system

Abstract

We provide the system. [Solution] A means for users to input exterior photos of the store, interior photos, sales data, and information about the store manager's personality, A means for receiving and storing the aforementioned exterior photographs, interior photographs, sales data, and store manager personality information, A means for analyzing the aforementioned stored data and evaluating the exterior and interior of the store using an image processing algorithm, A means for analyzing the aforementioned sales data and evaluating the sales trends and patterns of the stores, A means for analyzing the personality information of the store manager and evaluating the personality traits of the store manager, Based on these evaluation results, a total score is calculated and the results are provided in a report format. A system that includes means for providing feedback to the user on the results of the aforementioned report.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Small-scale stores such as restaurants and retail stores have problems such as sales improvement and a shortage of personnel. Conventional consulting services are expensive and not easily accessible to small-scale stores. In addition, there have been few opportunities to obtain specific improvement proposals based on detailed information such as the characteristics of each store and the personality of the store manager. The present invention aims to solve these problems and provide a system for simultaneously achieving sales improvement and personnel matching in stores.

Means for Solving the Problems

[0005] The system according to the present invention includes means for a user to input exterior photos of a store, interior photos, sales data, and information about the store manager's personality; means for receiving the exterior photos, interior photos, sales data, and store manager's personality information and storing the data; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the store manager's personality information and evaluating the store manager's personality traits; means for calculating an overall score based on these evaluation results and providing the results in a report format; and means for providing feedback on the report results to the user.

[0006] Furthermore, the system includes means for registering the aforementioned overall score and evaluation results on a consumer-facing website and publishing information on highly-rated stores. It also includes means for comparing the personality information of store managers entered by users with the personality assessment results of part-time job applicants to evaluate compatibility and calculate the degree of matching, as well as means for recommending appropriate stores to part-time job applicants based on the matching results. This makes it possible to provide low-cost and effective diagnosis and advice, thereby improving sales and alleviating labor shortages in small businesses.

[0007] "User" refers to the individuals or entities on the store side that use the system, such as business owners and managers.

[0008] "Exterior photos" refer to image data taken of the exterior of a store.

[0009] "Interior photos" refer to image data that captures the condition of the inside of a store.

[0010] "Sales data" refers to numerical information that shows a store's past sales performance, and includes specific data such as total sales, monthly sales, and the number of customers.

[0011] "Store manager personality information" refers to questionnaire data used to evaluate the store manager's personality traits, including items such as communication skills, leadership, and stress management ability.

[0012] "Data storage means" refers to a part of the system that stores photos, sales data, and personality information received from users.

[0013] An "image processing algorithm" refers to a computational method used to analyze photographic data and evaluate the exterior and interior appearance of a store.

[0014] "Sales data analysis method" refers to a part of a system that evaluates sales trends and patterns based on stored sales data.

[0015] "Store manager personality analysis method" refers to a part of a system that evaluates the personality traits of store managers based on stored personality information of store managers.

[0016] "Total score" refers to the overall evaluation score calculated based on the results of each evaluation item.

[0017] "Report format" refers to the format of a document or digital file that summarizes analysis results and evaluations.

[0018] A "consumer-oriented website" refers to a website that publishes evaluation results and information on highly-rated stores, making it accessible to consumers.

[0019] "Personality assessment results" refers to the results of a personality assessment taken by a part-time job applicant, and includes data that indicates specific personality traits.

[0020] "Compatibility evaluation method" refers to a part of a system that compares the personality information of the store manager and the part-time job applicant to evaluate their compatibility.

[0021] "Matching score" refers to an indicator that shows how well the store manager and the part-time job applicant are compatible in terms of personality.

[0022] A "suitable store" refers to a store that, based on the results of a compatibility assessment, is deemed suitable for the applicant. [Brief explanation of the drawing]

[0023] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0024] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0025] First, let's explain the terminology used in the following explanation.

[0026] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0027] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0028] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0029] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0031] [First Embodiment]

[0032] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0033] As shown in Figure 1, the 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.

[0034] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0036] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0039] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0043] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0044] This invention is a system for comprehensive diagnosis and consulting for small businesses. Specific embodiments are described below.

[0045] System Configuration

[0046] This system mainly consists of user terminals, servers, and consumer terminals.

[0047] 1. User terminal

[0048] The user terminal is a device used by the store's owner or manager (e.g., a PC, tablet, or smartphone), and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[0049] 2. Server

[0050] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating the overall score to providing feedback.

[0051] 3. Consumer devices

[0052] Consumer terminals are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the rating results are provided via dedicated websites or applications.

[0053] Program processing

[0054] 1. User data entry

[0055] The user logs into the system and enters basic information (store name, address, contact information, etc.).

[0056] Take photos of the store's exterior and interior and upload them to the system.

[0057] Enter sales data for the past few months and send it to the system.

[0058] Answer the questionnaire regarding the store manager's personality and submit the results.

[0059] 2. Server Data Analysis

[0060] The server stores photos, sales data, and personality assessment data received from users.

[0061] Based on saved exterior and interior photographs, image analysis algorithms are used to evaluate the store's exterior and interior. For example, elements such as lighting, cleanliness, and layout are analyzed.

[0062] Based on sales data, we evaluate sales trends, peak hours, and periods of stagnation. For example, we analyze monthly sales trends and fluctuations in customer numbers.

[0063] Based on personality questionnaire data, the store manager's personality traits will be evaluated. For example, communication skills, leadership abilities, and stress management skills will be analyzed.

[0064] 3. Calculation of overall score and feedback

[0065] The server calculates an overall score based on the image analysis results, sales analysis results, and store manager personality analysis results, and determines the evaluation for each item.

[0066] The system generates a report containing the calculated total score and detailed evaluation results. For example, it might show: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Total score 30 points.

[0067] Reports and improvement suggestions are sent to user terminals to provide feedback to the store. For example, specific advice such as "making the interior brighter and changing the seating layout will improve customer satisfaction" is provided.

[0068] 4. Launch and customer acquisition support

[0069] The server registers information about highly-rated restaurants on consumer-facing websites, making it accessible to consumers. For example, it displays rankings and reviews for "restaurants with high customer satisfaction."

[0070] Consumers can view information about highly-rated stores through consumer-facing terminals and decide whether to visit them.

[0071] 5. Talent matching

[0072] Applicants for part-time work take a personality test and submit the results to the system.

[0073] The server compares the store manager's personality information with the personality assessment results of the part-time job applicant to evaluate compatibility. For example, it evaluates compatibility in areas such as communication skills and stress management abilities.

[0074] Based on the evaluation results, the system recommends suitable stores to job applicants. For example, an evaluation such as "You have good personality chemistry with the store manager at this location" might be presented.

[0075] The above describes a specific embodiment of the present invention. This makes it possible to simultaneously improve store sales and alleviate labor shortages.

[0076] The following describes the processing flow.

[0077] Step 1:

[0078] The user logs into the system. They enter their username and password to be authenticated. Upon successful authentication, they are redirected to the dashboard screen.

[0079] Step 2:

[0080] The user enters basic information. They enter basic information such as the store name, address, and contact information into the form and submit it.

[0081] Step 3:

[0082] Users upload photos of the store's exterior. They take photos of the exterior and either drag and drop them into the designated area of ​​the system or select the file to upload.

[0083] Step 4:

[0084] Users upload interior photos of their stores. Similarly, they take interior photos and upload them to the system.

[0085] Step 5:

[0086] Users input sales data for the past few months. They enter numerical data such as total sales, monthly sales, and customer numbers, and then submit it.

[0087] Step 6:

[0088] The user answers a personality questionnaire for the store manager. They click on the appropriate option for each questionnaire item, and after answering all questions, they click the submit button.

[0089] Step 7:

[0090] The server stores exterior photos, interior photos, sales data, and personality assessment data received from the user. The data is then stored in a designated database.

[0091] Step 8:

[0092] The server processes the received exterior photos using an image analysis algorithm. For example, it evaluates the lighting conditions, cleanliness, and design elements of the photos.

[0093] Step 9:

[0094] The server processes the received interior photos using an image analysis algorithm. This algorithm evaluates the store's layout, decoration, cleanliness, and lighting.

[0095] Step 10:

[0096] The server analyzes the stored sales data. It identifies and evaluates patterns such as sales trends, peak hours, and periods of stagnation.

[0097] Step 11:

[0098] The server analyzes the personality questionnaire data of the store manager, evaluating personality traits such as communication skills, leadership, and stress management abilities.

[0099] Step 12:

[0100] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results. The score is calculated for each item, and the final overall score is determined.

[0101] Step 13:

[0102] The server generates a comprehensive evaluation report. It compiles detailed information and an overall score for exterior, interior, sales, and manager personality evaluations into a report format.

[0103] Step 14:

[0104] The server sends an evaluation report to the user's terminal. The user can view the evaluation results and improvement advice on the dashboard.

[0105] Step 15:

[0106] The server registers information about highly-rated stores on a consumer-facing website. Based on the evaluation results, rankings and reviews of highly-rated stores are published.

[0107] Step 16:

[0108] Consumers browse consumer-facing websites. They check information on highly-rated stores and select a store to visit.

[0109] Step 17:

[0110] Applicants for part-time work will take a free personality test. They will answer a personality test form and submit the results to the system.

[0111] Step 18:

[0112] The server compares the store manager's personality information with the personality assessment results of the part-time job applicants. An AI algorithm is used to evaluate compatibility.

[0113] Step 19:

[0114] The server recommends suitable stores to job applicants based on compatibility evaluation results. It generates a list of compatible stores and presents it to the applicants.

[0115] Step 20:

[0116] Applicants for part-time jobs review the information of the stores recommended to them. They view the reasons for the recommendation and detailed information, and then decide which store to apply to.

[0117] (Example 1)

[0118] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0119] In managing small businesses, there is a lack of understanding of the quality of the store's exterior and interior, the analysis of sales trends, and the personality traits of the store manager. Therefore, it is necessary to comprehensively evaluate this information and clearly identify areas for improvement. Furthermore, there is a need for means to communicate the store's appeal to consumers and for effective methods to assess the compatibility between the store and prospective part-time workers.

[0120] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0121] In this invention, the server includes means for users to input images of the store's exterior, interior, sales information, and information regarding the store manager's characteristics; means for receiving the exterior, interior, sales information, and store manager characteristics information and storing the data; and means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm. This makes it possible to comprehensively evaluate the quality of the store's exterior and interior, sales trends, and the store manager's personality traits, and to clearly suggest areas for improvement.

[0122] A "user" is a store owner or manager who uses the system to evaluate stores.

[0123] "Store exterior image" refers to an image file that shows the condition of the store's exterior.

[0124] "Store interior images" are image files that show the condition of the store's interior.

[0125] "Sales information" refers to information that shows past sales data for a store.

[0126] "Store manager's characteristic information" refers to data that includes information about the personality and characteristics of the store manager, who is the person in charge of the store.

[0127] "Means of saving data" refers to methods of storing received data in a storage device such as a database.

[0128] An "image processing algorithm" is a set of computational procedures used to analyze image data and extract specific features or patterns.

[0129] A "sales trend" is a pattern that shows the tendency or fluctuation of sales over a certain period of time.

[0130] "Store manager characteristics" refers to personality traits such as the store manager's leadership skills, communication abilities, and stress tolerance.

[0131] The "overall score" is a score calculated by integrating evaluation results such as the store's exterior, interior, sales trends, and the store manager's characteristics.

[0132] A "report format" is a format used to notify users of evaluation results or analysis results.

[0133] A "consumer-facing website" is a website that consumers can access to view store reviews and ratings.

[0134] "Part-time job applicants" refers to individuals who wish to work at a specific store.

[0135] "Compatibility" is an indicator of the degree of personality compatibility between the store manager and the part-time job applicant.

[0136] "Fit" is a score that indicates how well the personalities and characteristics of the store manager and the part-time job applicant are compatible.

[0137] "Recommendation methods" refer to methods of suggesting the most suitable store to part-time job applicants based on evaluation results.

[0138] This invention is a system for comprehensive diagnosis and consulting of small businesses. The system consists of a user terminal, a server, and a consumer terminal. A detailed embodiment of the system is described below.

[0139] User terminal

[0140] The user terminal is a device (such as a PC, tablet, or smartphone) used by the store's owner or manager, and provides an interface for inputting exterior and interior images of the store, sales information, and the store manager's characteristics.

[0141] server

[0142] The server is the central component of the system, receiving, storing, and analyzing data from users. The server contains a high-performance computer, a database, image analysis algorithms (such as OpenCV), and data analysis engines (such as Python's Pandas or Scikit-learn).

[0143] Consumer devices

[0144] Consumer terminals are devices (such as personal computers and smartphones) used to view information on highly-rated stores, and the evaluation results are provided via dedicated websites or applications.

[0145] Specific processing of the program

[0146] Basic information (store name, location, etc.), images, sales data, and personality information entered from the user's terminal are first sent to the server. The server receives this data and stores it in a database. Specific software used at this stage includes database management systems such as MySQL and PostgreSQL.

[0147] Image analysis

[0148] The server first analyzes exterior and interior images. Using OpenCV, it extracts and evaluates elements such as lighting, cleanliness, and layout from the images. For example, it analyzes the brightness distribution to evaluate the lighting conditions of a store.

[0149] Sales data analysis

[0150] Next, we will analyze the sales data. Using the Python Pandas library, we will evaluate sales trends, peak times, and periods of stagnation. For example, we will graph and analyze monthly sales trends.

[0151] Personality data analysis

[0152] Furthermore, the store manager's characteristics will be evaluated based on personality questionnaire data. Scikit-learn will be used to evaluate characteristics such as communication skills, leadership, and stress management ability. For example, the scores for each questionnaire item will be added together to calculate an overall score.

[0153] Overall score and feedback

[0154] The server calculates an overall score based on image analysis results, sales analysis results, and store manager personality analysis results, and generates a detailed evaluation result and improvement advice in report format. For example, it may include specific advice such as, "We recommend adding more lighting to improve the brightness of the store." The generated report is sent to the user's terminal.

[0155] Public release and customer acquisition support

[0156] The server registers information on highly-rated stores on a consumer-facing website and publishes details about these stores (e.g., store name, rating score, areas for improvement, etc.). Consumers can view this information through consumer-facing devices and decide whether to visit a store.

[0157] Talent matching

[0158] Applicants for part-time jobs take a personality test and submit the results to the system. The server compares the store manager's characteristics with the applicant's personality test results and calculates a compatibility score. Based on this, it recommends a suitable store to the applicant.

[0159] Examples of specific actions and prompt statements

[0160] For example, if a cafe owner were to use the system, they would enter the data following these steps:

[0161] 1. Log in to the system and enter your basic information (cafe name, address, contact information).

[0162] 2. Take photos of the store's exterior and interior and upload them to the system.

[0163] 3. Enter sales data for the past six months and submit it to the system.

[0164] 4. Answer the questionnaire regarding your (the store manager's) personality and submit the results.

[0165] An example of a prompt message is, "Analyze the exterior photos of this cafe and evaluate its cleanliness and layout."

[0166] The above describes a specific embodiment of this invention. By utilizing this system, it is possible to simultaneously improve store sales and alleviate labor shortages.

[0167] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0168] Step 1:

[0169] The user logs into the system. The user is required to enter their email address and password. The server receives the authentication information and verifies it against the user information in the database. Upon successful authentication, the user is redirected to the dashboard. The output displays the user's dashboard screen.

[0170] Step 2:

[0171] The user enters the store's basic information (store name, address, contact information). The entered data is sent to the system via an HTML form. The server saves the received data to a database. As output, the basic information is recorded in the database.

[0172] Step 3:

[0173] Users upload images of the store's exterior and interior. The input must be image files in JPEG or PNG format. The server receives the images and stores them in a database. The output is the image files themselves, recorded in the database.

[0174] Step 4:

[0175] The user inputs sales information for the past few months. The input must be in a format such as a CSV file. The server saves the received sales data to a database. The output is the sales data recorded in the database.

[0176] Step 5:

[0177] Users answer a questionnaire about the characteristics of the store manager. The input is in the form of selecting answers from multiple options. The server saves the received questionnaire results to a database. The output is the questionnaire data recorded in the database.

[0178] Step 6:

[0179] The server analyzes stored exterior and interior images. It requires received image files in JPEG or PNG format as input. The server uses OpenCV to extract elements such as lighting, cleanliness, and layout from the images. For example, it analyzes the brightness distribution to evaluate the lighting conditions of a store. The output is an evaluation score.

[0180] Step 7:

[0181] The server analyzes the stored sales data. The input requires sales information in CSV file format. The server uses the Python Pandas library to evaluate sales trends, peak times, and periods of low sales. For example, it can graph and analyze monthly sales trends. The output is a sales evaluation score.

[0182] Step 8:

[0183] The server analyzes stored data on the store manager's characteristics. The input requires survey results data. The server uses Scikit-learn to evaluate characteristics such as communication skills, leadership, and stress management. For example, it calculates an overall score by summing the scores for each characteristic. The output is the characteristic evaluation score.

[0184] Step 9:

[0185] The server calculates an overall score based on image analysis results, sales analysis results, and store manager characteristics results. The input requires evaluation scores obtained from each analysis. The server combines these scores to calculate the overall score. The output generates an overall evaluation report.

[0186] Step 10:

[0187] The server sends the overall evaluation report to the user's terminal. The generated overall evaluation report is required as input. The server sends the report to the user's terminal, allowing the user to view it. The output allows the user to review the evaluation results and improvement advice.

[0188] Step 11:

[0189] The server registers information about highly-rated stores on a consumer-facing website. The server requires data from the overall rating report as input. The server then publishes details about the highly-rated stores (e.g., store name, rating score, areas for improvement, etc.) on the website. As output, consumers can view the information about the highly-rated stores.

[0190] Step 12:

[0191] Applicants for part-time work take a personality assessment and submit the results to the system. The input requires answers to multiple questions in a questionnaire format. The server saves the received assessment results to a database. The output is the personality assessment data, which is then recorded in the database.

[0192] Step 13:

[0193] The server compares the store manager's characteristics with the personality assessment results of part-time job applicants and calculates a compatibility score. The required inputs are the store manager's characteristics data and the applicant's personality assessment results. The server calculates the compatibility score based on this data. The output is a compatibility evaluation report.

[0194] Step 14:

[0195] The server sends a compatibility assessment report to job applicants. The generated compatibility assessment report is required as input. The server sends the report to the applicant and recommends suitable stores. The output allows the applicant to review the appropriate stores.

[0196] (Application Example 1)

[0197] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0198] Traditional small-scale store management support systems typically focused on individual evaluations of exterior and interior design, sales trends, and store managers' personalities, making it difficult to provide comprehensive assessments or specific improvement advice. Furthermore, there was a lack of effective means to publicize highly-rated stores to consumers to support customer acquisition, and to evaluate and match employees with store managers based on their compatibility. As a result, store operations were often inefficient, leading to missed sales opportunities.

[0199] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0200] In this invention, the server includes means for users to input exterior and interior photos of a store, sales data, and information about the store manager's personality; means for receiving and storing the exterior and interior photos, sales data, and information about the store manager's personality; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the store manager's personality information and evaluating the store manager's personality traits; means for calculating an overall score based on these evaluation results and providing the results in a report format; means for providing feedback on the report results to the user; means for using an image processing algorithm to analyze the exterior and interior photos; means for evaluating sales trends and peak hours based on the analysis of the sales data; and means for evaluating the store manager's leadership traits based on the personality information. This makes it possible to provide a comprehensive evaluation of the store and specific improvement advice. Furthermore, it is possible to publish information about highly-rated stores on consumer-facing websites to support customer acquisition. In addition, it is possible to achieve efficient personnel allocation by evaluating the personality compatibility between the store manager and part-time job applicants and performing appropriate matching.

[0201] A "user terminal" is a device used by store owners and managers, providing an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[0202] "Photographs" refer to image data of the store's exterior and interior, which are taken and uploaded to the system.

[0203] "Sales data" refers to data that shows a store's past sales performance and is used to analyze sales trends and patterns.

[0204] "Store manager personality information" refers to data that indicates the store manager's personality traits, and is provided as a result of a personality assessment questionnaire.

[0205] A "server" is the central component that stores, analyzes, and generates evaluation results from data received from users.

[0206] An "image processing algorithm" is a technical method used to analyze photographic data and evaluate its external and internal appearance.

[0207] "Sales trends" refer to analytical results that show the trends and patterns of store sales based on sales data.

[0208] "Personality traits" are data that represent the characteristics of an individual's personality, and include leadership, stress coping ability, communication skills, and so on.

[0209] The "overall score" is the result of comprehensively calculating the evaluation of the store's exterior, interior, sales, and manager's personality, and is provided in report format.

[0210] A "report" is a document that summarizes the analysis results and improvement advice, and is provided as feedback to the user.

[0211] A "consumer-oriented website" is an online platform where consumers can view information about highly-rated stores.

[0212] "Matching score" is the result of evaluating the compatibility between the personality traits of the store manager and the personality traits of the part-time job applicant.

[0213] This invention is a system for comprehensive diagnosis and consulting for small businesses. The detailed implementation method is described below.

[0214] System Configuration

[0215] This system consists of user terminals, servers, and consumer terminals.

[0216] User terminal

[0217] User terminals are used by store owners and managers and include devices such as personal computers, tablets, and smartphones. User terminals have the following functions:

[0218] 1. Take photos of the store's exterior and interior and upload them to the system.

[0219] 2. Enter sales data for the past few months and submit it to the system.

[0220] 3. Answer the questionnaire regarding the store manager's personality and submit the results.

[0221] server

[0222] The server is the central component for receiving, storing, and analyzing data sent from user terminals. Specifically, it has the following functions:

[0223] 1. Use image processing algorithms to evaluate the exterior and interior of the store.

[0224] 2. Analyze sales data to evaluate sales trends, peak times, and periods of stagnation.

[0225] 3. Evaluate the store manager's personality traits based on personality questionnaire data.

[0226] 4. Calculate the overall score and generate the evaluation results in report format.

[0227] Consumer devices

[0228] Consumer devices are devices that allow consumers to view information on highly-rated stores, and include personal computers and smartphones. This allows consumers to view rating results via dedicated websites or applications.

[0229] Hardware and software to be used

[0230] This system uses the following hardware and software:

[0231] Hardware: Smartphones, tablets, PCs (Windows, Mac), servers (physical servers or cloud servers)

[0232] Software: Python, OpenCV (image analysis library), Pandas (data analysis library)

[0233] Data adjustment and calculation flow

[0234] The user terminal takes photos of the store's exterior and interior, inputs sales data, and collects data about the store manager's personality. This data is then sent to the server.

[0235] The server performs the following actions:

[0236] 1. Image Analysis: Using OpenCV, analyze exterior and interior photos of the store. The analysis results will be used to evaluate factors such as lighting brightness, cleanliness, and layout appropriateness.

[0237] 2. Sales Analysis: Use Pandas to analyze sales data and evaluate sales trends and quality. For example, statistically analyze monthly sales trends and fluctuations in customer numbers.

[0238] 3. Personality Assessment: Based on the store manager's personality assessment data, a generative AI model will be used to evaluate leadership, communication skills, stress management abilities, and other aspects.

[0239] Examples

[0240] The following are examples of how this system can be used:

[0241] 1. The store owner takes exterior and interior photos with their smartphone and uploads these photos to the server via the application.

[0242] 2. The manager enters sales data for the past three months in a table format and sends it to the system.

[0243] 3. The manager completes a personality assessment questionnaire for the store manager via the application and submits the results.

[0244] Specific input examples:

[0245] Store name: ABC Store

[0246] Location: Shibuya Ward, Tokyo

[0247] Contact number: 03-1234-5678

[0248] Exterior photo: exterior.jpg

[0249] Interior photo: interior.jpg

[0250] Sales data:

[0251] Monthly sales

[0252] January, 100000

[0253] February, 120000

[0254] Personality test results:

[0255] Communication: 8

[0256] Leadership: 7

[0257] Once the above data is sent to the server, the server performs the analysis described above and provides the management with an overall evaluation and improvement advice. In addition, by being registered as a highly-rated store, the store will also receive support for attracting customers through consumer terminals.

[0258] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0259] Step 1:

[0260] Data entry at the user terminal

[0261] Users take photos of the store's exterior and interior with their smartphones and upload these photos to the system via the application. In addition, they input sales data for the past few months in a table format and send it to the system. They also answer a personality assessment questionnaire about the store manager and submit the results.

[0262] Input data: Exterior photos, interior photos, sales data, store manager's personality information

[0263] Output data: Sending data to the server

[0264] Step 2:

[0265] Data storage on the server

[0266] The server temporarily stores exterior and interior photos, sales data, and manager personality information received from user terminals. The stored data serves as foundational data for analysis.

[0267] Input data: Exterior photos, interior photos, sales data, store manager's personality information

[0268] Output data: Saved data

[0269] Step 3:

[0270] Store evaluation using image analysis

[0271] The server uses OpenCV to analyze exterior and interior photographs. Specifically, it evaluates factors such as lighting, cleanliness, and layout in the photographs and calculates a score. Image processing algorithms utilize techniques such as edge detection and color analysis.

[0272] Input data: Exterior photos, interior photos

[0273] Output data: Appearance evaluation score, interior evaluation score

[0274] Step 4:

[0275] Analysis of sales data

[0276] The server analyzes the sales data using Pandas. It evaluates trends in sales, peak time periods, and periods of decline, etc., and calculates scores. Specific operations include data reading, graph generation, and statistical analysis.

[0277] Input data: Sales data

[0278] Output data: Sales evaluation score, trend analysis result

[0279] Step 5:

[0280] Evaluation of the store manager's personality

[0281] The server analyzes the store manager's personality diagnosis results using a generative AI model. It evaluates characteristics such as leadership, communication skills, stress response ability, etc., and calculates scores.

[0282] Input data: Store manager's personality information

[0283] Output data: Personality evaluation score

[0284] Step 6:

[0285] Comprehensive evaluation and report generation

[0286] The server calculates a comprehensive score based on the appearance evaluation score, interior evaluation score, sales evaluation score, and personality evaluation score, and generates an evaluation result and specific improvement advice in report form. This report includes a detailed analysis of each evaluation item and improvement measures.

[0287] Input data: Exterior evaluation score, Interior evaluation score, Sales evaluation score, Personality evaluation score

[0288] Output data: Overall evaluation report

[0289] Step 7:

[0290] User Feedback

[0291] The server sends a comprehensive evaluation report to the user's terminal and provides feedback. Users can view the report and see specific improvement measures.

[0292] Input data: Overall evaluation report

[0293] Output data: Sending reports to users

[0294] Step 8:

[0295] Information disclosure on consumer-facing websites

[0296] The server registers information on highly-rated stores on a consumer-facing website, making it accessible to consumers. This makes it easier for consumers to find highly-rated stores, thus supporting their ability to attract customers.

[0297] Input data: Overall rating report of highly-rated stores

[0298] Output data: Registration information for consumer-facing websites

[0299] Step 9:

[0300] Matching store managers with job seekers

[0301] The server compares the personality information of the store manager entered by the user with the personality assessment results of the part-time job applicant, evaluates compatibility, and calculates the degree of matching. Based on the matching results, it recommends a suitable store to the part-time job applicant.

[0302] Input data: Store manager's personality information, personality diagnosis results of part-time job applicants

[0303] Output data: Matching results, recommendation information for part-time job applicants

[0304] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0305] The present invention is a system that combines an emotion engine with a system for comprehensive diagnosis and consulting of small-scale stores and provides feedback considering the user's emotional state. Specific embodiments will be described below.

[0306] System configuration

[0307] This system is mainly composed of a user terminal, a server, an emotion engine, and a consumer terminal.

[0308] 1. User terminal

[0309] The user terminal is a device (e.g., a personal computer, a tablet, a smartphone) used by store managers or administrators on the store side, and provides an interface for inputting exterior photos, interior photos, sales data, and information related to the store manager's personality of the store.

[0310] 2. Server

[0311] The server is a central component that receives, stores, and analyzes data from the user. It incorporates an image analysis algorithm and a data analysis engine and is responsible for a series of processes from calculating the total score to providing feedback.

[0312] 3. Emotion engine

[0313] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. This allows it to analyze the user's emotional state and adjust the content, presentation method, and timing of evaluation reports and advice based on that state.

[0314] 4. Consumer devices

[0315] Consumer terminals are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the rating results are provided via dedicated websites or applications.

[0316] Program processing

[0317] 1. User data entry

[0318] The user logs into the system and enters basic information (store name, address, contact information, etc.).

[0319] Take photos of the store's exterior and interior and upload them to the system.

[0320] Enter sales data for the past few months and send it to the system.

[0321] Answer the questionnaire regarding the store manager's personality and submit the results.

[0322] 2. Server Data Analysis

[0323] The server stores photos, sales data, and personality assessment data received from users.

[0324] Based on saved exterior and interior photographs, an image analysis algorithm is used to evaluate the store's exterior and interior. For example, the lighting conditions, cleanliness, and design elements of the photographs are evaluated.

[0325] Based on sales data, we evaluate sales trends, peak hours, and periods of stagnation. For example, we analyze monthly sales trends and fluctuations in customer numbers.

[0326] Based on personality questionnaire data, the store manager's personality traits will be evaluated. For example, communication skills, leadership abilities, and stress management skills will be analyzed.

[0327] 3. Calculation of overall score and feedback

[0328] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results, and determines the evaluation for each item.

[0329] The system generates a report containing the calculated total score and detailed evaluation results. For example, it might show: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Total score 30 points.

[0330] Reports and improvement suggestions are sent to user terminals to provide feedback to the store. For example, specific advice such as "making the interior brighter and changing the seating layout will improve customer satisfaction" is provided.

[0331] 4. Emotion recognition by an emotion engine

[0332] The server uses an emotion engine to analyze user reactions during the report submission and feedback process. For example, it analyzes the user's facial expressions and tone of voice during input.

[0333] The emotion engine evaluates the user's emotional state and identifies emotions such as "satisfied," "anxious," or "angry."

[0334] The presentation method of evaluation reports and the content of advice are adjusted according to the user's emotional state. For example, if a user is feeling anxious, more specific and reassuring advice is provided.

[0335] 5. Publication and customer acquisition support

[0336] The server registers information about highly-rated restaurants on consumer-facing websites, making it accessible to consumers. For example, it displays rankings and reviews for "restaurants with high customer satisfaction."

[0337] Consumers can view information about highly-rated stores through consumer-facing terminals and decide whether to visit them.

[0338] 6. Talent Matching

[0339] Applicants for part-time work take a personality test and submit the results to the system.

[0340] The server compares the store manager's personality information with the personality assessment results of the part-time job applicant to evaluate compatibility. For example, it evaluates compatibility in areas such as communication skills and stress management abilities.

[0341] Based on the evaluation results, the system recommends suitable stores to job applicants. For example, an evaluation such as "You have good personality chemistry with the store manager at this location" might be presented.

[0342] The above describes a specific embodiment of the present invention. This simultaneously achieves improved store sales and the resolution of labor shortages, and further provides feedback that takes into account the user's emotional state.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The user logs into the system. They enter their username and password to be authenticated. Upon successful authentication, they are redirected to the dashboard screen.

[0346] Step 2:

[0347] The user enters basic information. They enter basic information such as the store name, address, and contact information into the form and submit it.

[0348] Step 3:

[0349] Users upload photos of the store's exterior. They take photos of the exterior and either drag and drop them into the designated area of ​​the system or select the file to upload.

[0350] Step 4:

[0351] Users upload interior photos of their stores. Similarly, they take interior photos and upload them to the system.

[0352] Step 5:

[0353] Users input sales data for the past few months. They enter numerical data such as total sales, monthly sales, and customer numbers, and then submit it.

[0354] Step 6:

[0355] The user answers a personality questionnaire for the store manager. They click on the appropriate option for each questionnaire item, and after answering all questions, they click the submit button.

[0356] Step 7:

[0357] The server stores exterior photos, interior photos, sales data, and personality assessment data received from the user. The data is then stored in a designated database.

[0358] Step 8:

[0359] The server processes the received exterior photos using an image analysis algorithm. For example, it evaluates the lighting conditions, cleanliness, and design elements of the photos.

[0360] Step 9:

[0361] The server processes the received interior photos using an image analysis algorithm. This algorithm evaluates the store's layout, decoration, cleanliness, and lighting.

[0362] Step 10:

[0363] The server analyzes the stored sales data. It identifies and evaluates patterns such as sales trends, peak hours, and periods of stagnation.

[0364] Step 11:

[0365] The server analyzes the personality questionnaire data of the store manager, evaluating personality traits such as communication skills, leadership, and stress management abilities.

[0366] Step 12:

[0367] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results. The score is calculated for each item, and the final overall score is determined.

[0368] Step 13:

[0369] The server generates a comprehensive evaluation report. It compiles detailed information and an overall score for exterior, interior, sales, and manager personality evaluations into a report format.

[0370] Step 14:

[0371] The server sends an evaluation report to the user's terminal. The user can view the evaluation results and improvement advice on the dashboard.

[0372] Step 15:

[0373] The server uses an emotion engine during the feedback process to analyze user responses in real time. For example, it monitors input speed and the context of responses.

[0374] Step 16:

[0375] The emotion engine evaluates the user's emotional state. For example, it identifies states such as "satisfied," "anxious," and "frustrated," and sends that information to the server.

[0376] Step 17:

[0377] The server adjusts how reports are presented based on the user's emotional state. For example, if the user is feeling anxious, it provides more detailed and reassuring advice.

[0378] Step 18:

[0379] The server sends users emotion-based evaluation reports. For example, it might offer specific advice such as, "The store's interior is good, but it would be even better if it were a little brighter."

[0380] Step 19:

[0381] The server registers information about highly-rated stores on a consumer-facing website. Based on the evaluation results, rankings and reviews of highly-rated stores are published.

[0382] Step 20:

[0383] Consumers browse consumer-facing websites. They check information on highly-rated stores and select a store to visit.

[0384] Step 21:

[0385] Applicants for part-time work will take a free personality test. They will answer a personality test form and submit the results to the system.

[0386] Step 22:

[0387] The server compares the store manager's personality information with the personality assessment results of the part-time job applicants. An AI algorithm is used to evaluate compatibility.

[0388] Step 23:

[0389] The server recommends suitable stores to job applicants based on compatibility evaluation results. It generates a list of compatible stores and presents it to the applicants.

[0390] Step 24:

[0391] Applicants for part-time jobs review the information of the stores recommended to them. They view the reasons for the recommendation and detailed information, and then decide which store to apply to.

[0392] (Example 2)

[0393] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0394] Conventional store diagnostic systems lacked the ability to provide feedback that considered the user's emotional state, resulting in insufficient features to make users more receptive to suggestions. Furthermore, the accuracy of store evaluations and the amount of information provided in feedback were often inadequate, preventing users from receiving sufficient improvement suggestions. Additionally, there were challenges in providing accurate evaluations and recommendations for consumer-facing information and matching with part-time job applicants.

[0395] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0396] In this invention, the server includes means for the user to input basic store information, exterior photos, interior photos, sales data, and information about the store manager's personality; means for receiving the exterior photos, interior photos, sales data, and store manager's personality information and storing the data; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the store manager's personality information and evaluating the store manager's personality traits; means for calculating an overall score based on these evaluation results and providing the results in a report format; means for providing feedback on the report results to the user, further analyzing the user's reaction using an emotion engine and providing feedback according to the user's emotional state; means for publishing the evaluation results to a consumer terminal and registering information on highly-rated stores; and means for comparing the personality diagnosis results of users and part-time job applicants, evaluating compatibility, and recommending appropriate stores to part-time job applicants. This makes it possible to provide more effective feedback that takes into account the user's emotional state, enabling highly accurate comprehensive diagnosis and consulting of stores. Furthermore, it will enable more accurate evaluations and recommendations in consumer-facing information disclosure and talent matching.

[0397] A "user terminal" is a device used by a user to access the system, and includes personal computers, tablets, smartphones, and other similar devices.

[0398] An "exterior photograph" is image data taken of the exterior of a store, and is used to evaluate the store's appearance.

[0399] "Interior photographs" are image data taken of the inside of a store and are used to evaluate the store's interior.

[0400] "Sales data" refers to numerical information about store sales, and is used to evaluate sales trends and patterns.

[0401] "Personality questionnaire data" refers to information used to evaluate the personality traits of users and job applicants, and is collected through questionnaires and surveys.

[0402] An "image processing algorithm" is a mathematical and computational method for analyzing image data, and is used to evaluate the exterior and interior appearance of a store.

[0403] The "overall score" is a numerical value calculated by integrating multiple evaluation factors, and it represents the overall evaluation of the store.

[0404] The "emotion engine" is a system module that analyzes the user's emotional state and adjusts the feedback based on that analysis.

[0405] "Consumer-facing devices" refer to devices used by consumers to view store ratings and information, and include personal computers and smartphones.

[0406] A "matching method" is a technique for comparing the personality assessment results of users and job applicants, evaluating compatibility, and making appropriate recommendations.

[0407] "Feedback methods" refer to methods for communicating analysis results and suggestions to the user, and include reports and advice.

[0408] This invention combines an emotion engine with a system for comprehensive diagnosis and consulting of small businesses, providing feedback that takes into account the user's emotional state. This system mainly consists of a user terminal, a server, an emotion engine, and a consumer terminal.

[0409] User terminal

[0410] User terminals are devices (PCs, tablets, smartphones) used by store owners and managers, and they provide an interface for inputting basic store information, exterior photos, interior photos, sales data, and information about the store manager's personality. For example, users can use their smartphones to take exterior and interior photos of the store and upload them to the system.

[0411] server

[0412] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating overall scores to providing feedback. Examples of image processing algorithms used include OpenCV. Examples of data analysis using Python libraries such as Scikit-learn and Pandas are also used. The server performs the following operations, for example:

[0413] 1. Store exterior and interior photos received from users and evaluate them using image analysis algorithms. Specifically, evaluate the lighting conditions, cleanliness, and design elements of the photos.

[0414] 2. Based on sales data, evaluate sales trends, peak hours, and periods of stagnation. For example, analyze monthly sales trends and fluctuations in customer numbers.

[0415] 3. Analyze the store manager's personality traits based on personality questionnaire data. For example, analyze communication skills, leadership skills, stress coping abilities, etc.

[0416] 4. Based on these analysis results, an overall score is calculated, and a detailed evaluation result is generated in report format. For example, it may be listed as follows: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Overall score 30 points.

[0417] Emotional Engine

[0418] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. This allows it to analyze the user's emotional state and adjust the content, presentation method, and timing of evaluation reports and advice accordingly. For example, it can recognize the user's facial expressions during input to identify emotions such as "satisfied" or "anxious."

[0419] Consumer devices

[0420] Consumer terminals are devices (PCs, smartphones) for viewing information on highly-rated stores, and the evaluation results are provided via dedicated websites or applications. Consumers can view information on highly-rated stores in formats such as rankings or customer reviews, and then decide whether to visit a store.

[0421] Specific examples and prompt statements

[0422] For example, the following specific examples can be considered.

[0423] Users log into the system, upload basic information, exterior and interior photos, and enter sales data.

[0424] The server analyzes the received data and uses image analysis algorithms to evaluate the exterior and interior appearance.

[0425] Provide users with specific feedback such as, "Improving the cleanliness of the interior will increase customer satisfaction."

[0426] Example of a prompt

[0427] Please rate the exterior and interior photos of the store.

[0428] "Please analyze the sales data from the past few months to evaluate sales trends."

[0429] "Please analyze the personality traits based on the store manager's personality assessment data."

[0430] "Analyze the user's emotional state and provide appropriate feedback."

[0431] The above describes the form for carrying out the invention. This system is expected to improve store sales, enhance customer satisfaction, and facilitate effective personnel matching.

[0432] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0433] Step 1: The user logs into the system.

[0434] Users access the system login screen using their device (PC, tablet, or smartphone). They enter their email address and password to authenticate. Upon successful authentication, the user can access the system dashboard.

[0435] Enter: Email address, password

[0436] Output: Authentication result (success / failure)

[0437] Step 2: The user enters basic information.

[0438] Users enter basic information such as store name, address, and contact information on the dashboard. This information is sent from the user's device to the server and stored on the server.

[0439] Input: Store name, address, contact information

[0440] Output: Basic information is saved on the server.

[0441] Step 3: The user uploads photos of the store's exterior and interior.

[0442] Users take photos of the store's exterior and interior using their smartphone's camera function. They then upload the photos to the system. The server receives and stores these photos.

[0443] Input: Exterior photos, interior photos

[0444] Output: Photo data is saved to the server.

[0445] Step 4: The user enters the sales data.

[0446] The user imports sales data for the past few months in Excel file format. The server saves the received sales data and prepares it for analysis.

[0447] Input: Sales data (Excel file)

[0448] Output: Sales data is saved to the server.

[0449] Step 5: The user answers a questionnaire about the store manager's personality.

[0450] The user enters their answers into a question form on the system. This is a questionnaire about personality traits. The server receives and stores this response data.

[0451] Input: Personality questionnaire data (survey responses)

[0452] Output: Personality questionnaire data is saved to the server.

[0453] Step 6: The server analyzes the exterior and interior photos.

[0454] The server uses image analysis algorithms to evaluate the stored exterior and interior photographs. Specifically, it uses OpenCV to analyze lighting conditions, cleanliness, design elements, and other factors.

[0455] Input: Exterior photos, interior photos

[0456] Output: Evaluation results (e.g., scores for lighting conditions and cleanliness)

[0457] Step 7: The server analyzes the sales data.

[0458] The server analyzes sales trends, peak times, and periods of stagnation based on stored sales data. Time series analysis and statistical methods are used for the analysis.

[0459] Input: Sales data

[0460] Output: Analysis results (e.g., sales trend graph, peak time zones)

[0461] Step 8: The server analyzes the store manager's personality traits.

[0462] The server analyzes the store manager's personality traits based on personality questionnaire data. Specifically, it uses psychology-based evaluation indicators to assess communication skills, leadership, stress management abilities, and other aspects.

[0463] Input: Personality questionnaire data

[0464] Output: Evaluation results of personality traits

[0465] Step 9: The server calculates the total score.

[0466] The server integrates image analysis results, sales analysis results, and personality analysis results to calculate an overall score. The overall score is determined by taking a weighted average of the evaluations of each item.

[0467] Input: Image evaluation results, sales analysis results, personality trait evaluation results

[0468] Output: Total score (e.g., 30 points)

[0469] Step 10: The server generates a report and provides feedback to the user.

[0470] The server generates a report-style feedback based on the overall score and detailed evaluation results for each item. The report is generated in PDF format and sent to the user's terminal.

[0471] Input: Total score, evaluation results for each item

[0472] Output: Report (PDF format)

[0473] Step 11: The server uses the emotion engine to analyze the user's response.

[0474] The server analyzes the user's reaction after the report is submitted. Specifically, it analyzes the user's facial expressions captured by the camera and uses an emotion engine to identify emotional states such as "satisfied" or "anxious."

[0475] Input: User reaction data (facial expression images)

[0476] Output: Sentiment assessment result (e.g., anxiety)

[0477] Step 12: The server adjusts the feedback based on the emotional state.

[0478] Based on the analysis results, the server provides feedback tailored to the user's emotional state. For example, it provides more detailed and reassuring advice to users who are feeling anxious.

[0479] Input: Sentiment evaluation result

[0480] Output: Adjusted feedback

[0481] Step 13: The server registers information about highly-rated stores on the consumer website.

[0482] The server registers information about highly-rated stores that have achieved a certain overall score on a consumer-facing website. This information is displayed in ranking or review format and can be viewed by consumers.

[0483] Input: Total score, store information

[0484] Output: Page update for consumer-facing website

[0485] Step 14: The server compares the personality assessment results of the user and the job applicant to evaluate compatibility.

[0486] The server compares the personality assessment results of saved users and job applicants to evaluate compatibility. The evaluation involves scoring the degree of agreement for each characteristic.

[0487] Input: User's personality assessment results, Part-time job applicant's personality assessment results

[0488] Output: Compatibility evaluation results (e.g., 80% compatibility)

[0489] Step 15: The server recommends a suitable store to the job applicant.

[0490] The server recommends suitable stores to job applicants based on compatibility evaluation results. The recommendation results are sent to the applicant's email address.

[0491] Input: Compatibility evaluation result

[0492] Output: Recommendation results (email notification)

[0493] The above outlines the specific processing steps of this system. This enables the provision of feedback that takes into account the user's emotional state and comprehensive store diagnostics.

[0494] (Application Example 2)

[0495] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0496] In modern society, small business owners often find it difficult to accurately analyze multifaceted information such as the store's exterior and interior, and sales data, and to receive a comprehensive diagnosis. Furthermore, they often lack feedback that takes their emotional state into account, resulting in a failure to implement concrete improvement measures. Additionally, there is a lack of features to publicly disclose store evaluation results to consumers, and a lack of features to assess the personality compatibility between business owners and part-time job applicants. Therefore, a system that provides effective solutions to these challenges is necessary.

[0497] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0498] In this invention, the server includes means for the user to input exterior photos, interior photos, sales data, and information about the manager's personality; means for receiving the exterior photos, interior photos, sales data, and manager's personality information and storing the data; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the manager's personality information and evaluating the manager's personality traits; means for analyzing the user's emotional state using an emotion engine; means for adjusting the evaluation results and report content based on the emotional state and providing feedback; means for calculating an overall score based on these evaluation results and providing the result in report format; and means for providing feedback on the report results to the user. This makes it possible to provide multifaceted evaluations of small stores and specific improvement measures that take into account the manager's emotional state. Furthermore, it is possible to make the store evaluation results public to consumers and to judge the personality compatibility between the manager and prospective part-time workers.

[0499] A "user terminal" is a device used by store owners and managers, providing an interface for inputting exterior and interior photos of the store, sales data, and information about the owner's personality.

[0500] A "server" is the central component that receives, stores, and analyzes data from users, and it contains built-in image processing algorithms and data analysis engines.

[0501] The "emotion engine" is a module that analyzes user input data and responses during feedback to recognize the user's emotional state, and analyzes what kind of emotional state the user is in.

[0502] An "image processing algorithm" is a computational method used to evaluate the exterior and interior appearance of a store by analyzing factors such as lighting conditions, cleanliness, and design elements in a photograph.

[0503] "Sales data" refers to the basic data used to evaluate store sales trends and patterns, peak hours, and periods of stagnation.

[0504] "Personality information" refers to information about the personality traits of business leaders, such as their communication skills, leadership abilities, and stress management skills.

[0505] The "overall score" is an evaluation score calculated based on the analysis results of saved data, and represents a comprehensive evaluation of the store's exterior, interior, sales performance, and the personality information of the manager.

[0506] "Feedback" is the process of providing users with a report containing their calculated overall score and detailed evaluation results.

[0507] A "consumer-oriented website" is a website where consumers can view information about highly-rated stores, and it displays rankings and customer reviews.

[0508] A "personality assessment" refers to the results of questionnaires or tests used to evaluate the personality traits of employers or job applicants.

[0509] "Matching" is a process that compares the personality information of business owners entered by users with the personality assessment results of part-time job applicants to evaluate compatibility.

[0510] A "report" is a document that includes various evaluation results and provides evaluations and improvement advice to users.

[0511] This invention provides a system that combines an emotion engine with a system for comprehensive diagnosis and consulting of small businesses, thereby providing feedback that takes into account the user's emotional state. This system consists of a user terminal, a server, an emotion engine, and a consumer terminal.

[0512] System Configuration

[0513] This system mainly consists of the following components:

[0514] 1. User terminal

[0515] The user terminal is a device (e.g., smartphone, tablet, PC) used by the store owner or manager, and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the owner's personality. Data is entered using the user terminal as follows:

[0516] Specific example:

[0517] Take exterior and interior photos and upload them to the system.

[0518] Enter sales data for the past few months and send it to the system.

[0519] Answer the questionnaire regarding the personality of the business owner and submit the results.

[0520] 2. Server

[0521] The server is the central component that receives, stores, and analyzes data from users. The server performs the following processes:

[0522] We will evaluate the exterior and interior of the store using an image analysis algorithm (e.g., OpenCV).

[0523] We will use data analysis tools (e.g., pandas) to evaluate sales trends and patterns.

[0524] We evaluate the personality traits of business owners based on personality questionnaire data.

[0525] Specific example:

[0526] Based on the saved exterior and interior photographs, lighting conditions, cleanliness, and design elements were analyzed.

[0527] We analyze sales data to examine monthly sales trends and fluctuations in customer numbers.

[0528] We analyze the personality traits of business leaders (communication skills, leadership, stress management skills, etc.).

[0529] 3. Emotional Engine

[0530] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. The emotion engine performs the following processes:

[0531] The system analyzes the user's facial expressions and tone of writing during input to evaluate their emotional state.

[0532] Based on the emotional state, the content, presentation method, and timing of evaluation reports and advice will be adjusted.

[0533] Specific example:

[0534] If a user is feeling anxious, we provide more specific and reassuring advice.

[0535] If the user is satisfied, we will suggest further improvements or new suggestions.

[0536] 4. Consumer devices

[0537] Consumer-facing devices are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the evaluation results are provided through dedicated websites or applications.

[0538] Specific example:

[0539] Rankings and customer reviews are displayed for stores with high customer satisfaction.

[0540] Consumers can view detailed information about highly-rated stores and decide whether to visit them.

[0541] Example of a prompt

[0542] The following are specific examples of prompts the system receives:

[0543] Image upload prompt: "Upload exterior_photo.jpg"

[0544] Prompt message when uploading sales data: "Upload sales data (January-March 2023)"

[0545] Prompt text when entering personality questionnaire data: "Evaluation of communication skills: High, Leadership: Medium, Stress coping ability: Low"

[0546] The above describes the embodiments for carrying out the present invention. This system makes it possible to provide multifaceted evaluations of small businesses and specific improvement measures that take into account the emotional state of the business owners. Furthermore, it enables the public disclosure of store evaluation results to consumers and the assessment of personality compatibility between business owners and part-time job applicants.

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The user terminal logs into the system and enters basic store information (store name, address, contact information, etc.). The server then receives this basic information and saves it to the database. The input data is basic information (store name, address, contact information, etc.), and the output is the saved basic information.

[0550] Step 2:

[0551] The user terminal takes photos of the store's exterior and interior and uploads them to the system. The server receives these photos and stores them in a database. The input data consists of exterior and interior photos, and the output is the stored photo data.

[0552] Step 3:

[0553] The user terminal inputs sales data for the past several months and sends it to the system. The server receives the sales data, saves it to a database, and performs data analysis. The input data is sales data, and the output is the saved sales data and the analysis results.

[0554] Step 4:

[0555] The user terminal answers a questionnaire about the manager's personality and sends the results to the system. The server stores the received personality data and performs analysis. The input data is the answers to the personality questionnaire, and the output is the stored personality data and analysis results.

[0556] Step 5:

[0557] The server uses image analysis algorithms (e.g., OpenCV) to evaluate the exterior and interior of a store based on stored exterior and interior photographs. It assesses lighting conditions, cleanliness, and design elements in the photographs and saves the evaluation results. The input data consists of exterior and interior photographs, and the output is the evaluation result.

[0558] Step 6:

[0559] The server analyzes sales trends and patterns based on stored sales data. Using data analysis tools (e.g., pandas), it evaluates monthly sales trends and fluctuations in customer numbers, and saves the results. The input data is sales data, and the output is the sales trend analysis results.

[0560] Step 7:

[0561] The server analyzes stored personality questionnaire data and evaluates personality traits (communication skills, leadership, stress coping ability, etc.). The analysis results are saved. The input data is personality data, and the output is the evaluation results of personality traits.

[0562] Step 8:

[0563] The server uses an emotion engine to analyze user input data and responses to feedback to recognize the user's emotional state. The input data consists of the user's input data and responses to feedback, and the output is the result of the emotional state evaluation.

[0564] Step 9:

[0565] The server adjusts the content, presentation method, and timing of evaluation results and reports based on the emotional state to provide feedback. The input data is the emotional state evaluation result, and the output is the adjusted evaluation result and report.

[0566] Step 10:

[0567] The server calculates an overall score based on these evaluation results and provides the result in a report format. The report is sent to the user's terminal and serves as feedback to the user. The input data is the evaluation result, and the output is a report containing the overall score and detailed evaluation results.

[0568] Step 11:

[0569] The server registers the overall score and evaluation results on a consumer-facing website, and publishes information on highly-rated stores. The input data is the overall score and evaluation results, and the output is the information registered on the consumer-facing website.

[0570] Step 12:

[0571] The server compares the personality information of the business owner entered by the user with the personality assessment results of the part-time job applicant to evaluate compatibility. Based on the compatibility results, it recommends a suitable store to the part-time job applicant. The input data is the personality information of the business owner and the personality assessment results of the part-time job applicant, and the output is the compatibility evaluation results and recommendation information.

[0572] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0573] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0574] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0575] [Second Embodiment]

[0576] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0577] As shown in Figure 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.

[0578] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0579] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0580] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0581] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0582] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0583] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0584] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0585] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0586] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0587] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0588] This invention is a system for comprehensive diagnosis and consulting for small businesses. Specific embodiments are described below.

[0589] System Configuration

[0590] This system mainly consists of user terminals, servers, and consumer terminals.

[0591] 1. User terminal

[0592] The user terminal is a device used by the store's owner or manager (e.g., a PC, tablet, or smartphone), and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[0593] 2. Server

[0594] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating the overall score to providing feedback.

[0595] 3. Consumer devices

[0596] Consumer terminals are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the rating results are provided via dedicated websites or applications.

[0597] Program processing

[0598] 1. User data entry

[0599] The user logs into the system and enters basic information (store name, address, contact information, etc.).

[0600] Take photos of the store's exterior and interior and upload them to the system.

[0601] Enter sales data for the past few months and send it to the system.

[0602] Answer the questionnaire regarding the store manager's personality and submit the results.

[0603] 2. Server Data Analysis

[0604] The server stores photos, sales data, and personality assessment data received from users.

[0605] Based on saved exterior and interior photographs, image analysis algorithms are used to evaluate the store's exterior and interior. For example, elements such as lighting, cleanliness, and layout are analyzed.

[0606] Based on sales data, we evaluate sales trends, peak hours, and periods of stagnation. For example, we analyze monthly sales trends and fluctuations in customer numbers.

[0607] Based on personality questionnaire data, the store manager's personality traits will be evaluated. For example, communication skills, leadership abilities, and stress management skills will be analyzed.

[0608] 3. Calculation of overall score and feedback

[0609] The server calculates an overall score based on the image analysis results, sales analysis results, and store manager personality analysis results, and determines the evaluation for each item.

[0610] The system generates a report containing the calculated total score and detailed evaluation results. For example, it might show: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Total score 30 points.

[0611] Reports and improvement suggestions are sent to user terminals to provide feedback to the store. For example, specific advice such as "making the interior brighter and changing the seating layout will improve customer satisfaction" is provided.

[0612] 4. Launch and customer acquisition support

[0613] The server registers information about highly-rated restaurants on consumer-facing websites, making it accessible to consumers. For example, it displays rankings and reviews for "restaurants with high customer satisfaction."

[0614] Consumers can view information about highly-rated stores through consumer-facing terminals and decide whether to visit them.

[0615] 5. Talent matching

[0616] Applicants for part-time work take a personality test and submit the results to the system.

[0617] The server compares the store manager's personality information with the personality assessment results of the part-time job applicant to evaluate compatibility. For example, it evaluates compatibility in areas such as communication skills and stress management abilities.

[0618] Based on the evaluation results, the system recommends suitable stores to job applicants. For example, an evaluation such as "You have good personality chemistry with the store manager at this location" might be presented.

[0619] The above describes a specific embodiment of the present invention. This makes it possible to simultaneously improve store sales and alleviate labor shortages.

[0620] The following describes the processing flow.

[0621] Step 1:

[0622] The user logs into the system. They enter their username and password to be authenticated. Upon successful authentication, they are redirected to the dashboard screen.

[0623] Step 2:

[0624] The user enters basic information. They enter basic information such as the store name, address, and contact information into the form and submit it.

[0625] Step 3:

[0626] Users upload photos of the store's exterior. They take photos of the exterior and either drag and drop them into the designated area of ​​the system or select the file to upload.

[0627] Step 4:

[0628] Users upload interior photos of their stores. Similarly, they take interior photos and upload them to the system.

[0629] Step 5:

[0630] Users input sales data for the past few months. They enter numerical data such as total sales, monthly sales, and customer numbers, and then submit it.

[0631] Step 6:

[0632] The user answers a personality questionnaire for the store manager. They click on the appropriate option for each questionnaire item, and after answering all questions, they click the submit button.

[0633] Step 7:

[0634] The server stores exterior photos, interior photos, sales data, and personality assessment data received from the user. The data is then stored in a designated database.

[0635] Step 8:

[0636] The server processes the received exterior photos using an image analysis algorithm. For example, it evaluates the lighting conditions, cleanliness, and design elements of the photos.

[0637] Step 9:

[0638] The server processes the received interior photos using an image analysis algorithm. This algorithm evaluates the store's layout, decoration, cleanliness, and lighting.

[0639] Step 10:

[0640] The server analyzes the stored sales data. It identifies and evaluates patterns such as sales trends, peak hours, and periods of stagnation.

[0641] Step 11:

[0642] The server analyzes the personality questionnaire data of the store manager, evaluating personality traits such as communication skills, leadership, and stress management abilities.

[0643] Step 12:

[0644] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results. The score is calculated for each item, and the final overall score is determined.

[0645] Step 13:

[0646] The server generates a comprehensive evaluation report. It compiles detailed information and an overall score for exterior, interior, sales, and manager personality evaluations into a report format.

[0647] Step 14:

[0648] The server sends an evaluation report to the user's terminal. The user can view the evaluation results and improvement advice on the dashboard.

[0649] Step 15:

[0650] The server registers information about highly-rated stores on a consumer-facing website. Based on the evaluation results, rankings and reviews of highly-rated stores are published.

[0651] Step 16:

[0652] Consumers browse consumer-facing websites. They check information on highly-rated stores and select a store to visit.

[0653] Step 17:

[0654] Applicants for part-time work will take a free personality test. They will answer a personality test form and submit the results to the system.

[0655] Step 18:

[0656] The server compares the store manager's personality information with the personality assessment results of the part-time job applicants. An AI algorithm is used to evaluate compatibility.

[0657] Step 19:

[0658] The server recommends suitable stores to job applicants based on compatibility evaluation results. It generates a list of compatible stores and presents it to the applicants.

[0659] Step 20:

[0660] Applicants for part-time jobs review the information of the stores recommended to them. They view the reasons for the recommendation and detailed information, and then decide which store to apply to.

[0661] (Example 1)

[0662] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0663] In managing small businesses, there is a lack of understanding of the quality of the store's exterior and interior, the analysis of sales trends, and the personality traits of the store manager. Therefore, it is necessary to comprehensively evaluate this information and clearly identify areas for improvement. Furthermore, there is a need for means to communicate the store's appeal to consumers and for effective methods to assess the compatibility between the store and prospective part-time workers.

[0664] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0665] In this invention, the server includes means for users to input images of the store's exterior, interior, sales information, and information regarding the store manager's characteristics; means for receiving the exterior, interior, sales information, and store manager characteristics information and storing the data; and means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm. This makes it possible to comprehensively evaluate the quality of the store's exterior and interior, sales trends, and the store manager's personality traits, and to clearly suggest areas for improvement.

[0666] A "user" is a store owner or manager who uses the system to evaluate stores.

[0667] "Store exterior image" refers to an image file that shows the condition of the store's exterior.

[0668] "Store interior images" are image files that show the condition of the store's interior.

[0669] "Sales information" refers to information that shows past sales data for a store.

[0670] "Store manager's characteristic information" refers to data that includes information about the personality and characteristics of the store manager, who is the person in charge of the store.

[0671] "Means of saving data" refers to methods of storing received data in a storage device such as a database.

[0672] An "image processing algorithm" is a set of computational procedures used to analyze image data and extract specific features or patterns.

[0673] A "sales trend" is a pattern that shows the tendency or fluctuation of sales over a certain period of time.

[0674] "Store manager characteristics" refers to personality traits such as the store manager's leadership skills, communication abilities, and stress tolerance.

[0675] The "overall score" is a score calculated by integrating evaluation results such as the store's exterior, interior, sales trends, and the store manager's characteristics.

[0676] A "report format" is a format used to notify users of evaluation results or analysis results.

[0677] A "consumer-facing website" is a website that consumers can access to view store reviews and ratings.

[0678] "Part-time job applicants" refers to individuals who wish to work at a specific store.

[0679] "Compatibility" is an indicator of the degree of personality compatibility between the store manager and the part-time job applicant.

[0680] "Fit" is a score that indicates how well the personalities and characteristics of the store manager and the part-time job applicant are compatible.

[0681] "Recommendation methods" refer to methods of suggesting the most suitable store to part-time job applicants based on evaluation results.

[0682] This invention is a system for comprehensive diagnosis and consulting of small businesses. The system consists of a user terminal, a server, and a consumer terminal. A detailed embodiment of the system is described below.

[0683] User terminal

[0684] The user terminal is a device (such as a PC, tablet, or smartphone) used by the store's owner or manager, and provides an interface for inputting exterior and interior images of the store, sales information, and the store manager's characteristics.

[0685] server

[0686] The server is the central component of the system, receiving, storing, and analyzing data from users. The server contains a high-performance computer, a database, image analysis algorithms (such as OpenCV), and data analysis engines (such as Python's Pandas or Scikit-learn).

[0687] Consumer devices

[0688] Consumer terminals are devices (such as personal computers and smartphones) used to view information on highly-rated stores, and the evaluation results are provided via dedicated websites or applications.

[0689] Specific processing of the program

[0690] Basic information (store name, location, etc.), images, sales data, and personality information entered from the user's terminal are first sent to the server. The server receives this data and stores it in a database. Specific software used at this stage includes database management systems such as MySQL and PostgreSQL.

[0691] Image analysis

[0692] The server first analyzes exterior and interior images. Using OpenCV, it extracts and evaluates elements such as lighting, cleanliness, and layout from the images. For example, it analyzes the brightness distribution to evaluate the lighting conditions of a store.

[0693] Sales data analysis

[0694] Next, we will analyze the sales data. Using the Python Pandas library, we will evaluate sales trends, peak times, and periods of stagnation. For example, we will graph and analyze monthly sales trends.

[0695] Personality data analysis

[0696] Furthermore, the store manager's characteristics will be evaluated based on personality questionnaire data. Scikit-learn will be used to evaluate characteristics such as communication skills, leadership, and stress management ability. For example, the scores for each questionnaire item will be added together to calculate an overall score.

[0697] Overall score and feedback

[0698] The server calculates an overall score based on image analysis results, sales analysis results, and store manager personality analysis results, and generates a detailed evaluation result and improvement advice in report format. For example, it may include specific advice such as, "We recommend adding more lighting to improve the brightness of the store." The generated report is sent to the user's terminal.

[0699] Public release and customer acquisition support

[0700] The server registers information on highly-rated stores on a consumer-facing website and publishes details about these stores (e.g., store name, rating score, areas for improvement, etc.). Consumers can view this information through consumer-facing devices and decide whether to visit the store.

[0701] Talent matching

[0702] Applicants for part-time jobs take a personality test and submit the results to the system. The server compares the store manager's characteristics with the applicant's personality test results and calculates a compatibility score. Based on this, it recommends a suitable store to the applicant.

[0703] Examples of specific actions and prompt statements

[0704] For example, if a cafe owner were to use the system, they would enter the data following these steps:

[0705] 1. Log in to the system and enter your basic information (cafe name, address, contact information).

[0706] 2. Take photos of the store's exterior and interior and upload them to the system.

[0707] 3. Enter sales data for the past six months and submit it to the system.

[0708] 4. Answer the questionnaire regarding your (the store manager's) personality and submit the results.

[0709] An example of a prompt message is, "Analyze the exterior photos of this cafe and evaluate its cleanliness and layout."

[0710] The above describes a specific embodiment of this invention. By utilizing this system, it is possible to simultaneously improve store sales and alleviate labor shortages.

[0711] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0712] Step 1:

[0713] The user logs into the system. The user is required to enter their email address and password. The server receives the authentication information and verifies it against the user information in the database. Upon successful authentication, the user is redirected to the dashboard. The output displays the user's dashboard screen.

[0714] Step 2:

[0715] The user enters the store's basic information (store name, address, contact information). The entered data is sent to the system via an HTML form. The server saves the received data to a database. As output, the basic information is recorded in the database.

[0716] Step 3:

[0717] Users upload images of the store's exterior and interior. The input must be image files in JPEG or PNG format. The server receives the images and stores them in a database. The output is the image files themselves, recorded in the database.

[0718] Step 4:

[0719] The user inputs sales information for the past few months. The input must be in a format such as a CSV file. The server saves the received sales data to a database. The output is the sales data recorded in the database.

[0720] Step 5:

[0721] Users answer a questionnaire about the characteristics of the store manager. The input is in the form of selecting answers from multiple options. The server saves the received questionnaire results to a database. The output is the questionnaire data recorded in the database.

[0722] Step 6:

[0723] The server analyzes stored exterior and interior images. It requires received image files in JPEG or PNG format as input. The server uses OpenCV to extract elements such as lighting, cleanliness, and layout from the images. For example, it analyzes the brightness distribution to evaluate the lighting conditions of a store. The output is an evaluation score.

[0724] Step 7:

[0725] The server analyzes the stored sales data. The input requires sales information in CSV file format. The server uses the Python Pandas library to evaluate sales trends, peak times, and periods of low sales. For example, it can graph and analyze monthly sales trends. The output is a sales evaluation score.

[0726] Step 8:

[0727] The server analyzes stored data on the store manager's characteristics. The input requires survey results data. The server uses Scikit-learn to evaluate characteristics such as communication skills, leadership, and stress management. For example, it calculates an overall score by summing the scores for each characteristic. The output is the characteristic evaluation score.

[0728] Step 9:

[0729] The server calculates an overall score based on image analysis results, sales analysis results, and store manager characteristics results. The input requires evaluation scores obtained from each analysis. The server combines these scores to calculate the overall score. The output generates an overall evaluation report.

[0730] Step 10:

[0731] The server sends the overall evaluation report to the user's terminal. The generated overall evaluation report is required as input. The server sends the report to the user's terminal, allowing the user to view it. The output allows the user to review the evaluation results and improvement advice.

[0732] Step 11:

[0733] The server registers information about highly-rated stores on a consumer-facing website. The server requires data from the overall rating report as input. The server then publishes details about the highly-rated stores (e.g., store name, rating score, areas for improvement, etc.) on the website. As output, consumers can view the information about the highly-rated stores.

[0734] Step 12:

[0735] Applicants for part-time work take a personality assessment and submit the results to the system. The input requires answers to multiple questions in a questionnaire format. The server saves the received assessment results to a database. The output is the personality assessment data, which is then recorded in the database.

[0736] Step 13:

[0737] The server compares the store manager's characteristics with the personality assessment results of part-time job applicants and calculates a compatibility score. The required inputs are the store manager's characteristics data and the applicant's personality assessment results. The server calculates the compatibility score based on this data. The output is a compatibility evaluation report.

[0738] Step 14:

[0739] The server sends a compatibility assessment report to job applicants. The generated compatibility assessment report is required as input. The server sends the report to the applicant and recommends suitable stores. The output allows the applicant to review the appropriate stores.

[0740] (Application Example 1)

[0741] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0742] Traditional small-scale store management support systems typically focused on individual evaluations of exterior and interior design, sales trends, and store managers' personalities, making it difficult to provide comprehensive assessments or specific improvement advice. Furthermore, there was a lack of effective means to publicize highly-rated stores to consumers to support customer acquisition, and to evaluate and match employees with store managers based on their compatibility. As a result, store operations were often inefficient, leading to missed sales opportunities.

[0743] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0744] In this invention, the server includes means for users to input exterior and interior photos of a store, sales data, and information about the store manager's personality; means for receiving and storing the exterior and interior photos, sales data, and information about the store manager's personality; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the store manager's personality information and evaluating the store manager's personality traits; means for calculating an overall score based on these evaluation results and providing the results in a report format; means for providing feedback on the report results to the user; means for using an image processing algorithm to analyze the exterior and interior photos; means for evaluating sales trends and peak hours based on the analysis of the sales data; and means for evaluating the store manager's leadership traits based on the personality information. This makes it possible to provide a comprehensive evaluation of the store and specific improvement advice. Furthermore, it is possible to publish information about highly-rated stores on consumer-facing websites to support customer acquisition. In addition, it is possible to achieve efficient personnel allocation by evaluating the personality compatibility between the store manager and part-time job applicants and performing appropriate matching.

[0745] A "user terminal" is a device used by store owners and managers, providing an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[0746] "Photographs" refer to image data of the store's exterior and interior, which are taken and uploaded to the system.

[0747] "Sales data" refers to data that shows a store's past sales performance and is used to analyze sales trends and patterns.

[0748] "Store manager personality information" refers to data that indicates the store manager's personality traits, and is provided as a result of a personality assessment questionnaire.

[0749] A "server" is the central component that stores, analyzes, and generates evaluation results from data received from users.

[0750] An "image processing algorithm" is a technical method used to analyze photographic data and evaluate its external and internal appearance.

[0751] "Sales trends" refer to analytical results that show the trends and patterns of store sales based on sales data.

[0752] "Personality traits" are data that represent the characteristics of an individual's personality, and include leadership, stress coping ability, communication skills, and so on.

[0753] The "overall score" is the result of comprehensively calculating the evaluation of the store's exterior, interior, sales, and manager's personality, and is provided in report format.

[0754] A "report" is a document that summarizes the analysis results and improvement advice, and is provided as feedback to the user.

[0755] A "consumer-oriented website" is an online platform where consumers can view information about highly-rated stores.

[0756] "Matching score" is the result of evaluating the compatibility between the personality traits of the store manager and the personality traits of the part-time job applicant.

[0757] This invention is a system for comprehensive diagnosis and consulting for small businesses. The detailed implementation method is described below.

[0758] System Configuration

[0759] This system consists of user terminals, servers, and consumer terminals.

[0760] User terminal

[0761] User terminals are used by store owners and managers and include devices such as personal computers, tablets, and smartphones. User terminals have the following functions:

[0762] 1. Take photos of the store's exterior and interior and upload them to the system.

[0763] 2. Enter sales data for the past few months and submit it to the system.

[0764] 3. Answer the questionnaire regarding the store manager's personality and submit the results.

[0765] server

[0766] The server is the central component for receiving, storing, and analyzing data sent from user terminals. Specifically, it has the following functions:

[0767] 1. Use image processing algorithms to evaluate the exterior and interior of the store.

[0768] 2. Analyze sales data to evaluate sales trends, peak times, and periods of stagnation.

[0769] 3. Evaluate the store manager's personality traits based on personality questionnaire data.

[0770] 4. Calculate the overall score and generate the evaluation results in report format.

[0771] Consumer devices

[0772] Consumer devices are devices that allow consumers to view information on highly-rated stores, and include personal computers and smartphones. This allows consumers to view rating results via dedicated websites or applications.

[0773] Hardware and software to be used

[0774] This system uses the following hardware and software:

[0775] Hardware: Smartphones, tablets, PCs (Windows, Mac), servers (physical servers or cloud servers)

[0776] Software: Python, OpenCV (image analysis library), Pandas (data analysis library)

[0777] Data adjustment and calculation flow

[0778] The user terminal takes photos of the store's exterior and interior, inputs sales data, and collects data about the store manager's personality. This data is then sent to the server.

[0779] The server performs the following actions:

[0780] 1. Image Analysis: Using OpenCV, analyze exterior and interior photos of the store. The analysis results will be used to evaluate factors such as lighting brightness, cleanliness, and layout appropriateness.

[0781] 2. Sales Analysis: Use Pandas to analyze sales data and evaluate sales trends and quality. For example, statistically analyze monthly sales trends and fluctuations in customer numbers.

[0782] 3. Personality Assessment: Based on the store manager's personality assessment data, a generative AI model will be used to evaluate leadership, communication skills, stress management abilities, and other aspects.

[0783] Examples

[0784] The following are examples of how this system can be used:

[0785] 1. The store owner takes exterior and interior photos with their smartphone and uploads these photos to the server via the application.

[0786] 2. The manager enters sales data for the past three months in a table format and sends it to the system.

[0787] 3. The manager completes a personality assessment questionnaire for the store manager via the application and submits the results.

[0788] Specific input examples:

[0789] Store name: ABC Store

[0790] Location: Shibuya Ward, Tokyo

[0791] Contact number: 03-1234-5678

[0792] Exterior photo: exterior.jpg

[0793] Interior photo: interior.jpg

[0794] Sales data:

[0795] Monthly sales

[0796] January, 100000

[0797] February, 120000

[0798] Personality test results:

[0799] Communication: 8

[0800] Leadership: 7

[0801] Once the above data is sent to the server, the server performs the analysis described above and provides the management with an overall evaluation and improvement advice. In addition, by being registered as a highly-rated store, the store will also receive support for attracting customers through consumer terminals.

[0802] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0803] Step 1:

[0804] Data entry at the user terminal

[0805] Users take photos of the store's exterior and interior with their smartphones and upload these photos to the system via the application. In addition, they input sales data for the past few months in a table format and send it to the system. They also answer a personality assessment questionnaire about the store manager and submit the results.

[0806] Input data: Exterior photos, interior photos, sales data, store manager's personality information

[0807] Output data: Sending data to the server

[0808] Step 2:

[0809] Data storage on the server

[0810] The server temporarily stores exterior and interior photos, sales data, and manager personality information received from user terminals. The stored data serves as foundational data for analysis.

[0811] Input data: Exterior photos, interior photos, sales data, store manager's personality information

[0812] Output data: Saved data

[0813] Step 3:

[0814] Store evaluation using image analysis

[0815] The server uses OpenCV to analyze exterior and interior photographs. Specifically, it evaluates factors such as lighting, cleanliness, and layout in the photographs and calculates a score. Image processing algorithms utilize techniques such as edge detection and color analysis.

[0816] Input data: Exterior photos, interior photos

[0817] Output data: Exterior evaluation score, Interior evaluation score

[0818] Step 4:

[0819] Sales data analysis

[0820] The server analyzes sales data using Pandas. It evaluates sales trends, peak times, periods of stagnation, etc., and calculates a score. Specific operations include data loading, graph generation, and statistical analysis.

[0821] Input data: Sales data

[0822] Output data: Sales evaluation score, trend analysis results

[0823] Step 5:

[0824] Manager's Personality Assessment

[0825] The server uses a generative AI model to analyze the store manager's personality assessment results. It evaluates characteristics such as leadership, communication skills, and stress management abilities, and calculates a score.

[0826] Input data: Store manager's personality information

[0827] Output data: Personality assessment score

[0828] Step 6:

[0829] Overall evaluation and report generation

[0830] The server calculates an overall score based on exterior evaluation scores, interior evaluation scores, sales evaluation scores, and personality evaluation scores, and generates an evaluation result and specific improvement advice in report format. This report includes a detailed analysis and improvement measures for each evaluation item.

[0831] Input data: Exterior evaluation score, Interior evaluation score, Sales evaluation score, Personality evaluation score

[0832] Output data: Overall evaluation report

[0833] Step 7:

[0834] User Feedback

[0835] The server sends a comprehensive evaluation report to the user's terminal and provides feedback. Users can view the report and see specific improvement measures.

[0836] Input data: Overall evaluation report

[0837] Output data: Sending reports to users

[0838] Step 8:

[0839] Information disclosure on consumer-facing websites

[0840] The server registers information on highly-rated stores on a consumer-facing website, making it accessible to consumers. This makes it easier for consumers to find highly-rated stores, thus supporting stores in attracting more customers.

[0841] Input data: Overall rating report of highly-rated stores

[0842] Output data: Registration information for consumer-facing websites

[0843] Step 9:

[0844] Matching store managers with part-time job applicants

[0845] The server compares the personality information of the store manager entered by the user with the personality assessment results of the part-time job applicant, evaluates compatibility, and calculates the degree of matching. Based on the matching results, it recommends a suitable store to the part-time job applicant.

[0846] Input data: Store manager's personality information, personality assessment results of part-time job applicants

[0847] Output data: Matching results, recommendation information for part-time job applicants

[0848] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0849] This invention is a system that combines an emotion engine with a system for comprehensive diagnosis and consulting of small businesses, providing feedback that takes into account the user's emotional state. Specific embodiments are described below.

[0850] System Configuration

[0851] This system primarily consists of user terminals, servers, an emotion engine, and consumer terminals.

[0852] 1. User terminal

[0853] The user terminal is a device (e.g., PC, tablet, smartphone) used by the store's owner or manager, and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[0854] 2. Server

[0855] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating the overall score to providing feedback.

[0856] 3. Emotional Engine

[0857] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. This allows it to analyze the user's emotional state and adjust the content, presentation method, and timing of evaluation reports and advice based on that state.

[0858] 4. Consumer devices

[0859] Consumer terminals are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the rating results are provided via dedicated websites or applications.

[0860] Program processing

[0861] 1. User data entry

[0862] The user logs into the system and enters basic information (store name, address, contact information, etc.).

[0863] Take photos of the store's exterior and interior and upload them to the system.

[0864] Enter sales data for the past few months and send it to the system.

[0865] Answer the questionnaire regarding the store manager's personality and submit the results.

[0866] 2. Server Data Analysis

[0867] The server stores photos, sales data, and personality assessment data received from users.

[0868] Based on saved exterior and interior photographs, an image analysis algorithm is used to evaluate the store's exterior and interior. For example, the lighting conditions, cleanliness, and design elements of the photographs are evaluated.

[0869] Based on sales data, we evaluate sales trends, peak hours, and periods of stagnation. For example, we analyze monthly sales trends and fluctuations in customer numbers.

[0870] Based on personality questionnaire data, the store manager's personality traits will be evaluated. For example, communication skills, leadership abilities, and stress management skills will be analyzed.

[0871] 3. Calculation of overall score and feedback

[0872] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results, and determines the evaluation for each item.

[0873] The system generates a report containing the calculated total score and detailed evaluation results. For example, it might show: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Total score 30 points.

[0874] Reports and improvement suggestions are sent to user terminals to provide feedback to the store. For example, specific advice such as "making the interior brighter and changing the seating layout will improve customer satisfaction" is provided.

[0875] 4. Emotion recognition by an emotion engine

[0876] The server uses an emotion engine to analyze user reactions during the report submission and feedback process. For example, it analyzes the user's facial expressions and tone of voice during input.

[0877] The emotion engine evaluates the user's emotional state and identifies emotions such as "satisfied," "anxious," or "angry."

[0878] The presentation method of evaluation reports and the content of advice are adjusted according to the user's emotional state. For example, if a user is feeling anxious, more specific and reassuring advice is provided.

[0879] 5. Publication and customer acquisition support

[0880] The server registers information about highly-rated restaurants on consumer-facing websites, making it accessible to consumers. For example, it displays rankings and reviews for "restaurants with high customer satisfaction."

[0881] Consumers can view information about highly-rated stores through consumer-facing terminals and decide whether to visit them.

[0882] 6. Talent Matching

[0883] Applicants for part-time work take a personality test and submit the results to the system.

[0884] The server compares the store manager's personality information with the personality assessment results of the part-time job applicant to evaluate compatibility. For example, it evaluates compatibility in areas such as communication skills and stress management abilities.

[0885] Based on the evaluation results, the system recommends suitable stores to job applicants. For example, an evaluation such as "You have good personality chemistry with the store manager at this location" might be presented.

[0886] The above describes a specific embodiment of the present invention. This simultaneously achieves improved store sales and the resolution of labor shortages, and further provides feedback that takes into account the user's emotional state.

[0887] The following describes the processing flow.

[0888] Step 1:

[0889] The user logs into the system. They enter their username and password to be authenticated. Upon successful authentication, they are redirected to the dashboard screen.

[0890] Step 2:

[0891] The user enters basic information. They enter basic information such as the store name, address, and contact information into the form and submit it.

[0892] Step 3:

[0893] Users upload photos of the store's exterior. They take photos of the exterior and either drag and drop them into the designated area of ​​the system or select the file to upload.

[0894] Step 4:

[0895] Users upload interior photos of their stores. Similarly, they take interior photos and upload them to the system.

[0896] Step 5:

[0897] Users input sales data for the past few months. They enter numerical data such as total sales, monthly sales, and customer numbers, and then submit it.

[0898] Step 6:

[0899] The user answers a personality questionnaire for the store manager. They click on the appropriate option for each questionnaire item, and after answering all questions, they click the submit button.

[0900] Step 7:

[0901] The server stores exterior photos, interior photos, sales data, and personality assessment data received from the user. The data is then stored in a designated database.

[0902] Step 8:

[0903] The server processes the received exterior photos using an image analysis algorithm. For example, it evaluates the lighting conditions, cleanliness, and design elements of the photos.

[0904] Step 9:

[0905] The server processes the received interior photos using an image analysis algorithm. This algorithm evaluates the store's layout, decoration, cleanliness, and lighting.

[0906] Step 10:

[0907] The server analyzes the stored sales data. It identifies and evaluates patterns such as sales trends, peak hours, and periods of stagnation.

[0908] Step 11:

[0909] The server analyzes the personality questionnaire data of the store manager, evaluating personality traits such as communication skills, leadership, and stress management abilities.

[0910] Step 12:

[0911] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results. The score is calculated for each item, and the final overall score is determined.

[0912] Step 13:

[0913] The server generates a comprehensive evaluation report. It compiles detailed information and an overall score for exterior, interior, sales, and manager personality evaluations into a report format.

[0914] Step 14:

[0915] The server sends an evaluation report to the user's terminal. The user can view the evaluation results and improvement advice on the dashboard.

[0916] Step 15:

[0917] The server uses an emotion engine during the feedback process to analyze user responses in real time. For example, it monitors input speed and the context of responses.

[0918] Step 16:

[0919] The emotion engine evaluates the user's emotional state. For example, it identifies states such as "satisfied," "anxious," and "frustrated," and sends that information to the server.

[0920] Step 17:

[0921] The server adjusts how reports are presented based on the user's emotional state. For example, if the user is feeling anxious, it provides more detailed and reassuring advice.

[0922] Step 18:

[0923] The server sends users emotion-based evaluation reports. For example, it might offer specific advice such as, "The store's interior is good, but it would be even better if it were a little brighter."

[0924] Step 19:

[0925] The server registers information about highly-rated stores on a consumer-facing website. Based on the evaluation results, rankings and reviews of highly-rated stores are published.

[0926] Step 20:

[0927] Consumers browse consumer-facing websites. They check information on highly-rated stores and select a store to visit.

[0928] Step 21:

[0929] Applicants for part-time work will take a free personality test. They will answer a personality test form and submit the results to the system.

[0930] Step 22:

[0931] The server compares the store manager's personality information with the personality assessment results of the part-time job applicants. An AI algorithm is used to evaluate compatibility.

[0932] Step 23:

[0933] The server recommends suitable stores to job applicants based on compatibility evaluation results. It generates a list of compatible stores and presents it to the applicants.

[0934] Step 24:

[0935] Applicants for part-time jobs review the information of the stores recommended to them. They view the reasons for the recommendation and detailed information, and then decide which store to apply to.

[0936] (Example 2)

[0937] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0938] Conventional store diagnostic systems lacked the ability to provide feedback that considered the user's emotional state, resulting in insufficient features to make users more receptive to suggestions. Furthermore, the accuracy of store evaluations and the amount of information provided in feedback were often inadequate, preventing users from receiving sufficient improvement suggestions. Additionally, there were challenges in providing accurate evaluations and recommendations for consumer-facing information and matching with part-time job applicants.

[0939] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0940] In this invention, the server includes means for the user to input basic store information, exterior photos, interior photos, sales data, and information about the store manager's personality; means for receiving the exterior photos, interior photos, sales data, and store manager's personality information and storing the data; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the store manager's personality information and evaluating the store manager's personality traits; means for calculating an overall score based on these evaluation results and providing the results in a report format; means for providing feedback on the report results to the user, further analyzing the user's reaction using an emotion engine and providing feedback according to the user's emotional state; means for publishing the evaluation results to a consumer terminal and registering information on highly-rated stores; and means for comparing the personality diagnosis results of users and part-time job applicants, evaluating compatibility, and recommending appropriate stores to part-time job applicants. This makes it possible to provide more effective feedback that takes into account the user's emotional state, enabling highly accurate comprehensive diagnosis and consulting of stores. Furthermore, it will enable more accurate evaluations and recommendations in consumer-facing information disclosure and talent matching.

[0941] A "user terminal" is a device used by a user to access the system, and includes personal computers, tablets, smartphones, and other similar devices.

[0942] An "exterior photograph" is image data taken of the exterior of a store, and is used to evaluate the store's appearance.

[0943] "Interior photographs" are image data taken of the inside of a store and are used to evaluate the store's interior.

[0944] "Sales data" refers to numerical information about store sales, and is used to evaluate sales trends and patterns.

[0945] "Personality questionnaire data" refers to information used to evaluate the personality traits of users and job applicants, and is collected through questionnaires and surveys.

[0946] An "image processing algorithm" is a mathematical and computational method for analyzing image data, and is used to evaluate the exterior and interior appearance of a store.

[0947] The "overall score" is a numerical value calculated by integrating multiple evaluation factors, and it represents the overall evaluation of the store.

[0948] The "emotion engine" is a system module that analyzes the user's emotional state and adjusts the feedback based on that analysis.

[0949] "Consumer-facing devices" refer to devices used by consumers to view store ratings and information, and include personal computers and smartphones.

[0950] A "matching method" is a technique for comparing the personality assessment results of users and job applicants, evaluating compatibility, and making appropriate recommendations.

[0951] "Feedback methods" refer to methods for communicating analysis results and suggestions to the user, and include reports and advice.

[0952] This invention combines an emotion engine with a system for comprehensive diagnosis and consulting of small businesses, providing feedback that takes into account the user's emotional state. This system mainly consists of a user terminal, a server, an emotion engine, and a consumer terminal.

[0953] User terminal

[0954] User terminals are devices (PCs, tablets, smartphones) used by store owners and managers, and they provide an interface for inputting basic store information, exterior photos, interior photos, sales data, and information about the store manager's personality. For example, users can use their smartphones to take exterior and interior photos of the store and upload them to the system.

[0955] server

[0956] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating overall scores to providing feedback. Examples of image processing algorithms used include OpenCV. Examples of data analysis using Python libraries such as Scikit-learn and Pandas are also used. The server performs the following operations, for example:

[0957] 1. Store exterior and interior photos received from users and evaluate them using image analysis algorithms. Specifically, evaluate the lighting conditions, cleanliness, and design elements of the photos.

[0958] 2. Based on sales data, evaluate sales trends, peak hours, and periods of stagnation. For example, analyze monthly sales trends and fluctuations in customer numbers.

[0959] 3. Analyze the store manager's personality traits based on personality questionnaire data. For example, analyze communication skills, leadership skills, stress coping abilities, etc.

[0960] 4. Based on these analysis results, an overall score is calculated, and a detailed evaluation report is generated. For example, it may be listed as follows: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Overall score 30 points.

[0961] Emotional Engine

[0962] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. This allows it to analyze the user's emotional state and adjust the content, presentation method, and timing of evaluation reports and advice accordingly. For example, it can recognize the user's facial expressions during input to identify emotions such as "satisfied" or "anxious."

[0963] Consumer devices

[0964] Consumer terminals are devices (PCs, smartphones) for viewing information on highly-rated stores, and the evaluation results are provided via dedicated websites or applications. Consumers can view information on highly-rated stores in formats such as rankings or customer reviews, and then decide whether to visit a store.

[0965] Specific examples and prompt statements

[0966] For example, the following specific examples can be considered.

[0967] Users log into the system, upload basic information, exterior and interior photos, and enter sales data.

[0968] The server analyzes the received data and uses image analysis algorithms to evaluate the exterior and interior appearance.

[0969] Provide users with specific feedback such as, "Improving the cleanliness of the interior will increase customer satisfaction."

[0970] Example of a prompt

[0971] Please rate the exterior and interior photos of the store.

[0972] "Please analyze the sales data from the past few months to evaluate sales trends."

[0973] "Please analyze the personality traits based on the store manager's personality assessment data."

[0974] "Analyze the user's emotional state and provide appropriate feedback."

[0975] The above describes the form for carrying out the invention. This system is expected to improve store sales, enhance customer satisfaction, and facilitate effective personnel matching.

[0976] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0977] Step 1: The user logs into the system.

[0978] Users access the system login screen using their device (PC, tablet, or smartphone). They enter their email address and password to authenticate. Upon successful authentication, the user can access the system dashboard.

[0979] Enter: Email address, password

[0980] Output: Authentication result (success / failure)

[0981] Step 2: The user enters basic information.

[0982] Users enter basic information such as store name, address, and contact information on the dashboard. This information is sent from the user's device to the server and stored on the server.

[0983] Input: Store name, address, contact information

[0984] Output: Basic information is saved on the server.

[0985] Step 3: The user uploads photos of the store's exterior and interior.

[0986] Users take photos of the store's exterior and interior using their smartphone's camera function. They then upload the photos to the system. The server receives and stores these photos.

[0987] Input: Exterior photos, interior photos

[0988] Output: Photo data is saved to the server.

[0989] Step 4: The user enters the sales data.

[0990] The user imports sales data for the past few months in Excel file format. The server saves the received sales data and prepares it for analysis.

[0991] Input: Sales data (Excel file)

[0992] Output: Sales data is saved to the server.

[0993] Step 5: The user answers a questionnaire about the store manager's personality.

[0994] The user enters their answers into a question form on the system. This is a questionnaire about personality traits. The server receives and stores this response data.

[0995] Input: Personality questionnaire data (survey responses)

[0996] Output: Personality questionnaire data is saved to the server.

[0997] Step 6: The server analyzes the exterior and interior photos.

[0998] The server uses image analysis algorithms to evaluate the stored exterior and interior photographs. Specifically, it uses OpenCV to analyze lighting conditions, cleanliness, design elements, and other factors.

[0999] Input: Exterior photos, interior photos

[1000] Output: Evaluation results (e.g., scores for lighting conditions and cleanliness)

[1001] Step 7: The server analyzes the sales data.

[1002] The server analyzes sales trends, peak times, and periods of stagnation based on stored sales data. Time series analysis and statistical methods are used for the analysis.

[1003] Input: Sales data

[1004] Output: Analysis results (e.g., sales trend graph, peak time zones)

[1005] Step 8: The server analyzes the store manager's personality traits.

[1006] The server analyzes the store manager's personality traits based on personality questionnaire data. Specifically, it uses psychology-based evaluation indicators to assess communication skills, leadership, stress management abilities, and other aspects.

[1007] Input: Personality questionnaire data

[1008] Output: Evaluation results of personality traits

[1009] Step 9: The server calculates the total score.

[1010] The server integrates image analysis results, sales analysis results, and personality analysis results to calculate an overall score. The overall score is determined by taking a weighted average of the evaluations of each item.

[1011] Input: Image evaluation results, sales analysis results, personality trait evaluation results

[1012] Output: Total score (e.g., 30 points)

[1013] Step 10: The server generates a report and provides feedback to the user.

[1014] The server generates a report-style feedback based on the overall score and detailed evaluation results for each item. The report is generated in PDF format and sent to the user's terminal.

[1015] Input: Total score, evaluation results for each item

[1016] Output: Report (PDF format)

[1017] Step 11: The server uses the emotion engine to analyze the user's response.

[1018] The server analyzes the user's reaction after the report is submitted. Specifically, it analyzes the user's facial expressions captured by the camera and uses an emotion engine to identify emotional states such as "satisfied" or "anxious."

[1019] Input: User reaction data (facial expression images)

[1020] Output: Sentiment assessment result (e.g., anxiety)

[1021] Step 12: The server adjusts the feedback based on the emotional state.

[1022] Based on the analysis results, the server provides feedback tailored to the user's emotional state. For example, it provides more detailed and reassuring advice to users who are feeling anxious.

[1023] Input: Sentiment evaluation result

[1024] Output: Adjusted feedback

[1025] Step 13: The server registers information about highly-rated stores on the consumer website.

[1026] The server registers information about highly-rated stores that have achieved a certain overall score on a consumer-facing website. This information is displayed in ranking or review format and can be viewed by consumers.

[1027] Input: Total score, store information

[1028] Output: Page update for consumer-facing website

[1029] Step 14: The server compares the personality assessment results of the user and the job applicant to evaluate compatibility.

[1030] The server compares the personality assessment results of saved users and job applicants to evaluate compatibility. The evaluation involves scoring the degree of agreement for each characteristic.

[1031] Input: User's personality assessment results, Part-time job applicant's personality assessment results

[1032] Output: Compatibility evaluation results (e.g., 80% compatibility)

[1033] Step 15: The server recommends a suitable store to the job applicant.

[1034] The server recommends suitable stores to job applicants based on compatibility evaluation results. The recommendation results are sent to the applicant's email address.

[1035] Input: Compatibility evaluation result

[1036] Output: Recommendation results (email notification)

[1037] The above outlines the specific processing steps of this system. This enables the provision of feedback that takes into account the user's emotional state and comprehensive store diagnostics.

[1038] (Application Example 2)

[1039] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1040] In modern society, small business owners often find it difficult to accurately analyze multifaceted information such as the store's exterior and interior, and sales data, and to receive a comprehensive diagnosis. Furthermore, they often lack feedback that takes their emotional state into account, resulting in a failure to implement concrete improvement measures. Additionally, there is a lack of features to publicly disclose store evaluation results to consumers, and a lack of features to assess the personality compatibility between business owners and part-time job applicants. Therefore, a system that provides effective solutions to these challenges is necessary.

[1041] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1042] In this invention, the server includes means for the user to input exterior photos, interior photos, sales data, and information about the manager's personality; means for receiving the exterior photos, interior photos, sales data, and manager's personality information and storing the data; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the manager's personality information and evaluating the manager's personality traits; means for analyzing the user's emotional state using an emotion engine; means for adjusting the evaluation results and report content based on the emotional state and providing feedback; means for calculating an overall score based on these evaluation results and providing the result in report format; and means for providing feedback on the report results to the user. This makes it possible to provide multifaceted evaluations of small stores and specific improvement measures that take into account the manager's emotional state. Furthermore, it is possible to make the store evaluation results public to consumers and to judge the personality compatibility between the manager and prospective part-time workers.

[1043] A "user terminal" is a device used by store owners and managers, providing an interface for inputting exterior and interior photos of the store, sales data, and information about the owner's personality.

[1044] A "server" is the central component that receives, stores, and analyzes data from users, and it contains built-in image processing algorithms and data analysis engines.

[1045] The "emotion engine" is a module that analyzes user input data and responses during feedback to recognize the user's emotional state, and analyzes what kind of emotional state the user is in.

[1046] An "image processing algorithm" is a computational method used to evaluate the exterior and interior appearance of a store by analyzing factors such as lighting conditions, cleanliness, and design elements in a photograph.

[1047] "Sales data" refers to the basic data used to evaluate store sales trends and patterns, peak hours, and periods of stagnation.

[1048] "Personality information" refers to information about the personality traits of business leaders, such as their communication skills, leadership abilities, and stress management skills.

[1049] The "overall score" is an evaluation score calculated based on the analysis results of saved data, and represents a comprehensive evaluation of the store's exterior, interior, sales performance, and the personality information of the manager.

[1050] "Feedback" is the process of providing users with a report containing their calculated overall score and detailed evaluation results.

[1051] A "consumer-oriented website" is a website where consumers can view information about highly-rated stores, and it displays rankings and customer reviews.

[1052] A "personality assessment" refers to the results of questionnaires or tests used to evaluate the personality traits of employers or job applicants.

[1053] "Matching" is a process that compares the personality information of business owners entered by users with the personality assessment results of part-time job applicants to evaluate compatibility.

[1054] A "report" is a document that includes various evaluation results and provides evaluations and improvement advice to users.

[1055] This invention provides a system that combines an emotion engine with a system for comprehensive diagnosis and consulting of small businesses, thereby providing feedback that takes into account the user's emotional state. This system consists of a user terminal, a server, an emotion engine, and a consumer terminal.

[1056] System Configuration

[1057] This system mainly consists of the following components:

[1058] 1. User terminal

[1059] The user terminal is a device (e.g., smartphone, tablet, PC) used by the store owner or manager, and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the owner's personality. Data is entered using the user terminal as follows:

[1060] Specific example:

[1061] Take exterior and interior photos and upload them to the system.

[1062] Enter sales data for the past few months and send it to the system.

[1063] Answer the questionnaire regarding the personality of the business owner and submit the results.

[1064] 2. Server

[1065] The server is the central component that receives, stores, and analyzes data from users. The server performs the following processes:

[1066] We will evaluate the exterior and interior of the store using an image analysis algorithm (e.g., OpenCV).

[1067] We will use data analysis tools (e.g., pandas) to evaluate sales trends and patterns.

[1068] We evaluate the personality traits of business owners based on personality questionnaire data.

[1069] Specific example:

[1070] Based on the saved exterior and interior photographs, lighting conditions, cleanliness, and design elements were analyzed.

[1071] We analyze sales data to examine monthly sales trends and fluctuations in customer numbers.

[1072] We analyze the personality traits of business leaders (communication skills, leadership, stress management skills, etc.).

[1073] 3. Emotional Engine

[1074] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. The emotion engine performs the following processes:

[1075] The system analyzes the user's facial expressions and tone of writing during input to evaluate their emotional state.

[1076] Based on the emotional state, the content, presentation method, and timing of evaluation reports and advice will be adjusted.

[1077] Specific example:

[1078] If a user is feeling anxious, we provide more specific and reassuring advice.

[1079] If the user is satisfied, we will suggest further improvements or new suggestions.

[1080] 4. Consumer devices

[1081] Consumer-facing devices are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the evaluation results are provided through dedicated websites or applications.

[1082] Specific example:

[1083] Rankings and customer reviews are displayed for stores with high customer satisfaction.

[1084] Consumers can view detailed information about highly-rated stores and decide whether to visit them.

[1085] Example of a prompt

[1086] The following are specific examples of prompts the system receives:

[1087] Image upload prompt: "Upload exterior_photo.jpg"

[1088] Prompt message when uploading sales data: "Upload sales data (January-March 2023)"

[1089] Prompt text when entering personality questionnaire data: "Evaluation of communication skills: High, Leadership: Medium, Stress coping ability: Low"

[1090] The above describes the embodiments for carrying out the present invention. This system makes it possible to provide multifaceted evaluations of small businesses and specific improvement measures that take into account the emotional state of the business owners. Furthermore, it enables the public disclosure of store evaluation results to consumers and the assessment of personality compatibility between business owners and part-time job applicants.

[1091] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1092] Step 1:

[1093] The user terminal logs into the system and enters basic store information (store name, address, contact information, etc.). The server then receives this basic information and saves it to the database. The input data is basic information (store name, address, contact information, etc.), and the output is the saved basic information.

[1094] Step 2:

[1095] The user terminal takes photos of the store's exterior and interior and uploads them to the system. The server receives these photos and stores them in a database. The input data consists of exterior and interior photos, and the output is the stored photo data.

[1096] Step 3:

[1097] The user terminal inputs sales data for the past several months and sends it to the system. The server receives the sales data, saves it to a database, and performs data analysis. The input data is sales data, and the output is the saved sales data and the analysis results.

[1098] Step 4:

[1099] The user terminal answers a questionnaire about the manager's personality and sends the results to the system. The server stores the received personality data and performs analysis. The input data is the answers to the personality questionnaire, and the output is the stored personality data and analysis results.

[1100] Step 5:

[1101] The server uses image analysis algorithms (e.g., OpenCV) to evaluate the exterior and interior of a store based on stored exterior and interior photographs. It assesses the lighting conditions, cleanliness, and design elements of the photographs and saves the evaluation results. The input data consists of exterior and interior photographs, and the output is the evaluation result.

[1102] Step 6:

[1103] The server analyzes sales trends and patterns based on stored sales data. Using data analysis tools (e.g., pandas), it evaluates monthly sales trends and fluctuations in customer numbers, and saves the results. The input data is sales data, and the output is the sales trend analysis results.

[1104] Step 7:

[1105] The server analyzes stored personality questionnaire data and evaluates personality traits (communication skills, leadership, stress coping ability, etc.). The analysis results are saved. The input data is personality data, and the output is the evaluation results of personality traits.

[1106] Step 8:

[1107] The server uses an emotion engine to analyze user input data and responses to feedback to recognize the user's emotional state. The input data consists of the user's input data and responses to feedback, and the output is the result of the emotional state evaluation.

[1108] Step 9:

[1109] The server adjusts the content, presentation method, and timing of evaluation results and reports based on the emotional state to provide feedback. The input data is the emotional state evaluation result, and the output is the adjusted evaluation result and report.

[1110] Step 10:

[1111] The server calculates an overall score based on these evaluation results and provides the result in a report format. The report is sent to the user's terminal and serves as feedback to the user. The input data is the evaluation result, and the output is a report containing the overall score and detailed evaluation results.

[1112] Step 11:

[1113] The server registers the overall score and evaluation results on a consumer-facing website, and publishes information on highly-rated stores. The input data is the overall score and evaluation results, and the output is the information registered on the consumer-facing website.

[1114] Step 12:

[1115] The server compares the personality information of the business owner entered by the user with the personality assessment results of the part-time job applicant to evaluate compatibility. Based on the compatibility results, it recommends a suitable store to the part-time job applicant. The input data is the personality information of the business owner and the personality assessment results of the part-time job applicant, and the output is the compatibility evaluation results and recommendation information.

[1116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1117] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1118] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1119] [Third Embodiment]

[1120] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1124] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1128] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[1129] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1130] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1131] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1132] This invention is a system for comprehensive diagnosis and consulting for small businesses. Specific embodiments are described below.

[1133] System Configuration

[1134] This system mainly consists of user terminals, servers, and consumer terminals.

[1135] 1. User terminal

[1136] The user terminal is a device used by the store's owner or manager (e.g., a PC, tablet, or smartphone), and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[1137] 2. Server

[1138] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating the overall score to providing feedback.

[1139] 3. Consumer devices

[1140] Consumer terminals are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the rating results are provided via dedicated websites or applications.

[1141] Program processing

[1142] 1. User data entry

[1143] The user logs into the system and enters basic information (store name, address, contact information, etc.).

[1144] Take photos of the store's exterior and interior and upload them to the system.

[1145] Enter sales data for the past few months and send it to the system.

[1146] Answer the questionnaire regarding the store manager's personality and submit the results.

[1147] 2. Server Data Analysis

[1148] The server stores photos, sales data, and personality assessment data received from users.

[1149] Based on saved exterior and interior photographs, image analysis algorithms are used to evaluate the store's exterior and interior. For example, elements such as lighting, cleanliness, and layout are analyzed.

[1150] Based on sales data, we evaluate sales trends, peak hours, and periods of stagnation. For example, we analyze monthly sales trends and fluctuations in customer numbers.

[1151] Based on personality questionnaire data, the store manager's personality traits will be evaluated. For example, communication skills, leadership abilities, and stress management skills will be analyzed.

[1152] 3. Calculation of overall score and feedback

[1153] The server calculates an overall score based on the image analysis results, sales analysis results, and store manager personality analysis results, and determines the evaluation for each item.

[1154] The system generates a report containing the calculated total score and detailed evaluation results. For example, it might show: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Total score 30 points.

[1155] Reports and improvement suggestions are sent to user terminals to provide feedback to the store. For example, specific advice such as "making the interior brighter and changing the seating layout will improve customer satisfaction" is provided.

[1156] 4. Launch and customer acquisition support

[1157] The server registers information about highly-rated restaurants on consumer-facing websites, making it accessible to consumers. For example, it displays rankings and reviews for "restaurants with high customer satisfaction."

[1158] Consumers can view information about highly-rated stores through consumer-facing terminals and decide whether to visit them.

[1159] 5. Talent matching

[1160] Applicants for part-time work take a personality test and submit the results to the system.

[1161] The server compares the store manager's personality information with the personality assessment results of the part-time job applicant to evaluate compatibility. For example, it evaluates compatibility in areas such as communication skills and stress management abilities.

[1162] Based on the evaluation results, the system recommends suitable stores to job applicants. For example, an evaluation such as "You have good personality chemistry with the store manager at this location" might be presented.

[1163] The above describes a specific embodiment of the present invention. This makes it possible to simultaneously improve store sales and alleviate labor shortages.

[1164] The following describes the processing flow.

[1165] Step 1:

[1166] The user logs into the system. They enter their username and password to be authenticated. Upon successful authentication, they are redirected to the dashboard screen.

[1167] Step 2:

[1168] The user enters basic information. They enter basic information such as the store name, address, and contact information into the form and submit it.

[1169] Step 3:

[1170] Users upload photos of the store's exterior. They take photos of the exterior and either drag and drop them into the designated area of ​​the system or select the file to upload.

[1171] Step 4:

[1172] Users upload interior photos of their stores. Similarly, they take interior photos and upload them to the system.

[1173] Step 5:

[1174] Users input sales data for the past few months. They enter numerical data such as total sales, monthly sales, and customer numbers, and then submit it.

[1175] Step 6:

[1176] The user answers a personality questionnaire for the store manager. They click on the appropriate option for each questionnaire item, and after answering all questions, they click the submit button.

[1177] Step 7:

[1178] The server stores exterior photos, interior photos, sales data, and personality assessment data received from the user. The data is then stored in a designated database.

[1179] Step 8:

[1180] The server processes the received exterior photos using an image analysis algorithm. For example, it evaluates the lighting conditions, cleanliness, and design elements of the photos.

[1181] Step 9:

[1182] The server processes the received interior photos using an image analysis algorithm. This algorithm evaluates the store's layout, decoration, cleanliness, and lighting.

[1183] Step 10:

[1184] The server analyzes the stored sales data. It identifies and evaluates patterns such as sales trends, peak hours, and periods of stagnation.

[1185] Step 11:

[1186] The server analyzes the personality questionnaire data of the store manager, evaluating personality traits such as communication skills, leadership, and stress management abilities.

[1187] Step 12:

[1188] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results. The score is calculated for each item, and the final overall score is determined.

[1189] Step 13:

[1190] The server generates a comprehensive evaluation report. It compiles detailed information and an overall score for exterior, interior, sales, and manager personality evaluations into a report format.

[1191] Step 14:

[1192] The server sends an evaluation report to the user's terminal. The user can view the evaluation results and improvement advice on the dashboard.

[1193] Step 15:

[1194] The server registers information about highly-rated stores on a consumer-facing website. Based on the evaluation results, rankings and reviews of highly-rated stores are published.

[1195] Step 16:

[1196] Consumers browse consumer-facing websites. They check information on highly-rated stores and select a store to visit.

[1197] Step 17:

[1198] Applicants for part-time work will take a free personality test. They will answer a personality test form and submit the results to the system.

[1199] Step 18:

[1200] The server compares the store manager's personality information with the personality assessment results of the part-time job applicants. An AI algorithm is used to evaluate compatibility.

[1201] Step 19:

[1202] The server recommends suitable stores to job applicants based on compatibility evaluation results. It generates a list of compatible stores and presents it to the applicants.

[1203] Step 20:

[1204] Applicants for part-time jobs review the information of the stores recommended to them. They view the reasons for the recommendation and detailed information, and then decide which store to apply to.

[1205] (Example 1)

[1206] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1207] In managing small businesses, there is a lack of understanding of the quality of the store's exterior and interior, the analysis of sales trends, and the personality traits of the store manager. Therefore, it is necessary to comprehensively evaluate this information and clearly identify areas for improvement. Furthermore, there is a need for means to communicate the store's appeal to consumers and for effective methods to assess the compatibility between the store and prospective part-time workers.

[1208] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1209] In this invention, the server includes means for users to input images of the store's exterior, interior, sales information, and information regarding the store manager's characteristics; means for receiving the exterior, interior, sales information, and store manager characteristics information and storing the data; and means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm. This makes it possible to comprehensively evaluate the quality of the store's exterior and interior, sales trends, and the store manager's personality traits, and to clearly suggest areas for improvement.

[1210] A "user" is a store owner or manager who uses the system to evaluate stores.

[1211] "Store exterior image" refers to an image file that shows the condition of the store's exterior.

[1212] "Store interior images" are image files that show the condition of the store's interior.

[1213] "Sales information" refers to information that shows past sales data for a store.

[1214] "Store manager's characteristic information" refers to data that includes information about the personality and characteristics of the store manager, who is the person in charge of the store.

[1215] "Means of saving data" refers to methods of storing received data in a storage device such as a database.

[1216] An "image processing algorithm" is a set of computational procedures used to analyze image data and extract specific features or patterns.

[1217] A "sales trend" is a pattern that shows the tendency or fluctuation of sales over a certain period of time.

[1218] "Store manager characteristics" refers to personality traits such as the store manager's leadership skills, communication abilities, and stress tolerance.

[1219] The "overall score" is a score calculated by integrating evaluation results such as the store's exterior, interior, sales trends, and the store manager's characteristics.

[1220] A "report format" is a format used to notify users of evaluation results or analysis results.

[1221] A "consumer-facing website" is a website that consumers can access to view store reviews and ratings.

[1222] "Part-time job applicants" refers to individuals who wish to work at a specific store.

[1223] "Compatibility" is an indicator of the degree of personality compatibility between the store manager and the part-time job applicant.

[1224] "Fit" is a score that indicates how well the personalities and characteristics of the store manager and the part-time job applicant are compatible.

[1225] "Recommendation methods" refer to methods of suggesting the most suitable store to part-time job applicants based on evaluation results.

[1226] This invention is a system for comprehensive diagnosis and consulting of small businesses. The system consists of a user terminal, a server, and a consumer terminal. A detailed embodiment of the system is described below.

[1227] User terminal

[1228] The user terminal is a device (such as a PC, tablet, or smartphone) used by the store's owner or manager, and provides an interface for inputting exterior and interior images of the store, sales information, and the store manager's characteristics.

[1229] server

[1230] The server is the central component of the system, receiving, storing, and analyzing data from users. The server contains a high-performance computer, a database, image analysis algorithms (such as OpenCV), and data analysis engines (such as Python's Pandas or Scikit-learn).

[1231] Consumer devices

[1232] Consumer terminals are devices (such as personal computers and smartphones) used to view information on highly-rated stores, and the evaluation results are provided via dedicated websites or applications.

[1233] Specific processing of the program

[1234] Basic information (store name, location, etc.), images, sales data, and personality information entered from the user's terminal are first sent to the server. The server receives this data and stores it in a database. Specific software used at this stage includes database management systems such as MySQL and PostgreSQL.

[1235] Image analysis

[1236] The server first analyzes exterior and interior images. Using OpenCV, it extracts and evaluates elements such as lighting, cleanliness, and layout from the images. For example, it analyzes the brightness distribution to evaluate the lighting conditions of a store.

[1237] Sales data analysis

[1238] Next, we will analyze the sales data. Using the Python Pandas library, we will evaluate sales trends, peak times, and periods of stagnation. For example, we will graph and analyze monthly sales trends.

[1239] Personality data analysis

[1240] Furthermore, the store manager's characteristics will be evaluated based on personality questionnaire data. Scikit-learn will be used to evaluate characteristics such as communication skills, leadership, and stress management ability. For example, the scores for each questionnaire item will be added together to calculate an overall score.

[1241] Overall score and feedback

[1242] The server calculates an overall score based on image analysis results, sales analysis results, and store manager personality analysis results, and generates a detailed evaluation result and improvement advice in report format. For example, it may include specific advice such as, "We recommend adding more lighting to improve the brightness of the store." The generated report is sent to the user's terminal.

[1243] Public release and customer acquisition support

[1244] The server registers information on highly-rated stores on a consumer-facing website and publishes details about these stores (e.g., store name, rating score, areas for improvement, etc.). Consumers can view this information through consumer-facing devices and decide whether to visit the store.

[1245] Talent matching

[1246] Applicants for part-time jobs take a personality test and submit the results to the system. The server compares the store manager's characteristics with the applicant's personality test results and calculates a compatibility score. Based on this, it recommends a suitable store to the applicant.

[1247] Examples of specific actions and prompt statements

[1248] For example, if a cafe owner were to use the system, they would enter the data following these steps:

[1249] 1. Log in to the system and enter your basic information (cafe name, address, contact information).

[1250] 2. Take photos of the store's exterior and interior and upload them to the system.

[1251] 3. Enter sales data for the past six months and submit it to the system.

[1252] 4. Answer the questionnaire regarding your (the store manager's) personality and submit the results.

[1253] An example of a prompt message is, "Analyze the exterior photos of this cafe and evaluate its cleanliness and layout."

[1254] The above describes a specific embodiment of this invention. By utilizing this system, it is possible to simultaneously improve store sales and alleviate labor shortages.

[1255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1256] Step 1:

[1257] The user logs into the system. The user is required to enter their email address and password. The server receives the authentication information and verifies it against the user information in the database. Upon successful authentication, the user is redirected to the dashboard. The output displays the user's dashboard screen.

[1258] Step 2:

[1259] The user enters the store's basic information (store name, address, contact information). The entered data is sent to the system via an HTML form. The server saves the received data to a database. As output, the basic information is recorded in the database.

[1260] Step 3:

[1261] Users upload images of the store's exterior and interior. The input must be image files in JPEG or PNG format. The server receives the images and stores them in a database. The output is the image files themselves, recorded in the database.

[1262] Step 4:

[1263] The user inputs sales information for the past few months. The input must be in a format such as a CSV file. The server saves the received sales data to a database. The output is the sales data recorded in the database.

[1264] Step 5:

[1265] Users answer a questionnaire about the characteristics of the store manager. The input is in the form of selecting answers from multiple options. The server saves the received questionnaire results to a database. The output is the questionnaire data recorded in the database.

[1266] Step 6:

[1267] The server analyzes stored exterior and interior images. It requires received image files in JPEG or PNG format as input. The server uses OpenCV to extract elements such as lighting, cleanliness, and layout from the images. For example, it analyzes the brightness distribution to evaluate the lighting conditions of a store. The output is an evaluation score.

[1268] Step 7:

[1269] The server analyzes the stored sales data. The input requires sales information in CSV file format. The server uses the Python Pandas library to evaluate sales trends, peak times, and periods of low sales. For example, it can graph and analyze monthly sales trends. The output is a sales evaluation score.

[1270] Step 8:

[1271] The server analyzes stored data on the store manager's characteristics. The input requires survey results data. The server uses Scikit-learn to evaluate characteristics such as communication skills, leadership, and stress management. For example, it calculates an overall score by summing the scores for each characteristic. The output is the characteristic evaluation score.

[1272] Step 9:

[1273] The server calculates an overall score based on image analysis results, sales analysis results, and store manager characteristics results. The input requires evaluation scores obtained from each analysis. The server combines these scores to calculate the overall score. The output generates an overall evaluation report.

[1274] Step 10:

[1275] The server sends the overall evaluation report to the user's terminal. The generated overall evaluation report is required as input. The server sends the report to the user's terminal, allowing the user to view it. The output allows the user to review the evaluation results and improvement advice.

[1276] Step 11:

[1277] The server registers information about highly-rated stores on a consumer-facing website. The server requires data from the overall rating report as input. The server then publishes details about the highly-rated stores (e.g., store name, rating score, areas for improvement, etc.) on the website. As output, consumers can view the information about the highly-rated stores.

[1278] Step 12:

[1279] Applicants for part-time work take a personality assessment and submit the results to the system. The input requires answers to multiple questions in a questionnaire format. The server saves the received assessment results to a database. The output is the personality assessment data, which is then recorded in the database.

[1280] Step 13:

[1281] The server compares the store manager's characteristics with the personality assessment results of part-time job applicants and calculates a compatibility score. The required inputs are the store manager's characteristics data and the applicant's personality assessment results. The server calculates the compatibility score based on this data. The output is a compatibility evaluation report.

[1282] Step 14:

[1283] The server sends a compatibility assessment report to job applicants. The generated compatibility assessment report is required as input. The server sends the report to the applicant and recommends suitable stores. The output allows the applicant to review the appropriate stores.

[1284] (Application Example 1)

[1285] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1286] Traditional small-scale store management support systems typically focused on individual evaluations of exterior and interior design, sales trends, and store managers' personalities, making it difficult to provide comprehensive assessments or specific improvement advice. Furthermore, there was a lack of effective means to publicize highly-rated stores to consumers to support customer acquisition, and to evaluate and match employees with store managers based on their compatibility. As a result, store operations were often inefficient, leading to missed sales opportunities.

[1287] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1288] In this invention, the server includes means for users to input exterior and interior photos of a store, sales data, and information about the store manager's personality; means for receiving and storing the exterior and interior photos, sales data, and information about the store manager's personality; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the store manager's personality information and evaluating the store manager's personality traits; means for calculating an overall score based on these evaluation results and providing the results in a report format; means for providing feedback on the report results to the user; means for using an image processing algorithm to analyze the exterior and interior photos; means for evaluating sales trends and peak hours based on the analysis of the sales data; and means for evaluating the store manager's leadership traits based on the personality information. This makes it possible to provide a comprehensive evaluation of the store and specific improvement advice. Furthermore, it is possible to publish information about highly-rated stores on consumer-facing websites to support customer acquisition. In addition, it is possible to achieve efficient personnel allocation by evaluating the personality compatibility between the store manager and part-time job applicants and performing appropriate matching.

[1289] A "user terminal" is a device used by store owners and managers, providing an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[1290] "Photographs" refer to image data of the store's exterior and interior, which are taken and uploaded to the system.

[1291] "Sales data" refers to data that shows a store's past sales performance and is used to analyze sales trends and patterns.

[1292] "Store manager personality information" refers to data that indicates the store manager's personality traits, and is provided as a result of a personality assessment questionnaire.

[1293] A "server" is the central component that stores, analyzes, and generates evaluation results from data received from users.

[1294] An "image processing algorithm" is a technical method used to analyze photographic data and evaluate its external and internal appearance.

[1295] "Sales trends" refer to analytical results that show the trends and patterns of store sales based on sales data.

[1296] "Personality traits" are data that represent the characteristics of an individual's personality, and include leadership, stress coping ability, communication skills, and so on.

[1297] The "overall score" is the result of comprehensively calculating the evaluation of the store's exterior, interior, sales, and manager's personality, and is provided in report format.

[1298] A "report" is a document that summarizes the analysis results and improvement advice, and is provided as feedback to the user.

[1299] A "consumer-oriented website" is an online platform where consumers can view information about highly-rated stores.

[1300] "Matching score" is the result of evaluating the compatibility between the personality traits of the store manager and the personality traits of the part-time job applicant.

[1301] This invention is a system for comprehensive diagnosis and consulting for small businesses. The detailed implementation method is described below.

[1302] System Configuration

[1303] This system consists of user terminals, servers, and consumer terminals.

[1304] User terminal

[1305] User terminals are used by store owners and managers and include devices such as personal computers, tablets, and smartphones. User terminals have the following functions:

[1306] 1. Take photos of the store's exterior and interior and upload them to the system.

[1307] 2. Enter sales data for the past few months and submit it to the system.

[1308] 3. Answer the questionnaire regarding the store manager's personality and submit the results.

[1309] server

[1310] The server is the central component for receiving, storing, and analyzing data sent from user terminals. Specifically, it has the following functions:

[1311] 1. Use image processing algorithms to evaluate the exterior and interior of the store.

[1312] 2. Analyze sales data to evaluate sales trends, peak times, and periods of stagnation.

[1313] 3. Evaluate the store manager's personality traits based on personality questionnaire data.

[1314] 4. Calculate the overall score and generate the evaluation results in report format.

[1315] Consumer devices

[1316] Consumer devices are devices that allow consumers to view information on highly-rated stores, and include personal computers and smartphones. This allows consumers to view rating results via dedicated websites or applications.

[1317] Hardware and software to be used

[1318] This system uses the following hardware and software:

[1319] Hardware: Smartphones, tablets, PCs (Windows, Mac), servers (physical servers or cloud servers)

[1320] Software: Python, OpenCV (image analysis library), Pandas (data analysis library)

[1321] Data adjustment and calculation flow

[1322] The user terminal takes photos of the store's exterior and interior, inputs sales data, and collects data about the store manager's personality. This data is then sent to the server.

[1323] The server performs the following actions:

[1324] 1. Image Analysis: Using OpenCV, analyze exterior and interior photos of the store. The analysis results will be used to evaluate factors such as lighting brightness, cleanliness, and layout appropriateness.

[1325] 2. Sales Analysis: Use Pandas to analyze sales data and evaluate sales trends and quality. For example, statistically analyze monthly sales trends and fluctuations in customer numbers.

[1326] 3. Personality Assessment: Based on the store manager's personality assessment data, a generative AI model will be used to evaluate leadership, communication skills, stress management abilities, and other aspects.

[1327] Examples

[1328] The following are examples of how this system can be used:

[1329] 1. The store owner takes exterior and interior photos with their smartphone and uploads these photos to the server via the application.

[1330] 2. The manager enters sales data for the past three months in a table format and sends it to the system.

[1331] 3. The manager completes a personality assessment questionnaire for the store manager via the application and submits the results.

[1332] Specific input examples:

[1333] Store name: ABC Store

[1334] Location: Shibuya Ward, Tokyo

[1335] Contact number: 03-1234-5678

[1336] Exterior photo: exterior.jpg

[1337] Interior photo: interior.jpg

[1338] Sales data:

[1339] Monthly sales

[1340] January, 100000

[1341] February, 120000

[1342] Personality test results:

[1343] Communication: 8

[1344] Leadership: 7

[1345] Once the above data is sent to the server, the server performs the analysis described above and provides the management with an overall evaluation and improvement advice. In addition, by being registered as a highly-rated store, the store will also receive support for attracting customers through consumer terminals.

[1346] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1347] Step 1:

[1348] Data entry at the user terminal

[1349] Users take photos of the store's exterior and interior with their smartphones and upload these photos to the system via the application. In addition, they input sales data for the past few months in a table format and send it to the system. They also answer a personality assessment questionnaire about the store manager and submit the results.

[1350] Input data: Exterior photos, interior photos, sales data, store manager's personality information

[1351] Output data: Sending data to the server

[1352] Step 2:

[1353] Data storage on the server

[1354] The server temporarily stores exterior and interior photos, sales data, and manager personality information received from user terminals. The stored data serves as foundational data for analysis.

[1355] Input data: Exterior photos, interior photos, sales data, store manager's personality information

[1356] Output data: Saved data

[1357] Step 3:

[1358] Store evaluation using image analysis

[1359] The server uses OpenCV to analyze exterior and interior photographs. Specifically, it evaluates factors such as lighting, cleanliness, and layout in the photographs and calculates a score. Image processing algorithms utilize techniques such as edge detection and color analysis.

[1360] Input data: Exterior photos, interior photos

[1361] Output data: Exterior evaluation score, Interior evaluation score

[1362] Step 4:

[1363] Sales data analysis

[1364] The server analyzes sales data using Pandas. It evaluates sales trends, peak times, periods of stagnation, etc., and calculates a score. Specific operations include data loading, graph generation, and statistical analysis.

[1365] Input data: Sales data

[1366] Output data: Sales evaluation score, trend analysis results

[1367] Step 5:

[1368] Manager's Personality Assessment

[1369] The server uses a generative AI model to analyze the store manager's personality assessment results. It evaluates characteristics such as leadership, communication skills, and stress management abilities, and calculates a score.

[1370] Input data: Store manager's personality information

[1371] Output data: Personality assessment score

[1372] Step 6:

[1373] Overall evaluation and report generation

[1374] The server calculates an overall score based on exterior evaluation scores, interior evaluation scores, sales evaluation scores, and personality evaluation scores, and generates an evaluation result and specific improvement advice in report format. This report includes a detailed analysis and improvement measures for each evaluation item.

[1375] Input data: Exterior evaluation score, Interior evaluation score, Sales evaluation score, Personality evaluation score

[1376] Output data: Overall evaluation report

[1377] Step 7:

[1378] User Feedback

[1379] The server sends a comprehensive evaluation report to the user's terminal and provides feedback. Users can view the report and see specific improvement measures.

[1380] Input data: Overall evaluation report

[1381] Output data: Sending reports to users

[1382] Step 8:

[1383] Information disclosure on consumer-facing websites

[1384] The server registers information on highly-rated stores on a consumer-facing website, making it accessible to consumers. This makes it easier for consumers to find highly-rated stores, thus supporting stores in attracting more customers.

[1385] Input data: Overall rating report of highly-rated stores

[1386] Output data: Registration information for consumer-facing websites

[1387] Step 9:

[1388] Matching store managers with part-time job applicants

[1389] The server compares the personality information of the store manager entered by the user with the personality assessment results of the part-time job applicant, evaluates compatibility, and calculates the degree of matching. Based on the matching results, it recommends a suitable store to the part-time job applicant.

[1390] Input data: Store manager's personality information, personality assessment results of part-time job applicants

[1391] Output data: Matching results, recommendation information for part-time job applicants

[1392] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1393] This invention is a system that combines an emotion engine with a system for comprehensive diagnosis and consulting of small businesses, providing feedback that takes into account the user's emotional state. Specific embodiments are described below.

[1394] System Configuration

[1395] This system primarily consists of user terminals, servers, an emotion engine, and consumer terminals.

[1396] 1. User terminal

[1397] The user terminal is a device (e.g., PC, tablet, smartphone) used by the store's owner or manager, and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[1398] 2. Server

[1399] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating the overall score to providing feedback.

[1400] 3. Emotional Engine

[1401] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. This allows it to analyze the user's emotional state and adjust the content, presentation method, and timing of evaluation reports and advice based on that state.

[1402] 4. Consumer devices

[1403] Consumer terminals are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the rating results are provided via dedicated websites or applications.

[1404] Program processing

[1405] 1. User data entry

[1406] The user logs into the system and enters basic information (store name, address, contact information, etc.).

[1407] Take photos of the store's exterior and interior and upload them to the system.

[1408] Enter sales data for the past few months and send it to the system.

[1409] Answer the questionnaire regarding the store manager's personality and submit the results.

[1410] 2. Server Data Analysis

[1411] The server stores photos, sales data, and personality assessment data received from users.

[1412] Based on saved exterior and interior photographs, an image analysis algorithm is used to evaluate the store's exterior and interior. For example, the lighting conditions, cleanliness, and design elements of the photographs are evaluated.

[1413] Based on sales data, we evaluate sales trends, peak hours, and periods of stagnation. For example, we analyze monthly sales trends and fluctuations in customer numbers.

[1414] Based on personality questionnaire data, the store manager's personality traits will be evaluated. For example, communication skills, leadership abilities, and stress management skills will be analyzed.

[1415] 3. Calculation of overall score and feedback

[1416] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results, and determines the evaluation for each item.

[1417] The system generates a report containing the calculated total score and detailed evaluation results. For example, it might show: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Total score 30 points.

[1418] Reports and improvement suggestions are sent to user terminals to provide feedback to the store. For example, specific advice such as "making the interior brighter and changing the seating layout will improve customer satisfaction" is provided.

[1419] 4. Emotion recognition by an emotion engine

[1420] The server uses an emotion engine to analyze user reactions during the report submission and feedback process. For example, it analyzes the user's facial expressions and tone of voice during input.

[1421] The emotion engine evaluates the user's emotional state and identifies emotions such as "satisfied," "anxious," or "angry."

[1422] The presentation method of evaluation reports and the content of advice are adjusted according to the user's emotional state. For example, if a user is feeling anxious, more specific and reassuring advice is provided.

[1423] 5. Publication and customer acquisition support

[1424] The server registers information about highly-rated restaurants on consumer-facing websites, making it accessible to consumers. For example, it displays rankings and reviews for "restaurants with high customer satisfaction."

[1425] Consumers can view information about highly-rated stores through consumer-facing terminals and decide whether to visit them.

[1426] 6. Talent Matching

[1427] Applicants for part-time work take a personality test and submit the results to the system.

[1428] The server compares the store manager's personality information with the personality assessment results of the part-time job applicant to evaluate compatibility. For example, it evaluates compatibility in areas such as communication skills and stress management abilities.

[1429] Based on the evaluation results, the system recommends suitable stores to job applicants. For example, an evaluation such as "You have good personality chemistry with the store manager at this location" might be presented.

[1430] The above describes a specific embodiment of the present invention. This simultaneously achieves improved store sales and the resolution of labor shortages, and further provides feedback that takes into account the user's emotional state.

[1431] The following describes the processing flow.

[1432] Step 1:

[1433] The user logs into the system. They enter their username and password to be authenticated. Upon successful authentication, they are redirected to the dashboard screen.

[1434] Step 2:

[1435] The user enters basic information. They enter basic information such as the store name, address, and contact information into the form and submit it.

[1436] Step 3:

[1437] Users upload photos of the store's exterior. They take photos of the exterior and either drag and drop them into the designated area of ​​the system or select the file to upload.

[1438] Step 4:

[1439] Users upload interior photos of their stores. Similarly, they take interior photos and upload them to the system.

[1440] Step 5:

[1441] Users input sales data for the past few months. They enter numerical data such as total sales, monthly sales, and customer numbers, and then submit it.

[1442] Step 6:

[1443] The user answers a personality questionnaire for the store manager. They click on the appropriate option for each questionnaire item, and after answering all questions, they click the submit button.

[1444] Step 7:

[1445] The server stores exterior photos, interior photos, sales data, and personality assessment data received from the user. The data is then stored in a designated database.

[1446] Step 8:

[1447] The server processes the received exterior photos using an image analysis algorithm. For example, it evaluates the lighting conditions, cleanliness, and design elements of the photos.

[1448] Step 9:

[1449] The server processes the received interior photos using an image analysis algorithm. This algorithm evaluates the store's layout, decoration, cleanliness, and lighting.

[1450] Step 10:

[1451] The server analyzes the stored sales data. It identifies and evaluates patterns such as sales trends, peak hours, and periods of stagnation.

[1452] Step 11:

[1453] The server analyzes the personality questionnaire data of the store manager, evaluating personality traits such as communication skills, leadership, and stress management abilities.

[1454] Step 12:

[1455] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results. The score is calculated for each item, and the final overall score is determined.

[1456] Step 13:

[1457] The server generates a comprehensive evaluation report. It compiles detailed information and an overall score for exterior, interior, sales, and manager personality evaluations into a report format.

[1458] Step 14:

[1459] The server sends an evaluation report to the user's terminal. The user can view the evaluation results and improvement advice on the dashboard.

[1460] Step 15:

[1461] The server uses an emotion engine during the feedback process to analyze user responses in real time. For example, it monitors input speed and the context of responses.

[1462] Step 16:

[1463] The emotion engine evaluates the user's emotional state. For example, it identifies states such as "satisfied," "anxious," and "frustrated," and sends that information to the server.

[1464] Step 17:

[1465] The server adjusts how reports are presented based on the user's emotional state. For example, if the user is feeling anxious, it provides more detailed and reassuring advice.

[1466] Step 18:

[1467] The server sends users emotion-based evaluation reports. For example, it might offer specific advice such as, "The store's interior is good, but it would be even better if it were a little brighter."

[1468] Step 19:

[1469] The server registers information about highly-rated stores on a consumer-facing website. Based on the evaluation results, rankings and reviews of highly-rated stores are published.

[1470] Step 20:

[1471] Consumers browse consumer-facing websites. They check information on highly-rated stores and select a store to visit.

[1472] Step 21:

[1473] Applicants for part-time work will take a free personality test. They will answer a personality test form and submit the results to the system.

[1474] Step 22:

[1475] The server compares the store manager's personality information with the personality assessment results of the part-time job applicants. An AI algorithm is used to evaluate compatibility.

[1476] Step 23:

[1477] The server recommends suitable stores to job applicants based on compatibility evaluation results. It generates a list of compatible stores and presents it to the applicants.

[1478] Step 24:

[1479] Applicants for part-time jobs review the information of the stores recommended to them. They view the reasons for the recommendation and detailed information, and then decide which store to apply to.

[1480] (Example 2)

[1481] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1482] Conventional store diagnostic systems lacked the ability to provide feedback that considered the user's emotional state, resulting in insufficient features to make users more receptive to suggestions. Furthermore, the accuracy of store evaluations and the amount of information provided in feedback were often inadequate, preventing users from receiving sufficient improvement suggestions. Additionally, there were challenges in providing accurate evaluations and recommendations for consumer-facing information and matching with part-time job applicants.

[1483] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1484] In this invention, the server includes means for the user to input basic store information, exterior photos, interior photos, sales data, and information about the store manager's personality; means for receiving the exterior photos, interior photos, sales data, and store manager's personality information and storing the data; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the store manager's personality information and evaluating the store manager's personality traits; means for calculating an overall score based on these evaluation results and providing the results in a report format; means for providing feedback on the report results to the user, further analyzing the user's reaction using an emotion engine and providing feedback according to the user's emotional state; means for publishing the evaluation results to a consumer terminal and registering information on highly-rated stores; and means for comparing the personality diagnosis results of users and part-time job applicants, evaluating compatibility, and recommending appropriate stores to part-time job applicants. This makes it possible to provide more effective feedback that takes into account the user's emotional state, enabling highly accurate comprehensive diagnosis and consulting of stores. Furthermore, it will enable more accurate evaluations and recommendations in consumer-facing information disclosure and talent matching.

[1485] A "user terminal" is a device used by a user to access the system, and includes personal computers, tablets, smartphones, and other similar devices.

[1486] An "exterior photograph" is image data taken of the exterior of a store, and is used to evaluate the store's appearance.

[1487] "Interior photographs" are image data taken of the inside of a store and are used to evaluate the store's interior.

[1488] "Sales data" refers to numerical information about store sales, and is used to evaluate sales trends and patterns.

[1489] "Personality questionnaire data" refers to information used to evaluate the personality traits of users and job applicants, and is collected through questionnaires and surveys.

[1490] An "image processing algorithm" is a mathematical and computational method for analyzing image data, and is used to evaluate the exterior and interior appearance of a store.

[1491] The "overall score" is a numerical value calculated by integrating multiple evaluation factors, and it represents the overall evaluation of the store.

[1492] The "emotion engine" is a system module that analyzes the user's emotional state and adjusts the feedback based on that analysis.

[1493] "Consumer-facing devices" refer to devices used by consumers to view store ratings and information, and include personal computers and smartphones.

[1494] A "matching method" is a technique for comparing the personality assessment results of users and job applicants, evaluating compatibility, and making appropriate recommendations.

[1495] "Feedback methods" refer to methods for communicating analysis results and suggestions to the user, and include reports and advice.

[1496] This invention combines an emotion engine with a system for comprehensive diagnosis and consulting of small businesses, providing feedback that takes into account the user's emotional state. This system mainly consists of a user terminal, a server, an emotion engine, and a consumer terminal.

[1497] User terminal

[1498] User terminals are devices (PCs, tablets, smartphones) used by store owners and managers, and they provide an interface for inputting basic store information, exterior photos, interior photos, sales data, and information about the store manager's personality. For example, users can use their smartphones to take exterior and interior photos of the store and upload them to the system.

[1499] server

[1500] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating overall scores to providing feedback. Examples of image processing algorithms used include OpenCV. Examples of data analysis using Python libraries such as Scikit-learn and Pandas are also used. The server performs the following operations, for example:

[1501] 1. Store exterior and interior photos received from users and evaluate them using image analysis algorithms. Specifically, evaluate the lighting conditions, cleanliness, and design elements of the photos.

[1502] 2. Based on sales data, evaluate sales trends, peak hours, and periods of stagnation. For example, analyze monthly sales trends and fluctuations in customer numbers.

[1503] 3. Analyze the store manager's personality traits based on personality questionnaire data. For example, analyze communication skills, leadership skills, stress coping abilities, etc.

[1504] 4. Based on these analysis results, an overall score is calculated, and a detailed evaluation report is generated. For example, it may be listed as follows: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Overall score 30 points.

[1505] Emotional Engine

[1506] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. This allows it to analyze the user's emotional state and adjust the content, presentation method, and timing of evaluation reports and advice accordingly. For example, it can recognize the user's facial expressions during input to identify emotions such as "satisfied" or "anxious."

[1507] Consumer devices

[1508] Consumer terminals are devices (PCs, smartphones) for viewing information on highly-rated stores, and the evaluation results are provided via dedicated websites or applications. Consumers can view information on highly-rated stores in formats such as rankings or customer reviews, and then decide whether to visit a store.

[1509] Specific examples and prompt statements

[1510] For example, the following specific examples can be considered.

[1511] Users log into the system, upload basic information, exterior and interior photos, and enter sales data.

[1512] The server analyzes the received data and uses image analysis algorithms to evaluate the exterior and interior appearance.

[1513] Provide users with specific feedback such as, "Improving the cleanliness of the interior will increase customer satisfaction."

[1514] Example of a prompt

[1515] Please rate the exterior and interior photos of the store.

[1516] "Please analyze the sales data from the past few months to evaluate sales trends."

[1517] "Please analyze the personality traits based on the store manager's personality assessment data."

[1518] "Analyze the user's emotional state and provide appropriate feedback."

[1519] The above describes the form for carrying out the invention. This system is expected to improve store sales, enhance customer satisfaction, and facilitate effective personnel matching.

[1520] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1521] Step 1: The user logs into the system.

[1522] Users access the system login screen using their device (PC, tablet, or smartphone). They enter their email address and password to authenticate. Upon successful authentication, the user can access the system dashboard.

[1523] Enter: Email address, password

[1524] Output: Authentication result (success / failure)

[1525] Step 2: The user enters basic information.

[1526] Users enter basic information such as store name, address, and contact information on the dashboard. This information is sent from the user's device to the server and stored on the server.

[1527] Input: Store name, address, contact information

[1528] Output: Basic information is saved on the server.

[1529] Step 3: The user uploads photos of the store's exterior and interior.

[1530] Users take photos of the store's exterior and interior using their smartphone's camera function. They then upload the photos to the system. The server receives and stores these photos.

[1531] Input: Exterior photos, interior photos

[1532] Output: Photo data is saved to the server.

[1533] Step 4: The user enters the sales data.

[1534] The user imports sales data for the past few months in Excel file format. The server saves the received sales data and prepares it for analysis.

[1535] Input: Sales data (Excel file)

[1536] Output: Sales data is saved to the server.

[1537] Step 5: The user answers a questionnaire about the store manager's personality.

[1538] The user enters their answers into a question form on the system. This is a questionnaire about personality traits. The server receives and stores this response data.

[1539] Input: Personality questionnaire data (survey responses)

[1540] Output: Personality questionnaire data is saved to the server.

[1541] Step 6: The server analyzes the exterior and interior photos.

[1542] The server uses image analysis algorithms to evaluate the stored exterior and interior photographs. Specifically, it uses OpenCV to analyze lighting conditions, cleanliness, design elements, and other factors.

[1543] Input: Exterior photos, interior photos

[1544] Output: Evaluation results (e.g., scores for lighting conditions and cleanliness)

[1545] Step 7: The server analyzes the sales data.

[1546] The server analyzes sales trends, peak times, and periods of stagnation based on stored sales data. Time series analysis and statistical methods are used for the analysis.

[1547] Input: Sales data

[1548] Output: Analysis results (e.g., sales trend graph, peak time zones)

[1549] Step 8: The server analyzes the store manager's personality traits.

[1550] The server analyzes the store manager's personality traits based on personality questionnaire data. Specifically, it uses psychology-based evaluation indicators to assess communication skills, leadership, stress management abilities, and other aspects.

[1551] Input: Personality questionnaire data

[1552] Output: Evaluation results of personality traits

[1553] Step 9: The server calculates the total score.

[1554] The server integrates image analysis results, sales analysis results, and personality analysis results to calculate an overall score. The overall score is determined by taking a weighted average of the evaluations of each item.

[1555] Input: Image evaluation results, sales analysis results, personality trait evaluation results

[1556] Output: Total score (e.g., 30 points)

[1557] Step 10: The server generates a report and provides feedback to the user.

[1558] The server generates a report-style feedback based on the overall score and detailed evaluation results for each item. The report is generated in PDF format and sent to the user's terminal.

[1559] Input: Total score, evaluation results for each item

[1560] Output: Report (PDF format)

[1561] Step 11: The server uses the emotion engine to analyze the user's response.

[1562] The server analyzes the user's reaction after the report is submitted. Specifically, it analyzes the user's facial expressions captured by the camera and uses an emotion engine to identify emotional states such as "satisfied" or "anxious."

[1563] Input: User reaction data (facial expression images)

[1564] Output: Sentiment assessment result (e.g., anxiety)

[1565] Step 12: The server adjusts the feedback based on the emotional state.

[1566] Based on the analysis results, the server provides feedback tailored to the user's emotional state. For example, it provides more detailed and reassuring advice to users who are feeling anxious.

[1567] Input: Sentiment evaluation result

[1568] Output: Adjusted feedback

[1569] Step 13: The server registers information about highly-rated stores on the consumer website.

[1570] The server registers information about highly-rated stores that have achieved a certain overall score on a consumer-facing website. This information is displayed in ranking or review format and can be viewed by consumers.

[1571] Input: Total score, store information

[1572] Output: Page update for consumer-facing website

[1573] Step 14: The server compares the personality assessment results of the user and the job applicant to evaluate compatibility.

[1574] The server compares the personality assessment results of saved users and job applicants to evaluate compatibility. The evaluation involves scoring the degree of agreement for each characteristic.

[1575] Input: User's personality assessment results, Part-time job applicant's personality assessment results

[1576] Output: Compatibility evaluation results (e.g., 80% compatibility)

[1577] Step 15: The server recommends a suitable store to the job applicant.

[1578] The server recommends suitable stores to job applicants based on compatibility evaluation results. The recommendation results are sent to the applicant's email address.

[1579] Input: Compatibility evaluation result

[1580] Output: Recommendation results (email notification)

[1581] The above outlines the specific processing steps of this system. This enables the provision of feedback that takes into account the user's emotional state and comprehensive store diagnostics.

[1582] (Application Example 2)

[1583] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1584] In modern society, small business owners often find it difficult to accurately analyze multifaceted information such as the store's exterior and interior, and sales data, and to receive a comprehensive diagnosis. Furthermore, they often lack feedback that takes their emotional state into account, resulting in a failure to implement concrete improvement measures. Additionally, there is a lack of features to publicly disclose store evaluation results to consumers, and a lack of features to assess the personality compatibility between business owners and part-time job applicants. Therefore, a system that provides effective solutions to these challenges is necessary.

[1585] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1586] In this invention, the server includes means for the user to input exterior photos, interior photos, sales data, and information about the manager's personality; means for receiving the exterior photos, interior photos, sales data, and manager's personality information and storing the data; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the manager's personality information and evaluating the manager's personality traits; means for analyzing the user's emotional state using an emotion engine; means for adjusting the evaluation results and report content based on the emotional state and providing feedback; means for calculating an overall score based on these evaluation results and providing the result in report format; and means for providing feedback on the report results to the user. This makes it possible to provide multifaceted evaluations of small stores and specific improvement measures that take into account the manager's emotional state. Furthermore, it is possible to make the store evaluation results public to consumers and to judge the personality compatibility between the manager and prospective part-time workers.

[1587] A "user terminal" is a device used by store owners and managers, providing an interface for inputting exterior and interior photos of the store, sales data, and information about the owner's personality.

[1588] A "server" is the central component that receives, stores, and analyzes data from users, and it contains built-in image processing algorithms and data analysis engines.

[1589] The "emotion engine" is a module that analyzes user input data and responses during feedback to recognize the user's emotional state, and analyzes what kind of emotional state the user is in.

[1590] An "image processing algorithm" is a computational method used to evaluate the exterior and interior appearance of a store by analyzing factors such as lighting conditions, cleanliness, and design elements in a photograph.

[1591] "Sales data" refers to the basic data used to evaluate store sales trends and patterns, peak hours, and periods of stagnation.

[1592] "Personality information" refers to information about the personality traits of business leaders, such as their communication skills, leadership abilities, and stress management skills.

[1593] The "overall score" is an evaluation score calculated based on the analysis results of saved data, and represents a comprehensive evaluation of the store's exterior, interior, sales performance, and the personality information of the manager.

[1594] "Feedback" is the process of providing users with a report containing their calculated overall score and detailed evaluation results.

[1595] A "consumer-oriented website" is a website where consumers can view information about highly-rated stores, and it displays rankings and customer reviews.

[1596] A "personality assessment" refers to the results of questionnaires or tests used to evaluate the personality traits of employers or job applicants.

[1597] "Matching" is a process that compares the personality information of business owners entered by users with the personality assessment results of part-time job applicants to evaluate compatibility.

[1598] A "report" is a document that includes various evaluation results and provides evaluations and improvement advice to users.

[1599] This invention provides a system that combines an emotion engine with a system for comprehensive diagnosis and consulting of small businesses, thereby providing feedback that takes into account the user's emotional state. This system consists of a user terminal, a server, an emotion engine, and a consumer terminal.

[1600] System Configuration

[1601] This system mainly consists of the following components:

[1602] 1. User terminal

[1603] The user terminal is a device (e.g., smartphone, tablet, PC) used by the store owner or manager, and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the owner's personality. Data is entered using the user terminal as follows:

[1604] Specific example:

[1605] Take exterior and interior photos and upload them to the system.

[1606] Enter sales data for the past few months and send it to the system.

[1607] Answer the questionnaire regarding the personality of the business owner and submit the results.

[1608] 2. Server

[1609] The server is the central component that receives, stores, and analyzes data from users. The server performs the following processes:

[1610] We will evaluate the exterior and interior of the store using an image analysis algorithm (e.g., OpenCV).

[1611] We will use data analysis tools (e.g., pandas) to evaluate sales trends and patterns.

[1612] We evaluate the personality traits of business owners based on personality questionnaire data.

[1613] Specific example:

[1614] Based on the saved exterior and interior photographs, lighting conditions, cleanliness, and design elements were analyzed.

[1615] We analyze sales data to examine monthly sales trends and fluctuations in customer numbers.

[1616] We analyze the personality traits of business leaders (communication skills, leadership, stress management skills, etc.).

[1617] 3. Emotional Engine

[1618] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. The emotion engine performs the following processes:

[1619] The system analyzes the user's facial expressions and tone of writing during input to evaluate their emotional state.

[1620] Based on the emotional state, the content, presentation method, and timing of evaluation reports and advice will be adjusted.

[1621] Specific example:

[1622] If a user is feeling anxious, we provide more specific and reassuring advice.

[1623] If the user is satisfied, we will suggest further improvements or new suggestions.

[1624] 4. Consumer devices

[1625] Consumer-facing devices are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the evaluation results are provided through dedicated websites or applications.

[1626] Specific example:

[1627] Rankings and customer reviews are displayed for stores with high customer satisfaction.

[1628] Consumers can view detailed information about highly-rated stores and decide whether to visit them.

[1629] Example of a prompt

[1630] The following are specific examples of prompts the system receives:

[1631] Image upload prompt: "Upload exterior_photo.jpg"

[1632] Prompt message when uploading sales data: "Upload sales data (January-March 2023)"

[1633] Prompt text when entering personality questionnaire data: "Evaluation of communication skills: High, Leadership: Medium, Stress coping ability: Low"

[1634] The above describes the embodiments for carrying out the present invention. This system makes it possible to provide multifaceted evaluations of small businesses and specific improvement measures that take into account the emotional state of the business owners. Furthermore, it enables the public disclosure of store evaluation results to consumers and the assessment of personality compatibility between business owners and part-time job applicants.

[1635] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1636] Step 1:

[1637] The user terminal logs into the system and enters basic store information (store name, address, contact information, etc.). The server then receives this basic information and saves it to the database. The input data is basic information (store name, address, contact information, etc.), and the output is the saved basic information.

[1638] Step 2:

[1639] The user terminal takes photos of the store's exterior and interior and uploads them to the system. The server receives these photos and stores them in a database. The input data consists of exterior and interior photos, and the output is the stored photo data.

[1640] Step 3:

[1641] The user terminal inputs sales data for the past several months and sends it to the system. The server receives the sales data, saves it to a database, and performs data analysis. The input data is sales data, and the output is the saved sales data and the analysis results.

[1642] Step 4:

[1643] The user terminal answers a questionnaire about the manager's personality and sends the results to the system. The server stores the received personality data and performs analysis. The input data is the answers to the personality questionnaire, and the output is the stored personality data and analysis results.

[1644] Step 5:

[1645] The server uses image analysis algorithms (e.g., OpenCV) to evaluate the exterior and interior of a store based on stored exterior and interior photographs. It assesses the lighting conditions, cleanliness, and design elements of the photographs and saves the evaluation results. The input data consists of exterior and interior photographs, and the output is the evaluation result.

[1646] Step 6:

[1647] The server analyzes sales trends and patterns based on stored sales data. Using data analysis tools (e.g., pandas), it evaluates monthly sales trends and fluctuations in customer numbers, and saves the results. The input data is sales data, and the output is the sales trend analysis results.

[1648] Step 7:

[1649] The server analyzes stored personality questionnaire data and evaluates personality traits (communication skills, leadership, stress coping ability, etc.). The analysis results are saved. The input data is personality data, and the output is the evaluation results of personality traits.

[1650] Step 8:

[1651] The server uses an emotion engine to analyze user input data and responses to feedback to recognize the user's emotional state. The input data consists of the user's input data and responses to feedback, and the output is the result of the emotional state evaluation.

[1652] Step 9:

[1653] The server adjusts the content, presentation method, and timing of evaluation results and reports based on the emotional state to provide feedback. The input data is the emotional state evaluation result, and the output is the adjusted evaluation result and report.

[1654] Step 10:

[1655] The server calculates an overall score based on these evaluation results and provides the result in a report format. The report is sent to the user's terminal and serves as feedback to the user. The input data is the evaluation result, and the output is a report containing the overall score and detailed evaluation results.

[1656] Step 11:

[1657] The server registers the overall score and evaluation results on a consumer-facing website, and publishes information on highly-rated stores. The input data is the overall score and evaluation results, and the output is the information registered on the consumer-facing website.

[1658] Step 12:

[1659] The server compares the personality information of the business owner entered by the user with the personality assessment results of the part-time job applicant to evaluate compatibility. Based on the compatibility results, it recommends a suitable store to the part-time job applicant. The input data is the personality information of the business owner and the personality assessment results of the part-time job applicant, and the output is the compatibility evaluation results and recommendation information.

[1660] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1661] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1662] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1663] [Fourth Embodiment]

[1664] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1665] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1666] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1667] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1668] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1669] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1670] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1671] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1672] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1673] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[1674] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1675] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1676] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1677] This invention is a system for comprehensive diagnosis and consulting for small businesses. Specific embodiments are described below.

[1678] System Configuration

[1679] This system mainly consists of user terminals, servers, and consumer terminals.

[1680] 1. User terminal

[1681] The user terminal is a device used by the store's owner or manager (e.g., a PC, tablet, or smartphone), and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[1682] 2. Server

[1683] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating the overall score to providing feedback.

[1684] 3. Consumer devices

[1685] Consumer terminals are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the rating results are provided via dedicated websites or applications.

[1686] Program processing

[1687] 1. User data entry

[1688] The user logs into the system and enters basic information (store name, address, contact information, etc.).

[1689] Take photos of the store's exterior and interior and upload them to the system.

[1690] Enter sales data for the past few months and send it to the system.

[1691] Answer the questionnaire regarding the store manager's personality and submit the results.

[1692] 2. Server Data Analysis

[1693] The server stores photos, sales data, and personality assessment data received from users.

[1694] Based on saved exterior and interior photographs, image analysis algorithms are used to evaluate the store's exterior and interior. For example, elements such as lighting, cleanliness, and layout are analyzed.

[1695] Based on sales data, we evaluate sales trends, peak hours, and periods of stagnation. For example, we analyze monthly sales trends and fluctuations in customer numbers.

[1696] Based on personality questionnaire data, the store manager's personality traits will be evaluated. For example, communication skills, leadership abilities, and stress management skills will be analyzed.

[1697] 3. Calculation of overall score and feedback

[1698] The server calculates an overall score based on the image analysis results, sales analysis results, and store manager personality analysis results, and determines the evaluation for each item.

[1699] The system generates a report containing the calculated total score and detailed evaluation results. For example, it might show: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Total score 30 points.

[1700] Reports and improvement suggestions are sent to user terminals to provide feedback to the store. For example, specific advice such as "making the interior brighter and changing the seating layout will improve customer satisfaction" is provided.

[1701] 4. Launch and customer acquisition support

[1702] The server registers information about highly-rated restaurants on consumer-facing websites, making it accessible to consumers. For example, it displays rankings and reviews for "restaurants with high customer satisfaction."

[1703] Consumers can view information about highly-rated stores through consumer-facing terminals and decide whether to visit them.

[1704] 5. Talent matching

[1705] Applicants for part-time work take a personality test and submit the results to the system.

[1706] The server compares the store manager's personality information with the personality assessment results of the part-time job applicant to evaluate compatibility. For example, it evaluates compatibility in areas such as communication skills and stress management abilities.

[1707] Based on the evaluation results, the system recommends suitable stores to job applicants. For example, an evaluation such as "You have good personality chemistry with the store manager at this location" might be presented.

[1708] The above describes a specific embodiment of the present invention. This makes it possible to simultaneously improve store sales and alleviate labor shortages.

[1709] The following describes the processing flow.

[1710] Step 1:

[1711] The user logs into the system. They enter their username and password to be authenticated. Upon successful authentication, they are redirected to the dashboard screen.

[1712] Step 2:

[1713] The user enters basic information. They enter basic information such as the store name, address, and contact information into the form and submit it.

[1714] Step 3:

[1715] Users upload photos of the store's exterior. They take photos of the exterior and either drag and drop them into the designated area of ​​the system or select the file to upload.

[1716] Step 4:

[1717] Users upload interior photos of their stores. Similarly, they take interior photos and upload them to the system.

[1718] Step 5:

[1719] Users input sales data for the past few months. They enter numerical data such as total sales, monthly sales, and customer numbers, and then submit it.

[1720] Step 6:

[1721] The user answers a personality questionnaire for the store manager. They click on the appropriate option for each questionnaire item, and after answering all questions, they click the submit button.

[1722] Step 7:

[1723] The server stores exterior photos, interior photos, sales data, and personality assessment data received from the user. The data is then stored in a designated database.

[1724] Step 8:

[1725] The server processes the received exterior photos using an image analysis algorithm. For example, it evaluates the lighting conditions, cleanliness, and design elements of the photos.

[1726] Step 9:

[1727] The server processes the received interior photos using an image analysis algorithm. This algorithm evaluates the store's layout, decoration, cleanliness, and lighting.

[1728] Step 10:

[1729] The server analyzes the stored sales data. It identifies and evaluates patterns such as sales trends, peak hours, and periods of stagnation.

[1730] Step 11:

[1731] The server analyzes the personality questionnaire data of the store manager, evaluating personality traits such as communication skills, leadership, and stress management abilities.

[1732] Step 12:

[1733] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results. The score is calculated for each item, and the final overall score is determined.

[1734] Step 13:

[1735] The server generates a comprehensive evaluation report. It compiles detailed information and an overall score for exterior, interior, sales, and manager personality evaluations into a report format.

[1736] Step 14:

[1737] The server sends an evaluation report to the user's terminal. The user can view the evaluation results and improvement advice on the dashboard.

[1738] Step 15:

[1739] The server registers information about highly-rated stores on a consumer-facing website. Based on the evaluation results, rankings and reviews of highly-rated stores are published.

[1740] Step 16:

[1741] Consumers browse consumer-facing websites. They check information on highly-rated stores and select a store to visit.

[1742] Step 17:

[1743] Applicants for part-time work will take a free personality test. They will answer a personality test form and submit the results to the system.

[1744] Step 18:

[1745] The server compares the store manager's personality information with the personality assessment results of the part-time job applicants. An AI algorithm is used to evaluate compatibility.

[1746] Step 19:

[1747] The server recommends suitable stores to job applicants based on compatibility evaluation results. It generates a list of compatible stores and presents it to the applicants.

[1748] Step 20:

[1749] Applicants for part-time jobs review the information of the stores recommended to them. They view the reasons for the recommendation and detailed information, and then decide which store to apply to.

[1750] (Example 1)

[1751] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1752] In managing small businesses, there is a lack of understanding of the quality of the store's exterior and interior, the analysis of sales trends, and the personality traits of the store manager. Therefore, it is necessary to comprehensively evaluate this information and clearly identify areas for improvement. Furthermore, there is a need for means to communicate the store's appeal to consumers and for effective methods to assess the compatibility between the store and prospective part-time workers.

[1753] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1754] In this invention, the server includes means for users to input images of the store's exterior, interior, sales information, and information regarding the store manager's characteristics; means for receiving the exterior, interior, sales information, and store manager characteristics information and storing the data; and means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm. This makes it possible to comprehensively evaluate the quality of the store's exterior and interior, sales trends, and the store manager's personality traits, and to clearly suggest areas for improvement.

[1755] A "user" is a store owner or manager who uses the system to evaluate stores.

[1756] "Store exterior image" refers to an image file that shows the condition of the store's exterior.

[1757] "Store interior images" are image files that show the condition of the store's interior.

[1758] "Sales information" refers to information that shows past sales data for a store.

[1759] "Store manager's characteristic information" refers to data that includes information about the personality and characteristics of the store manager, who is the person in charge of the store.

[1760] "Means of saving data" refers to methods of storing received data in a storage device such as a database.

[1761] An "image processing algorithm" is a set of computational procedures used to analyze image data and extract specific features or patterns.

[1762] A "sales trend" is a pattern that shows the tendency or fluctuation of sales over a certain period of time.

[1763] "Store manager characteristics" refers to personality traits such as the store manager's leadership skills, communication abilities, and stress tolerance.

[1764] The "overall score" is a score calculated by integrating evaluation results such as the store's exterior, interior, sales trends, and the store manager's characteristics.

[1765] A "report format" is a format used to notify users of evaluation results or analysis results.

[1766] A "consumer-facing website" is a website that consumers can access to view store reviews and ratings.

[1767] "Part-time job applicants" refers to individuals who wish to work at a specific store.

[1768] "Compatibility" is an indicator of the degree of personality compatibility between the store manager and the part-time job applicant.

[1769] "Fit" is a score that indicates how well the personalities and characteristics of the store manager and the part-time job applicant are compatible.

[1770] "Recommendation methods" refer to methods of suggesting the most suitable store to part-time job applicants based on evaluation results.

[1771] This invention is a system for comprehensive diagnosis and consulting of small businesses. The system consists of a user terminal, a server, and a consumer terminal. A detailed embodiment of the system is described below.

[1772] User terminal

[1773] The user terminal is a device (such as a PC, tablet, or smartphone) used by the store's owner or manager, and provides an interface for inputting exterior and interior images of the store, sales information, and the store manager's characteristics.

[1774] server

[1775] The server is the central component of the system, receiving, storing, and analyzing data from users. The server contains a high-performance computer, a database, image analysis algorithms (such as OpenCV), and data analysis engines (such as Python's Pandas or Scikit-learn).

[1776] Consumer devices

[1777] Consumer terminals are devices (such as personal computers and smartphones) used to view information on highly-rated stores, and the evaluation results are provided via dedicated websites or applications.

[1778] Specific processing of the program

[1779] Basic information (store name, location, etc.), images, sales data, and personality information entered from the user's terminal are first sent to the server. The server receives this data and stores it in a database. Specific software used at this stage includes database management systems such as MySQL and PostgreSQL.

[1780] Image analysis

[1781] The server first analyzes exterior and interior images. Using OpenCV, it extracts and evaluates elements such as lighting, cleanliness, and layout from the images. For example, it analyzes the brightness distribution to evaluate the lighting conditions of a store.

[1782] Sales data analysis

[1783] Next, we will analyze the sales data. Using the Python Pandas library, we will evaluate sales trends, peak times, and periods of stagnation. For example, we will graph and analyze monthly sales trends.

[1784] Personality data analysis

[1785] Furthermore, the store manager's characteristics will be evaluated based on personality questionnaire data. Scikit-learn will be used to evaluate characteristics such as communication skills, leadership, and stress management ability. For example, the scores for each questionnaire item will be added together to calculate an overall score.

[1786] Overall score and feedback

[1787] The server calculates an overall score based on image analysis results, sales analysis results, and store manager personality analysis results, and generates a detailed evaluation result and improvement advice in report format. For example, it may include specific advice such as, "We recommend adding more lighting to improve the brightness of the store." The generated report is sent to the user's terminal.

[1788] Public release and customer acquisition support

[1789] The server registers information on highly-rated stores on a consumer-facing website and publishes details about these stores (e.g., store name, rating score, areas for improvement, etc.). Consumers can view this information through consumer-facing devices and decide whether to visit the store.

[1790] Talent matching

[1791] Applicants for part-time jobs take a personality test and submit the results to the system. The server compares the store manager's characteristics with the applicant's personality test results and calculates a compatibility score. Based on this, it recommends a suitable store to the applicant.

[1792] Examples of specific actions and prompt statements

[1793] For example, if a cafe owner were to use the system, they would enter the data following these steps:

[1794] 1. Log in to the system and enter your basic information (cafe name, address, contact information).

[1795] 2. Take photos of the store's exterior and interior and upload them to the system.

[1796] 3. Enter sales data for the past six months and submit it to the system.

[1797] 4. Answer the questionnaire regarding your (the store manager's) personality and submit the results.

[1798] An example of a prompt message is, "Analyze the exterior photos of this cafe and evaluate its cleanliness and layout."

[1799] The above describes a specific embodiment of this invention. By utilizing this system, it is possible to simultaneously improve store sales and alleviate labor shortages.

[1800] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1801] Step 1:

[1802] The user logs into the system. The user is required to enter their email address and password. The server receives the authentication information and verifies it against the user information in the database. Upon successful authentication, the user is redirected to the dashboard. The output displays the user's dashboard screen.

[1803] Step 2:

[1804] The user enters the store's basic information (store name, address, contact information). The entered data is sent to the system via an HTML form. The server saves the received data to a database. As output, the basic information is recorded in the database.

[1805] Step 3:

[1806] Users upload images of the store's exterior and interior. The input must be image files in JPEG or PNG format. The server receives the images and stores them in a database. The output is the image files themselves, recorded in the database.

[1807] Step 4:

[1808] The user inputs sales information for the past few months. The input must be in a format such as a CSV file. The server saves the received sales data to a database. The output is the sales data recorded in the database.

[1809] Step 5:

[1810] Users answer a questionnaire about the characteristics of the store manager. The input is in the form of selecting answers from multiple options. The server saves the received questionnaire results to a database. The output is the questionnaire data recorded in the database.

[1811] Step 6:

[1812] The server analyzes stored exterior and interior images. It requires received image files in JPEG or PNG format as input. The server uses OpenCV to extract elements such as lighting, cleanliness, and layout from the images. For example, it analyzes the brightness distribution to evaluate the lighting conditions of a store. The output is an evaluation score.

[1813] Step 7:

[1814] The server analyzes the stored sales data. The input requires sales information in CSV file format. The server uses the Python Pandas library to evaluate sales trends, peak times, and periods of low sales. For example, it can graph and analyze monthly sales trends. The output is a sales evaluation score.

[1815] Step 8:

[1816] The server analyzes stored data on the store manager's characteristics. The input requires survey results data. The server uses Scikit-learn to evaluate characteristics such as communication skills, leadership, and stress management. For example, it calculates an overall score by summing the scores for each characteristic. The output is the characteristic evaluation score.

[1817] Step 9:

[1818] The server calculates an overall score based on image analysis results, sales analysis results, and store manager characteristics results. The input requires evaluation scores obtained from each analysis. The server combines these scores to calculate the overall score. The output generates an overall evaluation report.

[1819] Step 10:

[1820] The server sends the overall evaluation report to the user's terminal. The generated overall evaluation report is required as input. The server sends the report to the user's terminal, allowing the user to view it. The output allows the user to review the evaluation results and improvement advice.

[1821] Step 11:

[1822] The server registers information about highly-rated stores on a consumer-facing website. The server requires data from the overall rating report as input. The server then publishes details about the highly-rated stores (e.g., store name, rating score, areas for improvement, etc.) on the website. As output, consumers can view the information about the highly-rated stores.

[1823] Step 12:

[1824] Applicants for part-time work take a personality assessment and submit the results to the system. The input requires answers to multiple questions in a questionnaire format. The server saves the received assessment results to a database. The output is the personality assessment data, which is then recorded in the database.

[1825] Step 13:

[1826] The server compares the store manager's characteristics with the personality assessment results of part-time job applicants and calculates a compatibility score. The required inputs are the store manager's characteristics data and the applicant's personality assessment results. The server calculates the compatibility score based on this data. The output is a compatibility evaluation report.

[1827] Step 14:

[1828] The server sends a compatibility assessment report to job applicants. The generated compatibility assessment report is required as input. The server sends the report to the applicant and recommends suitable stores. The output allows the applicant to review the appropriate stores.

[1829] (Application Example 1)

[1830] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1831] Traditional small-scale store management support systems typically focused on individual evaluations of exterior and interior design, sales trends, and store managers' personalities, making it difficult to provide comprehensive assessments or specific improvement advice. Furthermore, there was a lack of effective means to publicize highly-rated stores to consumers to support customer acquisition, and to evaluate and match employees with store managers based on their compatibility. As a result, store operations were often inefficient, leading to missed sales opportunities.

[1832] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1833] In this invention, the server includes means for users to input exterior and interior photos of a store, sales data, and information about the store manager's personality; means for receiving and storing the exterior and interior photos, sales data, and information about the store manager's personality; means for analyzing the stored data and evaluating the store's exterior and interior using an image processing algorithm; means for analyzing the sales data and evaluating the store's sales trends and patterns; means for analyzing the store manager's personality information and evaluating the store manager's personality traits; means for calculating an overall score based on these evaluation results and providing the results in a report format; means for providing feedback on the report results to the user; means for using an image processing algorithm to analyze the exterior and interior photos; means for evaluating sales trends and peak hours based on the analysis of the sales data; and means for evaluating the store manager's leadership traits based on the personality information. This makes it possible to provide a comprehensive evaluation of the store and specific improvement advice. Furthermore, it is possible to publish information about highly-rated stores on consumer-facing websites to support customer acquisition. In addition, it is possible to achieve efficient personnel allocation by evaluating the personality compatibility between the store manager and part-time job applicants and performing appropriate matching.

[1834] A "user terminal" is a device used by store owners and managers, providing an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[1835] "Photographs" refer to image data of the store's exterior and interior, which are taken and uploaded to the system.

[1836] "Sales data" refers to data that shows a store's past sales performance and is used to analyze sales trends and patterns.

[1837] "Store manager personality information" refers to data that indicates the store manager's personality traits, and is provided as a result of a personality assessment questionnaire.

[1838] A "server" is the central component that stores, analyzes, and generates evaluation results from data received from users.

[1839] An "image processing algorithm" is a technical method used to analyze photographic data and evaluate its external and internal appearance.

[1840] "Sales trends" refer to analytical results that show the trends and patterns of store sales based on sales data.

[1841] "Personality traits" are data that represent the characteristics of an individual's personality, and include leadership, stress coping ability, communication skills, and so on.

[1842] The "overall score" is the result of comprehensively calculating the evaluation of the store's exterior, interior, sales, and manager's personality, and is provided in report format.

[1843] A "report" is a document that summarizes the analysis results and improvement advice, and is provided as feedback to the user.

[1844] A "consumer-oriented website" is an online platform where consumers can view information about highly-rated stores.

[1845] "Matching score" is the result of evaluating the compatibility between the personality traits of the store manager and the personality traits of the part-time job applicant.

[1846] This invention is a system for comprehensive diagnosis and consulting for small businesses. The detailed implementation method is described below.

[1847] System Configuration

[1848] This system consists of user terminals, servers, and consumer terminals.

[1849] User terminal

[1850] User terminals are used by store owners and managers and include devices such as personal computers, tablets, and smartphones. User terminals have the following functions:

[1851] 1. Take photos of the store's exterior and interior and upload them to the system.

[1852] 2. Enter sales data for the past few months and submit it to the system.

[1853] 3. Answer the questionnaire regarding the store manager's personality and submit the results.

[1854] server

[1855] The server is the central component for receiving, storing, and analyzing data sent from user terminals. Specifically, it has the following functions:

[1856] 1. Use image processing algorithms to evaluate the exterior and interior of the store.

[1857] 2. Analyze sales data to evaluate sales trends, peak times, and periods of stagnation.

[1858] 3. Evaluate the store manager's personality traits based on personality questionnaire data.

[1859] 4. Calculate the overall score and generate the evaluation results in report format.

[1860] Consumer devices

[1861] Consumer devices are devices that allow consumers to view information on highly-rated stores, and include personal computers and smartphones. This allows consumers to view rating results via dedicated websites or applications.

[1862] Hardware and software to be used

[1863] This system uses the following hardware and software:

[1864] Hardware: Smartphones, tablets, PCs (Windows, Mac), servers (physical servers or cloud servers)

[1865] Software: Python, OpenCV (image analysis library), Pandas (data analysis library)

[1866] Data adjustment and calculation flow

[1867] The user terminal takes photos of the store's exterior and interior, inputs sales data, and collects data about the store manager's personality. This data is then sent to the server.

[1868] The server performs the following actions:

[1869] 1. Image Analysis: Using OpenCV, analyze exterior and interior photos of the store. The analysis results will be used to evaluate factors such as lighting brightness, cleanliness, and layout appropriateness.

[1870] 2. Sales Analysis: Use Pandas to analyze sales data and evaluate sales trends and quality. For example, statistically analyze monthly sales trends and fluctuations in customer numbers.

[1871] 3. Personality Assessment: Based on the store manager's personality assessment data, a generative AI model will be used to evaluate leadership, communication skills, stress management abilities, and other aspects.

[1872] Examples

[1873] The following are examples of how this system can be used:

[1874] 1. The store owner takes exterior and interior photos with their smartphone and uploads these photos to the server via the application.

[1875] 2. The manager enters sales data for the past three months in a table format and sends it to the system.

[1876] 3. The manager completes a personality assessment questionnaire for the store manager via the application and submits the results.

[1877] Specific input examples:

[1878] Store name: ABC Store

[1879] Location: Shibuya Ward, Tokyo

[1880] Contact number: 03-1234-5678

[1881] Exterior photo: exterior.jpg

[1882] Interior photo: interior.jpg

[1883] Sales data:

[1884] Monthly sales

[1885] January, 100000

[1886] February, 120000

[1887] Personality test results:

[1888] Communication: 8

[1889] Leadership: 7

[1890] Once the above data is sent to the server, the server performs the analysis described above and provides the management with an overall evaluation and improvement advice. In addition, by being registered as a highly-rated store, the store will also receive support for attracting customers through consumer terminals.

[1891] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1892] Step 1:

[1893] Data entry at the user terminal

[1894] Users take photos of the store's exterior and interior with their smartphones and upload these photos to the system via the application. In addition, they input sales data for the past few months in a table format and send it to the system. They also answer a personality assessment questionnaire about the store manager and submit the results.

[1895] Input data: Exterior photos, interior photos, sales data, store manager's personality information

[1896] Output data: Sending data to the server

[1897] Step 2:

[1898] Data storage on the server

[1899] The server temporarily stores exterior and interior photos, sales data, and manager personality information received from user terminals. The stored data serves as foundational data for analysis.

[1900] Input data: Exterior photos, interior photos, sales data, store manager's personality information

[1901] Output data: Saved data

[1902] Step 3:

[1903] Store evaluation using image analysis

[1904] The server uses OpenCV to analyze exterior and interior photographs. Specifically, it evaluates factors such as lighting, cleanliness, and layout in the photographs and calculates a score. Image processing algorithms utilize techniques such as edge detection and color analysis.

[1905] Input data: Exterior photos, interior photos

[1906] Output data: Exterior evaluation score, Interior evaluation score

[1907] Step 4:

[1908] Sales data analysis

[1909] The server analyzes sales data using Pandas. It evaluates sales trends, peak times, periods of stagnation, etc., and calculates a score. Specific operations include data loading, graph generation, and statistical analysis.

[1910] Input data: Sales data

[1911] Output data: Sales evaluation score, trend analysis results

[1912] Step 5:

[1913] Manager's Personality Assessment

[1914] The server uses a generative AI model to analyze the store manager's personality assessment results. It evaluates characteristics such as leadership, communication skills, and stress management abilities, and calculates a score.

[1915] Input data: Store manager's personality information

[1916] Output data: Personality assessment score

[1917] Step 6:

[1918] Overall evaluation and report generation

[1919] The server calculates an overall score based on exterior evaluation scores, interior evaluation scores, sales evaluation scores, and personality evaluation scores, and generates an evaluation result and specific improvement advice in report format. This report includes a detailed analysis and improvement measures for each evaluation item.

[1920] Input data: Exterior evaluation score, Interior evaluation score, Sales evaluation score, Personality evaluation score

[1921] Output data: Overall evaluation report

[1922] Step 7:

[1923] User Feedback

[1924] The server sends a comprehensive evaluation report to the user's terminal and provides feedback. Users can view the report and see specific improvement measures.

[1925] Input data: Overall evaluation report

[1926] Output data: Sending reports to users

[1927] Step 8:

[1928] Information disclosure on consumer-facing websites

[1929] The server registers information on highly-rated stores on a consumer-facing website, making it accessible to consumers. This makes it easier for consumers to find highly-rated stores, thus supporting stores in attracting more customers.

[1930] Input data: Overall rating report of highly-rated stores

[1931] Output data: Registration information for consumer-facing websites

[1932] Step 9:

[1933] Matching store managers with part-time job applicants

[1934] The server compares the personality information of the store manager entered by the user with the personality assessment results of the part-time job applicant, evaluates compatibility, and calculates the degree of matching. Based on the matching results, it recommends a suitable store to the part-time job applicant.

[1935] Input data: Store manager's personality information, personality assessment results of part-time job applicants

[1936] Output data: Matching results, recommendation information for part-time job applicants

[1937] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1938] This invention is a system that combines an emotion engine with a system for comprehensive diagnosis and consulting of small businesses, providing feedback that takes into account the user's emotional state. Specific embodiments are described below.

[1939] System Configuration

[1940] This system primarily consists of user terminals, servers, an emotion engine, and consumer terminals.

[1941] 1. User terminal

[1942] The user terminal is a device (e.g., PC, tablet, smartphone) used by the store's owner or manager, and provides an interface for inputting exterior and interior photos of the store, sales data, and information about the store manager's personality.

[1943] 2. Server

[1944] The server is the central component that receives, stores, and analyzes data from users. It incorporates image analysis algorithms and data analysis engines, and is responsible for a series of processes from calculating the overall score to providing feedback.

[1945] 3. Emotional Engine

[1946] The emotion engine is a module that analyzes user input data and responses during feedback to recognize the user's emotional state. This allows it to analyze the user's emotional state and adjust the content, presentation method, and timing of evaluation reports and advice based on that state.

[1947] 4. Consumer devices

[1948] Consumer terminals are devices (e.g., personal computers, smartphones) used to view information on highly-rated stores, and the rating results are provided via dedicated websites or applications.

[1949] Program processing

[1950] 1. User data entry

[1951] The user logs into the system and enters basic information (store name, address, contact information, etc.).

[1952] Take photos of the store's exterior and interior and upload them to the system.

[1953] Enter sales data for the past few months and send it to the system.

[1954] Answer the questionnaire regarding the store manager's personality and submit the results.

[1955] 2. Server Data Analysis

[1956] The server stores photos, sales data, and personality assessment data received from users.

[1957] Based on saved exterior and interior photographs, an image analysis algorithm is used to evaluate the store's exterior and interior. For example, the lighting conditions, cleanliness, and design elements of the photographs are evaluated.

[1958] Based on sales data, we evaluate sales trends, peak hours, and periods of stagnation. For example, we analyze monthly sales trends and fluctuations in customer numbers.

[1959] Based on personality questionnaire data, the store manager's personality traits will be evaluated. For example, communication skills, leadership abilities, and stress management skills will be analyzed.

[1960] 3. Calculation of overall score and feedback

[1961] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results, and determines the evaluation for each item.

[1962] The system generates a report containing the calculated total score and detailed evaluation results. For example, it might show: Exterior evaluation 8 points, Interior evaluation 7 points, Sales evaluation 6 points, Store manager personality evaluation 9 points, Total score 30 points.

[1963] Reports and improvement suggestions are sent to user terminals to provide feedback to the store. For example, specific advice such as "making the interior brighter and changing the seating layout will improve customer satisfaction" is provided.

[1964] 4. Emotion recognition by an emotion engine

[1965] The server uses an emotion engine to analyze user reactions during the report submission and feedback process. For example, it analyzes the user's facial expressions and tone of voice during input.

[1966] The emotion engine evaluates the user's emotional state and identifies emotions such as "satisfied," "anxious," or "angry."

[1967] The presentation method of evaluation reports and the content of advice are adjusted according to the user's emotional state. For example, if a user is feeling anxious, more specific and reassuring advice is provided.

[1968] 5. Publication and customer acquisition support

[1969] The server registers information about highly-rated restaurants on consumer-facing websites, making it accessible to consumers. For example, it displays rankings and reviews for "restaurants with high customer satisfaction."

[1970] Consumers can view information about highly-rated stores through consumer-facing terminals and decide whether to visit them.

[1971] 6. Talent Matching

[1972] Applicants for part-time work take a personality test and submit the results to the system.

[1973] The server compares the store manager's personality information with the personality assessment results of the part-time job applicant to evaluate compatibility. For example, it evaluates compatibility in areas such as communication skills and stress management abilities.

[1974] Based on the evaluation results, the system recommends suitable stores to job applicants. For example, an evaluation such as "You have good personality chemistry with the store manager at this location" might be presented.

[1975] The above describes a specific embodiment of the present invention. This simultaneously achieves improved store sales and the resolution of labor shortages, and further provides feedback that takes into account the user's emotional state.

[1976] The following describes the processing flow.

[1977] Step 1:

[1978] The user logs into the system. They enter their username and password to be authenticated. Upon successful authentication, they are redirected to the dashboard screen.

[1979] Step 2:

[1980] The user enters basic information. They enter basic information such as the store name, address, and contact information into the form and submit it.

[1981] Step 3:

[1982] Users upload photos of the store's exterior. They take photos of the exterior and either drag and drop them into the designated area of ​​the system or select the file to upload.

[1983] Step 4:

[1984] Users upload interior photos of their stores. Similarly, they take interior photos and upload them to the system.

[1985] Step 5:

[1986] Users input sales data for the past few months. They enter numerical data such as total sales, monthly sales, and customer numbers, and then submit it.

[1987] Step 6:

[1988] The user answers a personality questionnaire for the store manager. They click on the appropriate option for each questionnaire item, and after answering all questions, they click the submit button.

[1989] Step 7:

[1990] The server stores exterior photos, interior photos, sales data, and personality assessment data received from the user. The data is then stored in a designated database.

[1991] Step 8:

[1992] The server processes the received exterior photos using an image analysis algorithm. For example, it evaluates the lighting conditions, cleanliness, and design elements of the photos.

[1993] Step 9:

[1994] The server processes the received interior photos using an image analysis algorithm. This algorithm evaluates the store's layout, decoration, cleanliness, and lighting.

[1995] Step 10:

[1996] The server analyzes the stored sales data. It identifies and evaluates patterns such as sales trends, peak hours, and periods of stagnation.

[1997] Step 11:

[1998] The server analyzes the personality questionnaire data of the store manager, evaluating personality traits such as communication skills, leadership, and stress management abilities.

[1999] Step 12:

[2000] The server calculates an overall score based on image analysis results, sales analysis results, and personality analysis results. The score is calculated for each item, and the final overall score is determined.

[2001] Step 13:

[2002] The server generates a comprehensive evaluation report. It compiles detailed information and an overall score for exterior, interior, sales, and manager personality evaluations into a report format.

[2003] Step 14:

[2004] The server sends an evaluation report to the user's terminal. The user can view the evaluation results and improvement advice on the dashboard.

[2005] Step 15:

[2006] The server uses an emotion engine during the feedback process to analyze user responses...

Claims

1. A means for users to input exterior photos of the store, interior photos, sales data, and information about the store manager's personality, A means for receiving and storing the aforementioned exterior photographs, interior photographs, sales data, and store manager personality information, A means for analyzing the aforementioned stored data and evaluating the exterior and interior of the store using an image processing algorithm, A means for analyzing the aforementioned sales data and evaluating the sales trends and patterns of the stores, A means for analyzing the personality information of the store manager and evaluating the personality traits of the store manager, Based on these evaluation results, a total score is calculated and the results are provided in a report format. A system that includes means for providing feedback to the user on the results of the aforementioned report.

2. The system according to claim 1, further comprising means for registering the aforementioned overall score and evaluation results on a consumer-facing website and publishing information on highly-rated stores.

3. A method for comparing the personality information of the store manager entered by the user with the personality assessment results of the part-time job applicant, evaluating compatibility, and calculating the degree of matching, The system according to claim 1, further comprising means for recommending a suitable store to a part-time job applicant based on the matching results.

Images