system

Abstract

Provide a system. 【Solution means】 Means for inputting the target sales amount and profit rate, Means for acquiring past sales data, customer data, and market trend data, Means for making a sales prediction based on the acquired data, Means for calculating the number of sales required to achieve the target, Means for generating an appropriate product list, Means for automatically generating the sales prediction and product list in document form, Means for outputting the document, 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Create "Problems to be Solved by the Invention" and "Means for Solving the Problems".

[0005] In order for salespersons to achieve profit targets, they need to efficiently handle a wide range of tasks such as creating detailed plans, selecting products to propose, creating meeting minutes, managing schedules after winning orders, and handling daily inquiries. Such tasks are time-consuming and error-prone, and are factors that significantly reduce sales efficiency. Therefore, there is a need to streamline and automate these tasks to reduce the burden on salespersons.

Means for Solving the Problems

[0006] To solve this problem, the present invention provides a system that includes means for inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating sales forecasts and product lists in document format, and means for outputting documents. Furthermore, by including means for automatically generating meeting minutes and means for generating the next action items based on the meeting minutes, and means for automatically generating a Gantt chart from the time of order acceptance until implementation, the work efficiency of sales representatives can be greatly improved.

[0007] "Target sales amount" refers to the sales figure that a sales representative aims to achieve over a period such as the next quarter or fiscal year.

[0008] "Profit margin" is a numerical value that shows the ratio of profit to sales, and is an indicator set as part of sales targets.

[0009] "Past sales data" refers to data that records sales performance over a certain period, and serves as basic information for sales forecasting.

[0010] "Customer data" refers to data that includes information such as customer attributes, purchase history, and needs, and is used to customize proposals.

[0011] "Market trend data" refers to data that includes information on market trends, developments, and economic conditions, and is useful for sales forecasting.

[0012] "Sales forecasting" is a calculation method that predicts future sales based on past sales data and market trend data.

[0013] "Sales volume" refers to the quantity of goods or services that are expected to be sold within a specific period.

[0014] The "product list" is a list of products proposed to customers and is selected based on customer characteristics and needs.

[0015] "Automatic generation" is a process in which the system automatically creates documents and data without the need for manual work.

[0016] A "Gantt chart" is a tool for visually representing schedules and progress in project management.

[0017] The "minutes of the meeting" is a document that records the content of speeches and decisions made at a meeting.

[0018] An "action item" is a specific action or task to be taken next based on the minutes of the meeting.

[0019] Based on these definitions, the scope of the claims can be described specifically and clearly.

Brief Description of the Drawings

[0020] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0021] 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.

[0022] First, the language used in the following description will be explained.

[0023] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Further, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] 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).

[0027] 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."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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".

[0041] The present invention is a system for improving sales efficiency, and its embodiments will be described in detail below.

[0042] Goal input and data collection

[0043] 1. Enter your goal

[0044] The user (sales representative) logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system.

[0045] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal. The entered data is then sent to the server.

[0046] 2. Data Collection

[0047] The server retrieves historical sales data and customer data from the database.

[0048] The server retrieves market trend data from external APIs and internal databases.

[0049] Sales forecasting and plan generation

[0050] 3. Data Analysis

[0051] The server analyzes sales trends using past sales data.

[0052] The server uses statistical models (e.g., time series analysis, regression analysis) based on market trend data to forecast sales.

[0053] 4. Profitability analysis

[0054] The server estimates a realistic profit margin based on the acquired data.

[0055] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (20%).

[0056] 5. Plan generation

[0057] The server calculates the sales figures needed to achieve the target and generates a product list.

[0058] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists.

[0059] The server sends the generated plan document to the user's terminal.

[0060] Gantt chart creation and meeting minutes management

[0061] 6. Create a Gantt chart

[0062] When a project is accepted, the user enters the project details into the terminal.

[0063] The server calculates the project's timeline and automatically generates a Gantt chart.

[0064] A Gantt chart is displayed on the device, allowing users to manage their schedules.

[0065] 7. Minutes generation

[0066] The user enters the meeting agenda and key points into the terminal.

[0067] The server analyzes meeting audio and documents and automatically generates meeting minutes.

[0068] The server generates the following action items and displays the meeting minutes and action items on the terminal.

[0069] Inquiry response

[0070] 8. Inquiries regarding products and tools

[0071] Users enter questions about products or internal tools into the terminal.

[0072] The server searches the FAQ database and related documents and generates an automated response.

[0073] The answer will be displayed on your device.

[0074] Specific example

[0075] As a concrete example, consider a scenario where a user inputs, "The sales target for the next quarter is 10 million yen, and the profit margin is 20%."

[0076] 1. The user enters the goal into their device and sends it to the server.

[0077] 2. The server retrieves historical data and market trends to perform sales forecasts.

[0078] 3. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[0079] 4. The server automatically generates a "Next Quarterly Goal Achievement Plan" document and sends it to the user's terminal.

[0080] 5. Users review plan documents on their devices and plan and execute sales activities.

[0081] As described above, the present invention provides a system that streamlines the work of sales representatives and supports them in achieving their goals.

[0082] The following describes the processing flow.

[0083] Creating a profit target achievement plan

[0084] Step 1:

[0085] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[0086] Step 2:

[0087] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal and clicks the "Submit" button.

[0088] Step 3:

[0089] The server receives data on target sales and profit margins sent by the user.

[0090] Step 4:

[0091] The server retrieves historical sales data from the database. For example, it might retrieve quarterly sales figures for the past three years.

[0092] Step 5:

[0093] The server retrieves customer data from the database. It collects information such as each customer's purchase history and contract status.

[0094] Step 6:

[0095] The server retrieves market trend data from external APIs and internal databases. Specifically, it collects the latest data such as industry reports and economic indicators.

[0096] Step 7:

[0097] The server analyzes sales trends using historical sales data. For example, it uses time series analysis to understand seasonal fluctuations and trends in sales.

[0098] Step 8:

[0099] Based on market trend data acquired by the server, future sales are predicted. Regression analysis and machine learning models are applied to achieve highly accurate predictions.

[0100] Step 9:

[0101] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%). For example, it calculates the optimal product mix while considering the profit margin.

[0102] Step 10:

[0103] The server calculates the sales figures needed to achieve the target. Specifically, it calculates the required sales figures for each product.

[0104] Step 11:

[0105] The server matches customer data with product data and generates a list of products optimized for each customer. For example, it might list products recommended based on a customer's past purchase history.

[0106] Step 12:

[0107] The server automatically generates a "Next Quarterly Target Achievement Plan" document based on sales forecasts and product lists. The document includes sales forecast graphs and product list tables.

[0108] Step 13:

[0109] The server sends the generated plan document to the user's terminal.

[0110] Step 14:

[0111] The device displays the received plan document, allowing the user to review the document.

[0112] Gantt chart creation and meeting minutes management

[0113] Step 1:

[0114] When a user accepts a project, they enter the project details from their device.

[0115] Step 2:

[0116] The server receives the project details and calculates the project schedule.

[0117] Step 3:

[0118] The server automatically generates a Gantt chart, displaying the project's start date, end date, and schedule for each stage.

[0119] Step 4:

[0120] The device displays a Gantt chart, allowing the user to manage their schedule.

[0121] Inquiry response

[0122] Step 1:

[0123] Users enter questions about products or internal tools from their devices.

[0124] Step 2:

[0125] The server receives the user's question and searches the FAQ database and related documents.

[0126] Step 3:

[0127] The server generates an automated response and obtains an answer to the user's question.

[0128] Step 4:

[0129] The device displays the answer, allowing the user to check the necessary information.

[0130] (Example 1)

[0131] 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."

[0132] Traditional sales support systems made sales forecasting and profit margin analysis time-consuming, making it difficult to develop efficient sales plans. Furthermore, automatic generation of meeting minutes, creation of project Gantt charts, and handling inquiries about personnel and products also required significant time and effort. This resulted in a heavy workload for sales representatives and decreased efficiency in achieving sales targets.

[0133] 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.

[0134] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring historical sales data, customer data, and market trend data, and means for performing sales forecasts based on the acquired data. This enables efficient data collection and analysis to be performed automatically, allowing for the rapid and highly accurate creation of sales forecasts and sales plans.

[0135] "Target sales amount" refers to the target sales amount set by the user.

[0136] "Profit margin" is an indicator that shows the ratio of profit to sales.

[0137] "Sales data" refers to numerical information about past sales performance.

[0138] "Customer data" refers to information about customers with whom transactions are conducted.

[0139] "Market trend data" refers to information about current and past trends in a specific market or industry.

[0140] "Sales forecasting" refers to the process of estimating future sales based on collected data.

[0141] "Sales volume" refers to the quantity of goods or services that need to be sold to achieve a goal.

[0142] A "product list" is a list of products or services that are offered for sale.

[0143] "Document format" refers to the format of a document, whether digital or on paper.

[0144] A Gantt chart is a visual chart used for managing project schedules.

[0145] "Meeting minutes" are documents that record the content and key points of a meeting.

[0146] An "action item" is an item that indicates the specific next steps to be taken based on the meeting minutes.

[0147] "Automatic response" refers to a system function that automatically generates pre-set answers.

[0148] A "server" is a computer system that processes data and responds to client requests.

[0149] "Terminal" refers to a computer or mobile device used by a user to operate.

[0150] The present invention is a system for improving sales efficiency, and its embodiments will be described in detail below.

[0151] System Overview

[0152] This system automatically generates a sales plan by inputting target sales figures and profit margins, collecting and analyzing historical data, and ultimately performing sales forecasts and profit margin analysis. Furthermore, it provides means for generating product lists, automatically creating Gantt charts, automatically generating meeting minutes, and handling inquiries. This significantly improves the work efficiency of sales representatives and supports them in achieving their targets.

[0153] Hardware and software to be used

[0154] 1. Server

[0155] Main processor: Server equipped with a high-speed CPU

[0156] Database: MySQL (registered trademark) database

[0157] External APIs: Google® Trends API, Google Cloud Speech-to-Text API

[0158] Analysis tools: Python, pandas, scikit-learn, numpy

[0159] Document generation: Generative AI models (e.g., GPT-3(registered trademark))

[0160] 2. Terminal

[0161] User Interface (UI): A user interface that operates on a web browser.

[0162] Display devices: PC, tablet, smartphone

[0163] Specific example

[0164] Goal input and data collection

[0165] 1. Enter your goal

[0166] The user logs into their device and selects the "Create Profit Target Achievement Plan" option. For example, the user might enter "Sales target for the next quarter: 10 million yen, profit margin: 20%."

[0167] 2. Data Collection

[0168] The server automatically retrieves historical sales and customer data from a MySQL database. Furthermore, it obtains market trend data via the Google Trends API and an internal database.

[0169] Sales forecasting and plan generation

[0170] The server performs sales forecasts based on the acquired data. Specifically, it preprocesses the data using Python's pandas library and scikit-learn, and applies time series analysis and regression analysis models. Profit margin analysis is performed similarly, and the server calculates the required sales volume and cost structure considering the target profit margin entered by the user.

[0171] The server generates a suitable product list based on sales forecast results and profit margin analysis results. Along with this product list, it automatically generates a "Goal Achievement Plan" document using a generation AI model (e.g., GPT-3). The generated plan document is sent to the user's terminal and displayed.

[0172] Example of a prompt:

[0173] "Our sales target for the next quarter is 10 million yen, with a profit margin of 20%. Please create a plan to achieve these targets."

[0174] Gantt chart creation and meeting minutes management

[0175] The server calculates the project schedule and automatically generates a Gantt chart when the user enters project details. The Gantt chart is displayed in a web browser.

[0176] To automatically generate meeting minutes, the system uses the Google Cloud Speech-to-Text API to convert meeting audio into text based on the meeting agenda and key points entered by the user, and then generates the minutes using NLP (Natural Language Processing) technology. The generated minutes are displayed on the user's device, and the next action items are also automatically generated.

[0177] Inquiry response

[0178] When a user enters a question about a product or internal tool into their terminal, the server uses ElasticSearch® to search the FAQ database and related documents, and automatically generates an appropriate answer. The generated answer is then displayed on the user's terminal.

[0179] Based on the above, the present invention provides a system that streamlines the work of sales representatives and supports them in achieving their goals.

[0180] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0181] Step 1: Enter your goal

[0182] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[0183] The user enters "A sales target of 10 million yen and a profit margin of 20% for the next quarter."

[0184] The input data is packaged in JSON format as form data and sent to the server using a REST API.

[0185] Input: User-entered data on sales target amount and profit margin.

[0186] Output: Target amount and profit margin data sent to the server

[0187] Step 2: Data Collection

[0188] The server retrieves historical sales and customer data from a MySQL database.

[0189] The server retrieves market trend data through the Google Trends API and its internal database.

[0190] Data is retrieved using SQL queries, and data obtained from external APIs is stored in JSON format.

[0191] Input: Sales target amount and profit margin data

[0192] Output: Historical sales data, customer data, and market trend data retrieved from databases and external APIs.

[0193] Step 3: Data Analysis (Sales Forecast)

[0194] The server performs sales forecasts based on acquired historical sales data and market trend data.

[0195] The data is preprocessed using the Python pandas library, and predictions are made by applying statistical models (e.g., time series analysis, regression analysis).

[0196] The forecast results output a numerical representation of the likelihood of achieving the sales target.

[0197] Input: Historical sales data, customer data, market trend data

[0198] Output: Sales forecast data

[0199] Step 4: Profit Margin Analysis

[0200] Based on the predicted sales data, the server uses the NumPy library to perform sales and cost analysis, taking into account the required profit margin of 20%.

[0201] Calculate the required sales volume and cost structure, and output specific figures.

[0202] Input: Sales forecast data, sales target amount, profit margin

[0203] Output: Data on required sales volume and cost structure

[0204] Step 5: Plan Generation

[0205] The server generates a list of products necessary to achieve sales targets based on sales forecasts and profit margin analysis results.

[0206] The server uses a generative AI model (e.g., GPT-3) to automatically generate a "Goal Achievement Plan" document.

[0207] The document is exported in PDF or Word format and sent to the user's device.

[0208] Input: Sales forecast, profit margin analysis results, product list

[0209] Output: "Goal Achievement Plan" document

[0210] Step 6: Create a Gantt chart

[0211] The user enters the details of a new case into the terminal.

[0212] The server automatically generates a Gantt chart using D3.js or Chart.js based on the input data.

[0213] The Gantt chart is displayed on the user's device, enabling project schedule management.

[0214] Input: Project details data

[0215] Output: Gantt chart

[0216] Step 7: Generate meeting minutes

[0217] The user enters the meeting agenda and key points into the terminal.

[0218] The server uses the Google Cloud Speech-to-Text API to convert meeting audio into text and generates meeting minutes using an NLP model.

[0219] The meeting minutes and next action items will be displayed in document format on the user's device.

[0220] Input: Meeting agenda, key points, meeting audio

[0221] Output: Meeting minutes, next action items

[0222] Step 8: Handling inquiries

[0223] Users enter questions about products or internal tools into the terminal.

[0224] The server uses Elasticsearch to search the FAQ database and related documents, and generates appropriate automated responses.

[0225] The automated response will be displayed on the user's device.

[0226] Input: User's question

[0227] Output: Automated response

[0228] (Application Example 1)

[0229] 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."

[0230] Traditional sales efficiency improvement systems were limited to sales forecasting and target setting functions, lacking the tools and features to effectively utilize the analysis results. Furthermore, features such as schedule management, meeting minute generation, and automated inquiry handling were not sufficiently integrated, making it difficult for sales representatives to efficiently manage their diverse tasks.

[0231] 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.

[0232] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means for generating a Gantt chart for schedule management, means for automatically generating meeting minutes, and means for automatically generating inquiry responses. This enables the user to efficiently perform tasks from sales target planning and schedule management to meeting minute generation and inquiry handling in a consistent manner.

[0233] "Target sales amount" refers to a specific sales figure that you want to achieve in your sales activities or business plan.

[0234] "Profit margin" is an indicator that shows the ratio of profit earned to sales revenue.

[0235] "Past sales data" refers to historical sales information recorded up to the present.

[0236] "Customer data" refers to aggregated data about customers, including age, gender, and purchase history.

[0237] "Market trend data" refers to data related to market trends and consumer behavior.

[0238] "Means of sales forecasting" refers to methods or devices for predicting future sales based on collected data.

[0239] "Sales volume required to achieve the target" refers to the number of products that need to be sold to achieve the set target sales amount and profit margin.

[0240] A "product list" is a list of the products and services that are sold.

[0241] "Means of automatically generating documents in document format" refers to methods or devices for analyzing data and automatically converting the results into documents such as reports and proposals.

[0242] "Means for outputting documents" refers to methods or devices for providing the generated documents to the user.

[0243] A "Gantt chart for schedule management" is a diagram in the form of a Gantt chart used to visually display the schedule of a project or task.

[0244] "Methods for automatically generating meeting minutes" refer to methods or devices that analyze the content of a meeting and automatically create a summary of it as meeting minutes.

[0245] "Means for automatically generating inquiry responses" refers to methods or devices for automatically providing appropriate responses to questions and inquiries from users.

[0246] The present invention relates to a system for improving sales efficiency, and its embodiments will be described in detail below.

[0247] Goal input and data collection

[0248] 1. Enter your goal

[0249] The user (sales representative) logs into the terminal and selects the displayed "Create Profit Target Achievement Plan" option. The user enters the target sales amount and profit margin into the terminal, and that data is sent to the server.

[0250] 2. Data Collection

[0251] The server automatically retrieves historical sales and customer data from the store's point-of-sale (POS) system. Market trend data is obtained from external APIs and internal databases.

[0252] Sales forecasting and plan generation

[0253] 3. Data Analysis

[0254] The server analyzes sales trends using collected data. Based on market trend data, it uses statistical models (e.g., time series analysis, regression analysis) to predict future sales.

[0255] 4. Profitability analysis

[0256] The server estimates realistic profit margins based on the acquired data. Considering the target profit margin, it calculates the necessary sales revenue and cost structure.

[0257] 5. Plan generation

[0258] The server calculates the sales volume required to achieve the target and generates a suitable product list. Based on the sales forecast and product list, it automatically generates a "Target Achievement Plan" document and sends this document to the user's terminal.

[0259] Gantt chart creation and meeting minutes management

[0260] 6. Create a Gantt chart

[0261] When a project is accepted, the user enters the project details into the terminal, and the server calculates the project's timeline and automatically generates a Gantt chart. The Gantt chart is displayed on the terminal, allowing the user to manage their schedule.

[0262] 7. Minutes generation

[0263] When a user enters meeting agenda items and key points into their terminal, the server analyzes the meeting audio and documents and automatically generates meeting minutes. Next action items are also generated and displayed on the terminal along with the meeting minutes.

[0264] Inquiry response

[0265] 8. Inquiries regarding products and tools

[0266] When a user enters a question about a product or internal tool into the terminal, the server searches the FAQ database and relevant documents and generates an automated response. The answer is then displayed on the terminal.

[0267] Specific example

[0268] As a concrete example, consider a scenario where a user inputs "The sales target for the next quarter is 10 million yen, with a profit margin of 20%." The user inputs the target into their terminal, and this data is sent to the server. The server retrieves past sales data and market trends and makes a sales forecast. Subsequently, the server performs an analysis based on profit margins, calculates the necessary sales volume and product list, and sends the generated "Next Quarterly Target Achievement Plan" document to the user's terminal. The user reviews this plan document, plans and executes their sales activities.

[0269] Example of a prompt

[0270] "Our sales target for the next quarter is 10 million yen, with a profit margin of 20%. Please analyze past sales data and market trends to generate the optimal sales promotion plan."

[0271] This system significantly improves the efficiency of sales representatives' work and enables them to develop effective strategies for achieving sales targets and profit margins.

[0272] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0273] Step 1:

[0274] The user enters their target sales amount and profit margin into the terminal. The input data is in the following format: "Target sales amount: 10 million yen", "Profit margin: 20%". This data is sent from the terminal to the server. The server stores the received data in its database.

[0275] Step 2:

[0276] The server retrieves historical sales and customer data from the store's POS system and related databases. Furthermore, it collects market trend data through external APIs and internal databases. This data is temporarily stored on the server for analysis.

[0277] Step 3:

[0278] The server uses time series analysis and regression analysis techniques to forecast sales based on historical sales data. The input data consists of past sales records, while the output data represents future sales forecasts. The server performs data analysis using Python's Pandas and scikit-learn libraries.

[0279] Step 4:

[0280] The server uses collected market trend data to perform detailed analysis based on sales forecasts. Specifically, it readjusts the forecasting model and calculates the final forecast values ​​that take into account fluctuations. The output data consists of predicted sales and profit margins.

[0281] Step 5:

[0282] Based on the set target profit rate, the server calculates the required sales revenue and costs. As a result, the number of sales required to achieve the target and an appropriate list of merchandise are generated. The input data is the profit rate and the predicted sales value, and the output data is the list of merchandise.

[0283] Step 6:

[0284] Based on the sales prediction and the data of the merchandise list, the server automatically generates a "Target Achievement Plan" document. This document describes the sales strategy, the required number of sales, and the recommended merchandise. The server sends the generated document to the user's terminal.

[0285] Step 7:

[0286] When the user inputs the details of the project into the terminal, the server automatically generates the project process and creates a Gantt chart. The input data is the detailed information of the project, and the output data is a schedule in Gantt chart format.

[0287] Step 8:

[0288] When the user inputs the topics and key points for the meeting into the terminal, the server analyzes the audio data and documents of the meeting and automatically generates meeting minutes. Specifically, using speech recognition technology, the meeting content is texturized, and the necessary information is extracted and summarized as meeting minutes. The output data is the automatically generated meeting minutes.

[0289] Step 9:

[0290] When the user inputs questions about merchandise or in-house tools into the terminal, the server searches the FAQ database and related documents and generates an inquiry response. The input data is the question from the user, and the output data is the automatically generated response. The server uses a natural language generation model such as the GPT-4 (registered trademark) API to create the response.

[0291] Through these steps, this system dramatically improves the efficiency of sales representatives and provides concrete plans for achieving goals.

[0292] 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.

[0293] This invention is a system for improving sales efficiency, combining an emotion engine that recognizes user emotions to enable more appropriate suggestions and two-way communication. The embodiments are described in detail below.

[0294] Goal input and data collection

[0295] 1. Enter your goal

[0296] The user (sales representative) logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system.

[0297] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal. The entered data is then sent to the server.

[0298] 2. Data Collection

[0299] The server retrieves historical sales data and customer data from the database.

[0300] The server retrieves market trend data from external APIs and internal databases.

[0301] Sales forecasting and plan generation

[0302] 3. Data Analysis

[0303] The server analyzes sales trends using past sales data.

[0304] The server uses statistical models (e.g., time series analysis, regression analysis) based on market trend data to make sales forecasts.

[0305] 4. Profit Rate Analysis

[0306] The server estimates a realistic profit rate based on the acquired data.

[0307] The server considers the target profit rate (e.g., 20%) and calculates the required sales volume and cost structure.

[0308] 5. Plan Generation

[0309] The server calculates the number of sales required to achieve the goal and generates a product list.

[0310] The server automatically generates a "Goal Achievement Plan" document based on the sales forecast and the product list.

[0311] The server sends the generated plan document to the user's terminal.

[0312] Utilization of the Emotion Engine

[0313] 6. Emotion Recognition

[0314] When the user inputs goals or inquiries, the terminal uses the emotion engine to recognize emotions from the user's expression and voice.

[0315] The server receives the emotion analysis result and supplements the user's input information.

[0316] 7. Proposals Based on Emotions

[0317] The server makes more appropriate proposals to the user considering the emotion analysis result.

[0318] For example, when the user is feeling stressed, the system provides advice and resources to reduce the burden as much as possible.

[0319] Gantt chart creation and meeting minutes management

[0320] 8. Create a Gantt chart

[0321] When a project is accepted, the user enters the project details into the terminal.

[0322] The server calculates the project's timeline and automatically generates a Gantt chart.

[0323] The device displays a Gantt chart, allowing the user to manage their schedule.

[0324] 9. Minutes generation

[0325] The user enters the meeting agenda and key points into the terminal.

[0326] The server analyzes meeting audio and documents and automatically generates meeting minutes.

[0327] The server generates the following action items and displays the meeting minutes and action items on the terminal.

[0328] Sentiment analysis of meeting minutes

[0329] 10. Recognizing emotions during meetings

[0330] The device analyzes audio data from the meeting using an emotion engine and records the speaker's emotions, which are then reflected in the meeting minutes.

[0331] Based on the analysis results, the server records the emotional state and its changes in the meeting minutes.

[0332] Inquiry response

[0333] 11. Inquiries regarding products and tools

[0334] Users enter questions about products or internal tools from their devices.

[0335] The server receives the user's question and searches the FAQ database and related documents.

[0336] The server generates an automated response and obtains an answer to the user's question.

[0337] The device displays the answer, allowing the user to check the necessary information.

[0338] Specific example

[0339] As a concrete example, consider a scenario where a user inputs, "The sales target for the next quarter is 10 million yen, and the profit margin is 20%."

[0340] 1. The user enters the goal into their device and sends it to the server.

[0341] 2. The server uses an emotion engine to analyze the user's emotions at the time of input and obtain information such as stress levels and satisfaction levels.

[0342] 3. The server retrieves historical data and market trends to perform sales forecasts.

[0343] 4. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[0344] 5. Based on the sentiment analysis results, the server automatically generates a "Next Quarterly Goal Achievement Plan" document containing optimal advice for the user.

[0345] 6. The server sends the generated plan document to the user's terminal.

[0346] 7. Users review plan documents on their devices and plan and execute sales activities.

[0347] As described above, the present invention realizes a system that streamlines the work of sales representatives and provides more appropriate support by utilizing emotion recognition.

[0348] The following describes the processing flow.

[0349] Creating profit target achievement plans and utilizing the emotional engine

[0350] Step 1:

[0351] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[0352] Step 2:

[0353] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal and clicks the "Submit" button.

[0354] Step 3:

[0355] The device analyzes the user's facial expressions and voice during input using an emotion engine to detect their emotional state (e.g., stress, satisfaction level, excitement level).

[0356] Step 4:

[0357] The device sends the detected emotional state to the server as data.

[0358] Step 5:

[0359] The server receives data from the user regarding target sales figures, profit margins, and emotional states.

[0360] Step 6:

[0361] The server retrieves historical sales data from the database. For example, it might retrieve quarterly sales figures for the past three years.

[0362] Step 7:

[0363] The server retrieves customer data from the database. It collects information such as each customer's purchase history and contract status.

[0364] Step 8:

[0365] The server retrieves market trend data from external APIs and internal databases. For example, it collects the latest data such as industry reports and economic indicators.

[0366] Step 9:

[0367] The server analyzes sales trends using historical sales data. For example, it uses time series analysis to understand seasonal fluctuations and trends in sales.

[0368] Step 10:

[0369] The server predicts future sales based on market trend data. Regression analysis and machine learning models are applied to achieve highly accurate predictions.

[0370] Step 11:

[0371] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%). For example, it calculates the optimal product mix while considering the profit margin.

[0372] Step 12:

[0373] The server calculates the sales figures needed to achieve the target. Specifically, it calculates the required sales figures for each product.

[0374] Step 13:

[0375] The server matches customer data with product data and generates a list of products optimized for each customer. For example, it might list products recommended based on a customer's past purchase history.

[0376] Step 14:

[0377] The server automatically generates a "Next Quarterly Target Achievement Plan" document based on sales forecasts and product lists. The document includes sales forecast graphs and product list tables.

[0378] Step 15:

[0379] Based on the sentiment analysis results, the server adds advice and support messages tailored to the user's emotional state to the plan document.

[0380] Step 16:

[0381] The server sends the generated plan document to the user's terminal.

[0382] Step 17:

[0383] The device displays the received plan document, allowing the user to review the document.

[0384] Gantt chart creation and meeting minutes management

[0385] Step 1:

[0386] When a user accepts a project, they enter the project details from their device.

[0387] Step 2:

[0388] The server receives the project details and calculates the project schedule.

[0389] Step 3:

[0390] The server automatically generates a Gantt chart, displaying the project's start date, end date, and schedule for each stage.

[0391] Step 4:

[0392] The device displays a Gantt chart, allowing the user to manage their schedule.

[0393] Sentiment analysis of meeting minutes

[0394] Step 1:

[0395] The user enters the meeting agenda and key points into the terminal.

[0396] Step 2:

[0397] The device analyzes the audio data during the meeting using an emotion engine to detect the emotional state of the speaker.

[0398] Step 3:

[0399] The device sends the detected emotional state to the server as meeting minutes data.

[0400] Step 4:

[0401] The server automatically generates meeting minutes based on audio data and emotional states. Emotional states are also reflected in the minutes.

[0402] Step 5:

[0403] The server generates the next action item and displays the meeting minutes and action item on the terminal.

[0404] Inquiry response

[0405] Step 1:

[0406] Users enter questions about products or internal tools from their devices.

[0407] Step 2:

[0408] The device uses an emotion engine to analyze facial expressions and voice during questioning and detect the emotional state.

[0409] Step 3:

[0410] The device sends question data, including emotional state, to the server.

[0411] Step 4:

[0412] The server receives the user's question and sentiment state, and searches the FAQ database and related documents.

[0413] Step 5:

[0414] The server automatically generates responses based on the user's emotional state and outputs responses that include appropriate advice.

[0415] Step 6:

[0416] The device displays the answer, allowing the user to check the necessary information.

[0417] (Example 2)

[0418] 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".

[0419] Traditional sales support systems often provided uniform proposals and support without considering the user's emotions or state of mind. As a result, they failed to alleviate user stress and anxiety, hindering effective sales activities. Furthermore, tasks such as creating meeting minutes and managing Gantt charts were time-consuming and cumbersome, making efficient work difficult. In addition, there was a lack of technology to recognize emotional states during meetings and provide appropriate feedback. These problems increased the burden on sales representatives and decreased work efficiency.

[0420] 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.

[0421] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means including an emotion engine for recognizing user emotions, means for making appropriate suggestions to the user based on the emotion analysis results by the emotion engine, means for automatically generating meeting minutes, means for generating the next action items based on the meeting minutes, means for analyzing the emotions of speakers during the meeting and reflecting them in the meeting minutes, and means for automatically generating a Gantt chart from the time of order acceptance until implementation. This enables effective suggestions that take user emotions into consideration, thereby improving the efficiency of sales activities. In addition, it eliminates the effort of creating meeting minutes and managing Gantt charts, thus improving operational efficiency. Furthermore, it enables appropriate feedback through emotion analysis during meetings, which is expected to improve meeting productivity.

[0422] "Target sales amount" refers to the amount of sales that should be achieved through sales activities.

[0423] "Profit margin" refers to the ratio of profit to sales revenue and is an indicator used to evaluate the profitability of business activities.

[0424] "Past sales data" refers to data that includes historical sales information for the period up to now.

[0425] "Customer data" refers to data containing information about a customer, including their name, contact information, and purchase history.

[0426] "Market trend data" refers to data that includes information on the latest trends and developments in an industry or market.

[0427] "Sales forecasting" is the process of estimating future sales based on acquired data.

[0428] "Sales volume" refers to the quantity of goods or services sold within a specific period.

[0429] A "product list" is a list that shows a list of products and services that are sold.

[0430] "Automatically generating a document in a document format" refers to the process by which a system automatically creates a document based on input data and calculation results.

[0431] An "emotion engine" is a technology that analyzes a user's emotional state from their facial expressions, voice, and other data.

[0432] "Emotion analysis results" refer to data about the user's emotions obtained as a result of analysis using an emotion engine.

[0433] An "appropriate suggestion" is one that indicates the optimal action or strategy based on the user's emotional state and sales objectives.

[0434] "Meeting minutes" are documents that record the content of discussions held during meetings or conferences.

[0435] An "action item" is an item that indicates the specific tasks or action plan to be taken next based on the meeting minutes.

[0436] A Gantt chart is a diagram that visually represents a schedule used in project management.

[0437] This invention is a system that streamlines sales activities and provides support that takes user emotions into consideration. The system consists of a user interface for inputting target sales figures and profit margins, a server for data collection and analysis, and an emotion engine for recognizing emotions. The specific configuration and processing methods are described below.

[0438] Goal input and data collection

[0439] The user logs into their terminal and selects the "Create Profit Target Achievement Plan" option on the system. Next, they enter their target sales amount and profit margin. For example, a user might set a sales target of 10 million yen and a profit margin of 20% for the next quarter. This information is then sent from the terminal to the server.

[0440] The server retrieves historical sales and customer data from the database and collects market trend data using external APIs. Specifically, the server uses an SQL database to retrieve historical data and a REST API to collect market trends.

[0441] Sales forecasting and plan generation

[0442] The server uses statistical models such as time series analysis and regression analysis to make sales forecasts based on the acquired data. For example, Python libraries such as Pandas and scikit-learn could be used. Sales trends are graphed, and sales for the next quarter are forecasted.

[0443] The server calculates the necessary sales revenue and cost structure based on the target profit margin. For example, it performs simulations using the MICROSOFT® EXCEL® API. Based on the simulation results, it generates a product list and automatically creates a "Target Achievement Plan" in document format, which includes the sales forecast and product list. The generated document is sent to the terminal and displayed to the user.

[0444] Utilizing the Emotion Engine

[0445] When a user enters their goals or inquiries, the device uses its camera and microphone to analyze their facial expressions and voice using an emotion engine. For example, this could involve using the OpenCV library and the Google Cloud Speech-to-Text API. The analysis results are sent to a server and supplemented with the user's input information.

[0446] Based on the emotion analysis results, the server provides situation-appropriate advice. For example, if the user is feeling stressed, the system will add advice such as "Try to relax and approach this task" to the document.

[0447] Gantt chart creation and meeting minutes management

[0448] When a project is accepted, the user enters the project details into their terminal. The server calculates the project's timeline based on this information and automatically generates a Gantt chart using a JavaScript® library (e.g., DHTMLX Gantt). This chart is displayed on the terminal, allowing the user to manage their schedule.

[0449] During a meeting, users input agenda items and key points into their terminals. The server collects meeting audio, uses natural language processing (NLP) technology to transcribe the speech into text, and automatically generates meeting minutes. Python's NLTK library or the Google Cloud Speech-to-Text API are commonly used for this purpose. Furthermore, the server generates the following action items, which are also displayed on the terminals.

[0450] Examples of specific cases and prompts for generative AI models.

[0451] As a concrete example, consider a scenario where a user inputs "The sales target for the next quarter is 10 million yen, with a profit margin of 20%." The server uses an emotion engine to analyze the user's emotions at the time of input and obtains information such as stress and satisfaction levels. Based on the analysis results, it performs a sales forecast and generates a planning document that includes an appropriate sales plan and advice. The generated document is sent to the user's terminal, and the user plans and executes sales activities based on it.

[0452] Examples of prompts for a generative AI model are as follows:

[0453] The sales target for the next quarter is 10 million yen, with a profit margin of 20%. Based on this, generate a sales forecast that takes historical data and market trends into account, along with a planning document that includes an appropriate sales plan. Also, include advice based on sentiment analysis during data entry.

[0454] As described above, the present invention provides a system that takes user emotions into consideration and effectively supports sales activities.

[0455] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0456] Step 1:

[0457] The user logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system. The user then enters a target sales amount (e.g., 10 million yen) and a profit margin (e.g., 20%). This input data is temporarily stored on the terminal and sent to the server.

[0458] Step 2:

[0459] The server retrieves historical sales and customer data from the database based on the received target sales amount and profit margin. Specifically, it extracts data by executing SQL queries. Furthermore, it uses an external API to obtain market trend data. This external API is accessed via a RESTful API. The input data consists of the target sales amount and profit margin, and based on this, database searches and API calls are performed, yielding historical sales data, market trend data, and other output.

[0460] Step 3:

[0461] The server analyzes sales trends and patterns based on the acquired data. Specifically, it uses Python's Pandas and scikit-learn libraries to perform time series analysis and regression analysis. The input data consists of historical sales data and market trend data. Based on this, a sales forecasting model is built, and the predicted sales data is obtained as output.

[0462] Step 4:

[0463] The server calculates the sales volume required to achieve the target based on sales forecast data. For example, it uses the Excel API to perform calculations that take into account profit margins and cost structures based on the predicted sales data. The input data is sales forecast data and target profit margins, and based on this, it estimates the required sales volume and cost structure, and outputs the specific sales volume required to achieve the target.

[0464] Step 5:

[0465] The server generates a product list based on the required sales volume. For example, sales targets and strategies are set for each product. The input data is the sales volume required to achieve the target, and the server creates a product list based on this, generating the product list as output.

[0466] Step 6:

[0467] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists. This document includes specific sales strategies and implementation plans. A Python library could be used for document generation. The input data consists of sales forecast data and product lists; the server creates a document based on this data, and outputs the "Goal Achievement Plan" document.

[0468] Step 7:

[0469] The server sends the generated "Goal Achievement Plan" document to the user's terminal. The user reviews the document received on their terminal, plans and executes their sales activities. The input data is the generated document, which is then sent to the terminal and displayed to the user, resulting in the output.

[0470] Step 8:

[0471] When a user enters their goals or inquiries, the device uses an emotion engine to analyze the user's facial expressions and voice. Specifically, it uses the camera and microphone to utilize the OpenCV library and the Google Cloud Speech-to-Text API. The input data consists of the user's facial expressions and voice, which are used to analyze emotions, and the emotion analysis results are output.

[0472] Step 9:

[0473] The server receives the sentiment analysis results and supplements them with the user's input information. For example, if the user is feeling stressed, that information is added. The input data is the sentiment analysis results, which are supplemented based on the user's input information, and the output is user data with emotions.

[0474] Step 10:

[0475] The server provides optimal suggestions to the user based on the emotion analysis results. For example, if the user is feeling stressed, it will provide "advice on how to relax." The input data consists of the emotion analysis results and the user's input information. Based on this, the server forms advice and provides appropriate suggestions as output.

[0476] Step 11:

[0477] When a project is accepted, the user enters the project details into their terminal. The server calculates the project's timeline based on this information and automatically generates a Gantt chart using a JavaScript library (e.g., DHTMLX Gantt). The input data consists of detailed project information, which is used to generate the Gantt chart, and the output is a schedule for project management.

[0478] Step 12:

[0479] The user enters the meeting agenda and key points into a terminal. The server collects the meeting audio, converts it to text using natural language processing technology (e.g., Python's NLTK library or Google Cloud Speech-to-Text API), and automatically generates meeting minutes. The input data consists of meeting audio and agenda information, and the server generates the meeting minutes based on this data, providing the minutes as output.

[0480] Step 13:

[0481] The server generates the next set of action items based on the generated meeting minutes. The input data is the meeting minutes, and based on this, it lists the next set of action items and sends them to the terminal. The user reviews these action items, plans the next steps, and executes them.

[0482] Step 14:

[0483] During a meeting, the terminal analyzes the audio data using an emotion engine and records the speaker's emotions, which are then reflected in the meeting minutes. Based on the analysis results, the server records the emotional state and its changes in the meeting minutes. The input data consists of audio data and emotion analysis results. The meeting minutes are updated based on this data, and the output is meeting minutes with added emotions.

[0484] (Application Example 2)

[0485] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0486] In modern advertising planning and sales activities, it is crucial to make appropriate proposals that take into account the emotional state of the target audience. However, traditional systems have struggled to grasp user emotions and make flexible proposals based on them. Furthermore, the process from proposing advertising campaigns to automatically registering them with each advertising platform is inconsistent, resulting in manual work and significant time and effort required.

[0487] 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 inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means for analyzing the user's emotions using an emotion recognition engine, and means for making optimal suggestions to the user based on the emotion analysis results. This enables the proposal of flexible advertising plans that take into account the user's emotions and the efficient management of advertising campaigns.

[0488] "Target sales amount" refers to the target sales amount that a user aims to achieve within a specific period.

[0489] "Profit margin" is an indicator that shows the ratio of profit to sales, and it indicates the efficiency of management.

[0490] "Sales data" refers to data on past sales performance, including information such as the number of units sold and the sales amount for each product.

[0491] "Customer data" refers to information about past and present customers, including purchase history, attribute information, and behavioral data.

[0492] "Market trend data" refers to data on current market trends and future predictions, including consumer behavior and the activities of competitors.

[0493] "Sales forecasting" is the process of predicting future sales based on past sales data and market trend data.

[0494] "Sales volume" refers to the actual number of a particular product or service sold within a certain period of time.

[0495] A "product list" is a list of products or services that are targeted for sale or advertising.

[0496] "Automatically generating documents in a specified format" refers to the process by which a system automatically creates documents based on specific data.

[0497] An "emotion recognition engine" is a technology that analyzes a person's facial expressions and voice data to identify their emotions.

[0498] An "advertising campaign" is a series of advertising activities planned to promote a specific product or service.

[0499] An "advertising platform" is a term that refers to the entire system and service used to deliver advertisements over the internet.

[0500] This invention is a system that improves the efficiency of advertising planning by combining an emotion recognition engine. The embodiments thereof are described in detail below.

[0501] System Configuration and Operation Overview

[0502] User login and targeting

[0503] The user (advertiser) logs in to their smartphone or tablet and selects the "Create Ad Plan" option in the application. The user sets a specific target audience. For example, they might select "Men aged 25-34, Asia region." This setting data is sent to the server.

[0504] Data collection

[0505] The server retrieves data from past advertising campaigns from the advertising database. It also uses external APIs to obtain market trend data. For example, it retrieves past advertising data from "https: / / api.pastadsdata.com" and market trend data from "https: / / api.markettrends.com".

[0506] Advertising plan generation

[0507] The server analyzes collected historical data and market trend data using statistical methods (e.g., time series analysis, clustering) to generate the optimal advertising plan. The generated advertising plan includes information on suggested creatives, messages, and advertising delivery platforms (e.g., Facebook, Google AdWords®).

[0508] Utilization of emotion recognition engines

[0509] When a user reviews an ad plan, the system uses the camera and microphone on their smart device to analyze their emotions from their facial expressions and voice using an emotion recognition engine. The results of the emotion analysis are sent to a server, and the suggestions are adjusted based on the user's state. For example, if the user is feeling stressed, the system will offer alternative plans or advice for relaxation.

[0510] Advertising campaign development

[0511] Once the final advertising plan is confirmed, the server automatically registers the campaign with each advertising platform and begins delivery. This virtually eliminates the need for manual intervention, streamlining the management and deployment of advertising plans.

[0512] Usage example

[0513] For example, in a scenario where the user inputs "The sales target for the next quarter is 10 million yen, and the profit margin is 20%":

[0514] 1. The user enters the goal into their device and sends it to the server.

[0515] 2. The server uses an emotion recognition engine to analyze the user's emotions at the time of input. It obtains information such as stress levels and satisfaction levels.

[0516] 3. The server retrieves historical data and market trends to perform sales forecasts.

[0517] 4. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[0518] 5. Based on the sentiment analysis results, the server automatically generates a "Next Quarterly Goal Achievement Plan" document that includes optimal advice for the user.

[0519] 6. The ad campaign targeting the defined audience is automatically registered with each ad delivery platform, and delivery begins.

[0520] Thus, the present invention is a system that proposes flexible advertising plans that take user emotions into consideration, and streamlines the management and deployment of advertising campaigns.

[0521] Example of a prompt

[0522] Use the following API to generate an ad plan for your target audience in the Asian region, aged 25-34.

[0523] 1. Retrieve past advertising campaign data from https: / / api.pastadsdata.com.

[0524] 2. Obtain the latest market trend data from https: / / api.markettrends.com.

[0525] 3. Consider the user's emotions and provide the optimal advertising plan.

[0526] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0527] Step 1:

[0528] The user logs in on their smart device and selects the ad plan creation option. The user sets a specific target audience (e.g., men aged 25-34, Asian region), and this information is sent to the server. The input data is attribute information of the target group, which is used for data collection and analysis in the next step.

[0529] Step 2:

[0530] The server retrieves historical advertising campaign data from the advertising database and also obtains market trend data from external APIs (e.g., https: / / api.pastadsdata.com and https: / / api.markettrends.com). The retrieved data includes information such as sales performance and market trends, and is stored in the database for analysis.

[0531] Step 3:

[0532] The server uses statistical models (e.g., time series analysis, clustering) to process and perform calculations on historical advertising campaign data and market trend data. This analysis predicts the most effective advertising plan for the target audience. The output includes predicted sales and response rates, as well as a list of suggested ad creatives and messages.

[0533] Step 4:

[0534] The server automatically generates the created ad plan in document format (e.g., PDF or HTML report). The ad plan document includes detailed information such as specific suggestions for the target audience and recommended ad serving platforms. The generated document is then sent to the user's device.

[0535] Step 5:

[0536] When a user reviews an ad plan, an emotion recognition engine analyzes the user's facial expressions and voice through the device's camera and microphone. The input data consists of camera images and audio data, which are analyzed in real time to detect the user's emotional state (e.g., stress, satisfaction).

[0537] Step 6:

[0538] The analysis results from the emotion recognition engine are sent to the server, and the suggested advertising plan is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the server will provide alternative plans or advice for relaxation. This ultimately determines the optimal advertising plan for the user.

[0539] Step 7:

[0540] The server automatically registers the finalized ad plan with each ad delivery platform (e.g., Facebook, Google AdWords) and begins delivery. The input data is the details of the final ad plan, which is sent to each ad delivery platform. The output is the start of the ad campaign on each platform.

[0541] The above processing steps ensure that the process from ad plan creation to delivery proceeds efficiently, enabling flexible suggestions based on user emotions.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] [Second Embodiment]

[0546] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0547] 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.

[0548] 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).

[0549] 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.

[0550] 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.

[0551] 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).

[0552] 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.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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".

[0558] The present invention is a system for improving sales efficiency, and its embodiments will be described in detail below.

[0559] Goal input and data collection

[0560] 1. Enter your goal

[0561] The user (sales representative) logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system.

[0562] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal. The entered data is then sent to the server.

[0563] 2. Data Collection

[0564] The server retrieves historical sales data and customer data from the database.

[0565] The server retrieves market trend data from external APIs and internal databases.

[0566] Sales forecasting and plan generation

[0567] 3. Data Analysis

[0568] The server analyzes sales trends using past sales data.

[0569] The server uses statistical models (e.g., time series analysis, regression analysis) based on market trend data to forecast sales.

[0570] 4. Profitability analysis

[0571] The server estimates a realistic profit margin based on the acquired data.

[0572] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (20%).

[0573] 5. Plan generation

[0574] The server calculates the sales figures needed to achieve the target and generates a product list.

[0575] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists.

[0576] The server sends the generated plan document to the user's terminal.

[0577] Gantt chart creation and meeting minutes management

[0578] 6. Create a Gantt chart

[0579] When a project is accepted, the user enters the project details into the terminal.

[0580] The server calculates the project's timeline and automatically generates a Gantt chart.

[0581] A Gantt chart is displayed on the device, allowing users to manage their schedules.

[0582] 7. Minutes generation

[0583] The user enters the meeting agenda and key points into the terminal.

[0584] The server analyzes meeting audio and documents and automatically generates meeting minutes.

[0585] The server generates the following action items and displays the meeting minutes and action items on the terminal.

[0586] Inquiry response

[0587] 8. Inquiries regarding products and tools

[0588] Users enter questions about products or internal tools into the terminal.

[0589] The server searches the FAQ database and related documents and generates an automated response.

[0590] The answer will be displayed on your device.

[0591] Specific example

[0592] As a concrete example, consider a scenario where a user inputs, "The sales target for the next quarter is 10 million yen, and the profit margin is 20%."

[0593] 1. The user enters the goal into their device and sends it to the server.

[0594] 2. The server retrieves historical data and market trends to perform sales forecasts.

[0595] 3. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[0596] 4. The server automatically generates a "Next Quarterly Goal Achievement Plan" document and sends it to the user's terminal.

[0597] 5. Users review plan documents on their devices and plan and execute sales activities.

[0598] As described above, the present invention provides a system that streamlines the work of sales representatives and supports them in achieving their goals.

[0599] The following describes the processing flow.

[0600] Creating a profit target achievement plan

[0601] Step 1:

[0602] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[0603] Step 2:

[0604] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal and clicks the "Submit" button.

[0605] Step 3:

[0606] The server receives data on target sales and profit margins sent by the user.

[0607] Step 4:

[0608] The server retrieves historical sales data from the database. For example, it might retrieve quarterly sales figures for the past three years.

[0609] Step 5:

[0610] The server retrieves customer data from the database. It collects information such as each customer's purchase history and contract status.

[0611] Step 6:

[0612] The server retrieves market trend data from external APIs and internal databases. Specifically, it collects the latest data such as industry reports and economic indicators.

[0613] Step 7:

[0614] The server analyzes sales trends using historical sales data. For example, it uses time series analysis to understand seasonal fluctuations and trends in sales.

[0615] Step 8:

[0616] Based on market trend data acquired by the server, future sales are predicted. Regression analysis and machine learning models are applied to achieve highly accurate predictions.

[0617] Step 9:

[0618] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%). For example, it calculates the optimal product mix while considering the profit margin.

[0619] Step 10:

[0620] The server calculates the sales figures needed to achieve the target. Specifically, it calculates the required sales figures for each product.

[0621] Step 11:

[0622] The server matches customer data with product data and generates a list of products optimized for each customer. For example, it might list products recommended based on a customer's past purchase history.

[0623] Step 12:

[0624] The server automatically generates a "Next Quarterly Target Achievement Plan" document based on sales forecasts and product lists. The document includes sales forecast graphs and product list tables.

[0625] Step 13:

[0626] The server sends the generated plan document to the user's terminal.

[0627] Step 14:

[0628] The device displays the received plan document, allowing the user to review the document.

[0629] Gantt chart creation and meeting minutes management

[0630] Step 1:

[0631] When a user accepts a project, they enter the project details from their device.

[0632] Step 2:

[0633] The server receives the project details and calculates the project schedule.

[0634] Step 3:

[0635] The server automatically generates a Gantt chart, displaying the project's start date, end date, and schedule for each stage.

[0636] Step 4:

[0637] The device displays a Gantt chart, allowing the user to manage their schedule.

[0638] Inquiry response

[0639] Step 1:

[0640] Users enter questions about products or internal tools from their devices.

[0641] Step 2:

[0642] The server receives the user's question and searches the FAQ database and related documents.

[0643] Step 3:

[0644] The server generates an automated response and obtains an answer to the user's question.

[0645] Step 4:

[0646] The device displays the answer, allowing the user to check the necessary information.

[0647] (Example 1)

[0648] 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".

[0649] Traditional sales support systems made sales forecasting and profit margin analysis time-consuming, making it difficult to develop efficient sales plans. Furthermore, automatic generation of meeting minutes, creation of project Gantt charts, and handling inquiries about personnel and products also required significant time and effort. This resulted in a heavy workload for sales representatives and decreased efficiency in achieving sales targets.

[0650] 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.

[0651] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring historical sales data, customer data, and market trend data, and means for performing sales forecasts based on the acquired data. This enables efficient data collection and analysis to be performed automatically, allowing for the rapid and highly accurate creation of sales forecasts and sales plans.

[0652] "Target sales amount" refers to the target sales amount set by the user.

[0653] "Profit margin" is an indicator that shows the ratio of profit to sales.

[0654] "Sales data" refers to numerical information about past sales performance.

[0655] "Customer data" refers to information about customers with whom transactions are conducted.

[0656] "Market trend data" refers to information about current and past trends in a specific market or industry.

[0657] "Sales forecasting" refers to the process of estimating future sales based on collected data.

[0658] "Sales volume" refers to the quantity of goods or services that need to be sold to achieve a goal.

[0659] A "product list" is a list of products or services that are offered for sale.

[0660] "Document format" refers to the format of a document, whether digital or on paper.

[0661] A Gantt chart is a visual chart used for managing project schedules.

[0662] "Meeting minutes" are documents that record the content and key points of a meeting.

[0663] An "action item" is an item that indicates the specific next steps to be taken based on the meeting minutes.

[0664] "Automatic response" refers to a system function that automatically generates pre-set answers.

[0665] A "server" is a computer system that processes data and responds to client requests.

[0666] "Terminal" refers to a computer or mobile device used by a user to operate.

[0667] The present invention is a system for improving sales efficiency, and its embodiments will be described in detail below.

[0668] System Overview

[0669] This system automatically generates a sales plan by inputting target sales figures and profit margins, collecting and analyzing historical data, and ultimately performing sales forecasts and profit margin analysis. Furthermore, it provides means for generating product lists, automatically creating Gantt charts, automatically generating meeting minutes, and handling inquiries. This significantly improves the work efficiency of sales representatives and supports them in achieving their targets.

[0670] Hardware and software to be used

[0671] 1. Server

[0672] Main processor: Server equipped with a high-speed CPU

[0673] Database: MySQL database

[0674] External API: Google Trends API, Google Cloud Speech-to-Text API

[0675] Analysis tools: Python, pandas, scikit-learn, numpy

[0676] Document generation: Generative AI models (e.g., GPT-3)

[0677] 2. Terminal

[0678] User Interface (UI): A user interface that operates on a web browser.

[0679] Display devices: PC, tablet, smartphone

[0680] Specific example

[0681] Goal input and data collection

[0682] 1. Enter your goal

[0683] The user logs into their device and selects the "Create Profit Target Achievement Plan" option. For example, the user might enter "Sales target for the next quarter: 10 million yen, profit margin: 20%."

[0684] 2. Data Collection

[0685] The server automatically retrieves historical sales and customer data from a MySQL database. Furthermore, it obtains market trend data via the Google Trends API and an internal database.

[0686] Sales forecasting and plan generation

[0687] The server performs sales forecasts based on the acquired data. Specifically, it preprocesses the data using Python's pandas library and scikit-learn, and applies time series analysis and regression analysis models. Profit margin analysis is performed similarly, and the server calculates the required sales volume and cost structure considering the target profit margin entered by the user.

[0688] The server generates a suitable product list based on sales forecast results and profit margin analysis results. Along with this product list, it automatically generates a "Goal Achievement Plan" document using a generation AI model (e.g., GPT-3). The generated plan document is sent to the user's terminal and displayed.

[0689] Example of a prompt:

[0690] "Our sales target for the next quarter is 10 million yen, with a profit margin of 20%. Please create a plan to achieve these targets."

[0691] Gantt chart creation and meeting minutes management

[0692] The server calculates the project schedule and automatically generates a Gantt chart when the user enters project details. The Gantt chart is displayed in a web browser.

[0693] To automatically generate meeting minutes, the system uses the Google Cloud Speech-to-Text API to convert meeting audio into text based on the meeting agenda and key points entered by the user, and then generates the minutes using NLP (Natural Language Processing) technology. The generated minutes are displayed on the user's device, and the next action items are also automatically generated.

[0694] Inquiry response

[0695] When a user enters a question about a product or internal tool into their terminal, the server uses Elasticsearch to search the FAQ database and relevant documents, automatically generating an appropriate answer. The generated answer is then displayed on the user's terminal.

[0696] Based on the above, the present invention provides a system that streamlines the work of sales representatives and supports them in achieving their goals.

[0697] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0698] Step 1: Enter your goal

[0699] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[0700] The user enters "A sales target of 10 million yen and a profit margin of 20% for the next quarter."

[0701] The input data is packaged in JSON format as form data and sent to the server using a REST API.

[0702] Input: User-entered data on sales target amount and profit margin.

[0703] Output: Target amount and profit margin data sent to the server

[0704] Step 2: Data Collection

[0705] The server retrieves historical sales and customer data from a MySQL database.

[0706] The server retrieves market trend data through the Google Trends API and its internal database.

[0707] Data is retrieved using SQL queries, and data obtained from external APIs is stored in JSON format.

[0708] Input: Sales target amount and profit margin data

[0709] Output: Historical sales data, customer data, and market trend data retrieved from databases and external APIs.

[0710] Step 3: Data Analysis (Sales Forecast)

[0711] The server performs sales forecasts based on acquired historical sales data and market trend data.

[0712] The data is preprocessed using the Python pandas library, and predictions are made by applying statistical models (e.g., time series analysis, regression analysis).

[0713] The forecast results output a numerical representation of the likelihood of achieving the sales target.

[0714] Input: Historical sales data, customer data, market trend data

[0715] Output: Sales forecast data

[0716] Step 4: Profit Margin Analysis

[0717] Based on the predicted sales data, the server uses the NumPy library to perform sales and cost analysis, taking into account the required profit margin of 20%.

[0718] Calculate the required sales volume and cost structure, and output specific figures.

[0719] Input: Sales forecast data, sales target amount, profit margin

[0720] Output: Data on required sales volume and cost structure

[0721] Step 5: Plan Generation

[0722] The server generates a list of products necessary to achieve sales targets based on sales forecasts and profit margin analysis results.

[0723] The server uses a generative AI model (e.g., GPT-3) to automatically generate a "Goal Achievement Plan" document.

[0724] The document is exported in PDF or Word format and sent to the user's device.

[0725] Input: Sales forecast, profit margin analysis results, product list

[0726] Output: "Goal Achievement Plan" document

[0727] Step 6: Create a Gantt chart

[0728] The user enters the details of a new case into the terminal.

[0729] The server automatically generates a Gantt chart using D3.js or Chart.js based on the input data.

[0730] The Gantt chart is displayed on the user's device, enabling project schedule management.

[0731] Input: Project details data

[0732] Output: Gantt chart

[0733] Step 7: Generate meeting minutes

[0734] The user enters the meeting agenda and key points into the terminal.

[0735] The server uses the Google Cloud Speech-to-Text API to convert meeting audio into text and generates meeting minutes using an NLP model.

[0736] The meeting minutes and next action items will be displayed in document format on the user's device.

[0737] Input: Meeting agenda, key points, meeting audio

[0738] Output: Meeting minutes, next action items

[0739] Step 8: Handling inquiries

[0740] Users enter questions about products or internal tools into the terminal.

[0741] The server uses Elasticsearch to search the FAQ database and related documents, and generates appropriate automated responses.

[0742] The automated response will be displayed on the user's device.

[0743] Input: User's question

[0744] Output: Automated response

[0745] (Application Example 1)

[0746] 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."

[0747] Traditional sales efficiency improvement systems were limited to sales forecasting and target setting functions, lacking the tools and features to effectively utilize the analysis results. Furthermore, features such as schedule management, meeting minute generation, and automated inquiry handling were not sufficiently integrated, making it difficult for sales representatives to efficiently manage their diverse tasks.

[0748] 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.

[0749] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means for generating a Gantt chart for schedule management, means for automatically generating meeting minutes, and means for automatically generating inquiry responses. This enables the user to efficiently perform tasks from sales target planning and schedule management to meeting minute generation and inquiry handling in a consistent manner.

[0750] "Target sales amount" refers to a specific sales figure that you want to achieve in your sales activities or business plan.

[0751] "Profit margin" is an indicator that shows the ratio of profit earned to sales revenue.

[0752] "Past sales data" refers to historical sales information recorded up to the present.

[0753] "Customer data" refers to aggregated data about customers, including age, gender, and purchase history.

[0754] "Market trend data" refers to data related to market trends and consumer behavior.

[0755] "Means of sales forecasting" refers to methods or devices for predicting future sales based on collected data.

[0756] "Sales volume required to achieve the target" refers to the number of products that need to be sold to achieve the set target sales amount and profit margin.

[0757] A "product list" is a list of the products and services that are sold.

[0758] "Means of automatically generating documents in document format" refers to methods or devices for analyzing data and automatically converting the results into documents such as reports and proposals.

[0759] "Means for outputting documents" refers to methods or devices for providing the generated documents to the user.

[0760] A "Gantt chart for schedule management" is a diagram in the form of a Gantt chart used to visually display the schedule of a project or task.

[0761] "Methods for automatically generating meeting minutes" refer to methods or devices that analyze the content of a meeting and automatically create a summary of it as meeting minutes.

[0762] "Means for automatically generating inquiry responses" refers to methods or devices for automatically providing appropriate responses to questions and inquiries from users.

[0763] The present invention relates to a system for improving sales efficiency, and its embodiments will be described in detail below.

[0764] Goal input and data collection

[0765] 1. Enter your goal

[0766] The user (sales representative) logs into the terminal and selects the displayed "Create Profit Target Achievement Plan" option. The user enters the target sales amount and profit margin into the terminal, and that data is sent to the server.

[0767] 2. Data Collection

[0768] The server automatically retrieves historical sales and customer data from the store's point-of-sale (POS) system. Market trend data is obtained from external APIs and internal databases.

[0769] Sales forecasting and plan generation

[0770] 3. Data Analysis

[0771] The server analyzes sales trends using collected data. Based on market trend data, it uses statistical models (e.g., time series analysis, regression analysis) to predict future sales.

[0772] 4. Profitability analysis

[0773] The server estimates realistic profit margins based on the acquired data. Considering the target profit margin, it calculates the necessary sales revenue and cost structure.

[0774] 5. Plan generation

[0775] The server calculates the sales volume required to achieve the target and generates a suitable product list. Based on the sales forecast and product list, it automatically generates a "Target Achievement Plan" document and sends this document to the user's terminal.

[0776] Gantt chart creation and meeting minutes management

[0777] 6. Create a Gantt chart

[0778] When a project is accepted, the user enters the project details into the terminal, and the server calculates the project's timeline and automatically generates a Gantt chart. The Gantt chart is displayed on the terminal, allowing the user to manage their schedule.

[0779] 7. Minutes generation

[0780] When a user enters meeting agenda items and key points into their terminal, the server analyzes the meeting audio and documents and automatically generates meeting minutes. Next action items are also generated and displayed on the terminal along with the meeting minutes.

[0781] Inquiry response

[0782] 8. Inquiries regarding products and tools

[0783] When a user enters a question about a product or internal tool into the terminal, the server searches the FAQ database and relevant documents and generates an automated response. The answer is then displayed on the terminal.

[0784] Specific example

[0785] As a concrete example, consider a scenario where a user inputs "The sales target for the next quarter is 10 million yen, with a profit margin of 20%." The user inputs the target into their terminal, and this data is sent to the server. The server retrieves past sales data and market trends and makes a sales forecast. Subsequently, the server performs an analysis based on profit margins, calculates the necessary sales volume and product list, and sends the generated "Next Quarterly Target Achievement Plan" document to the user's terminal. The user reviews this plan document, plans and executes their sales activities.

[0786] Example of a prompt

[0787] "Our sales target for the next quarter is 10 million yen, with a profit margin of 20%. Please analyze past sales data and market trends to generate the optimal sales promotion plan."

[0788] This system significantly improves the efficiency of sales representatives' work and enables them to develop effective strategies for achieving sales targets and profit margins.

[0789] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0790] Step 1:

[0791] The user enters their target sales amount and profit margin into the terminal. The input data is in the following format: "Target sales amount: 10 million yen", "Profit margin: 20%". This data is sent from the terminal to the server. The server stores the received data in its database.

[0792] Step 2:

[0793] The server retrieves historical sales and customer data from the store's POS system and related databases. Furthermore, it collects market trend data through external APIs and internal databases. This data is temporarily stored on the server for analysis.

[0794] Step 3:

[0795] The server uses time series analysis and regression analysis techniques to forecast sales based on historical sales data. The input data consists of past sales records, while the output data represents future sales forecasts. The server performs data analysis using Python's Pandas and scikit-learn libraries.

[0796] Step 4:

[0797] The server uses collected market trend data to perform detailed analysis based on sales forecasts. Specifically, it readjusts the forecasting model and calculates the final forecast values ​​that take into account fluctuations. The output data consists of predicted sales and profit margins.

[0798] Step 5:

[0799] The server calculates the required sales revenue and costs based on the set target profit margin. This generates the sales volume and appropriate product list needed to achieve the target. The input data is the profit margin and sales forecast, and the output data is the product list.

[0800] Step 6:

[0801] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product list data. This document includes sales strategies, required sales figures, and recommended products. The server then sends the generated document to the user's terminal.

[0802] Step 7:

[0803] When a user enters project details into their terminal, the server automatically generates the project schedule and creates a Gantt chart. The input data is detailed project information, and the output data is a schedule in Gantt chart format.

[0804] Step 8:

[0805] When a user enters meeting agenda items and key points into their terminal, the server analyzes the meeting's audio data and documents to automatically generate meeting minutes. Specifically, it uses speech recognition technology to transcribe the meeting content into text, extracts necessary information, and compiles it into meeting minutes. The output data is the automatically generated meeting minutes.

[0806] Step 9:

[0807] When a user enters a question about a product or internal tool into a terminal, the server searches the FAQ database and relevant documents and generates a response to the inquiry. The input data is the user's question, and the output data is the automatically generated response. The server uses a natural language generation model such as the GPT-4 API to create the response.

[0808] Through these steps, this system dramatically improves the efficiency of sales representatives and provides concrete plans for achieving goals.

[0809] 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.

[0810] This invention is a system for improving sales efficiency, combining an emotion engine that recognizes user emotions to enable more appropriate suggestions and two-way communication. The embodiments are described in detail below.

[0811] Goal input and data collection

[0812] 1. Enter your goal

[0813] The user (sales representative) logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system.

[0814] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal. The entered data is then sent to the server.

[0815] 2. Data Collection

[0816] The server retrieves historical sales data and customer data from the database.

[0817] The server retrieves market trend data from external APIs and internal databases.

[0818] Sales forecasting and plan generation

[0819] 3. Data Analysis

[0820] The server analyzes sales trends using past sales data.

[0821] The server uses statistical models (e.g., time series analysis, regression analysis) based on market trend data to forecast sales.

[0822] 4. Profitability analysis

[0823] The server estimates a realistic profit margin based on the acquired data.

[0824] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%).

[0825] 5. Plan generation

[0826] The server calculates the sales figures needed to achieve the target and generates a product list.

[0827] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists.

[0828] The server sends the generated plan document to the user's terminal.

[0829] Utilizing the Emotion Engine

[0830] 6. Emotion recognition

[0831] When a user enters a goal or inquiry, the device uses an emotion engine to recognize emotions from the user's facial expressions and voice.

[0832] The server receives the sentiment analysis results and supplements them with the user's input information.

[0833] 7. Emotion-based proposals

[0834] The server takes the sentiment analysis results into consideration to provide more appropriate suggestions to the user.

[0835] For example, if a user is experiencing stress, the system will provide advice and resources to help alleviate that burden.

[0836] Gantt chart creation and meeting minutes management

[0837] 8. Create a Gantt chart

[0838] When a project is accepted, the user enters the project details into the terminal.

[0839] The server calculates the project's timeline and automatically generates a Gantt chart.

[0840] The device displays a Gantt chart, allowing the user to manage their schedule.

[0841] 9. Minutes generation

[0842] The user enters the meeting agenda and key points into the terminal.

[0843] The server analyzes meeting audio and documents and automatically generates meeting minutes.

[0844] The server generates the following action items and displays the meeting minutes and action items on the terminal.

[0845] Sentiment analysis of meeting minutes

[0846] 10. Recognizing emotions during meetings

[0847] The device analyzes audio data from the meeting using an emotion engine and records the speaker's emotions, which are then reflected in the meeting minutes.

[0848] Based on the analysis results, the server records the emotional state and its changes in the meeting minutes.

[0849] Inquiry response

[0850] 11. Inquiries regarding products and tools

[0851] Users enter questions about products or internal tools from their devices.

[0852] The server receives the user's question and searches the FAQ database and related documents.

[0853] The server generates an automated response and obtains an answer to the user's question.

[0854] The device displays the answer, allowing the user to check the necessary information.

[0855] Specific example

[0856] As a concrete example, consider a scenario where a user inputs, "The sales target for the next quarter is 10 million yen, and the profit margin is 20%."

[0857] 1. The user enters the goal into their device and sends it to the server.

[0858] 2. The server uses an emotion engine to analyze the user's emotions at the time of input and obtain information such as stress levels and satisfaction levels.

[0859] 3. The server retrieves historical data and market trends to perform sales forecasts.

[0860] 4. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[0861] 5. Based on the sentiment analysis results, the server automatically generates a "Next Quarterly Goal Achievement Plan" document containing optimal advice for the user.

[0862] 6. The server sends the generated plan document to the user's terminal.

[0863] 7. Users review plan documents on their devices and plan and execute sales activities.

[0864] As described above, the present invention realizes a system that streamlines the work of sales representatives and provides more appropriate support by utilizing emotion recognition.

[0865] The following describes the processing flow.

[0866] Creating profit target achievement plans and utilizing the emotional engine

[0867] Step 1:

[0868] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[0869] Step 2:

[0870] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal and clicks the "Submit" button.

[0871] Step 3:

[0872] The device analyzes the user's facial expressions and voice during input using an emotion engine to detect their emotional state (e.g., stress, satisfaction level, excitement level).

[0873] Step 4:

[0874] The device sends the detected emotional state to the server as data.

[0875] Step 5:

[0876] The server receives data from the user regarding target sales figures, profit margins, and emotional states.

[0877] Step 6:

[0878] The server retrieves historical sales data from the database. For example, it might retrieve quarterly sales figures for the past three years.

[0879] Step 7:

[0880] The server retrieves customer data from the database. It collects information such as each customer's purchase history and contract status.

[0881] Step 8:

[0882] The server retrieves market trend data from external APIs and internal databases. For example, it collects the latest data such as industry reports and economic indicators.

[0883] Step 9:

[0884] The server analyzes sales trends using historical sales data. For example, it uses time series analysis to understand seasonal fluctuations and trends in sales.

[0885] Step 10:

[0886] The server predicts future sales based on market trend data. Regression analysis and machine learning models are applied to achieve highly accurate predictions.

[0887] Step 11:

[0888] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%). For example, it calculates the optimal product mix while considering the profit margin.

[0889] Step 12:

[0890] The server calculates the sales figures needed to achieve the target. Specifically, it calculates the required sales figures for each product.

[0891] Step 13:

[0892] The server matches customer data with product data and generates a list of products optimized for each customer. For example, it might list products recommended based on a customer's past purchase history.

[0893] Step 14:

[0894] The server automatically generates a "Next Quarterly Target Achievement Plan" document based on sales forecasts and product lists. The document includes sales forecast graphs and product list tables.

[0895] Step 15:

[0896] Based on the sentiment analysis results, the server adds advice and support messages tailored to the user's emotional state to the plan document.

[0897] Step 16:

[0898] The server sends the generated plan document to the user's terminal.

[0899] Step 17:

[0900] The device displays the received plan document, allowing the user to review the document.

[0901] Gantt chart creation and meeting minutes management

[0902] Step 1:

[0903] When a user accepts a project, they enter the project details from their device.

[0904] Step 2:

[0905] The server receives the project details and calculates the project schedule.

[0906] Step 3:

[0907] The server automatically generates a Gantt chart, displaying the project's start date, end date, and schedule for each stage.

[0908] Step 4:

[0909] The device displays a Gantt chart, allowing the user to manage their schedule.

[0910] Sentiment analysis of meeting minutes

[0911] Step 1:

[0912] The user enters the meeting agenda and key points into the terminal.

[0913] Step 2:

[0914] The device analyzes the audio data during the meeting using an emotion engine to detect the emotional state of the speaker.

[0915] Step 3:

[0916] The device sends the detected emotional state to the server as meeting minutes data.

[0917] Step 4:

[0918] The server automatically generates meeting minutes based on audio data and emotional states. Emotional states are also reflected in the minutes.

[0919] Step 5:

[0920] The server generates the next action item and displays the meeting minutes and action item on the terminal.

[0921] Inquiry response

[0922] Step 1:

[0923] Users enter questions about products or internal tools from their devices.

[0924] Step 2:

[0925] The device uses an emotion engine to analyze facial expressions and voice during questioning and detect the emotional state.

[0926] Step 3:

[0927] The device sends question data, including emotional state, to the server.

[0928] Step 4:

[0929] The server receives the user's question and sentiment state, and searches the FAQ database and related documents.

[0930] Step 5:

[0931] The server automatically generates responses based on the user's emotional state and outputs responses that include appropriate advice.

[0932] Step 6:

[0933] The device displays the answer, allowing the user to check the necessary information.

[0934] (Example 2)

[0935] 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".

[0936] Traditional sales support systems often provided uniform proposals and support without considering the user's emotions or state of mind. As a result, they failed to alleviate user stress and anxiety, hindering effective sales activities. Furthermore, tasks such as creating meeting minutes and managing Gantt charts were time-consuming and cumbersome, making efficient work difficult. In addition, there was a lack of technology to recognize emotional states during meetings and provide appropriate feedback. These problems increased the burden on sales representatives and decreased work efficiency.

[0937] 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.

[0938] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means including an emotion engine for recognizing user emotions, means for making appropriate suggestions to the user based on the emotion analysis results by the emotion engine, means for automatically generating meeting minutes, means for generating the next action items based on the meeting minutes, means for analyzing the emotions of speakers during the meeting and reflecting them in the meeting minutes, and means for automatically generating a Gantt chart from the time of order acceptance until implementation. This enables effective suggestions that take user emotions into consideration, thereby improving the efficiency of sales activities. In addition, it eliminates the effort of creating meeting minutes and managing Gantt charts, thus improving operational efficiency. Furthermore, it enables appropriate feedback through emotion analysis during meetings, which is expected to improve meeting productivity.

[0939] "Target sales amount" refers to the amount of sales that should be achieved through sales activities.

[0940] "Profit margin" refers to the ratio of profit to sales revenue and is an indicator used to evaluate the profitability of business activities.

[0941] "Past sales data" refers to data that includes historical sales information for the period up to now.

[0942] "Customer data" refers to data containing information about a customer, including their name, contact information, and purchase history.

[0943] "Market trend data" refers to data that includes information on the latest trends and developments in an industry or market.

[0944] "Sales forecasting" is the process of estimating future sales based on acquired data.

[0945] "Sales volume" refers to the quantity of goods or services sold within a specific period.

[0946] A "product list" is a list that shows a list of products and services that are sold.

[0947] "Automatically generating a document in a document format" refers to the process by which a system automatically creates a document based on input data and calculation results.

[0948] An "emotion engine" is a technology that analyzes a user's emotional state from their facial expressions, voice, and other data.

[0949] "Emotion analysis results" refer to data about the user's emotions obtained as a result of analysis using an emotion engine.

[0950] An "appropriate suggestion" is one that indicates the optimal action or strategy based on the user's emotional state and sales objectives.

[0951] "Meeting minutes" are documents that record the content of discussions held during meetings or conferences.

[0952] An "action item" is an item that indicates the specific tasks or action plan to be taken next based on the meeting minutes.

[0953] A Gantt chart is a diagram that visually represents a schedule used in project management.

[0954] This invention is a system that streamlines sales activities and provides support that takes user emotions into consideration. The system consists of a user interface for inputting target sales figures and profit margins, a server for data collection and analysis, and an emotion engine for recognizing emotions. The specific configuration and processing methods are described below.

[0955] Goal input and data collection

[0956] The user logs into their terminal and selects the "Create Profit Target Achievement Plan" option on the system. Next, they enter their target sales amount and profit margin. For example, a user might set a sales target of 10 million yen and a profit margin of 20% for the next quarter. This information is then sent from the terminal to the server.

[0957] The server retrieves historical sales and customer data from the database and collects market trend data using external APIs. Specifically, the server uses an SQL database to retrieve historical data and a REST API to collect market trends.

[0958] Sales forecasting and plan generation

[0959] The server uses statistical models such as time series analysis and regression analysis to make sales forecasts based on the acquired data. For example, Python libraries such as Pandas and scikit-learn could be used. Sales trends are graphed, and sales for the next quarter are forecasted.

[0960] The server calculates the necessary sales revenue and cost structure based on the target profit margin. For example, it performs simulations using the Microsoft Excel API. Based on the simulation results, it generates a product list and automatically creates a "Target Achievement Plan" in document format, which includes the sales forecast and product list. The generated document is sent to the terminal and displayed to the user.

[0961] Utilizing the Emotion Engine

[0962] When a user enters their goals or inquiries, the device uses its camera and microphone to analyze their facial expressions and voice using an emotion engine. For example, this could involve using the OpenCV library and the Google Cloud Speech-to-Text API. The analysis results are sent to a server and supplemented with the user's input information.

[0963] Based on the emotion analysis results, the server provides situation-appropriate advice. For example, if the user is feeling stressed, the system will add advice such as "Try to relax and approach this task" to the document.

[0964] Gantt chart creation and meeting minutes management

[0965] When a project is accepted, the user enters the project details into their terminal. The server calculates the project's timeline based on this information and automatically generates a Gantt chart using a JavaScript library (e.g., DHTMLX Gantt). This chart is displayed on the terminal, allowing the user to manage their schedule.

[0966] During a meeting, users input agenda items and key points into their terminals. The server collects meeting audio, uses natural language processing (NLP) technology to transcribe the speech into text, and automatically generates meeting minutes. Python's NLTK library or the Google Cloud Speech-to-Text API are commonly used for this purpose. Furthermore, the server generates the following action items, which are also displayed on the terminals.

[0967] Examples of specific cases and prompts for generative AI models.

[0968] As a concrete example, consider a scenario where a user inputs "The sales target for the next quarter is 10 million yen, with a profit margin of 20%." The server uses an emotion engine to analyze the user's emotions at the time of input and obtains information such as stress and satisfaction levels. Based on the analysis results, it performs a sales forecast and generates a planning document that includes an appropriate sales plan and advice. The generated document is sent to the user's terminal, and the user plans and executes sales activities based on it.

[0969] Examples of prompts for a generative AI model are as follows:

[0970] The sales target for the next quarter is 10 million yen, with a profit margin of 20%. Based on this, generate a sales forecast that takes historical data and market trends into account, along with a planning document that includes an appropriate sales plan. Also, include advice based on sentiment analysis during data entry.

[0971] As described above, the present invention provides a system that takes user emotions into consideration and effectively supports sales activities.

[0972] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0973] Step 1:

[0974] The user logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system. The user then enters a target sales amount (e.g., 10 million yen) and a profit margin (e.g., 20%). This input data is temporarily stored on the terminal and sent to the server.

[0975] Step 2:

[0976] The server retrieves historical sales and customer data from the database based on the received target sales amount and profit margin. Specifically, it extracts data by executing SQL queries. Furthermore, it uses an external API to obtain market trend data. This external API is accessed via a RESTful API. The input data consists of the target sales amount and profit margin, and based on this, database searches and API calls are performed, yielding historical sales data, market trend data, and other output.

[0977] Step 3:

[0978] The server analyzes sales trends and patterns based on the acquired data. Specifically, it uses Python's Pandas and scikit-learn libraries to perform time series analysis and regression analysis. The input data consists of historical sales data and market trend data. Based on this, a sales forecasting model is built, and the predicted sales data is obtained as output.

[0979] Step 4:

[0980] The server calculates the sales volume required to achieve the target based on sales forecast data. For example, it uses the Excel API to perform calculations that take into account profit margins and cost structures based on the predicted sales data. The input data is sales forecast data and target profit margins, and based on this, it estimates the required sales volume and cost structure, and outputs the specific sales volume required to achieve the target.

[0981] Step 5:

[0982] The server generates a product list based on the required sales volume. For example, sales targets and strategies are set for each product. The input data is the sales volume required to achieve the target, and the server creates a product list based on this, generating the product list as output.

[0983] Step 6:

[0984] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists. This document includes specific sales strategies and implementation plans. A Python library could be used for document generation. The input data consists of sales forecast data and product lists; the server creates a document based on this data, and outputs the "Goal Achievement Plan" document.

[0985] Step 7:

[0986] The server sends the generated "Goal Achievement Plan" document to the user's terminal. The user reviews the document received on their terminal, plans and executes their sales activities. The input data is the generated document, which is then sent to the terminal and displayed to the user, resulting in the output.

[0987] Step 8:

[0988] When a user enters their goals or inquiries, the device uses an emotion engine to analyze the user's facial expressions and voice. Specifically, it uses the camera and microphone to utilize the OpenCV library and the Google Cloud Speech-to-Text API. The input data consists of the user's facial expressions and voice, which are used to analyze emotions, and the emotion analysis results are output.

[0989] Step 9:

[0990] The server receives the sentiment analysis results and supplements them with the user's input information. For example, if the user is feeling stressed, that information is added. The input data is the sentiment analysis results, which are supplemented based on the user's input information, and the output is user data with emotions.

[0991] Step 10:

[0992] The server provides optimal suggestions to the user based on the emotion analysis results. For example, if the user is feeling stressed, it will provide "advice on how to relax." The input data consists of the emotion analysis results and the user's input information. Based on this, the server forms advice and provides appropriate suggestions as output.

[0993] Step 11:

[0994] When a project is accepted, the user enters the project details into their terminal. The server calculates the project's timeline based on this information and automatically generates a Gantt chart using a JavaScript library (e.g., DHTMLX Gantt). The input data consists of detailed project information, which is used to generate the Gantt chart, and the output is a schedule for project management.

[0995] Step 12:

[0996] The user enters the meeting agenda and key points into a terminal. The server collects the meeting audio, converts it to text using natural language processing technology (e.g., Python's NLTK library or Google Cloud Speech-to-Text API), and automatically generates meeting minutes. The input data consists of meeting audio and agenda information, and the server generates the meeting minutes based on this data, providing the minutes as output.

[0997] Step 13:

[0998] The server generates the next set of action items based on the generated meeting minutes. The input data is the meeting minutes, and based on this, it lists the next set of action items and sends them to the terminal. The user reviews these action items, plans the next steps, and executes them.

[0999] Step 14:

[1000] During a meeting, the terminal analyzes the audio data using an emotion engine and records the speaker's emotions, which are then reflected in the meeting minutes. Based on the analysis results, the server records the emotional state and its changes in the meeting minutes. The input data consists of audio data and emotion analysis results. The meeting minutes are updated based on this data, and the output is meeting minutes with added emotions.

[1001] (Application Example 2)

[1002] 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."

[1003] In modern advertising planning and sales activities, it is crucial to make appropriate proposals that take into account the emotional state of the target audience. However, traditional systems have struggled to grasp user emotions and make flexible proposals based on them. Furthermore, the process from proposing advertising campaigns to automatically registering them with each advertising platform is inconsistent, resulting in manual work and significant time and effort required.

[1004] 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 inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means for analyzing the user's emotions using an emotion recognition engine, and means for making optimal suggestions to the user based on the emotion analysis results. This enables the proposal of flexible advertising plans that take into account the user's emotions and the efficient management of advertising campaigns.

[1005] "Target sales amount" refers to the target sales amount that a user aims to achieve within a specific period.

[1006] "Profit margin" is an indicator that shows the ratio of profit to sales, and it indicates the efficiency of management.

[1007] "Sales data" refers to data on past sales performance, including information such as the number of units sold and the sales amount for each product.

[1008] "Customer data" refers to information about past and present customers, including purchase history, attribute information, and behavioral data.

[1009] "Market trend data" refers to data on current market trends and future predictions, including consumer behavior and the activities of competitors.

[1010] "Sales forecasting" is the process of predicting future sales based on past sales data and market trend data.

[1011] "Sales volume" refers to the actual number of a particular product or service sold within a certain period of time.

[1012] A "product list" is a list of products or services that are targeted for sale or advertising.

[1013] "Automatically generating documents in a specified format" refers to the process by which a system automatically creates documents based on specific data.

[1014] An "emotion recognition engine" is a technology that analyzes a person's facial expressions and voice data to identify their emotions.

[1015] An "advertising campaign" is a series of advertising activities planned to promote a specific product or service.

[1016] An "advertising platform" is a term that refers to the entire system and service used to deliver advertisements over the internet.

[1017] This invention is a system that improves the efficiency of advertising planning by combining an emotion recognition engine. The embodiments thereof are described in detail below.

[1018] System Configuration and Operation Overview

[1019] User login and targeting

[1020] The user (advertiser) logs in to their smartphone or tablet and selects the "Create Ad Plan" option in the application. The user sets a specific target audience. For example, they might select "Men aged 25-34, Asia region." This setting data is sent to the server.

[1021] Data collection

[1022] The server retrieves data from past advertising campaigns from the advertising database. It also uses external APIs to obtain market trend data. For example, it retrieves past advertising data from "https: / / api.pastadsdata.com" and market trend data from "https: / / api.markettrends.com".

[1023] Advertising plan generation

[1024] The server analyzes collected historical data and market trend data using statistical methods (e.g., time series analysis, clustering) to generate the optimal advertising plan. The generated advertising plan includes information on suggested creatives, messages, and advertising delivery platforms (e.g., Facebook, Google AdWords).

[1025] Utilization of emotion recognition engines

[1026] When a user reviews an ad plan, the system uses the camera and microphone on their smart device to analyze their emotions from their facial expressions and voice using an emotion recognition engine. The results of the emotion analysis are sent to a server, and the suggestions are adjusted based on the user's state. For example, if the user is feeling stressed, the system will offer alternative plans or advice for relaxation.

[1027] Advertising campaign development

[1028] Once the final advertising plan is confirmed, the server automatically registers the campaign with each advertising platform and begins delivery. This virtually eliminates the need for manual intervention, streamlining the management and deployment of advertising plans.

[1029] Usage example

[1030] For example, in a scenario where the user inputs "The sales target for the next quarter is 10 million yen, and the profit margin is 20%":

[1031] 1. The user enters the goal into their device and sends it to the server.

[1032] 2. The server uses an emotion recognition engine to analyze the user's emotions at the time of input. It obtains information such as stress levels and satisfaction levels.

[1033] 3. The server retrieves historical data and market trends to perform sales forecasts.

[1034] 4. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[1035] 5. Based on the sentiment analysis results, the server automatically generates a "Next Quarterly Goal Achievement Plan" document that includes optimal advice for the user.

[1036] 6. The ad campaign targeting the defined audience is automatically registered with each ad delivery platform, and delivery begins.

[1037] Thus, the present invention is a system that proposes flexible advertising plans that take user emotions into consideration, and streamlines the management and deployment of advertising campaigns.

[1038] Example of a prompt

[1039] Use the following API to generate an ad plan for your target audience in the Asian region, aged 25-34.

[1040] 1. Retrieve past advertising campaign data from https: / / api.pastadsdata.com.

[1041] 2. Obtain the latest market trend data from https: / / api.markettrends.com.

[1042] 3. Consider the user's emotions and provide the optimal advertising plan.

[1043] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1044] Step 1:

[1045] The user logs in on their smart device and selects the ad plan creation option. The user sets a specific target audience (e.g., men aged 25-34, Asian region), and this information is sent to the server. The input data is attribute information of the target group, which is used for data collection and analysis in the next step.

[1046] Step 2:

[1047] The server retrieves historical advertising campaign data from the advertising database and also obtains market trend data from external APIs (e.g., https: / / api.pastadsdata.com and https: / / api.markettrends.com). The retrieved data includes information such as sales performance and market trends, and is stored in the database for analysis.

[1048] Step 3:

[1049] The server uses statistical models (e.g., time series analysis, clustering) to process and perform calculations on historical advertising campaign data and market trend data. This analysis predicts the most effective advertising plan for the target audience. The output includes predicted sales and response rates, as well as a list of suggested ad creatives and messages.

[1050] Step 4:

[1051] The server automatically generates the created ad plan in document format (e.g., PDF or HTML report). The ad plan document includes detailed information such as specific suggestions for the target audience and recommended ad serving platforms. The generated document is then sent to the user's device.

[1052] Step 5:

[1053] When a user reviews an ad plan, an emotion recognition engine analyzes the user's facial expressions and voice through the device's camera and microphone. The input data consists of camera images and audio data, which are analyzed in real time to detect the user's emotional state (e.g., stress, satisfaction).

[1054] Step 6:

[1055] The analysis results from the emotion recognition engine are sent to the server, and the suggested advertising plan is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the server will provide alternative plans or advice for relaxation. This ultimately determines the optimal advertising plan for the user.

[1056] Step 7:

[1057] The server automatically registers the finalized ad plan with each ad delivery platform (e.g., Facebook, Google AdWords) and begins delivery. The input data is the details of the final ad plan, which is sent to each ad delivery platform. The output is the start of the ad campaign on each platform.

[1058] The above processing steps ensure that the process from ad plan creation to delivery proceeds efficiently, enabling flexible suggestions based on user emotions.

[1059] 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.

[1060] 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.

[1061] 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.

[1062] [Third Embodiment]

[1063] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1064] 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.

[1065] 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).

[1066] 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.

[1067] 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.

[1068] 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).

[1069] 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.

[1070] 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.

[1071] 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.

[1072] 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.

[1073] 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.

[1074] 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".

[1075] The present invention is a system for improving sales efficiency, and its embodiments will be described in detail below.

[1076] Goal input and data collection

[1077] 1. Enter your goal

[1078] The user (sales representative) logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system.

[1079] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal. The entered data is then sent to the server.

[1080] 2. Data Collection

[1081] The server retrieves historical sales data and customer data from the database.

[1082] The server retrieves market trend data from external APIs and internal databases.

[1083] Sales forecasting and plan generation

[1084] 3. Data Analysis

[1085] The server analyzes sales trends using past sales data.

[1086] The server uses statistical models (e.g., time series analysis, regression analysis) based on market trend data to forecast sales.

[1087] 4. Profitability analysis

[1088] The server estimates a realistic profit margin based on the acquired data.

[1089] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (20%).

[1090] 5. Plan generation

[1091] The server calculates the sales figures needed to achieve the target and generates a product list.

[1092] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists.

[1093] The server sends the generated plan document to the user's terminal.

[1094] Gantt chart creation and meeting minutes management

[1095] 6. Create a Gantt chart

[1096] When a project is accepted, the user enters the project details into the terminal.

[1097] The server calculates the project's timeline and automatically generates a Gantt chart.

[1098] A Gantt chart is displayed on the device, allowing users to manage their schedules.

[1099] 7. Minutes generation

[1100] The user enters the meeting agenda and key points into the terminal.

[1101] The server analyzes meeting audio and documents and automatically generates meeting minutes.

[1102] The server generates the following action items and displays the meeting minutes and action items on the terminal.

[1103] Inquiry response

[1104] 8. Inquiries regarding products and tools

[1105] Users enter questions about products or internal tools into the terminal.

[1106] The server searches the FAQ database and related documents and generates an automated response.

[1107] The answer will be displayed on your device.

[1108] Specific example

[1109] As a concrete example, consider a scenario where a user inputs, "The sales target for the next quarter is 10 million yen, and the profit margin is 20%."

[1110] 1. The user enters the goal into their device and sends it to the server.

[1111] 2. The server retrieves historical data and market trends to perform sales forecasts.

[1112] 3. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[1113] 4. The server automatically generates a "Next Quarterly Goal Achievement Plan" document and sends it to the user's terminal.

[1114] 5. Users review plan documents on their devices and plan and execute sales activities.

[1115] As described above, the present invention provides a system that streamlines the work of sales representatives and supports them in achieving their goals.

[1116] The following describes the processing flow.

[1117] Creating a profit target achievement plan

[1118] Step 1:

[1119] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[1120] Step 2:

[1121] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal and clicks the "Submit" button.

[1122] Step 3:

[1123] The server receives data on target sales and profit margins sent by the user.

[1124] Step 4:

[1125] The server retrieves historical sales data from the database. For example, it might retrieve quarterly sales figures for the past three years.

[1126] Step 5:

[1127] The server retrieves customer data from the database. It collects information such as each customer's purchase history and contract status.

[1128] Step 6:

[1129] The server retrieves market trend data from external APIs and internal databases. Specifically, it collects the latest data such as industry reports and economic indicators.

[1130] Step 7:

[1131] The server analyzes sales trends using historical sales data. For example, it uses time series analysis to understand seasonal fluctuations and trends in sales.

[1132] Step 8:

[1133] Based on market trend data acquired by the server, future sales are predicted. Regression analysis and machine learning models are applied to achieve highly accurate predictions.

[1134] Step 9:

[1135] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%). For example, it calculates the optimal product mix while considering the profit margin.

[1136] Step 10:

[1137] The server calculates the sales figures needed to achieve the target. Specifically, it calculates the required sales figures for each product.

[1138] Step 11:

[1139] The server matches customer data with product data and generates a list of products optimized for each customer. For example, it might list products recommended based on a customer's past purchase history.

[1140] Step 12:

[1141] The server automatically generates a "Next Quarterly Target Achievement Plan" document based on sales forecasts and product lists. The document includes sales forecast graphs and product list tables.

[1142] Step 13:

[1143] The server sends the generated plan document to the user's terminal.

[1144] Step 14:

[1145] The device displays the received plan document, allowing the user to review the document.

[1146] Gantt chart creation and meeting minutes management

[1147] Step 1:

[1148] When a user accepts a project, they enter the project details from their device.

[1149] Step 2:

[1150] The server receives the project details and calculates the project schedule.

[1151] Step 3:

[1152] The server automatically generates a Gantt chart, displaying the project's start date, end date, and schedule for each stage.

[1153] Step 4:

[1154] The device displays a Gantt chart, allowing the user to manage their schedule.

[1155] Inquiry response

[1156] Step 1:

[1157] Users enter questions about products or internal tools from their devices.

[1158] Step 2:

[1159] The server receives the user's question and searches the FAQ database and related documents.

[1160] Step 3:

[1161] The server generates an automated response and obtains an answer to the user's question.

[1162] Step 4:

[1163] The device displays the answer, allowing the user to check the necessary information.

[1164] (Example 1)

[1165] 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."

[1166] Traditional sales support systems made sales forecasting and profit margin analysis time-consuming, making it difficult to develop efficient sales plans. Furthermore, automatic generation of meeting minutes, creation of project Gantt charts, and handling inquiries about personnel and products also required significant time and effort. This resulted in a heavy workload for sales representatives and decreased efficiency in achieving sales targets.

[1167] 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.

[1168] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring historical sales data, customer data, and market trend data, and means for performing sales forecasts based on the acquired data. This enables efficient data collection and analysis to be performed automatically, allowing for the rapid and highly accurate creation of sales forecasts and sales plans.

[1169] "Target sales amount" refers to the target sales amount set by the user.

[1170] "Profit margin" is an indicator that shows the ratio of profit to sales.

[1171] "Sales data" refers to numerical information about past sales performance.

[1172] "Customer data" refers to information about customers with whom transactions are conducted.

[1173] "Market trend data" refers to information about current and past trends in a specific market or industry.

[1174] "Sales forecasting" refers to the process of estimating future sales based on collected data.

[1175] "Sales volume" refers to the quantity of goods or services that need to be sold to achieve a goal.

[1176] A "product list" is a list of products or services that are offered for sale.

[1177] "Document format" refers to the format of a document, whether digital or on paper.

[1178] A Gantt chart is a visual chart used for managing project schedules.

[1179] "Meeting minutes" are documents that record the content and key points of a meeting.

[1180] An "action item" is an item that indicates the specific next steps to be taken based on the meeting minutes.

[1181] "Automatic response" refers to a system function that automatically generates pre-set answers.

[1182] A "server" is a computer system that processes data and responds to client requests.

[1183] "Terminal" refers to a computer or mobile device used by a user to operate.

[1184] The present invention is a system for improving sales efficiency, and its embodiments will be described in detail below.

[1185] System Overview

[1186] This system automatically generates a sales plan by inputting target sales figures and profit margins, collecting and analyzing historical data, and ultimately performing sales forecasts and profit margin analysis. Furthermore, it provides means for generating product lists, automatically creating Gantt charts, automatically generating meeting minutes, and handling inquiries. This significantly improves the work efficiency of sales representatives and supports them in achieving their targets.

[1187] Hardware and software to be used

[1188] 1. Server

[1189] Main processor: Server equipped with a high-speed CPU

[1190] Database: MySQL database

[1191] External API: Google Trends API, Google Cloud Speech-to-Text API

[1192] Analysis tools: Python, pandas, scikit-learn, numpy

[1193] Document generation: Generative AI models (e.g., GPT-3)

[1194] 2. Terminal

[1195] User Interface (UI): A user interface that operates on a web browser.

[1196] Display devices: PC, tablet, smartphone

[1197] Specific example

[1198] Goal input and data collection

[1199] 1. Enter your goal

[1200] The user logs into their device and selects the "Create Profit Target Achievement Plan" option. For example, the user might enter "Sales target for the next quarter: 10 million yen, profit margin: 20%."

[1201] 2. Data Collection

[1202] The server automatically retrieves historical sales and customer data from a MySQL database. Furthermore, it obtains market trend data via the Google Trends API and an internal database.

[1203] Sales forecasting and plan generation

[1204] The server performs sales forecasts based on the acquired data. Specifically, it preprocesses the data using Python's pandas library and scikit-learn, and applies time series analysis and regression analysis models. Profit margin analysis is performed similarly, and the server calculates the required sales volume and cost structure considering the target profit margin entered by the user.

[1205] The server generates a suitable product list based on sales forecast results and profit margin analysis results. Along with this product list, it automatically generates a "Goal Achievement Plan" document using a generation AI model (e.g., GPT-3). The generated plan document is sent to the user's terminal and displayed.

[1206] Example of a prompt:

[1207] "Our sales target for the next quarter is 10 million yen, with a profit margin of 20%. Please create a plan to achieve these targets."

[1208] Gantt chart creation and meeting minutes management

[1209] The server calculates the project schedule and automatically generates a Gantt chart when the user enters project details. The Gantt chart is displayed in a web browser.

[1210] To automatically generate meeting minutes, the system uses the Google Cloud Speech-to-Text API to convert meeting audio into text based on the meeting agenda and key points entered by the user, and then generates the minutes using NLP (Natural Language Processing) technology. The generated minutes are displayed on the user's device, and the next action items are also automatically generated.

[1211] Inquiry response

[1212] When a user enters a question about a product or internal tool into their terminal, the server uses Elasticsearch to search the FAQ database and relevant documents, automatically generating an appropriate answer. The generated answer is then displayed on the user's terminal.

[1213] Based on the above, the present invention provides a system that streamlines the work of sales representatives and supports them in achieving their goals.

[1214] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1215] Step 1: Enter your goal

[1216] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[1217] The user enters "A sales target of 10 million yen and a profit margin of 20% for the next quarter."

[1218] The input data is packaged in JSON format as form data and sent to the server using a REST API.

[1219] Input: User-entered data on sales target amount and profit margin.

[1220] Output: Target amount and profit margin data sent to the server

[1221] Step 2: Data Collection

[1222] The server retrieves historical sales and customer data from a MySQL database.

[1223] The server retrieves market trend data through the Google Trends API and its internal database.

[1224] Data is retrieved using SQL queries, and data obtained from external APIs is stored in JSON format.

[1225] Input: Sales target amount and profit margin data

[1226] Output: Historical sales data, customer data, and market trend data retrieved from databases and external APIs.

[1227] Step 3: Data Analysis (Sales Forecast)

[1228] The server performs sales forecasts based on acquired historical sales data and market trend data.

[1229] The data is preprocessed using the Python pandas library, and predictions are made by applying statistical models (e.g., time series analysis, regression analysis).

[1230] The forecast results output a numerical representation of the likelihood of achieving the sales target.

[1231] Input: Historical sales data, customer data, market trend data

[1232] Output: Sales forecast data

[1233] Step 4: Profit Margin Analysis

[1234] Based on the predicted sales data, the server uses the NumPy library to perform sales and cost analysis, taking into account the required profit margin of 20%.

[1235] Calculate the required sales volume and cost structure, and output specific figures.

[1236] Input: Sales forecast data, sales target amount, profit margin

[1237] Output: Data on required sales volume and cost structure

[1238] Step 5: Plan Generation

[1239] The server generates a list of products necessary to achieve sales targets based on sales forecasts and profit margin analysis results.

[1240] The server uses a generative AI model (e.g., GPT-3) to automatically generate a "Goal Achievement Plan" document.

[1241] The document is exported in PDF or Word format and sent to the user's device.

[1242] Input: Sales forecast, profit margin analysis results, product list

[1243] Output: "Goal Achievement Plan" document

[1244] Step 6: Create a Gantt chart

[1245] The user enters the details of a new case into the terminal.

[1246] The server automatically generates a Gantt chart using D3.js or Chart.js based on the input data.

[1247] The Gantt chart is displayed on the user's device, enabling project schedule management.

[1248] Input: Project details data

[1249] Output: Gantt chart

[1250] Step 7: Generate meeting minutes

[1251] The user enters the meeting agenda and key points into the terminal.

[1252] The server uses the Google Cloud Speech-to-Text API to convert meeting audio into text and generates meeting minutes using an NLP model.

[1253] The meeting minutes and next action items will be displayed in document format on the user's device.

[1254] Input: Meeting agenda, key points, meeting audio

[1255] Output: Meeting minutes, next action items

[1256] Step 8: Handling inquiries

[1257] Users enter questions about products or internal tools into the terminal.

[1258] The server uses Elasticsearch to search the FAQ database and related documents, and generates appropriate automated responses.

[1259] The automated response will be displayed on the user's device.

[1260] Input: User's question

[1261] Output: Automated response

[1262] (Application Example 1)

[1263] 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."

[1264] Traditional sales efficiency improvement systems were limited to sales forecasting and target setting functions, lacking the tools and features to effectively utilize the analysis results. Furthermore, features such as schedule management, meeting minute generation, and automated inquiry handling were not sufficiently integrated, making it difficult for sales representatives to efficiently manage their diverse tasks.

[1265] 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.

[1266] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means for generating a Gantt chart for schedule management, means for automatically generating meeting minutes, and means for automatically generating inquiry responses. This enables the user to efficiently perform tasks from sales target planning and schedule management to meeting minute generation and inquiry handling in a consistent manner.

[1267] "Target sales amount" refers to a specific sales figure that you want to achieve in your sales activities or business plan.

[1268] "Profit margin" is an indicator that shows the ratio of profit earned to sales revenue.

[1269] "Past sales data" refers to historical sales information recorded up to the present.

[1270] "Customer data" refers to aggregated data about customers, including age, gender, and purchase history.

[1271] "Market trend data" refers to data related to market trends and consumer behavior.

[1272] "Means of sales forecasting" refers to methods or devices for predicting future sales based on collected data.

[1273] "Sales volume required to achieve the target" refers to the number of products that need to be sold to achieve the set target sales amount and profit margin.

[1274] A "product list" is a list of the products and services that are sold.

[1275] "Means of automatically generating documents in document format" refers to methods or devices for analyzing data and automatically converting the results into documents such as reports and proposals.

[1276] "Means for outputting documents" refers to methods or devices for providing the generated documents to the user.

[1277] A "Gantt chart for schedule management" is a diagram in the form of a Gantt chart used to visually display the schedule of a project or task.

[1278] "Methods for automatically generating meeting minutes" refer to methods or devices that analyze the content of a meeting and automatically create a summary of it as meeting minutes.

[1279] "Means for automatically generating inquiry responses" refers to methods or devices for automatically providing appropriate responses to questions and inquiries from users.

[1280] The present invention relates to a system for improving sales efficiency, and its embodiments will be described in detail below.

[1281] Goal input and data collection

[1282] 1. Enter your goal

[1283] The user (sales representative) logs into the terminal and selects the displayed "Create Profit Target Achievement Plan" option. The user enters the target sales amount and profit margin into the terminal, and that data is sent to the server.

[1284] 2. Data Collection

[1285] The server automatically retrieves historical sales and customer data from the store's point-of-sale (POS) system. Market trend data is obtained from external APIs and internal databases.

[1286] Sales forecasting and plan generation

[1287] 3. Data Analysis

[1288] The server analyzes sales trends using collected data. Based on market trend data, it uses statistical models (e.g., time series analysis, regression analysis) to predict future sales.

[1289] 4. Profitability analysis

[1290] The server estimates realistic profit margins based on the acquired data. Considering the target profit margin, it calculates the necessary sales revenue and cost structure.

[1291] 5. Plan generation

[1292] The server calculates the sales volume required to achieve the target and generates a suitable product list. Based on the sales forecast and product list, it automatically generates a "Target Achievement Plan" document and sends this document to the user's terminal.

[1293] Gantt chart creation and meeting minutes management

[1294] 6. Create a Gantt chart

[1295] When a project is accepted, the user enters the project details into the terminal, and the server calculates the project's timeline and automatically generates a Gantt chart. The Gantt chart is displayed on the terminal, allowing the user to manage their schedule.

[1296] 7. Minutes generation

[1297] When a user enters meeting agenda items and key points into their terminal, the server analyzes the meeting audio and documents and automatically generates meeting minutes. Next action items are also generated and displayed on the terminal along with the meeting minutes.

[1298] Inquiry response

[1299] 8. Inquiries regarding products and tools

[1300] When a user enters a question about a product or internal tool into the terminal, the server searches the FAQ database and relevant documents and generates an automated response. The answer is then displayed on the terminal.

[1301] Specific example

[1302] As a concrete example, consider a scenario where a user inputs "The sales target for the next quarter is 10 million yen, with a profit margin of 20%." The user inputs the target into their terminal, and this data is sent to the server. The server retrieves past sales data and market trends and makes a sales forecast. Subsequently, the server performs an analysis based on profit margins, calculates the necessary sales volume and product list, and sends the generated "Next Quarterly Target Achievement Plan" document to the user's terminal. The user reviews this plan document, plans and executes their sales activities.

[1303] Example of a prompt

[1304] "Our sales target for the next quarter is 10 million yen, with a profit margin of 20%. Please analyze past sales data and market trends to generate the optimal sales promotion plan."

[1305] This system significantly improves the efficiency of sales representatives' work and enables them to develop effective strategies for achieving sales targets and profit margins.

[1306] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1307] Step 1:

[1308] The user enters their target sales amount and profit margin into the terminal. The input data is in the following format: "Target sales amount: 10 million yen", "Profit margin: 20%". This data is sent from the terminal to the server. The server stores the received data in its database.

[1309] Step 2:

[1310] The server retrieves historical sales and customer data from the store's POS system and related databases. Furthermore, it collects market trend data through external APIs and internal databases. This data is temporarily stored on the server for analysis.

[1311] Step 3:

[1312] The server uses time series analysis and regression analysis techniques to forecast sales based on historical sales data. The input data consists of past sales records, while the output data represents future sales forecasts. The server performs data analysis using Python's Pandas and scikit-learn libraries.

[1313] Step 4:

[1314] The server uses collected market trend data to perform detailed analysis based on sales forecasts. Specifically, it readjusts the forecasting model and calculates the final forecast values ​​that take into account fluctuations. The output data consists of predicted sales and profit margins.

[1315] Step 5:

[1316] The server calculates the required sales revenue and costs based on the set target profit margin. This generates the sales volume and appropriate product list needed to achieve the target. The input data is the profit margin and sales forecast, and the output data is the product list.

[1317] Step 6:

[1318] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product list data. This document includes sales strategies, required sales figures, and recommended products. The server then sends the generated document to the user's terminal.

[1319] Step 7:

[1320] When a user enters project details into their terminal, the server automatically generates the project schedule and creates a Gantt chart. The input data is detailed project information, and the output data is a schedule in Gantt chart format.

[1321] Step 8:

[1322] When a user enters meeting agenda items and key points into their terminal, the server analyzes the meeting's audio data and documents to automatically generate meeting minutes. Specifically, it uses speech recognition technology to transcribe the meeting content into text, extracts necessary information, and compiles it into meeting minutes. The output data is the automatically generated meeting minutes.

[1323] Step 9:

[1324] When a user enters a question about a product or internal tool into a terminal, the server searches the FAQ database and relevant documents and generates a response to the inquiry. The input data is the user's question, and the output data is the automatically generated response. The server uses a natural language generation model such as the GPT-4 API to create the response.

[1325] Through these steps, this system dramatically improves the efficiency of sales representatives and provides concrete plans for achieving goals.

[1326] 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.

[1327] This invention is a system for improving sales efficiency, combining an emotion engine that recognizes user emotions to enable more appropriate suggestions and two-way communication. The embodiments are described in detail below.

[1328] Goal input and data collection

[1329] 1. Enter your goal

[1330] The user (sales representative) logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system.

[1331] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal. The entered data is then sent to the server.

[1332] 2. Data Collection

[1333] The server retrieves historical sales data and customer data from the database.

[1334] The server retrieves market trend data from external APIs and internal databases.

[1335] Sales forecasting and plan generation

[1336] 3. Data Analysis

[1337] The server analyzes sales trends using past sales data.

[1338] The server uses statistical models (e.g., time series analysis, regression analysis) based on market trend data to forecast sales.

[1339] 4. Profitability analysis

[1340] The server estimates a realistic profit margin based on the acquired data.

[1341] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%).

[1342] 5. Plan generation

[1343] The server calculates the sales figures needed to achieve the target and generates a product list.

[1344] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists.

[1345] The server sends the generated plan document to the user's terminal.

[1346] Utilizing the Emotion Engine

[1347] 6. Emotion recognition

[1348] When a user enters a goal or inquiry, the device uses an emotion engine to recognize emotions from the user's facial expressions and voice.

[1349] The server receives the sentiment analysis results and supplements them with the user's input information.

[1350] 7. Emotion-based proposals

[1351] The server takes the sentiment analysis results into consideration to provide more appropriate suggestions to the user.

[1352] For example, if a user is experiencing stress, the system will provide advice and resources to help alleviate that burden.

[1353] Gantt chart creation and meeting minutes management

[1354] 8. Create a Gantt chart

[1355] When a project is accepted, the user enters the project details into the terminal.

[1356] The server calculates the project's timeline and automatically generates a Gantt chart.

[1357] The device displays a Gantt chart, allowing the user to manage their schedule.

[1358] 9. Minutes generation

[1359] The user enters the meeting agenda and key points into the terminal.

[1360] The server analyzes meeting audio and documents and automatically generates meeting minutes.

[1361] The server generates the following action items and displays the meeting minutes and action items on the terminal.

[1362] Sentiment analysis of meeting minutes

[1363] 10. Recognizing emotions during meetings

[1364] The device analyzes audio data from the meeting using an emotion engine and records the speaker's emotions, which are then reflected in the meeting minutes.

[1365] Based on the analysis results, the server records the emotional state and its changes in the meeting minutes.

[1366] Inquiry response

[1367] 11. Inquiries regarding products and tools

[1368] Users enter questions about products or internal tools from their devices.

[1369] The server receives the user's question and searches the FAQ database and related documents.

[1370] The server generates an automated response and obtains an answer to the user's question.

[1371] The device displays the answer, allowing the user to check the necessary information.

[1372] Specific example

[1373] As a concrete example, consider a scenario where a user inputs, "The sales target for the next quarter is 10 million yen, and the profit margin is 20%."

[1374] 1. The user enters the goal into their device and sends it to the server.

[1375] 2. The server uses an emotion engine to analyze the user's emotions at the time of input and obtain information such as stress levels and satisfaction levels.

[1376] 3. The server retrieves historical data and market trends to perform sales forecasts.

[1377] 4. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[1378] 5. Based on the sentiment analysis results, the server automatically generates a "Next Quarterly Goal Achievement Plan" document containing optimal advice for the user.

[1379] 6. The server sends the generated plan document to the user's terminal.

[1380] 7. Users review plan documents on their devices and plan and execute sales activities.

[1381] As described above, the present invention realizes a system that streamlines the work of sales representatives and provides more appropriate support by utilizing emotion recognition.

[1382] The following describes the processing flow.

[1383] Creating profit target achievement plans and utilizing the emotional engine

[1384] Step 1:

[1385] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[1386] Step 2:

[1387] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal and clicks the "Submit" button.

[1388] Step 3:

[1389] The device analyzes the user's facial expressions and voice during input using an emotion engine to detect their emotional state (e.g., stress, satisfaction level, excitement level).

[1390] Step 4:

[1391] The device sends the detected emotional state to the server as data.

[1392] Step 5:

[1393] The server receives data from the user regarding target sales figures, profit margins, and emotional states.

[1394] Step 6:

[1395] The server retrieves historical sales data from the database. For example, it might retrieve quarterly sales figures for the past three years.

[1396] Step 7:

[1397] The server retrieves customer data from the database. It collects information such as each customer's purchase history and contract status.

[1398] Step 8:

[1399] The server retrieves market trend data from external APIs and internal databases. For example, it collects the latest data such as industry reports and economic indicators.

[1400] Step 9:

[1401] The server analyzes sales trends using historical sales data. For example, it uses time series analysis to understand seasonal fluctuations and trends in sales.

[1402] Step 10:

[1403] The server predicts future sales based on market trend data. Regression analysis and machine learning models are applied to achieve highly accurate predictions.

[1404] Step 11:

[1405] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%). For example, it calculates the optimal product mix while considering the profit margin.

[1406] Step 12:

[1407] The server calculates the sales figures needed to achieve the target. Specifically, it calculates the required sales figures for each product.

[1408] Step 13:

[1409] The server matches customer data with product data and generates a list of products optimized for each customer. For example, it might list products recommended based on a customer's past purchase history.

[1410] Step 14:

[1411] The server automatically generates a "Next Quarterly Target Achievement Plan" document based on sales forecasts and product lists. The document includes sales forecast graphs and product list tables.

[1412] Step 15:

[1413] Based on the sentiment analysis results, the server adds advice and support messages tailored to the user's emotional state to the plan document.

[1414] Step 16:

[1415] The server sends the generated plan document to the user's terminal.

[1416] Step 17:

[1417] The device displays the received plan document, allowing the user to review the document.

[1418] Gantt chart creation and meeting minutes management

[1419] Step 1:

[1420] When a user accepts a project, they enter the project details from their device.

[1421] Step 2:

[1422] The server receives the project details and calculates the project schedule.

[1423] Step 3:

[1424] The server automatically generates a Gantt chart, displaying the project's start date, end date, and schedule for each stage.

[1425] Step 4:

[1426] The device displays a Gantt chart, allowing the user to manage their schedule.

[1427] Sentiment analysis of meeting minutes

[1428] Step 1:

[1429] The user enters the meeting agenda and key points into the terminal.

[1430] Step 2:

[1431] The device analyzes the audio data during the meeting using an emotion engine to detect the emotional state of the speaker.

[1432] Step 3:

[1433] The device sends the detected emotional state to the server as meeting minutes data.

[1434] Step 4:

[1435] The server automatically generates meeting minutes based on audio data and emotional states. Emotional states are also reflected in the minutes.

[1436] Step 5:

[1437] The server generates the next action item and displays the meeting minutes and action item on the terminal.

[1438] Inquiry response

[1439] Step 1:

[1440] Users enter questions about products or internal tools from their devices.

[1441] Step 2:

[1442] The device uses an emotion engine to analyze facial expressions and voice during questioning and detect the emotional state.

[1443] Step 3:

[1444] The device sends question data, including emotional state, to the server.

[1445] Step 4:

[1446] The server receives the user's question and sentiment state, and searches the FAQ database and related documents.

[1447] Step 5:

[1448] The server automatically generates responses based on the user's emotional state and outputs responses that include appropriate advice.

[1449] Step 6:

[1450] The device displays the answer, allowing the user to check the necessary information.

[1451] (Example 2)

[1452] 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."

[1453] Traditional sales support systems often provided uniform proposals and support without considering the user's emotions or state of mind. As a result, they failed to alleviate user stress and anxiety, hindering effective sales activities. Furthermore, tasks such as creating meeting minutes and managing Gantt charts were time-consuming and cumbersome, making efficient work difficult. In addition, there was a lack of technology to recognize emotional states during meetings and provide appropriate feedback. These problems increased the burden on sales representatives and decreased work efficiency.

[1454] 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.

[1455] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means including an emotion engine for recognizing user emotions, means for making appropriate suggestions to the user based on the emotion analysis results by the emotion engine, means for automatically generating meeting minutes, means for generating the next action items based on the meeting minutes, means for analyzing the emotions of speakers during the meeting and reflecting them in the meeting minutes, and means for automatically generating a Gantt chart from the time of order acceptance until implementation. This enables effective suggestions that take user emotions into consideration, thereby improving the efficiency of sales activities. In addition, it eliminates the effort of creating meeting minutes and managing Gantt charts, thus improving operational efficiency. Furthermore, it enables appropriate feedback through emotion analysis during meetings, which is expected to improve meeting productivity.

[1456] "Target sales amount" refers to the amount of sales that should be achieved through sales activities.

[1457] "Profit margin" refers to the ratio of profit to sales revenue and is an indicator used to evaluate the profitability of business activities.

[1458] "Past sales data" refers to data that includes historical sales information for the period up to now.

[1459] "Customer data" refers to data containing information about a customer, including their name, contact information, and purchase history.

[1460] "Market trend data" refers to data that includes information on the latest trends and developments in an industry or market.

[1461] "Sales forecasting" is the process of estimating future sales based on acquired data.

[1462] "Sales volume" refers to the quantity of goods or services sold within a specific period.

[1463] A "product list" is a list that shows a list of products and services that are sold.

[1464] "Automatically generating a document in a document format" refers to the process by which a system automatically creates a document based on input data and calculation results.

[1465] An "emotion engine" is a technology that analyzes a user's emotional state from their facial expressions, voice, and other data.

[1466] "Emotion analysis results" refer to data about the user's emotions obtained as a result of analysis using an emotion engine.

[1467] An "appropriate suggestion" is one that indicates the optimal action or strategy based on the user's emotional state and sales objectives.

[1468] "Meeting minutes" are documents that record the content of discussions held during meetings or conferences.

[1469] An "action item" is an item that indicates the specific tasks or action plan to be taken next based on the meeting minutes.

[1470] A Gantt chart is a diagram that visually represents a schedule used in project management.

[1471] This invention is a system that streamlines sales activities and provides support that takes user emotions into consideration. The system consists of a user interface for inputting target sales figures and profit margins, a server for data collection and analysis, and an emotion engine for recognizing emotions. The specific configuration and processing methods are described below.

[1472] Goal input and data collection

[1473] The user logs into their terminal and selects the "Create Profit Target Achievement Plan" option on the system. Next, they enter their target sales amount and profit margin. For example, a user might set a sales target of 10 million yen and a profit margin of 20% for the next quarter. This information is then sent from the terminal to the server.

[1474] The server retrieves historical sales and customer data from the database and collects market trend data using external APIs. Specifically, the server uses an SQL database to retrieve historical data and a REST API to collect market trends.

[1475] Sales forecasting and plan generation

[1476] The server uses statistical models such as time series analysis and regression analysis to make sales forecasts based on the acquired data. For example, Python libraries such as Pandas and scikit-learn could be used. Sales trends are graphed, and sales for the next quarter are forecasted.

[1477] The server calculates the necessary sales revenue and cost structure based on the target profit margin. For example, it performs simulations using the Microsoft Excel API. Based on the simulation results, it generates a product list and automatically creates a "Target Achievement Plan" in document format, which includes the sales forecast and product list. The generated document is sent to the terminal and displayed to the user.

[1478] Utilizing the Emotion Engine

[1479] When a user enters their goals or inquiries, the device uses its camera and microphone to analyze their facial expressions and voice using an emotion engine. For example, this could involve using the OpenCV library and the Google Cloud Speech-to-Text API. The analysis results are sent to a server and supplemented with the user's input information.

[1480] Based on the emotion analysis results, the server provides situation-appropriate advice. For example, if the user is feeling stressed, the system will add advice such as "Try to relax and approach this task" to the document.

[1481] Gantt chart creation and meeting minutes management

[1482] When a project is accepted, the user enters the project details into their terminal. The server calculates the project's timeline based on this information and automatically generates a Gantt chart using a JavaScript library (e.g., DHTMLX Gantt). This chart is displayed on the terminal, allowing the user to manage their schedule.

[1483] During a meeting, users input agenda items and key points into their terminals. The server collects meeting audio, uses natural language processing (NLP) technology to transcribe the speech into text, and automatically generates meeting minutes. Python's NLTK library or the Google Cloud Speech-to-Text API are commonly used for this purpose. Furthermore, the server generates the following action items, which are also displayed on the terminals.

[1484] Examples of specific cases and prompts for generative AI models.

[1485] As a concrete example, consider a scenario where a user inputs "The sales target for the next quarter is 10 million yen, with a profit margin of 20%." The server uses an emotion engine to analyze the user's emotions at the time of input and obtains information such as stress and satisfaction levels. Based on the analysis results, it performs a sales forecast and generates a planning document that includes an appropriate sales plan and advice. The generated document is sent to the user's terminal, and the user plans and executes sales activities based on it.

[1486] Examples of prompts for a generative AI model are as follows:

[1487] The sales target for the next quarter is 10 million yen, with a profit margin of 20%. Based on this, generate a sales forecast that takes historical data and market trends into account, along with a planning document that includes an appropriate sales plan. Also, include advice based on sentiment analysis during data entry.

[1488] As described above, the present invention provides a system that takes user emotions into consideration and effectively supports sales activities.

[1489] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1490] Step 1:

[1491] The user logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system. The user then enters a target sales amount (e.g., 10 million yen) and a profit margin (e.g., 20%). This input data is temporarily stored on the terminal and sent to the server.

[1492] Step 2:

[1493] The server retrieves historical sales and customer data from the database based on the received target sales amount and profit margin. Specifically, it extracts data by executing SQL queries. Furthermore, it uses an external API to obtain market trend data. This external API is accessed via a RESTful API. The input data consists of the target sales amount and profit margin, and based on this, database searches and API calls are performed, yielding historical sales data, market trend data, and other output.

[1494] Step 3:

[1495] The server analyzes sales trends and patterns based on the acquired data. Specifically, it uses Python's Pandas and scikit-learn libraries to perform time series analysis and regression analysis. The input data consists of historical sales data and market trend data. Based on this, a sales forecasting model is built, and the predicted sales data is obtained as output.

[1496] Step 4:

[1497] The server calculates the sales volume required to achieve the target based on sales forecast data. For example, it uses the Excel API to perform calculations that take into account profit margins and cost structures based on the predicted sales data. The input data is sales forecast data and target profit margins, and based on this, it estimates the required sales volume and cost structure, and outputs the specific sales volume required to achieve the target.

[1498] Step 5:

[1499] The server generates a product list based on the required sales volume. For example, sales targets and strategies are set for each product. The input data is the sales volume required to achieve the target, and the server creates a product list based on this, generating the product list as output.

[1500] Step 6:

[1501] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists. This document includes specific sales strategies and implementation plans. A Python library could be used for document generation. The input data consists of sales forecast data and product lists; the server creates a document based on this data, and outputs the "Goal Achievement Plan" document.

[1502] Step 7:

[1503] The server sends the generated "Goal Achievement Plan" document to the user's terminal. The user reviews the document received on their terminal, plans and executes their sales activities. The input data is the generated document, which is then sent to the terminal and displayed to the user, resulting in the output.

[1504] Step 8:

[1505] When a user enters their goals or inquiries, the device uses an emotion engine to analyze the user's facial expressions and voice. Specifically, it uses the camera and microphone to utilize the OpenCV library and the Google Cloud Speech-to-Text API. The input data consists of the user's facial expressions and voice, which are used to analyze emotions, and the emotion analysis results are output.

[1506] Step 9:

[1507] The server receives the sentiment analysis results and supplements them with the user's input information. For example, if the user is feeling stressed, that information is added. The input data is the sentiment analysis results, which are supplemented based on the user's input information, and the output is user data with emotions.

[1508] Step 10:

[1509] The server provides optimal suggestions to the user based on the emotion analysis results. For example, if the user is feeling stressed, it will provide "advice on how to relax." The input data consists of the emotion analysis results and the user's input information. Based on this, the server forms advice and provides appropriate suggestions as output.

[1510] Step 11:

[1511] When a project is accepted, the user enters the project details into their terminal. The server calculates the project's timeline based on this information and automatically generates a Gantt chart using a JavaScript library (e.g., DHTMLX Gantt). The input data consists of detailed project information, which is used to generate the Gantt chart, and the output is a schedule for project management.

[1512] Step 12:

[1513] The user enters the meeting agenda and key points into a terminal. The server collects the meeting audio, converts it to text using natural language processing technology (e.g., Python's NLTK library or Google Cloud Speech-to-Text API), and automatically generates meeting minutes. The input data consists of meeting audio and agenda information, and the server generates the meeting minutes based on this data, providing the minutes as output.

[1514] Step 13:

[1515] The server generates the next set of action items based on the generated meeting minutes. The input data is the meeting minutes, and based on this, it lists the next set of action items and sends them to the terminal. The user reviews these action items, plans the next steps, and executes them.

[1516] Step 14:

[1517] During a meeting, the terminal analyzes the audio data using an emotion engine and records the speaker's emotions, which are then reflected in the meeting minutes. Based on the analysis results, the server records the emotional state and its changes in the meeting minutes. The input data consists of audio data and emotion analysis results. The meeting minutes are updated based on this data, and the output is meeting minutes with added emotions.

[1518] (Application Example 2)

[1519] 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."

[1520] In modern advertising planning and sales activities, it is crucial to make appropriate proposals that take into account the emotional state of the target audience. However, traditional systems have struggled to grasp user emotions and make flexible proposals based on them. Furthermore, the process from proposing advertising campaigns to automatically registering them with each advertising platform is inconsistent, resulting in manual work and significant time and effort required.

[1521] 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 inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means for analyzing the user's emotions using an emotion recognition engine, and means for making optimal suggestions to the user based on the emotion analysis results. This enables the proposal of flexible advertising plans that take into account the user's emotions and the efficient management of advertising campaigns.

[1522] "Target sales amount" refers to the target sales amount that a user aims to achieve within a specific period.

[1523] "Profit margin" is an indicator that shows the ratio of profit to sales, and it indicates the efficiency of management.

[1524] "Sales data" refers to data on past sales performance, including information such as the number of units sold and the sales amount for each product.

[1525] "Customer data" refers to information about past and present customers, including purchase history, attribute information, and behavioral data.

[1526] "Market trend data" refers to data on current market trends and future predictions, including consumer behavior and the activities of competitors.

[1527] "Sales forecasting" is the process of predicting future sales based on past sales data and market trend data.

[1528] "Sales volume" refers to the actual number of a particular product or service sold within a certain period of time.

[1529] A "product list" is a list of products or services that are targeted for sale or advertising.

[1530] "Automatically generating documents in a specified format" refers to the process by which a system automatically creates documents based on specific data.

[1531] An "emotion recognition engine" is a technology that analyzes a person's facial expressions and voice data to identify their emotions.

[1532] An "advertising campaign" is a series of advertising activities planned to promote a specific product or service.

[1533] An "advertising platform" is a term that refers to the entire system and service used to deliver advertisements over the internet.

[1534] This invention is a system that improves the efficiency of advertising planning by combining an emotion recognition engine. The embodiments thereof are described in detail below.

[1535] System Configuration and Operation Overview

[1536] User login and targeting

[1537] The user (advertiser) logs in to their smartphone or tablet and selects the "Create Ad Plan" option in the application. The user sets a specific target audience. For example, they might select "Men aged 25-34, Asia region." This setting data is sent to the server.

[1538] Data collection

[1539] The server retrieves data from past advertising campaigns from the advertising database. It also uses external APIs to obtain market trend data. For example, it retrieves past advertising data from "https: / / api.pastadsdata.com" and market trend data from "https: / / api.markettrends.com".

[1540] Advertising plan generation

[1541] The server analyzes collected historical data and market trend data using statistical methods (e.g., time series analysis, clustering) to generate the optimal advertising plan. The generated advertising plan includes information on suggested creatives, messages, and advertising delivery platforms (e.g., Facebook, Google AdWords).

[1542] Utilization of emotion recognition engines

[1543] When a user reviews an ad plan, the system uses the camera and microphone on their smart device to analyze their emotions from their facial expressions and voice using an emotion recognition engine. The results of the emotion analysis are sent to a server, and the suggestions are adjusted based on the user's state. For example, if the user is feeling stressed, the system will offer alternative plans or advice for relaxation.

[1544] Advertising campaign development

[1545] Once the final advertising plan is confirmed, the server automatically registers the campaign with each advertising platform and begins delivery. This virtually eliminates the need for manual intervention, streamlining the management and deployment of advertising plans.

[1546] Usage example

[1547] For example, in a scenario where the user inputs "The sales target for the next quarter is 10 million yen, and the profit margin is 20%":

[1548] 1. The user enters the goal into their device and sends it to the server.

[1549] 2. The server uses an emotion recognition engine to analyze the user's emotions at the time of input. It obtains information such as stress levels and satisfaction levels.

[1550] 3. The server retrieves historical data and market trends to perform sales forecasts.

[1551] 4. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[1552] 5. Based on the sentiment analysis results, the server automatically generates a "Next Quarterly Goal Achievement Plan" document that includes optimal advice for the user.

[1553] 6. The ad campaign targeting the defined audience is automatically registered with each ad delivery platform, and delivery begins.

[1554] Thus, the present invention is a system that proposes flexible advertising plans that take user emotions into consideration, and streamlines the management and deployment of advertising campaigns.

[1555] Example of a prompt

[1556] Use the following API to generate an ad plan for your target audience in the Asian region, aged 25-34.

[1557] 1. Retrieve past advertising campaign data from https: / / api.pastadsdata.com.

[1558] 2. Obtain the latest market trend data from https: / / api.markettrends.com.

[1559] 3. Consider the user's emotions and provide the optimal advertising plan.

[1560] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1561] Step 1:

[1562] The user logs in on their smart device and selects the ad plan creation option. The user sets a specific target audience (e.g., men aged 25-34, Asian region), and this information is sent to the server. The input data is attribute information of the target group, which is used for data collection and analysis in the next step.

[1563] Step 2:

[1564] The server retrieves historical advertising campaign data from the advertising database and also obtains market trend data from external APIs (e.g., https: / / api.pastadsdata.com and https: / / api.markettrends.com). The retrieved data includes information such as sales performance and market trends, and is stored in the database for analysis.

[1565] Step 3:

[1566] The server uses statistical models (e.g., time series analysis, clustering) to process and perform calculations on historical advertising campaign data and market trend data. This analysis predicts the most effective advertising plan for the target audience. The output includes predicted sales and response rates, as well as a list of suggested ad creatives and messages.

[1567] Step 4:

[1568] The server automatically generates the created ad plan in document format (e.g., PDF or HTML report). The ad plan document includes detailed information such as specific suggestions for the target audience and recommended ad serving platforms. The generated document is then sent to the user's device.

[1569] Step 5:

[1570] When a user reviews an ad plan, an emotion recognition engine analyzes the user's facial expressions and voice through the device's camera and microphone. The input data consists of camera images and audio data, which are analyzed in real time to detect the user's emotional state (e.g., stress, satisfaction).

[1571] Step 6:

[1572] The analysis results from the emotion recognition engine are sent to the server, and the suggested advertising plan is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the server will provide alternative plans or advice for relaxation. This ultimately determines the optimal advertising plan for the user.

[1573] Step 7:

[1574] The server automatically registers the finalized ad plan with each ad delivery platform (e.g., Facebook, Google AdWords) and begins delivery. The input data is the details of the final ad plan, which is sent to each ad delivery platform. The output is the start of the ad campaign on each platform.

[1575] The above processing steps ensure that the process from ad plan creation to delivery proceeds efficiently, enabling flexible suggestions based on user emotions.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] [Fourth Embodiment]

[1580] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1581] 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.

[1582] 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).

[1583] 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.

[1584] 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.

[1585] 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).

[1586] 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.

[1587] 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.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] 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.

[1592] 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".

[1593] The present invention is a system for improving sales efficiency, and its embodiments will be described in detail below.

[1594] Goal input and data collection

[1595] 1. Enter your goal

[1596] The user (sales representative) logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system.

[1597] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal. The entered data is then sent to the server.

[1598] 2. Data Collection

[1599] The server retrieves historical sales data and customer data from the database.

[1600] The server retrieves market trend data from external APIs and internal databases.

[1601] Sales forecasting and plan generation

[1602] 3. Data Analysis

[1603] The server analyzes sales trends using past sales data.

[1604] The server uses statistical models (e.g., time series analysis, regression analysis) based on market trend data to forecast sales.

[1605] 4. Profitability analysis

[1606] The server estimates a realistic profit margin based on the acquired data.

[1607] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (20%).

[1608] 5. Plan generation

[1609] The server calculates the sales figures needed to achieve the target and generates a product list.

[1610] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists.

[1611] The server sends the generated plan document to the user's terminal.

[1612] Gantt chart creation and meeting minutes management

[1613] 6. Create a Gantt chart

[1614] When a project is accepted, the user enters the project details into the terminal.

[1615] The server calculates the project's timeline and automatically generates a Gantt chart.

[1616] A Gantt chart is displayed on the device, allowing users to manage their schedules.

[1617] 7. Minutes generation

[1618] The user enters the meeting agenda and key points into the terminal.

[1619] The server analyzes meeting audio and documents and automatically generates meeting minutes.

[1620] The server generates the following action items and displays the meeting minutes and action items on the terminal.

[1621] Inquiry response

[1622] 8. Inquiries regarding products and tools

[1623] Users enter questions about products or internal tools into the terminal.

[1624] The server searches the FAQ database and related documents and generates an automated response.

[1625] The answer will be displayed on your device.

[1626] Specific example

[1627] As a concrete example, consider a scenario where a user inputs, "The sales target for the next quarter is 10 million yen, and the profit margin is 20%."

[1628] 1. The user enters the goal into their device and sends it to the server.

[1629] 2. The server retrieves historical data and market trends to perform sales forecasts.

[1630] 3. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[1631] 4. The server automatically generates a "Next Quarterly Goal Achievement Plan" document and sends it to the user's terminal.

[1632] 5. Users review plan documents on their devices and plan and execute sales activities.

[1633] As described above, the present invention provides a system that streamlines the work of sales representatives and supports them in achieving their goals.

[1634] The following describes the processing flow.

[1635] Creating a profit target achievement plan

[1636] Step 1:

[1637] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[1638] Step 2:

[1639] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal and clicks the "Submit" button.

[1640] Step 3:

[1641] The server receives data on target sales and profit margins sent by the user.

[1642] Step 4:

[1643] The server retrieves historical sales data from the database. For example, it might retrieve quarterly sales figures for the past three years.

[1644] Step 5:

[1645] The server retrieves customer data from the database. It collects information such as each customer's purchase history and contract status.

[1646] Step 6:

[1647] The server retrieves market trend data from external APIs and internal databases. Specifically, it collects the latest data such as industry reports and economic indicators.

[1648] Step 7:

[1649] The server analyzes sales trends using historical sales data. For example, it uses time series analysis to understand seasonal fluctuations and trends in sales.

[1650] Step 8:

[1651] Based on market trend data acquired by the server, future sales are predicted. Regression analysis and machine learning models are applied to achieve highly accurate predictions.

[1652] Step 9:

[1653] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%). For example, it calculates the optimal product mix while considering the profit margin.

[1654] Step 10:

[1655] The server calculates the sales figures needed to achieve the target. Specifically, it calculates the required sales figures for each product.

[1656] Step 11:

[1657] The server matches customer data with product data and generates a list of products optimized for each customer. For example, it might list products recommended based on a customer's past purchase history.

[1658] Step 12:

[1659] The server automatically generates a "Next Quarterly Target Achievement Plan" document based on sales forecasts and product lists. The document includes sales forecast graphs and product list tables.

[1660] Step 13:

[1661] The server sends the generated plan document to the user's terminal.

[1662] Step 14:

[1663] The device displays the received plan document, allowing the user to review the document.

[1664] Gantt chart creation and meeting minutes management

[1665] Step 1:

[1666] When a user accepts a project, they enter the project details from their device.

[1667] Step 2:

[1668] The server receives the project details and calculates the project schedule.

[1669] Step 3:

[1670] The server automatically generates a Gantt chart, displaying the project's start date, end date, and schedule for each stage.

[1671] Step 4:

[1672] The device displays a Gantt chart, allowing the user to manage their schedule.

[1673] Inquiry response

[1674] Step 1:

[1675] Users enter questions about products or internal tools from their devices.

[1676] Step 2:

[1677] The server receives the user's question and searches the FAQ database and related documents.

[1678] Step 3:

[1679] The server generates an automated response and obtains an answer to the user's question.

[1680] Step 4:

[1681] The device displays the answer, allowing the user to check the necessary information.

[1682] (Example 1)

[1683] 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".

[1684] Traditional sales support systems made sales forecasting and profit margin analysis time-consuming, making it difficult to develop efficient sales plans. Furthermore, automatic generation of meeting minutes, creation of project Gantt charts, and handling inquiries about personnel and products also required significant time and effort. This resulted in a heavy workload for sales representatives and decreased efficiency in achieving sales targets.

[1685] 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.

[1686] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring historical sales data, customer data, and market trend data, and means for performing sales forecasts based on the acquired data. This enables efficient data collection and analysis to be performed automatically, allowing for the rapid and highly accurate creation of sales forecasts and sales plans.

[1687] "Target sales amount" refers to the target sales amount set by the user.

[1688] "Profit margin" is an indicator that shows the ratio of profit to sales.

[1689] "Sales data" refers to numerical information about past sales performance.

[1690] "Customer data" refers to information about customers with whom transactions are conducted.

[1691] "Market trend data" refers to information about current and past trends in a specific market or industry.

[1692] "Sales forecasting" refers to the process of estimating future sales based on collected data.

[1693] "Sales volume" refers to the quantity of goods or services that need to be sold to achieve a goal.

[1694] A "product list" is a list of products or services that are offered for sale.

[1695] "Document format" refers to the format of a document, whether digital or on paper.

[1696] A Gantt chart is a visual chart used for managing project schedules.

[1697] "Meeting minutes" are documents that record the content and key points of a meeting.

[1698] An "action item" is an item that indicates the specific next steps to be taken based on the meeting minutes.

[1699] "Automatic response" refers to a system function that automatically generates pre-set answers.

[1700] A "server" is a computer system that processes data and responds to client requests.

[1701] "Terminal" refers to a computer or mobile device used by a user to operate.

[1702] The present invention is a system for improving sales efficiency, and its embodiments will be described in detail below.

[1703] System Overview

[1704] This system automatically generates a sales plan by inputting target sales figures and profit margins, collecting and analyzing historical data, and ultimately performing sales forecasts and profit margin analysis. Furthermore, it provides means for generating product lists, automatically creating Gantt charts, automatically generating meeting minutes, and handling inquiries. This significantly improves the work efficiency of sales representatives and supports them in achieving their targets.

[1705] Hardware and software to be used

[1706] 1. Server

[1707] Main processor: Server equipped with a high-speed CPU

[1708] Database: MySQL database

[1709] External API: Google Trends API, Google Cloud Speech-to-Text API

[1710] Analysis tools: Python, pandas, scikit-learn, numpy

[1711] Document generation: Generative AI models (e.g., GPT-3)

[1712] 2. Terminal

[1713] User Interface (UI): A user interface that operates on a web browser.

[1714] Display devices: PC, tablet, smartphone

[1715] Specific example

[1716] Goal input and data collection

[1717] 1. Enter your goal

[1718] The user logs into their device and selects the "Create Profit Target Achievement Plan" option. For example, the user might enter "Sales target for the next quarter: 10 million yen, profit margin: 20%."

[1719] 2. Data Collection

[1720] The server automatically retrieves historical sales and customer data from a MySQL database. Furthermore, it obtains market trend data via the Google Trends API and an internal database.

[1721] Sales forecasting and plan generation

[1722] The server performs sales forecasts based on the acquired data. Specifically, it preprocesses the data using Python's pandas library and scikit-learn, and applies time series analysis and regression analysis models. Profit margin analysis is performed similarly, and the server calculates the required sales volume and cost structure considering the target profit margin entered by the user.

[1723] The server generates a suitable product list based on sales forecast results and profit margin analysis results. Along with this product list, it automatically generates a "Goal Achievement Plan" document using a generation AI model (e.g., GPT-3). The generated plan document is sent to the user's terminal and displayed.

[1724] Example of a prompt:

[1725] "Our sales target for the next quarter is 10 million yen, with a profit margin of 20%. Please create a plan to achieve these targets."

[1726] Gantt chart creation and meeting minutes management

[1727] The server calculates the project schedule and automatically generates a Gantt chart when the user enters project details. The Gantt chart is displayed in a web browser.

[1728] To automatically generate meeting minutes, the system uses the Google Cloud Speech-to-Text API to convert meeting audio into text based on the meeting agenda and key points entered by the user, and then generates the minutes using NLP (Natural Language Processing) technology. The generated minutes are displayed on the user's device, and the next action items are also automatically generated.

[1729] Inquiry response

[1730] When a user enters a question about a product or internal tool into their terminal, the server uses Elasticsearch to search the FAQ database and relevant documents, automatically generating an appropriate answer. The generated answer is then displayed on the user's terminal.

[1731] Based on the above, the present invention provides a system that streamlines the work of sales representatives and supports them in achieving their goals.

[1732] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1733] Step 1: Enter your goal

[1734] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[1735] The user enters "A sales target of 10 million yen and a profit margin of 20% for the next quarter."

[1736] The input data is packaged in JSON format as form data and sent to the server using a REST API.

[1737] Input: User-entered data on sales target amount and profit margin.

[1738] Output: Target amount and profit margin data sent to the server

[1739] Step 2: Data Collection

[1740] The server retrieves historical sales and customer data from a MySQL database.

[1741] The server retrieves market trend data through the Google Trends API and its internal database.

[1742] Data is retrieved using SQL queries, and data obtained from external APIs is stored in JSON format.

[1743] Input: Sales target amount and profit margin data

[1744] Output: Historical sales data, customer data, and market trend data retrieved from databases and external APIs.

[1745] Step 3: Data Analysis (Sales Forecast)

[1746] The server performs sales forecasts based on acquired historical sales data and market trend data.

[1747] The data is preprocessed using the Python pandas library, and predictions are made by applying statistical models (e.g., time series analysis, regression analysis).

[1748] The forecast results output a numerical representation of the likelihood of achieving the sales target.

[1749] Input: Historical sales data, customer data, market trend data

[1750] Output: Sales forecast data

[1751] Step 4: Profit Margin Analysis

[1752] Based on the predicted sales data, the server uses the NumPy library to perform sales and cost analysis, taking into account the required profit margin of 20%.

[1753] Calculate the required sales volume and cost structure, and output specific figures.

[1754] Input: Sales forecast data, sales target amount, profit margin

[1755] Output: Data on required sales volume and cost structure

[1756] Step 5: Plan Generation

[1757] The server generates a list of products necessary to achieve sales targets based on sales forecasts and profit margin analysis results.

[1758] The server uses a generative AI model (e.g., GPT-3) to automatically generate a "Goal Achievement Plan" document.

[1759] The document is exported in PDF or Word format and sent to the user's device.

[1760] Input: Sales forecast, profit margin analysis results, product list

[1761] Output: "Goal Achievement Plan" document

[1762] Step 6: Create a Gantt chart

[1763] The user enters the details of a new case into the terminal.

[1764] The server automatically generates a Gantt chart using D3.js or Chart.js based on the input data.

[1765] The Gantt chart is displayed on the user's device, enabling project schedule management.

[1766] Input: Project details data

[1767] Output: Gantt chart

[1768] Step 7: Generate meeting minutes

[1769] The user enters the meeting agenda and key points into the terminal.

[1770] The server uses the Google Cloud Speech-to-Text API to convert meeting audio into text and generates meeting minutes using an NLP model.

[1771] The meeting minutes and next action items will be displayed in document format on the user's device.

[1772] Input: Meeting agenda, key points, meeting audio

[1773] Output: Meeting minutes, next action items

[1774] Step 8: Handling inquiries

[1775] Users enter questions about products or internal tools into the terminal.

[1776] The server uses Elasticsearch to search the FAQ database and related documents, and generates appropriate automated responses.

[1777] The automated response will be displayed on the user's device.

[1778] Input: User's question

[1779] Output: Automated response

[1780] (Application Example 1)

[1781] 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".

[1782] Traditional sales efficiency improvement systems were limited to sales forecasting and target setting functions, lacking the tools and features to effectively utilize the analysis results. Furthermore, features such as schedule management, meeting minute generation, and automated inquiry handling were not sufficiently integrated, making it difficult for sales representatives to efficiently manage their diverse tasks.

[1783] 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.

[1784] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means for generating a Gantt chart for schedule management, means for automatically generating meeting minutes, and means for automatically generating inquiry responses. This enables the user to efficiently perform tasks from sales target planning and schedule management to meeting minute generation and inquiry handling in a consistent manner.

[1785] "Target sales amount" refers to a specific sales figure that you want to achieve in your sales activities or business plan.

[1786] "Profit margin" is an indicator that shows the ratio of profit earned to sales revenue.

[1787] "Past sales data" refers to historical sales information recorded up to the present.

[1788] "Customer data" refers to aggregated data about customers, including age, gender, and purchase history.

[1789] "Market trend data" refers to data related to market trends and consumer behavior.

[1790] "Means of sales forecasting" refers to methods or devices for predicting future sales based on collected data.

[1791] "Sales volume required to achieve the target" refers to the number of products that need to be sold to achieve the set target sales amount and profit margin.

[1792] A "product list" is a list of the products and services that are sold.

[1793] "Means of automatically generating documents in document format" refers to methods or devices for analyzing data and automatically converting the results into documents such as reports and proposals.

[1794] "Means for outputting documents" refers to methods or devices for providing the generated documents to the user.

[1795] A "Gantt chart for schedule management" is a diagram in the form of a Gantt chart used to visually display the schedule of a project or task.

[1796] "Methods for automatically generating meeting minutes" refer to methods or devices that analyze the content of a meeting and automatically create a summary of it as meeting minutes.

[1797] "Means for automatically generating inquiry responses" refers to methods or devices for automatically providing appropriate responses to questions and inquiries from users.

[1798] The present invention relates to a system for improving sales efficiency, and its embodiments will be described in detail below.

[1799] Goal input and data collection

[1800] 1. Enter your goal

[1801] The user (sales representative) logs into the terminal and selects the displayed "Create Profit Target Achievement Plan" option. The user enters the target sales amount and profit margin into the terminal, and that data is sent to the server.

[1802] 2. Data Collection

[1803] The server automatically retrieves historical sales and customer data from the store's point-of-sale (POS) system. Market trend data is obtained from external APIs and internal databases.

[1804] Sales forecasting and plan generation

[1805] 3. Data Analysis

[1806] The server analyzes sales trends using collected data. Based on market trend data, it uses statistical models (e.g., time series analysis, regression analysis) to predict future sales.

[1807] 4. Profitability analysis

[1808] The server estimates realistic profit margins based on the acquired data. Considering the target profit margin, it calculates the necessary sales revenue and cost structure.

[1809] 5. Plan generation

[1810] The server calculates the sales volume required to achieve the target and generates a suitable product list. Based on the sales forecast and product list, it automatically generates a "Target Achievement Plan" document and sends this document to the user's terminal.

[1811] Gantt chart creation and meeting minutes management

[1812] 6. Create a Gantt chart

[1813] When a project is accepted, the user enters the project details into the terminal, and the server calculates the project's timeline and automatically generates a Gantt chart. The Gantt chart is displayed on the terminal, allowing the user to manage their schedule.

[1814] 7. Minutes generation

[1815] When a user enters meeting agenda items and key points into their terminal, the server analyzes the meeting audio and documents and automatically generates meeting minutes. Next action items are also generated and displayed on the terminal along with the meeting minutes.

[1816] Inquiry response

[1817] 8. Inquiries regarding products and tools

[1818] When a user enters a question about a product or internal tool into the terminal, the server searches the FAQ database and relevant documents and generates an automated response. The answer is then displayed on the terminal.

[1819] Specific example

[1820] As a concrete example, consider a scenario where a user inputs "The sales target for the next quarter is 10 million yen, with a profit margin of 20%." The user inputs the target into their terminal, and this data is sent to the server. The server retrieves past sales data and market trends and makes a sales forecast. Subsequently, the server performs an analysis based on profit margins, calculates the necessary sales volume and product list, and sends the generated "Next Quarterly Target Achievement Plan" document to the user's terminal. The user reviews this plan document, plans and executes their sales activities.

[1821] Example of a prompt

[1822] "Our sales target for the next quarter is 10 million yen, with a profit margin of 20%. Please analyze past sales data and market trends to generate the optimal sales promotion plan."

[1823] This system significantly improves the efficiency of sales representatives' work and enables them to develop effective strategies for achieving sales targets and profit margins.

[1824] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1825] Step 1:

[1826] The user enters their target sales amount and profit margin into the terminal. The input data is in the following format: "Target sales amount: 10 million yen", "Profit margin: 20%". This data is sent from the terminal to the server. The server stores the received data in its database.

[1827] Step 2:

[1828] The server retrieves historical sales and customer data from the store's POS system and related databases. Furthermore, it collects market trend data through external APIs and internal databases. This data is temporarily stored on the server for analysis.

[1829] Step 3:

[1830] The server uses time series analysis and regression analysis techniques to forecast sales based on historical sales data. The input data consists of past sales records, while the output data represents future sales forecasts. The server performs data analysis using Python's Pandas and scikit-learn libraries.

[1831] Step 4:

[1832] The server uses collected market trend data to perform detailed analysis based on sales forecasts. Specifically, it readjusts the forecasting model and calculates the final forecast values ​​that take into account fluctuations. The output data consists of predicted sales and profit margins.

[1833] Step 5:

[1834] The server calculates the required sales revenue and costs based on the set target profit margin. This generates the sales volume and appropriate product list needed to achieve the target. The input data is the profit margin and sales forecast, and the output data is the product list.

[1835] Step 6:

[1836] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product list data. This document includes sales strategies, required sales figures, and recommended products. The server then sends the generated document to the user's terminal.

[1837] Step 7:

[1838] When a user enters project details into their terminal, the server automatically generates the project schedule and creates a Gantt chart. The input data is detailed project information, and the output data is a schedule in Gantt chart format.

[1839] Step 8:

[1840] When a user enters meeting agenda items and key points into their terminal, the server analyzes the meeting's audio data and documents to automatically generate meeting minutes. Specifically, it uses speech recognition technology to transcribe the meeting content into text, extracts necessary information, and compiles it into meeting minutes. The output data is the automatically generated meeting minutes.

[1841] Step 9:

[1842] When a user enters a question about a product or internal tool into a terminal, the server searches the FAQ database and relevant documents and generates a response to the inquiry. The input data is the user's question, and the output data is the automatically generated response. The server uses a natural language generation model such as the GPT-4 API to create the response.

[1843] Through these steps, this system dramatically improves the efficiency of sales representatives and provides concrete plans for achieving goals.

[1844] 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.

[1845] This invention is a system for improving sales efficiency, combining an emotion engine that recognizes user emotions to enable more appropriate suggestions and two-way communication. The embodiments are described in detail below.

[1846] Goal input and data collection

[1847] 1. Enter your goal

[1848] The user (sales representative) logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system.

[1849] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal. The entered data is then sent to the server.

[1850] 2. Data Collection

[1851] The server retrieves historical sales data and customer data from the database.

[1852] The server retrieves market trend data from external APIs and internal databases.

[1853] Sales forecasting and plan generation

[1854] 3. Data Analysis

[1855] The server analyzes sales trends using past sales data.

[1856] The server uses statistical models (e.g., time series analysis, regression analysis) based on market trend data to forecast sales.

[1857] 4. Profitability analysis

[1858] The server estimates a realistic profit margin based on the acquired data.

[1859] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%).

[1860] 5. Plan generation

[1861] The server calculates the sales figures needed to achieve the target and generates a product list.

[1862] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists.

[1863] The server sends the generated plan document to the user's terminal.

[1864] Utilizing the Emotion Engine

[1865] 6. Emotion recognition

[1866] When a user enters a goal or inquiry, the device uses an emotion engine to recognize emotions from the user's facial expressions and voice.

[1867] The server receives the sentiment analysis results and supplements them with the user's input information.

[1868] 7. Emotion-based proposals

[1869] The server takes the sentiment analysis results into consideration to provide more appropriate suggestions to the user.

[1870] For example, if a user is experiencing stress, the system will provide advice and resources to help alleviate that burden.

[1871] Gantt chart creation and meeting minutes management

[1872] 8. Create a Gantt chart

[1873] When a project is accepted, the user enters the project details into the terminal.

[1874] The server calculates the project's timeline and automatically generates a Gantt chart.

[1875] The device displays a Gantt chart, allowing the user to manage their schedule.

[1876] 9. Minutes generation

[1877] The user enters the meeting agenda and key points into the terminal.

[1878] The server analyzes meeting audio and documents and automatically generates meeting minutes.

[1879] The server generates the following action items and displays the meeting minutes and action items on the terminal.

[1880] Sentiment analysis of meeting minutes

[1881] 10. Recognizing emotions during meetings

[1882] The device analyzes audio data from the meeting using an emotion engine and records the speaker's emotions, which are then reflected in the meeting minutes.

[1883] Based on the analysis results, the server records the emotional state and its changes in the meeting minutes.

[1884] Inquiry response

[1885] 11. Inquiries regarding products and tools

[1886] Users enter questions about products or internal tools from their devices.

[1887] The server receives the user's question and searches the FAQ database and related documents.

[1888] The server generates an automated response and obtains an answer to the user's question.

[1889] The device displays the answer, allowing the user to check the necessary information.

[1890] Specific example

[1891] As a concrete example, consider a scenario where a user inputs, "The sales target for the next quarter is 10 million yen, and the profit margin is 20%."

[1892] 1. The user enters the goal into their device and sends it to the server.

[1893] 2. The server uses an emotion engine to analyze the user's emotions at the time of input and obtain information such as stress levels and satisfaction levels.

[1894] 3. The server retrieves historical data and market trends to perform sales forecasts.

[1895] 4. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[1896] 5. Based on the sentiment analysis results, the server automatically generates a "Next Quarterly Goal Achievement Plan" document containing optimal advice for the user.

[1897] 6. The server sends the generated plan document to the user's terminal.

[1898] 7. Users review plan documents on their devices and plan and execute sales activities.

[1899] As described above, the present invention realizes a system that streamlines the work of sales representatives and provides more appropriate support by utilizing emotion recognition.

[1900] The following describes the processing flow.

[1901] Creating profit target achievement plans and utilizing the emotional engine

[1902] Step 1:

[1903] The user logs into their device and selects the "Create Profit Target Achievement Plan" option.

[1904] Step 2:

[1905] The user enters their target sales amount (e.g., 10 million yen) and profit margin (e.g., 20%) into the terminal and clicks the "Submit" button.

[1906] Step 3:

[1907] The device analyzes the user's facial expressions and voice during input using an emotion engine to detect their emotional state (e.g., stress, satisfaction level, excitement level).

[1908] Step 4:

[1909] The device sends the detected emotional state to the server as data.

[1910] Step 5:

[1911] The server receives data from the user regarding target sales figures, profit margins, and emotional states.

[1912] Step 6:

[1913] The server retrieves historical sales data from the database. For example, it might retrieve quarterly sales figures for the past three years.

[1914] Step 7:

[1915] The server retrieves customer data from the database. It collects information such as each customer's purchase history and contract status.

[1916] Step 8:

[1917] The server retrieves market trend data from external APIs and internal databases. For example, it collects the latest data such as industry reports and economic indicators.

[1918] Step 9:

[1919] The server analyzes sales trends using historical sales data. For example, it uses time series analysis to understand seasonal fluctuations and trends in sales.

[1920] Step 10:

[1921] The server predicts future sales based on market trend data. Regression analysis and machine learning models are applied to achieve highly accurate predictions.

[1922] Step 11:

[1923] The server calculates the necessary sales revenue and cost structure, taking into account the target profit margin (e.g., 20%). For example, it calculates the optimal product mix while considering the profit margin.

[1924] Step 12:

[1925] The server calculates the sales figures needed to achieve the target. Specifically, it calculates the required sales figures for each product.

[1926] Step 13:

[1927] The server matches customer data with product data and generates a list of products optimized for each customer. For example, it might list products recommended based on a customer's past purchase history.

[1928] Step 14:

[1929] The server automatically generates a "Next Quarterly Target Achievement Plan" document based on sales forecasts and product lists. The document includes sales forecast graphs and product list tables.

[1930] Step 15:

[1931] Based on the sentiment analysis results, the server adds advice and support messages tailored to the user's emotional state to the plan document.

[1932] Step 16:

[1933] The server sends the generated plan document to the user's terminal.

[1934] Step 17:

[1935] The device displays the received plan document, allowing the user to review the document.

[1936] Gantt chart creation and meeting minutes management

[1937] Step 1:

[1938] When a user accepts a project, they enter the project details from their device.

[1939] Step 2:

[1940] The server receives the project details and calculates the project schedule.

[1941] Step 3:

[1942] The server automatically generates a Gantt chart, displaying the project's start date, end date, and schedule for each stage.

[1943] Step 4:

[1944] The device displays a Gantt chart, allowing the user to manage their schedule.

[1945] Sentiment analysis of meeting minutes

[1946] Step 1:

[1947] The user enters the meeting agenda and key points into the terminal.

[1948] Step 2:

[1949] The device analyzes the audio data during the meeting using an emotion engine to detect the emotional state of the speaker.

[1950] Step 3:

[1951] The device sends the detected emotional state to the server as meeting minutes data.

[1952] Step 4:

[1953] The server automatically generates meeting minutes based on audio data and emotional states. Emotional states are also reflected in the minutes.

[1954] Step 5:

[1955] The server generates the next action item and displays the meeting minutes and action item on the terminal.

[1956] Inquiry response

[1957] Step 1:

[1958] Users enter questions about products or internal tools from their devices.

[1959] Step 2:

[1960] The device uses an emotion engine to analyze facial expressions and voice during questioning and detect the emotional state.

[1961] Step 3:

[1962] The device sends question data, including emotional state, to the server.

[1963] Step 4:

[1964] The server receives the user's question and sentiment state, and searches the FAQ database and related documents.

[1965] Step 5:

[1966] The server automatically generates responses based on the user's emotional state and outputs responses that include appropriate advice.

[1967] Step 6:

[1968] The device displays the answer, allowing the user to check the necessary information.

[1969] (Example 2)

[1970] 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".

[1971] Traditional sales support systems often provided uniform proposals and support without considering the user's emotions or state of mind. As a result, they failed to alleviate user stress and anxiety, hindering effective sales activities. Furthermore, tasks such as creating meeting minutes and managing Gantt charts were time-consuming and cumbersome, making efficient work difficult. In addition, there was a lack of technology to recognize emotional states during meetings and provide appropriate feedback. These problems increased the burden on sales representatives and decreased work efficiency.

[1972] 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.

[1973] In this invention, the server includes means for inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means including an emotion engine for recognizing user emotions, means for making appropriate suggestions to the user based on the emotion analysis results by the emotion engine, means for automatically generating meeting minutes, means for generating the next action items based on the meeting minutes, means for analyzing the emotions of speakers during the meeting and reflecting them in the meeting minutes, and means for automatically generating a Gantt chart from the time of order acceptance until implementation. This enables effective suggestions that take user emotions into consideration, thereby improving the efficiency of sales activities. In addition, it eliminates the effort of creating meeting minutes and managing Gantt charts, thus improving operational efficiency. Furthermore, it enables appropriate feedback through emotion analysis during meetings, which is expected to improve meeting productivity.

[1974] "Target sales amount" refers to the amount of sales that should be achieved through sales activities.

[1975] "Profit margin" refers to the ratio of profit to sales revenue and is an indicator used to evaluate the profitability of business activities.

[1976] "Past sales data" refers to data that includes historical sales information for the period up to now.

[1977] "Customer data" refers to data containing information about a customer, including their name, contact information, and purchase history.

[1978] "Market trend data" refers to data that includes information on the latest trends and developments in an industry or market.

[1979] "Sales forecasting" is the process of estimating future sales based on acquired data.

[1980] "Sales volume" refers to the quantity of goods or services sold within a specific period.

[1981] A "product list" is a list that shows a list of products and services that are sold.

[1982] "Automatically generating a document in a document format" refers to the process by which a system automatically creates a document based on input data and calculation results.

[1983] An "emotion engine" is a technology that analyzes a user's emotional state from their facial expressions, voice, and other data.

[1984] "Emotion analysis results" refer to data about the user's emotions obtained as a result of analysis using an emotion engine.

[1985] An "appropriate suggestion" is one that indicates the optimal action or strategy based on the user's emotional state and sales objectives.

[1986] "Meeting minutes" are documents that record the content of discussions held during meetings or conferences.

[1987] An "action item" is an item that indicates the specific tasks or action plan to be taken next based on the meeting minutes.

[1988] A Gantt chart is a diagram that visually represents a schedule used in project management.

[1989] This invention is a system that streamlines sales activities and provides support that takes user emotions into consideration. The system consists of a user interface for inputting target sales figures and profit margins, a server for data collection and analysis, and an emotion engine for recognizing emotions. The specific configuration and processing methods are described below.

[1990] Goal input and data collection

[1991] The user logs into their terminal and selects the "Create Profit Target Achievement Plan" option on the system. Next, they enter their target sales amount and profit margin. For example, a user might set a sales target of 10 million yen and a profit margin of 20% for the next quarter. This information is then sent from the terminal to the server.

[1992] The server retrieves historical sales and customer data from the database and collects market trend data using external APIs. Specifically, the server uses an SQL database to retrieve historical data and a REST API to collect market trends.

[1993] Sales forecasting and plan generation

[1994] The server uses statistical models such as time series analysis and regression analysis to make sales forecasts based on the acquired data. For example, Python libraries such as Pandas and scikit-learn could be used. Sales trends are graphed, and sales for the next quarter are forecasted.

[1995] The server calculates the necessary sales revenue and cost structure based on the target profit margin. For example, it performs simulations using the Microsoft Excel API. Based on the simulation results, it generates a product list and automatically creates a "Target Achievement Plan" in document format, which includes the sales forecast and product list. The generated document is sent to the terminal and displayed to the user.

[1996] Utilizing the Emotion Engine

[1997] When a user enters their goals or inquiries, the device uses its camera and microphone to analyze their facial expressions and voice using an emotion engine. For example, this could involve using the OpenCV library and the Google Cloud Speech-to-Text API. The analysis results are sent to a server and supplemented with the user's input information.

[1998] Based on the emotion analysis results, the server provides situation-appropriate advice. For example, if the user is feeling stressed, the system will add advice such as "Try to relax and approach this task" to the document.

[1999] Gantt chart creation and meeting minutes management

[2000] When a project is accepted, the user enters the project details into their terminal. The server calculates the project's timeline based on this information and automatically generates a Gantt chart using a JavaScript library (e.g., DHTMLX Gantt). This chart is displayed on the terminal, allowing the user to manage their schedule.

[2001] During a meeting, users input agenda items and key points into their terminals. The server collects meeting audio, uses natural language processing (NLP) technology to transcribe the speech into text, and automatically generates meeting minutes. Python's NLTK library or the Google Cloud Speech-to-Text API are commonly used for this purpose. Furthermore, the server generates the following action items, which are also displayed on the terminals.

[2002] Examples of specific cases and prompts for generative AI models.

[2003] As a concrete example, consider a scenario where a user inputs "The sales target for the next quarter is 10 million yen, with a profit margin of 20%." The server uses an emotion engine to analyze the user's emotions at the time of input and obtains information such as stress and satisfaction levels. Based on the analysis results, it performs a sales forecast and generates a planning document that includes an appropriate sales plan and advice. The generated document is sent to the user's terminal, and the user plans and executes sales activities based on it.

[2004] Examples of prompts for a generative AI model are as follows:

[2005] The sales target for the next quarter is 10 million yen, with a profit margin of 20%. Based on this, generate a sales forecast that takes historical data and market trends into account, along with a planning document that includes an appropriate sales plan. Also, include advice based on sentiment analysis during data entry.

[2006] As described above, the present invention provides a system that takes user emotions into consideration and effectively supports sales activities.

[2007] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2008] Step 1:

[2009] The user logs into the terminal and selects the "Create Profit Target Achievement Plan" option in the system. The user then enters a target sales amount (e.g., 10 million yen) and a profit margin (e.g., 20%). This input data is temporarily stored on the terminal and sent to the server.

[2010] Step 2:

[2011] The server retrieves historical sales and customer data from the database based on the received target sales amount and profit margin. Specifically, it extracts data by executing SQL queries. Furthermore, it uses an external API to obtain market trend data. This external API is accessed via a RESTful API. The input data consists of the target sales amount and profit margin, and based on this, database searches and API calls are performed, yielding historical sales data, market trend data, and other output.

[2012] Step 3:

[2013] The server analyzes sales trends and patterns based on the acquired data. Specifically, it uses Python's Pandas and scikit-learn libraries to perform time series analysis and regression analysis. The input data consists of historical sales data and market trend data. Based on this, a sales forecasting model is built, and the predicted sales data is obtained as output.

[2014] Step 4:

[2015] The server calculates the sales volume required to achieve the target based on sales forecast data. For example, it uses the Excel API to perform calculations that take into account profit margins and cost structures based on the predicted sales data. The input data is sales forecast data and target profit margins, and based on this, it estimates the required sales volume and cost structure, and outputs the specific sales volume required to achieve the target.

[2016] Step 5:

[2017] The server generates a product list based on the required sales volume. For example, sales targets and strategies are set for each product. The input data is the sales volume required to achieve the target, and the server creates a product list based on this, generating the product list as output.

[2018] Step 6:

[2019] The server automatically generates a "Goal Achievement Plan" document based on sales forecasts and product lists. This document includes specific sales strategies and implementation plans. A Python library could be used for document generation. The input data consists of sales forecast data and product lists; the server creates a document based on this data, and outputs the "Goal Achievement Plan" document.

[2020] Step 7:

[2021] The server sends the generated "Goal Achievement Plan" document to the user's terminal. The user reviews the document received on their terminal, plans and executes their sales activities. The input data is the generated document, which is then sent to the terminal and displayed to the user, resulting in the output.

[2022] Step 8:

[2023] When a user enters their goals or inquiries, the device uses an emotion engine to analyze the user's facial expressions and voice. Specifically, it uses the camera and microphone to utilize the OpenCV library and the Google Cloud Speech-to-Text API. The input data consists of the user's facial expressions and voice, which are used to analyze emotions, and the emotion analysis results are output.

[2024] Step 9:

[2025] The server receives the sentiment analysis results and supplements them with the user's input information. For example, if the user is feeling stressed, that information is added. The input data is the sentiment analysis results, which are supplemented based on the user's input information, and the output is user data with emotions.

[2026] Step 10:

[2027] The server provides optimal suggestions to the user based on the emotion analysis results. For example, if the user is feeling stressed, it will provide "advice on how to relax." The input data consists of the emotion analysis results and the user's input information. Based on this, the server forms advice and provides appropriate suggestions as output.

[2028] Step 11:

[2029] When a project is accepted, the user enters the project details into their terminal. The server calculates the project's timeline based on this information and automatically generates a Gantt chart using a JavaScript library (e.g., DHTMLX Gantt). The input data consists of detailed project information, which is used to generate the Gantt chart, and the output is a schedule for project management.

[2030] Step 12:

[2031] The user enters the meeting agenda and key points into a terminal. The server collects the meeting audio, converts it to text using natural language processing technology (e.g., Python's NLTK library or Google Cloud Speech-to-Text API), and automatically generates meeting minutes. The input data consists of meeting audio and agenda information, and the server generates the meeting minutes based on this data, providing the minutes as output.

[2032] Step 13:

[2033] The server generates the next set of action items based on the generated meeting minutes. The input data is the meeting minutes, and based on this, it lists the next set of action items and sends them to the terminal. The user reviews these action items, plans the next steps, and executes them.

[2034] Step 14:

[2035] During a meeting, the terminal analyzes the audio data using an emotion engine and records the speaker's emotions, which are then reflected in the meeting minutes. Based on the analysis results, the server records the emotional state and its changes in the meeting minutes. The input data consists of audio data and emotion analysis results. The meeting minutes are updated based on this data, and the output is meeting minutes with added emotions.

[2036] (Application Example 2)

[2037] 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".

[2038] In modern advertising planning and sales activities, it is crucial to make appropriate proposals that take into account the emotional state of the target audience. However, traditional systems have struggled to grasp user emotions and make flexible proposals based on them. Furthermore, the process from proposing advertising campaigns to automatically registering them with each advertising platform is inconsistent, resulting in manual work and significant time and effort required.

[2039] 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 inputting target sales amount and profit margin, means for acquiring past sales data, customer data, and market trend data, means for making sales forecasts based on the acquired data, means for calculating the number of sales required to achieve the target, means for generating an appropriate product list, means for automatically generating the sales forecast and product list in document format, means for outputting the document, means for analyzing the user's emotions using an emotion recognition engine, and means for making optimal suggestions to the user based on the emotion analysis results. This enables the proposal of flexible advertising plans that take into account the user's emotions and the efficient management of advertising campaigns.

[2040] "Target sales amount" refers to the target sales amount that a user aims to achieve within a specific period.

[2041] "Profit margin" is an indicator that shows the ratio of profit to sales, and it indicates the efficiency of management.

[2042] "Sales data" refers to data on past sales performance, including information such as the number of units sold and the sales amount for each product.

[2043] "Customer data" refers to information about past and present customers, including purchase history, attribute information, and behavioral data.

[2044] "Market trend data" refers to data on current market trends and future predictions, including consumer behavior and the activities of competitors.

[2045] "Sales forecasting" is the process of predicting future sales based on past sales data and market trend data.

[2046] "Sales volume" refers to the actual number of a particular product or service sold within a certain period of time.

[2047] A "product list" is a list of products or services that are targeted for sale or advertising.

[2048] "Automatically generating documents in a specified format" refers to the process by which a system automatically creates documents based on specific data.

[2049] An "emotion recognition engine" is a technology that analyzes a person's facial expressions and voice data to identify their emotions.

[2050] An "advertising campaign" is a series of advertising activities planned to promote a specific product or service.

[2051] An "advertising platform" is a term that refers to the entire system and service used to deliver advertisements over the internet.

[2052] This invention is a system that improves the efficiency of advertising planning by combining an emotion recognition engine. The embodiments thereof are described in detail below.

[2053] System Configuration and Operation Overview

[2054] User login and targeting

[2055] The user (advertiser) logs in to their smartphone or tablet and selects the "Create Ad Plan" option in the application. The user sets a specific target audience. For example, they might select "Men aged 25-34, Asia region." This setting data is sent to the server.

[2056] Data collection

[2057] The server retrieves data from past advertising campaigns from the advertising database. It also uses external APIs to obtain market trend data. For example, it retrieves past advertising data from "https: / / api.pastadsdata.com" and market trend data from "https: / / api.markettrends.com".

[2058] Advertising plan generation

[2059] The server analyzes collected historical data and market trend data using statistical methods (e.g., time series analysis, clustering) to generate the optimal advertising plan. The generated advertising plan includes information on suggested creatives, messages, and advertising delivery platforms (e.g., Facebook, Google AdWords).

[2060] Utilization of emotion recognition engines

[2061] When a user reviews an ad plan, the system uses the camera and microphone on their smart device to analyze their emotions from their facial expressions and voice using an emotion recognition engine. The results of the emotion analysis are sent to a server, and the suggestions are adjusted based on the user's state. For example, if the user is feeling stressed, the system will offer alternative plans or advice for relaxation.

[2062] Advertising campaign development

[2063] Once the final advertising plan is confirmed, the server automatically registers the campaign with each advertising platform and begins delivery. This virtually eliminates the need for manual intervention, streamlining the management and deployment of advertising plans.

[2064] Usage example

[2065] For example, in a scenario where the user inputs "The sales target for the next quarter is 10 million yen, and the profit margin is 20%":

[2066] 1. The user enters the goal into their device and sends it to the server.

[2067] 2. The server uses an emotion recognition engine to analyze the user's emotions at the time of input. It obtains information such as stress levels and satisfaction levels.

[2068] 3. The server retrieves historical data and market trends to perform sales forecasts.

[2069] 4. The server performs an analysis based on profit margins and calculates the required sales volume and product list.

[2070] 5. Based on the sentiment analysis results, the server automatically generates a "Next Quarterly Goal Achievement Plan" document that includes optimal advice for the user.

[2071] 6. The ad campaign targeting the defined audience is automatically registered with each ad delivery platform, and delivery begins.

[2072] Thus, the present invention is a system that proposes flexible advertising plans that take user emotions into consideration, and streamlines the management and deployment of advertising campaigns.

[2073] Example of a prompt

[2074] Use the following API to generate an ad plan for your target audience in the Asian region, aged 25-34.

[2075] 1. Retrieve past advertising campaign data from https: / / api.pastadsdata.com.

[2076] 2. Obtain the latest market trend data from https: / / api.markettrends.com.

[2077] 3. Consider the user's emotions and provide the optimal advertising plan.

[2078] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2079] Step 1:

[2080] The user logs in on their smart device and selects the ad plan creation option. The user sets a specific target audience (e.g., men aged 25-34, Asian region), and this information is sent to the server. The input data is attribute information of the target group, which is used for data collection and analysis in the next step.

[2081] Step 2:

[2082] The server retrieves historical advertising campaign data from the advertising database and also obtains market trend data from external APIs (e.g., https: / / api.pastadsdata.com and https: / / api.markettrends.com). The retrieved data includes information such as sales performance and market trends, and is stored in the database for analysis.

[2083] Step 3:

[2084] The server uses statistical models (e.g., time series analysis, clustering) to process and perform calculations on historical advertising campaign data and market trend data. This analysis predicts the most effective advertising plan for the target audience. The output includes predicted sales and response rates, as well as a list of suggested ad creatives and messages.

[2085] Step 4:

[2086] The server automatically generates the created ad plan in document format (e.g., PDF or HTML report). The ad plan document includes detailed information such as specific suggestions for the target audience and recommended ad serving platforms. The generated document is then sent to the user's device.

[2087] Step 5:

[2088] When a user reviews an ad plan, an emotion recognition engine analyzes the user's facial expressions and voice through the device's camera and microphone. The input data consists of camera images and audio data, which are analyzed in real time to detect the user's emotional state (e.g., stress, satisfaction).

[2089] Step 6:

[2090] The analysis results from the emotion recognition engine are sent to the server, and the suggested advertising plan is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the server will provide alternative plans or advice for relaxation. This ultimately determines the optimal advertising plan for the user.

[2091] Step 7:

[2092] The server automatically registers the finalized ad plan with each ad delivery platform (e.g., Facebook, Google AdWords) and begins delivery. The input data is the details of the final ad plan, which is sent to each ad delivery platform. The output is the start of the ad campaign on each platform.

[2093] The above processing steps ensure that the process from ad plan creation to delivery proceeds efficiently, enabling flexible suggestions based on user emotions.

[2094] 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.

[2095] 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.

[2096] In the above embodiment, an example was given in which the 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.

[2097] 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.

[2098] 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.

[2099] 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.

[2100] 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.

[2101] 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.

[2102] 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."

[2103] 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.

[2104] 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.

[2105] 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.

[2106] 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.

[2107] 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.

[2108] 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.

[2109] 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 executin...

Claims

1. A means to input target sales amount and profit margin, Means for obtaining historical sales data, customer data, and market trend data, A means of making sales forecasts based on the acquired data, A means of calculating the sales figures needed to achieve the target, A means of generating an appropriate product list, A means for automatically generating the aforementioned sales forecast and product list in document format, Means for outputting the aforementioned document, A system that includes this.

2. A method for automatically generating meeting minutes, A means for generating the following action items based on the aforementioned meeting minutes, The system according to claim 1, further comprising:

3. A method for automatically generating a Gantt chart from the time of project acceptance until implementation, The system according to claim 1, further comprising:

Images