system

Abstract

We provide the system. [Solution] A means for receiving product information and conditions in natural language format from users, A means for analyzing the aforementioned product information and conditions and extracting the type of product, period, and plan, A means for obtaining relevant cost and fee information from an internal database based on the aforementioned product information and conditions, A means for calculating total costs and break-even points using acquired cost and fee information, A means of generating calculation results in natural language format and returning them to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional proposal phase, the methods for calculating the proposed amount and gross profit require a lot of time and labor, and it is difficult to respond quickly in situations where quick decisions are required. Also, human errors may occur in the calculation process, lacking accuracy. Therefore, there is a need to provide a system that can immediately and accurately calculate the proposed amount and gross profit based on the product information and conditions in the form of natural language input by the user.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for receiving product information and conditions in natural language format from a user; means for analyzing the product information and conditions and extracting the type of product, period, and plan; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total cost and break-even point using the obtained cost and fee information; and means for generating the calculation results in natural language format and returning them to the user. With this system, users can instantly calculate proposed prices and gross profit simply by inputting product information and conditions in natural language, enabling quick decision-making and eliminating human error in the calculation process.

[0006] A "user" refers to an entity that uses the system to input product information and conditions and receives the calculation results.

[0007] "Natural language form" refers to the language form that humans use on a daily basis, and is primarily input as text.

[0008] "Product information" refers to information related to a specific product or service, and specifically includes product name, model, price, etc.

[0009] "Conditions" refer to the detailed specifications and requirements regarding the use or provision of the product or service, which are entered along with the product information. Specifically, these include the duration, plan, and pricing structure.

[0010] "Means" refers to methods or devices used to achieve a specific function.

[0011] "Analyzing" refers to the process of breaking down input information in natural language form and extracting specific meanings or data.

[0012] "Extracting" refers to taking out the necessary elements from the analyzed information.

[0013] An "internal database" refers to a system of information where data used for calculations and references is stored.

[0014] "Relevant cost and price information" refers to cost and price information related to specific merchandise or services.

[0015] "Total cost" refers to the total cost calculated based on specific periods or conditions.

[0016] "Break-even point" refers to the minimum revenue point required for revenue to exceed costs.

[0017] "Calculate" refers to performing numerical processing based on specified data.

[0018] "Generate in natural language form" refers to formulating the calculation results into a text in a form that is easy for humans to understand.

[0019] "Return" refers to returning the calculation results or information to the input source.

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 the 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 the emotion engine is combined.

Mode 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 terms used in the following description will be described.

[0023] In the following embodiments, the signed 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 system of this invention analyzes product information and conditions entered by the user in natural language, calculates total cost and break-even point based on the results, and returns the calculation results in natural language. This system consists of a user terminal, a server, and an internal database.

[0042] User input

[0043] Users input product information and conditions in natural language format using their terminals. For example, the following inputs are possible:

[0044] What is the break-even point (ARPU) for offering an iPhone 14 (registered trademark) as a 36-month rental with a fixed 20GB data allowance?

[0045] Server-based input analysis

[0046] The server receives input text sent by the user and parses it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the type of product, duration, and plan. This process yields information such as the following:

[0047] Product: iPhone 14

[0048] Duration: 36 months

[0049] Plan: 20GB flat rate

[0050] Cost information acquisition via server

[0051] Next, the server connects to an internal database to retrieve cost and pricing information related to the products and plans. For example, the internal database may store cost data such as the following:

[0052] iPhone 14 cost: ¥100,000

[0053] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0054] Server-based computation

[0055] The server uses the acquired cost information to calculate the total cost and the break-even point. The specific calculation is as follows:

[0056] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[0057] Break-even ARPU = Total Cost / 36 months

[0058] In this example, the total cost is ¥208,000 (¥100,000 + ¥3,000 x 36), and the break-even ARPU is ¥5,777.78.

[0059] Server returns results to the user.

[0060] Once the calculation is complete, the server generates the result in natural language format and sends it back to the user. The user can receive the calculation result on their terminal as follows:

[0061] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[0062] In this way, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, this system can eliminate human error and significantly improve operational efficiency.

[0063] As a concrete example, if a user enters "What is the break-even ARPU for a 36-month rental of an iPhone 14 with a fixed 20GB data plan?", the server immediately performs the calculation according to the above procedure, determining the break-even ARPU to be ¥5,777.78, and returning the result to the user. This allows the user to make quick decisions in the proposal phase.

[0064] The following describes the processing flow.

[0065] Step 1:

[0066] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[0067] Step 2:

[0068] The terminal transmits product information and conditions entered by the user in natural language format to the server. The user's input data is transmitted to the server via the network.

[0069] Step 3:

[0070] The server analyzes the user's input data. The server uses natural language processing (NLP) techniques to extract information such as the type of product (e.g., iPhone 14), the duration (e.g., 36 months), and the plan (e.g., 20GB flat rate).

[0071] Step 4:

[0072] Based on the analyzed product information and conditions, the server accesses an internal database to retrieve the necessary cost and fee information. Specifically, it extracts information such as the cost (e.g., ¥100,000) and plan (¥3,000 per month) of a product (iPhone 14) from the internal database.

[0073] Step 5:

[0074] The server uses the acquired cost and pricing information to calculate the total cost and break-even point (ARPU). The calculation is performed as follows:

[0075] Total cost = Product cost (¥100,000) + (Monthly plan cost (¥3,000) × period (36 months)) = ¥208,000

[0076] Break-even ARPU = Total cost (¥208,000) / Period (36 months) = ¥5,777.78

[0077] Step 6:

[0078] The server generates the calculation results in natural language format and sends them back to the user. The specific message in natural language format would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[0079] Step 7:

[0080] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[0081] In this way, throughout each step, the system can instantly calculate the proposed price and gross profit based on the conditions entered by the user in natural language, and return the results.

[0082] (Example 1)

[0083] 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."

[0084] Conventional systems faced the challenge of requiring significant time and effort to manually analyze product information and conditions to calculate total costs and break-even points. Furthermore, they were prone to errors due to human error, making it difficult to provide information quickly and accurately. This invention aims to solve these problems by automating the analysis of product information and the calculation of total costs and break-even points, thereby improving operational efficiency.

[0085] 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.

[0086] In this invention, the server includes means for receiving product information and conditions in natural language format from a user; means for analyzing the product information and conditions and extracting product types, periods, and plans; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total costs and break-even points using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; means for the user to input product information and conditions in natural language format using a terminal; means for the server to receive and analyze the input; means for the server to obtain cost information from the database; means for the server to perform calculations based on the acquired cost information; and means for returning the calculation results in natural language format. This enables the user to quickly and accurately obtain the proposed price and gross profit.

[0087] A "user" refers to a person or organization that uses the system to input product information and conditions in natural language format and receives calculation results.

[0088] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to input product information and conditions in natural language format and receive calculation results.

[0089] A "server" refers to a computing system that receives input information in natural language format, performs analysis and calculations, and returns the calculation results to the user.

[0090] "Natural language processing technology" refers to the techniques and algorithms used to automatically analyze natural language text and extract necessary information, rather than manually.

[0091] An "internal database" refers to a database system used to store data such as cost and fee information related to product information.

[0092] "Product information" refers to information about the products handled by the system, including data in natural language format that users input, such as the type of product.

[0093] "Conditions" refers to additional information such as the duration and plan that accompanies the product information.

[0094] "Cost information" refers to data that stores cost and fee information related to products and plans.

[0095] "Total cost" refers to the sum of all costs associated with the product and plan.

[0096] The "break-even point" refers to a figure used to determine the profit margin by dividing total costs over a specific period.

[0097] The system of this invention analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language. This system consists of a user terminal, a server, and an internal database.

[0098] First, the user uses their device to input product information and conditions in natural language format. For example, the following inputs are possible:

[0099] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[0100] The server receives input text sent by the user and parses it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the product type, duration, and plan. This process yields information such as the following:

[0101] Product: iPhone 14

[0102] Duration: 36 months

[0103] Plan: 20GB flat rate

[0104] The server then connects to an internal database to retrieve cost and pricing information related to the product or plan. For example, the internal database may contain cost data such as the following:

[0105] iPhone 14 cost: ¥100,000

[0106] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0107] The server uses the acquired cost information to calculate the total cost and the break-even point. The specific calculation is as follows:

[0108] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[0109] Break-even ARPU = Total Expenses / 36 months

[0110] In this example, the total cost is ¥208,000 (¥100,000 + ¥3,000 x 36), and the break-even ARPU is ¥5,777.78.

[0111] Once the calculation is complete, the server generates the results in natural language format and sends them back to the user. The user can receive the calculation results on their terminal as follows:

[0112] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[0113] In this way, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, this system can eliminate human error and significantly improve operational efficiency.

[0114] As a concrete example, if a user inputs "What is the break-even ARPU for a 24-month lease of an iPad® Pro with a fixed 10GB data plan?", the server immediately performs the calculation according to the above procedure, determining the break-even ARPU to be ¥5,833.33, and returning the result to the user. This allows the user to make quick decisions during the proposal phase.

[0115] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0116] Step 1:

[0117] Users enter product information and conditions in natural language format using their devices.

[0118] Input: Natural language text (Example: "What is the break-even ARPU for offering an iPhone 14 on a 36-month rental basis with a fixed 20GB data plan?")

[0119] Output: Sending input data from the user

[0120] Specific action: The user types text into the input field on the device and clicks the "Send" button.

[0121] Step 2:

[0122] The server receives the input text from the user.

[0123] Input: Natural language text submitted by the user.

[0124] Output: Receiving input data on the server side

[0125] Specific operation: The server receives an HTTP request at a specific API endpoint.

[0126] Step 3:

[0127] The server uses natural language processing (NLP) techniques to analyze the input text.

[0128] Input: Received text in natural language format

[0129] Output: Extraction results for product type, duration, and plan (e.g., "Product: iPhone 14", "Duration: 36 months", "Plan: 20GB flat rate")

[0130] Specific operation: The server analyzes the text using a natural language processing library (e.g., spaCy or NLTK) and extracts the necessary information.

[0131] Step 4:

[0132] The server connects to an internal database to retrieve relevant cost and pricing information.

[0133] Input: Extracted product type, duration, plan

[0134] Output: Cost information and pricing information (e.g., "Cost of iPhone 14: ¥100,000", "Monthly cost of 20GB flat-rate plan: ¥3,000")

[0135] Specific operation: The server connects to a database management system (e.g., SQLite, MySQL®) and issues SQL queries to retrieve the necessary data.

[0136] Step 5:

[0137] Based on the cost information acquired by the server, the total cost and break-even point are calculated.

[0138] Input: Cost information and fee information (e.g., ¥100,000, ¥3,000)

[0139] Output: Calculation results (Example: Total cost: ¥208,000, Break-even ARPU: ¥5,777.78)

[0140] Specific operation: The server sums up each cost to calculate the total cost. Then, it divides the total cost by the period (number of months) to calculate the break-even point.

[0141] Step 6:

[0142] The server generates the calculation results in natural language format and sends them back to the user.

[0143] Input: Calculation result (Example: Total cost: ¥208,000, Break-even ARPU: ¥5,777.78)

[0144] Output: Result report in natural language format (Example: "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.")

[0145] Specific operation: The server uses a template engine (e.g., Jinja2) to assemble a report in natural language format and sends it back to the user's terminal as an HTTP response.

[0146] Step 7:

[0147] The user checks the calculation results on their device.

[0148] Input: Natural language result report returned from the server

[0149] Output: Visualization of calculation results

[0150] Specific action: The user checks the returned result on their device screen.

[0151] (Application Example 1)

[0152] 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."

[0153] Traditional retail businesses lacked the appropriate tools for effectively sourcing and pricing goods, making it difficult to accurately calculate total costs and break-even points in advance, especially when handling multiple products or plans simultaneously. Furthermore, the time required for data collection and analysis hindered quick decision-making. There was also the risk of losing profits due to inappropriate pricing.

[0154] 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.

[0155] In this invention, the server includes means for receiving product information and conditions in natural language format from a user; means for analyzing the product information and conditions and extracting product types, periods, and plans; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total costs and break-even points using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; and means for calculating the break-even ARPU on a smartphone to support the decision-making process for pricing when a physical store operator procures and sets prices for products. This enables physical store operators to quickly and accurately calculate product costs and break-even points and set optimal prices.

[0156] A "user" is an individual or organization that uses the system.

[0157] "Natural language forms" refer to the forms of written and spoken language that humans use in everyday life.

[0158] "Product information" refers to information that includes specific details and characteristics of a product.

[0159] "Conditions" refer to specific requirements or conditions set when purchasing or using a product.

[0160] "Type" refers to the category or classification to which a product or service belongs.

[0161] "Period" refers to the length of time during which a product or service is used or provided.

[0162] A "plan" refers to the method and content of service provision based on specific conditions and pricing structures.

[0163] "Cost information" refers to the details of the costs involved in providing goods or services.

[0164] "Pricing information" refers to the details of the fees that users pay for using a product or service.

[0165] "Total cost" refers to the total expenses incurred in providing a product or service.

[0166] The "break-even point" is the point at which revenue and costs equal each other, resulting in zero profit.

[0167] A "smartphone" is a portable information terminal equipped with advanced functions.

[0168] "Break-even ARPU" refers to the profit point obtained by dividing costs by the average revenue over a certain period.

[0169] A "physical store operator" is an individual or company that operates a physical store and sells goods or provides services.

[0170] "Decision-making" is the process of choosing a specific action or solution.

[0171] This invention provides a smartphone application for brick-and-mortar store operators to manage product procurement and pricing. The specific steps for implementing this system are outlined below.

[0172] System Configuration

[0173] The system includes the following components:

[0174] 1. User device (smartphone)

[0175] 2. Server

[0176] 3. Internal Database

[0177] System operation

[0178] User input

[0179] The user inputs text in natural language format on their smartphone. For example, they might input the following:

[0180] text

[0181] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[0182] Input analysis

[0183] The server receives input text sent by the user and analyzes it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the type of product, duration, and plan. This process obtains specific information such as the product (product information), duration, and plan.

[0184] Obtaining cost information

[0185] Next, the server connects to an internal database to retrieve cost and pricing information related to products and plans. For example, the internal database stores the purchase cost of products and the monthly cost of plans.

[0186] calculation

[0187] The server uses the acquired cost information to calculate the total cost and the break-even point. Specifically, the break-even ARPU is determined by adding up the cost of the product and the monthly cost of the plan over a specific period and dividing it by the period.

[0188] Result generation and return

[0189] Once the calculation is complete, the server generates the calculation result in natural language format and sends it back to the user. The user can receive the calculation result on their smartphone as follows:

[0190] text

[0191] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[0192] The technology used

[0193] hardware

[0194] Smartphone (user device)

[0195] Server (computation and database management)

[0196] software

[0197] Natural Language Processing (NLP): SpaCy, BERT

[0198] Database access modules: SQLite, MongoDB

[0199] Specific example

[0200] For example, if a brick-and-mortar store operator enters the question, "What is the break-even point (ARPU) for renting a MacBook Pro for 24 months with a fixed 200GB of storage?", the response will be as follows:

[0201] text

[0202] The break-even point for renting a MacBook Pro for 24 months with a fixed 200GB data allowance is ¥X,XXX.XX.

[0203] This allows brick-and-mortar store operators to easily calculate costs and profits and set optimal prices.

[0204] This invention enables brick-and-mortar store operators to quickly and accurately calculate product costs and break-even points, and to set optimal prices.

[0205] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0206] Step 1:

[0207] The user enters text in natural language format on their smartphone. The input includes conditions such as product type, duration, and plan. For example, the text might be, "What is the break-even ARPU for a 36-month rental of an iPhone 14 with a 20GB flat rate?" This input is then sent to the server.

[0208] Step 2:

[0209] The server analyzes the natural language input text received from the user. Natural language processing (NLP) techniques are used for the analysis. Specifically, tools such as SpaCy and BERT are used to extract product types, durations, and plans. The extracted product information is then output from the received text input.

[0210] Step 3:

[0211] The server retrieves relevant cost and pricing information from its internal database based on the extracted product information. Database access uses tools such as SQLite or MongoDB. For example, the cost of an iPhone 14 and the monthly cost of a 20GB flat-rate plan are retrieved as follows:

[0212] The cost of the iPhone 14 = ¥100,000

[0213] Monthly cost for the 20GB flat-rate plan = ¥3,000

[0214] Step 4:

[0215] The server calculates the total cost using the acquired cost information. The specific calculation formula is as follows:

[0216] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[0217] The cost information entered here becomes the input data, and the total cost is output. For example, the total cost is ¥208,000 (¥100,000 + ¥3,000 × 36).

[0218] Step 5:

[0219] The server calculates the break-even point (ARPU) based on total costs. The formula is as follows:

[0220] Break-even ARPU = Total Cost / 36 months

[0221] The total cost here becomes the input data, and the break-even ARPU is output. For example, the break-even ARPU is ¥5,777.78.

[0222] Step 6:

[0223] The server generates the calculation results in natural language format and sends them back to the user. The generated results are displayed on the smartphone. For example, it might display something like, "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[0224] This process allows users to quickly and accurately obtain product cost and break-even point information on their smartphones. This helps brick-and-mortar store operators make informed decisions about optimal pricing.

[0225] 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.

[0226] The present invention combines a system that analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language with an emotion engine that recognizes the user's emotions. This system consists of a user terminal, a server, an internal database, and an emotion engine.

[0227] User input

[0228] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[0229] Server-based input analysis

[0230] The server receives input text sent by the user and parses it. The server uses natural language processing (NLP) techniques to extract the type of product, duration, and plan. This process yields information such as:

[0231] Product: iPhone 14

[0232] Duration: 36 months

[0233] Plan: 20GB flat rate

[0234] Cost information acquisition via server

[0235] Next, the server accesses an internal database to retrieve cost and pricing information related to the product or plan. The internal database may contain cost data such as:

[0236] iPhone 14 cost: ¥100,000

[0237] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0238] Server-based computation

[0239] The server uses the acquired cost information to calculate the total cost and the break-even point (ARPU). The specific calculation is as follows:

[0240] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[0241] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[0242] Emotion recognition by an emotion engine

[0243] The server uses natural language processing techniques to analyze the sentiment of the user's input text. For example, if the text entered by the user contains an expression like "I'm in a hurry," the sentiment engine will recognize that the user is in a hurry.

[0244] Server returns results to the user.

[0245] Once the calculation is complete, the server generates the results in natural language and adjusts how the results are presented based on the emotions recognized by the emotion engine. For example, if it detects that the user is in a hurry, it will immediately return the calculation results. A specific message in natural language would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[0246] User result confirmation

[0247] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[0248] As a concrete example, if a user enters "I'm in a hurry. What is the break-even ARPU for a 36-month rental of an iPhone 14 with a fixed 20GB data plan?", the server will immediately perform the calculation according to the above procedure, estimating the break-even ARPU as ¥5,777.78, and quickly return the result to the user. This allows the user to make quick decisions in the proposal phase.

[0249] Thus, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, the introduction of an emotion engine enables flexible responses tailored to the user's situation, significantly improving operational efficiency.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[0253] Step 2:

[0254] The terminal transmits product information and conditions entered by the user in natural language format to the server. The user's input data is transmitted to the server via the network.

[0255] Step 3:

[0256] The server analyzes the user's input data. The server uses natural language processing (NLP) techniques to extract information such as the type of product (e.g., iPhone 14), the duration (e.g., 36 months), and the plan (e.g., 20GB flat rate).

[0257] Step 4:

[0258] Based on the analyzed product information and conditions, the server accesses an internal database to retrieve the necessary cost and fee information. Specifically, it extracts information such as the cost (e.g., ¥100,000) and plan (¥3,000 per month) of a product (iPhone 14) from the internal database.

[0259] Step 5:

[0260] The server uses the acquired cost and pricing information to calculate the total cost and break-even point (ARPU). The calculation is performed as follows:

[0261] Total cost = Product cost (¥100,000) + (Monthly plan cost (¥3,000) × period (36 months)) = ¥208,000

[0262] Break-even ARPU = Total cost (¥208,000) / Period (36 months) = ¥5,777.78

[0263] Step 6:

[0264] The server uses an emotion engine to analyze the emotions expressed in the user's input text. For example, if the user types "I'm in a hurry," the emotion engine recognizes that the user is in a hurry.

[0265] Step 7:

[0266] The server generates calculation results in natural language and adjusts how the results are presented based on the perceived emotion. For example, if it detects that the user is in a hurry, it performs expedited processing to immediately return the calculation results. A specific message in natural language would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[0267] Step 8:

[0268] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[0269] Thus, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, the introduction of an emotion engine enables flexible responses tailored to the user's situation, significantly improving operational efficiency.

[0270] (Example 2)

[0271] 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".

[0272] Conventional product information and condition input systems struggle to process user-provided information accurately and quickly, and particularly lack the functionality to consider user emotions and circumstances. This has led to problems such as inappropriate responses in certain situations, resulting in decreased user work efficiency.

[0273] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving product information and conditions in natural language format input from the user; means for analyzing the product information and conditions and extracting the type of product, period, and plan; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total cost and break-even point using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; and means for recognizing emotions from the user's input text and adjusting the method of presenting the results. As a result, the user can quickly and accurately obtain the proposed amount and gross profit, and flexible responses can be made according to the user's situation.

[0274] "Product information" refers to information about specific products or services provided by users.

[0275] "Conditions" refer to the detailed requirements and restrictions in transactions and uses related to product information.

[0276] "Natural language form" means a form using language expressions commonly used by humans in daily life.

[0277] "User" refers to an individual or a legal entity using this system.

[0278] "Terminal" refers to electronic devices such as computers, smartphones, and tablets used by users to access the system.

[0279] "Server" refers to a central computer that receives and processes information sent from users' terminals.

[0280] "Internal database" means a collection of data that the server accesses to obtain information.

[0281] "Total cost" refers to the overall cost related to specific products or conditions.

[0282] "Break-even point" means the point where revenue and cost are balanced, and is often expressed as ARPU (Average Revenue Per User) in English.

[0283] "Emotion recognition" refers to the process of analyzing users' input text to identify their emotions and intentions.

[0284] "Natural language processing technology" refers to the technology for computers to understand and analyze human language.

[0285] "Calculation" refers to the process of calculating specific metrics (total cost or break-even point) based on product information and conditions.

[0286] The system of the present invention combines a function of recognizing the user's sentiment with a system that analyzes the merchandise information and conditions input by the user in natural language form, calculates the total cost and the break-even point based on the results, and returns the calculation results in natural language form. This system is composed of a user terminal, a server, an internal database, and a sentiment recognition engine.

[0287] User Input

[0288] The user uses the terminal to input merchandise information and conditions in natural language form. Specifically, the user accesses an input form using an application or web browser on the terminal, enters text such as "What is the break-even ARPU when renting an iPhone 14 for 36 months with a 20GB flat rate?", and clicks the send button.

[0289] Input Analysis by Server

[0290] The server receives the input text sent from the user and analyzes the text. The server uses a natural language processing engine (such as SpaCy or NLTK) to extract the type of merchandise, the period, and the plan from the input text. For example, the following information is extracted from the above input text:

[0291] Merchandise: iPhone 14

[0292] Period: 36 months

[0293] Plan: 20GB flat rate

[0294] Cost Information Acquisition by Server

[0295] Next, the server accesses the internal database and acquires cost and fee information related to the extracted merchandise and plan. The following cost data is stored in the internal database:

[0296] iPhone 14 cost: ¥100,000

[0297] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0298] Server-based computation

[0299] The server uses the acquired cost information to calculate the total cost and the break-even point (ARPU). The specific calculation is as follows:

[0300] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[0301] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[0302] Emotion analysis using an emotion recognition engine

[0303] The server uses an emotion recognition engine (such as Microsoft® Text Analytics API) to recognize emotions from the user's input text. For example, if the input text contains a phrase like "I'm in a hurry," the emotion recognition engine will interpret that the user is in a hurry.

[0304] Server returns results to the user.

[0305] Once the calculation is complete, the server generates the results in natural language format and adjusts how the results are presented based on the emotions recognized by the emotion recognition engine. For example, if the server recognizes that the user is in a hurry, it will immediately return the calculation results. A concrete example would be a message like, "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78," which is then sent back to the user.

[0306] User result confirmation

[0307] The user receives the calculation results sent from the server through the terminal and checks the results displayed on the screen. Based on these results, the user can quickly determine the proposed amount and gross profit.

[0308] The above are the specific embodiments of the present invention. Thereby, the user can quickly and accurately obtain the proposed amount and gross profit, and it is also possible to provide optimal information according to the user's situation.

[0309] The flow of the specific process in Example 2 will be described with reference to FIG. 13.

[0310] Step 1: The user inputs merchandise information and conditions

[0311] Explanation

[0312] The user uses the terminal to input merchandise information and conditions in the form of natural language. The input content is sent to the server.

[0313] Input

[0314] The text "What is the break-even ARPU when offering the iPhone 14 for 36 months rental with a fixed 20GB?"

[0315] Output

[0316] The user's input text sent to the server.

[0317] Specific operation

[0318] The user launches an application or web browser on the terminal, enters the merchandise information and conditions in the input field in the form of natural language, and clicks the send button.

[0319] Step 2: The server analyzes the input text

[0320] Explanation

[0321] The server analyzes the received input text. It uses a natural language processing engine to extract the type of product, duration, and plan.

[0322] input

[0323] Natural language text sent by the user.

[0324] output

[0325] Extracted information regarding products, periods, and plans.

[0326] Specific actions

[0327] The server uses an NLP engine (e.g., SpaCy or NLTK) to analyze the text "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?" and extracts the following information:

[0328] Product: iPhone 14

[0329] Duration: 36 months

[0330] Plan: 20GB flat rate

[0331] Step 3: The server retrieves internal cost information.

[0332] explanation

[0333] The server accesses an internal database to retrieve cost and pricing information related to products and plans.

[0334] input

[0335] Extracted information regarding products and plans.

[0336] output

[0337] Related cost and fee information.

[0338] Specific actions

[0339] The server executes the following SQL query on its internal database:

[0340] sql

[0341] SELECT cost FROM products WHERE name = 'iPhone 14';

[0342] SELECT monthly_cost FROM plans WHERE plan_name = '20GB flat rate';

[0343] As a result, the following information is obtained:

[0344] iPhone 14 cost: ¥100,000

[0345] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0346] Step 4: The server calculates the total cost and break-even point.

[0347] explanation

[0348] The server calculates the total cost and break-even point (ARPU) based on the acquired cost information.

[0349] input

[0350] Acquired cost and fee information.

[0351] output

[0352] The calculated total cost and break-even point.

[0353] Specific actions

[0354] The server performs the following calculation:

[0355] Total cost = ¥100,000 + (¥3,000 × 36) = ¥208,000

[0356] Break-even ARPU = ¥208,000 ÷ 36 = ¥5,777.78

[0357] Step 5: The server recognizes the user's emotions.

[0358] explanation

[0359] The server uses an emotion recognition engine to analyze the emotions in the user's input text.

[0360] input

[0361] User input text.

[0362] output

[0363] Analyzed user sentiment information.

[0364] Specific actions

[0365] The server passes the text to an emotion recognition engine (for example, the Microsoft Text Analytics API) and recognizes that the user is in a hurry based on keywords such as "hurry."

[0366] Step 6: The server generates and returns the results.

[0367] explanation

[0368] Based on the calculation results and emotion recognition results, the server generates the results in natural language format and sends them back to the user.

[0369] input

[0370] Calculation results and emotion recognition results.

[0371] output

[0372] Result message in natural language format.

[0373] Specific actions

[0374] The server generates the following message:

[0375] "The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[0376] If the system detects that the user is in a hurry, this message will be sent back to the user immediately.

[0377] Step 7: The user confirms the results.

[0378] explanation

[0379] Users review the results received from the server via their terminals and use them to inform their business decisions.

[0380] input

[0381] A result message in natural language format sent from the server.

[0382] output

[0383] User review of results and appropriate action.

[0384] Specific actions

[0385] The results are displayed on the user's device, which the user reviews and uses to determine the proposed price and gross profit.

[0386] (Application Example 2)

[0387] 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".

[0388] Existing product information management systems struggle to properly analyze product information and conditions provided by users in natural language and to quickly deliver cost calculation results. Furthermore, they display results uniformly without considering user sentiment, potentially impairing the user experience. This creates a challenge, for example, when a company seeks to quickly determine a proposed price and improve performance; the system's response delays can hinder rapid decision-making.

[0389] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving product information and conditions in natural language format input from the user; means for analyzing the product information and conditions and extracting the type of product, period, and plan; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total cost and break-even point using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; means for recognizing emotions from the user's input text; and means for adjusting the method of presenting the results to the user based on the recognized emotions. As a result, the user can quickly and accurately obtain cost calculation results for products based on the input conditions, and it is also possible to display results that take the user's emotions into consideration. This is expected to improve operational efficiency and user experience.

[0390] "Natural language product information and conditions" refers to information about products and services, as well as the conditions for their provision, entered by the user in simple language.

[0391] "Analysis" refers to the process of analyzing input information to understand it and identify the necessary elements.

[0392] "Type of product" refers to the category or type of goods or services identified from the analyzed information.

[0393] "Duration" refers to the length of time over which a product or service is provided.

[0394] "Plan" refers to the terms and conditions of service or pricing plan related to a product or service.

[0395] An "internal database" refers to a database that a system uses to store cost and fee information.

[0396] "Cost and fee information" refers to detailed information about the costs and fees associated with the goods or services.

[0397] "Total cost" refers to the sum of all expenses necessary to provide goods or services under specific conditions.

[0398] The "break-even point" refers to the point at which income and expenses are equal when providing goods or services, meaning that profit is zero.

[0399] "Emotion recognition" refers to the process of analyzing a user's emotional state based on the text they input.

[0400] "Adjusting the presentation method of results" refers to optimizing how calculation results are displayed based on the recognized emotions of the user.

[0401] The system of this invention analyzes product information and conditions entered by the user in natural language, quickly and accurately calculates the relevant costs and break-even points, and returns the results to the user in an appropriate format. It also has the function of recognizing emotions from the user's input text and adjusting the way the results are presented. This system consists of a user terminal, a server, an internal database, and an emotion engine.

[0402] Explanation of the program's processing

[0403] 1. Acceptance of user input

[0404] User terminal: Users use devices such as smartphones or computers to input product information and conditions in natural language format. For example, they might input text such as, "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat-rate plan?"

[0405] 2. Parsing of input text

[0406] Server: The server receives the input text and analyzes it using natural language processing (NLP) techniques. It uses the spacy library (ja_core_news_sm model) to extract the product type (e.g., "iPhone 14"), duration (e.g., "36 months"), and plan (e.g., "20GB flat-rate plan").

[0407] 3. Obtaining cost information

[0408] Server: The server accesses the internal database to retrieve cost and pricing information related to products and plans. The internal database stores details such as the cost of goods sold and monthly costs.

[0409] 4. Calculation of costs and break-even point

[0410] Server: The server calculates total cost and break-even point based on the acquired cost information. For example, it calculates total cost using the cost of an iPhone 14 and the monthly cost of a 20GB flat-rate plan, and then calculates the break-even point ARPU.

[0411] 5. Emotion recognition

[0412] Server: Uses the transformers library's pipeline("sentiment-analysis") model to recognize emotions from user input text. For example, it analyzes text containing expressions like "I'm in a hurry" to recognize that the user is in a hurry.

[0413] 6. Generating and returning results

[0414] Server: Generates calculation results in natural language format and adjusts the presentation method based on sentiment recognition. For example, if the server recognizes that the user is in a hurry, it immediately returns the calculation results. A specific natural language message might be sent such as, "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[0415] Adding specific examples

[0416] If a user enters "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan?", the system will process it as follows: Based on this input, the server will immediately analyze the data, calculate the total cost and break-even ARPU, and finally return a result of ¥5,777.78 as the break-even ARPU.

[0417] This enables users to make quick decisions in the proposal phase, resulting in significant improvements in operational efficiency and user experience.

[0418] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0419] Step 1:

[0420] The user uses their device to input product information and conditions in natural language format. The input is in the form of text such as, "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?" The entered information is sent to the server.

[0421] Input: User-generated text in natural language format

[0422] Output: Input text data

[0423] Step 2:

[0424] The server receives the input text and analyzes it using natural language processing techniques. Specifically, it uses the ja_core_news_sm model from the spacy library to extract the product type (iPhone 14), duration (36 months), and plan (20GB flat-rate plan). This converts the user input into structured data.

[0425] Input: Input text data

[0426] Output: Structured data on product type, duration, and plan.

[0427] Step 3:

[0428] The server accesses an internal database to retrieve cost and pricing information related to the type of product and plan. Specifically, it retrieves the cost of an iPhone 14 (¥100,000) and the monthly cost of a 20GB flat-rate plan (¥3,000) from the database. This provides the basic cost information necessary for calculations.

[0429] Input: Structured data on product type, duration, and plan.

[0430] Output: Cost data retrieved from the internal database

[0431] Step 4:

[0432] The total cost and break-even point are calculated using the cost data acquired by the server. The specific calculation method is as follows:

[0433] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[0434] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[0435] Input: Cost data retrieved from an internal database

[0436] Output: Calculation results of total cost and break-even point

[0437] Step 5:

[0438] The server uses the transformers library's pipeline("sentiment-analysis") model to recognize emotions from the user's input text. If the input text contains expressions such as "I'm in a hurry," the emotion engine recognizes that the user is in a hurry. The results of the emotion recognition are used for subsequent processing.

[0439] Input: Input text data

[0440] Output: Emotion recognition results

[0441] Step 6:

[0442] The server generates calculation results in natural language format and adjusts how the results are presented to the user based on the sentiment recognition results. Specifically, if sentiment recognition determines that the user is in a hurry, the results are returned promptly. An example of the generated text is the message, "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance is ¥5,777.78."

[0443] Input: Calculation results of total cost and break-even point, results of sentiment recognition

[0444] Output: Message in natural language format

[0445] Step 7:

[0446] The user receives the results sent from the server via their device and checks the calculation results displayed on the screen. This allows the user to quickly determine the proposed price and gross profit.

[0447] Input: Message in natural language format

[0448] Output: Calculation results displayed on the screen

[0449] 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.

[0450] 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.

[0451] 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.

[0452] [Second Embodiment]

[0453] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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".

[0465] The system of this invention analyzes product information and conditions entered by the user in natural language, calculates total cost and break-even point based on the results, and returns the calculation results in natural language. This system consists of a user terminal, a server, and an internal database.

[0466] User input

[0467] Users input product information and conditions in natural language format using their terminals. For example, the following inputs are possible:

[0468] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[0469] Server-based input analysis

[0470] The server receives input text sent by the user and parses it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the type of product, duration, and plan. This process yields information such as the following:

[0471] Product: iPhone 14

[0472] Duration: 36 months

[0473] Plan: 20GB flat rate

[0474] Cost information acquisition via server

[0475] Next, the server connects to an internal database to retrieve cost and pricing information related to the products and plans. For example, the internal database may store cost data such as the following:

[0476] iPhone 14 cost: ¥100,000

[0477] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0478] Server-based computation

[0479] The server uses the acquired cost information to calculate the total cost and the break-even point. The specific calculation is as follows:

[0480] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[0481] Break-even ARPU = Total Cost / 36 months

[0482] In this example, the total cost is ¥208,000 (¥100,000 + ¥3,000 x 36), and the break-even ARPU is ¥5,777.78.

[0483] Server returns results to the user.

[0484] Once the calculation is complete, the server generates the result in natural language format and sends it back to the user. The user can receive the calculation result on their terminal as follows:

[0485] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[0486] In this way, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, this system can eliminate human error and significantly improve operational efficiency.

[0487] As a concrete example, if a user enters "What is the break-even ARPU for a 36-month rental of an iPhone 14 with a fixed 20GB data plan?", the server immediately performs the calculation according to the above procedure, determining the break-even ARPU to be ¥5,777.78, and returning the result to the user. This allows the user to make quick decisions in the proposal phase.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[0491] Step 2:

[0492] The terminal transmits product information and conditions entered by the user in natural language format to the server. The user's input data is transmitted to the server via the network.

[0493] Step 3:

[0494] The server analyzes the user's input data. The server uses natural language processing (NLP) techniques to extract information such as the type of product (e.g., iPhone 14), the duration (e.g., 36 months), and the plan (e.g., 20GB flat rate).

[0495] Step 4:

[0496] Based on the analyzed product information and conditions, the server accesses an internal database to retrieve the necessary cost and fee information. Specifically, it extracts information such as the cost (e.g., ¥100,000) and plan (¥3,000 per month) of a product (iPhone 14) from the internal database.

[0497] Step 5:

[0498] The server uses the acquired cost and pricing information to calculate the total cost and break-even point (ARPU). The calculation is performed as follows:

[0499] Total cost = Product cost (¥100,000) + (Monthly plan cost (¥3,000) × period (36 months)) = ¥208,000

[0500] Break-even ARPU = Total cost (¥208,000) / Period (36 months) = ¥5,777.78

[0501] Step 6:

[0502] The server generates the calculation results in natural language format and sends them back to the user. The specific message in natural language format would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[0503] Step 7:

[0504] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[0505] In this way, throughout each step, the system can instantly calculate the proposed price and gross profit based on the conditions entered by the user in natural language, and return the results.

[0506] (Example 1)

[0507] 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."

[0508] Conventional systems faced the challenge of requiring significant time and effort to manually analyze product information and conditions to calculate total costs and break-even points. Furthermore, they were prone to errors due to human error, making it difficult to provide information quickly and accurately. This invention aims to solve these problems by automating the analysis of product information and the calculation of total costs and break-even points, thereby improving operational efficiency.

[0509] 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.

[0510] In this invention, the server includes means for receiving product information and conditions in natural language format from a user; means for analyzing the product information and conditions and extracting product types, periods, and plans; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total costs and break-even points using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; means for the user to input product information and conditions in natural language format using a terminal; means for the server to receive and analyze the input; means for the server to obtain cost information from the database; means for the server to perform calculations based on the acquired cost information; and means for returning the calculation results in natural language format. This enables the user to quickly and accurately obtain the proposed price and gross profit.

[0511] A "user" refers to a person or organization that uses the system to input product information and conditions in natural language format and receives calculation results.

[0512] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to input product information and conditions in natural language format and receive calculation results.

[0513] A "server" refers to a computing system that receives input information in natural language format, performs analysis and calculations, and returns the calculation results to the user.

[0514] "Natural language processing technology" refers to the techniques and algorithms used to automatically analyze natural language text and extract necessary information, rather than manually.

[0515] An "internal database" refers to a database system used to store data such as cost and fee information related to product information.

[0516] "Product information" refers to information about the products handled by the system, including data in natural language format that users input, such as the type of product.

[0517] "Conditions" refers to additional information such as the duration and plan that accompanies the product information.

[0518] "Cost information" refers to data that stores cost and fee information related to products and plans.

[0519] "Total cost" refers to the sum of all costs associated with the product and plan.

[0520] The "break-even point" refers to a figure used to determine the profit margin by dividing total costs over a specific period.

[0521] The system of this invention analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language. This system consists of a user terminal, a server, and an internal database.

[0522] First, the user uses their device to input product information and conditions in natural language format. For example, the following inputs are possible:

[0523] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[0524] The server receives input text sent by the user and parses it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the product type, duration, and plan. This process yields information such as the following:

[0525] Product: iPhone 14

[0526] Duration: 36 months

[0527] Plan: 20GB flat rate

[0528] The server then connects to an internal database to retrieve cost and pricing information related to the product or plan. For example, the internal database may contain cost data such as the following:

[0529] iPhone 14 cost: ¥100,000

[0530] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0531] The server uses the acquired cost information to calculate the total cost and the break-even point. The specific calculation is as follows:

[0532] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[0533] Break-even ARPU = Total Expenses / 36 months

[0534] In this example, the total cost is ¥208,000 (¥100,000 + ¥3,000 x 36), and the break-even ARPU is ¥5,777.78.

[0535] Once the calculation is complete, the server generates the results in natural language format and sends them back to the user. The user can receive the calculation results on their terminal as follows:

[0536] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[0537] In this way, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, this system can eliminate human error and significantly improve operational efficiency.

[0538] As a concrete example, if a user inputs "What is the break-even ARPU for a 24-month lease of an iPad Pro with a fixed 10GB data plan?", the server immediately performs the calculation according to the above procedure, determining the break-even ARPU to be ¥5,833.33, and returning the result to the user. This allows the user to make quick decisions during the proposal phase.

[0539] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0540] Step 1:

[0541] Users enter product information and conditions in natural language format using their devices.

[0542] Input: Natural language text (Example: "What is the break-even ARPU for offering an iPhone 14 on a 36-month rental basis with a fixed 20GB data plan?")

[0543] Output: Sending input data from the user

[0544] Specific action: The user types text into the input field on the device and clicks the "Send" button.

[0545] Step 2:

[0546] The server receives the input text from the user.

[0547] Input: Natural language text submitted by the user.

[0548] Output: Receiving input data on the server side

[0549] Specific operation: The server receives an HTTP request at a specific API endpoint.

[0550] Step 3:

[0551] The server uses natural language processing (NLP) techniques to analyze the input text.

[0552] Input: Received text in natural language format

[0553] Output: Extraction results for product type, duration, and plan (e.g., "Product: iPhone 14", "Duration: 36 months", "Plan: 20GB flat rate")

[0554] Specific operation: The server analyzes the text using a natural language processing library (e.g., spaCy or NLTK) and extracts the necessary information.

[0555] Step 4:

[0556] The server connects to an internal database to retrieve relevant cost and pricing information.

[0557] Input: Extracted product type, duration, plan

[0558] Output: Cost information and pricing information (e.g., "Cost of iPhone 14: ¥100,000", "Monthly cost of 20GB flat-rate plan: ¥3,000")

[0559] Specific operation: The server connects to a database management system (e.g., SQLite, MySQL) and issues SQL queries to retrieve the necessary data.

[0560] Step 5:

[0561] Based on the cost information acquired by the server, the total cost and break-even point are calculated.

[0562] Input: Cost information and fee information (e.g., ¥100,000, ¥3,000)

[0563] Output: Calculation results (Example: Total cost: ¥208,000, Break-even ARPU: ¥5,777.78)

[0564] Specific operation: The server sums up each cost to calculate the total cost. Then, it divides the total cost by the period (number of months) to calculate the break-even point.

[0565] Step 6:

[0566] The server generates the calculation results in natural language format and sends them back to the user.

[0567] Input: Calculation result (Example: Total cost: ¥208,000, Break-even ARPU: ¥5,777.78)

[0568] Output: Result report in natural language format (Example: "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.")

[0569] Specific operation: The server uses a template engine (e.g., Jinja2) to assemble a report in natural language format and sends it back to the user's terminal as an HTTP response.

[0570] Step 7:

[0571] The user checks the calculation results on their device.

[0572] Input: Natural language result report returned from the server

[0573] Output: Visualization of calculation results

[0574] Specific action: The user checks the returned result on their device screen.

[0575] (Application Example 1)

[0576] 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."

[0577] Traditional retail businesses lacked the appropriate tools for effectively sourcing and pricing goods, making it difficult to accurately calculate total costs and break-even points in advance, especially when handling multiple products or plans simultaneously. Furthermore, the time required for data collection and analysis hindered quick decision-making. There was also the risk of losing profits due to inappropriate pricing.

[0578] 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.

[0579] In this invention, the server includes means for receiving product information and conditions in natural language format from a user; means for analyzing the product information and conditions and extracting product types, periods, and plans; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total costs and break-even points using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; and means for calculating the break-even ARPU on a smartphone to support the decision-making process for pricing when a physical store operator procures and sets prices for products. This enables physical store operators to quickly and accurately calculate product costs and break-even points and set optimal prices.

[0580] A "user" is an individual or organization that uses the system.

[0581] "Natural language forms" refer to the forms of written and spoken language that humans use in everyday life.

[0582] "Product information" refers to information that includes specific details and characteristics of a product.

[0583] "Conditions" refer to specific requirements or conditions set when purchasing or using a product.

[0584] "Type" refers to the category or classification to which a product or service belongs.

[0585] "Period" refers to the length of time during which a product or service is used or provided.

[0586] A "plan" refers to the method and content of service provision based on specific conditions and pricing structures.

[0587] "Cost information" refers to the details of the costs involved in providing goods or services.

[0588] "Pricing information" refers to the details of the fees that users pay for using a product or service.

[0589] "Total cost" refers to the total expenses incurred in providing a product or service.

[0590] The "break-even point" is the point at which revenue and costs equal each other, resulting in zero profit.

[0591] A "smartphone" is a portable information terminal equipped with advanced functions.

[0592] "Break-even ARPU" refers to the profit point obtained by dividing costs by the average revenue over a certain period.

[0593] A "physical store operator" is an individual or company that operates a physical store and sells goods or provides services.

[0594] "Decision-making" is the process of choosing a specific action or solution.

[0595] This invention provides a smartphone application for brick-and-mortar store operators to manage product procurement and pricing. The specific steps for implementing this system are outlined below.

[0596] System Configuration

[0597] The system includes the following components:

[0598] 1. User device (smartphone)

[0599] 2. Server

[0600] 3. Internal Database

[0601] System operation

[0602] User input

[0603] The user inputs text in natural language format on their smartphone. For example, they might input the following:

[0604] text

[0605] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[0606] Input analysis

[0607] The server receives input text sent by the user and analyzes it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the type of product, duration, and plan. This process obtains specific information such as the product (product information), duration, and plan.

[0608] Obtaining cost information

[0609] Next, the server connects to an internal database to retrieve cost and pricing information related to products and plans. For example, the internal database stores the purchase cost of products and the monthly cost of plans.

[0610] calculation

[0611] The server uses the acquired cost information to calculate the total cost and the break-even point. Specifically, the break-even ARPU is determined by adding up the cost of the product and the monthly cost of the plan over a specific period and dividing it by the period.

[0612] Result generation and return

[0613] Once the calculation is complete, the server generates the calculation result in natural language format and sends it back to the user. The user can receive the calculation result on their smartphone as follows:

[0614] text

[0615] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[0616] The technology used

[0617] hardware

[0618] Smartphone (user device)

[0619] Server (computation and database management)

[0620] software

[0621] Natural Language Processing (NLP): SpaCy, BERT

[0622] Database access modules: SQLite, MongoDB

[0623] Specific example

[0624] For example, if a brick-and-mortar store operator enters the question, "What is the break-even point (ARPU) for renting a MacBook Pro for 24 months with a fixed 200GB of storage?", the response will be as follows:

[0625] text

[0626] The break-even point for renting a MacBook Pro for 24 months with a fixed 200GB data allowance is ¥X,XXX.XX.

[0627] This allows brick-and-mortar store operators to easily calculate costs and profits and set optimal prices.

[0628] This invention enables brick-and-mortar store operators to quickly and accurately calculate product costs and break-even points, and to set optimal prices.

[0629] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0630] Step 1:

[0631] The user enters text in natural language format on their smartphone. The input includes conditions such as product type, duration, and plan. For example, the text might be, "What is the break-even ARPU for a 36-month rental of an iPhone 14 with a 20GB flat rate?" This input is then sent to the server.

[0632] Step 2:

[0633] The server analyzes the natural language input text received from the user. Natural language processing (NLP) techniques are used for the analysis. Specifically, tools such as SpaCy and BERT are used to extract product types, durations, and plans. The extracted product information is then output from the received text input.

[0634] Step 3:

[0635] The server retrieves relevant cost and pricing information from its internal database based on the extracted product information. Database access uses tools such as SQLite or MongoDB. For example, the cost of an iPhone 14 and the monthly cost of a 20GB flat-rate plan are retrieved as follows:

[0636] The cost of the iPhone 14 = ¥100,000

[0637] Monthly cost for the 20GB flat-rate plan = ¥3,000

[0638] Step 4:

[0639] The server calculates the total cost using the acquired cost information. The specific calculation formula is as follows:

[0640] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[0641] The cost information entered here becomes the input data, and the total cost is output. For example, the total cost is ¥208,000 (¥100,000 + ¥3,000 × 36).

[0642] Step 5:

[0643] The server calculates the break-even point (ARPU) based on total costs. The formula is as follows:

[0644] Break-even ARPU = Total Cost / 36 months

[0645] The total cost here becomes the input data, and the break-even ARPU is output. For example, the break-even ARPU is ¥5,777.78.

[0646] Step 6:

[0647] The server generates the calculation results in natural language format and sends them back to the user. The generated results are displayed on the smartphone. For example, it might display something like, "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[0648] This process allows users to quickly and accurately obtain product cost and break-even point information on their smartphones. This helps brick-and-mortar store operators make informed decisions about optimal pricing.

[0649] 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.

[0650] The present invention combines a system that analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language with an emotion engine that recognizes the user's emotions. This system consists of a user terminal, a server, an internal database, and an emotion engine.

[0651] User input

[0652] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[0653] Server-based input analysis

[0654] The server receives input text sent by the user and parses it. The server uses natural language processing (NLP) techniques to extract the type of product, duration, and plan. This process yields information such as:

[0655] Product: iPhone 14

[0656] Duration: 36 months

[0657] Plan: 20GB flat rate

[0658] Cost information acquisition via server

[0659] Next, the server accesses an internal database to retrieve cost and pricing information related to the product or plan. The internal database may contain cost data such as:

[0660] iPhone 14 cost: ¥100,000

[0661] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0662] Server-based computation

[0663] The server uses the acquired cost information to calculate the total cost and the break-even point (ARPU). The specific calculation is as follows:

[0664] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[0665] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[0666] Emotion recognition by an emotion engine

[0667] The server uses natural language processing techniques to analyze the sentiment of the user's input text. For example, if the text entered by the user contains an expression like "I'm in a hurry," the sentiment engine will recognize that the user is in a hurry.

[0668] Server returns results to the user.

[0669] Once the calculation is complete, the server generates the results in natural language and adjusts how the results are presented based on the emotions recognized by the emotion engine. For example, if it detects that the user is in a hurry, it will immediately return the calculation results. A specific message in natural language would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[0670] User result confirmation

[0671] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[0672] As a concrete example, if a user enters "I'm in a hurry. What is the break-even ARPU for a 36-month rental of an iPhone 14 with a fixed 20GB data plan?", the server will immediately perform the calculation according to the above procedure, estimating the break-even ARPU as ¥5,777.78, and quickly return the result to the user. This allows the user to make quick decisions in the proposal phase.

[0673] Thus, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, the introduction of an emotion engine enables flexible responses tailored to the user's situation, significantly improving operational efficiency.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[0677] Step 2:

[0678] The terminal transmits product information and conditions entered by the user in natural language format to the server. The user's input data is transmitted to the server via the network.

[0679] Step 3:

[0680] The server analyzes the user's input data. The server uses natural language processing (NLP) techniques to extract information such as the type of product (e.g., iPhone 14), the duration (e.g., 36 months), and the plan (e.g., 20GB flat rate).

[0681] Step 4:

[0682] Based on the analyzed product information and conditions, the server accesses an internal database to retrieve the necessary cost and fee information. Specifically, it extracts information such as the cost (e.g., ¥100,000) and plan (¥3,000 per month) of a product (iPhone 14) from the internal database.

[0683] Step 5:

[0684] The server uses the acquired cost and pricing information to calculate the total cost and break-even point (ARPU). The calculation is performed as follows:

[0685] Total cost = Product cost (¥100,000) + (Monthly plan cost (¥3,000) × period (36 months)) = ¥208,000

[0686] Break-even ARPU = Total cost (¥208,000) / Period (36 months) = ¥5,777.78

[0687] Step 6:

[0688] The server uses an emotion engine to analyze the emotions expressed in the user's input text. For example, if the user types "I'm in a hurry," the emotion engine recognizes that the user is in a hurry.

[0689] Step 7:

[0690] The server generates calculation results in natural language and adjusts how the results are presented based on the perceived emotion. For example, if it detects that the user is in a hurry, it performs expedited processing to immediately return the calculation results. A specific message in natural language would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[0691] Step 8:

[0692] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[0693] Thus, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, the introduction of an emotion engine enables flexible responses tailored to the user's situation, significantly improving operational efficiency.

[0694] (Example 2)

[0695] 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".

[0696] Conventional product information and condition input systems struggle to process user-provided information accurately and quickly, and particularly lack the functionality to consider user emotions and circumstances. This has led to problems such as inappropriate responses in certain situations, resulting in decreased user work efficiency.

[0697] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving product information and conditions in natural language format input from the user; means for analyzing the product information and conditions and extracting the type of product, period, and plan; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total cost and break-even point using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; and means for recognizing emotions from the user's input text and adjusting the method of presenting the results. As a result, the user can quickly and accurately obtain the proposed amount and gross profit, and flexible responses can be made according to the user's situation.

[0698] "Product information" refers to information about specific products or services offered by the user.

[0699] "Conditions" refers to the detailed requirements and restrictions on transactions and use related to product information.

[0700] "Natural language forms" refer to forms that use language expressions that humans use on a daily basis.

[0701] "User" refers to an individual or legal entity that uses this system.

[0702] "Terminal" refers to electronic devices such as computers, smartphones, and tablets that users use to access a system.

[0703] A "server" refers to a central computer that receives and processes information sent from a user's terminal.

[0704] An "internal database" refers to a collection of data that a server accesses to retrieve information.

[0705] "Total cost" refers to the overall expenses associated with a particular product or condition.

[0706] The "break-even point" refers to the point where revenue and costs are balanced, and is often expressed in English as ARPU (Average Revenue Per User).

[0707] "Emotion recognition" refers to the process of analyzing user input text to identify their emotions and intentions.

[0708] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0709] "Calculation" refers to the process of calculating specific indicators (such as total cost or break-even point) based on product information and conditions.

[0710] The present invention combines a system that analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language with a function that recognizes the user's emotions. This system consists of a user terminal, a server, an internal database, and an emotion recognition engine.

[0711] User input

[0712] Users input product information and conditions in natural language format using their devices. Specifically, users access an input form using an application or web browser on their device, enter text such as "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?", and click the submit button.

[0713] Server-based input analysis

[0714] The server receives input text sent by the user and parses it. Using a natural language processing engine (such as SpaCy or NLTK), the server extracts the type of product, duration, and plan from the input text. For example, the following information is extracted from the above input text:

[0715] Product: iPhone 14

[0716] Duration: 36 months

[0717] Plan: 20GB flat rate

[0718] Cost information acquisition via server

[0719] Next, the server accesses the internal database to retrieve cost and pricing information related to the extracted products and plans. The internal database contains cost data such as the following:

[0720] iPhone 14 cost: ¥100,000

[0721] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0722] Server-based computation

[0723] The server uses the acquired cost information to calculate the total cost and the break-even point (ARPU). The specific calculation is as follows:

[0724] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[0725] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[0726] Emotion analysis using an emotion recognition engine

[0727] The server uses an emotion recognition engine (such as the Microsoft Text Analytics API) to recognize emotions from the user's input text. For example, if the input text contains a phrase like "I'm in a hurry," the emotion recognition engine will interpret that the user is in a hurry.

[0728] Server returns results to the user.

[0729] Once the calculation is complete, the server generates the results in natural language format and adjusts how the results are presented based on the emotions recognized by the emotion recognition engine. For example, if the server recognizes that the user is in a hurry, it will immediately return the calculation results. A concrete example would be a message like, "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78," which is then sent back to the user.

[0730] User result confirmation

[0731] The user receives the calculation results sent from the server via their terminal and checks the results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[0732] The above describes a specific embodiment of the present invention. This enables users to quickly and accurately obtain proposed amounts and gross profits, and also allows for the provision of optimal information tailored to the user's situation.

[0733] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0734] Step 1: The user enters product information and conditions.

[0735] explanation

[0736] The user uses a terminal to input product information and conditions in natural language format. The entered information is then sent to the server.

[0737] input

[0738] The text asks, "What is the break-even point (ARPU) for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[0739] output

[0740] The user's input text sent to the server.

[0741] Specific actions

[0742] The user launches the application or web browser on their device, enters product information and conditions in natural language format into the input fields, and clicks the submit button.

[0743] Step 2: The server parses the input text.

[0744] explanation

[0745] The server analyzes the received input text. It uses a natural language processing engine to extract the type of product, duration, and plan.

[0746] input

[0747] Natural language text sent by the user.

[0748] output

[0749] Extracted information regarding products, periods, and plans.

[0750] Specific actions

[0751] The server uses an NLP engine (e.g., SpaCy or NLTK) to analyze the text "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?" and extracts the following information:

[0752] Product: iPhone 14

[0753] Duration: 36 months

[0754] Plan: 20GB flat rate

[0755] Step 3: The server retrieves internal cost information.

[0756] explanation

[0757] The server accesses an internal database to retrieve cost and pricing information related to products and plans.

[0758] input

[0759] Extracted information regarding products and plans.

[0760] output

[0761] Related cost and fee information.

[0762] Specific actions

[0763] The server executes the following SQL query on its internal database:

[0764] sql

[0765] SELECT cost FROM products WHERE name = 'iPhone 14';

[0766] SELECT monthly_cost FROM plans WHERE plan_name = '20GB flat rate';

[0767] As a result, the following information is obtained:

[0768] iPhone 14 cost: ¥100,000

[0769] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0770] Step 4: The server calculates the total cost and break-even point.

[0771] explanation

[0772] The server calculates the total cost and break-even point (ARPU) based on the acquired cost information.

[0773] input

[0774] Acquired cost and fee information.

[0775] output

[0776] The calculated total cost and break-even point.

[0777] Specific actions

[0778] The server performs the following calculation:

[0779] Total cost = ¥100,000 + (¥3,000 × 36) = ¥208,000

[0780] Break-even ARPU = ¥208,000 ÷ 36 = ¥5,777.78

[0781] Step 5: The server recognizes the user's emotions.

[0782] explanation

[0783] The server uses an emotion recognition engine to analyze the emotions in the user's input text.

[0784] input

[0785] User input text.

[0786] output

[0787] Analyzed user sentiment information.

[0788] Specific actions

[0789] The server passes the text to an emotion recognition engine (for example, the Microsoft Text Analytics API) and recognizes that the user is in a hurry based on keywords such as "hurry."

[0790] Step 6: The server generates and returns the results.

[0791] explanation

[0792] Based on the calculation results and emotion recognition results, the server generates the results in natural language format and sends them back to the user.

[0793] input

[0794] Calculation results and emotion recognition results.

[0795] output

[0796] Result message in natural language format.

[0797] Specific actions

[0798] The server generates the following message:

[0799] "The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[0800] If the system detects that the user is in a hurry, this message will be sent back to the user immediately.

[0801] Step 7: The user confirms the results.

[0802] explanation

[0803] Users review the results received from the server via their terminals and use them to inform their business decisions.

[0804] input

[0805] A result message in natural language format sent from the server.

[0806] output

[0807] User review of results and appropriate action.

[0808] Specific actions

[0809] The results are displayed on the user's device, which the user reviews and uses to determine the proposed price and gross profit.

[0810] (Application Example 2)

[0811] 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."

[0812] Existing product information management systems struggle to properly analyze product information and conditions provided by users in natural language and to quickly deliver cost calculation results. Furthermore, they display results uniformly without considering user sentiment, potentially impairing the user experience. This creates a challenge, for example, when a company seeks to quickly determine a proposed price and improve performance; the system's response delays can hinder rapid decision-making.

[0813] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving product information and conditions in natural language format input from the user; means for analyzing the product information and conditions and extracting the type of product, period, and plan; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total cost and break-even point using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; means for recognizing emotions from the user's input text; and means for adjusting the method of presenting the results to the user based on the recognized emotions. As a result, the user can quickly and accurately obtain cost calculation results for products based on the input conditions, and it is also possible to display results that take the user's emotions into consideration. This is expected to improve operational efficiency and user experience.

[0814] "Natural language product information and conditions" refers to information about products and services, as well as the conditions for their provision, entered by the user in simple language.

[0815] "Analysis" refers to the process of analyzing input information to understand it and identify the necessary elements.

[0816] "Type of product" refers to the category or type of goods or services identified from the analyzed information.

[0817] "Duration" refers to the length of time over which a product or service is provided.

[0818] "Plan" refers to the terms and conditions of service or pricing plan related to a product or service.

[0819] An "internal database" refers to a database that a system uses to store cost and fee information.

[0820] "Cost and fee information" refers to detailed information about the costs and fees associated with the goods or services.

[0821] "Total cost" refers to the sum of all expenses necessary to provide goods or services under specific conditions.

[0822] The "break-even point" refers to the point at which income and expenses are equal when providing goods or services, meaning that profit is zero.

[0823] "Emotion recognition" refers to the process of analyzing a user's emotional state based on the text they input.

[0824] "Adjusting the presentation method of results" refers to optimizing how calculation results are displayed based on the recognized emotions of the user.

[0825] The system of this invention analyzes product information and conditions entered by the user in natural language, quickly and accurately calculates the relevant costs and break-even points, and returns the results to the user in an appropriate format. It also has the function of recognizing emotions from the user's input text and adjusting the way the results are presented. This system consists of a user terminal, a server, an internal database, and an emotion engine.

[0826] Explanation of the program's processing

[0827] 1. Acceptance of user input

[0828] User terminal: Users use devices such as smartphones or computers to input product information and conditions in natural language format. For example, they might input text such as, "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat-rate plan?"

[0829] 2. Parsing of input text

[0830] Server: The server receives the input text and analyzes it using natural language processing (NLP) techniques. It uses the spacy library (ja_core_news_sm model) to extract the product type (e.g., "iPhone 14"), duration (e.g., "36 months"), and plan (e.g., "20GB flat-rate plan").

[0831] 3. Obtaining cost information

[0832] Server: The server accesses the internal database to retrieve cost and pricing information related to products and plans. The internal database stores details such as the cost of goods sold and monthly costs.

[0833] 4. Calculation of costs and break-even point

[0834] Server: The server calculates total cost and break-even point based on the acquired cost information. For example, it calculates total cost using the cost of an iPhone 14 and the monthly cost of a 20GB flat-rate plan, and then calculates the break-even point ARPU.

[0835] 5. Emotion recognition

[0836] Server: Uses the transformers library's pipeline("sentiment-analysis") model to recognize emotions from user input text. For example, it analyzes text containing expressions like "I'm in a hurry" to recognize that the user is in a hurry.

[0837] 6. Generating and returning results

[0838] Server: Generates calculation results in natural language format and adjusts the presentation method based on sentiment recognition. For example, if the server recognizes that the user is in a hurry, it immediately returns the calculation results. A specific natural language message might be sent such as, "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[0839] Adding specific examples

[0840] If a user enters "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan?", the system will process it as follows: Based on this input, the server will immediately analyze the data, calculate the total cost and break-even ARPU, and finally return a result of ¥5,777.78 as the break-even ARPU.

[0841] This enables users to make quick decisions in the proposal phase, resulting in significant improvements in operational efficiency and user experience.

[0842] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0843] Step 1:

[0844] The user uses their device to input product information and conditions in natural language format. The input is in the form of text such as, "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?" The entered information is sent to the server.

[0845] Input: User-generated text in natural language format

[0846] Output: Input text data

[0847] Step 2:

[0848] The server receives the input text and analyzes it using natural language processing techniques. Specifically, it uses the ja_core_news_sm model from the spacy library to extract the product type (iPhone 14), duration (36 months), and plan (20GB flat-rate plan). This converts the user input into structured data.

[0849] Input: Input text data

[0850] Output: Structured data on product type, duration, and plan.

[0851] Step 3:

[0852] The server accesses an internal database to retrieve cost and pricing information related to the type of product and plan. Specifically, it retrieves the cost of an iPhone 14 (¥100,000) and the monthly cost of a 20GB flat-rate plan (¥3,000) from the database. This provides the basic cost information necessary for calculations.

[0853] Input: Structured data on product type, duration, and plan.

[0854] Output: Cost data retrieved from the internal database

[0855] Step 4:

[0856] The total cost and break-even point are calculated using the cost data acquired by the server. The specific calculation method is as follows:

[0857] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[0858] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[0859] Input: Cost data retrieved from an internal database

[0860] Output: Calculation results of total cost and break-even point

[0861] Step 5:

[0862] The server uses the transformers library's pipeline("sentiment-analysis") model to recognize emotions from the user's input text. If the input text contains expressions such as "I'm in a hurry," the emotion engine recognizes that the user is in a hurry. The results of the emotion recognition are used for subsequent processing.

[0863] Input: Input text data

[0864] Output: Emotion recognition results

[0865] Step 6:

[0866] The server generates calculation results in natural language format and adjusts how the results are presented to the user based on the sentiment recognition results. Specifically, if sentiment recognition determines that the user is in a hurry, the results are returned promptly. An example of the generated text is the message, "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance is ¥5,777.78."

[0867] Input: Calculation results of total cost and break-even point, results of sentiment recognition

[0868] Output: Message in natural language format

[0869] Step 7:

[0870] The user receives the results sent from the server via their device and checks the calculation results displayed on the screen. This allows the user to quickly determine the proposed price and gross profit.

[0871] Input: Message in natural language format

[0872] Output: Calculation results displayed on the screen

[0873] 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.

[0874] 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.

[0875] 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.

[0876] [Third Embodiment]

[0877] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0878] 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.

[0879] 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).

[0880] 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.

[0881] 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.

[0882] 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).

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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".

[0889] The system of this invention analyzes product information and conditions entered by the user in natural language, calculates total cost and break-even point based on the results, and returns the calculation results in natural language. This system consists of a user terminal, a server, and an internal database.

[0890] User input

[0891] Users input product information and conditions in natural language format using their terminals. For example, the following inputs are possible:

[0892] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[0893] Server-based input analysis

[0894] The server receives input text sent by the user and parses it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the type of product, duration, and plan. This process yields information such as the following:

[0895] Product: iPhone 14

[0896] Duration: 36 months

[0897] Plan: 20GB flat rate

[0898] Cost information acquisition via server

[0899] Next, the server connects to an internal database to retrieve cost and pricing information related to the products and plans. For example, the internal database may store cost data such as the following:

[0900] iPhone 14 cost: ¥100,000

[0901] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0902] Server-based computation

[0903] The server uses the acquired cost information to calculate the total cost and the break-even point. The specific calculation is as follows:

[0904] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[0905] Break-even ARPU = Total Cost / 36 months

[0906] In this example, the total cost is ¥208,000 (¥100,000 + ¥3,000 x 36), and the break-even ARPU is ¥5,777.78.

[0907] Server returns results to the user.

[0908] Once the calculation is complete, the server generates the result in natural language format and sends it back to the user. The user can receive the calculation result on their terminal as follows:

[0909] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[0910] In this way, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, this system can eliminate human error and significantly improve operational efficiency.

[0911] As a concrete example, if a user enters "What is the break-even ARPU for a 36-month rental of an iPhone 14 with a fixed 20GB data plan?", the server immediately performs the calculation according to the above procedure, determining the break-even ARPU to be ¥5,777.78, and returning the result to the user. This allows the user to make quick decisions in the proposal phase.

[0912] The following describes the processing flow.

[0913] Step 1:

[0914] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[0915] Step 2:

[0916] The terminal transmits product information and conditions entered by the user in natural language format to the server. The user's input data is transmitted to the server via the network.

[0917] Step 3:

[0918] The server analyzes the user's input data. The server uses natural language processing (NLP) techniques to extract information such as the type of product (e.g., iPhone 14), the duration (e.g., 36 months), and the plan (e.g., 20GB flat rate).

[0919] Step 4:

[0920] Based on the analyzed product information and conditions, the server accesses an internal database to retrieve the necessary cost and fee information. Specifically, it extracts information such as the cost (e.g., ¥100,000) and plan (¥3,000 per month) of a product (iPhone 14) from the internal database.

[0921] Step 5:

[0922] The server uses the acquired cost and pricing information to calculate the total cost and break-even point (ARPU). The calculation is performed as follows:

[0923] Total cost = Product cost (¥100,000) + (Monthly plan cost (¥3,000) × period (36 months)) = ¥208,000

[0924] Break-even ARPU = Total cost (¥208,000) / Period (36 months) = ¥5,777.78

[0925] Step 6:

[0926] The server generates the calculation results in natural language format and sends them back to the user. The specific message in natural language format would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[0927] Step 7:

[0928] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[0929] In this way, throughout each step, the system can instantly calculate the proposed price and gross profit based on the conditions entered by the user in natural language, and return the results.

[0930] (Example 1)

[0931] 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."

[0932] Conventional systems faced the challenge of requiring significant time and effort to manually analyze product information and conditions to calculate total costs and break-even points. Furthermore, they were prone to errors due to human error, making it difficult to provide information quickly and accurately. This invention aims to solve these problems by automating the analysis of product information and the calculation of total costs and break-even points, thereby improving operational efficiency.

[0933] 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.

[0934] In this invention, the server includes means for receiving product information and conditions in natural language format from a user; means for analyzing the product information and conditions and extracting product types, periods, and plans; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total costs and break-even points using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; means for the user to input product information and conditions in natural language format using a terminal; means for the server to receive and analyze the input; means for the server to obtain cost information from the database; means for the server to perform calculations based on the acquired cost information; and means for returning the calculation results in natural language format. This enables the user to quickly and accurately obtain the proposed price and gross profit.

[0935] A "user" refers to a person or organization that uses the system to input product information and conditions in natural language format and receives calculation results.

[0936] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to input product information and conditions in natural language format and receive calculation results.

[0937] A "server" refers to a computing system that receives input information in natural language format, performs analysis and calculations, and returns the calculation results to the user.

[0938] "Natural language processing technology" refers to the techniques and algorithms used to automatically analyze natural language text and extract necessary information, rather than manually.

[0939] An "internal database" refers to a database system used to store data such as cost and fee information related to product information.

[0940] "Product information" refers to information about the products handled by the system, including data in natural language format that users input, such as the type of product.

[0941] "Conditions" refers to additional information such as the duration and plan that accompanies the product information.

[0942] "Cost information" refers to data that stores cost and fee information related to products and plans.

[0943] "Total cost" refers to the sum of all costs associated with the product and plan.

[0944] The "break-even point" refers to a figure used to determine the profit margin by dividing total costs over a specific period.

[0945] The system of this invention analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language. This system consists of a user terminal, a server, and an internal database.

[0946] First, the user uses their device to input product information and conditions in natural language format. For example, the following inputs are possible:

[0947] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[0948] The server receives input text sent by the user and parses it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the product type, duration, and plan. This process yields information such as the following:

[0949] Product: iPhone 14

[0950] Duration: 36 months

[0951] Plan: 20GB flat rate

[0952] The server then connects to an internal database to retrieve cost and pricing information related to the product or plan. For example, the internal database may contain cost data such as the following:

[0953] iPhone 14 cost: ¥100,000

[0954] Monthly cost for the 20GB flat-rate plan: ¥3,000

[0955] The server uses the acquired cost information to calculate the total cost and the break-even point. The specific calculation is as follows:

[0956] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[0957] Break-even ARPU = Total Expenses / 36 months

[0958] In this example, the total cost is ¥208,000 (¥100,000 + ¥3,000 x 36), and the break-even ARPU is ¥5,777.78.

[0959] Once the calculation is complete, the server generates the results in natural language format and sends them back to the user. The user can receive the calculation results on their terminal as follows:

[0960] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[0961] In this way, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, this system can eliminate human error and significantly improve operational efficiency.

[0962] As a concrete example, if a user inputs "What is the break-even ARPU for a 24-month lease of an iPad Pro with a fixed 10GB data plan?", the server immediately performs the calculation according to the above procedure, determining the break-even ARPU to be ¥5,833.33, and returning the result to the user. This allows the user to make quick decisions during the proposal phase.

[0963] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0964] Step 1:

[0965] Users enter product information and conditions in natural language format using their devices.

[0966] Input: Natural language text (Example: "What is the break-even ARPU for offering an iPhone 14 on a 36-month rental basis with a fixed 20GB data plan?")

[0967] Output: Sending input data from the user

[0968] Specific action: The user types text into the input field on the device and clicks the "Send" button.

[0969] Step 2:

[0970] The server receives the input text from the user.

[0971] Input: Natural language text submitted by the user.

[0972] Output: Receiving input data on the server side

[0973] Specific operation: The server receives an HTTP request at a specific API endpoint.

[0974] Step 3:

[0975] The server uses natural language processing (NLP) techniques to analyze the input text.

[0976] Input: Received text in natural language format

[0977] Output: Extraction results for product type, duration, and plan (e.g., "Product: iPhone 14", "Duration: 36 months", "Plan: 20GB flat rate")

[0978] Specific operation: The server analyzes the text using a natural language processing library (e.g., spaCy or NLTK) and extracts the necessary information.

[0979] Step 4:

[0980] The server connects to an internal database to retrieve relevant cost and pricing information.

[0981] Input: Extracted product type, duration, plan

[0982] Output: Cost information and pricing information (e.g., "Cost of iPhone 14: ¥100,000", "Monthly cost of 20GB flat-rate plan: ¥3,000")

[0983] Specific operation: The server connects to a database management system (e.g., SQLite, MySQL) and issues SQL queries to retrieve the necessary data.

[0984] Step 5:

[0985] Based on the cost information acquired by the server, the total cost and break-even point are calculated.

[0986] Input: Cost information and fee information (e.g., ¥100,000, ¥3,000)

[0987] Output: Calculation results (Example: Total cost: ¥208,000, Break-even ARPU: ¥5,777.78)

[0988] Specific operation: The server sums up each cost to calculate the total cost. Then, it divides the total cost by the period (number of months) to calculate the break-even point.

[0989] Step 6:

[0990] The server generates the calculation results in natural language format and sends them back to the user.

[0991] Input: Calculation result (Example: Total cost: ¥208,000, Break-even ARPU: ¥5,777.78)

[0992] Output: Result report in natural language format (Example: "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.")

[0993] Specific operation: The server uses a template engine (e.g., Jinja2) to assemble a report in natural language format and sends it back to the user's terminal as an HTTP response.

[0994] Step 7:

[0995] The user checks the calculation results on their device.

[0996] Input: Natural language result report returned from the server

[0997] Output: Visualization of calculation results

[0998] Specific action: The user checks the returned result on their device screen.

[0999] (Application Example 1)

[1000] 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."

[1001] Traditional retail businesses lacked the appropriate tools for effectively sourcing and pricing goods, making it difficult to accurately calculate total costs and break-even points in advance, especially when handling multiple products or plans simultaneously. Furthermore, the time required for data collection and analysis hindered quick decision-making. There was also the risk of losing profits due to inappropriate pricing.

[1002] 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.

[1003] In this invention, the server includes means for receiving product information and conditions in natural language format from a user; means for analyzing the product information and conditions and extracting product types, periods, and plans; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total costs and break-even points using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; and means for calculating the break-even ARPU on a smartphone to support the decision-making process for pricing when a physical store operator procures and sets prices for products. This enables physical store operators to quickly and accurately calculate product costs and break-even points and set optimal prices.

[1004] A "user" is an individual or organization that uses the system.

[1005] "Natural language forms" refer to the forms of written and spoken language that humans use in everyday life.

[1006] "Product information" refers to information that includes specific details and characteristics of a product.

[1007] "Conditions" refer to specific requirements or conditions set when purchasing or using a product.

[1008] "Type" refers to the category or classification to which a product or service belongs.

[1009] "Period" refers to the length of time during which a product or service is used or provided.

[1010] A "plan" refers to the method and content of service provision based on specific conditions and pricing structures.

[1011] "Cost information" refers to the details of the costs involved in providing goods or services.

[1012] "Pricing information" refers to the details of the fees that users pay for using a product or service.

[1013] "Total cost" refers to the total expenses incurred in providing a product or service.

[1014] The "break-even point" is the point at which revenue and costs equal each other, resulting in zero profit.

[1015] A "smartphone" is a portable information terminal equipped with advanced functions.

[1016] "Break-even ARPU" refers to the profit point obtained by dividing costs by the average revenue over a certain period.

[1017] A "physical store operator" is an individual or company that operates a physical store and sells goods or provides services.

[1018] "Decision-making" is the process of choosing a specific action or solution.

[1019] This invention provides a smartphone application for brick-and-mortar store operators to manage product procurement and pricing. The specific steps for implementing this system are outlined below.

[1020] System Configuration

[1021] The system includes the following components:

[1022] 1. User device (smartphone)

[1023] 2. Server

[1024] 3. Internal Database

[1025] System operation

[1026] User input

[1027] The user inputs text in natural language format on their smartphone. For example, they might input the following:

[1028] text

[1029] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[1030] Input analysis

[1031] The server receives input text sent by the user and analyzes it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the type of product, duration, and plan. This process obtains specific information such as the product (product information), duration, and plan.

[1032] Obtaining cost information

[1033] Next, the server connects to an internal database to retrieve cost and pricing information related to products and plans. For example, the internal database stores the purchase cost of products and the monthly cost of plans.

[1034] calculation

[1035] The server uses the acquired cost information to calculate the total cost and the break-even point. Specifically, the break-even ARPU is determined by adding up the cost of the product and the monthly cost of the plan over a specific period and dividing it by the period.

[1036] Result generation and return

[1037] Once the calculation is complete, the server generates the calculation result in natural language format and sends it back to the user. The user can receive the calculation result on their smartphone as follows:

[1038] text

[1039] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[1040] The technology used

[1041] hardware

[1042] Smartphone (user device)

[1043] Server (computation and database management)

[1044] software

[1045] Natural Language Processing (NLP): SpaCy, BERT

[1046] Database access modules: SQLite, MongoDB

[1047] Specific example

[1048] For example, if a brick-and-mortar store operator enters the question, "What is the break-even point (ARPU) for renting a MacBook Pro for 24 months with a fixed 200GB of storage?", the response will be as follows:

[1049] text

[1050] The break-even point for renting a MacBook Pro for 24 months with a fixed 200GB data allowance is ¥X,XXX.XX.

[1051] This allows brick-and-mortar store operators to easily calculate costs and profits and set optimal prices.

[1052] This invention enables brick-and-mortar store operators to quickly and accurately calculate product costs and break-even points, and to set optimal prices.

[1053] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1054] Step 1:

[1055] The user enters text in natural language format on their smartphone. The input includes conditions such as product type, duration, and plan. For example, the text might be, "What is the break-even ARPU for a 36-month rental of an iPhone 14 with a 20GB flat rate?" This input is then sent to the server.

[1056] Step 2:

[1057] The server analyzes the natural language input text received from the user. Natural language processing (NLP) techniques are used for the analysis. Specifically, tools such as SpaCy and BERT are used to extract product types, durations, and plans. The extracted product information is then output from the received text input.

[1058] Step 3:

[1059] The server retrieves relevant cost and pricing information from its internal database based on the extracted product information. Database access uses tools such as SQLite or MongoDB. For example, the cost of an iPhone 14 and the monthly cost of a 20GB flat-rate plan are retrieved as follows:

[1060] The cost of the iPhone 14 = ¥100,000

[1061] Monthly cost for the 20GB flat-rate plan = ¥3,000

[1062] Step 4:

[1063] The server calculates the total cost using the acquired cost information. The specific calculation formula is as follows:

[1064] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[1065] The cost information entered here becomes the input data, and the total cost is output. For example, the total cost is ¥208,000 (¥100,000 + ¥3,000 × 36).

[1066] Step 5:

[1067] The server calculates the break-even point (ARPU) based on total costs. The formula is as follows:

[1068] Break-even ARPU = Total Cost / 36 months

[1069] The total cost here becomes the input data, and the break-even ARPU is output. For example, the break-even ARPU is ¥5,777.78.

[1070] Step 6:

[1071] The server generates the calculation results in natural language format and sends them back to the user. The generated results are displayed on the smartphone. For example, it might display something like, "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[1072] This process allows users to quickly and accurately obtain product cost and break-even point information on their smartphones. This helps brick-and-mortar store operators make informed decisions about optimal pricing.

[1073] 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.

[1074] The present invention combines a system that analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language with an emotion engine that recognizes the user's emotions. This system consists of a user terminal, a server, an internal database, and an emotion engine.

[1075] User input

[1076] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[1077] Server-based input analysis

[1078] The server receives input text sent by the user and parses it. The server uses natural language processing (NLP) techniques to extract the type of product, duration, and plan. This process yields information such as:

[1079] Product: iPhone 14

[1080] Duration: 36 months

[1081] Plan: 20GB flat rate

[1082] Cost information acquisition via server

[1083] Next, the server accesses an internal database to retrieve cost and pricing information related to the product or plan. The internal database may contain cost data such as:

[1084] iPhone 14 cost: ¥100,000

[1085] Monthly cost for the 20GB flat-rate plan: ¥3,000

[1086] Server-based computation

[1087] The server uses the acquired cost information to calculate the total cost and the break-even point (ARPU). The specific calculation is as follows:

[1088] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[1089] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[1090] Emotion recognition by an emotion engine

[1091] The server uses natural language processing techniques to analyze the sentiment of the user's input text. For example, if the text entered by the user contains an expression like "I'm in a hurry," the sentiment engine will recognize that the user is in a hurry.

[1092] Server returns results to the user.

[1093] Once the calculation is complete, the server generates the results in natural language and adjusts how the results are presented based on the emotions recognized by the emotion engine. For example, if it detects that the user is in a hurry, it will immediately return the calculation results. A specific message in natural language would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[1094] User result confirmation

[1095] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[1096] As a concrete example, if a user enters "I'm in a hurry. What is the break-even ARPU for a 36-month rental of an iPhone 14 with a fixed 20GB data plan?", the server will immediately perform the calculation according to the above procedure, estimating the break-even ARPU as ¥5,777.78, and quickly return the result to the user. This allows the user to make quick decisions in the proposal phase.

[1097] Thus, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, the introduction of an emotion engine enables flexible responses tailored to the user's situation, significantly improving operational efficiency.

[1098] The following describes the processing flow.

[1099] Step 1:

[1100] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[1101] Step 2:

[1102] The terminal transmits product information and conditions entered by the user in natural language format to the server. The user's input data is transmitted to the server via the network.

[1103] Step 3:

[1104] The server analyzes the user's input data. The server uses natural language processing (NLP) techniques to extract information such as the type of product (e.g., iPhone 14), the duration (e.g., 36 months), and the plan (e.g., 20GB flat rate).

[1105] Step 4:

[1106] Based on the analyzed product information and conditions, the server accesses an internal database to retrieve the necessary cost and fee information. Specifically, it extracts information such as the cost (e.g., ¥100,000) and plan (¥3,000 per month) of a product (iPhone 14) from the internal database.

[1107] Step 5:

[1108] The server uses the acquired cost and pricing information to calculate the total cost and break-even point (ARPU). The calculation is performed as follows:

[1109] Total cost = Product cost (¥100,000) + (Monthly plan cost (¥3,000) × period (36 months)) = ¥208,000

[1110] Break-even ARPU = Total cost (¥208,000) / Period (36 months) = ¥5,777.78

[1111] Step 6:

[1112] The server uses an emotion engine to analyze the emotions expressed in the user's input text. For example, if the user types "I'm in a hurry," the emotion engine recognizes that the user is in a hurry.

[1113] Step 7:

[1114] The server generates calculation results in natural language and adjusts how the results are presented based on the perceived emotion. For example, if it detects that the user is in a hurry, it performs expedited processing to immediately return the calculation results. A specific message in natural language would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[1115] Step 8:

[1116] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[1117] Thus, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, the introduction of an emotion engine enables flexible responses tailored to the user's situation, significantly improving operational efficiency.

[1118] (Example 2)

[1119] 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."

[1120] Conventional product information and condition input systems struggle to process user-provided information accurately and quickly, and particularly lack the functionality to consider user emotions and circumstances. This has led to problems such as inappropriate responses in certain situations, resulting in decreased user work efficiency.

[1121] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving product information and conditions in natural language format input from the user; means for analyzing the product information and conditions and extracting the type of product, period, and plan; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total cost and break-even point using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; and means for recognizing emotions from the user's input text and adjusting the method of presenting the results. As a result, the user can quickly and accurately obtain the proposed amount and gross profit, and flexible responses can be made according to the user's situation.

[1122] "Product information" refers to information about specific products or services offered by the user.

[1123] "Conditions" refers to the detailed requirements and restrictions on transactions and use related to product information.

[1124] "Natural language forms" refer to forms that use language expressions that humans use on a daily basis.

[1125] "User" refers to an individual or legal entity that uses this system.

[1126] "Terminal" refers to electronic devices such as computers, smartphones, and tablets that users use to access a system.

[1127] A "server" refers to a central computer that receives and processes information sent from a user's terminal.

[1128] An "internal database" refers to a collection of data that a server accesses to retrieve information.

[1129] "Total cost" refers to the overall expenses associated with a particular product or condition.

[1130] The "break-even point" refers to the point where revenue and costs are balanced, and is often expressed in English as ARPU (Average Revenue Per User).

[1131] "Emotion recognition" refers to the process of analyzing user input text to identify their emotions and intentions.

[1132] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[1133] "Calculation" refers to the process of calculating specific indicators (such as total cost or break-even point) based on product information and conditions.

[1134] The present invention combines a system that analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language with a function that recognizes the user's emotions. This system consists of a user terminal, a server, an internal database, and an emotion recognition engine.

[1135] User input

[1136] Users input product information and conditions in natural language format using their devices. Specifically, users access an input form using an application or web browser on their device, enter text such as "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?", and click the submit button.

[1137] Server-based input analysis

[1138] The server receives input text sent by the user and parses it. Using a natural language processing engine (such as SpaCy or NLTK), the server extracts the type of product, duration, and plan from the input text. For example, the following information is extracted from the above input text:

[1139] Product: iPhone 14

[1140] Duration: 36 months

[1141] Plan: 20GB flat rate

[1142] Cost information acquisition via server

[1143] Next, the server accesses the internal database to retrieve cost and pricing information related to the extracted products and plans. The internal database contains cost data such as the following:

[1144] iPhone 14 cost: ¥100,000

[1145] Monthly cost for the 20GB flat-rate plan: ¥3,000

[1146] Server-based computation

[1147] The server uses the acquired cost information to calculate the total cost and the break-even point (ARPU). The specific calculation is as follows:

[1148] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[1149] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[1150] Emotion analysis using an emotion recognition engine

[1151] The server uses an emotion recognition engine (such as the Microsoft Text Analytics API) to recognize emotions from the user's input text. For example, if the input text contains a phrase like "I'm in a hurry," the emotion recognition engine will interpret that the user is in a hurry.

[1152] Server returns results to the user.

[1153] Once the calculation is complete, the server generates the results in natural language format and adjusts how the results are presented based on the emotions recognized by the emotion recognition engine. For example, if the server recognizes that the user is in a hurry, it will immediately return the calculation results. A concrete example would be a message like, "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78," which is then sent back to the user.

[1154] User result confirmation

[1155] The user receives the calculation results sent from the server via their terminal and checks the results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[1156] The above describes a specific embodiment of the present invention. This enables users to quickly and accurately obtain proposed amounts and gross profits, and also allows for the provision of optimal information tailored to the user's situation.

[1157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1158] Step 1: The user enters product information and conditions.

[1159] explanation

[1160] The user uses a terminal to input product information and conditions in natural language format. The entered information is then sent to the server.

[1161] input

[1162] The text asks, "What is the break-even point (ARPU) for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[1163] output

[1164] The user's input text sent to the server.

[1165] Specific actions

[1166] The user launches the application or web browser on their device, enters product information and conditions in natural language format into the input fields, and clicks the submit button.

[1167] Step 2: The server parses the input text.

[1168] explanation

[1169] The server analyzes the received input text. It uses a natural language processing engine to extract the type of product, duration, and plan.

[1170] input

[1171] Natural language text sent by the user.

[1172] output

[1173] Extracted information regarding products, periods, and plans.

[1174] Specific actions

[1175] The server uses an NLP engine (e.g., SpaCy or NLTK) to analyze the text "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?" and extracts the following information:

[1176] Product: iPhone 14

[1177] Duration: 36 months

[1178] Plan: 20GB flat rate

[1179] Step 3: The server retrieves internal cost information.

[1180] explanation

[1181] The server accesses an internal database to retrieve cost and pricing information related to products and plans.

[1182] input

[1183] Extracted information regarding products and plans.

[1184] output

[1185] Related cost and fee information.

[1186] Specific actions

[1187] The server executes the following SQL query on its internal database:

[1188] sql

[1189] SELECT cost FROM products WHERE name = 'iPhone 14';

[1190] SELECT monthly_cost FROM plans WHERE plan_name = '20GB flat rate';

[1191] As a result, the following information is obtained:

[1192] iPhone 14 cost: ¥100,000

[1193] Monthly cost for the 20GB flat-rate plan: ¥3,000

[1194] Step 4: The server calculates the total cost and break-even point.

[1195] explanation

[1196] The server calculates the total cost and break-even point (ARPU) based on the acquired cost information.

[1197] input

[1198] Acquired cost and fee information.

[1199] output

[1200] The calculated total cost and break-even point.

[1201] Specific actions

[1202] The server performs the following calculation:

[1203] Total cost = ¥100,000 + (¥3,000 × 36) = ¥208,000

[1204] Break-even ARPU = ¥208,000 ÷ 36 = ¥5,777.78

[1205] Step 5: The server recognizes the user's emotions.

[1206] explanation

[1207] The server uses an emotion recognition engine to analyze the emotions in the user's input text.

[1208] input

[1209] User input text.

[1210] output

[1211] Analyzed user sentiment information.

[1212] Specific actions

[1213] The server passes the text to an emotion recognition engine (for example, the Microsoft Text Analytics API) and recognizes that the user is in a hurry based on keywords such as "hurry."

[1214] Step 6: The server generates and returns the results.

[1215] explanation

[1216] Based on the calculation results and emotion recognition results, the server generates the results in natural language format and sends them back to the user.

[1217] input

[1218] Calculation results and emotion recognition results.

[1219] output

[1220] Result message in natural language format.

[1221] Specific actions

[1222] The server generates the following message:

[1223] "The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[1224] If the system detects that the user is in a hurry, this message will be sent back to the user immediately.

[1225] Step 7: The user confirms the results.

[1226] explanation

[1227] Users review the results received from the server via their terminals and use them to inform their business decisions.

[1228] input

[1229] A result message in natural language format sent from the server.

[1230] output

[1231] User review of results and appropriate action.

[1232] Specific actions

[1233] The results are displayed on the user's device, which the user reviews and uses to determine the proposed price and gross profit.

[1234] (Application Example 2)

[1235] 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."

[1236] Existing product information management systems struggle to properly analyze product information and conditions provided by users in natural language and to quickly deliver cost calculation results. Furthermore, they display results uniformly without considering user sentiment, potentially impairing the user experience. This creates a challenge, for example, when a company seeks to quickly determine a proposed price and improve performance; the system's response delays can hinder rapid decision-making.

[1237] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving product information and conditions in natural language format input from the user; means for analyzing the product information and conditions and extracting the type of product, period, and plan; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total cost and break-even point using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; means for recognizing emotions from the user's input text; and means for adjusting the method of presenting the results to the user based on the recognized emotions. As a result, the user can quickly and accurately obtain cost calculation results for products based on the input conditions, and it is also possible to display results that take the user's emotions into consideration. This is expected to improve operational efficiency and user experience.

[1238] "Natural language product information and conditions" refers to information about products and services, as well as the conditions for their provision, entered by the user in simple language.

[1239] "Analysis" refers to the process of analyzing input information to understand it and identify the necessary elements.

[1240] "Type of product" refers to the category or type of goods or services identified from the analyzed information.

[1241] "Duration" refers to the length of time over which a product or service is provided.

[1242] "Plan" refers to the terms and conditions of service or pricing plan related to a product or service.

[1243] An "internal database" refers to a database that a system uses to store cost and fee information.

[1244] "Cost and fee information" refers to detailed information about the costs and fees associated with the goods or services.

[1245] "Total cost" refers to the sum of all expenses necessary to provide goods or services under specific conditions.

[1246] The "break-even point" refers to the point at which income and expenses are equal when providing goods or services, meaning that profit is zero.

[1247] "Emotion recognition" refers to the process of analyzing a user's emotional state based on the text they input.

[1248] "Adjusting the presentation method of results" refers to optimizing how calculation results are displayed based on the recognized emotions of the user.

[1249] The system of this invention analyzes product information and conditions entered by the user in natural language, quickly and accurately calculates the relevant costs and break-even points, and returns the results to the user in an appropriate format. It also has the function of recognizing emotions from the user's input text and adjusting the way the results are presented. This system consists of a user terminal, a server, an internal database, and an emotion engine.

[1250] Explanation of the program's processing

[1251] 1. Acceptance of user input

[1252] User terminal: Users use devices such as smartphones or computers to input product information and conditions in natural language format. For example, they might input text such as, "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat-rate plan?"

[1253] 2. Parsing of input text

[1254] Server: The server receives the input text and analyzes it using natural language processing (NLP) techniques. It uses the spacy library (ja_core_news_sm model) to extract the product type (e.g., "iPhone 14"), duration (e.g., "36 months"), and plan (e.g., "20GB flat-rate plan").

[1255] 3. Obtaining cost information

[1256] Server: The server accesses the internal database to retrieve cost and pricing information related to products and plans. The internal database stores details such as the cost of goods sold and monthly costs.

[1257] 4. Calculation of costs and break-even point

[1258] Server: The server calculates total cost and break-even point based on the acquired cost information. For example, it calculates total cost using the cost of an iPhone 14 and the monthly cost of a 20GB flat-rate plan, and then calculates the break-even point ARPU.

[1259] 5. Emotion recognition

[1260] Server: Uses the transformers library's pipeline("sentiment-analysis") model to recognize emotions from user input text. For example, it analyzes text containing expressions like "I'm in a hurry" to recognize that the user is in a hurry.

[1261] 6. Generating and returning results

[1262] Server: Generates calculation results in natural language format and adjusts the presentation method based on sentiment recognition. For example, if the server recognizes that the user is in a hurry, it immediately returns the calculation results. A specific natural language message might be sent such as, "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[1263] Adding specific examples

[1264] If a user enters "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan?", the system will process it as follows: Based on this input, the server will immediately analyze the data, calculate the total cost and break-even ARPU, and finally return a result of ¥5,777.78 as the break-even ARPU.

[1265] This enables users to make quick decisions in the proposal phase, resulting in significant improvements in operational efficiency and user experience.

[1266] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1267] Step 1:

[1268] The user uses their device to input product information and conditions in natural language format. The input is in the form of text such as, "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?" The entered information is sent to the server.

[1269] Input: User-generated text in natural language format

[1270] Output: Input text data

[1271] Step 2:

[1272] The server receives the input text and analyzes it using natural language processing techniques. Specifically, it uses the ja_core_news_sm model from the spacy library to extract the product type (iPhone 14), duration (36 months), and plan (20GB flat-rate plan). This converts the user input into structured data.

[1273] Input: Input text data

[1274] Output: Structured data on product type, duration, and plan.

[1275] Step 3:

[1276] The server accesses an internal database to retrieve cost and pricing information related to the type of product and plan. Specifically, it retrieves the cost of an iPhone 14 (¥100,000) and the monthly cost of a 20GB flat-rate plan (¥3,000) from the database. This provides the basic cost information necessary for calculations.

[1277] Input: Structured data on product type, duration, and plan.

[1278] Output: Cost data retrieved from the internal database

[1279] Step 4:

[1280] The total cost and break-even point are calculated using the cost data acquired by the server. The specific calculation method is as follows:

[1281] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[1282] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[1283] Input: Cost data retrieved from an internal database

[1284] Output: Calculation results of total cost and break-even point

[1285] Step 5:

[1286] The server uses the transformers library's pipeline("sentiment-analysis") model to recognize emotions from the user's input text. If the input text contains expressions such as "I'm in a hurry," the emotion engine recognizes that the user is in a hurry. The results of the emotion recognition are used for subsequent processing.

[1287] Input: Input text data

[1288] Output: Emotion recognition results

[1289] Step 6:

[1290] The server generates calculation results in natural language format and adjusts how the results are presented to the user based on the sentiment recognition results. Specifically, if sentiment recognition determines that the user is in a hurry, the results are returned promptly. An example of the generated text is the message, "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance is ¥5,777.78."

[1291] Input: Calculation results of total cost and break-even point, results of sentiment recognition

[1292] Output: Message in natural language format

[1293] Step 7:

[1294] The user receives the results sent from the server via their device and checks the calculation results displayed on the screen. This allows the user to quickly determine the proposed price and gross profit.

[1295] Input: Message in natural language format

[1296] Output: Calculation results displayed on the screen

[1297] 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.

[1298] 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.

[1299] 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.

[1300] [Fourth Embodiment]

[1301] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1302] 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.

[1303] 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).

[1304] 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.

[1305] 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.

[1306] 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).

[1307] 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.

[1308] 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.

[1309] 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.

[1310] 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.

[1311] 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.

[1312] 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.

[1313] 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".

[1314] The system of this invention analyzes product information and conditions entered by the user in natural language, calculates total cost and break-even point based on the results, and returns the calculation results in natural language. This system consists of a user terminal, a server, and an internal database.

[1315] User input

[1316] Users input product information and conditions in natural language format using their terminals. For example, the following inputs are possible:

[1317] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[1318] Server-based input analysis

[1319] The server receives input text sent by the user and parses it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the type of product, duration, and plan. This process yields information such as the following:

[1320] Product: iPhone 14

[1321] Duration: 36 months

[1322] Plan: 20GB flat rate

[1323] Cost information acquisition via server

[1324] Next, the server connects to an internal database to retrieve cost and pricing information related to the products and plans. For example, the internal database may contain cost data such as the following:

[1325] iPhone 14 cost: ¥100,000

[1326] Monthly cost for the 20GB flat-rate plan: ¥3,000

[1327] Server-based computation

[1328] The server uses the acquired cost information to calculate the total cost and the break-even point. The specific calculation is as follows:

[1329] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[1330] Break-even ARPU = Total Cost / 36 months

[1331] In this example, the total cost is ¥208,000 (¥100,000 + ¥3,000 x 36), and the break-even ARPU is ¥5,777.78.

[1332] Server returns results to the user.

[1333] Once the calculation is complete, the server generates the result in natural language format and sends it back to the user. The user can receive the calculation result on their terminal as follows:

[1334] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[1335] In this way, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, this system can eliminate human error and significantly improve operational efficiency.

[1336] As a concrete example, if a user enters "What is the break-even ARPU for a 36-month rental of an iPhone 14 with a fixed 20GB data plan?", the server immediately performs the calculation according to the above procedure, determining the break-even ARPU to be ¥5,777.78, and returning the result to the user. This allows the user to make quick decisions in the proposal phase.

[1337] The following describes the processing flow.

[1338] Step 1:

[1339] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[1340] Step 2:

[1341] The terminal transmits product information and conditions entered by the user in natural language format to the server. The user's input data is transmitted to the server via the network.

[1342] Step 3:

[1343] The server analyzes the user's input data. The server uses natural language processing (NLP) techniques to extract information such as the type of product (e.g., iPhone 14), the duration (e.g., 36 months), and the plan (e.g., 20GB flat rate).

[1344] Step 4:

[1345] Based on the analyzed product information and conditions, the server accesses an internal database to retrieve the necessary cost and fee information. Specifically, it extracts information such as the cost (e.g., ¥100,000) and plan (¥3,000 per month) of a product (iPhone 14) from the internal database.

[1346] Step 5:

[1347] The server uses the acquired cost and pricing information to calculate the total cost and break-even point (ARPU). The calculation is performed as follows:

[1348] Total cost = Product cost (¥100,000) + (Monthly plan cost (¥3,000) × period (36 months)) = ¥208,000

[1349] Break-even ARPU = Total cost (¥208,000) / Period (36 months) = ¥5,777.78

[1350] Step 6:

[1351] The server generates the calculation results in natural language format and sends them back to the user. The specific message in natural language format would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[1352] Step 7:

[1353] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[1354] Throughout each step, the system can instantly calculate the proposed price and gross profit based on the conditions entered by the user in natural language, and return the results.

[1355] (Example 1)

[1356] 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".

[1357] Conventional systems faced the challenge of requiring significant time and effort to manually analyze product information and conditions to calculate total costs and break-even points. Furthermore, they were prone to errors due to human error, making it difficult to provide information quickly and accurately. This invention aims to solve these problems by automating the analysis of product information and the calculation of total costs and break-even points, thereby improving operational efficiency.

[1358] 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.

[1359] In this invention, the server includes means for receiving product information and conditions in natural language format from a user; means for analyzing the product information and conditions and extracting product types, periods, and plans; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total costs and break-even points using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; means for the user to input product information and conditions in natural language format using a terminal; means for the server to receive and analyze the input; means for the server to obtain cost information from the database; means for the server to perform calculations based on the acquired cost information; and means for returning the calculation results in natural language format. This enables the user to quickly and accurately obtain the proposed price and gross profit.

[1360] A "user" refers to a person or organization that uses the system to input product information and conditions in natural language format and receives calculation results.

[1361] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to input product information and conditions in natural language format and receive calculation results.

[1362] A "server" refers to a computing system that receives input information in natural language format, performs analysis and calculations, and returns the calculation results to the user.

[1363] "Natural language processing technology" refers to the techniques and algorithms used to automatically analyze natural language text and extract necessary information, rather than manually.

[1364] An "internal database" refers to a database system used to store data such as cost and fee information related to product information.

[1365] "Product information" refers to information about the products handled by the system, including data in natural language format that users input, such as the type of product.

[1366] "Conditions" refers to additional information such as the duration and plan that accompanies the product information.

[1367] "Cost information" refers to data that stores cost and fee information related to products and plans.

[1368] "Total cost" refers to the sum of all costs associated with the product and plan.

[1369] The "break-even point" refers to a figure used to determine the profit margin by dividing total costs over a specific period.

[1370] The system of this invention analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language. This system consists of a user terminal, a server, and an internal database.

[1371] First, the user uses their device to input product information and conditions in natural language format. For example, the following inputs are possible:

[1372] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[1373] The server receives input text sent by the user and parses it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the product type, duration, and plan. This process yields information such as the following:

[1374] Product: iPhone 14

[1375] Duration: 36 months

[1376] Plan: 20GB flat rate

[1377] The server then connects to an internal database to retrieve cost and pricing information related to the product or plan. For example, the internal database may contain cost data such as the following:

[1378] iPhone 14 cost: ¥100,000

[1379] Monthly cost for the 20GB flat-rate plan: ¥3,000

[1380] The server uses the acquired cost information to calculate the total cost and the break-even point. The specific calculation is as follows:

[1381] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[1382] Break-even ARPU = Total Expenses / 36 months

[1383] In this example, the total cost is ¥208,000 (¥100,000 + ¥3,000 x 36), and the break-even ARPU is ¥5,777.78.

[1384] Once the calculation is complete, the server generates the results in natural language format and sends them back to the user. The user can receive the calculation results on their terminal as follows:

[1385] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[1386] In this way, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, this system can eliminate human error and significantly improve operational efficiency.

[1387] As a concrete example, if a user inputs "What is the break-even ARPU for a 24-month lease of an iPad Pro with a fixed 10GB data plan?", the server immediately performs the calculation according to the above procedure, determining the break-even ARPU to be ¥5,833.33, and returning the result to the user. This allows the user to make quick decisions during the proposal phase.

[1388] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1389] Step 1:

[1390] Users enter product information and conditions in natural language format using their devices.

[1391] Input: Natural language text (Example: "What is the break-even ARPU for offering an iPhone 14 on a 36-month rental basis with a fixed 20GB data plan?")

[1392] Output: Sending input data from the user

[1393] Specific action: The user types text into the input field on the device and clicks the "Send" button.

[1394] Step 2:

[1395] The server receives the input text from the user.

[1396] Input: Natural language text submitted by the user.

[1397] Output: Receiving input data on the server side

[1398] Specific operation: The server receives an HTTP request at a specific API endpoint.

[1399] Step 3:

[1400] The server uses natural language processing (NLP) techniques to analyze the input text.

[1401] Input: Received text in natural language format

[1402] Output: Extraction results for product type, duration, and plan (e.g., "Product: iPhone 14", "Duration: 36 months", "Plan: 20GB flat rate")

[1403] Specific operation: The server analyzes the text using a natural language processing library (e.g., spaCy or NLTK) and extracts the necessary information.

[1404] Step 4:

[1405] The server connects to an internal database to retrieve relevant cost and pricing information.

[1406] Input: Extracted product type, duration, plan

[1407] Output: Cost information and pricing information (e.g., "Cost of iPhone 14: ¥100,000", "Monthly cost of 20GB flat-rate plan: ¥3,000")

[1408] Specific operation: The server connects to a database management system (e.g., SQLite, MySQL) and issues SQL queries to retrieve the necessary data.

[1409] Step 5:

[1410] Based on the cost information acquired by the server, the total cost and break-even point are calculated.

[1411] Input: Cost information and fee information (e.g., ¥100,000, ¥3,000)

[1412] Output: Calculation results (Example: Total cost: ¥208,000, Break-even ARPU: ¥5,777.78)

[1413] Specific operation: The server sums up each cost to calculate the total cost. Then, it divides the total cost by the period (number of months) to calculate the break-even point.

[1414] Step 6:

[1415] The server generates the calculation results in natural language format and sends them back to the user.

[1416] Input: Calculation result (Example: Total cost: ¥208,000, Break-even ARPU: ¥5,777.78)

[1417] Output: Result report in natural language format (Example: "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.")

[1418] Specific operation: The server uses a template engine (e.g., Jinja2) to assemble a report in natural language format and sends it back to the user's terminal as an HTTP response.

[1419] Step 7:

[1420] The user checks the calculation results on their device.

[1421] Input: Natural language result report returned from the server

[1422] Output: Visualization of calculation results

[1423] Specific action: The user checks the returned result on their device screen.

[1424] (Application Example 1)

[1425] 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".

[1426] Traditional retail businesses lacked the appropriate tools for effectively sourcing and pricing goods, making it difficult to accurately calculate total costs and break-even points in advance, especially when handling multiple products or plans simultaneously. Furthermore, the time required for data collection and analysis hindered quick decision-making. There was also the risk of losing profits due to inappropriate pricing.

[1427] 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.

[1428] In this invention, the server includes means for receiving product information and conditions in natural language format from a user; means for analyzing the product information and conditions and extracting product types, periods, and plans; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total costs and break-even points using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; and means for calculating the break-even ARPU on a smartphone to support the decision-making process for pricing when a physical store operator procures and sets prices for products. This enables physical store operators to quickly and accurately calculate product costs and break-even points and set optimal prices.

[1429] A "user" is an individual or organization that uses the system.

[1430] "Natural language forms" refer to the forms of written and spoken language that humans use in everyday life.

[1431] "Product information" refers to information that includes specific details and characteristics of a product.

[1432] "Conditions" refer to specific requirements or conditions set when purchasing or using a product.

[1433] "Type" refers to the category or classification to which a product or service belongs.

[1434] "Period" refers to the length of time during which a product or service is used or provided.

[1435] A "plan" refers to the method and content of service provision based on specific conditions and pricing structures.

[1436] "Cost information" refers to the details of the costs involved in providing goods or services.

[1437] "Pricing information" refers to the details of the fees that users pay for using a product or service.

[1438] "Total cost" refers to the total expenses incurred in providing a product or service.

[1439] The "break-even point" is the point at which revenue and costs equal each other, resulting in zero profit.

[1440] A "smartphone" is a portable information terminal equipped with advanced functions.

[1441] "Break-even ARPU" refers to the profit point obtained by dividing costs by the average revenue over a certain period.

[1442] A "physical store operator" is an individual or company that operates a physical store and sells goods or provides services.

[1443] "Decision-making" is the process of choosing a specific action or solution.

[1444] This invention provides a smartphone application for brick-and-mortar store operators to manage product procurement and pricing. The specific steps for implementing this system are outlined below.

[1445] System Configuration

[1446] The system includes the following components:

[1447] 1. User device (smartphone)

[1448] 2. Server

[1449] 3. Internal Database

[1450] System operation

[1451] User input

[1452] The user inputs text in natural language format on their smartphone. For example, they might input the following:

[1453] text

[1454] What is the break-even point (ARPU) for offering an iPhone 14 as a 36-month rental with a fixed 20GB data allowance?

[1455] Input analysis

[1456] The server receives input text sent by the user and analyzes it. Specifically, it uses natural language processing (NLP) techniques to analyze the input content and extract the type of product, duration, and plan. This process obtains specific information such as the product (product information), duration, and plan.

[1457] Obtaining cost information

[1458] Next, the server connects to an internal database to retrieve cost and pricing information related to products and plans. For example, the internal database stores the purchase cost of products and the monthly cost of plans.

[1459] calculation

[1460] The server uses the acquired cost information to calculate the total cost and the break-even point. Specifically, the break-even ARPU is determined by adding up the cost of the product and the monthly cost of the plan over a specific period and dividing it by the period.

[1461] Result generation and return

[1462] Once the calculation is complete, the server generates the calculation result in natural language format and sends it back to the user. The user can receive the calculation result on their smartphone as follows:

[1463] text

[1464] The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78.

[1465] The technology used

[1466] hardware

[1467] Smartphone (user device)

[1468] Server (computation and database management)

[1469] software

[1470] Natural Language Processing (NLP): SpaCy, BERT

[1471] Database access modules: SQLite, MongoDB

[1472] Specific example

[1473] For example, if a brick-and-mortar store operator enters the question, "What is the break-even point (ARPU) for renting a MacBook Pro for 24 months with a fixed 200GB of storage?", the response will be as follows:

[1474] text

[1475] The break-even point for renting a MacBook Pro for 24 months with a fixed 200GB data allowance is ¥X,XXX.XX.

[1476] This allows brick-and-mortar store operators to easily calculate costs and profits and set optimal prices.

[1477] This invention enables brick-and-mortar store operators to quickly and accurately calculate product costs and break-even points, and to set optimal prices.

[1478] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1479] Step 1:

[1480] The user enters text in natural language format on their smartphone. The input includes conditions such as product type, duration, and plan. For example, the text might be, "What is the break-even ARPU for a 36-month rental of an iPhone 14 with a 20GB flat rate?" This input is then sent to the server.

[1481] Step 2:

[1482] The server analyzes the natural language input text received from the user. Natural language processing (NLP) techniques are used for the analysis. Specifically, tools such as SpaCy and BERT are used to extract product types, durations, and plans. The extracted product information is then output from the received text input.

[1483] Step 3:

[1484] The server retrieves relevant cost and pricing information from its internal database based on the extracted product information. Database access uses tools such as SQLite or MongoDB. For example, the cost of an iPhone 14 and the monthly cost of a 20GB flat-rate plan are retrieved as follows:

[1485] The cost of the iPhone 14 = ¥100,000

[1486] Monthly cost for the 20GB flat-rate plan = ¥3,000

[1487] Step 4:

[1488] The server calculates the total cost using the acquired cost information. The specific calculation formula is as follows:

[1489] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months)

[1490] The cost information entered here becomes the input data, and the total cost is output. For example, the total cost is ¥208,000 (¥100,000 + ¥3,000 × 36).

[1491] Step 5:

[1492] The server calculates the break-even point (ARPU) based on total costs. The formula is as follows:

[1493] Break-even ARPU = Total Cost / 36 months

[1494] The total cost here becomes the input data, and the break-even ARPU is output. For example, the break-even ARPU is ¥5,777.78.

[1495] Step 6:

[1496] The server generates the calculation results in natural language format and sends them back to the user. The generated results are displayed on the smartphone. For example, it might display something like, "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[1497] This process allows users to quickly and accurately obtain product cost and break-even point information on their smartphones. This helps brick-and-mortar store operators make informed decisions about optimal pricing.

[1498] 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.

[1499] The present invention combines a system that analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language with an emotion engine that recognizes the user's emotions. This system consists of a user terminal, a server, an internal database, and an emotion engine.

[1500] User input

[1501] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[1502] Server-based input analysis

[1503] The server receives input text sent by the user and parses it. The server uses natural language processing (NLP) techniques to extract the type of product, duration, and plan. This process yields information such as:

[1504] Product: iPhone 14

[1505] Duration: 36 months

[1506] Plan: 20GB flat rate

[1507] Cost information acquisition via server

[1508] Next, the server accesses an internal database to retrieve cost and pricing information related to the product or plan. The internal database may contain cost data such as:

[1509] iPhone 14 cost: ¥100,000

[1510] Monthly cost for the 20GB flat-rate plan: ¥3,000

[1511] Server-based computation

[1512] The server uses the acquired cost information to calculate the total cost and the break-even point (ARPU). The specific calculation is as follows:

[1513] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[1514] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[1515] Emotion recognition by an emotion engine

[1516] The server uses natural language processing techniques to analyze the sentiment of the user's input text. For example, if the text entered by the user contains an expression like "I'm in a hurry," the sentiment engine will recognize that the user is in a hurry.

[1517] Server returns results to the user.

[1518] Once the calculation is complete, the server generates the results in natural language and adjusts how the results are presented based on the emotions recognized by the emotion engine. For example, if it detects that the user is in a hurry, it will immediately return the calculation results. A specific message in natural language would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[1519] User result confirmation

[1520] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[1521] As a concrete example, if a user enters "I'm in a hurry. What is the break-even ARPU for a 36-month rental of an iPhone 14 with a fixed 20GB data plan?", the server will immediately perform the calculation according to the above procedure, estimating the break-even ARPU as ¥5,777.78, and quickly return the result to the user. This allows the user to make quick decisions in the proposal phase.

[1522] Thus, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, the introduction of an emotion engine enables flexible responses tailored to the user's situation, significantly improving operational efficiency.

[1523] The following describes the processing flow.

[1524] Step 1:

[1525] Users input product information and conditions in natural language format using their devices. For example, they might input text such as, "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[1526] Step 2:

[1527] The terminal transmits product information and conditions entered by the user in natural language format to the server. The user's input data is transmitted to the server via the network.

[1528] Step 3:

[1529] The server analyzes the user's input data. The server uses natural language processing (NLP) techniques to extract information such as the type of product (e.g., iPhone 14), the duration (e.g., 36 months), and the plan (e.g., 20GB flat rate).

[1530] Step 4:

[1531] Based on the analyzed product information and conditions, the server accesses an internal database to retrieve the necessary cost and fee information. Specifically, it extracts information such as the cost (e.g., ¥100,000) and plan (¥3,000 per month) of a product (iPhone 14) from the internal database.

[1532] Step 5:

[1533] The server uses the acquired cost and pricing information to calculate the total cost and break-even point (ARPU). The calculation is performed as follows:

[1534] Total cost = Product cost (¥100,000) + (Monthly plan cost (¥3,000) × period (36 months)) = ¥208,000

[1535] Break-even ARPU = Total cost (¥208,000) / Period (36 months) = ¥5,777.78

[1536] Step 6:

[1537] The server uses an emotion engine to analyze the emotions expressed in the user's input text. For example, if the user types "I'm in a hurry," the emotion engine recognizes that the user is in a hurry.

[1538] Step 7:

[1539] The server generates calculation results in natural language and adjusts how the results are presented based on the perceived emotion. For example, if it detects that the user is in a hurry, it performs expedited processing to immediately return the calculation results. A specific message in natural language would be: "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[1540] Step 8:

[1541] The user receives the results sent from the server via their terminal and checks the calculation results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[1542] Thus, by implementing the system of the present invention, users can quickly and accurately obtain the proposed amount and gross profit. Furthermore, the introduction of an emotion engine enables flexible responses tailored to the user's situation, significantly improving operational efficiency.

[1543] (Example 2)

[1544] 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".

[1545] Conventional product information and condition input systems struggle to process user-provided information accurately and quickly, and particularly lack the functionality to consider user emotions and circumstances. This has led to problems such as inappropriate responses in certain situations, resulting in decreased user work efficiency.

[1546] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving product information and conditions in natural language format input from the user; means for analyzing the product information and conditions and extracting the type of product, period, and plan; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total cost and break-even point using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; and means for recognizing emotions from the user's input text and adjusting the method of presenting the results. As a result, the user can quickly and accurately obtain the proposed amount and gross profit, and flexible responses can be made according to the user's situation.

[1547] "Product information" refers to information about specific products or services offered by the user.

[1548] "Conditions" refers to the detailed requirements and restrictions on transactions and use related to product information.

[1549] "Natural language forms" refer to forms that use language expressions that humans use on a daily basis.

[1550] "User" refers to an individual or legal entity that uses this system.

[1551] "Terminal" refers to electronic devices such as computers, smartphones, and tablets that users use to access a system.

[1552] A "server" refers to a central computer that receives and processes information sent from a user's terminal.

[1553] An "internal database" refers to a collection of data that a server accesses to retrieve information.

[1554] "Total cost" refers to the overall expenses associated with a particular product or condition.

[1555] The "break-even point" refers to the point where revenue and costs are balanced, and is often expressed in English as ARPU (Average Revenue Per User).

[1556] "Emotion recognition" refers to the process of analyzing user input text to identify their emotions and intentions.

[1557] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[1558] "Calculation" refers to the process of calculating specific indicators (such as total cost or break-even point) based on product information and conditions.

[1559] The present invention combines a system that analyzes product information and conditions entered by the user in natural language, calculates total costs and break-even points based on the results, and returns the calculation results in natural language with a function that recognizes the user's emotions. This system consists of a user terminal, a server, an internal database, and an emotion recognition engine.

[1560] User input

[1561] Users input product information and conditions in natural language format using their devices. Specifically, users access an input form using an application or web browser on their device, enter text such as "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?", and click the submit button.

[1562] Server-based input analysis

[1563] The server receives input text sent by the user and parses it. Using a natural language processing engine (such as SpaCy or NLTK), the server extracts the type of product, duration, and plan from the input text. For example, the following information is extracted from the above input text:

[1564] Product: iPhone 14

[1565] Duration: 36 months

[1566] Plan: 20GB flat rate

[1567] Cost information acquisition via server

[1568] Next, the server accesses the internal database to retrieve cost and pricing information related to the extracted products and plans. The internal database contains cost data such as the following:

[1569] iPhone 14 cost: ¥100,000

[1570] Monthly cost for the 20GB flat-rate plan: ¥3,000

[1571] Server-based computation

[1572] The server uses the acquired cost information to calculate the total cost and the break-even point (ARPU). The specific calculation is as follows:

[1573] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[1574] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[1575] Emotion analysis using an emotion recognition engine

[1576] The server uses an emotion recognition engine (such as the Microsoft Text Analytics API) to recognize emotions from the user's input text. For example, if the input text contains a phrase like "I'm in a hurry," the emotion recognition engine will interpret that the user is in a hurry.

[1577] Server returns results to the user.

[1578] Once the calculation is complete, the server generates the results in natural language format and adjusts how the results are presented based on the emotions recognized by the emotion recognition engine. For example, if the server recognizes that the user is in a hurry, it will immediately return the calculation results. A concrete example would be a message like, "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78," which is then sent back to the user.

[1579] User result confirmation

[1580] The user receives the calculation results sent from the server via their terminal and checks the results displayed on the screen. Based on these results, the user can quickly determine the proposed price and gross profit.

[1581] The above describes a specific embodiment of the present invention. This enables users to quickly and accurately obtain proposed amounts and gross profits, and also allows for the provision of optimal information tailored to the user's situation.

[1582] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1583] Step 1: The user enters product information and conditions.

[1584] explanation

[1585] The user uses a terminal to input product information and conditions in natural language format. The entered information is then sent to the server.

[1586] input

[1587] The text asks, "What is the break-even point (ARPU) for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?"

[1588] output

[1589] The user's input text sent to the server.

[1590] Specific actions

[1591] The user launches the application or web browser on their device, enters product information and conditions in natural language format into the input fields, and clicks the submit button.

[1592] Step 2: The server parses the input text.

[1593] explanation

[1594] The server analyzes the received input text. It uses a natural language processing engine to extract the type of product, duration, and plan.

[1595] input

[1596] Natural language text sent by the user.

[1597] output

[1598] Extracted information regarding products, periods, and plans.

[1599] Specific actions

[1600] The server uses an NLP engine (e.g., SpaCy or NLTK) to analyze the text "What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?" and extracts the following information:

[1601] Product: iPhone 14

[1602] Duration: 36 months

[1603] Plan: 20GB flat rate

[1604] Step 3: The server retrieves internal cost information.

[1605] explanation

[1606] The server accesses an internal database to retrieve cost and pricing information related to products and plans.

[1607] input

[1608] Extracted information regarding products and plans.

[1609] output

[1610] Related cost and fee information.

[1611] Specific actions

[1612] The server executes the following SQL query on its internal database:

[1613] SQL

[1614] SELECT cost FROM products WHERE name = 'iPhone 14';

[1615] SELECT monthly_cost FROM plans WHERE plan_name = '20GB flat rate';

[1616] As a result, the following information is obtained:

[1617] iPhone 14 cost: ¥100,000

[1618] Monthly cost for the 20GB flat-rate plan: ¥3,000

[1619] Step 4: The server calculates the total cost and break-even point.

[1620] explanation

[1621] The server calculates the total cost and break-even point (ARPU) based on the acquired cost information.

[1622] input

[1623] Acquired cost and fee information.

[1624] output

[1625] The calculated total cost and break-even point.

[1626] Specific actions

[1627] The server performs the following calculation:

[1628] Total cost = ¥100,000 + (¥3,000 × 36) = ¥208,000

[1629] Break-even ARPU = ¥208,000 ÷ 36 = ¥5,777.78

[1630] Step 5: The server recognizes the user's emotions.

[1631] explanation

[1632] The server uses an emotion recognition engine to analyze the emotions in the user's input text.

[1633] input

[1634] User input text.

[1635] output

[1636] Analyzed user sentiment information.

[1637] Specific actions

[1638] The server passes the text to an emotion recognition engine (for example, the Microsoft Text Analytics API) and recognizes that the user is in a hurry based on keywords such as "hurry."

[1639] Step 6: The server generates and returns the results.

[1640] explanation

[1641] Based on the calculation results and emotion recognition results, the server generates the results in natural language format and sends them back to the user.

[1642] input

[1643] Calculation results and emotion recognition results.

[1644] output

[1645] Result message in natural language format.

[1646] Specific actions

[1647] The server generates the following message:

[1648] "The break-even point for renting an iPhone 14 for 36 months with a fixed 20GB data plan is ¥5,777.78."

[1649] If the system detects that the user is in a hurry, this message will be sent back to the user immediately.

[1650] Step 7: The user confirms the results.

[1651] explanation

[1652] Users review the results received from the server via their terminals and use them to inform their business decisions.

[1653] input

[1654] A result message in natural language format sent from the server.

[1655] output

[1656] User review of results and appropriate action.

[1657] Specific actions

[1658] The results are displayed on the user's device, which the user reviews and uses to determine the proposed price and gross profit.

[1659] (Application Example 2)

[1660] 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".

[1661] Existing product information management systems struggle to properly analyze product information and conditions provided by users in natural language and to quickly deliver cost calculation results. Furthermore, they display results uniformly without considering user sentiment, potentially impairing the user experience. This creates a challenge, for example, when a company seeks to quickly determine a proposed price and improve performance; the system's response delays can hinder rapid decision-making.

[1662] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving product information and conditions in natural language format input from the user; means for analyzing the product information and conditions and extracting the type of product, period, and plan; means for obtaining relevant cost and fee information from an internal database based on the product information and conditions; means for calculating total cost and break-even point using the obtained cost and fee information; means for generating the calculation results in natural language format and returning them to the user; means for recognizing emotions from the user's input text; and means for adjusting the method of presenting the results to the user based on the recognized emotions. As a result, the user can quickly and accurately obtain cost calculation results for products based on the input conditions, and it is also possible to display results that take the user's emotions into consideration. This is expected to improve operational efficiency and user experience.

[1663] "Natural language product information and conditions" refers to information about products and services, as well as the conditions for their provision, entered by the user in simple language.

[1664] "Analysis" refers to the process of analyzing input information to understand it and identify the necessary elements.

[1665] "Type of product" refers to the category or type of goods or services identified from the analyzed information.

[1666] "Duration" refers to the length of time over which a product or service is provided.

[1667] "Plan" refers to the terms and conditions of service or pricing plan related to a product or service.

[1668] An "internal database" refers to a database that a system uses to store cost and fee information.

[1669] "Cost and fee information" refers to detailed information about the costs and fees associated with the goods or services.

[1670] "Total cost" refers to the sum of all expenses necessary to provide goods or services under specific conditions.

[1671] The "break-even point" refers to the point at which income and expenses are equal when providing goods or services, meaning that profit is zero.

[1672] "Emotion recognition" refers to the process of analyzing a user's emotional state based on the text they input.

[1673] "Adjusting the presentation method of results" refers to optimizing how calculation results are displayed based on the recognized emotions of the user.

[1674] The system of this invention analyzes product information and conditions entered by the user in natural language, quickly and accurately calculates the relevant costs and break-even points, and returns the results to the user in an appropriate format. It also has the function of recognizing emotions from the user's input text and adjusting the way the results are presented. This system consists of a user terminal, a server, an internal database, and an emotion engine.

[1675] Explanation of the program's processing

[1676] 1. Acceptance of user input

[1677] User terminal: Users use devices such as smartphones or computers to input product information and conditions in natural language format. For example, they might input text such as, "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat-rate plan?"

[1678] 2. Parsing of input text

[1679] Server: The server receives the input text and analyzes it using natural language processing (NLP) techniques. It uses the spacy library (ja_core_news_sm model) to extract the product type (e.g., "iPhone 14"), duration (e.g., "36 months"), and plan (e.g., "20GB flat-rate plan").

[1680] 3. Obtaining cost information

[1681] Server: The server accesses the internal database to retrieve cost and pricing information related to products and plans. The internal database stores details such as the cost of goods sold and monthly costs.

[1682] 4. Calculation of costs and break-even point

[1683] Server: The server calculates total cost and break-even point based on the acquired cost information. For example, it calculates total cost using the cost of an iPhone 14 and the monthly cost of a 20GB flat-rate plan, and then calculates the break-even point ARPU.

[1684] 5. Emotion recognition

[1685] Server: Uses the transformers library's pipeline("sentiment-analysis") model to recognize emotions from user input text. For example, it analyzes text containing expressions like "I'm in a hurry" to recognize that the user is in a hurry.

[1686] 6. Generating and returning results

[1687] Server: Generates calculation results in natural language format and adjusts the presentation method based on sentiment recognition. For example, if the server recognizes that the user is in a hurry, it immediately returns the calculation results. A specific natural language message might be sent such as, "The break-even ARPU for renting an iPhone 14 for 36 months with a 20GB flat rate is ¥5,777.78."

[1688] Adding specific examples

[1689] If a user enters "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data plan?", the system will process it as follows: Based on this input, the server will immediately analyze the data, calculate the total cost and break-even ARPU, and finally return a result of ¥5,777.78 as the break-even ARPU.

[1690] This enables users to make quick decisions in the proposal phase, resulting in significant improvements in operational efficiency and user experience.

[1691] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1692] Step 1:

[1693] The user uses their device to input product information and conditions in natural language format. The input is in the form of text such as, "I'm in a hurry. What is the break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance?" The entered information is sent to the server.

[1694] Input: User-generated text in natural language format

[1695] Output: Input text data

[1696] Step 2:

[1697] The server receives the input text and analyzes it using natural language processing techniques. Specifically, it uses the ja_core_news_sm model from the spacy library to extract the product type (iPhone 14), duration (36 months), and plan (20GB flat-rate plan). This converts the user input into structured data.

[1698] Input: Input text data

[1699] Output: Structured data on product type, duration, and plan.

[1700] Step 3:

[1701] The server accesses an internal database to retrieve cost and pricing information related to the type of product and plan. Specifically, it retrieves the cost of an iPhone 14 (¥100,000) and the monthly cost of a 20GB flat-rate plan (¥3,000) from the database. This provides the basic cost information necessary for calculations.

[1702] Input: Structured data on product type, duration, and plan.

[1703] Output: Cost data retrieved from the internal database

[1704] Step 4:

[1705] The total cost and break-even point are calculated using the cost data acquired by the server. The specific calculation method is as follows:

[1706] Total cost = iPhone 14 cost + (monthly cost of 20GB flat-rate plan × 36 months) = ¥208,000

[1707] Break-even ARPU = Total Cost / 36 months = ¥5,777.78

[1708] Input: Cost data retrieved from an internal database

[1709] Output: Calculation results of total cost and break-even point

[1710] Step 5:

[1711] The server uses the transformers library's pipeline("sentiment-analysis") model to recognize emotions from the user's input text. If the input text contains expressions such as "I'm in a hurry," the emotion engine recognizes that the user is in a hurry. The results of the emotion recognition are used for subsequent processing.

[1712] Input: Input text data

[1713] Output: Emotion recognition results

[1714] Step 6:

[1715] The server generates calculation results in natural language format and adjusts how the results are presented to the user based on the sentiment recognition results. Specifically, if sentiment recognition determines that the user is in a hurry, the results are returned promptly. An example of the generated text is the message, "The break-even ARPU for renting an iPhone 14 for 36 months with a fixed 20GB data allowance is ¥5,777.78."

[1716] Input: Calculation results of total cost and break-even point, results of sentiment recognition

[1717] Output: Message in natural language format

[1718] Step 7:

[1719] The user receives the results sent from the server via their device and checks the calculation results displayed on the screen. This allows the user to quickly determine the proposed price and gross profit.

[1720] Input: Message in natural language format

[1721] Output: Calculation results displayed on the screen

[1722] 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.

[1723] 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.

[1724] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1725] 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.

[1726] 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.

[1727] 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.

[1728] 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.

[1729] 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.

[1730] 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."

[1731] 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.

[1732] 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.

[1733] 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.

[1734] 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.

[1735] 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.

[1736] 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.

[1737] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1738] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1739] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1740] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1741] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1742] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1743] The following is further disclosed regarding the embodiments described above.

[1744] (Claim 1)

[1745] A means for receiving product information and conditions in natural language format from users,

[1746] A means for analyzing the aforementioned product information and conditions and extracting the type of product, period, and plan,

[1747] A means for obtaining relevant cost and fee information from an internal database based on the aforementioned product information and conditions,

[1748] A means for calculating total costs and break-even points using acquired cost and fee information,

[1749] A means of generating calculation results in natural language format and returning them to the user,

[1750] A system that includes this.

[1751] (Claim 2)

[1752] The system according to claim 1, characterized in that the means for analyzing the aforementioned product information and conditions uses natural language processing technology.

[1753] (Claim 3)

[1754] The system according to claim 1, further comprising means for calculating gross profit using the calculated break-even point and providing the result to the user.

[1755]

[1756] "Example 1"

[1757] (Claim 1)

[1758] A means for receiving product information and conditions in natural language format from users,

[1759] A means for analyzing the aforementioned product information and conditions and extracting the product type, period, and plan,

[1760] A means for obtaining related cost and fee information from an internal database based on the aforementioned product information and conditions,

[1761] A means for calculating total costs and break-even points using acquired cost and fee information,

[1762] A means of generating calculation results in natural language format and returning them to the user,

[1763] A means by which the user inputs product information and conditions in natural language format using a terminal,

[1764] A means for the server to receive and analyze input,

[1765] A means by which the server retrieves cost information from the database,

[1766] A method for calculating based on cost information acquired by the server,

[1767] A means of returning the calculation results in natural language format,

[1768] A system that includes this.

[1769] (Claim 2)

[1770] The system according to claim 1, characterized in that the means for analyzing the aforementioned product information and conditions uses natural language processing technology.

[1771] (Claim 3)

[1772] The system according to claim 1, further comprising means for calculating the profit margin using the break-even point calculated above and providing the result to the user.

[1773] "Application Example 1"

[1774] (Claim 1)

[1775] A means for receiving product information and conditions in natural language format from users,

[1776] A means for analyzing the aforementioned product information and conditions and extracting the product type, period, and plan,

[1777] A means for obtaining relevant cost and fee information from an internal database based on the aforementioned product information and conditions,

[1778] A means for calculating total costs and break-even points using acquired cost and fee information,

[1779] A means of generating calculation results in natural language format and returning them to the user,

[1780] To assist brick-and-mortar store operators in sourcing and pricing products, this system uses smartphones to calculate the break-even point (ARPU) and support pricing decision-making.

[1781] A system that includes this.

[1782] (Claim 2)

[1783] The system according to claim 1, characterized in that the means for analyzing the aforementioned product information and conditions uses natural language processing technology.

[1784] (Claim 3)

[1785] The system according to claim 1, further comprising means for calculating gross profit using the calculated break-even point and providing the result to the user.

[1786] "Example 2 of combining an emotion engine"

[1787] (Claim 1)

[1788] A means for receiving product information and conditions in natural language format from users,

[1789] A means for analyzing the aforementioned product information and conditions and extracting the type of product, period, and plan,

[1790] A means for obtaining relevant cost and fee information from an internal database based on the aforementioned product information and conditions,

[1791] A means for calculating total costs and break-even points using acquired cost and fee information,

[1792] A means of generating calculation results in natural language format and returning them to the user,

[1793] A means of recognizing emotions from user input text and adjusting the way results are presented,

[1794] A system that includes this.

[1795] (Claim 2)

[1796] The system according to claim 1, characterized in that the means for analyzing the aforementioned product information and conditions uses natural language processing technology.

[1797] (Claim 3)

[1798] The system according to claim 1, further comprising means for calculating gross profit using the calculated break-even point and providing the result to the user.

[1799] "Application example 2 when combining with an emotional engine"

[1800] (Claim 1)

[1801] A means for receiving product information and conditions in natural language format from users,

[1802] A means for analyzing the aforementioned product information and conditions and extracting the type of product, period, and plan,

[1803] A means for obtaining relevant cost and fee information from an internal database based on the aforementioned product information and conditions,

[1804] A means for calculating total costs and break-even points using acquired cost and fee information,

[1805] A means of generating calculation results in natural language format and returning them to the user,

[1806] A means of recognizing emotions from user input text,

[1807] A means of adjusting the way results are presented to the user based on recognized emotions,

[1808] A system that includes this.

[1809] (Claim 2)

[1810] The system according to claim 1, characterized in that the means for analyzing the aforementioned product information and conditions uses natural language processing technology.

[1811] (Claim 3)

[1812] The system according to claim 1, further comprising means for calculating gross profit using the calculated break-even point and providing the result to the user. [Explanation of Symbols]

[1813] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving product information and conditions in natural language format from users, A means for analyzing the aforementioned product information and conditions and extracting the type of product, period, and plan, A means for obtaining relevant cost and fee information from an internal database based on the aforementioned product information and conditions, A means for calculating total costs and break-even points using acquired cost and fee information, A means of generating calculation results in natural language format and returning them to the user, A system that includes this.

2. The system according to claim 1, characterized in that the means for analyzing the aforementioned product information and conditions uses natural language processing technology.

3. The system according to claim 1, further comprising means for calculating gross profit using the calculated break-even point and providing the result to the user.

Images