the data acquired by the sales data acquisition unit. The promotional item creation unit creates promotional items. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently order products and create promotional materials for local events. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The local event response system according to an embodiment of the present invention is a system that collects local event information, extracts store sales and order data from an internal database, adjusts dates, and then uses a generation AI to present the necessary product orders, and creates promotional materials using image generation AI and text generation AI. As a result, the local event response system enables retailers with nationwide operations to appropriately respond to local events and carry out efficient and effective promotional activities.

[0029] A local event response system according to an embodiment includes an event information collection unit, a sales data acquisition unit, an order submission unit, and a sales promotion material creation unit. The event information collection unit collects local event information. For example, it uses a generation AI to collect event information in real time from local social media sites and blogs, and constantly updates the database with the latest information. The event information collection unit also uses a generation AI to simultaneously collect examples of past event successes and failures, which can be used to respond to future events. For example, it analyzes the number of participants and sales data of past events to identify factors behind success and failure. Furthermore, the event information collection unit uses an emotion estimation function to estimate expectations and interest in events from local residents' social media posts, and evaluate the importance of the event based on that information. For example, it analyzes posts with a high percentage of positive emotions to evaluate the importance of the event. The sales data acquisition unit acquires sales and order data from stores. For example, it uses a generation AI to collect not only past sales data but also sales data from competing stores, enabling more accurate demand forecasting. The sales data acquisition unit can also take external factors such as seasons, weather, and local economic conditions into account when analyzing sales and order data, allowing for more accurate adjustments. For example, it can analyze seasonal demand fluctuations and the effects of weather and adjust order quantities accordingly. Furthermore, the sales data acquisition unit can use the emotion estimation function to analyze customer sentiment toward specific products based on past sales data and prioritize ordering of emotionally preferred products. For example, it can analyze comments on social media and review sites and calculate an emotion score. The order presentation unit presents necessary product orders. For example, it can use generative AI to optimize not only product ordering but also inventory management and delivery schedules. Furthermore, the order presentation unit can prioritize ordering environmentally friendly products and packaging from an ecological perspective when ordering products. For example, it can select products made from renewable materials. Furthermore, the order presentation unit can use the emotion estimation function to predict demand for specific products based on customer sentiment and prioritize ordering of emotionally preferred products. For example, it can analyze comments on social media and review sites and calculate an emotion score. The promotional material creation unit creates promotional materials. For example, image generation AI can be used to automatically generate designs that reflect local culture and traditions, creating promotional materials that are familiar to local residents.The promotional material creation unit can also use text generation AI to create catchy slogans and descriptions that incorporate local dialects and unique expressions. For example, it can generate friendly catchy slogans using local dialects. Furthermore, the promotional material creation unit can use an emotion estimation function to analyze customers' emotional responses to promotional materials in real time and select the most effective designs and text. For example, it can analyze customers' facial expressions and voices and calculate emotion scores. This allows the local event response system according to the embodiment to appropriately respond to local events and realize efficient and effective promotional activities. For example, it can quickly order products and create promotional materials tailored to local festivals and events, which is expected to increase sales. It also enables responses tailored to local needs, contributing to improved customer satisfaction.

[0030] The event information collection unit uses generation AI to collect event information in real time from local SNS and blogs, and can always reflect the latest information in the database. The event information collection unit uses generation AI to collect event information in real time from local SNS and blogs. For example, it can analyze posts on Twitter and Facebook, automatically extract information about local events and festivals, and store it in the database. This allows the latest event information to always be reflected in the database.

[0031] The event information collection unit uses the generation AI to simultaneously collect examples of success and failure from past events, and can utilize this information to handle the next event. The event information collection unit, for example, uses the generation AI to collect examples of success and failure from past events. For example, it analyzes the number of participants and sales data from past events to identify factors that led to success and failure. This makes it possible to utilize examples of success and failure from past events to handle the next event.

[0032] The event information collection unit uses a drone to capture footage of the local event, and the generation AI can estimate the scale of the event and the number of participants from that footage. The event information collection unit, for example, uses a drone to capture footage of a local event, and the generation AI analyzes that footage. For example, it estimates the number of participants and the scale of the event from the footage and stores it in a database. This makes it possible to capture footage of the local event and estimate the scale and number of participants of the event.

[0033] When collecting local event information, the event information collection unit simultaneously collects local weather data, allowing it to propose countermeasures according to the weather. For example, when collecting local event information, the event information collection unit simultaneously collects weather data. For example, it analyzes weather forecasts and past weather data to propose suitable dates for holding events. This allows it to propose countermeasures according to the weather.

[0034] The sales data acquisition unit uses the generation AI to collect not only past sales data but also sales data of competing stores, enabling more accurate demand forecasting. The sales data acquisition unit, for example, uses the generation AI to collect past sales data and sales data of competing stores and performs demand forecasting. For example, it analyzes sales data of competing stores and predicts fluctuations in demand. This allows sales data of competing stores to be collected as well, enabling more accurate demand forecasting.

[0035] The sales data acquisition unit takes into account external factors such as the season, weather, and local economic conditions when analyzing sales and order data, allowing for more accurate adjustments.The sales data acquisition unit, for example, takes into account external factors such as the season, weather, and local economic conditions when analyzing sales and order data.For example, it analyzes seasonal demand fluctuations and the effects of weather and adjusts order quantities.This allows for more accurate adjustments by taking external factors into account.

[0036] The sales data acquisition unit also collects opinions and feedback from store staff, and the generation AI can make adjustments taking this into account. The sales data acquisition unit, for example, collects opinions and feedback from store staff, and the generation AI can analyze sales and order data taking this into account. For example, the order quantity can be adjusted based on the experience and knowledge of the staff. This allows adjustments to be made taking into account the opinions and feedback of store staff.

[0037] The sales data acquisition unit can combine regional demographic data with the analysis of sales and order data to predict future demand fluctuations. The sales data acquisition unit, for example, combines regional demographic data with the analysis of sales and order data. For example, it analyzes trends in population increase and decrease to predict future demand fluctuations. This makes it possible to predict future demand fluctuations by combining regional demographic data.

[0038] The order submission unit can prioritize ordering environmentally friendly products and packaging from an ecological perspective when ordering products. For example, the order submission unit selects products that use recyclable materials. This allows environmentally friendly products and packaging to be prioritized when ordering products.

[0039] The order submission unit prioritizes local specialty products and products from local companies when placing an order, thereby revitalizing the local economy. The order submission unit, for example, prioritizes local specialty products and products from local companies when placing an order. For example, it adds local agricultural products and crafts to the order list. This prioritizes local specialty products and products from local companies, thereby revitalizing the local economy.

[0040] The order submission unit can consider the sales target and campaign information for each store when ordering products and present the optimal order quantity. The order submission unit, for example, considers the sales target and campaign information for each store when ordering products. For example, it predicts demand during a specific campaign period and adjusts the order quantity. This makes it possible to consider the sales target and campaign information for each store and present the optimal order quantity.

[0041] The promotional material creation unit uses image generation AI to automatically generate designs that reflect the local culture and traditions, making it possible to create promotional materials that are familiar to local residents. The promotional material creation unit, for example, uses image generation AI to automatically generate designs that reflect the local culture and traditions. For example, it creates designs that use motifs of local festivals and traditional events. This makes it possible to automatically generate designs that reflect the local culture and traditions, making it possible to create promotional materials that are familiar to local residents.

[0042] The promotional material creation department can use text generation AI to create catchy slogans and descriptions that incorporate regional dialects and unique expressions. The promotional material creation department can, for example, use text generation AI to create catchy slogans and descriptions that incorporate regional dialects and unique expressions. For example, it can generate friendly catchy slogans that use regional dialects. This makes it possible to create catchy slogans and descriptions that incorporate regional dialects and unique expressions.

[0043] The promotional material creation unit can use image generation AI to automatically generate not only promotional materials but also store display and interior designs. The promotional material creation unit can use image generation AI to automatically generate not only promotional materials but also store display and interior designs. For example, it can create store window displays and interior designs. This makes it possible to automatically generate not only promotional materials but also store display and interior designs.

[0044] The promotional material creation unit can use text generation AI to automatically generate not only promotional materials but also the content for store websites and SNS posts. The promotional material creation unit, for example, uses text generation AI to automatically generate not only promotional materials but also the content for store websites and SNS posts. For example, it creates campaign information and product descriptions. This allows the automatic generation of not only promotional materials but also the content for store websites and SNS posts.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The local event response system can further include an opinion collection unit that collects the opinions of local residents. The opinion collection unit collects, for example, the opinions and requests that local residents have regarding events through questionnaires or online forms. This makes it possible to respond to events based on the needs and expectations of local residents, thereby strengthening ties with the community. For example, the event content and areas for improvement desired by local residents can be collected and reflected in the next event. The opinion collection unit can also collect opinions from local residents in real time, allowing for flexible response even while the event is in progress. For example, areas for improvement or additional requests can be immediately reflected during the event.

[0047] The local event response system can further include a tourist information collection unit that collects local tourist information. The tourist information collection unit collects, for example, information on local tourist attractions, accommodations, and restaurants, and provides it to event participants. This allows event participants to enjoy local sightseeing and contributes to revitalizing the local economy. For example, it can introduce tourist spots and recommended restaurants to visit before and after the event. The tourist information collection unit can also update local tourist information in real time and provide the latest information. For example, it can instantly reflect information on newly opened stores and seasonal tourist attractions.

[0048] The local event response system can further include a traffic information collection unit that collects local traffic information. The traffic information collection unit collects, for example, local traffic conditions and public transportation operation information and provides it to event participants. This can support event participants in moving smoothly. For example, it can provide information on traffic congestion and the availability of nearby parking lots. The traffic information collection unit can also update traffic information in real time to provide the latest information. For example, it can immediately reflect information on road closures due to traffic accidents or construction work.

[0049] The local event response system can further include a disaster prevention information collection unit that collects local disaster prevention information. The disaster prevention information collection unit collects, for example, local disaster risk and evacuation shelter information and provides it to event participants. This can support event participants in safely enjoying the event. For example, it can provide information on evacuation sites and evacuation routes in the event of a disaster such as an earthquake or typhoon. The disaster prevention information collection unit can also update disaster prevention information in real time and provide the latest information. For example, it can instantly reflect emergency information and evacuation instructions in the event of a disaster.

[0050] The local event response system may further include a health information collection unit that collects local health information. The health information collection unit, for example, collects information on local medical institutions and health events and provides it to event participants. This can support event participants in enjoying the event while taking their health into consideration. For example, it can provide information on the nearest hospitals and clinics, and information on health check events held during the event. The health information collection unit can also update the health information in real time to provide the latest information. For example, it can instantly reflect information on influenza epidemics and vaccinations.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The event information collection unit collects local event information. For example, it uses generation AI to collect event information in real time from local social media and blogs, and constantly updates the database with the latest information. It also simultaneously collects examples of past event successes and failures, which can be used to handle future events. Furthermore, it uses an emotion estimation function to estimate the expectations and interest in the event from local residents' social media posts, and evaluates the importance of the event based on this information. Step 2: The sales data acquisition unit acquires sales and order data from the store. For example, using generative AI, it can collect not only past sales data but also sales data from competing stores to make more accurate demand forecasts. It can also take into account external factors such as season, weather, and local economic conditions when analyzing sales and order data, allowing for more accurate adjustments. Furthermore, it can use emotion estimation functionality to analyze customer sentiment toward specific products based on past sales data, and prioritize orders for emotionally preferred products. Step 3: The order suggestion unit suggests the necessary product orders. For example, generative AI can be used to not only order products, but also optimize inventory management and delivery schedules. It can also prioritize ordering eco-friendly products and packaging from an ecological perspective. Furthermore, it can use emotion estimation to predict demand for specific products based on customer emotions, and prioritize ordering emotionally preferred products. Step 4: The promotional materials creation department creates promotional materials. For example, image generation AI can be used to automatically generate designs that reflect the local culture and traditions, creating promotional materials that are familiar to local residents. Text generation AI can also be used to create catchy slogans and descriptions that incorporate local dialects and unique expressions. Furthermore, emotion estimation functionality can be used to analyze customers' emotional reactions to promotional materials in real time, allowing the most effective designs and text to be selected.

[0053] (Example 2) The local event response system according to an embodiment of the present invention is a system that collects local event information, extracts store sales and order data from an internal database, adjusts dates, and then uses a generation AI to present the necessary product orders, and creates promotional materials using image generation AI and text generation AI. As a result, the local event response system enables retailers with nationwide operations to appropriately respond to local events and carry out efficient and effective promotional activities.

[0054] A local event response system according to an embodiment includes an event information collection unit, a sales data acquisition unit, an order submission unit, and a sales promotion material creation unit. The event information collection unit collects local event information. For example, it uses a generation AI to collect event information in real time from local social media sites and blogs, and constantly updates the database with the latest information. The event information collection unit also uses a generation AI to simultaneously collect examples of past event successes and failures, which can be used to respond to future events. For example, it analyzes the number of participants and sales data of past events to identify factors behind success and failure. Furthermore, the event information collection unit uses an emotion estimation function to estimate expectations and interest in events from local residents' social media posts, and evaluate the importance of the event based on that information. For example, it analyzes posts with a high percentage of positive emotions to evaluate the importance of the event. The sales data acquisition unit acquires sales and order data from stores. For example, it uses a generation AI to collect not only past sales data but also sales data from competing stores, enabling more accurate demand forecasting. The sales data acquisition unit can also take external factors such as seasons, weather, and local economic conditions into account when analyzing sales and order data, allowing for more accurate adjustments. For example, it can analyze seasonal demand fluctuations and the effects of weather and adjust order quantities accordingly. Furthermore, the sales data acquisition unit can use the emotion estimation function to analyze customer sentiment toward specific products based on past sales data and prioritize ordering of emotionally preferred products. For example, it can analyze comments on social media and review sites and calculate an emotion score. The order presentation unit presents necessary product orders. For example, it can use generative AI to optimize not only product ordering but also inventory management and delivery schedules. Furthermore, the order presentation unit can prioritize ordering environmentally friendly products and packaging from an ecological perspective when ordering products. For example, it can select products made from renewable materials. Furthermore, the order presentation unit can use the emotion estimation function to predict demand for specific products based on customer sentiment and prioritize ordering of emotionally preferred products. For example, it can analyze comments on social media and review sites and calculate an emotion score. The promotional material creation unit creates promotional materials. For example, image generation AI can be used to automatically generate designs that reflect local culture and traditions, creating promotional materials that are familiar to local residents.The promotional material creation unit can also use text generation AI to create catchy slogans and descriptions that incorporate local dialects and unique expressions. For example, it can generate friendly catchy slogans using local dialects. Furthermore, the promotional material creation unit can use an emotion estimation function to analyze customers' emotional responses to promotional materials in real time and select the most effective designs and text. For example, it can analyze customers' facial expressions and voices and calculate emotion scores. This allows the local event response system according to the embodiment to appropriately respond to local events and realize efficient and effective promotional activities. For example, it can quickly order products and create promotional materials tailored to local festivals and events, which is expected to increase sales. It also enables responses tailored to local needs, contributing to improved customer satisfaction.

[0055] The event information collection unit uses generation AI to collect event information in real time from local SNS and blogs, and can always reflect the latest information in the database. The event information collection unit uses generation AI to collect event information in real time from local SNS and blogs. For example, it can analyze posts on Twitter and Facebook, automatically extract information about local events and festivals, and store it in the database. This allows the latest event information to always be reflected in the database.

[0056] The event information collection unit uses the generation AI to simultaneously collect examples of success and failure from past events, and can utilize this information to handle the next event. The event information collection unit, for example, uses the generation AI to collect examples of success and failure from past events. For example, it analyzes the number of participants and sales data from past events to identify factors that led to success and failure. This makes it possible to utilize examples of success and failure from past events to handle the next event.

[0057] The event information collection unit can use the emotion estimation function to estimate the expectations and interest levels for an event from local residents' SNS posts, and evaluate the importance of the event based on that information. The event information collection unit, for example, uses the emotion estimation function to estimate the expectations and interest levels for an event from local residents' SNS posts. For example, it analyzes posts with a high percentage of positive emotions and evaluates the importance of the event. This makes it possible to evaluate the importance of the event based on the expectations and interest levels of local residents.

[0058] The event information collection unit uses a drone to capture footage of the local event, and the generation AI can estimate the scale of the event and the number of participants from that footage. The event information collection unit, for example, uses a drone to capture footage of a local event, and the generation AI analyzes that footage. For example, it estimates the number of participants and the scale of the event from the footage and stores it in a database. This makes it possible to capture footage of the local event and estimate the scale and number of participants of the event.

[0059] When collecting local event information, the event information collection unit simultaneously collects local weather data, allowing it to propose countermeasures according to the weather. For example, when collecting local event information, the event information collection unit simultaneously collects weather data. For example, it analyzes weather forecasts and past weather data to propose suitable dates for holding events. This allows it to propose countermeasures according to the weather.

[0060] The event information collection unit uses the emotion estimation function to analyze the emotions of local residents toward events and can prioritize responding to events that are associated with a lot of positive emotions. The event information collection unit, for example, uses the emotion estimation function to analyze the emotions of local residents toward events. For example, it analyzes social media posts and blog comments and prioritizes responding to events that are associated with a lot of positive emotions. This makes it possible to prioritize responding to events that are associated with a lot of positive emotions.

[0061] The sales data acquisition unit uses the generation AI to collect not only past sales data but also sales data of competing stores, enabling more accurate demand forecasting. The sales data acquisition unit, for example, uses the generation AI to collect past sales data and sales data of competing stores and performs demand forecasting. For example, it analyzes sales data of competing stores and predicts fluctuations in demand. This allows sales data of competing stores to be collected as well, enabling more accurate demand forecasting.

[0062] The sales data acquisition unit takes into account external factors such as the season, weather, and local economic conditions when analyzing sales and order data, allowing for more accurate adjustments.The sales data acquisition unit, for example, takes into account external factors such as the season, weather, and local economic conditions when analyzing sales and order data.For example, it analyzes seasonal demand fluctuations and the effects of weather and adjusts order quantities.This allows for more accurate adjustments by taking external factors into account.

[0063] The sales data acquisition unit can use the emotion estimation function to analyze customer emotions toward specific products based on past sales data and prioritize ordering of products that are emotionally preferred. The sales data acquisition unit can, for example, use the emotion estimation function to analyze customer emotions toward specific products based on past sales data. For example, it can analyze comments on social media and review sites and calculate an emotion score. This makes it possible to prioritize ordering of products that are preferred based on customer emotions.

[0064] The sales data acquisition unit also collects opinions and feedback from store staff, and the generation AI can make adjustments taking this into account. The sales data acquisition unit, for example, collects opinions and feedback from store staff, and the generation AI can analyze sales and order data taking this into account. For example, the order quantity can be adjusted based on the experience and knowledge of the staff. This allows adjustments to be made taking into account the opinions and feedback of store staff.

[0065] The sales data acquisition unit can combine regional demographic data with the analysis of sales and order data to predict future demand fluctuations. The sales data acquisition unit, for example, combines regional demographic data with the analysis of sales and order data. For example, it analyzes trends in population increase and decrease to predict future demand fluctuations. This makes it possible to predict future demand fluctuations by combining regional demographic data.

[0066] The sales data acquisition unit can use the emotion estimation function to analyze the emotions of store staff and adjust orders to create a comfortable working environment for the staff. The sales data acquisition unit can, for example, use the emotion estimation function to analyze the emotions of store staff and adjust orders to create a comfortable working environment. For example, the sales data acquisition unit can analyze the stress levels of staff and adjust order quantities. This makes it possible to analyze the emotions of store staff and adjust orders to create a comfortable working environment.

[0067] The order submission unit can prioritize ordering environmentally friendly products and packaging from an ecological perspective when ordering products. For example, the order submission unit selects products that use recyclable materials. This allows environmentally friendly products and packaging to be prioritized when ordering products.

[0068] The order presentation unit can use the emotion estimation function to predict demand for a specific product based on customer emotions and prioritize ordering of products that are emotionally preferred. The order presentation unit can, for example, use the emotion estimation function to predict demand for a specific product based on customer emotions. For example, it can analyze comments on social media or review sites and calculate an emotion score. This makes it possible to prioritize ordering of products that are preferred based on customer emotions.

[0069] The order submission unit prioritizes local specialty products and products from local companies when placing an order, thereby revitalizing the local economy. The order submission unit, for example, prioritizes local specialty products and products from local companies when placing an order. For example, it adds local agricultural products and crafts to the order list. This prioritizes local specialty products and products from local companies, thereby revitalizing the local economy.

[0070] The order submission unit can consider the sales target and campaign information for each store when ordering products and present the optimal order quantity. The order submission unit, for example, considers the sales target and campaign information for each store when ordering products. For example, it predicts demand during a specific campaign period and adjusts the order quantity. This makes it possible to consider the sales target and campaign information for each store and present the optimal order quantity.

[0071] The order presentation unit can use the emotion estimation function to analyze the emotions of the store staff and prioritize ordering of products recommended by the staff. The order presentation unit, for example, uses the emotion estimation function to analyze the emotions of the store staff and prioritize ordering of products recommended by the staff. For example, products that have a high level of satisfaction among the staff are added to the order list. This makes it possible to analyze the emotions of the store staff and prioritize ordering of products recommended by the staff.

[0072] The promotional material creation unit uses image generation AI to automatically generate designs that reflect the local culture and traditions, making it possible to create promotional materials that are familiar to local residents. The promotional material creation unit, for example, uses image generation AI to automatically generate designs that reflect the local culture and traditions. For example, it creates designs that use motifs of local festivals and traditional events. This makes it possible to automatically generate designs that reflect the local culture and traditions, making it possible to create promotional materials that are familiar to local residents.

[0073] The promotional material creation department can use text generation AI to create catchy slogans and descriptions that incorporate regional dialects and unique expressions. The promotional material creation department can, for example, use text generation AI to create catchy slogans and descriptions that incorporate regional dialects and unique expressions. For example, it can generate friendly catchy slogans that use regional dialects. This makes it possible to create catchy slogans and descriptions that incorporate regional dialects and unique expressions.

[0074] The promotional material creation unit uses the emotion estimation function to analyze customers' emotional responses to promotional materials in real time and can select the most effective designs and text. The promotional material creation unit, for example, uses the emotion estimation function to analyze customers' emotional responses to promotional materials in real time. For example, it analyzes the customer's facial expressions and voice and calculates an emotion score. This allows the customer's emotional responses to be analyzed in real time and the most effective designs and text to be selected.

[0075] The promotional material creation unit can use image generation AI to automatically generate not only promotional materials but also store display and interior designs. The promotional material creation unit can use image generation AI to automatically generate not only promotional materials but also store display and interior designs. For example, it can create store window displays and interior designs. This makes it possible to automatically generate not only promotional materials but also store display and interior designs.

[0076] The promotional material creation unit can use text generation AI to automatically generate not only promotional materials but also the content for store websites and SNS posts. The promotional material creation unit, for example, uses text generation AI to automatically generate not only promotional materials but also the content for store websites and SNS posts. For example, it creates campaign information and product descriptions. This allows the automatic generation of not only promotional materials but also the content for store websites and SNS posts.

[0077] The promotional material creation unit uses the emotion estimation function to analyze the emotional reactions of staff members to promotional materials, and can create promotional materials that the staff members can recommend with confidence. The promotional material creation unit, for example, uses the emotion estimation function to analyze the emotional reactions of staff members to promotional materials. For example, it analyzes the facial expressions and voices of staff members and calculates an emotion score. This makes it possible to analyze the emotional reactions of staff members and create promotional materials that the staff members can recommend with confidence.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The local event response system can further include an opinion collection unit that collects the opinions of local residents. The opinion collection unit collects, for example, the opinions and requests that local residents have regarding events through questionnaires or online forms. This makes it possible to respond to events based on the needs and expectations of local residents, thereby strengthening ties with the community. For example, the event content and areas for improvement desired by local residents can be collected and reflected in the next event. The opinion collection unit can also collect opinions from local residents in real time, allowing for flexible response even while the event is in progress. For example, areas for improvement or additional requests can be immediately reflected during the event.

[0080] The local event response system can further include a tourist information collection unit that collects local tourist information. The tourist information collection unit collects, for example, information on local tourist attractions, accommodations, and restaurants, and provides it to event participants. This allows event participants to enjoy local sightseeing and contributes to revitalizing the local economy. For example, it can introduce tourist spots and recommended restaurants to visit before and after the event. The tourist information collection unit can also update local tourist information in real time and provide the latest information. For example, it can instantly reflect information on newly opened stores and seasonal tourist attractions.

[0081] The local event response system can further include a traffic information collection unit that collects local traffic information. The traffic information collection unit collects, for example, local traffic conditions and public transportation operation information and provides it to event participants. This can support event participants in moving smoothly. For example, it can provide information on traffic congestion and the availability of nearby parking lots. The traffic information collection unit can also update traffic information in real time to provide the latest information. For example, it can immediately reflect information on road closures due to traffic accidents or construction work.

[0082] The local event response system can further include a disaster prevention information collection unit that collects local disaster prevention information. The disaster prevention information collection unit collects, for example, local disaster risk and evacuation shelter information and provides it to event participants. This can support event participants in safely enjoying the event. For example, it can provide information on evacuation sites and evacuation routes in the event of a disaster such as an earthquake or typhoon. The disaster prevention information collection unit can also update disaster prevention information in real time and provide the latest information. For example, it can instantly reflect emergency information and evacuation instructions in the event of a disaster.

[0083] The local event response system may further include a health information collection unit that collects local health information. The health information collection unit, for example, collects information on local medical institutions and health events and provides it to event participants. This can support event participants in enjoying the event while taking their health into consideration. For example, it can provide information on the nearest hospitals and clinics, and information on health check events held during the event. The health information collection unit can also update the health information in real time to provide the latest information. For example, it can instantly reflect information on influenza epidemics and vaccinations.

[0084] The local event response system can also use emotion estimation to monitor the emotions of event participants in real time and adjust the progress of the event. For example, it can analyze participants' facial expressions and voices during the event to detect changes in their emotions. This allows the content and progress of the event to be flexibly changed to increase participant satisfaction. For example, if participants are bored, entertainment elements can be added. The emotion estimation function can also be used to analyze participants' emotional data after the event ends to identify areas for improvement for the next event. For example, it can identify which activities participants particularly enjoyed and which points they were dissatisfied with.

[0085] The local event response system can also use emotion estimation to analyze the emotional responses of local residents to event announcements and promotions, and select the optimal announcement method. For example, it can analyze responses to social media and advertisements and prioritize announcement methods that generate a lot of positive emotions. This allows event information to be communicated effectively to local residents. For example, if video advertisements are well received, video can be used more frequently in future announcements. The emotion estimation function can also be used to adjust the content and timing of announcements to attract the attention of local residents. For example, it can make announcements that increase positive emotions just before the event.

[0086] The local event response system can also use emotion estimation to analyze the emotional interactions between event participants and provide an environment in which participants can enjoy themselves more. For example, it can analyze the conversations and interactions between participants and form groups with a high proportion of positive emotions. This allows participants to have a more enjoyable time. For example, it can match participants with common hobbies and interests. The emotion estimation function can also be used to monitor changes in participants' emotions during the event in real time and provide support as needed. For example, if a participant is feeling anxious, staff can provide support.

[0087] The local event response system can also use the emotion estimation function to analyze participants' emotional data after the event and use the results to plan the next event. For example, emotional data from participants can be collected after the event and activities and content that generated a lot of positive emotions can be identified. This makes it possible to incorporate similar activities and content into the next event. For example, it can re-plan games or workshops that participants particularly enjoyed. The emotion estimation function can also be used to identify areas for improvement at the event and improve the quality of the next event. For example, it can take specific measures to improve points that participants were dissatisfied with.

[0088] The local event response system can also use the emotion estimation function to evaluate the success of an event based on the emotional data of event participants. For example, it can collect emotional data from participants during the event and evaluate whether there were many positive emotions. This allows the success of the event to be quantitatively evaluated. For example, it can analyze participants' smiles and happy expressions to calculate an emotional score. The emotion estimation function can also be used to identify factors that made an event successful and areas for improvement, which can be used for future events. For example, it can specifically understand the reasons why a particular activity was successful and areas that need improvement.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The event information collection unit collects local event information. For example, it uses generation AI to collect event information in real time from local social media and blogs, and constantly updates the database with the latest information. It also simultaneously collects examples of past event successes and failures, which can be used to handle future events. Furthermore, it uses an emotion estimation function to estimate the expectations and interest in the event from local residents' social media posts, and evaluates the importance of the event based on this information. Step 2: The sales data acquisition unit acquires sales and order data from the store. For example, using generative AI, it can collect not only past sales data but also sales data from competing stores to make more accurate demand forecasts. It can also take into account external factors such as season, weather, and local economic conditions when analyzing sales and order data, allowing for more accurate adjustments. Furthermore, it can use emotion estimation functionality to analyze customer sentiment toward specific products based on past sales data, and prioritize orders for emotionally preferred products. Step 3: The order suggestion unit suggests the necessary product orders. For example, generative AI can be used to not only order products, but also optimize inventory management and delivery schedules. It can also prioritize ordering eco-friendly products and packaging from an ecological perspective. Furthermore, it can use emotion estimation to predict demand for specific products based on customer emotions, and prioritize ordering emotionally preferred products. Step 4: The promotional materials creation department creates promotional materials. For example, image generation AI can be used to automatically generate designs that reflect the local culture and traditions, creating promotional materials that are familiar to local residents. Text generation AI can also be used to create catchy slogans and descriptions that incorporate local dialects and unique expressions. Furthermore, emotion estimation functionality can be used to analyze customers' emotional reactions to promotional materials in real time, allowing the most effective designs and text to be selected.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0132] 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.

[0133] 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.

[0134] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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. [Explanation of symbols]

[0158] 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. An event information gathering department that collects information on local events; a sales data acquisition unit that acquires sales and order data for the store; an order submission unit that submits a necessary product order based on the data acquired by the sales data acquisition unit; a sales promotion material creation unit that creates sales promotion materials; A system characterized by:

2. The event information collection unit Drones are used to capture images of the event, and the AI ​​used to generate the images estimates the scale of the event and the number of participants.

2. The system of claim 1.

3. The sales data acquisition unit Generative AI is used to collect not only past sales data but also sales data from competing stores to make more accurate demand forecasts.

2. The system of claim 1.

4. The order submission unit Using generative AI to optimize not only product ordering but also inventory management and delivery schedules 2. The system of claim 1.

5. The sales promotion material creation unit Using image generation AI, designs that reflect the culture and traditions of the region are automatically generated, creating promotional materials that are familiar to local residents.

2. The system of claim 1.

6. The event information collection unit Estimate the expectations and interest of local residents in the event from their SNS posts, and evaluate the importance of the event based on that information.

2. The system of claim 1.

7. The sales data acquisition unit Analyze customer sentiment toward specific products based on past sales data and prioritize orders for emotionally preferred products 2. The system of claim 1.

8. The order submission unit Predicting demand for specific products based on customer sentiment and prioritizing orders for emotionally preferred products 2. The system of claim 1.

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