system

Abstract

Provide a system. 【Solution means】 Means for receiving daily business report data, Means for receiving sales floor photo data, Means for analyzing the daily business report data by natural language processing to extract important keywords and sales performance, Means for recognizing the arrangement of products and inventory status by image analysis of the sales floor photo data, Means for evaluating the sales performance and product arrangement data to identify strengths and weaknesses, Means for generating proposals for maintaining the identified strengths and improving weaknesses, Means for generating negotiation materials based on the proposed content, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] In the analysis of sales data, the task of manually analyzing daily sales reports and sales floor photos requires time and effort, which imposes a significant burden on sales activities that demand quick and accurate judgment. Therefore, it is necessary to quickly grasp where the strengths and weaknesses of the sales situation are and provide effective improvement proposals. Also, means for automatically creating negotiation materials based on this information are required.

Means for Solving the Problems

[0005] The present invention solves the above problems with a system comprising: means for receiving sales report data; means for receiving store photo data; means for analyzing the sales report data using natural language processing to extract important keywords and sales performance; means for recognizing product placement and inventory status from the store photo data using image analysis; means for evaluating sales performance and product placement data to identify strengths and weaknesses; means for generating proposals to maintain the identified strengths and improve weaknesses; and means for generating sales negotiation materials based on the content of the proposals.

[0006] "Sales report data" refers to the data from reports in which salespeople record their daily work activities and sales performance.

[0007] "Store floor photo data" refers to image data that captures the appearance of the sales floor within a store and the arrangement of products.

[0008] "Natural language processing" is a technology that analyzes sentences and text data to extract meaning and keywords.

[0009] "Image analysis" is a technology that uses algorithms to analyze image data and recognize specific objects or patterns.

[0010] "Sales performance" refers to data that shows the sales and sales volume of a product within a certain period.

[0011] "Product placement" refers to information indicating the location and layout of each product within a sales area or store.

[0012] A "strength" is an element or characteristic that demonstrates significant results or effects in sales data or product placement.

[0013] A "weakness" refers to any element or characteristic in sales data or product placement that presents problems or needs improvement.

[0014] A "proposal" is a recommendation that outlines specific actions to maintain strengths and improve weaknesses.

[0015] The "negotiation materials" are reports and presentation materials used in negotiations, created based on the analysis results of sales data and improvement proposals.

Brief Explanation of Drawings

[0016] [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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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 a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. The system allows users to upload sales report data and store photo data, and automatically generates sales materials based on that information.

[0038] Program processing

[0039] 1. Data Collection

[0040] Users upload sales report data and store photo data. This data is sent from the terminal to the server.

[0041] 2. Data Analysis

[0042] The server analyzes sales report data using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[0043] 3. Analysis of store photos

[0044] The server analyzes store layout photos using an image recognition algorithm. It recognizes the placement and inventory status of products in the photos and determines whether a product is located at the front or back of a shelf. For example, the store layout photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[0045] 4. Data Evaluation

[0046] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[0047] 5. Generating improvement suggestions

[0048] The server generates specific suggestions to maintain strengths and improve weaknesses. For example, for product X, it might suggest "maintain this placement to improve visibility," and for product Y, it might suggest "increase inventory and place it at the front of the shelf."

[0049] 6. Generating sales negotiation materials

[0050] The server automatically generates sales materials based on the proposal. These materials include an assessment of strengths, an assessment of weaknesses, and specific proposals. These sales materials are output in PDF or PowerPoint format and provided to the terminal. The terminal displays the generated sales materials to the user and provides a download link.

[0051] Specific example

[0052] For example, consider a scenario where a user uploads a sales report stating, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the sales floor shows product X placed at the store entrance and product Y at the back of the shelf. The system would process this as follows:

[0053] 1. Users upload sales reports and photos of the sales floor.

[0054] 2. The device sends this data to the server.

[0055] 3. The server analyzes the contents of the sales report using natural language processing and extracts keywords and sales performance data.

[0056] 4. The server analyzes images of the store floor to recognize the product layout and inventory status.

[0057] 5. The server evaluates this data and determines the product's strengths and weaknesses.

[0058] 6. The server generates suggestions to maintain its strengths and address its weaknesses.

[0059] 7. The server automatically generates sales materials and provides them to the terminal.

[0060] In this way, users can streamline their sales analysis tasks and implement improvements quickly.

[0061] The following describes the processing flow.

[0062] Step 1:

[0063] User: Upload sales report data to the system. Specifically, enter text data related to sales activities and press the submit button.

[0064] Step 2:

[0065] Terminal: Receives sales report data and sends it to the server. Specifically, it converts the text data submitted by the user into the appropriate format and sends it to the server as an HTTP request.

[0066] Step 3:

[0067] User: Upload store photos to the system. Specifically, select photos of products taken inside the store and press the upload button.

[0068] Step 4:

[0069] Terminal: Receives store photo data and sends it to the server. Specifically, it converts the image files uploaded by the user into the appropriate format and sends them to the server as an HTTP request.

[0070] Step 5:

[0071] Server: Receives sales report data and analyzes the data using a natural language processing (NLP) engine. Specifically, it tokenizes the text and extracts keywords such as nouns, verbs, and adjectives.

[0072] Step 6:

[0073] Server: Receives storefront photo data and analyzes it using image analysis algorithms. Specifically, it performs object detection and classification within the images to identify product placement and inventory status.

[0074] Step 7:

[0075] Server: Analyzes keywords extracted from sales reports and sales performance data, and stores them in an evaluation database. Specifically, it extracts elements such as increased sales and insufficient inventory, and saves this data to the database.

[0076] Step 8:

[0077] Server: Evaluates product placement data obtained from store photos and stores it in an evaluation database. Specifically, it stores product location (front, back of shelf, etc.) and inventory status (in stock, out of stock, etc.) in the database.

[0078] Step 9:

[0079] Server: Based on the evaluation database, identify the strengths and weaknesses of sales data. Specifically, analyze the reasons for increases and decreases in sales and identify the elements that constitute strengths and weaknesses.

[0080] Step 10:

[0081] Server: Generates specific proposals to maintain strengths and improve weaknesses. Specifically, it identifies the factors contributing to strengths and proposes measures to maintain them. For weaknesses, it analyzes the causes and proposes solutions to address them.

[0082] Step 11:

[0083] Server: Generates sales materials based on the proposed content. Specifically, it organizes the evaluation results and proposed content and creates sales materials in PDF or PowerPoint format.

[0084] Step 12:

[0085] Server: Sends the completed sales materials to the terminal. Specifically, it provides the generated files to the terminal as a download link, allowing the user to access them.

[0086] Step 13:

[0087] Terminal: Displays sales materials to users and provides download links. Specifically, it displays sales materials containing store and product evaluation results and improvement suggestions to users in a visible format and maintains a downloadable state as needed.

[0088] (Example 1)

[0089] 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."

[0090] Traditional sales analysis systems require manual analysis of sales report data and store photo data, which is time-consuming, labor-intensive, and inefficient. Furthermore, manual analysis carries the risk of human error, resulting in a lack of accuracy and reliability. Additionally, the process of incorporating data insights into sales materials is time-consuming, hindering rapid decision-making.

[0091] 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.

[0092] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales negotiation materials based on the proposed content, and means for providing the sales negotiation materials to the terminal in PDF or presentation format and presenting the user with a download link. This enables the user to perform sales analysis efficiently and quickly and make rapid decisions based on accurate improvement proposals.

[0093] "Sales report data" refers to data that records the results, observations, and sales performance of a day's sales activities.

[0094] "Store floor photo data" refers to image data that captures the arrangement of products and inventory status within a store or sales floor.

[0095] Natural language processing is a technology that enables computers to understand and analyze human language, and to extract useful information from text data.

[0096] "Image analysis" is a technology that processes and analyzes image data to recognize and extract objects and features within the image.

[0097] A "strength" is an element that demonstrates a competitive advantage or improved performance in areas such as sales performance or product placement.

[0098] A "weakness" is an element that indicates a decline in performance or room for improvement in areas such as sales figures or product placement.

[0099] A "proposal" is a concrete action plan or strategy to maintain sales strengths and improve weaknesses.

[0100] "Sales negotiation materials" are documents that summarize the results of sales analysis and proposals, and are used during sales negotiations.

[0101] A "terminal" refers to a device used by a user, such as a computer or smartphone.

[0102] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. The following describes each processing step and the hardware and software used.

[0103] Uploading and sending data

[0104] Users upload sales report data (e.g., Excel or CSV files) and store photo data (image files such as JPEG or PNG) through the system interface. The terminal sends this data to the server using secure HTTP communication. Specifically, the SSL / TLS protocol is used to ensure data security.

[0105] Analysis of sales report data

[0106] The server saves the received sales report data to a database. Then, it analyzes the text data using an NLP library (e.g., Python's NLTK or spaCy). By employing techniques such as text tokenization, part-of-speech tagging, and entity recognition, it extracts important information such as the date, sales performance, product name, and sales quantity. The analysis results are then saved back to the database.

[0107] Analysis of store photo data

[0108] The server analyzes store photo data using image recognition algorithms (for example, models using OpenCV or TENSORFLOW®). It detects products from the images and recognizes the location (front, back, etc.) and inventory status of each product. This analysis result is also stored in a database.

[0109] Data Evaluation

[0110] The server integrates sales performance data extracted from sales reports with product placement data obtained from store photos to evaluate sales strengths and weaknesses. For example, if product X is selling well because it is prominently displayed in a particular store, this is judged as a strength. Conversely, if product Y is selling poorly due to insufficient stock or being placed at the back of the shelf, this is judged as a weakness. This evaluation result is also stored in the database.

[0111] Generating improvement suggestions

[0112] Based on the evaluation results of strengths and weaknesses, the server generates specific improvement suggestions through a template engine (e.g., Jinja2). For example, for product X, a suggestion is generated that "this placement should be maintained to improve visibility," and for product Y, a suggestion is generated that "inventory should be increased and it should be placed at the front of the shelf."

[0113] Generation and provision of business negotiation materials

[0114] The server automatically generates sales materials based on the proposal. These materials are generated in PDF or presentation format, and the Python libraries ReportLab and pptx are used for their creation. The generated sales materials are provided to the terminal, which notifies the user and provides a download link. The user can then download and use the generated sales materials.

[0115] Specific example

[0116] For example, if a user uploads a sales report stating, "October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the sales floor shows product X placed at the store entrance and product Y at the back of the shelf, the system would process the data as follows: First, the user uploads the sales report and the sales floor photo, and the terminal sends this data to the server. The server then analyzes the contents of the sales report using natural language processing to extract keywords and sales performance. Next, the server analyzes the sales floor photo to recognize the product placement and inventory status. Based on the analysis results, the server determines the strengths and weaknesses of the products and generates suggestions to maintain the strengths and eliminate the weaknesses. Finally, it automatically generates sales materials and provides them to the terminal.

[0117] Example of a prompt

[0118] "Please upload your sales report and photos of your sales floor. We will then generate specific improvement suggestions based on the analysis data."

[0119] This system allows users to streamline sales analysis tasks and implement improvements quickly.

[0120] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0121] Step 1: Upload Data

[0122] Users upload sales report data and store photo data through the system interface. Users log in to the system from a web browser and either drag and drop files onto the upload form or use the file selection dialog. These operations provide sales report data (e.g., Excel or CSV files) and store photo data (JPEG or PNG, etc.) as input.

[0123] Step 2: Send

[0124] The terminal transmits sales report data and storefront photo data uploaded by the user to the server using secure HTTP communication. SSL / TLS protocol is used to ensure the security of the transmitted data. Input is the data uploaded by the user, and output is the transmission of data to the server.

[0125] Step 3: Analysis of sales report data

[0126] The server saves the received sales report data to a database. Next, it uses an NLP library (e.g., Python's NLTK or spaCy) to analyze the text of the sales report data. This extracts important information such as date, sales performance, product name, and sales quantity using techniques such as tokenization, part-of-speech tagging, and entity recognition. The input is the received sales report data, and the output is the extracted keywords and sales performance.

[0127] Step 4: Analysis of store photo data

[0128] The server analyzes store layout photos using image recognition algorithms (e.g., models using OpenCV or TensorFlow). Through image analysis, it detects products and recognizes their location (front, back, etc.) and inventory status. The input is the received store layout photos, and the output is information on product placement and inventory status.

[0129] Step 5: Data Evaluation

[0130] The server integrates sales performance data extracted from sales reports with product placement data obtained from store photos to evaluate sales strengths and weaknesses. For example, if product X is placed at the front and sales are increasing, it is considered a strength, while if product Y is out of stock or placed at the back of the shelf and sales are sluggish, it is considered a weakness. The input is sales performance and product placement data, and the output is information on the evaluated strengths and weaknesses.

[0131] Step 6: Generating improvement proposals

[0132] The server uses a template engine (e.g., Jinja2) to generate specific improvement suggestions based on the strengths and weaknesses assessment results. For example, for product X, a suggestion is generated that "this placement should be maintained to improve visibility," and for product Y, a suggestion is generated that "inventory should be increased and the product should be placed at the front of the shelf." The input is the assessment results, and the output is the improvement suggestions.

[0133] Step 7: Generate and provide sales materials.

[0134] The server automatically generates sales materials based on the proposed content. These materials are generated in PDF or presentation format, and Python's ReportLab and pptx libraries are used for their creation. The generated sales materials are provided to the terminal, which notifies the user and provides a download link. The input is improvement proposals, and the output is sales materials in PDF or presentation format.

[0135] (Application Example 1)

[0136] 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."

[0137] Conventional sales analysis systems require manual analysis of sales report data and store photo data, which is time-consuming and labor-intensive, and results in inconsistent analysis accuracy. Furthermore, it is often difficult to quickly identify optimal product placement and promotional strategies, hindering improvements in sales efficiency. This invention aims to solve these problems and provide a system that performs sales analysis more efficiently and accurately, generating concrete improvement suggestions.

[0138] 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.

[0139] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales materials based on the proposals and outputting them in PDF or presentation format, means for collecting sales report data and store photo data using smartphones or robots, and means for performing data analysis and evaluation using a cloud server. This makes it possible to streamline sales analysis work and quickly generate concrete improvement proposals.

[0140] "Sales report data" refers to data that records daily performance and activities related to sales.

[0141] "Store display photo data" refers to photographic data that captures the arrangement of products and the condition of the store display.

[0142] "Natural language processing" is a technology that analyzes text data and performs processing such as semantic understanding and keyword extraction.

[0143] "Image analysis" is a technology that analyzes photographs and image data to recognize specific objects or features.

[0144] "Strengths" refer to positive elements or advantages in sales performance or product placement.

[0145] "Weaknesses" refer to negative elements or shortcomings in sales performance or product placement.

[0146] A "proposal" is a specific action or strategy to maintain identified strengths and improve weaknesses.

[0147] "Sales materials" are documents that summarize sales proposals and improvement measures in document or presentation format.

[0148] A "smartphone" is a type of mobile phone, a portable device that has both communication and computer functions.

[0149] A "robot" is a mechanical device that operates autonomously or semi-autonomously and has the function of performing a specific task.

[0150] A "cloud server" is a remote server provided via the internet that offers large-scale data processing and storage capabilities.

[0151] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. This system uses smartphones and robots and utilizes cloud servers to perform advanced data analysis.

[0152] Users collect sales report data and store photo data using smartphones or robots. This data is sent to a cloud server where it is analyzed. The sales report data is analyzed using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y are sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[0153] Similarly, store photo data is analyzed using image recognition algorithms (such as OpenCV or TensorFlow) to recognize the placement and inventory status of products. For example, a store photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[0154] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[0155] The server then generates specific suggestions to maintain strengths and improve weaknesses. For example, for product X, it might suggest "maintain this placement to improve visibility," and for product Y, it might suggest "increase inventory and place it at the front of the shelf."

[0156] Finally, the server automatically generates sales materials based on the proposal. These materials are output in PDF or presentation format (such as PowerPoint) and provided to the user's terminal. The user can review the generated sales materials and download them via a download link.

[0157] As a concrete example, input the following prompt into the generating AI model:

[0158] After users upload store layouts and sales reports using their smartphones, a cloud server analyzes the data, evaluates product strengths and weaknesses, and generates improvement suggestions based on that analysis. The generated sales materials are provided in PDF format, allowing store managers and sales representatives to quickly implement improvement measures based on the data. This application contributes to improved sales efficiency through intuitive data collection using smartphones and robots, and advanced data analysis using natural language processing and image recognition technologies.

[0159] This streamlines sales analysis and enables the rapid generation of concrete improvement suggestions.

[0160] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0161] Step 1:

[0162] Users collect sales report data and store photo data using smartphones or robots and upload them to a cloud server. Input data consists of daily report data in text format and store photos in JPEG format. Based on user actions, the collected data is transmitted to the cloud server in real time.

[0163] Step 2:

[0164] This system analyzes sales report data received by a cloud server using natural language processing (NLP) techniques. The input data consists of user-uploaded daily report text, and the output is extracted data containing important keywords and sales performance information. Specifically, it uses NLP libraries (e.g., NLTK, spaCy) to extract key information from the text.

[0165] Step 3:

[0166] The cloud server analyzes storefront photo data received using an image recognition algorithm. The input data consists of JPEG photos of the storefront uploaded by users, and the output data shows product placement information and inventory status. Specifically, an image recognition algorithm (e.g., OpenCV, TensorFlow) is used to identify the location and inventory quantity of each product.

[0167] Step 4:

[0168] The cloud server evaluates the analyzed sales report data and store photo data to identify sales strengths and weaknesses. The input data consists of the extracted data and placement data obtained in the previous step, and the output is the evaluation results of strengths and weaknesses. Specifically, data analysis tools (e.g., Pandas, NumPy) are used to compare and analyze the entire dataset.

[0169] Step 5:

[0170] The cloud server generates suggestions to maintain identified strengths and improve weaknesses. The input data is the evaluation results of strengths and weaknesses, and the output is a list of specific improvement suggestions. Specifically, it uses conditional branching and rule-based algorithms to automatically generate the optimal suggestions.

[0171] Step 6:

[0172] The cloud server automatically generates sales materials based on the generated proposals. The input data is a list of proposals, and the output is sales materials in PDF or presentation format. Specifically, it uses a document generation library (e.g., ReportLab, pptx) to construct the materials.

[0173] Step 7:

[0174] The cloud server provides the generated sales materials to the terminal and displays them to the user. The input data is sales materials in PDF or presentation format, and the output is a download link displayed on the terminal. Specifically, a file transfer protocol is used to send the materials to the terminal.

[0175] This allows users to streamline sales analysis tasks and quickly generate concrete improvement suggestions.

[0176] 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.

[0177] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides even more accurate suggestions. This system allows users to upload sales report data and store photo data, and based on that information, it automatically generates sales materials and presents optimal suggestions tailored to the user's emotions.

[0178] Program processing

[0179] 1. Data Collection

[0180] Users upload sales report data and store photo data. This data is sent from the terminal to the server.

[0181] 2. Data Analysis

[0182] The server analyzes sales report data using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[0183] 3. Analysis of store photos

[0184] The server analyzes store layout photos using an image recognition algorithm. It recognizes the placement and inventory status of products in the photos and determines whether a product is located at the front or back of a shelf. For example, the store layout photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[0185] 4. Data Evaluation

[0186] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[0187] 5. Emotion analysis

[0188] The server uses an emotion analysis engine to recognize the user's emotional state. For example, when a user enters sales report data, the server recognizes their emotion (positive, negative, or neutral) from the tone and context of the text. It also analyzes the user's facial expressions and posture in store photos to evaluate their emotional state.

[0189] 6. Generating improvement suggestions

[0190] The server generates specific suggestions to maintain strengths and improve weaknesses. Furthermore, it adjusts the suggestions based on the user's emotional data. For example, if negative emotions are detected, the improvement suggestions are adjusted to be more specific and encouraging.

[0191] 7. Generating sales negotiation materials

[0192] The server automatically generates sales materials based on the proposal. These materials include an assessment of strengths and weaknesses, as well as specific proposals. Furthermore, they may include supplementary information and words of encouragement tailored to the user's emotional state. These sales materials are output in PDF or PowerPoint format and provided to the user's device.

[0193] Specific example

[0194] For example, consider a scenario where a user uploads a sales report stating, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the store layout shows product X placed at the store entrance and product Y at the back of the shelf. Furthermore, if the sentiment analysis engine recognizes from the sales report that the user has negative emotions, the system will process the data as follows.

[0195] 1. Users upload sales reports and photos of the sales floor.

[0196] 2. The device sends this data to the server.

[0197] 3. The server analyzes the contents of the sales report using natural language processing and extracts keywords and sales performance data.

[0198] 4. The server analyzes images of the store floor to recognize the product layout and inventory status.

[0199] 5. The server uses an emotion analysis engine to recognize negative emotions from the user based on the content of the sales report.

[0200] 6. The server evaluates this data and determines the product's strengths and weaknesses.

[0201] 7. The server generates suggestions to maintain its strengths and address its weaknesses. Considering that users may have negative feelings, the suggestions are presented in a more specific and positive manner.

[0202] 8. The server automatically generates and provides sales materials to the terminal. The sales materials also include an encouraging message such as, "Let's review recent achievements and work towards the next step."

[0203] In this way, users can streamline their sales analysis work, implement improvements quickly, and receive emotional support.

[0204] The following describes the processing flow.

[0205] Step 1:

[0206] User: Upload sales report data to the system. Specifically, enter text data related to sales activities and press the submit button.

[0207] Step 2:

[0208] Terminal: Receives sales report data and sends it to the server. Specifically, it converts the text data submitted by the user into the appropriate format and sends it to the server as an HTTP request.

[0209] Step 3:

[0210] User: Upload store photos to the system. Specifically, select photos of products taken inside the store and press the upload button.

[0211] Step 4:

[0212] Terminal: Receives store photo data and sends it to the server. Specifically, it converts the image files uploaded by the user into the appropriate format and sends them to the server as an HTTP request.

[0213] Step 5:

[0214] Server: Receives sales report data and analyzes it using a natural language processing (NLP) engine. Specifically, it tokenizes the text and extracts keywords such as nouns, verbs, and adjectives. It also identifies keywords such as "Store A," "Product X," "Sales," and "20% increase compared to the previous month."

[0215] Step 6:

[0216] Server: Receives store floor photo data and analyzes it using image analysis algorithms. Specifically, it performs object detection and classification within the image to determine the placement and inventory status of products. For example, it determines that product X is placed at the front of the shelf and product Y is located at the back of the shelf.

[0217] Step 7:

[0218] Server: Based on keywords extracted from sales reports and sales performance data, the server stores this information in an evaluation database. Specifically, it extracts elements such as sales increases / decreases and inventory shortages, and saves this information in the database.

[0219] Step 8:

[0220] Server: Evaluates product placement data obtained from store photos and stores it in an evaluation database. Specifically, it saves the location and inventory status of products in the database.

[0221] Step 9:

[0222] Server: Using an emotion analysis engine, it analyzes user emotions from sales report data and store photo data. For example, it identifies negative, positive, and neutral tones from the text of sales reports and recognizes emotions from the facial expressions of users in store photos.

[0223] Step 10:

[0224] Server: Evaluates sales performance and product placement data, along with recognized sentiment data, to identify strengths and weaknesses. For example, product X is identified as a strength because its high visibility is leading to increased sales, while product Y is identified as a weakness because its low sales are due to insufficient stock and poor placement.

[0225] Step 11:

[0226] Server: Generates specific suggestions to maintain strengths and improve weaknesses. Specifically, it proposes strategies to maintain the factors contributing to strengths and improve inventory management and product placement to address weaknesses. The suggestions are adjusted to be more appropriate by considering user sentiment data.

[0227] Step 12:

[0228] Server: Automatically generates sales materials based on the proposed content. Specifically, it organizes evaluation results and proposal content and creates sales materials in PDF or PowerPoint format. It also includes messages of encouragement and appreciation tailored to the user's emotions.

[0229] Step 13:

[0230] Server: Sends the completed sales materials to the terminal. Specifically, it provides the generated files to the terminal as a download link, allowing the user to access them.

[0231] Step 14:

[0232] Terminal: Displays sales materials to the user and provides a download link. Specifically, it displays sales materials containing evaluation results and improvement suggestions to the user in a visible format and maintains a state where they can be downloaded as needed.

[0233] (Example 2)

[0234] 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".

[0235] Conventional sales analysis systems struggled to provide appropriate suggestions based on user emotions, in addition to analyzing sales report data and store photo data. This resulted in inaccurate evaluations of sales strengths and weaknesses, as well as the inability to provide suggestions that considered user emotions, ultimately leading to lower effectiveness of improvement suggestions. Furthermore, the generation of sales materials lacked adjustments that reflected user emotions, resulting in a failure to motivate users.

[0236] 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.

[0237] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for recognizing and evaluating user emotion data, means for generating suggestions to maintain identified strengths and improve weaknesses, means for generating sales negotiation materials based on the suggested content, and means for adjusting the sales negotiation materials according to the user's emotional state. This enables detailed sales analysis and the generation of improvement suggestions that take user emotions into consideration, increasing the effectiveness of improvement suggestions and enabling the maintenance or improvement of user motivation.

[0238] "Sales report data" refers to data containing detailed information about sales activities, including daily sales, customer trends, and product trends, recorded in digital or paper format.

[0239] "Sales floor photo data" refers to photographic data showing the arrangement and inventory status of products within a sales floor or store, and consists of image files taken with a digital camera or smartphone.

[0240] "Natural language processing" is a technology that enables computers to understand and analyze human language, and it involves methods for extracting, classifying, and analyzing important keywords and meanings from text.

[0241] "Image analysis" is a technology that uses computers to analyze digital images and recognize, classify, and analyze objects and patterns within those images.

[0242] "Sales performance" refers to actual data such as sales and sales volume of products over a specific period, and is an indicator that shows the results of a company's or store's business activities.

[0243] "Product placement data" refers to data about the location and layout of products within a store, and provides information that indicates the visibility and accessibility of products.

[0244] "Emotional data" refers to data that reflects the user's emotional state, and is information indicating positive, negative, or neutral emotions obtained through methods such as text tone and facial expression analysis.

[0245] A "strength" refers to an advantage in sales activities or product features, representing a competitive advantage that a company or store has over its competitors.

[0246] A "weakness" refers to an inferiority in sales activities or product characteristics, representing shortcomings or deficiencies that a company or store should address.

[0247] A "proposal" is a set of specific measures and policies to maintain strengths and improve weaknesses, and is useful information for sales strategies and store operations.

[0248] "Sales materials" are documents used in sales activities and negotiations with customers, and include proposals, sales performance, and evaluations of strengths and weaknesses.

[0249] "Adjusting based on the user's emotional state" is a method of modifying the content of proposals and sales materials based on the user's emotional data in order to provide users with optimal information and improve their motivation.

[0250] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides even more accurate suggestions.

[0251] The specific embodiments for carrying out the invention are described in detail below.

[0252] First, users upload sales report data and store photo data using a dedicated web portal or application. At this time, a file selection dialog will appear, and users will select the data files to upload. After selecting the files, the data is encrypted and sent from the terminal to the server. For example, this applies when a user selects and sends the daily report file "report_20231001.txt" and the store photo "store_photo.jpg".

[0253] The server analyzes the submitted sales report data using natural language processing (NLP) techniques. Specifically, it extracts important keywords and sales performance using Python and dedicated natural language processing libraries. This process utilizes Apache® Kafka to analyze the data stream. In addition, image recognition libraries such as OpenCV and TensorFlow are used for image analysis to recognize product placement and inventory status from store floor photo data. For example, from "store_photo.jpg", it recognizes that "product X is placed at the front of the shelf and product Y is placed at the back of the shelf".

[0254] Next, based on the extracted sales performance data and product placement data, the server evaluates its strengths and weaknesses. This evaluation uses an algorithm that compares and analyzes past sales data with current data. The evaluation process also includes comparison with existing databases within the system.

[0255] Furthermore, the server uses an emotion analysis engine to recognize and analyze the user's emotional data. Emotion analysis employs techniques that evaluate the user's emotions (positive, negative, or neutral) based on the tone and style of their writing. The user's facial expressions and posture in store photos are also included in the analysis.

[0256] Based on the evaluation results, the server generates improvement suggestions that reflect the user's emotions. For example, if negative emotions are detected, the improvement suggestions are adjusted to be more specific and encouraging.

[0257] Finally, the server automatically creates sales materials based on the generated improvement suggestions. These materials are generated in PDF or presentation format and may include supplementary information and encouraging messages tailored to the user's emotional state. Once the materials are complete, a download link is notified to the device, and the user can download and use them.

[0258] The following are examples of input prompts for the generated AI model when using this system.

[0259] A user has uploaded sales report data for store A. The report states, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock." A photo of the store layout shows product X placed near the entrance and product Y placed at the back of the shelf. The sentiment analysis engine indicates that the user is experiencing negative emotions. Based on this information, generate suggestions for increasing sales and create specific sales materials.

[0260] This invention allows users to quickly and efficiently conduct sales analysis and implement improvement measures, as well as receive appropriate emotional support.

[0261] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0262] Step 1:

[0263] Users upload sales report data and store photo data using a dedicated web portal or application. When a user selects files such as "report_20231001.txt" and "store_photo.jpg", these files are sent from the terminal to the server in an encrypted state.

[0264] Input: Sales report data file, store photo data file

[0265] Output: Encrypted data is sent to the server.

[0266] Step 2:

[0267] The server analyzes the received sales report data using natural language processing (NLP) techniques. Specifically, it uses Python and natural language processing libraries (e.g., NLTK and SpaCy) to extract important keywords and sales performance data. For example, it extracts keywords such as "Store A," "Product X," "Sales," "20% increase compared to the previous month," "Inventory shortage," and "Sales slump" from "report_20231001.txt."

[0268] Input: Sales report data file

[0269] Output: Extracted keywords and sales performance data

[0270] Step 3:

[0271] The server analyzes the received store photo data using an image recognition algorithm. Specifically, it uses image recognition libraries such as OpenCV or TensorFlow to analyze product placement and inventory status. For example, it recognizes from "store_photo.jpg" that "product X is placed at the front of the shelf and product Y is placed at the back of the shelf."

[0272] Input: Sales floor photo data file

[0273] Output: Product placement and inventory status data

[0274] Step 4:

[0275] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. This uses an algorithm that performs comparative analysis with past sales data and existing databases within the system. For example, product X is evaluated as having a strength because its high visibility is leading to increased sales, while product Y is evaluated as having a weakness because its low sales are due to insufficient inventory and inconspicuous placement.

[0276] Input: Extracted keywords, sales performance data, product placement and inventory status data

[0277] Output: Identified strengths and weaknesses

[0278] Step 5:

[0279] The server uses an emotion analysis engine to recognize and evaluate the user's emotional state. Specifically, it uses IBM Watson® and Microsoft® Azure® emotion analysis APIs to analyze emotions (positive, negative, neutral) from the content of sales reports. It also evaluates the emotional state by analyzing the user's facial expressions and posture in photos of the sales floor.

[0280] Input: Sales report data file, store photo data file

[0281] Output: User's emotional data

[0282] Step 6:

[0283] Based on the evaluated strengths and weaknesses, and the user's emotional data, the server generates specific improvement suggestions. If the user has negative emotions, the content of the suggestions is made more specific and positive. For example, suggestions such as "Continuously place Product X at the front of the shelf and replenish the inventory of Product Y to improve sales" are made.

[0284] Input: Identified strengths and weaknesses, user's emotional data

[0285] Output: Improvement suggestions

[0286] Step 7:

[0287] Based on the generated improvement suggestions, the server automatically creates negotiation materials. These negotiation materials are generated in PDF or presentation format and include charts and graphs. Additionally, supplementary information and encouraging messages according to the user's emotional state may be added.

[0288] Input: Improvement suggestions, user's emotional data

[0289] Output: Negotiation materials (in PDF or presentation format)

[0290] Through the above processing steps, the user can quickly and efficiently conduct sales analysis and take improvement measures, and can also receive appropriate support in terms of emotions.

[0291] (Application Example 2)

[0292] Next, Application Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0293] Currently, in brick-and-mortar store operations, it is essential to quickly and accurately grasp sales performance and product placement, and to make efficient improvement suggestions. However, these tasks are often performed manually, which is time-consuming and labor-intensive, and often relies on subjective judgment. Furthermore, because suggestions are not made flexibly based on customer emotions, there are problems with the acceptance and implementation rate of suggestions. In this situation, there is a need to develop a system that analyzes sales performance and product placement data to generate objective and effective improvement suggestions.

[0294] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing the placement and inventory status of products from the store photo data using image analysis, means for evaluating the sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales negotiation materials based on the proposed content, means for transmitting sales report data and store photo data from the user terminal to the server, emotion analysis means for recognizing the user's emotions, and means for adjusting the proposed content based on the user's emotions. This enables objective improvement proposals based on sales performance and store photo data, as well as flexible proposals that take into account the user's emotions.

[0295] "Sales report data" refers to data that records daily performance and information related to sales activities.

[0296] "Store photo data" refers to image data that visually records the conditions of a store, such as the arrangement of products and inventory levels.

[0297] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0298] "Important keywords" are words or phrases that are important for identifying specific information, extracted through data analysis.

[0299] "Sales performance" refers to performance data such as the sales amount and the number of sales of products or services during a specific period.

[0300] "Image analysis" is a technology that uses computer vision technology to extract useful information from image data.

[0301] "Product placement" refers to the display position and arrangement of products in the sales floor.

[0302] "Inventory status" is information indicating the inventory quantity and status of products at a specific point in time.

[0303] "Strengths" are characteristics or elements that are recognized as good in the object of evaluation.

[0304] "Weaknesses" are characteristics or elements that are recognized as needing improvement in the object of evaluation.

[0305] "Proposals" are recommendations for specific actions or strategies to maintain identified strengths and improve weaknesses.

[0306] "Negotiation materials" are materials that visually or documentarily summarize the proposal content.

[0307] "Sentiment analysis" is a technology that recognizes and evaluates the user's emotional state from text or images.

[0308] "User terminals" are computer devices or mobile devices that can transmit business daily report data and sales floor photo data to the server.

[0309] This invention is a system that analyzes sales report data and store photo data to identify sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides more accurate suggestions. The embodiments for carrying out this invention are as follows.

[0310] The user first uploads sales report data and store photo data using a terminal. The terminal sends this data to the server. The server analyzes the data and generates suggestions using the following methods.

[0311] The server analyzes sales report data using natural language processing (NLP) techniques to extract important keywords and sales performance data. Specific software used includes natural language processing libraries such as Spacy. This analysis extracts keywords such as "Store A," "Product X," and "20% increase compared to the previous month."

[0312] Next, the server analyzes the store layout photo data using an image recognition algorithm. The specific software includes image processing libraries using OpenCV and TensorFlow. This allows the system to recognize the placement and inventory status of products in the store, providing information such as "product X is placed at the front of the shelf" or "product Y is out of stock."

[0313] Next, the server evaluates the extracted sales performance data and product placement data to identify strengths and weaknesses. This evaluation identifies information such as, "Product X has high visibility and strong sales," or "Product Y is out of stock and sales are sluggish."

[0314] Furthermore, the server uses an emotion analysis engine to recognize the user's emotional state. This emotion analysis employs a generative AI model that evaluates the tone and context of the text. For example, if negative emotions are detected when a user enters sales report data, the server will recognize this.

[0315] The server generates specific improvement suggestions based on identified strengths and weaknesses, as well as the user's emotional state. These suggestions are tailored to the user's emotions and may be presented in a more positive and concrete format. For example, a suggestion such as "Maintain product X in its current position and replenish product Y's inventory immediately" might be generated.

[0316] Ultimately, the server automatically generates sales materials based on the improvement suggestions that have been created. These materials, including PDF or presentation formats, are provided to the user's terminal. This allows the user to efficiently analyze sales data from physical stores and implement improvement measures.

[0317] For example, the following format can be used as a prompt:

[0318] "On October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock. Based on this data, generate improvement proposals and prepare sales materials."

[0319] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0320] Step 1:

[0321] Users input sales report data and take photos of the sales floor. The sales report data includes daily sales performance and sales status, while the sales floor photos visually record product placement and inventory status. Users also upload this data using a smartphone or smart glasses.

[0322] Input: Sales report data, store photo data

[0323] Output: Data uploaded to the device

[0324] Step 2:

[0325] The terminal sends sales report data and store photo data to the server. A data transmission completion notification is displayed on the terminal to confirm that the data has reached the server correctly.

[0326] Input: Data uploaded to the device

[0327] Output: Data sent to the server

[0328] Step 3:

[0329] The server analyzes the received sales report data using natural language processing (NLP) techniques. Specifically, it uses NLP libraries such as spacy to extract important keywords and sales performance data from the sales reports.

[0330] Input: Sales report data

[0331] Output: Key keywords, sales performance

[0332] Step 4:

[0333] The server analyzes store photo data using image recognition algorithms. Using image processing libraries such as OpenCV and TensorFlow, it recognizes the placement and inventory status of products in the photos and stores this information in a database.

[0334] Input: Storefront photo data

[0335] Output: Product placement data, inventory status data

[0336] Step 5:

[0337] The server evaluates the extracted key keywords, sales performance, and product placement data, and uses this information to identify strengths and weaknesses. This results in evaluations such as, "Product X has high visibility and strong sales."

[0338] Input: Key keywords, sales performance, product placement data

[0339] Output: Evaluation results of strengths and weaknesses

[0340] Step 6:

[0341] The server uses an emotion analysis engine to recognize the user's emotional state from sales report data and store photo data. A generative AI model is used to evaluate the user's emotions (positive, negative, neutral) based on their input and photos.

[0342] Input: Sales report data, store photo data

[0343] Output: Sentiment evaluation

[0344] Step 7:

[0345] The server generates specific improvement suggestions based on the strengths and weaknesses assessment results and the sentiment assessment. The generated suggestions are adjusted based on the user's sentiment and include positive content, such as "Keep product X in its current position."

[0346] Input: Strengths and weaknesses assessment results, emotional assessment

[0347] Output: Specific improvement suggestions

[0348] Step 8:

[0349] The server automatically generates sales materials based on the improvement suggestions that have been created. The sales materials are generated in PDF or presentation format and provided to the user's terminal.

[0350] Input: Specific improvement suggestions

[0351] Output: Sales materials (PDF / presentation format)

[0352] As a concrete example of the operation, the prompt statement is as follows:

[0353] "On October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock. Based on this data, generate improvement proposals and prepare sales materials."

[0354] 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.

[0355] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0356] 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.

[0357] [Second Embodiment]

[0358] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0359] 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.

[0360] 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).

[0361] 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.

[0362] 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.

[0363] 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).

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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".

[0370] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. The system allows users to upload sales report data and store photo data, and automatically generates sales materials based on that information.

[0371] Program processing

[0372] 1. Data Collection

[0373] Users upload sales report data and store photo data. This data is sent from the terminal to the server.

[0374] 2. Data Analysis

[0375] The server analyzes sales report data using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[0376] 3. Analysis of store photos

[0377] The server analyzes store layout photos using an image recognition algorithm. It recognizes the placement and inventory status of products in the photos and determines whether a product is located at the front or back of a shelf. For example, the store layout photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[0378] 4. Data Evaluation

[0379] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[0380] 5. Generating improvement suggestions

[0381] The server generates specific suggestions to maintain strengths and improve weaknesses. For example, for product X, it might suggest "maintain this placement to improve visibility," and for product Y, it might suggest "increase inventory and place it at the front of the shelf."

[0382] 6. Generating sales negotiation materials

[0383] The server automatically generates sales materials based on the proposal. These materials include an assessment of strengths, an assessment of weaknesses, and specific proposals. These sales materials are output in PDF or PowerPoint format and provided to the terminal. The terminal displays the generated sales materials to the user and provides a download link.

[0384] Specific example

[0385] For example, consider a scenario where a user uploads a sales report stating, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the sales floor shows product X placed at the store entrance and product Y at the back of the shelf. The system would process this as follows:

[0386] 1. Users upload sales reports and photos of the sales floor.

[0387] 2. The device sends this data to the server.

[0388] 3. The server analyzes the contents of the sales report using natural language processing and extracts keywords and sales performance data.

[0389] 4. The server analyzes images of the store floor to recognize the product layout and inventory status.

[0390] 5. The server evaluates this data and determines the product's strengths and weaknesses.

[0391] 6. The server generates suggestions to maintain its strengths and address its weaknesses.

[0392] 7. The server automatically generates sales materials and provides them to the terminal.

[0393] In this way, users can streamline their sales analysis tasks and implement improvements quickly.

[0394] The following describes the processing flow.

[0395] Step 1:

[0396] User: Upload sales report data to the system. Specifically, enter text data related to sales activities and press the submit button.

[0397] Step 2:

[0398] Terminal: Receives sales report data and sends it to the server. Specifically, it converts the text data submitted by the user into the appropriate format and sends it to the server as an HTTP request.

[0399] Step 3:

[0400] User: Upload store photos to the system. Specifically, select photos of products taken inside the store and press the upload button.

[0401] Step 4:

[0402] Terminal: Receives store photo data and sends it to the server. Specifically, it converts the image files uploaded by the user into the appropriate format and sends them to the server as an HTTP request.

[0403] Step 5:

[0404] Server: Receives sales report data and analyzes the data using a natural language processing (NLP) engine. Specifically, it tokenizes the text and extracts keywords such as nouns, verbs, and adjectives.

[0405] Step 6:

[0406] Server: Receives storefront photo data and analyzes it using image analysis algorithms. Specifically, it performs object detection and classification within the images to identify product placement and inventory status.

[0407] Step 7:

[0408] Server: Analyzes keywords extracted from sales reports and sales performance data, and stores them in an evaluation database. Specifically, it extracts elements such as increased sales and insufficient inventory, and saves this data to the database.

[0409] Step 8:

[0410] Server: Evaluates product placement data obtained from store photos and stores it in an evaluation database. Specifically, it stores product location (front, back of shelf, etc.) and inventory status (in stock, out of stock, etc.) in the database.

[0411] Step 9:

[0412] Server: Based on the evaluation database, identify the strengths and weaknesses of sales data. Specifically, analyze the reasons for increases and decreases in sales and identify the elements that constitute strengths and weaknesses.

[0413] Step 10:

[0414] Server: Generates specific proposals to maintain strengths and improve weaknesses. Specifically, it identifies the factors contributing to strengths and proposes measures to maintain them. For weaknesses, it analyzes the causes and proposes solutions to address them.

[0415] Step 11:

[0416] Server: Generates sales materials based on the proposed content. Specifically, it organizes the evaluation results and proposed content and creates sales materials in PDF or PowerPoint format.

[0417] Step 12:

[0418] Server: Sends the completed sales materials to the terminal. Specifically, it provides the generated files to the terminal as a download link, allowing the user to access them.

[0419] Step 13:

[0420] Terminal: Displays sales materials to users and provides download links. Specifically, it displays sales materials containing store and product evaluation results and improvement suggestions to users in a visible format and maintains a downloadable state as needed.

[0421] (Example 1)

[0422] 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."

[0423] Traditional sales analysis systems require manual analysis of sales report data and store photo data, which is time-consuming, labor-intensive, and inefficient. Furthermore, manual analysis carries the risk of human error, resulting in a lack of accuracy and reliability. Additionally, the process of incorporating data insights into sales materials is time-consuming, hindering rapid decision-making.

[0424] 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.

[0425] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales negotiation materials based on the proposed content, and means for providing the sales negotiation materials to the terminal in PDF or presentation format and presenting the user with a download link. This enables the user to perform sales analysis efficiently and quickly and make rapid decisions based on accurate improvement proposals.

[0426] "Sales report data" refers to data that records the results, observations, and sales performance of a day's sales activities.

[0427] "Store floor photo data" refers to image data that captures the arrangement of products and inventory status within a store or sales floor.

[0428] Natural language processing is a technology that enables computers to understand and analyze human language, and to extract useful information from text data.

[0429] "Image analysis" is a technology that processes and analyzes image data to recognize and extract objects and features within the image.

[0430] A "strength" is an element that demonstrates a competitive advantage or improved performance in areas such as sales performance or product placement.

[0431] A "weakness" is an element that indicates a decline in performance or room for improvement in areas such as sales figures or product placement.

[0432] A "proposal" is a concrete action plan or strategy to maintain sales strengths and improve weaknesses.

[0433] "Sales negotiation materials" are documents that summarize the results of sales analysis and proposals, and are used during sales negotiations.

[0434] A "terminal" refers to a device used by a user, such as a computer or smartphone.

[0435] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. The following describes each processing step and the hardware and software used.

[0436] Uploading and sending data

[0437] Users upload sales report data (e.g., Excel or CSV files) and store photo data (image files such as JPEG or PNG) through the system interface. The terminal sends this data to the server using secure HTTP communication. Specifically, the SSL / TLS protocol is used to ensure data security.

[0438] Analysis of sales report data

[0439] The server saves the received sales report data to a database. Then, it analyzes the text data using an NLP library (e.g., Python's NLTK or spaCy). By employing techniques such as text tokenization, part-of-speech tagging, and entity recognition, it extracts important information such as the date, sales performance, product name, and sales quantity. The analysis results are then saved back to the database.

[0440] Analysis of store photo data

[0441] The server analyzes store photo data using image recognition algorithms (for example, models using OpenCV or TensorFlow). It detects products from the images and recognizes the location (front, back, etc.) and inventory status of each product. This analysis result is also stored in a database.

[0442] Data Evaluation

[0443] The server integrates sales performance data extracted from sales reports with product placement data obtained from store photos to evaluate sales strengths and weaknesses. For example, if product X is selling well because it is prominently displayed in a particular store, this is judged as a strength. Conversely, if product Y is selling poorly due to insufficient stock or being placed at the back of the shelf, this is judged as a weakness. This evaluation result is also stored in the database.

[0444] Generating improvement suggestions

[0445] Based on the evaluation results of strengths and weaknesses, the server generates specific improvement suggestions through a template engine (e.g., Jinja2). For example, for product X, a suggestion is generated that "this placement should be maintained to improve visibility," and for product Y, a suggestion is generated that "inventory should be increased and it should be placed at the front of the shelf."

[0446] Generation and provision of business negotiation materials

[0447] The server automatically generates sales materials based on the proposal. These materials are generated in PDF or presentation format, and the Python libraries ReportLab and pptx are used for their creation. The generated sales materials are provided to the terminal, which notifies the user and provides a download link. The user can then download and use the generated sales materials.

[0448] Specific example

[0449] For example, if a user uploads a sales report stating, "October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the sales floor shows product X placed at the store entrance and product Y at the back of the shelf, the system would process the data as follows: First, the user uploads the sales report and the sales floor photo, and the terminal sends this data to the server. The server then analyzes the contents of the sales report using natural language processing to extract keywords and sales performance. Next, the server analyzes the sales floor photo to recognize the product placement and inventory status. Based on the analysis results, the server determines the strengths and weaknesses of the products and generates suggestions to maintain the strengths and eliminate the weaknesses. Finally, it automatically generates sales materials and provides them to the terminal.

[0450] Example of a prompt

[0451] "Please upload your sales report and photos of your sales floor. We will then generate specific improvement suggestions based on the analysis data."

[0452] This system allows users to streamline sales analysis tasks and implement improvements quickly.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1: Upload Data

[0455] Users upload sales report data and store photo data through the system interface. Users log in to the system from a web browser and either drag and drop files onto the upload form or use the file selection dialog. These operations provide sales report data (e.g., Excel or CSV files) and store photo data (JPEG or PNG, etc.) as input.

[0456] Step 2: Send

[0457] The terminal transmits sales report data and storefront photo data uploaded by the user to the server using secure HTTP communication. SSL / TLS protocol is used to ensure the security of the transmitted data. Input is the data uploaded by the user, and output is the transmission of data to the server.

[0458] Step 3: Analysis of sales report data

[0459] The server saves the received sales report data to a database. Next, it uses an NLP library (e.g., Python's NLTK or spaCy) to analyze the text of the sales report data. This extracts important information such as date, sales performance, product name, and sales quantity using techniques such as tokenization, part-of-speech tagging, and entity recognition. The input is the received sales report data, and the output is the extracted keywords and sales performance.

[0460] Step 4: Analysis of store photo data

[0461] The server analyzes store layout photos using image recognition algorithms (e.g., models using OpenCV or TensorFlow). Through image analysis, it detects products and recognizes their location (front, back, etc.) and inventory status. The input is the received store layout photos, and the output is information on product placement and inventory status.

[0462] Step 5: Data Evaluation

[0463] The server integrates sales performance data extracted from sales reports with product placement data obtained from store photos to evaluate sales strengths and weaknesses. For example, if product X is placed at the front and sales are increasing, it is considered a strength, while if product Y is out of stock or placed at the back of the shelf and sales are sluggish, it is considered a weakness. The input is sales performance and product placement data, and the output is information on the evaluated strengths and weaknesses.

[0464] Step 6: Generating improvement proposals

[0465] The server uses a template engine (e.g., Jinja2) to generate specific improvement suggestions based on the strengths and weaknesses assessment results. For example, for product X, a suggestion is generated that "this placement should be maintained to improve visibility," and for product Y, a suggestion is generated that "inventory should be increased and the product should be placed at the front of the shelf." The input is the assessment results, and the output is the improvement suggestions.

[0466] Step 7: Generate and provide sales materials.

[0467] The server automatically generates sales materials based on the proposed content. These materials are generated in PDF or presentation format, and Python's ReportLab and pptx libraries are used for their creation. The generated sales materials are provided to the terminal, which notifies the user and provides a download link. The input is improvement proposals, and the output is sales materials in PDF or presentation format.

[0468] (Application Example 1)

[0469] 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."

[0470] Conventional sales analysis systems require manual analysis of sales report data and store photo data, which is time-consuming and labor-intensive, and results in inconsistent analysis accuracy. Furthermore, it is often difficult to quickly identify optimal product placement and promotional strategies, hindering improvements in sales efficiency. This invention aims to solve these problems and provide a system that performs sales analysis more efficiently and accurately, generating concrete improvement suggestions.

[0471] 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.

[0472] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales materials based on the proposals and outputting them in PDF or presentation format, means for collecting sales report data and store photo data using smartphones or robots, and means for performing data analysis and evaluation using a cloud server. This makes it possible to streamline sales analysis work and quickly generate concrete improvement proposals.

[0473] "Sales report data" refers to data that records daily performance and activities related to sales.

[0474] "Store display photo data" refers to photographic data that captures the arrangement of products and the condition of the store display.

[0475] "Natural language processing" is a technology that analyzes text data and performs processing such as semantic understanding and keyword extraction.

[0476] "Image analysis" is a technology that analyzes photographs and image data to recognize specific objects or features.

[0477] "Strengths" refer to positive elements or advantages in sales performance or product placement.

[0478] "Weaknesses" refer to negative elements or shortcomings in sales performance or product placement.

[0479] A "proposal" is a specific action or strategy to maintain identified strengths and improve weaknesses.

[0480] "Sales materials" are documents that summarize sales proposals and improvement measures in document or presentation format.

[0481] A "smartphone" is a type of mobile phone, a portable device that has both communication and computer functions.

[0482] A "robot" is a mechanical device that operates autonomously or semi-autonomously and has the function of performing a specific task.

[0483] A "cloud server" is a remote server provided via the internet that offers large-scale data processing and storage capabilities.

[0484] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. This system uses smartphones and robots and utilizes cloud servers to perform advanced data analysis.

[0485] Users collect sales report data and store photo data using smartphones or robots. This data is sent to a cloud server where it is analyzed. The sales report data is analyzed using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y are sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[0486] Similarly, store photo data is analyzed using image recognition algorithms (such as OpenCV or TensorFlow) to recognize the placement and inventory status of products. For example, a store photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[0487] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[0488] The server then generates specific suggestions to maintain strengths and improve weaknesses. For example, for product X, it might suggest "maintain this placement to improve visibility," and for product Y, it might suggest "increase inventory and place it at the front of the shelf."

[0489] Finally, the server automatically generates sales materials based on the proposal. These materials are output in PDF or presentation format (such as PowerPoint) and provided to the user's terminal. The user can review the generated sales materials and download them via a download link.

[0490] As a concrete example, input the following prompt into the generating AI model:

[0491] After users upload store layouts and sales reports using their smartphones, a cloud server analyzes the data, evaluates product strengths and weaknesses, and generates improvement suggestions based on that analysis. The generated sales materials are provided in PDF format, allowing store managers and sales representatives to quickly implement improvement measures based on the data. This application contributes to improved sales efficiency through intuitive data collection using smartphones and robots, and advanced data analysis using natural language processing and image recognition technologies.

[0492] This streamlines sales analysis and enables the rapid generation of concrete improvement suggestions.

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] Users collect sales report data and store photo data using smartphones or robots and upload them to a cloud server. Input data consists of daily report data in text format and store photos in JPEG format. Based on user actions, the collected data is transmitted to the cloud server in real time.

[0496] Step 2:

[0497] This system analyzes sales report data received by a cloud server using natural language processing (NLP) techniques. The input data consists of user-uploaded daily report text, and the output is extracted data containing important keywords and sales performance information. Specifically, it uses NLP libraries (e.g., NLTK, spaCy) to extract key information from the text.

[0498] Step 3:

[0499] The cloud server analyzes storefront photo data received using an image recognition algorithm. The input data consists of JPEG photos of the storefront uploaded by users, and the output data shows product placement information and inventory status. Specifically, an image recognition algorithm (e.g., OpenCV, TensorFlow) is used to identify the location and inventory quantity of each product.

[0500] Step 4:

[0501] The cloud server evaluates the analyzed sales report data and store photo data to identify sales strengths and weaknesses. The input data consists of the extracted data and placement data obtained in the previous step, and the output is the evaluation results of strengths and weaknesses. Specifically, data analysis tools (e.g., Pandas, NumPy) are used to compare and analyze the entire dataset.

[0502] Step 5:

[0503] The cloud server generates suggestions to maintain identified strengths and improve weaknesses. The input data is the evaluation results of strengths and weaknesses, and the output is a list of specific improvement suggestions. Specifically, it uses conditional branching and rule-based algorithms to automatically generate the optimal suggestions.

[0504] Step 6:

[0505] The cloud server automatically generates sales materials based on the generated proposals. The input data is a list of proposals, and the output is sales materials in PDF or presentation format. Specifically, it uses a document generation library (e.g., ReportLab, pptx) to construct the materials.

[0506] Step 7:

[0507] The cloud server provides the generated sales materials to the terminal and displays them to the user. The input data is sales materials in PDF or presentation format, and the output is a download link displayed on the terminal. Specifically, a file transfer protocol is used to send the materials to the terminal.

[0508] This allows users to streamline sales analysis tasks and quickly generate concrete improvement suggestions.

[0509] 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.

[0510] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides even more accurate suggestions. This system allows users to upload sales report data and store photo data, and based on that information, it automatically generates sales materials and presents optimal suggestions tailored to the user's emotions.

[0511] Program processing

[0512] 1. Data Collection

[0513] Users upload sales report data and store photo data. This data is sent from the terminal to the server.

[0514] 2. Data Analysis

[0515] The server analyzes sales report data using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[0516] 3. Analysis of store photos

[0517] The server analyzes store layout photos using an image recognition algorithm. It recognizes the placement and inventory status of products in the photos and determines whether a product is located at the front or back of a shelf. For example, the store layout photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[0518] 4. Data Evaluation

[0519] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[0520] 5. Emotion analysis

[0521] The server uses an emotion analysis engine to recognize the user's emotional state. For example, when a user enters sales report data, the server recognizes their emotion (positive, negative, or neutral) from the tone and context of the text. It also analyzes the user's facial expressions and posture in store photos to evaluate their emotional state.

[0522] 6. Generating improvement suggestions

[0523] The server generates specific suggestions to maintain strengths and improve weaknesses. Furthermore, it adjusts the suggestions based on the user's emotional data. For example, if negative emotions are detected, the improvement suggestions are adjusted to be more specific and encouraging.

[0524] 7. Generating sales negotiation materials

[0525] The server automatically generates sales materials based on the proposal. These materials include an assessment of strengths and weaknesses, as well as specific proposals. Furthermore, they may include supplementary information and words of encouragement tailored to the user's emotional state. These sales materials are output in PDF or PowerPoint format and provided to the user's device.

[0526] Specific example

[0527] For example, consider a scenario where a user uploads a sales report stating, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the store layout shows product X placed at the store entrance and product Y at the back of the shelf. Furthermore, if the sentiment analysis engine recognizes from the sales report that the user has negative emotions, the system will process the data as follows.

[0528] 1. Users upload sales reports and photos of the sales floor.

[0529] 2. The device sends this data to the server.

[0530] 3. The server analyzes the contents of the sales report using natural language processing and extracts keywords and sales performance data.

[0531] 4. The server analyzes images of the store floor to recognize the product layout and inventory status.

[0532] 5. The server uses an emotion analysis engine to recognize negative emotions from the user based on the content of the sales report.

[0533] 6. The server evaluates this data and determines the product's strengths and weaknesses.

[0534] 7. The server generates suggestions to maintain its strengths and address its weaknesses. Considering that users may have negative feelings, the suggestions are presented in a more specific and positive manner.

[0535] 8. The server automatically generates and provides sales materials to the terminal. The sales materials also include an encouraging message such as, "Let's review recent achievements and work towards the next step."

[0536] In this way, users can streamline their sales analysis work, implement improvements quickly, and receive emotional support.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] User: Upload sales report data to the system. Specifically, enter text data related to sales activities and press the submit button.

[0540] Step 2:

[0541] Terminal: Receives sales report data and sends it to the server. Specifically, it converts the text data submitted by the user into the appropriate format and sends it to the server as an HTTP request.

[0542] Step 3:

[0543] User: Upload store photos to the system. Specifically, select photos of products taken inside the store and press the upload button.

[0544] Step 4:

[0545] Terminal: Receives store photo data and sends it to the server. Specifically, it converts the image files uploaded by the user into the appropriate format and sends them to the server as an HTTP request.

[0546] Step 5:

[0547] Server: Receives sales report data and analyzes it using a natural language processing (NLP) engine. Specifically, it tokenizes the text and extracts keywords such as nouns, verbs, and adjectives. It also identifies keywords such as "Store A," "Product X," "Sales," and "20% increase compared to the previous month."

[0548] Step 6:

[0549] Server: Receives store floor photo data and analyzes it using image analysis algorithms. Specifically, it performs object detection and classification within the image to determine the placement and inventory status of products. For example, it determines that product X is placed at the front of the shelf and product Y is located at the back of the shelf.

[0550] Step 7:

[0551] Server: Based on keywords extracted from sales reports and sales performance data, the server stores this information in an evaluation database. Specifically, it extracts elements such as sales increases / decreases and inventory shortages, and saves this information in the database.

[0552] Step 8:

[0553] Server: Evaluates product placement data obtained from store photos and stores it in an evaluation database. Specifically, it saves the location and inventory status of products in the database.

[0554] Step 9:

[0555] Server: Using an emotion analysis engine, it analyzes user emotions from sales report data and store photo data. For example, it identifies negative, positive, and neutral tones from the text of sales reports and recognizes emotions from the facial expressions of users in store photos.

[0556] Step 10:

[0557] Server: Evaluates sales performance and product placement data, along with recognized sentiment data, to identify strengths and weaknesses. For example, product X is identified as a strength because its high visibility is leading to increased sales, while product Y is identified as a weakness because its low sales are due to insufficient stock and poor placement.

[0558] Step 11:

[0559] Server: Generates specific suggestions to maintain strengths and improve weaknesses. Specifically, it proposes strategies to maintain the factors contributing to strengths and improve inventory management and product placement to address weaknesses. The suggestions are adjusted to be more appropriate by considering user sentiment data.

[0560] Step 12:

[0561] Server: Automatically generates sales materials based on the proposed content. Specifically, it organizes evaluation results and proposal content and creates sales materials in PDF or PowerPoint format. It also includes messages of encouragement and appreciation tailored to the user's emotions.

[0562] Step 13:

[0563] Server: Sends the completed sales materials to the terminal. Specifically, it provides the generated files to the terminal as a download link, allowing the user to access them.

[0564] Step 14:

[0565] Terminal: Displays sales materials to the user and provides a download link. Specifically, it displays sales materials containing evaluation results and improvement suggestions to the user in a visible format and maintains a state where they can be downloaded as needed.

[0566] (Example 2)

[0567] 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".

[0568] Conventional sales analysis systems struggled to provide appropriate suggestions based on user emotions, in addition to analyzing sales report data and store photo data. This resulted in inaccurate evaluations of sales strengths and weaknesses, as well as the inability to provide suggestions that considered user emotions, ultimately leading to lower effectiveness of improvement suggestions. Furthermore, the generation of sales materials lacked adjustments that reflected user emotions, resulting in a failure to motivate users.

[0569] 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.

[0570] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for recognizing and evaluating user emotion data, means for generating suggestions to maintain identified strengths and improve weaknesses, means for generating sales negotiation materials based on the suggested content, and means for adjusting the sales negotiation materials according to the user's emotional state. This enables detailed sales analysis and the generation of improvement suggestions that take user emotions into consideration, increasing the effectiveness of improvement suggestions and enabling the maintenance or improvement of user motivation.

[0571] "Sales report data" refers to data containing detailed information about sales activities, including daily sales, customer trends, and product trends, recorded in digital or paper format.

[0572] "Sales floor photo data" refers to photographic data showing the arrangement and inventory status of products within a sales floor or store, and consists of image files taken with a digital camera or smartphone.

[0573] "Natural language processing" is a technology that enables computers to understand and analyze human language, and it involves methods for extracting, classifying, and analyzing important keywords and meanings from text.

[0574] "Image analysis" is a technology that uses computers to analyze digital images and recognize, classify, and analyze objects and patterns within those images.

[0575] "Sales performance" refers to actual data such as sales and sales volume of products over a specific period, and is an indicator that shows the results of a company's or store's business activities.

[0576] "Product placement data" refers to data about the location and layout of products within a store, and provides information that indicates the visibility and accessibility of products.

[0577] "Emotional data" refers to data that reflects the user's emotional state, and is information indicating positive, negative, or neutral emotions obtained through methods such as text tone and facial expression analysis.

[0578] A "strength" refers to an advantage in sales activities or product features, representing a competitive advantage that a company or store has over its competitors.

[0579] A "weakness" refers to an inferiority in sales activities or product characteristics, representing shortcomings or deficiencies that a company or store should address.

[0580] A "proposal" is a set of specific measures and policies to maintain strengths and improve weaknesses, and is useful information for sales strategies and store operations.

[0581] "Sales materials" are documents used in sales activities and negotiations with customers, and include proposals, sales performance, and evaluations of strengths and weaknesses.

[0582] "Adjusting based on the user's emotional state" is a method of modifying the content of proposals and sales materials based on the user's emotional data in order to provide users with optimal information and improve their motivation.

[0583] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides even more accurate suggestions.

[0584] The specific embodiments for carrying out the invention are described in detail below.

[0585] First, users upload sales report data and store photo data using a dedicated web portal or application. At this time, a file selection dialog will appear, and users will select the data files to upload. After selecting the files, the data is encrypted and sent from the terminal to the server. For example, this applies when a user selects and sends the daily report file "report_20231001.txt" and the store photo "store_photo.jpg".

[0586] The server analyzes the submitted sales report data using natural language processing (NLP) techniques. Specifically, it extracts important keywords and sales performance using Python and dedicated natural language processing libraries. This process utilizes Apache Kafka to analyze the data stream. In addition, image recognition libraries such as OpenCV and TensorFlow are used for image analysis to recognize product placement and inventory status from store floor photos. For example, from "store_photo.jpg", it recognizes that "product X is placed at the front of the shelf and product Y is placed at the back of the shelf".

[0587] Next, based on the extracted sales performance data and product placement data, the server evaluates its strengths and weaknesses. This evaluation uses an algorithm that compares and analyzes past sales data with current data. The evaluation process also includes comparison with existing databases within the system.

[0588] Furthermore, the server uses an emotion analysis engine to recognize and analyze the user's emotional data. Emotion analysis employs techniques that evaluate the user's emotions (positive, negative, or neutral) based on the tone and style of their writing. The user's facial expressions and posture in store photos are also included in the analysis.

[0589] Based on the evaluation results, the server generates improvement suggestions that reflect the user's emotions. For example, if negative emotions are detected, the improvement suggestions are adjusted to be more specific and encouraging.

[0590] Finally, the server automatically creates sales materials based on the generated improvement suggestions. These materials are generated in PDF or presentation format and may include supplementary information and encouraging messages tailored to the user's emotional state. Once the materials are complete, a download link is notified to the device, and the user can download and use them.

[0591] The following are examples of input prompts for the generated AI model when using this system.

[0592] A user has uploaded sales report data for store A. The report states, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock." A photo of the store layout shows product X placed near the entrance and product Y placed at the back of the shelf. The sentiment analysis engine indicates that the user is experiencing negative emotions. Based on this information, generate suggestions for increasing sales and create specific sales materials.

[0593] This invention allows users to quickly and efficiently conduct sales analysis and implement improvement measures, as well as receive appropriate emotional support.

[0594] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0595] Step 1:

[0596] Users upload sales report data and store photo data using a dedicated web portal or application. When a user selects files such as "report_20231001.txt" and "store_photo.jpg", these files are sent from the terminal to the server in an encrypted state.

[0597] Input: Sales report data file, store photo data file

[0598] Output: Encrypted data is sent to the server.

[0599] Step 2:

[0600] The server analyzes the received sales report data using natural language processing (NLP) techniques. Specifically, it uses Python and natural language processing libraries (e.g., NLTK and SpaCy) to extract important keywords and sales performance data. For example, it extracts keywords such as "Store A," "Product X," "Sales," "20% increase compared to the previous month," "Inventory shortage," and "Sales slump" from "report_20231001.txt."

[0601] Input: Sales report data file

[0602] Output: Extracted keywords and sales performance data

[0603] Step 3:

[0604] The server analyzes the received store photo data using an image recognition algorithm. Specifically, it uses image recognition libraries such as OpenCV or TensorFlow to analyze product placement and inventory status. For example, it recognizes from "store_photo.jpg" that "product X is placed at the front of the shelf and product Y is placed at the back of the shelf."

[0605] Input: Sales floor photo data file

[0606] Output: Product placement and inventory status data

[0607] Step 4:

[0608] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. This uses an algorithm that performs comparative analysis with past sales data and existing databases within the system. For example, product X is evaluated as having a strength because its high visibility is leading to increased sales, while product Y is evaluated as having a weakness because its low sales are due to insufficient inventory and inconspicuous placement.

[0609] Input: Extracted keywords, sales performance data, product placement and inventory status data

[0610] Output: Identified strengths and weaknesses

[0611] Step 5:

[0612] The server uses an emotion analysis engine to recognize and evaluate the user's emotional state. Specifically, it uses IBM Watson and Microsoft Azure emotion analysis APIs to analyze emotions (positive, negative, neutral) from the content of sales reports. It also evaluates the emotional state by analyzing the user's facial expressions and posture in photos of the sales floor.

[0613] Input: Sales report data file, store photo data file

[0614] Output: User sentiment data

[0615] Step 6:

[0616] The server generates specific improvement suggestions based on the evaluated strengths and weaknesses, as well as user sentiment data. If the user has negative emotions, the suggestions are made more specific and positive. For example, it might suggest, "Continue to place product X at the front of the shelf and replenish the stock of product Y to improve sales."

[0617] Input: Identified strengths and weaknesses, user sentiment data

[0618] Output: Improvement suggestions

[0619] Step 7:

[0620] The server automatically generates sales materials based on the improvement suggestions that have been created. These materials are generated in PDF or presentation format and include charts and graphs. In addition, supplementary information and encouraging messages tailored to the user's emotional state may be added.

[0621] Input: Improvement suggestions, user sentiment data

[0622] Output: Sales materials (PDF or presentation format)

[0623] Through these processing steps, users can quickly and efficiently conduct sales analysis and implement improvement measures, while also receiving appropriate emotional support.

[0624] (Application Example 2)

[0625] 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."

[0626] Currently, in brick-and-mortar store operations, it is essential to quickly and accurately grasp sales performance and product placement, and to make efficient improvement suggestions. However, these tasks are often performed manually, which is time-consuming and labor-intensive, and often relies on subjective judgment. Furthermore, because suggestions are not made flexibly based on customer emotions, there are problems with the acceptance and implementation rate of suggestions. In this situation, there is a need to develop a system that analyzes sales performance and product placement data to generate objective and effective improvement suggestions.

[0627] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing the placement and inventory status of products from the store photo data using image analysis, means for evaluating the sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales negotiation materials based on the proposed content, means for transmitting sales report data and store photo data from the user terminal to the server, emotion analysis means for recognizing the user's emotions, and means for adjusting the proposed content based on the user's emotions. This enables objective improvement proposals based on sales performance and store photo data, as well as flexible proposals that take into account the user's emotions.

[0628] "Sales report data" refers to data that records daily performance and information related to sales activities.

[0629] "Store photo data" refers to image data that visually records the conditions of a store, such as the arrangement of products and inventory levels.

[0630] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0631] "Key keywords" are words or phrases that are important for identifying specific information, extracted through data analysis.

[0632] "Sales performance" refers to performance data such as sales figures and sales volume of products or services over a specific period.

[0633] "Image analysis" is a technique that uses computer vision technology to extract useful information from image data.

[0634] "Product placement" refers to the location and arrangement of products on display in a store.

[0635] "Inventory status" refers to information indicating the quantity and condition of a product in stock at a specific point in time.

[0636] A "strength" is a characteristic or element that is perceived as positive in the area being evaluated.

[0637] A "weakness" is a characteristic or element of the subject being evaluated that is recognized as needing improvement.

[0638] A "suggestion" is a recommendation of specific actions or strategies to maintain identified strengths and improve weaknesses.

[0639] "Business negotiation materials" are documents that summarize the proposed content in a visual or written format.

[0640] "Emotion analysis" is a technology that recognizes and evaluates a user's emotional state from text and images.

[0641] A "user terminal" refers to a computer or mobile device that can send sales report data and store photo data to a server.

[0642] This invention is a system that analyzes sales report data and store photo data to identify sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides more accurate suggestions. The embodiments for carrying out this invention are as follows.

[0643] The user first uploads sales report data and store photo data using a terminal. The terminal sends this data to the server. The server analyzes the data and generates suggestions using the following methods.

[0644] The server analyzes sales report data using natural language processing (NLP) techniques to extract important keywords and sales performance data. Specific software used includes natural language processing libraries such as Spacy. This analysis extracts keywords such as "Store A," "Product X," and "20% increase compared to the previous month."

[0645] Next, the server analyzes the store layout photo data using an image recognition algorithm. The specific software includes image processing libraries using OpenCV and TensorFlow. This allows the system to recognize the placement and inventory status of products in the store, providing information such as "product X is placed at the front of the shelf" or "product Y is out of stock."

[0646] Next, the server evaluates the extracted sales performance data and product placement data to identify strengths and weaknesses. This evaluation identifies information such as, "Product X has high visibility and strong sales," or "Product Y is out of stock and sales are sluggish."

[0647] Furthermore, the server uses an emotion analysis engine to recognize the user's emotional state. This emotion analysis employs a generative AI model that evaluates the tone and context of the text. For example, if negative emotions are detected when a user enters sales report data, the server will recognize this.

[0648] The server generates specific improvement suggestions based on identified strengths and weaknesses, as well as the user's emotional state. These suggestions are tailored to the user's emotions and may be presented in a more positive and concrete format. For example, a suggestion such as "Maintain product X in its current position and replenish product Y's inventory immediately" might be generated.

[0649] Ultimately, the server automatically generates sales materials based on the improvement suggestions that have been created. These materials, including PDF or presentation formats, are provided to the user's terminal. This allows the user to efficiently analyze sales data from physical stores and implement improvement measures.

[0650] For example, the following format can be used as a prompt:

[0651] "On October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock. Based on this data, generate improvement proposals and prepare sales materials."

[0652] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0653] Step 1:

[0654] Users input sales report data and take photos of the sales floor. The sales report data includes daily sales performance and sales status, while the sales floor photos visually record product placement and inventory status. Users also upload this data using a smartphone or smart glasses.

[0655] Input: Sales report data, store photo data

[0656] Output: Data uploaded to the device

[0657] Step 2:

[0658] The terminal sends sales report data and store photo data to the server. A data transmission completion notification is displayed on the terminal to confirm that the data has reached the server correctly.

[0659] Input: Data uploaded to the device

[0660] Output: Data sent to the server

[0661] Step 3:

[0662] The server analyzes the received sales report data using natural language processing (NLP) techniques. Specifically, it uses NLP libraries such as spacy to extract important keywords and sales performance data from the sales reports.

[0663] Input: Sales report data

[0664] Output: Key keywords, sales performance

[0665] Step 4:

[0666] The server analyzes store photo data using image recognition algorithms. Using image processing libraries such as OpenCV and TensorFlow, it recognizes the placement and inventory status of products in the photos and stores this information in a database.

[0667] Input: Storefront photo data

[0668] Output: Product placement data, inventory status data

[0669] Step 5:

[0670] The server evaluates the extracted key keywords, sales performance, and product placement data, and uses this information to identify strengths and weaknesses. This results in evaluations such as, "Product X has high visibility and strong sales."

[0671] Input: Key keywords, sales performance, product placement data

[0672] Output: Evaluation results of strengths and weaknesses

[0673] Step 6:

[0674] The server uses an emotion analysis engine to recognize the user's emotional state from sales report data and store photo data. A generative AI model is used to evaluate the user's emotions (positive, negative, neutral) based on their input and photos.

[0675] Input: Sales report data, store photo data

[0676] Output: Sentiment evaluation

[0677] Step 7:

[0678] The server generates specific improvement suggestions based on the strengths and weaknesses assessment results and the sentiment assessment. The generated suggestions are adjusted based on the user's sentiment and include positive content, such as "Keep product X in its current position."

[0679] Input: Strengths and weaknesses assessment results, emotional assessment

[0680] Output: Specific improvement suggestions

[0681] Step 8:

[0682] The server automatically generates sales materials based on the improvement suggestions that have been created. The sales materials are generated in PDF or presentation format and provided to the user's terminal.

[0683] Input: Specific improvement suggestions

[0684] Output: Sales materials (PDF / presentation format)

[0685] As a concrete example of the operation, the prompt statement is as follows:

[0686] "On October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock. Based on this data, generate improvement proposals and prepare sales materials."

[0687] 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.

[0688] 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.

[0689] 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.

[0690] [Third Embodiment]

[0691] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0692] 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.

[0693] 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).

[0694] 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.

[0695] 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.

[0696] 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).

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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".

[0703] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. The system allows users to upload sales report data and store photo data, and automatically generates sales materials based on that information.

[0704] Program processing

[0705] 1. Data Collection

[0706] Users upload sales report data and store photo data. This data is sent from the terminal to the server.

[0707] 2. Data Analysis

[0708] The server analyzes sales report data using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[0709] 3. Analysis of store photos

[0710] The server analyzes store layout photos using an image recognition algorithm. It recognizes the placement and inventory status of products in the photos and determines whether a product is located at the front or back of a shelf. For example, the store layout photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[0711] 4. Data Evaluation

[0712] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[0713] 5. Generating improvement suggestions

[0714] The server generates specific suggestions to maintain strengths and improve weaknesses. For example, for product X, it might suggest "maintain this placement to improve visibility," and for product Y, it might suggest "increase inventory and place it at the front of the shelf."

[0715] 6. Generating sales negotiation materials

[0716] The server automatically generates sales materials based on the proposal. These materials include an assessment of strengths, an assessment of weaknesses, and specific proposals. These sales materials are output in PDF or PowerPoint format and provided to the terminal. The terminal displays the generated sales materials to the user and provides a download link.

[0717] Specific example

[0718] For example, consider a scenario where a user uploads a sales report stating, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the sales floor shows product X placed at the store entrance and product Y at the back of the shelf. The system would process this as follows:

[0719] 1. Users upload sales reports and photos of the sales floor.

[0720] 2. The device sends this data to the server.

[0721] 3. The server analyzes the contents of the sales report using natural language processing and extracts keywords and sales performance data.

[0722] 4. The server analyzes images of the store floor to recognize the product layout and inventory status.

[0723] 5. The server evaluates this data and determines the product's strengths and weaknesses.

[0724] 6. The server generates suggestions to maintain its strengths and address its weaknesses.

[0725] 7. The server automatically generates sales materials and provides them to the terminal.

[0726] In this way, users can streamline their sales analysis tasks and implement improvements quickly.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] User: Upload sales report data to the system. Specifically, enter text data related to sales activities and press the submit button.

[0730] Step 2:

[0731] Terminal: Receives sales report data and sends it to the server. Specifically, it converts the text data submitted by the user into the appropriate format and sends it to the server as an HTTP request.

[0732] Step 3:

[0733] User: Upload store photos to the system. Specifically, select photos of products taken inside the store and press the upload button.

[0734] Step 4:

[0735] Terminal: Receives store photo data and sends it to the server. Specifically, it converts the image files uploaded by the user into the appropriate format and sends them to the server as an HTTP request.

[0736] Step 5:

[0737] Server: Receives sales report data and analyzes the data using a natural language processing (NLP) engine. Specifically, it tokenizes the text and extracts keywords such as nouns, verbs, and adjectives.

[0738] Step 6:

[0739] Server: Receives storefront photo data and analyzes it using image analysis algorithms. Specifically, it performs object detection and classification within the images to identify product placement and inventory status.

[0740] Step 7:

[0741] Server: Analyzes keywords extracted from sales reports and sales performance data, and stores them in an evaluation database. Specifically, it extracts elements such as increased sales and insufficient inventory, and saves this data to the database.

[0742] Step 8:

[0743] Server: Evaluates product placement data obtained from store photos and stores it in an evaluation database. Specifically, it stores product location (front, back of shelf, etc.) and inventory status (in stock, out of stock, etc.) in the database.

[0744] Step 9:

[0745] Server: Based on the evaluation database, identify the strengths and weaknesses of sales data. Specifically, analyze the reasons for increases and decreases in sales and identify the elements that constitute strengths and weaknesses.

[0746] Step 10:

[0747] Server: Generates specific proposals to maintain strengths and improve weaknesses. Specifically, it identifies the factors contributing to strengths and proposes measures to maintain them. For weaknesses, it analyzes the causes and proposes solutions to address them.

[0748] Step 11:

[0749] Server: Generates sales materials based on the proposed content. Specifically, it organizes the evaluation results and proposed content and creates sales materials in PDF or PowerPoint format.

[0750] Step 12:

[0751] Server: Sends the completed sales materials to the terminal. Specifically, it provides the generated files to the terminal as a download link, allowing the user to access them.

[0752] Step 13:

[0753] Terminal: Displays sales materials to users and provides download links. Specifically, it displays sales materials containing store and product evaluation results and improvement suggestions to users in a visible format and maintains a downloadable state as needed.

[0754] (Example 1)

[0755] 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."

[0756] Traditional sales analysis systems require manual analysis of sales report data and store photo data, which is time-consuming, labor-intensive, and inefficient. Furthermore, manual analysis carries the risk of human error, resulting in a lack of accuracy and reliability. Additionally, the process of incorporating data insights into sales materials is time-consuming, hindering rapid decision-making.

[0757] 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.

[0758] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales negotiation materials based on the proposed content, and means for providing the sales negotiation materials to the terminal in PDF or presentation format and presenting the user with a download link. This enables the user to perform sales analysis efficiently and quickly and make rapid decisions based on accurate improvement proposals.

[0759] "Sales report data" refers to data that records the results, observations, and sales performance of a day's sales activities.

[0760] "Store floor photo data" refers to image data that captures the arrangement of products and inventory status within a store or sales floor.

[0761] Natural language processing is a technology that enables computers to understand and analyze human language, and to extract useful information from text data.

[0762] "Image analysis" is a technology that processes and analyzes image data to recognize and extract objects and features within the image.

[0763] A "strength" is an element that demonstrates a competitive advantage or improved performance in areas such as sales performance or product placement.

[0764] A "weakness" is an element that indicates a decline in performance or room for improvement in areas such as sales figures or product placement.

[0765] A "proposal" is a concrete action plan or strategy to maintain sales strengths and improve weaknesses.

[0766] "Sales negotiation materials" are documents that summarize the results of sales analysis and proposals, and are used during sales negotiations.

[0767] A "terminal" refers to a device used by a user, such as a computer or smartphone.

[0768] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. The following describes each processing step and the hardware and software used.

[0769] Uploading and sending data

[0770] Users upload sales report data (e.g., Excel or CSV files) and store photo data (image files such as JPEG or PNG) through the system interface. The terminal sends this data to the server using secure HTTP communication. Specifically, the SSL / TLS protocol is used to ensure data security.

[0771] Analysis of sales report data

[0772] The server saves the received sales report data to a database. Then, it analyzes the text data using an NLP library (e.g., Python's NLTK or spaCy). By employing techniques such as text tokenization, part-of-speech tagging, and entity recognition, it extracts important information such as the date, sales performance, product name, and sales quantity. The analysis results are then saved back to the database.

[0773] Analysis of store photo data

[0774] The server analyzes store photo data using image recognition algorithms (for example, models using OpenCV or TensorFlow). It detects products from the images and recognizes the location (front, back, etc.) and inventory status of each product. This analysis result is also stored in a database.

[0775] Data Evaluation

[0776] The server integrates sales performance data extracted from sales reports with product placement data obtained from store photos to evaluate sales strengths and weaknesses. For example, if product X is selling well because it is prominently displayed in a particular store, this is judged as a strength. Conversely, if product Y is selling poorly due to insufficient stock or being placed at the back of the shelf, this is judged as a weakness. This evaluation result is also stored in the database.

[0777] Generating improvement suggestions

[0778] Based on the evaluation results of strengths and weaknesses, the server generates specific improvement suggestions through a template engine (e.g., Jinja2). For example, for product X, a suggestion is generated that "this placement should be maintained to improve visibility," and for product Y, a suggestion is generated that "inventory should be increased and it should be placed at the front of the shelf."

[0779] Generation and provision of business negotiation materials

[0780] The server automatically generates sales materials based on the proposal. These materials are generated in PDF or presentation format, and the Python libraries ReportLab and pptx are used for their creation. The generated sales materials are provided to the terminal, which notifies the user and provides a download link. The user can then download and use the generated sales materials.

[0781] Specific example

[0782] For example, if a user uploads a sales report stating, "October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the sales floor shows product X placed at the store entrance and product Y at the back of the shelf, the system would process the data as follows: First, the user uploads the sales report and the sales floor photo, and the terminal sends this data to the server. The server then analyzes the contents of the sales report using natural language processing to extract keywords and sales performance. Next, the server analyzes the sales floor photo to recognize the product placement and inventory status. Based on the analysis results, the server determines the strengths and weaknesses of the products and generates suggestions to maintain the strengths and eliminate the weaknesses. Finally, it automatically generates sales materials and provides them to the terminal.

[0783] Example of a prompt

[0784] "Please upload your sales report and photos of your sales floor. We will then generate specific improvement suggestions based on the analysis data."

[0785] This system allows users to streamline sales analysis tasks and implement improvements quickly.

[0786] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0787] Step 1: Upload Data

[0788] Users upload sales report data and store photo data through the system interface. Users log in to the system from a web browser and either drag and drop files onto the upload form or use the file selection dialog. These operations provide sales report data (e.g., Excel or CSV files) and store photo data (JPEG or PNG, etc.) as input.

[0789] Step 2: Send

[0790] The terminal transmits sales report data and storefront photo data uploaded by the user to the server using secure HTTP communication. SSL / TLS protocol is used to ensure the security of the transmitted data. Input is the data uploaded by the user, and output is the transmission of data to the server.

[0791] Step 3: Analysis of sales report data

[0792] The server saves the received sales report data to a database. Next, it uses an NLP library (e.g., Python's NLTK or spaCy) to analyze the text of the sales report data. This extracts important information such as date, sales performance, product name, and sales quantity using techniques such as tokenization, part-of-speech tagging, and entity recognition. The input is the received sales report data, and the output is the extracted keywords and sales performance.

[0793] Step 4: Analysis of store photo data

[0794] The server analyzes store layout photos using image recognition algorithms (e.g., models using OpenCV or TensorFlow). Through image analysis, it detects products and recognizes their location (front, back, etc.) and inventory status. The input is the received store layout photos, and the output is information on product placement and inventory status.

[0795] Step 5: Data Evaluation

[0796] The server integrates sales performance data extracted from sales reports with product placement data obtained from store photos to evaluate sales strengths and weaknesses. For example, if product X is placed at the front and sales are increasing, it is considered a strength, while if product Y is out of stock or placed at the back of the shelf and sales are sluggish, it is considered a weakness. The input is sales performance and product placement data, and the output is information on the evaluated strengths and weaknesses.

[0797] Step 6: Generating improvement proposals

[0798] The server uses a template engine (e.g., Jinja2) to generate specific improvement suggestions based on the strengths and weaknesses assessment results. For example, for product X, a suggestion is generated that "this placement should be maintained to improve visibility," and for product Y, a suggestion is generated that "inventory should be increased and the product should be placed at the front of the shelf." The input is the assessment results, and the output is the improvement suggestions.

[0799] Step 7: Generate and provide sales materials.

[0800] The server automatically generates sales materials based on the proposed content. These materials are generated in PDF or presentation format, and Python's ReportLab and pptx libraries are used for their creation. The generated sales materials are provided to the terminal, which notifies the user and provides a download link. The input is improvement proposals, and the output is sales materials in PDF or presentation format.

[0801] (Application Example 1)

[0802] 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."

[0803] Conventional sales analysis systems require manual analysis of sales report data and store photo data, which is time-consuming and labor-intensive, and results in inconsistent analysis accuracy. Furthermore, it is often difficult to quickly identify optimal product placement and promotional strategies, hindering improvements in sales efficiency. This invention aims to solve these problems and provide a system that performs sales analysis more efficiently and accurately, generating concrete improvement suggestions.

[0804] 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.

[0805] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales materials based on the proposals and outputting them in PDF or presentation format, means for collecting sales report data and store photo data using smartphones or robots, and means for performing data analysis and evaluation using a cloud server. This makes it possible to streamline sales analysis work and quickly generate concrete improvement proposals.

[0806] "Sales report data" refers to data that records daily performance and activities related to sales.

[0807] "Store display photo data" refers to photographic data that captures the arrangement of products and the condition of the store display.

[0808] "Natural language processing" is a technology that analyzes text data and performs processing such as semantic understanding and keyword extraction.

[0809] "Image analysis" is a technology that analyzes photographs and image data to recognize specific objects or features.

[0810] "Strengths" refer to positive elements or advantages in sales performance or product placement.

[0811] "Weaknesses" refer to negative elements or shortcomings in sales performance or product placement.

[0812] A "proposal" is a specific action or strategy to maintain identified strengths and improve weaknesses.

[0813] "Sales materials" are documents that summarize sales proposals and improvement measures in document or presentation format.

[0814] A "smartphone" is a type of mobile phone, a portable device that has both communication and computer functions.

[0815] A "robot" is a mechanical device that operates autonomously or semi-autonomously and has the function of performing a specific task.

[0816] A "cloud server" is a remote server provided via the internet that offers large-scale data processing and storage capabilities.

[0817] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. This system uses smartphones and robots and utilizes cloud servers to perform advanced data analysis.

[0818] Users collect sales report data and store photo data using smartphones or robots. This data is sent to a cloud server where it is analyzed. The sales report data is analyzed using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y are sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[0819] Similarly, store photo data is analyzed using image recognition algorithms (such as OpenCV or TensorFlow) to recognize the placement and inventory status of products. For example, a store photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[0820] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[0821] The server then generates specific suggestions to maintain strengths and improve weaknesses. For example, for product X, it might suggest "maintain this placement to improve visibility," and for product Y, it might suggest "increase inventory and place it at the front of the shelf."

[0822] Finally, the server automatically generates sales materials based on the proposal. These materials are output in PDF or presentation format (such as PowerPoint) and provided to the user's terminal. The user can review the generated sales materials and download them via a download link.

[0823] As a concrete example, input the following prompt into the generating AI model:

[0824] After users upload store layouts and sales reports using their smartphones, a cloud server analyzes the data, evaluates product strengths and weaknesses, and generates improvement suggestions based on that analysis. The generated sales materials are provided in PDF format, allowing store managers and sales representatives to quickly implement improvement measures based on the data. This application contributes to improved sales efficiency through intuitive data collection using smartphones and robots, and advanced data analysis using natural language processing and image recognition technologies.

[0825] This streamlines sales analysis and enables the rapid generation of concrete improvement suggestions.

[0826] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0827] Step 1:

[0828] Users collect sales report data and store photo data using smartphones or robots and upload them to a cloud server. Input data consists of daily report data in text format and store photos in JPEG format. Based on user actions, the collected data is transmitted to the cloud server in real time.

[0829] Step 2:

[0830] This system analyzes sales report data received by a cloud server using natural language processing (NLP) techniques. The input data consists of user-uploaded daily report text, and the output is extracted data containing important keywords and sales performance information. Specifically, it uses NLP libraries (e.g., NLTK, spaCy) to extract key information from the text.

[0831] Step 3:

[0832] The cloud server analyzes storefront photo data received using an image recognition algorithm. The input data consists of JPEG photos of the storefront uploaded by users, and the output data shows product placement information and inventory status. Specifically, an image recognition algorithm (e.g., OpenCV, TensorFlow) is used to identify the location and inventory quantity of each product.

[0833] Step 4:

[0834] The cloud server evaluates the analyzed sales report data and store photo data to identify sales strengths and weaknesses. The input data consists of the extracted data and placement data obtained in the previous step, and the output is the evaluation results of strengths and weaknesses. Specifically, data analysis tools (e.g., Pandas, NumPy) are used to compare and analyze the entire dataset.

[0835] Step 5:

[0836] The cloud server generates suggestions to maintain identified strengths and improve weaknesses. The input data is the evaluation results of strengths and weaknesses, and the output is a list of specific improvement suggestions. Specifically, it uses conditional branching and rule-based algorithms to automatically generate the optimal suggestions.

[0837] Step 6:

[0838] The cloud server automatically generates sales materials based on the generated proposals. The input data is a list of proposals, and the output is sales materials in PDF or presentation format. Specifically, it uses a document generation library (e.g., ReportLab, pptx) to construct the materials.

[0839] Step 7:

[0840] The cloud server provides the generated sales materials to the terminal and displays them to the user. The input data is sales materials in PDF or presentation format, and the output is a download link displayed on the terminal. Specifically, a file transfer protocol is used to send the materials to the terminal.

[0841] This allows users to streamline sales analysis tasks and quickly generate concrete improvement suggestions.

[0842] 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.

[0843] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides even more accurate suggestions. This system allows users to upload sales report data and store photo data, and based on that information, it automatically generates sales materials and presents optimal suggestions tailored to the user's emotions.

[0844] Program processing

[0845] 1. Data Collection

[0846] Users upload sales report data and store photo data. This data is sent from the terminal to the server.

[0847] 2. Data Analysis

[0848] The server analyzes sales report data using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[0849] 3. Analysis of store photos

[0850] The server analyzes store layout photos using an image recognition algorithm. It recognizes the placement and inventory status of products in the photos and determines whether a product is located at the front or back of a shelf. For example, the store layout photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[0851] 4. Data Evaluation

[0852] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[0853] 5. Emotion analysis

[0854] The server uses an emotion analysis engine to recognize the user's emotional state. For example, when a user enters sales report data, the server recognizes their emotion (positive, negative, or neutral) from the tone and context of the text. It also analyzes the user's facial expressions and posture in store photos to evaluate their emotional state.

[0855] 6. Generating improvement suggestions

[0856] The server generates specific suggestions to maintain strengths and improve weaknesses. Furthermore, it adjusts the suggestions based on the user's emotional data. For example, if negative emotions are detected, the improvement suggestions are adjusted to be more specific and encouraging.

[0857] 7. Generating sales negotiation materials

[0858] The server automatically generates sales materials based on the proposal. These materials include an assessment of strengths and weaknesses, as well as specific proposals. Furthermore, they may include supplementary information and words of encouragement tailored to the user's emotional state. These sales materials are output in PDF or PowerPoint format and provided to the user's device.

[0859] Specific example

[0860] For example, consider a scenario where a user uploads a sales report stating, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the store layout shows product X placed at the store entrance and product Y at the back of the shelf. Furthermore, if the sentiment analysis engine recognizes from the sales report that the user has negative emotions, the system will process the data as follows.

[0861] 1. Users upload sales reports and photos of the sales floor.

[0862] 2. The device sends this data to the server.

[0863] 3. The server analyzes the contents of the sales report using natural language processing and extracts keywords and sales performance data.

[0864] 4. The server analyzes images of the store floor to recognize the product layout and inventory status.

[0865] 5. The server uses an emotion analysis engine to recognize negative emotions from the user based on the content of the sales report.

[0866] 6. The server evaluates this data and determines the product's strengths and weaknesses.

[0867] 7. The server generates suggestions to maintain its strengths and address its weaknesses. Considering that users may have negative feelings, the suggestions are presented in a more specific and positive manner.

[0868] 8. The server automatically generates and provides sales materials to the terminal. The sales materials also include an encouraging message such as, "Let's review recent achievements and work towards the next step."

[0869] In this way, users can streamline their sales analysis work, implement improvements quickly, and receive emotional support.

[0870] The following describes the processing flow.

[0871] Step 1:

[0872] User: Upload sales report data to the system. Specifically, enter text data related to sales activities and press the submit button.

[0873] Step 2:

[0874] Terminal: Receives sales report data and sends it to the server. Specifically, it converts the text data submitted by the user into the appropriate format and sends it to the server as an HTTP request.

[0875] Step 3:

[0876] User: Upload store photos to the system. Specifically, select photos of products taken inside the store and press the upload button.

[0877] Step 4:

[0878] Terminal: Receives store photo data and sends it to the server. Specifically, it converts the image files uploaded by the user into the appropriate format and sends them to the server as an HTTP request.

[0879] Step 5:

[0880] Server: Receives sales report data and analyzes it using a natural language processing (NLP) engine. Specifically, it tokenizes the text and extracts keywords such as nouns, verbs, and adjectives. It also identifies keywords such as "Store A," "Product X," "Sales," and "20% increase compared to the previous month."

[0881] Step 6:

[0882] Server: Receives store floor photo data and analyzes it using image analysis algorithms. Specifically, it performs object detection and classification within the image to determine the placement and inventory status of products. For example, it determines that product X is placed at the front of the shelf and product Y is located at the back of the shelf.

[0883] Step 7:

[0884] Server: Based on keywords extracted from sales reports and sales performance data, the server stores this information in an evaluation database. Specifically, it extracts elements such as sales increases / decreases and inventory shortages, and saves this information in the database.

[0885] Step 8:

[0886] Server: Evaluates product placement data obtained from store photos and stores it in an evaluation database. Specifically, it saves the location and inventory status of products in the database.

[0887] Step 9:

[0888] Server: Using an emotion analysis engine, it analyzes user emotions from sales report data and store photo data. For example, it identifies negative, positive, and neutral tones from the text of sales reports and recognizes emotions from the facial expressions of users in store photos.

[0889] Step 10:

[0890] Server: Evaluates sales performance and product placement data, along with recognized sentiment data, to identify strengths and weaknesses. For example, product X is identified as a strength because its high visibility is leading to increased sales, while product Y is identified as a weakness because its low sales are due to insufficient stock and poor placement.

[0891] Step 11:

[0892] Server: Generates specific suggestions to maintain strengths and improve weaknesses. Specifically, it proposes strategies to maintain the factors contributing to strengths and improve inventory management and product placement to address weaknesses. The suggestions are adjusted to be more appropriate by considering user sentiment data.

[0893] Step 12:

[0894] Server: Automatically generates sales materials based on the proposed content. Specifically, it organizes evaluation results and proposal content and creates sales materials in PDF or PowerPoint format. It also includes messages of encouragement and appreciation tailored to the user's emotions.

[0895] Step 13:

[0896] Server: Sends the completed sales materials to the terminal. Specifically, it provides the generated files to the terminal as a download link, allowing the user to access them.

[0897] Step 14:

[0898] Terminal: Displays sales materials to the user and provides a download link. Specifically, it displays sales materials containing evaluation results and improvement suggestions to the user in a visible format and maintains a state where they can be downloaded as needed.

[0899] (Example 2)

[0900] 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."

[0901] Conventional sales analysis systems struggled to provide appropriate suggestions based on user emotions, in addition to analyzing sales report data and store photo data. This resulted in inaccurate evaluations of sales strengths and weaknesses, as well as the inability to provide suggestions that considered user emotions, ultimately leading to lower effectiveness of improvement suggestions. Furthermore, the generation of sales materials lacked adjustments that reflected user emotions, resulting in a failure to motivate users.

[0902] 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.

[0903] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for recognizing and evaluating user emotion data, means for generating suggestions to maintain identified strengths and improve weaknesses, means for generating sales negotiation materials based on the suggested content, and means for adjusting the sales negotiation materials according to the user's emotional state. This enables detailed sales analysis and the generation of improvement suggestions that take user emotions into consideration, increasing the effectiveness of improvement suggestions and enabling the maintenance or improvement of user motivation.

[0904] "Sales report data" refers to data containing detailed information about sales activities, including daily sales, customer trends, and product trends, recorded in digital or paper format.

[0905] "Sales floor photo data" refers to photographic data showing the arrangement and inventory status of products within a sales floor or store, and consists of image files taken with a digital camera or smartphone.

[0906] "Natural language processing" is a technology that enables computers to understand and analyze human language, and it involves methods for extracting, classifying, and analyzing important keywords and meanings from text.

[0907] "Image analysis" is a technology that uses computers to analyze digital images and recognize, classify, and analyze objects and patterns within those images.

[0908] "Sales performance" refers to actual data such as sales and sales volume of products over a specific period, and is an indicator that shows the results of a company's or store's business activities.

[0909] "Product placement data" refers to data about the location and layout of products within a store, and provides information that indicates the visibility and accessibility of products.

[0910] "Emotional data" refers to data that reflects the user's emotional state, and is information indicating positive, negative, or neutral emotions obtained through methods such as text tone and facial expression analysis.

[0911] A "strength" refers to an advantage in sales activities or product features, representing a competitive advantage that a company or store has over its competitors.

[0912] A "weakness" refers to an inferiority in sales activities or product characteristics, representing shortcomings or deficiencies that a company or store should address.

[0913] A "proposal" is a set of specific measures and policies to maintain strengths and improve weaknesses, and is useful information for sales strategies and store operations.

[0914] "Sales materials" are documents used in sales activities and negotiations with customers, and include proposals, sales performance, and evaluations of strengths and weaknesses.

[0915] "Adjusting based on the user's emotional state" is a method of modifying the content of proposals and sales materials based on the user's emotional data in order to provide users with optimal information and improve their motivation.

[0916] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides even more accurate suggestions.

[0917] The specific embodiments for carrying out the invention are described in detail below.

[0918] First, users upload sales report data and store photo data using a dedicated web portal or application. At this time, a file selection dialog will appear, and users will select the data files to upload. After selecting the files, the data is encrypted and sent from the terminal to the server. For example, this applies when a user selects and sends the daily report file "report_20231001.txt" and the store photo "store_photo.jpg".

[0919] The server analyzes the submitted sales report data using natural language processing (NLP) techniques. Specifically, it extracts important keywords and sales performance using Python and dedicated natural language processing libraries. This process utilizes Apache Kafka to analyze the data stream. In addition, image recognition libraries such as OpenCV and TensorFlow are used for image analysis to recognize product placement and inventory status from store floor photos. For example, from "store_photo.jpg", it recognizes that "product X is placed at the front of the shelf and product Y is placed at the back of the shelf".

[0920] Next, based on the extracted sales performance data and product placement data, the server evaluates its strengths and weaknesses. This evaluation uses an algorithm that compares and analyzes past sales data with current data. The evaluation process also includes comparison with existing databases within the system.

[0921] Furthermore, the server uses an emotion analysis engine to recognize and analyze the user's emotional data. Emotion analysis employs techniques that evaluate the user's emotions (positive, negative, or neutral) based on the tone and style of their writing. The user's facial expressions and posture in store photos are also included in the analysis.

[0922] Based on the evaluation results, the server generates improvement suggestions that reflect the user's emotions. For example, if negative emotions are detected, the improvement suggestions are adjusted to be more specific and encouraging.

[0923] Finally, the server automatically creates sales materials based on the generated improvement suggestions. These materials are generated in PDF or presentation format and may include supplementary information and encouraging messages tailored to the user's emotional state. Once the materials are complete, a download link is notified to the device, and the user can download and use them.

[0924] The following are examples of input prompts for the generated AI model when using this system.

[0925] A user has uploaded sales report data for store A. The report states, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock." A photo of the store layout shows product X placed near the entrance and product Y placed at the back of the shelf. The sentiment analysis engine indicates that the user is experiencing negative emotions. Based on this information, generate suggestions for increasing sales and create specific sales materials.

[0926] This invention allows users to quickly and efficiently conduct sales analysis and implement improvement measures, as well as receive appropriate emotional support.

[0927] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0928] Step 1:

[0929] Users upload sales report data and store photo data using a dedicated web portal or application. When a user selects files such as "report_20231001.txt" and "store_photo.jpg", these files are sent from the terminal to the server in an encrypted state.

[0930] Input: Sales report data file, store photo data file

[0931] Output: Encrypted data is sent to the server.

[0932] Step 2:

[0933] The server analyzes the received sales report data using natural language processing (NLP) techniques. Specifically, it uses Python and natural language processing libraries (e.g., NLTK and SpaCy) to extract important keywords and sales performance data. For example, it extracts keywords such as "Store A," "Product X," "Sales," "20% increase compared to the previous month," "Inventory shortage," and "Sales slump" from "report_20231001.txt."

[0934] Input: Sales report data file

[0935] Output: Extracted keywords and sales performance data

[0936] Step 3:

[0937] The server analyzes the received store photo data using an image recognition algorithm. Specifically, it uses image recognition libraries such as OpenCV or TensorFlow to analyze product placement and inventory status. For example, it recognizes from "store_photo.jpg" that "product X is placed at the front of the shelf and product Y is placed at the back of the shelf."

[0938] Input: Sales floor photo data file

[0939] Output: Product placement and inventory status data

[0940] Step 4:

[0941] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. This uses an algorithm that performs comparative analysis with past sales data and existing databases within the system. For example, product X is evaluated as having a strength because its high visibility is leading to increased sales, while product Y is evaluated as having a weakness because its low sales are due to insufficient inventory and inconspicuous placement.

[0942] Input: Extracted keywords, sales performance data, product placement and inventory status data

[0943] Output: Identified strengths and weaknesses

[0944] Step 5:

[0945] The server uses an emotion analysis engine to recognize and evaluate the user's emotional state. Specifically, it uses IBM Watson and Microsoft Azure emotion analysis APIs to analyze emotions (positive, negative, neutral) from the content of sales reports. It also evaluates the emotional state by analyzing the user's facial expressions and posture in photos of the sales floor.

[0946] Input: Sales report data file, store photo data file

[0947] Output: User sentiment data

[0948] Step 6:

[0949] The server generates specific improvement suggestions based on the evaluated strengths and weaknesses, as well as user sentiment data. If the user has negative emotions, the suggestions are made more specific and positive. For example, it might suggest, "Continue to place product X at the front of the shelf and replenish the stock of product Y to improve sales."

[0950] Input: Identified strengths and weaknesses, user sentiment data

[0951] Output: Improvement suggestions

[0952] Step 7:

[0953] The server automatically generates sales materials based on the improvement suggestions that have been created. These materials are generated in PDF or presentation format and include charts and graphs. In addition, supplementary information and encouraging messages tailored to the user's emotional state may be added.

[0954] Input: Improvement suggestions, user sentiment data

[0955] Output: Sales materials (PDF or presentation format)

[0956] Through these processing steps, users can quickly and efficiently conduct sales analysis and implement improvement measures, while also receiving appropriate emotional support.

[0957] (Application Example 2)

[0958] 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."

[0959] Currently, in brick-and-mortar store operations, it is essential to quickly and accurately grasp sales performance and product placement, and to make efficient improvement suggestions. However, these tasks are often performed manually, which is time-consuming and labor-intensive, and often relies on subjective judgment. Furthermore, because suggestions are not made flexibly based on customer emotions, there are problems with the acceptance and implementation rate of suggestions. In this situation, there is a need to develop a system that analyzes sales performance and product placement data to generate objective and effective improvement suggestions.

[0960] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing the placement and inventory status of products from the store photo data using image analysis, means for evaluating the sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales negotiation materials based on the proposed content, means for transmitting sales report data and store photo data from the user terminal to the server, emotion analysis means for recognizing the user's emotions, and means for adjusting the proposed content based on the user's emotions. This enables objective improvement proposals based on sales performance and store photo data, as well as flexible proposals that take into account the user's emotions.

[0961] "Sales report data" refers to data that records daily performance and information related to sales activities.

[0962] "Store photo data" refers to image data that visually records the conditions of a store, such as the arrangement of products and inventory levels.

[0963] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0964] "Key keywords" are words or phrases that are important for identifying specific information, extracted through data analysis.

[0965] "Sales performance" refers to performance data such as sales figures and sales volume of products or services over a specific period.

[0966] "Image analysis" is a technique that uses computer vision technology to extract useful information from image data.

[0967] "Product placement" refers to the location and arrangement of products on display in a store.

[0968] "Inventory status" refers to information indicating the quantity and condition of a product in stock at a specific point in time.

[0969] A "strength" is a characteristic or element that is perceived as positive in the area being evaluated.

[0970] A "weakness" is a characteristic or element of the subject being evaluated that is recognized as needing improvement.

[0971] A "suggestion" is a recommendation of specific actions or strategies to maintain identified strengths and improve weaknesses.

[0972] "Business negotiation materials" are documents that summarize the proposed content in a visual or written format.

[0973] "Emotion analysis" is a technology that recognizes and evaluates a user's emotional state from text and images.

[0974] A "user terminal" refers to a computer or mobile device that can send sales report data and store photo data to a server.

[0975] This invention is a system that analyzes sales report data and store photo data to identify sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides more accurate suggestions. The embodiments for carrying out this invention are as follows.

[0976] The user first uploads sales report data and store photo data using a terminal. The terminal sends this data to the server. The server analyzes the data and generates suggestions using the following methods.

[0977] The server analyzes sales report data using natural language processing (NLP) techniques to extract important keywords and sales performance data. Specific software used includes natural language processing libraries such as Spacy. This analysis extracts keywords such as "Store A," "Product X," and "20% increase compared to the previous month."

[0978] Next, the server analyzes the store layout photo data using an image recognition algorithm. The specific software includes image processing libraries using OpenCV and TensorFlow. This allows the system to recognize the placement and inventory status of products in the store, providing information such as "product X is placed at the front of the shelf" or "product Y is out of stock."

[0979] Next, the server evaluates the extracted sales performance data and product placement data to identify strengths and weaknesses. This evaluation identifies information such as, "Product X has high visibility and strong sales," or "Product Y is out of stock and sales are sluggish."

[0980] Furthermore, the server uses an emotion analysis engine to recognize the user's emotional state. This emotion analysis employs a generative AI model that evaluates the tone and context of the text. For example, if negative emotions are detected when a user enters sales report data, the server will recognize this.

[0981] The server generates specific improvement suggestions based on identified strengths and weaknesses, as well as the user's emotional state. These suggestions are tailored to the user's emotions and may be presented in a more positive and concrete format. For example, a suggestion such as "Maintain product X in its current position and replenish product Y's inventory immediately" might be generated.

[0982] Ultimately, the server automatically generates sales materials based on the improvement suggestions that have been created. These materials, including PDF or presentation formats, are provided to the user's terminal. This allows the user to efficiently analyze sales data from physical stores and implement improvement measures.

[0983] For example, the following format can be used as a prompt:

[0984] "On October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock. Based on this data, generate improvement proposals and prepare sales materials."

[0985] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0986] Step 1:

[0987] Users input sales report data and take photos of the sales floor. The sales report data includes daily sales performance and sales status, while the sales floor photos visually record product placement and inventory status. Users also upload this data using a smartphone or smart glasses.

[0988] Input: Sales report data, store photo data

[0989] Output: Data uploaded to the device

[0990] Step 2:

[0991] The terminal sends sales report data and store photo data to the server. A data transmission completion notification is displayed on the terminal to confirm that the data has reached the server correctly.

[0992] Input: Data uploaded to the device

[0993] Output: Data sent to the server

[0994] Step 3:

[0995] The server analyzes the received sales report data using natural language processing (NLP) techniques. Specifically, it uses NLP libraries such as spacy to extract important keywords and sales performance data from the sales reports.

[0996] Input: Sales report data

[0997] Output: Key keywords, sales performance

[0998] Step 4:

[0999] The server analyzes store photo data using image recognition algorithms. Using image processing libraries such as OpenCV and TensorFlow, it recognizes the placement and inventory status of products in the photos and stores this information in a database.

[1000] Input: Storefront photo data

[1001] Output: Product placement data, inventory status data

[1002] Step 5:

[1003] The server evaluates the extracted key keywords, sales performance, and product placement data, and uses this information to identify strengths and weaknesses. This results in evaluations such as, "Product X has high visibility and strong sales."

[1004] Input: Key keywords, sales performance, product placement data

[1005] Output: Evaluation results of strengths and weaknesses

[1006] Step 6:

[1007] The server uses an emotion analysis engine to recognize the user's emotional state from sales report data and store photo data. A generative AI model is used to evaluate the user's emotions (positive, negative, neutral) based on their input and photos.

[1008] Input: Sales report data, store photo data

[1009] Output: Sentiment evaluation

[1010] Step 7:

[1011] The server generates specific improvement suggestions based on the strengths and weaknesses assessment results and the sentiment assessment. The generated suggestions are adjusted based on the user's sentiment and include positive content, such as "Keep product X in its current position."

[1012] Input: Strengths and weaknesses assessment results, emotional assessment

[1013] Output: Specific improvement suggestions

[1014] Step 8:

[1015] The server automatically generates sales materials based on the improvement suggestions that have been created. The sales materials are generated in PDF or presentation format and provided to the user's terminal.

[1016] Input: Specific improvement suggestions

[1017] Output: Sales materials (PDF / presentation format)

[1018] As a concrete example of the operation, the prompt statement is as follows:

[1019] "On October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock. Based on this data, generate improvement proposals and prepare sales materials."

[1020] 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.

[1021] 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.

[1022] 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.

[1023] [Fourth Embodiment]

[1024] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1025] 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.

[1026] 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).

[1027] 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.

[1028] 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.

[1029] 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).

[1030] 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.

[1031] 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.

[1032] 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.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] 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".

[1037] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. The system allows users to upload sales report data and store photo data, and automatically generates sales materials based on that information.

[1038] Program processing

[1039] 1. Data Collection

[1040] Users upload sales report data and store photo data. This data is sent from the terminal to the server.

[1041] 2. Data Analysis

[1042] The server analyzes sales report data using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[1043] 3. Analysis of store photos

[1044] The server analyzes store layout photos using an image recognition algorithm. It recognizes the placement and inventory status of products in the photos and determines whether a product is located at the front or back of a shelf. For example, the store layout photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[1045] 4. Data Evaluation

[1046] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[1047] 5. Generating improvement suggestions

[1048] The server generates specific suggestions to maintain strengths and improve weaknesses. For example, for product X, it might suggest "maintain this placement to improve visibility," and for product Y, it might suggest "increase inventory and place it at the front of the shelf."

[1049] 6. Generating sales negotiation materials

[1050] The server automatically generates sales materials based on the proposal. These materials include an assessment of strengths, an assessment of weaknesses, and specific proposals. These sales materials are output in PDF or PowerPoint format and provided to the terminal. The terminal displays the generated sales materials to the user and provides a download link.

[1051] Specific example

[1052] For example, consider a scenario where a user uploads a sales report stating, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the sales floor shows product X placed at the store entrance and product Y at the back of the shelf. The system would process this as follows:

[1053] 1. Users upload sales reports and photos of the sales floor.

[1054] 2. The device sends this data to the server.

[1055] 3. The server analyzes the contents of the sales report using natural language processing and extracts keywords and sales performance data.

[1056] 4. The server analyzes images of the store floor to recognize the product layout and inventory status.

[1057] 5. The server evaluates this data and determines the product's strengths and weaknesses.

[1058] 6. The server generates suggestions to maintain its strengths and address its weaknesses.

[1059] 7. The server automatically generates sales materials and provides them to the terminal.

[1060] In this way, users can streamline their sales analysis tasks and implement improvements quickly.

[1061] The following describes the processing flow.

[1062] Step 1:

[1063] User: Upload sales report data to the system. Specifically, enter text data related to sales activities and press the submit button.

[1064] Step 2:

[1065] Terminal: Receives sales report data and sends it to the server. Specifically, it converts the text data submitted by the user into the appropriate format and sends it to the server as an HTTP request.

[1066] Step 3:

[1067] User: Upload store photos to the system. Specifically, select photos of products taken inside the store and press the upload button.

[1068] Step 4:

[1069] Terminal: Receives store photo data and sends it to the server. Specifically, it converts the image files uploaded by the user into the appropriate format and sends them to the server as an HTTP request.

[1070] Step 5:

[1071] Server: Receives sales report data and analyzes the data using a natural language processing (NLP) engine. Specifically, it tokenizes the text and extracts keywords such as nouns, verbs, and adjectives.

[1072] Step 6:

[1073] Server: Receives storefront photo data and analyzes it using image analysis algorithms. Specifically, it performs object detection and classification within the images to identify product placement and inventory status.

[1074] Step 7:

[1075] Server: Analyzes keywords extracted from sales reports and sales performance data, and stores them in an evaluation database. Specifically, it extracts elements such as increased sales and insufficient inventory, and saves this data to the database.

[1076] Step 8:

[1077] Server: Evaluates product placement data obtained from store photos and stores it in an evaluation database. Specifically, it stores product location (front, back of shelf, etc.) and inventory status (in stock, out of stock, etc.) in the database.

[1078] Step 9:

[1079] Server: Based on the evaluation database, identify the strengths and weaknesses of sales data. Specifically, analyze the reasons for increases and decreases in sales and identify the elements that constitute strengths and weaknesses.

[1080] Step 10:

[1081] Server: Generates specific proposals to maintain strengths and improve weaknesses. Specifically, it identifies the factors contributing to strengths and proposes measures to maintain them. For weaknesses, it analyzes the causes and proposes solutions to address them.

[1082] Step 11:

[1083] Server: Generates sales materials based on the proposed content. Specifically, it organizes the evaluation results and proposed content and creates sales materials in PDF or PowerPoint format.

[1084] Step 12:

[1085] Server: Sends the completed sales materials to the terminal. Specifically, it provides the generated files to the terminal as a download link, allowing the user to access them.

[1086] Step 13:

[1087] Terminal: Displays sales materials to users and provides download links. Specifically, it displays sales materials containing store and product evaluation results and improvement suggestions to users in a visible format and maintains a downloadable state as needed.

[1088] (Example 1)

[1089] 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".

[1090] Traditional sales analysis systems require manual analysis of sales report data and store photo data, which is time-consuming, labor-intensive, and inefficient. Furthermore, manual analysis carries the risk of human error, resulting in a lack of accuracy and reliability. Additionally, the process of incorporating data insights into sales materials is time-consuming, hindering rapid decision-making.

[1091] 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.

[1092] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales negotiation materials based on the proposed content, and means for providing the sales negotiation materials to the terminal in PDF or presentation format and presenting the user with a download link. This enables the user to perform sales analysis efficiently and quickly and make rapid decisions based on accurate improvement proposals.

[1093] "Sales report data" refers to data that records the results, observations, and sales performance of a day's sales activities.

[1094] "Store floor photo data" refers to image data that captures the arrangement of products and inventory status within a store or sales floor.

[1095] Natural language processing is a technology that enables computers to understand and analyze human language, and to extract useful information from text data.

[1096] "Image analysis" is a technology that processes and analyzes image data to recognize and extract objects and features within the image.

[1097] A "strength" is an element that demonstrates a competitive advantage or improved performance in areas such as sales performance or product placement.

[1098] A "weakness" is an element that indicates a decline in performance or room for improvement in areas such as sales figures or product placement.

[1099] A "proposal" is a concrete action plan or strategy to maintain sales strengths and improve weaknesses.

[1100] "Sales negotiation materials" are documents that summarize the results of sales analysis and proposals, and are used during sales negotiations.

[1101] A "terminal" refers to a device used by a user, such as a computer or smartphone.

[1102] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. The following describes each processing step and the hardware and software used.

[1103] Uploading and sending data

[1104] Users upload sales report data (e.g., Excel or CSV files) and store photo data (image files such as JPEG or PNG) through the system interface. The terminal sends this data to the server using secure HTTP communication. Specifically, the SSL / TLS protocol is used to ensure data security.

[1105] Analysis of sales report data

[1106] The server saves the received sales report data to a database. Then, it analyzes the text data using an NLP library (e.g., Python's NLTK or spaCy). By employing techniques such as text tokenization, part-of-speech tagging, and entity recognition, it extracts important information such as the date, sales performance, product name, and sales quantity. The analysis results are then saved back to the database.

[1107] Analysis of store photo data

[1108] The server analyzes store photo data using image recognition algorithms (for example, models using OpenCV or TensorFlow). It detects products from the images and recognizes the location (front, back, etc.) and inventory status of each product. This analysis result is also stored in a database.

[1109] Data Evaluation

[1110] The server integrates sales performance data extracted from sales reports with product placement data obtained from store photos to evaluate sales strengths and weaknesses. For example, if product X is selling well because it is prominently displayed in a particular store, this is judged as a strength. Conversely, if product Y is selling poorly due to insufficient stock or being placed at the back of the shelf, this is judged as a weakness. This evaluation result is also stored in the database.

[1111] Generating improvement suggestions

[1112] Based on the evaluation results of strengths and weaknesses, the server generates specific improvement suggestions through a template engine (e.g., Jinja2). For example, for product X, a suggestion is generated that "this placement should be maintained to improve visibility," and for product Y, a suggestion is generated that "inventory should be increased and it should be placed at the front of the shelf."

[1113] Generation and provision of business negotiation materials

[1114] The server automatically generates sales materials based on the proposal. These materials are generated in PDF or presentation format, and the Python libraries ReportLab and pptx are used for their creation. The generated sales materials are provided to the terminal, which notifies the user and provides a download link. The user can then download and use the generated sales materials.

[1115] Specific example

[1116] For example, if a user uploads a sales report stating, "October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the sales floor shows product X placed at the store entrance and product Y at the back of the shelf, the system would process the data as follows: First, the user uploads the sales report and the sales floor photo, and the terminal sends this data to the server. The server then analyzes the contents of the sales report using natural language processing to extract keywords and sales performance. Next, the server analyzes the sales floor photo to recognize the product placement and inventory status. Based on the analysis results, the server determines the strengths and weaknesses of the products and generates suggestions to maintain the strengths and eliminate the weaknesses. Finally, it automatically generates sales materials and provides them to the terminal.

[1117] Example of a prompt

[1118] "Please upload your sales report and photos of your sales floor. We will then generate specific improvement suggestions based on the analysis data."

[1119] This system allows users to streamline sales analysis tasks and implement improvements quickly.

[1120] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1121] Step 1: Upload Data

[1122] Users upload sales report data and store photo data through the system interface. Users log in to the system from a web browser and either drag and drop files onto the upload form or use the file selection dialog. These operations provide sales report data (e.g., Excel or CSV files) and store photo data (JPEG or PNG, etc.) as input.

[1123] Step 2: Send

[1124] The terminal transmits sales report data and storefront photo data uploaded by the user to the server using secure HTTP communication. SSL / TLS protocol is used to ensure the security of the transmitted data. Input is the data uploaded by the user, and output is the transmission of data to the server.

[1125] Step 3: Analysis of sales report data

[1126] The server saves the received sales report data to a database. Next, it uses an NLP library (e.g., Python's NLTK or spaCy) to analyze the text of the sales report data. This extracts important information such as date, sales performance, product name, and sales quantity using techniques such as tokenization, part-of-speech tagging, and entity recognition. The input is the received sales report data, and the output is the extracted keywords and sales performance.

[1127] Step 4: Analysis of store photo data

[1128] The server analyzes store layout photos using image recognition algorithms (e.g., models using OpenCV or TensorFlow). Through image analysis, it detects products and recognizes their location (front, back, etc.) and inventory status. The input is the received store layout photos, and the output is information on product placement and inventory status.

[1129] Step 5: Data Evaluation

[1130] The server integrates sales performance data extracted from sales reports with product placement data obtained from store photos to evaluate sales strengths and weaknesses. For example, if product X is placed at the front and sales are increasing, it is considered a strength, while if product Y is out of stock or placed at the back of the shelf and sales are sluggish, it is considered a weakness. The input is sales performance and product placement data, and the output is information on the evaluated strengths and weaknesses.

[1131] Step 6: Generating improvement proposals

[1132] The server uses a template engine (e.g., Jinja2) to generate specific improvement suggestions based on the strengths and weaknesses assessment results. For example, for product X, a suggestion is generated that "this placement should be maintained to improve visibility," and for product Y, a suggestion is generated that "inventory should be increased and the product should be placed at the front of the shelf." The input is the assessment results, and the output is the improvement suggestions.

[1133] Step 7: Generate and provide sales materials.

[1134] The server automatically generates sales materials based on the proposed content. These materials are generated in PDF or presentation format, and Python's ReportLab and pptx libraries are used for their creation. The generated sales materials are provided to the terminal, which notifies the user and provides a download link. The input is improvement proposals, and the output is sales materials in PDF or presentation format.

[1135] (Application Example 1)

[1136] 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".

[1137] Conventional sales analysis systems require manual analysis of sales report data and store photo data, which is time-consuming and labor-intensive, and results in inconsistent analysis accuracy. Furthermore, it is often difficult to quickly identify optimal product placement and promotional strategies, hindering improvements in sales efficiency. This invention aims to solve these problems and provide a system that performs sales analysis more efficiently and accurately, generating concrete improvement suggestions.

[1138] 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.

[1139] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales materials based on the proposals and outputting them in PDF or presentation format, means for collecting sales report data and store photo data using smartphones or robots, and means for performing data analysis and evaluation using a cloud server. This makes it possible to streamline sales analysis work and quickly generate concrete improvement proposals.

[1140] "Sales report data" refers to data that records daily performance and activities related to sales.

[1141] "Store display photo data" refers to photographic data that captures the arrangement of products and the condition of the store display.

[1142] "Natural language processing" is a technology that analyzes text data and performs processing such as semantic understanding and keyword extraction.

[1143] "Image analysis" is a technology that analyzes photographs and image data to recognize specific objects or features.

[1144] "Strengths" refer to positive elements or advantages in sales performance or product placement.

[1145] "Weaknesses" refer to negative elements or shortcomings in sales performance or product placement.

[1146] A "proposal" is a specific action or strategy to maintain identified strengths and improve weaknesses.

[1147] "Sales materials" are documents that summarize sales proposals and improvement measures in document or presentation format.

[1148] A "smartphone" is a type of mobile phone, a portable device that has both communication and computer functions.

[1149] A "robot" is a mechanical device that operates autonomously or semi-autonomously and has the function of performing a specific task.

[1150] A "cloud server" is a remote server provided via the internet that offers large-scale data processing and storage capabilities.

[1151] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. This system uses smartphones and robots and utilizes cloud servers to perform advanced data analysis.

[1152] Users collect sales report data and store photo data using smartphones or robots. This data is sent to a cloud server where it is analyzed. The sales report data is analyzed using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y are sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[1153] Similarly, store photo data is analyzed using image recognition algorithms (such as OpenCV or TensorFlow) to recognize the placement and inventory status of products. For example, a store photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[1154] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[1155] The server then generates specific suggestions to maintain strengths and improve weaknesses. For example, for product X, it might suggest "maintain this placement to improve visibility," and for product Y, it might suggest "increase inventory and place it at the front of the shelf."

[1156] Finally, the server automatically generates sales materials based on the proposal. These materials are output in PDF or presentation format (such as PowerPoint) and provided to the user's terminal. The user can review the generated sales materials and download them via a download link.

[1157] As a concrete example, input the following prompt into the generating AI model:

[1158] After users upload store layouts and sales reports using their smartphones, a cloud server analyzes the data, evaluates product strengths and weaknesses, and generates improvement suggestions based on that analysis. The generated sales materials are provided in PDF format, allowing store managers and sales representatives to quickly implement improvement measures based on the data. This application contributes to improved sales efficiency through intuitive data collection using smartphones and robots, and advanced data analysis using natural language processing and image recognition technologies.

[1159] This streamlines sales analysis and enables the rapid generation of concrete improvement suggestions.

[1160] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1161] Step 1:

[1162] Users collect sales report data and store photo data using smartphones or robots and upload them to a cloud server. Input data consists of daily report data in text format and store photos in JPEG format. Based on user actions, the collected data is transmitted to the cloud server in real time.

[1163] Step 2:

[1164] This system analyzes sales report data received by a cloud server using natural language processing (NLP) techniques. The input data consists of user-uploaded daily report text, and the output is extracted data containing important keywords and sales performance information. Specifically, it uses NLP libraries (e.g., NLTK, spaCy) to extract key information from the text.

[1165] Step 3:

[1166] The cloud server analyzes storefront photo data received using an image recognition algorithm. The input data consists of JPEG photos of the storefront uploaded by users, and the output data shows product placement information and inventory status. Specifically, an image recognition algorithm (e.g., OpenCV, TensorFlow) is used to identify the location and inventory quantity of each product.

[1167] Step 4:

[1168] The cloud server evaluates the analyzed sales report data and store photo data to identify sales strengths and weaknesses. The input data consists of the extracted data and placement data obtained in the previous step, and the output is the evaluation results of strengths and weaknesses. Specifically, data analysis tools (e.g., Pandas, NumPy) are used to compare and analyze the entire dataset.

[1169] Step 5:

[1170] The cloud server generates suggestions to maintain identified strengths and improve weaknesses. The input data is the evaluation results of strengths and weaknesses, and the output is a list of specific improvement suggestions. Specifically, it uses conditional branching and rule-based algorithms to automatically generate the optimal suggestions.

[1171] Step 6:

[1172] The cloud server automatically generates sales materials based on the generated proposals. The input data is a list of proposals, and the output is sales materials in PDF or presentation format. Specifically, it uses a document generation library (e.g., ReportLab, pptx) to construct the materials.

[1173] Step 7:

[1174] The cloud server provides the generated sales materials to the terminal and displays them to the user. The input data is sales materials in PDF or presentation format, and the output is a download link displayed on the terminal. Specifically, a file transfer protocol is used to send the materials to the terminal.

[1175] This allows users to streamline sales analysis tasks and quickly generate concrete improvement suggestions.

[1176] 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.

[1177] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides even more accurate suggestions. This system allows users to upload sales report data and store photo data, and based on that information, it automatically generates sales materials and presents optimal suggestions tailored to the user's emotions.

[1178] Program processing

[1179] 1. Data Collection

[1180] Users upload sales report data and store photo data. This data is sent from the terminal to the server.

[1181] 2. Data Analysis

[1182] The server analyzes sales report data using natural language processing (NLP) technology to extract important keywords and sales performance. For example, from a daily report such as "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," the keywords "store A," "product X," "sales," "20% increase compared to the previous month," "insufficient stock," and "sluggish sales" are extracted.

[1183] 3. Analysis of store photos

[1184] The server analyzes store layout photos using an image recognition algorithm. It recognizes the placement and inventory status of products in the photos and determines whether a product is located at the front or back of a shelf. For example, the store layout photo might reveal that product X is placed at the front of the shelf and product Y is placed at the back.

[1185] 4. Data Evaluation

[1186] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. For example, the high visibility of product X, which is leading to increased sales, is identified as a strength, while the low sales of product Y, due to insufficient stock and inconspicuous placement, are identified as weaknesses.

[1187] 5. Emotion analysis

[1188] The server uses an emotion analysis engine to recognize the user's emotional state. For example, when a user enters sales report data, the server recognizes their emotion (positive, negative, or neutral) from the tone and context of the text. It also analyzes the user's facial expressions and posture in store photos to evaluate their emotional state.

[1189] 6. Generating improvement suggestions

[1190] The server generates specific suggestions to maintain strengths and improve weaknesses. Furthermore, it adjusts the suggestions based on the user's emotional data. For example, if negative emotions are detected, the improvement suggestions are adjusted to be more specific and encouraging.

[1191] 7. Generating sales negotiation materials

[1192] The server automatically generates sales materials based on the proposal. These materials include an assessment of strengths and weaknesses, as well as specific proposals. Furthermore, they may include supplementary information and words of encouragement tailored to the user's emotional state. These sales materials are output in PDF or PowerPoint format and provided to the user's device.

[1193] Specific example

[1194] For example, consider a scenario where a user uploads a sales report stating, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock," and a photo of the store layout shows product X placed at the store entrance and product Y at the back of the shelf. Furthermore, if the sentiment analysis engine recognizes from the sales report that the user has negative emotions, the system will process the data as follows.

[1195] 1. Users upload sales reports and photos of the sales floor.

[1196] 2. The device sends this data to the server.

[1197] 3. The server analyzes the contents of the sales report using natural language processing and extracts keywords and sales performance data.

[1198] 4. The server analyzes images of the store floor to recognize the product layout and inventory status.

[1199] 5. The server uses an emotion analysis engine to recognize negative emotions from the user based on the content of the sales report.

[1200] 6. The server evaluates this data and determines the product's strengths and weaknesses.

[1201] 7. The server generates suggestions to maintain its strengths and address its weaknesses. Considering that users may have negative feelings, the suggestions are presented in a more specific and positive manner.

[1202] 8. The server automatically generates and provides sales materials to the terminal. The sales materials also include an encouraging message such as, "Let's review recent achievements and work towards the next step."

[1203] In this way, users can streamline their sales analysis work, implement improvements quickly, and receive emotional support.

[1204] The following describes the processing flow.

[1205] Step 1:

[1206] User: Upload sales report data to the system. Specifically, enter text data related to sales activities and press the submit button.

[1207] Step 2:

[1208] Terminal: Receives sales report data and sends it to the server. Specifically, it converts the text data submitted by the user into the appropriate format and sends it to the server as an HTTP request.

[1209] Step 3:

[1210] User: Upload store photos to the system. Specifically, select photos of products taken inside the store and press the upload button.

[1211] Step 4:

[1212] Terminal: Receives store photo data and sends it to the server. Specifically, it converts the image files uploaded by the user into the appropriate format and sends them to the server as an HTTP request.

[1213] Step 5:

[1214] Server: Receives sales report data and analyzes it using a natural language processing (NLP) engine. Specifically, it tokenizes the text and extracts keywords such as nouns, verbs, and adjectives. It also identifies keywords such as "Store A," "Product X," "Sales," and "20% increase compared to the previous month."

[1215] Step 6:

[1216] Server: Receives store floor photo data and analyzes it using image analysis algorithms. Specifically, it performs object detection and classification within the image to determine the placement and inventory status of products. For example, it determines that product X is placed at the front of the shelf and product Y is located at the back of the shelf.

[1217] Step 7:

[1218] Server: Based on keywords extracted from sales reports and sales performance data, the server stores this information in an evaluation database. Specifically, it extracts elements such as sales increases / decreases and inventory shortages, and saves this information in the database.

[1219] Step 8:

[1220] Server: Evaluates product placement data obtained from store photos and stores it in an evaluation database. Specifically, it saves the location and inventory status of products in the database.

[1221] Step 9:

[1222] Server: Using an emotion analysis engine, it analyzes user emotions from sales report data and store photo data. For example, it identifies negative, positive, and neutral tones from the text of sales reports and recognizes emotions from the facial expressions of users in store photos.

[1223] Step 10:

[1224] Server: Evaluates sales performance and product placement data, along with recognized sentiment data, to identify strengths and weaknesses. For example, product X is identified as a strength because its high visibility is leading to increased sales, while product Y is identified as a weakness because its low sales are due to insufficient stock and poor placement.

[1225] Step 11:

[1226] Server: Generates specific suggestions to maintain strengths and improve weaknesses. Specifically, it proposes strategies to maintain the factors contributing to strengths and improve inventory management and product placement to address weaknesses. The suggestions are adjusted to be more appropriate by considering user sentiment data.

[1227] Step 12:

[1228] Server: Automatically generates sales materials based on the proposed content. Specifically, it organizes evaluation results and proposal content and creates sales materials in PDF or PowerPoint format. It also includes messages of encouragement and appreciation tailored to the user's emotions.

[1229] Step 13:

[1230] Server: Sends the completed sales materials to the terminal. Specifically, it provides the generated files to the terminal as a download link, allowing the user to access them.

[1231] Step 14:

[1232] Terminal: Displays sales materials to the user and provides a download link. Specifically, it displays sales materials containing evaluation results and improvement suggestions to the user in a visible format and maintains a state where they can be downloaded as needed.

[1233] (Example 2)

[1234] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1235] Conventional sales analysis systems struggled to provide appropriate suggestions based on user emotions, in addition to analyzing sales report data and store photo data. This resulted in inaccurate evaluations of sales strengths and weaknesses, as well as the inability to provide suggestions that considered user emotions, ultimately leading to lower effectiveness of improvement suggestions. Furthermore, the generation of sales materials lacked adjustments that reflected user emotions, resulting in a failure to motivate users.

[1236] 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.

[1237] In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing product placement and inventory status from the store photo data using image analysis, means for evaluating sales performance and product placement data to identify strengths and weaknesses, means for recognizing and evaluating user emotion data, means for generating suggestions to maintain identified strengths and improve weaknesses, means for generating sales negotiation materials based on the suggested content, and means for adjusting the sales negotiation materials according to the user's emotional state. This enables detailed sales analysis and the generation of improvement suggestions that take user emotions into consideration, increasing the effectiveness of improvement suggestions and enabling the maintenance or improvement of user motivation.

[1238] "Sales report data" refers to data containing detailed information about sales activities, including daily sales, customer trends, and product trends, recorded in digital or paper format.

[1239] "Sales floor photo data" refers to photographic data showing the arrangement and inventory status of products within a sales floor or store, and consists of image files taken with a digital camera or smartphone.

[1240] "Natural language processing" is a technology that enables computers to understand and analyze human language, and it involves methods for extracting, classifying, and analyzing important keywords and meanings from text.

[1241] "Image analysis" is a technology that uses computers to analyze digital images and recognize, classify, and analyze objects and patterns within those images.

[1242] "Sales performance" refers to actual data such as sales and sales volume of products over a specific period, and is an indicator that shows the results of a company's or store's business activities.

[1243] "Product placement data" refers to data about the location and layout of products within a store, and provides information that indicates the visibility and accessibility of products.

[1244] "Emotional data" refers to data that reflects the user's emotional state, and is information indicating positive, negative, or neutral emotions obtained through methods such as text tone and facial expression analysis.

[1245] A "strength" refers to an advantage in sales activities or product features, representing a competitive advantage that a company or store has over its competitors.

[1246] A "weakness" refers to an inferiority in sales activities or product characteristics, representing shortcomings or deficiencies that a company or store should address.

[1247] A "proposal" is a set of specific measures and policies to maintain strengths and improve weaknesses, and is useful information for sales strategies and store operations.

[1248] "Sales materials" are documents used in sales activities and negotiations with customers, and include proposals, sales performance, and evaluations of strengths and weaknesses.

[1249] "Adjusting based on the user's emotional state" is a method of modifying the content of proposals and sales materials based on the user's emotional data in order to provide users with optimal information and improve their motivation.

[1250] This invention is a system that analyzes sales report data and store photo data to automatically determine sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides even more accurate suggestions.

[1251] The specific embodiments for carrying out the invention are described in detail below.

[1252] First, users upload sales report data and store photo data using a dedicated web portal or application. At this time, a file selection dialog will appear, and users will select the data files to upload. After selecting the files, the data is encrypted and sent from the terminal to the server. For example, this applies when a user selects and sends the daily report file "report_20231001.txt" and the store photo "store_photo.jpg".

[1253] The server analyzes the submitted sales report data using natural language processing (NLP) techniques. Specifically, it extracts important keywords and sales performance using Python and dedicated natural language processing libraries. This process utilizes Apache Kafka to analyze the data stream. In addition, image recognition libraries such as OpenCV and TensorFlow are used for image analysis to recognize product placement and inventory status from store floor photos. For example, from "store_photo.jpg", it recognizes that "product X is placed at the front of the shelf and product Y is placed at the back of the shelf".

[1254] Next, based on the extracted sales performance data and product placement data, the server evaluates its strengths and weaknesses. This evaluation uses an algorithm that compares and analyzes past sales data with current data. The evaluation process also includes comparison with existing databases within the system.

[1255] Furthermore, the server uses an emotion analysis engine to recognize and analyze the user's emotional data. Emotion analysis employs techniques that evaluate the user's emotions (positive, negative, or neutral) based on the tone and style of their writing. The user's facial expressions and posture in store photos are also included in the analysis.

[1256] Based on the evaluation results, the server generates improvement suggestions that reflect the user's emotions. For example, if negative emotions are detected, the improvement suggestions are adjusted to be more specific and encouraging.

[1257] Finally, the server automatically creates sales materials based on the generated improvement suggestions. These materials are generated in PDF or presentation format and may include supplementary information and encouraging messages tailored to the user's emotional state. Once the materials are complete, a download link is notified to the device, and the user can download and use them.

[1258] The following are examples of input prompts for the generated AI model when using this system.

[1259] A user has uploaded sales report data for store A. The report states, "October 1, 2023: Sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock." A photo of the store layout shows product X placed near the entrance and product Y placed at the back of the shelf. The sentiment analysis engine indicates that the user is experiencing negative emotions. Based on this information, generate suggestions for increasing sales and create specific sales materials.

[1260] This invention allows users to quickly and efficiently conduct sales analysis and implement improvement measures, as well as receive appropriate emotional support.

[1261] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1262] Step 1:

[1263] Users upload sales report data and store photo data using a dedicated web portal or application. When a user selects files such as "report_20231001.txt" and "store_photo.jpg", these files are sent from the terminal to the server in an encrypted state.

[1264] Input: Sales report data file, store photo data file

[1265] Output: Encrypted data is sent to the server.

[1266] Step 2:

[1267] The server analyzes the received sales report data using natural language processing (NLP) techniques. Specifically, it uses Python and natural language processing libraries (e.g., NLTK and SpaCy) to extract important keywords and sales performance data. For example, it extracts keywords such as "Store A," "Product X," "Sales," "20% increase compared to the previous month," "Inventory shortage," and "Sales slump" from "report_20231001.txt."

[1268] Input: Sales report data file

[1269] Output: Extracted keywords and sales performance data

[1270] Step 3:

[1271] The server analyzes the received store photo data using an image recognition algorithm. Specifically, it uses image recognition libraries such as OpenCV or TensorFlow to analyze product placement and inventory status. For example, it recognizes from "store_photo.jpg" that "product X is placed at the front of the shelf and product Y is placed at the back of the shelf."

[1272] Input: Sales floor photo data file

[1273] Output: Product placement and inventory status data

[1274] Step 4:

[1275] The server evaluates strengths and weaknesses based on extracted sales performance data and product placement data. This uses an algorithm that performs comparative analysis with past sales data and existing databases within the system. For example, product X is evaluated as having a strength because its high visibility is leading to increased sales, while product Y is evaluated as having a weakness because its low sales are due to insufficient inventory and inconspicuous placement.

[1276] Input: Extracted keywords, sales performance data, product placement and inventory status data

[1277] Output: Identified strengths and weaknesses

[1278] Step 5:

[1279] The server uses an emotion analysis engine to recognize and evaluate the user's emotional state. Specifically, it uses IBM Watson and Microsoft Azure emotion analysis APIs to analyze emotions (positive, negative, neutral) from the content of sales reports. It also evaluates the emotional state by analyzing the user's facial expressions and posture in photos of the sales floor.

[1280] Input: Sales report data file, store photo data file

[1281] Output: User sentiment data

[1282] Step 6:

[1283] The server generates specific improvement suggestions based on the evaluated strengths and weaknesses, as well as user sentiment data. If the user has negative emotions, the suggestions are made more specific and positive. For example, it might suggest, "Continue to place product X at the front of the shelf and replenish the stock of product Y to improve sales."

[1284] Input: Identified strengths and weaknesses, user sentiment data

[1285] Output: Improvement suggestions

[1286] Step 7:

[1287] The server automatically generates sales materials based on the improvement suggestions that have been created. These materials are generated in PDF or presentation format and include charts and graphs. In addition, supplementary information and encouraging messages tailored to the user's emotional state may be added.

[1288] Input: Improvement suggestions, user sentiment data

[1289] Output: Sales materials (PDF or presentation format)

[1290] Through these processing steps, users can quickly and efficiently conduct sales analysis and implement improvement measures, while also receiving appropriate emotional support.

[1291] (Application Example 2)

[1292] 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 robot 414 as the "terminal".

[1293] Currently, in brick-and-mortar store operations, it is essential to quickly and accurately grasp sales performance and product placement, and to make efficient improvement suggestions. However, these tasks are often performed manually, which is time-consuming and labor-intensive, and often relies on subjective judgment. Furthermore, because suggestions are not made flexibly based on customer emotions, there are problems with the acceptance and implementation rate of suggestions. In this situation, there is a need to develop a system that analyzes sales performance and product placement data to generate objective and effective improvement suggestions.

[1294] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving sales report data, means for receiving store photo data, means for analyzing the sales report data using natural language processing to extract important keywords and sales performance, means for recognizing the placement and inventory status of products from the store photo data using image analysis, means for evaluating the sales performance and product placement data to identify strengths and weaknesses, means for generating proposals to maintain the identified strengths and improve weaknesses, means for generating sales negotiation materials based on the proposed content, means for transmitting sales report data and store photo data from the user terminal to the server, emotion analysis means for recognizing the user's emotions, and means for adjusting the proposed content based on the user's emotions. This enables objective improvement proposals based on sales performance and store photo data, as well as flexible proposals that take into account the user's emotions.

[1295] "Sales report data" refers to data that records daily performance and information related to sales activities.

[1296] "Store photo data" refers to image data that visually records the conditions of a store, such as the arrangement of products and inventory levels.

[1297] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[1298] "Key keywords" are words or phrases that are important for identifying specific information, extracted through data analysis.

[1299] "Sales performance" refers to performance data such as sales figures and sales volume of products or services over a specific period.

[1300] "Image analysis" is a technique that uses computer vision technology to extract useful information from image data.

[1301] "Product placement" refers to the location and arrangement of products on display in a store.

[1302] "Inventory status" refers to information indicating the quantity and condition of a product in stock at a specific point in time.

[1303] A "strength" is a characteristic or element that is perceived as positive in the area being evaluated.

[1304] A "weakness" is a characteristic or element of the subject being evaluated that is recognized as needing improvement.

[1305] A "suggestion" is a recommendation of specific actions or strategies to maintain identified strengths and improve weaknesses.

[1306] "Business negotiation materials" are documents that summarize the proposed content in a visual or written format.

[1307] "Emotion analysis" is a technology that recognizes and evaluates a user's emotional state from text and images.

[1308] A "user terminal" refers to a computer or mobile device that can send sales report data and store photo data to a server.

[1309] This invention is a system that analyzes sales report data and store photo data to identify sales strengths and weaknesses and generate specific improvement suggestions. Furthermore, by combining it with an emotion analysis engine that recognizes user emotions, it provides more accurate suggestions. The embodiments for carrying out this invention are as follows.

[1310] The user first uploads sales report data and store photo data using a terminal. The terminal sends this data to the server. The server analyzes the data and generates suggestions using the following methods.

[1311] The server analyzes sales report data using natural language processing (NLP) techniques to extract important keywords and sales performance data. Specific software used includes natural language processing libraries such as Spacy. This analysis extracts keywords such as "Store A," "Product X," and "20% increase compared to the previous month."

[1312] Next, the server analyzes the store layout photo data using an image recognition algorithm. The specific software includes image processing libraries using OpenCV and TensorFlow. This allows the system to recognize the placement and inventory status of products in the store, providing information such as "product X is placed at the front of the shelf" or "product Y is out of stock."

[1313] Next, the server evaluates the extracted sales performance data and product placement data to identify strengths and weaknesses. This evaluation identifies information such as, "Product X has high visibility and strong sales," or "Product Y is out of stock and sales are sluggish."

[1314] Furthermore, the server uses an emotion analysis engine to recognize the user's emotional state. This emotion analysis employs a generative AI model that evaluates the tone and context of the text. For example, if negative emotions are detected when a user enters sales report data, the server will recognize this.

[1315] The server generates specific improvement suggestions based on identified strengths and weaknesses, as well as the user's emotional state. These suggestions are tailored to the user's emotions and may be presented in a more positive and concrete format. For example, a suggestion such as "Maintain product X in its current position and replenish product Y's inventory immediately" might be generated.

[1316] Ultimately, the server automatically generates sales materials based on the improvement suggestions that have been created. These materials, including PDF or presentation formats, are provided to the user's terminal. This allows the user to efficiently analyze sales data from physical stores and implement improvement measures.

[1317] For example, the following format can be used as a prompt:

[1318] "On October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock. Based on this data, generate improvement proposals and prepare sales materials."

[1319] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1320] Step 1:

[1321] Users input sales report data and take photos of the sales floor. The sales report data includes daily sales performance and sales status, while the sales floor photos visually record product placement and inventory status. Users also upload this data using a smartphone or smart glasses.

[1322] Input: Sales report data, store photo data

[1323] Output: Data uploaded to the device

[1324] Step 2:

[1325] The terminal sends sales report data and store photo data to the server. A data transmission completion notification is displayed on the terminal to confirm that the data has reached the server correctly.

[1326] Input: Data uploaded to the device

[1327] Output: Data sent to the server

[1328] Step 3:

[1329] The server analyzes the received sales report data using natural language processing (NLP) techniques. Specifically, it uses NLP libraries such as spacy to extract important keywords and sales performance data from the sales reports.

[1330] Input: Sales report data

[1331] Output: Key keywords, sales performance

[1332] Step 4:

[1333] The server analyzes store photo data using image recognition algorithms. Using image processing libraries such as OpenCV and TensorFlow, it recognizes the placement and inventory status of products in the photos and stores this information in a database.

[1334] Input: Storefront photo data

[1335] Output: Product placement data, inventory status data

[1336] Step 5:

[1337] The server evaluates the extracted key keywords, sales performance, and product placement data, and uses this information to identify strengths and weaknesses. This results in evaluations such as, "Product X has high visibility and strong sales."

[1338] Input: Key keywords, sales performance, product placement data

[1339] Output: Evaluation results of strengths and weaknesses

[1340] Step 6:

[1341] The server uses an emotion analysis engine to recognize the user's emotional state from sales report data and store photo data. A generative AI model is used to evaluate the user's emotions (positive, negative, neutral) based on their input and photos.

[1342] Input: Sales report data, store photo data

[1343] Output: Sentiment evaluation

[1344] Step 7:

[1345] The server generates specific improvement suggestions based on the strengths and weaknesses assessment results and the sentiment assessment. The generated suggestions are adjusted based on the user's sentiment and include positive content, such as "Keep product X in its current position."

[1346] Input: Strengths and weaknesses assessment results, emotional assessment

[1347] Output: Specific improvement suggestions

[1348] Step 8:

[1349] The server automatically generates sales materials based on the improvement suggestions that have been created. The sales materials are generated in PDF or presentation format and provided to the user's terminal.

[1350] Input: Specific improvement suggestions

[1351] Output: Sales materials (PDF / presentation format)

[1352] As a concrete example of the operation, the prompt statement is as follows:

[1353] "On October 1, 2023, sales of product X at store A increased by 20% compared to the previous month. Sales of product Y were sluggish due to insufficient stock. Based on this data, generate improvement proposals and prepare sales materials."

[1354] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 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.

[1355] 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.

[1356] 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 robot 414.

[1357] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1358] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1359] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1360] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1361] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1362] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1363] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1364] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1365] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1366] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1367] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1368] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1369] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1370] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1371] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1372] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1373] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1374] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1375] The following is further disclosed regarding the embodiments described above.

[1376] (Claim 1)

[1377] A means of receiving sales report data,

[1378] A means of receiving store photo data,

[1379] A means for analyzing the aforementioned sales report data using natural language processing to extract important keywords and sales performance,

[1380] The aforementioned store photo data is used to recognize the arrangement and inventory status of products through image analysis,

[1381] A means for evaluating the aforementioned sales performance and product placement data to identify strengths and weaknesses,

[1382] Means for generating suggestions for maintaining the identified strengths and improving weaknesses,

[1383] A means for generating business negotiation materials based on the aforementioned proposal,

[1384] A system that includes this.

[1385] (Claim 2)

[1386] The system according to claim 1, further comprising a terminal that transmits the sales report data and the sales floor photo data to a server.

[1387] (Claim 3)

[1388] The system according to claim 1, further comprising means for generating the aforementioned business negotiation materials in PDF or PowerPoint format and presenting them on a terminal.

[1389] "Example 1"

[1390] (Claim 1)

[1391] A means of receiving sales report data,

[1392] A means of receiving store photo data,

[1393] A means for analyzing the aforementioned sales report data using natural language processing to extract important keywords and sales performance,

[1394] The aforementioned store photo data is used to recognize the arrangement and inventory status of products through image analysis,

[1395] A means for evaluating the aforementioned sales performance and product placement data to identify strengths and weaknesses,

[1396] Means for generating suggestions for maintaining the identified strengths and improving weaknesses,

[1397] A means for generating business negotiation materials based on the aforementioned proposal,

[1398] A means of providing the aforementioned business negotiation materials to the terminal in PDF or presentation format and presenting the user with a download link,

[1399] A system that includes this.

[1400] (Claim 2)

[1401] The system according to claim 1, further comprising a device for transmitting the sales report data and the sales floor photo data to a server.

[1402] (Claim 3)

[1403] The system according to claim 1, further comprising means for providing real-time feedback to the user on the status of the transmission, analysis, and material generation of sales floor photo data and sales floor report data.

[1404] "Application Example 1"

[1405] (Claim 1)

[1406] A means of receiving sales report data,

[1407] A means of receiving store photo data,

[1408] A means for analyzing the aforementioned sales report data using natural language processing to extract important keywords and sales performance,

[1409] The aforementioned store photo data is used to recognize the arrangement and inventory status of products through image analysis,

[1410] A means for evaluating the aforementioned sales performance and product placement data to identify strengths and weaknesses,

[1411] Means for generating suggestions for maintaining the identified strengths and improving weaknesses,

[1412] A means of generating negotiation materials based on the aforementioned proposal and outputting them in PDF or presentation format,

[1413] A method for collecting sales report data and store photo data using smartphones and robots,

[1414] A means of performing data analysis and evaluation using a cloud server,

[1415] A system that includes this.

[1416] (Claim 2)

[1417] The system according to claim 1, further comprising a terminal that transmits the sales report data and the sales floor photo data to a cloud server.

[1418] (Claim 3)

[1419] The system according to claim 1, further comprising means for generating the aforementioned business negotiation materials in PDF or presentation format and presenting them on a terminal.

[1420] "Example 2 of combining an emotion engine"

[1421] (Claim 1)

[1422] A means of receiving sales report data,

[1423] A means of receiving store photo data,

[1424] A means for analyzing the aforementioned sales report data using natural language processing to extract important keywords and sales performance,

[1425] The aforementioned store photo data is used to recognize the arrangement and inventory status of products through image analysis,

[1426] A means for evaluating the aforementioned sales performance and product placement data to identify strengths and weaknesses,

[1427] A means of recognizing and evaluating user sentiment data,

[1428] Means for generating suggestions for maintaining the identified strengths and improving weaknesses,

[1429] A means for generating business negotiation materials based on the aforementioned proposal,

[1430] A means for adjusting the aforementioned sales materials according to the user's emotional state,

[1431] A system that includes this.

[1432] (Claim 2)

[1433] The system according to claim 1, further comprising a terminal that transmits the sales report data and the sales floor photo data to a server.

[1434] (Claim 3)

[1435] The system according to claim 1, further comprising means for generating the aforementioned business negotiation materials in PDF or presentation format and presenting them on a terminal.

[1436] "Application example 2 when combining with an emotional engine"

[1437] (Claim 1)

[1438] A means of receiving sales report data,

[1439] A means of receiving store photo data,

[1440] A means for analyzing the aforementioned sales report data using natural language processing to extract important keywords and sales performance,

[1441] The aforementioned store photo data is used to recognize the arrangement and inventory status of products through image analysis,

[1442] A means for evaluating the aforementioned sales performance and product placement data to identify strengths and weaknesses,

[1443] Means for generating suggestions for maintaining the identified strengths and improving weaknesses,

[1444] A means for generating business negotiation materials based on the aforementioned proposal,

[1445] A means for transmitting sales report data and store photo data from a user terminal to a server,

[1446] A means of analyzing user emotions,

[1447] A means for adjusting the aforementioned proposal based on the user's emotions,

[1448] A system that includes this.

[1449] (Claim 2)

[1450] The system according to claim 1, further comprising means for generating the aforementioned business negotiation materials in PDF or presentation format and presenting them on a terminal.

[1451] (Claim 3)

[1452] The system according to claim 1, comprising an application installed on a smartphone or smart glasses. [Explanation of symbols]

[1453] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving sales report data, A means of receiving store photo data, A means for analyzing the aforementioned sales report data using natural language processing to extract important keywords and sales performance, The aforementioned storefront photograph data is used to recognize the arrangement and inventory status of products through image analysis, A means for evaluating the aforementioned sales performance and product placement data to identify strengths and weaknesses, Means for generating suggestions for maintaining the identified strengths and improving weaknesses, A means for generating business negotiation materials based on the aforementioned proposal, A system that includes this.

2. The system according to claim 1, further comprising a terminal that transmits the sales report data and the sales floor photo data to a server.

3. The system according to claim 1, further comprising means for generating the aforementioned business negotiation materials in PDF or PowerPoint format and presenting them on a terminal.

Images