System

Abstract

An object of a system according to an embodiment is to comprehensively analyze various data in a new store opening plan and make a specific proposal.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes a specific proposal based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to comprehensively analyze a variety of data and make specific proposals when planning new store openings.

[0005] The system according to the embodiment aims to comprehensively analyze various data in new store opening plans and make specific proposals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively analyze various data in new store opening plans and make specific proposals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The analysis service according to an embodiment of the present invention uses AI to perform multifaceted analysis and provide specific proposals for new restaurant opening plans. The analysis service uses AI to analyze "traffic flow data," "geographical characteristics," "population and household characteristics," and "competing stores" to provide specific proposals for pricing, customer acquisition strategies, store design, the introduction of electronic payment systems, and even sales / profit forecasts. For example, the analysis service performs a detailed analysis of traffic flow in a specific area to determine the number of people who will gather at certain times. The analysis service then analyzes the area's topography, transportation access, and the location of surrounding facilities to evaluate the convenience of a restaurant location. Furthermore, the analysis service analyzes the age group, family structure, income level, and other factors of residents in a specific area to identify target customer demographics. The analysis service also analyzes the number, business type, and price range of competing stores in a specific area to understand the competitive situation. Next, the analysis service proposes optimal pricing based on the collected data. Furthermore, as a customer acquisition strategy, the analysis service proposes campaigns offering discounts during specific times and promotions utilizing social media. Furthermore, the analysis service proposes store design, interior design, and layout tailored to the target customer demographic. Specific proposals for the introduction of electronic payment systems to enhance convenience are also made. Finally, specific sales and revenue forecasts are also made based on the collected data. This allows the analysis service to perform multifaceted analysis and make specific proposals when planning new restaurant openings. This increases the success rate of restaurant opening plans. For example, the analysis service can select the optimal restaurant location and propose pricing and customer attraction strategies tailored to the target customer demographic, thereby enabling efficient restaurant opening plans.

[0029] The analysis service according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data. Examples of the data include, but are not limited to, numerical data, text data, and image data. The collection unit collects data using, for example, a sensor. The collection unit can also collect data through a questionnaire survey. For example, the collection unit collects people flow data using GPS data. The collection unit can also collect people flow data using Wi-Fi data. The collection unit can also collect topographical data and land use data. For example, the collection unit collects topographical data to evaluate geographical characteristics. The collection unit can also collect demographical data and household income data. For example, the collection unit collects demographic data to understand the age groups and family structures of people living in a specific area. The collection unit can also collect location information and sales data of competing stores. For example, the collection unit collects location information to understand the number and business types of competing stores in a specific area. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes collected people flow data to understand the flow of people in a specific area. The analysis unit can also analyze collected geographical characteristic data to evaluate the convenience of a store location. The analysis unit can also analyze collected population household characteristic data to identify a target customer demographic. For example, the analysis unit can analyze the age group and family structure of people living in a specific area to identify a target customer demographic. The analysis unit can also analyze collected competing store data to understand the competitive situation. For example, the analysis unit can analyze the number and business types of competing stores in a specific area to understand the competitive situation. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The proposals include, for example, pricing and marketing strategies, but are not limited to these examples. For example, the proposal unit proposes optimal pricing based on the collected data. The proposal unit can also propose discount campaigns during specific time periods and promotions using social media.The proposal unit can also propose interior design and layout tailored to the target customer demographic. For example, the proposal unit can propose color designs and traffic flow designs tailored to the target customer demographic. The proposal unit can also propose the introduction of an electronic payment system. For example, the proposal unit can propose the introduction of credit card payments or mobile payments. The proposal unit can also predict sales / profits based on the collected data. For example, the proposal unit predicts sales / profits based on past sales data and economic indicators. This allows the analysis service according to the embodiment to consistently collect, analyze, and propose data.

[0030] The collection unit can collect data on people flow, geographic characteristics, population and household characteristics, and competing store data. The collection unit, for example, collects people flow data using GPS data. For example, the collection unit collects GPS data to understand the flow of people in a specific area. The collection unit can also collect people flow data using Wi-Fi data. For example, the collection unit collects Wi-Fi data around a commercial facility to identify peak and off-peak times. The collection unit can also collect topographical data and land use data. For example, the collection unit collects topographical data to evaluate the topography and transportation access of a specific area. The collection unit can also collect demographical data and household income data. For example, the collection unit collects demographical data to understand the age groups and family structures of people living in a specific area. The collection unit can also collect location information and sales data of competing stores. For example, the collection unit collects location information to understand the number and business types of competing stores in a specific area. This enables multifaceted data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input GPS data into a generation AI, which may then analyze the people flow data.

[0031] The analysis unit can analyze the collected data and select the optimal store location. For example, the analysis unit can analyze collected pedestrian flow data to understand the flow of people in a specific area. For example, the analysis unit can analyze pedestrian flow data in front of a station or around a commercial facility to identify peak and off-peak times. The analysis unit can also analyze collected geographical characteristic data to evaluate the convenience of a store location. For example, the analysis unit can analyze the topography and transportation access of a specific area to select a location with good accessibility and high customer attraction. The analysis unit can also analyze collected population and household characteristic data to identify a target customer demographic. For example, the analysis unit can analyze the age group and family structure of people living in a specific area to identify a target customer demographic. The analysis unit can also analyze collected competing store data to understand the competitive situation. For example, the analysis unit can analyze the number and business types of competing stores in a specific area to understand the competitive situation. This allows the optimal store location to be selected. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis department can input the collected data into the generation AI, which can then select the optimal location for opening a store.

[0032] The proposal unit can propose pricing, customer attraction strategies, store design, introduction of an electronic payment system, and sales / revenue forecasts. The proposal unit, for example, proposes optimal pricing based on collected data. For example, the proposal unit sets prices using the cost-plus method or the market price method. The proposal unit can also propose campaigns that offer discounts during specific time periods or promotions that utilize social media. For example, the proposal unit can propose campaigns that offer discounts during peak times. The proposal unit can also propose interior design and layout tailored to the target customer demographic. For example, the proposal unit can propose color designs and traffic flow designs tailored to the target customer demographic. The proposal unit can also propose the introduction of an electronic payment system. For example, the proposal unit can propose the introduction of credit card payments or mobile payments. The proposal unit can also make sales / revenue forecasts based on collected data. For example, the proposal unit makes sales / revenue forecasts based on past sales data and economic indicators. This enables specific proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal department can input the collected data into the generation AI, which can then propose pricing and customer acquisition strategies.

[0033] The proposal unit can propose setting a price range that matches the target customer segment. The proposal unit, for example, proposes setting a price range that matches the target customer segment. For example, the proposal unit sets a price range that matches a specific age group or income level. The proposal unit can also set a price range based on the purchase history of the target customer segment. For example, the proposal unit analyzes past purchase history and sets an optimal price range. This makes it possible to set prices that match the target customer segment. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the target customer segment into a generation AI, and the generation AI can set the price range.

[0034] The proposal unit can propose campaigns that offer discounts during specific time periods or promotions that utilize social media. For example, the proposal unit proposes campaigns that offer discounts during specific time periods. For example, the proposal unit proposes campaigns that offer discounts during peak times. The proposal unit can also propose promotions that utilize social media. For example, the proposal unit proposes promotions that utilize influencer marketing. The proposal unit can also attract customers through advertising campaigns. For example, the proposal unit proposes promotions that reach a specific target demographic using social media advertising. This enables an effective customer attraction strategy. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input collected data into a generation AI, which can then propose campaigns and promotions.

[0035] The proposal unit can propose interior design and layout tailored to the target customer base. The proposal unit, for example, proposes interior design and layout tailored to the target customer base. For example, the proposal unit proposes color designs and traffic flow designs tailored to the target customer base. The proposal unit can also propose interior designs based on a specific theme. For example, the proposal unit proposes interior designs based on a specific theme, such as a cafe style or a family restaurant style. The proposal unit can also propose store layout designs. For example, the proposal unit proposes efficient traffic flow designs and seating arrangements. This makes it possible to design a store tailored to the target customer base. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input collected data into a generation AI, which then proposes interior design and layout.

[0036] The proposal unit can propose the introduction of an electronic payment system. The proposal unit, for example, proposes the introduction of an electronic payment system. For example, the proposal unit proposes the introduction of credit card payment or mobile payment. The proposal unit can also propose the introduction of QR code (registered trademark) payment or electronic money payment. For example, the proposal unit proposes the introduction of a specific electronic payment system and explains its convenience. The proposal unit can also propose costs and operation methods associated with the introduction of the electronic payment system. For example, the proposal unit proposes an optimal electronic payment system after taking into account introduction costs and operation costs. This enables the introduction of a highly convenient payment system. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input collected data into a generation AI, which then proposes the introduction of an electronic payment system.

[0037] The proposal unit can make sales / revenue predictions based on the collected data. The proposal unit, for example, makes sales / revenue predictions based on the collected data. For example, the proposal unit makes sales / revenue predictions based on past sales data and economic indicators. The proposal unit can also make sales / revenue predictions based on specific events or seasons. For example, the proposal unit makes sales predictions when a specific event is held. The proposal unit can also make revenue predictions taking into account seasonal sales patterns. For example, the proposal unit makes revenue predictions based on sales patterns in summer and winter. This makes it possible to make specific sales / revenue predictions. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the collected data into a generation AI, and the generation AI can make sales / revenue predictions.

[0038] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, analyzes past data collection history and selects the optimal collection method. For example, the collection unit selects the most efficient collection method from the past data collection history. The collection unit can also concentrate data collection in a specific time period based on the past data collection history. The collection unit can also analyze past data collection history and improve the collection method. For example, the collection unit improves the collection frequency and collection means based on the past data collection history. This enables efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data collection history into a generation AI, which can select the optimal collection method.

[0039] The collection unit can adjust the collection range based on a specific event or season when collecting data. For example, when a specific event is held, the collection unit strengthens data collection around the event. For example, when a sporting event or cultural event is held, the collection unit collects people flow data around the event. The collection unit can also adjust the collection range taking into account seasonal people flow patterns. For example, the collection unit adjusts the collection range based on people flow patterns in summer and winter. The collection unit can also dynamically change the collection range based on a specific event or season. For example, the collection unit expands the collection range when a specific event is held. This enables data collection according to the event or season. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to a specific event or season into a generation AI, and the generation AI can adjust the collection range.

[0040] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit collects voice data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit collects text data using OCR technology. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. For example, the collection unit collects image data using image recognition technology. This enables data collection depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI, and the generation AI can select the optimal collection means.

[0041] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. The collection unit, for example, prioritizes collecting people flow data in a specific area. For example, the collection unit collects people flow data using GPS data for a specific area. The collection unit can also prioritize collecting highly relevant data based on geographical characteristics. For example, the collection unit collects topographical data and transportation access data for a specific area. The collection unit can also dynamically change the collection range by taking geographical location information into consideration. For example, the collection unit expands or reduces the collection range based on the geographical location information of a specific area. This enables data collection based on geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information to a generation AI, and the generation AI can prioritize collecting highly relevant data.

[0042] The collection unit can analyze social media activities and collect related data when collecting data. The collection unit collects related data based on, for example, check-in information on social media. For example, the collection unit analyzes check-in information on social media and collects people flow data in a specific area. The collection unit can also analyze posts on social media and collect related data. For example, the collection unit analyzes posts on social media and collects geographical characteristic data of a specific area. The collection unit can also collect related data by referring to the activities of friends on social media. For example, the collection unit collects people flow data in a specific area based on the check-in information of friends on social media. This makes it possible to collect data based on social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and use the generation AI to collect related data.

[0043] When collecting data, the collection unit can customize the collection method by reflecting past feedback. The collection unit, for example, improves the collection method based on past feedback. For example, the collection unit analyzes past feedback and improves the collection frequency or collection method. The collection unit can also adjust the collection range by reflecting past feedback. For example, the collection unit expands or reduces the collection range of a specific area based on past feedback. The collection unit can also customize the collection method by referring to past feedback. For example, the collection unit selects a collection method using voice recognition technology or OCR technology based on past feedback. This makes it possible to collect data based on past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into a generation AI and use the generation AI to customize the collection method.

[0044] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high business impact. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis when the reliability of the data is low. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance and postpones analysis of data with low importance. This enables analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0045] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific algorithm to people flow data for analysis. For example, the analysis unit analyzes people flow data using a clustering algorithm. The analysis unit can also apply different algorithms to geographical characteristic data for analysis. For example, the analysis unit analyzes geographical characteristic data using regression analysis. The analysis unit can also analyze population household characteristic data by applying an appropriate algorithm. For example, the analysis unit analyzes population household characteristic data using a decision tree algorithm. This enables analysis according to the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to a generation AI, which can then apply different analysis algorithms.

[0046] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, improves the analysis algorithm based on past analysis results. For example, the analysis unit improves the analysis algorithm by referring to past success cases and failure cases. The analysis unit can also adjust the level of detail of the analysis by referring to past analysis results. For example, the analysis unit creates a detailed report or a summary report based on past analysis results. The analysis unit can also improve the accuracy of the analysis by utilizing past analysis results. For example, the analysis unit evaluates the error rate and reproducibility based on past analysis results and improves the accuracy of the analysis. This enables highly accurate analysis by utilizing past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into a generation AI, which can improve the accuracy of the analysis.

[0047] During analysis, the analysis unit can determine the priority of the analysis based on the time when the data was collected. The analysis unit, for example, prioritizes the analysis of the most recent data. For example, the analysis unit prioritizes analysis based on the most recent data. The analysis unit can also determine the priority of the analysis by referring to past data. For example, the analysis unit determines the priority of the analysis based on past data. The analysis unit can also adjust the level of detail of the analysis based on the time when the data was collected. For example, the analysis unit performs a detailed analysis of the most recent data and a simplified analysis of past data. This makes it possible to prioritize the analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can determine the priority of the analysis.

[0048] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly correlated data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of data with a low causal relationship. The analysis unit can also dynamically change the order of analysis based on the relevance of the data. For example, the analysis unit dynamically changes the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to a generation AI, and the generation AI can adjust the order of analysis.

[0049] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. For example, the analysis unit evaluates the user's level of expertise and uses detailed technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. For example, the analysis unit evaluates the user's level of expertise and explains the analysis results in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit adjusts the presentation method, such as graph display or text display, based on the user's level of expertise. This makes it possible to provide analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise into a generation AI, and the generation AI can adjust the use of technical terms in the analysis.

[0050] The proposal unit can adjust the level of detail of the proposal based on the importance of the data when making a proposal. The proposal unit, for example, makes a detailed proposal based on data with high importance. For example, the proposal unit makes a detailed proposal based on data with high business impact. The proposal unit can also make a simplified proposal based on data with low importance. For example, the proposal unit makes a simplified proposal when the reliability of the data is low. The proposal unit can also determine the priority of the proposal based on the importance of the data. For example, the proposal unit prioritizes proposing data with high importance and postpones proposing data with low importance. This enables proposals based on the importance of the data. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance of the data to a generation AI, and the generation AI can adjust the level of detail of the proposal.

[0051] The suggestion unit can apply different suggestion algorithms depending on the data category when making a suggestion. The suggestion unit applies a specific suggestion algorithm based on, for example, people flow data. For example, the suggestion unit makes suggestions based on people flow data using a clustering algorithm. The suggestion unit can also apply different suggestion algorithms based on geographical characteristic data. For example, the suggestion unit makes suggestions based on geographical characteristic data using regression analysis. The suggestion unit can also apply an appropriate suggestion algorithm based on population household characteristic data. For example, the suggestion unit makes suggestions based on population household characteristic data using a decision tree algorithm. This enables suggestions according to the data category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the data category to a generation AI, which can then apply different suggestion algorithms.

[0052] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results. The proposal unit, for example, improves the proposal algorithm based on past proposal results. For example, the proposal unit improves the proposal algorithm by referring to past success cases and failure cases. The proposal unit can also adjust the level of detail of the proposal by referring to past proposal results. For example, the proposal unit creates a detailed report or a summary report based on past proposal results. The proposal unit can also improve the accuracy of the proposal by utilizing past proposal results. For example, the proposal unit evaluates the error rate and reproducibility based on past proposal results and improves the accuracy of the proposal. This enables highly accurate proposals by utilizing past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal results into a generation AI, which can improve the accuracy of the proposal.

[0053] When making a proposal, the proposal unit can determine the priority of the proposal based on the time when the data was collected. For example, the proposal unit prioritizes proposals based on the latest data. For example, the proposal unit prioritizes proposals based on the latest data. The proposal unit can also determine the priority of the proposals by referring to past data. For example, the proposal unit determines the priority of the proposals based on past data. The proposal unit can also adjust the level of detail of the proposal based on the time when the data was collected. For example, the proposal unit makes detailed proposals for the latest data and simplified proposals for past data. This makes it possible to prioritize the proposals based on the time when the data was collected. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the time when the data was collected into a generation AI, and the generation AI can determine the priority of the proposals.

[0054] The suggestion unit can adjust the order of suggestions based on the relevance of data when making suggestions. The suggestion unit, for example, prioritizes suggestions based on highly relevant data. For example, the suggestion unit prioritizes suggestions based on highly correlated data. The suggestion unit can also postpone suggestions based on less relevant data. For example, the suggestion unit postpones suggestions based on less causal data. The suggestion unit can also dynamically change the order of suggestions based on the relevance of data. For example, the suggestion unit dynamically changes the order of suggestions based on the relevance of data. This makes it possible to adjust the order of suggestions based on the relevance of data. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of data to a generation AI, and the generation AI can adjust the order of suggestions.

[0055] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses detailed technical terminology. For example, the suggestion unit evaluates the user's level of expertise and uses detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can explain the proposal in simple terms. For example, the suggestion unit evaluates the user's level of expertise and explains the proposal in simple terms. Furthermore, the suggestion unit can adjust the presentation method of the proposal according to the user's level of expertise. For example, the suggestion unit adjusts the presentation method, such as graph display or text display, based on the user's level of expertise. This enables proposals to be made according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise into a generation AI, which can then adjust the use of technical terminology in the proposal.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can also dynamically adjust the range of data collection based on specific events or seasons. For example, the collection unit can strengthen the collection of people flow data around sporting or cultural events when they are held. The collection unit can also adjust the collection range taking into account people flow patterns in summer and winter. Furthermore, the collection unit can expand or shrink the collection range based on specific events or seasons. This allows data collection according to events and seasons.

[0058] The collection unit may also analyze social media activity and collect related data when collecting data. For example, the collection unit may collect people flow data in a specific area based on check-in information on social media. The collection unit may also analyze social media posts to collect geographical characteristic data for a specific area. Furthermore, the collection unit may collect related data based on the activities of friends on social media. This makes it possible to collect data based on social media activity.

[0059] When collecting data, the collection unit can also customize the collection method by reflecting past feedback. For example, the collection unit can improve the collection frequency or collection means based on past feedback. The collection unit can also adjust the collection range by reflecting past feedback. Furthermore, the collection unit can also customize the collection means by referring to past feedback. This makes it possible to collect data based on past feedback.

[0060] During analysis, the analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. Also, a simplified analysis can be performed on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This makes it possible to perform analysis according to the importance of the data.

[0061] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a clustering algorithm can be applied to people flow data for analysis. Regression analysis can also be applied to geographical characteristic data for analysis. Furthermore, a decision tree algorithm can be applied to population household characteristic data for analysis. This allows for analysis according to the data category.

[0062] When making a proposal, the proposal unit can also adjust the level of detail of the proposal based on the importance of the data. For example, a detailed proposal can be made based on data with high importance. Alternatively, a simplified proposal can be made based on data with low importance. Furthermore, the proposal unit can also determine the priority of the proposal based on the importance of the data. This makes it possible to make proposals according to the importance of the data.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects data. This data includes numerical data, text data, and image data. The collection unit can collect data through sensors or questionnaire surveys. For example, it collects people flow data using GPS data and Wi-Fi data, as well as topographical data, land use data, demographic data, household income data, and location and sales data of competing stores. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out using statistical analysis and machine learning algorithms. For example, the collected people flow data is analyzed to understand the flow of people in a specific area. Geographical characteristic data is also analyzed to evaluate the convenience of store locations, and population and household characteristic data is analyzed to identify target customer segments. Furthermore, data on competing stores is analyzed to understand the competitive situation. Step 3: The proposal department makes specific proposals based on the analysis results obtained by the analysis department. These proposals include pricing and marketing strategies, interior design and layout tailored to the target customer demographic, the introduction of an electronic payment system, and sales / revenue forecasts. For example, they may propose optimal pricing, campaigns offering discounts at specific times, promotions using social media, color designs and traffic flow designs tailored to the target customer demographic, the introduction of credit card and mobile payments, and sales / revenue forecasts based on past sales data and economic indicators.

[0065] (Example 2) The analysis service according to an embodiment of the present invention uses AI to perform multifaceted analysis and provide specific proposals for new restaurant opening plans. The analysis service uses AI to analyze "traffic flow data," "geographical characteristics," "population and household characteristics," and "competing stores" to provide specific proposals for pricing, customer acquisition strategies, store design, the introduction of electronic payment systems, and even sales / profit forecasts. For example, the analysis service performs a detailed analysis of traffic flow in a specific area to determine the number of people who will gather at certain times. The analysis service then analyzes the area's topography, transportation access, and the location of surrounding facilities to evaluate the convenience of a restaurant location. Furthermore, the analysis service analyzes the age group, family structure, income level, and other factors of residents in a specific area to identify target customer demographics. The analysis service also analyzes the number, business type, and price range of competing stores in a specific area to understand the competitive situation. Next, the analysis service proposes optimal pricing based on the collected data. Furthermore, as a customer acquisition strategy, the analysis service proposes campaigns offering discounts during specific times and promotions utilizing social media. Furthermore, the analysis service proposes store design, interior design, and layout tailored to the target customer demographic. Specific proposals for the introduction of electronic payment systems to enhance convenience are also made. Finally, specific sales and revenue forecasts are also made based on the collected data. This allows the analysis service to perform multifaceted analysis and make specific proposals when planning new restaurant openings. This increases the success rate of restaurant opening plans. For example, the analysis service can select the optimal restaurant location and propose pricing and customer attraction strategies tailored to the target customer demographic, thereby enabling efficient restaurant opening plans.

[0066] The analysis service according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data. Examples of the data include, but are not limited to, numerical data, text data, and image data. The collection unit collects data using, for example, a sensor. The collection unit can also collect data through a questionnaire survey. For example, the collection unit collects people flow data using GPS data. The collection unit can also collect people flow data using Wi-Fi data. The collection unit can also collect topographical data and land use data. For example, the collection unit collects topographical data to evaluate geographical characteristics. The collection unit can also collect demographical data and household income data. For example, the collection unit collects demographic data to understand the age groups and family structures of people living in a specific area. The collection unit can also collect location information and sales data of competing stores. For example, the collection unit collects location information to understand the number and business types of competing stores in a specific area. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes collected people flow data to understand the flow of people in a specific area. The analysis unit can also analyze collected geographical characteristic data to evaluate the convenience of a store location. The analysis unit can also analyze collected population household characteristic data to identify a target customer demographic. For example, the analysis unit can analyze the age group and family structure of people living in a specific area to identify a target customer demographic. The analysis unit can also analyze collected competing store data to understand the competitive situation. For example, the analysis unit can analyze the number and business types of competing stores in a specific area to understand the competitive situation. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The proposals include, for example, pricing and marketing strategies, but are not limited to these examples. For example, the proposal unit proposes optimal pricing based on the collected data. The proposal unit can also propose discount campaigns during specific time periods and promotions using social media.The proposal unit can also propose interior design and layout tailored to the target customer demographic. For example, the proposal unit can propose color designs and traffic flow designs tailored to the target customer demographic. The proposal unit can also propose the introduction of an electronic payment system. For example, the proposal unit can propose the introduction of credit card payments or mobile payments. The proposal unit can also predict sales / profits based on the collected data. For example, the proposal unit predicts sales / profits based on past sales data and economic indicators. This allows the analysis service according to the embodiment to consistently collect, analyze, and propose data.

[0067] The collection unit can collect data on people flow, geographic characteristics, population and household characteristics, and competing store data. The collection unit, for example, collects people flow data using GPS data. For example, the collection unit collects GPS data to understand the flow of people in a specific area. The collection unit can also collect people flow data using Wi-Fi data. For example, the collection unit collects Wi-Fi data around a commercial facility to identify peak and off-peak times. The collection unit can also collect topographical data and land use data. For example, the collection unit collects topographical data to evaluate the topography and transportation access of a specific area. The collection unit can also collect demographical data and household income data. For example, the collection unit collects demographical data to understand the age groups and family structures of people living in a specific area. The collection unit can also collect location information and sales data of competing stores. For example, the collection unit collects location information to understand the number and business types of competing stores in a specific area. This enables multifaceted data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input GPS data into a generation AI, which may then analyze the people flow data.

[0068] The analysis unit can analyze the collected data and select the optimal store location. For example, the analysis unit can analyze collected pedestrian flow data to understand the flow of people in a specific area. For example, the analysis unit can analyze pedestrian flow data in front of a station or around a commercial facility to identify peak and off-peak times. The analysis unit can also analyze collected geographical characteristic data to evaluate the convenience of a store location. For example, the analysis unit can analyze the topography and transportation access of a specific area to select a location with good accessibility and high customer attraction. The analysis unit can also analyze collected population and household characteristic data to identify a target customer demographic. For example, the analysis unit can analyze the age group and family structure of people living in a specific area to identify a target customer demographic. The analysis unit can also analyze collected competing store data to understand the competitive situation. For example, the analysis unit can analyze the number and business types of competing stores in a specific area to understand the competitive situation. This allows the optimal store location to be selected. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis department can input the collected data into the generation AI, which can then select the optimal location for opening a store.

[0069] The proposal unit can propose pricing, customer attraction strategies, store design, introduction of an electronic payment system, and sales / revenue forecasts. The proposal unit, for example, proposes optimal pricing based on collected data. For example, the proposal unit sets prices using the cost-plus method or the market price method. The proposal unit can also propose campaigns that offer discounts during specific time periods or promotions that utilize social media. For example, the proposal unit can propose campaigns that offer discounts during peak times. The proposal unit can also propose interior design and layout tailored to the target customer demographic. For example, the proposal unit can propose color designs and traffic flow designs tailored to the target customer demographic. The proposal unit can also propose the introduction of an electronic payment system. For example, the proposal unit can propose the introduction of credit card payments or mobile payments. The proposal unit can also make sales / revenue forecasts based on collected data. For example, the proposal unit makes sales / revenue forecasts based on past sales data and economic indicators. This enables specific proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal department can input the collected data into the generation AI, which can then propose pricing and customer acquisition strategies.

[0070] The proposal unit can propose setting a price range that matches the target customer segment. The proposal unit, for example, proposes setting a price range that matches the target customer segment. For example, the proposal unit sets a price range that matches a specific age group or income level. The proposal unit can also set a price range based on the purchase history of the target customer segment. For example, the proposal unit analyzes past purchase history and sets an optimal price range. This makes it possible to set prices that match the target customer segment. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the target customer segment into a generation AI, and the generation AI can set the price range.

[0071] The proposal unit can propose campaigns that offer discounts during specific time periods or promotions that utilize social media. For example, the proposal unit proposes campaigns that offer discounts during specific time periods. For example, the proposal unit proposes campaigns that offer discounts during peak times. The proposal unit can also propose promotions that utilize social media. For example, the proposal unit proposes promotions that utilize influencer marketing. The proposal unit can also attract customers through advertising campaigns. For example, the proposal unit proposes promotions that reach a specific target demographic using social media advertising. This enables an effective customer attraction strategy. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input collected data into a generation AI, which can then propose campaigns and promotions.

[0072] The proposal unit can propose interior design and layout tailored to the target customer base. The proposal unit, for example, proposes interior design and layout tailored to the target customer base. For example, the proposal unit proposes color designs and traffic flow designs tailored to the target customer base. The proposal unit can also propose interior designs based on a specific theme. For example, the proposal unit proposes interior designs based on a specific theme, such as a cafe style or a family restaurant style. The proposal unit can also propose store layout designs. For example, the proposal unit proposes efficient traffic flow designs and seating arrangements. This makes it possible to design a store tailored to the target customer base. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input collected data into a generation AI, which then proposes interior design and layout.

[0073] The proposal unit can propose the introduction of an electronic payment system. The proposal unit, for example, proposes the introduction of an electronic payment system. For example, the proposal unit proposes the introduction of credit card payment or mobile payment. The proposal unit can also propose the introduction of QR code payment or electronic money payment. For example, the proposal unit proposes the introduction of a specific electronic payment system and explains its convenience. The proposal unit can also propose costs and operation methods associated with the introduction of the electronic payment system. For example, the proposal unit proposes an optimal electronic payment system after taking into account introduction costs and operation costs. This makes it possible to introduce a highly convenient payment system. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input collected data into a generation AI, which then proposes the introduction of an electronic payment system.

[0074] The proposal unit can make sales / revenue predictions based on the collected data. The proposal unit, for example, makes sales / revenue predictions based on the collected data. For example, the proposal unit makes sales / revenue predictions based on past sales data and economic indicators. The proposal unit can also make sales / revenue predictions based on specific events or seasons. For example, the proposal unit makes sales predictions when a specific event is held. The proposal unit can also make revenue predictions taking into account seasonal sales patterns. For example, the proposal unit makes revenue predictions based on sales patterns in summer and winter. This makes it possible to make specific sales / revenue predictions. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the collected data into a generation AI, and the generation AI can make sales / revenue predictions.

[0075] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can adjust the timing of data collection to quickly collect necessary data. This enables data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI, and the generation AI can adjust the timing of data collection.

[0076] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, analyzes past data collection history and selects the optimal collection method. For example, the collection unit selects the most efficient collection method from the past data collection history. The collection unit can also concentrate data collection in a specific time period based on the past data collection history. The collection unit can also analyze past data collection history and improve the collection method. For example, the collection unit improves the collection frequency and collection means based on the past data collection history. This enables efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data collection history into a generation AI, which can select the optimal collection method.

[0077] The collection unit can adjust the collection range based on a specific event or season when collecting data. For example, when a specific event is held, the collection unit strengthens data collection around the event. For example, when a sporting event or cultural event is held, the collection unit collects people flow data around the event. The collection unit can also adjust the collection range taking into account seasonal people flow patterns. For example, the collection unit adjusts the collection range based on people flow patterns in summer and winter. The collection unit can also dynamically change the collection range based on a specific event or season. For example, the collection unit expands the collection range when a specific event is held. This enables data collection according to the event or season. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to a specific event or season into a generation AI, and the generation AI can adjust the collection range.

[0078] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit collects voice data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit collects text data using OCR technology. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. For example, the collection unit collects image data using image recognition technology. This enables data collection depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI, and the generation AI can select the optimal collection means.

[0079] The collection unit can estimate the user's emotions and prioritize data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting data of high importance. For example, the collection unit estimates the user's stress level using an emotion estimation algorithm and prioritizes collecting data of high importance. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. For example, the collection unit estimates the user's relaxation level using an emotion estimation algorithm and prioritizes collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. For example, the collection unit estimates the user's hurry using an emotion estimation algorithm and prioritizes collecting data that can be collected quickly. This enables prioritization of data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and determine the priority of the data to be collected by the generation AI.

[0080] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. The collection unit, for example, prioritizes collecting people flow data in a specific area. For example, the collection unit collects people flow data using GPS data for a specific area. The collection unit can also prioritize collecting highly relevant data based on geographical characteristics. For example, the collection unit collects topographical data and transportation access data for a specific area. The collection unit can also dynamically change the collection range by taking geographical location information into consideration. For example, the collection unit expands or reduces the collection range based on the geographical location information of a specific area. This enables data collection based on geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information to a generation AI, and the generation AI can prioritize collecting highly relevant data.

[0081] The collection unit can analyze social media activities and collect related data when collecting data. The collection unit collects related data based on, for example, check-in information on social media. For example, the collection unit analyzes check-in information on social media and collects people flow data in a specific area. The collection unit can also analyze posts on social media and collect related data. For example, the collection unit analyzes posts on social media and collects geographical characteristic data of a specific area. The collection unit can also collect related data by referring to the activities of friends on social media. For example, the collection unit collects people flow data in a specific area based on the check-in information of friends on social media. This makes it possible to collect data based on social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and use the generation AI to collect related data.

[0082] When collecting data, the collection unit can customize the collection method by reflecting past feedback. The collection unit, for example, improves the collection method based on past feedback. For example, the collection unit analyzes past feedback and improves the collection frequency or collection method. The collection unit can also adjust the collection range by reflecting past feedback. For example, the collection unit expands or reduces the collection range of a specific area based on past feedback. The collection unit can also customize the collection method by referring to past feedback. For example, the collection unit selects a collection method using voice recognition technology or OCR technology based on past feedback. This makes it possible to collect data based on past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into a generation AI and use the generation AI to customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, the analysis unit estimates the user's level of tension using an emotion estimation algorithm and provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit estimates the user's level of relaxation using an emotion estimation algorithm and provides a detailed analysis result. The analysis unit can also provide a summary analysis result if the user is in a hurry. For example, the analysis unit estimates the user's state of hurry using an emotion estimation algorithm and provides a summary analysis result. This makes it possible to provide an analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then adjust the way the analysis is presented.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high business impact. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis when the reliability of the data is low. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance and postpones analysis of data with low importance. This enables analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific algorithm to people flow data for analysis. For example, the analysis unit analyzes people flow data using a clustering algorithm. The analysis unit can also apply different algorithms to geographical characteristic data for analysis. For example, the analysis unit analyzes geographical characteristic data using regression analysis. The analysis unit can also analyze population household characteristic data by applying an appropriate algorithm. For example, the analysis unit analyzes population household characteristic data using a decision tree algorithm. This enables analysis according to the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to a generation AI, which can then apply different analysis algorithms.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, improves the analysis algorithm based on past analysis results. For example, the analysis unit improves the analysis algorithm by referring to past success cases and failure cases. The analysis unit can also adjust the level of detail of the analysis by referring to past analysis results. For example, the analysis unit creates a detailed report or a summary report based on past analysis results. The analysis unit can also improve the accuracy of the analysis by utilizing past analysis results. For example, the analysis unit evaluates the error rate and reproducibility based on past analysis results and improves the accuracy of the analysis. This enables highly accurate analysis by utilizing past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into a generation AI, which can improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit can estimate the user's hurry using an emotion estimation algorithm and provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can estimate the user's relaxation level using an emotion estimation algorithm and provide a detailed analysis result. The analysis unit can also provide an analysis result with visually stimulating effects if the user is excited. For example, the analysis unit can estimate the user's excitement level using an emotion estimation algorithm and provide an analysis result with visually stimulating effects. This makes it possible to adjust the length of the analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI, and the generation AI may adjust the length of the analysis.

[0088] During analysis, the analysis unit can determine the priority of the analysis based on the time when the data was collected. The analysis unit, for example, prioritizes the analysis of the most recent data. For example, the analysis unit prioritizes analysis based on the most recent data. The analysis unit can also determine the priority of the analysis by referring to past data. For example, the analysis unit determines the priority of the analysis based on past data. The analysis unit can also adjust the level of detail of the analysis based on the time when the data was collected. For example, the analysis unit performs a detailed analysis of the most recent data and a simplified analysis of past data. This makes it possible to prioritize the analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can determine the priority of the analysis.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly correlated data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of data with a low causal relationship. The analysis unit can also dynamically change the order of analysis based on the relevance of the data. For example, the analysis unit dynamically changes the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to a generation AI, and the generation AI can adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. For example, the analysis unit evaluates the user's level of expertise and uses detailed technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. For example, the analysis unit evaluates the user's level of expertise and explains the analysis results in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit adjusts the presentation method, such as graph display or text display, based on the user's level of expertise. This makes it possible to provide analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise into a generation AI, and the generation AI can adjust the use of technical terms in the analysis.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, when the user is nervous, the suggestion unit makes a simple, highly visible suggestion. For example, the suggestion unit estimates the user's level of tension using an emotion estimation algorithm and makes a simple, highly visible suggestion. The suggestion unit can also make a detailed suggestion when the user is relaxed. For example, the suggestion unit estimates the user's level of relaxation using an emotion estimation algorithm and makes a detailed suggestion. The suggestion unit can also make a suggestion that focuses on the main points when the user is in a hurry. For example, the suggestion unit estimates the user's state of hurry using an emotion estimation algorithm and makes a suggestion that focuses on the main points. This makes it possible to adjust the way suggestions are expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotional data into the generation AI, which can then adjust the way the suggestion is expressed.

[0092] The proposal unit can adjust the level of detail of the proposal based on the importance of the data when making a proposal. The proposal unit, for example, makes a detailed proposal based on data with high importance. For example, the proposal unit makes a detailed proposal based on data with high business impact. The proposal unit can also make a simplified proposal based on data with low importance. For example, the proposal unit makes a simplified proposal when the reliability of the data is low. The proposal unit can also determine the priority of the proposal based on the importance of the data. For example, the proposal unit prioritizes proposing data with high importance and postpones proposing data with low importance. This enables proposals based on the importance of the data. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance of the data to a generation AI, and the generation AI can adjust the level of detail of the proposal.

[0093] The suggestion unit can apply different suggestion algorithms depending on the data category when making a suggestion. The suggestion unit applies a specific suggestion algorithm based on, for example, people flow data. For example, the suggestion unit makes suggestions based on people flow data using a clustering algorithm. The suggestion unit can also apply different suggestion algorithms based on geographical characteristic data. For example, the suggestion unit makes suggestions based on geographical characteristic data using regression analysis. The suggestion unit can also apply an appropriate suggestion algorithm based on population household characteristic data. For example, the suggestion unit makes suggestions based on population household characteristic data using a decision tree algorithm. This enables suggestions according to the data category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the data category to a generation AI, which can then apply different suggestion algorithms.

[0094] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results. The proposal unit, for example, improves the proposal algorithm based on past proposal results. For example, the proposal unit improves the proposal algorithm by referring to past success cases and failure cases. The proposal unit can also adjust the level of detail of the proposal by referring to past proposal results. For example, the proposal unit creates a detailed report or a summary report based on past proposal results. The proposal unit can also improve the accuracy of the proposal by utilizing past proposal results. For example, the proposal unit evaluates the error rate and reproducibility based on past proposal results and improves the accuracy of the proposal. This enables highly accurate proposals by utilizing past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal results into a generation AI, which can improve the accuracy of the proposal.

[0095] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can make a short and to-the-point suggestion. For example, the suggestion unit can estimate the user's hurry using an emotion estimation algorithm and make a short and to-the-point suggestion. The suggestion unit can also make a detailed suggestion if the user is relaxed. For example, the suggestion unit can estimate the user's relaxation level using an emotion estimation algorithm and make a detailed suggestion. The suggestion unit can also make a suggestion with a visually stimulating effect if the user is excited. For example, the suggestion unit can estimate the user's excitement level using an emotion estimation algorithm and make a suggestion with a visually stimulating effect. This makes it possible to adjust the length of the suggestion according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotional data into the generation AI, and the generation AI can adjust the length of the suggestion.

[0096] When making a proposal, the proposal unit can determine the priority of the proposal based on the time when the data was collected. For example, the proposal unit prioritizes proposals based on the latest data. For example, the proposal unit prioritizes proposals based on the latest data. The proposal unit can also determine the priority of the proposals by referring to past data. For example, the proposal unit determines the priority of the proposals based on past data. The proposal unit can also adjust the level of detail of the proposal based on the time when the data was collected. For example, the proposal unit makes detailed proposals for the latest data and simplified proposals for past data. This makes it possible to prioritize the proposals based on the time when the data was collected. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the time when the data was collected into a generation AI, and the generation AI can determine the priority of the proposals.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of data when making suggestions. The suggestion unit, for example, prioritizes suggestions based on highly relevant data. For example, the suggestion unit prioritizes suggestions based on highly correlated data. The suggestion unit can also postpone suggestions based on less relevant data. For example, the suggestion unit postpones suggestions based on less causal data. The suggestion unit can also dynamically change the order of suggestions based on the relevance of data. For example, the suggestion unit dynamically changes the order of suggestions based on the relevance of data. This makes it possible to adjust the order of suggestions based on the relevance of data. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the relevance of data to a generation AI, and the generation AI can adjust the order of suggestions.

[0098] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses detailed technical terminology. For example, the suggestion unit evaluates the user's level of expertise and uses detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can explain the proposal in simple terms. For example, the suggestion unit evaluates the user's level of expertise and explains the proposal in simple terms. Furthermore, the suggestion unit can adjust the presentation method of the proposal according to the user's level of expertise. For example, the suggestion unit adjusts the presentation method, such as graph display or text display, based on the user's level of expertise. This enables proposals to be made according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise into a generation AI, which can then adjust the use of technical terminology in the proposal. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit and the suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data using the camera 42 or the microphone 38B of the smart device 14. For example, the collection unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and estimate the user's emotions. For example, the suggestion unit can suggest pricing and marketing strategies based on the analysis results obtained by the specific processing unit 290 of the data processing device 12. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can adjust the timing of data collection. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit and the suggestion unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data using the camera 42 or the microphone 238 of the smart glasses 214. For example, the collection unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and estimate the user's emotions. For example, the suggestion unit can suggest pricing and marketing strategies based on the analysis results obtained by the specific processing unit 290 of the data processing device 12. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can adjust the timing of data collection. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit and the suggestion unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect data using the camera 42 or the microphone 238 of the headset type terminal 314. For example, the collection unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and estimate the user's emotions. For example, the suggestion unit can suggest pricing and marketing strategies based on the analysis results obtained by the specific processing unit 290 of the data processing device 12. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can adjust the timing of data collection. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit and the suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data using the camera 42 or the microphone 238 of the robot 414. For example, the collection unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and estimate the user's emotions. For example, the suggestion unit can suggest pricing and marketing strategies based on the analysis results obtained by the specific processing unit 290 of the data processing device 12. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can adjust the timing of data collection.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The collection unit can also estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can adjust the timing of data collection to quickly collect necessary data. This enables flexible data collection according to the user's emotions.

[0101] The collection unit can also dynamically adjust the range of data collection based on specific events or seasons. For example, the collection unit can strengthen the collection of people flow data around sporting or cultural events when they are held. The collection unit can also adjust the collection range taking into account people flow patterns in summer and winter. Furthermore, the collection unit can expand or shrink the collection range based on specific events or seasons. This allows data collection according to events and seasons.

[0102] The analysis unit can also estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This makes it possible to provide analysis results that correspond to the user's emotions.

[0103] The suggestion unit can also estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is nervous, the suggestion unit can make simple, highly visible suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions that focus on the main points. This makes it possible to adjust the way suggestions are expressed according to the user's emotions.

[0104] The suggestion unit can also estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can make short and to-the-point suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the user is excited, the suggestion unit can make suggestions with visually stimulating effects. This makes it possible to adjust the length of suggestions according to the user's emotions.

[0105] The collection unit may also analyze social media activity and collect related data when collecting data. For example, the collection unit may collect people flow data in a specific area based on check-in information on social media. The collection unit may also analyze social media posts to collect geographical characteristic data for a specific area. Furthermore, the collection unit may collect related data based on the activities of friends on social media. This makes it possible to collect data based on social media activity.

[0106] When collecting data, the collection unit can also customize the collection method by reflecting past feedback. For example, the collection unit can improve the collection frequency or collection means based on past feedback. The collection unit can also adjust the collection range by reflecting past feedback. Furthermore, the collection unit can also customize the collection means by referring to past feedback. This makes it possible to collect data based on past feedback.

[0107] During analysis, the analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. Also, a simplified analysis can be performed on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This makes it possible to perform analysis according to the importance of the data.

[0108] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a clustering algorithm can be applied to people flow data for analysis. Regression analysis can also be applied to geographical characteristic data for analysis. Furthermore, a decision tree algorithm can be applied to population household characteristic data for analysis. This allows for analysis according to the data category.

[0109] When making a proposal, the proposal unit can also adjust the level of detail of the proposal based on the importance of the data. For example, a detailed proposal can be made based on data with high importance. Alternatively, a simplified proposal can be made based on data with low importance. Furthermore, the proposal unit can also determine the priority of the proposal based on the importance of the data. This makes it possible to make proposals according to the importance of the data.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The collection unit collects data. This data includes numerical data, text data, and image data. The collection unit can collect data through sensors or questionnaire surveys. For example, it collects people flow data using GPS data and Wi-Fi data, as well as topographical data, land use data, demographic data, household income data, and location and sales data of competing stores. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out using statistical analysis and machine learning algorithms. For example, the collected people flow data is analyzed to understand the flow of people in a specific area. Geographical characteristic data is also analyzed to evaluate the convenience of store locations, and population and household characteristic data is analyzed to identify target customer segments. Furthermore, data on competing stores is analyzed to understand the competitive situation. Step 3: The proposal department makes specific proposals based on the analysis results obtained by the analysis department. These proposals include pricing and marketing strategies, interior design and layout tailored to the target customer demographic, the introduction of an electronic payment system, and sales / revenue forecasts. For example, they may propose optimal pricing, campaigns offering discounts at specific times, promotions using social media, color designs and traffic flow designs tailored to the target customer demographic, the introduction of credit card and mobile payments, and sales / revenue forecasts based on past sales data and economic indicators.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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 AI 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.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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 AI 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0160] 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.

[0161] 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.

[0162] 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 AI 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.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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, in order to avoid confusion and to 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.

[0182] 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.

[0183] [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that makes specific proposals based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect data on foot traffic, geographical characteristics, population and household characteristics, and competitor store data 2. The system of claim 1.

3. The analysis unit Analyze the collected data and select the optimal location for the store.

2. The system of claim 1.

4. The proposal unit Propose pricing, customer acquisition strategies, store design, electronic payment system implementation, and sales / revenue forecasts 2. The system of claim 1.

5. The proposal unit Propose price ranges tailored to your target customer base 2. The system of claim 1.

6. The proposal unit Propose campaigns offering discounts during specific times and promotions using social media 2. The system of claim 1.

7. The proposal unit Propose interior and layout ideas to suit your target customer base 2. The system of claim 1.

8. The proposal unit Proposing the introduction of an electronic payment system 2. The system of claim 1.

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