System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving an estimate from a vendor and converting it into digital data, a means for extracting necessary information from the digital data and registering it in a database, a means for collating the extracted information with a market market price, a means for automatically performing price negotiation with the vendor, a means for calculating an optimum price and recording it in the database, a means for automatically generating a credit application based on the contents of the estimate and the negotiation result, and a means for transmitting the credit application to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Current purchasing operations require various procedures, such as price negotiations and approvals by the purchasing department and internal approvals, after receiving a quote from a vendor, which makes it time-consuming and difficult to respond quickly. Furthermore, the same ordering process is often repeated, calling for greater efficiency. In addition, there is a risk of human error and mistakes, so solving these issues and improving the speed and efficiency of purchasing operations is an urgent task. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving quotations from vendors and converting them into digital data, a means for extracting necessary information from the digital data and registering it in a database, a means for comparing the extracted information with market prices, a means for automatically negotiating prices with vendors, a means for calculating the optimum price and recording it in a database, a means for automatically generating approval documents based on the contents of the quotation and the results of the negotiations, and a means for sending the approval documents to the user.

[0006] Specifically, optical character recognition (OCR) technology is used to extract necessary information from digital data, and market price data from national databases and the Internet is collected and used to verify market prices, enabling highly accurate price calculations and efficient business operations.

[0007] In this way, the present invention can shorten the overall time required for purchasing operations, improve efficiency, reduce the risk of human error, and provide a means for promoting the standardization of business processes.

[0008] "Vendor" refers to a supplier of goods or services to a business or organization.

[0009] A "quote" is a document that describes the costs and conditions for providing a specific product or service.

[0010] "Digital data" refers to data in a format that can be handled by electronic devices and is information that can be managed and used through computers and networks.

[0011] "Optical character recognition (OCR) technology" refers to technology that scans printed characters and extracts text data from the image.

[0012] "Market price" refers to the average price at which a particular product or service is traded in the market.

[0013] A "negotiation email" refers to a message sent via email to negotiate prices and terms.

[0014] A "database" refers to a collection of information constructed to efficiently manage and search large amounts of data.

[0015] A "reisho" is a document used for formal decision-making within a company or organization, requesting approval for a specific proposal or resolution.

[0016] "User" refers to a person or role that uses this system to perform purchasing and approval tasks. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The unmanned purchasing system of the present invention receives quotations from vendors and then automates a series of purchasing processes. This system can continuously execute each step of the process: quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval.

[0039] The main components of this system are the user terminal, a server, and peripheral devices as needed. The user processes the quotation received from the vendor on the terminal, and the system then proceeds automatically.

[0040] Program processing details

[0041] 1. Receipt of quotation

[0042] A user receives a quote from a vendor.

[0043] The terminal scans the estimate with a scanner and generates image data.

[0044] The device uses OCR software to convert the image data into text data.

[0045] The terminal transmits the text data to the server.

[0046] 2. Estimate registration

[0047] The server analyzes the received text data and divides it into items.

[0048] The server organizes the information extracted, such as "vendor name," "item," "quantity," "unit price," and "total."

[0049] The server registers the extracted information in the purchasing system database.

[0050] 3. Market price comparison

[0051] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[0052] The server evaluates the fairness of the received quote based on the market price collected by the server.

[0053] 4. Price Negotiation

[0054] The server automatically generates and sends a negotiation email to the vendor.

[0055] If the server receives a response from the vendor and adjusts the price again, it will conduct any necessary additional negotiations.

[0056] If the negotiations are deemed appropriate, the negotiations will be terminated.

[0057] 5. Price Calculation

[0058] The server calculates the optimal price based on the confirmed price and stores it in the database.

[0059] The server compares the original quoted price with the confirmed price and selects the best price.

[0060] 6. Creating approval documents

[0061] The server automatically generates a request form based on the estimate, negotiation details, and optimal price.

[0062] The server generates a request form and sends it to the user's device in PDF format.

[0063] 7. Final approval

[0064] The user checks the contents of the received request.

[0065] The user logs in to the approval system, checks the final approval request, and approves it.

[0066] Specific examples

[0067] For example, when an estimate for electrical equipment construction is received from a vendor, it is processed as follows:

[0068] 1. Receipt of quotation

[0069] The user receives a quote for "electrical wiring work" from a vendor.

[0070] The terminal scans the estimate and creates image data.

[0071] The device uses OCR technology to convert the image data into text and send it to the server.

[0072] 2. Estimate registration

[0073] The server analyzes the received text data and extracts information such as "Vendor name: ABC Company," "Item: Electric wire," "Quantity: 100m," "Unit price: 500 yen," and "Total: 50,000 yen."

[0074] The server registers the extracted information in a database.

[0075] 3. Market price comparison

[0076] The server collects the market price for "electrical wiring work" from a national database and market price data on the Internet, and determines that "the market price is 450 yen per meter."

[0077] The server evaluates the appropriateness of the estimated price (500 yen per meter) it received based on the market price and determines that it is too expensive.

[0078] 4. Price Negotiation

[0079] The server sends a negotiation email to the vendor saying, "Could you please set the unit price at 450 yen based on the market price?"

[0080] The server receives the vendor's reply, "How about 470 yen?" and continues negotiations if necessary.

[0081] 5. Price Calculation

[0082] The server records the information that was agreed upon as a result of negotiations with the vendor: "unit price 470 yen, total 47,000 yen."

[0083] The server calculates the difference (3,000 yen) from the original estimated price (50,000 yen) and displays this to the user.

[0084] 6. Creating approval documents

[0085] The server creates a request form based on the estimate and negotiation results, and includes information such as "electrical wiring work," "agreed price: 47,000 yen," and "price negotiation details."

[0086] The server generates the request form in PDF format and sends it to the user's device.

[0087] 7. Final approval

[0088] The user checks the submitted request and logs into the approval system.

[0089] The user approves the contents of the request and makes the final decision.

[0090] The above is a specific example of the program processing of the unmanned purchasing system based on the present invention. This system will improve the efficiency and speed of purchasing operations and reduce errors.

[0091] The processing flow will be explained below.

[0092] Step 1:

[0093] A user receives a quote from a vendor.

[0094] The terminal scans the estimate with a scanner and generates image data.

[0095] The device uses OCR software to convert the image data into text data.

[0096] The terminal transmits the text data to the server.

[0097] Step 2:

[0098] The server analyzes the received text data.

[0099] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[0100] The server registers the extracted information in the purchasing system database.

[0101] Step 3:

[0102] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[0103] The server evaluates the fairness of the received quote based on the market price collected by the server.

[0104] Step 4:

[0105] The server generates a template for a price negotiation email.

[0106] The server automatically sends a negotiation email to the vendor, which contains specific negotiation terms based on the market price.

[0107] The vendor responds to the negotiation email.

[0108] The server receives the vendor's response and renegotiates if necessary.

[0109] Step 5:

[0110] The server calculates the optimal price based on the finalized negotiation results.

[0111] The server records the determined price in a database.

[0112] The server calculates the difference between the original quote and the confirmed price.

[0113] The server displays the differences in a spreadsheet or report format.

[0114] Step 6:

[0115] The server prepares a template for the approval document.

[0116] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[0117] The server generates the request form in PDF format and sends it to the user.

[0118] Step 7:

[0119] The user receives the request sent from the server.

[0120] The user checks the contents of the approval request and logs in to the approval system.

[0121] The user clicks the confirmation button on the approval screen for the request form to approve it.

[0122] Example 1

[0123] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0124] In conventional purchasing operations, the entire process from receiving quotations to final approval is performed manually, which is time-consuming and prone to human error. Furthermore, because matching with market prices and price negotiations are also performed manually, efficiency is lacking. The present invention aims to solve these problems and improve the efficiency and accuracy of purchasing operations.

[0125] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0126] In this invention, the server includes means for receiving quotations from vendors and converting them into digital data, means for extracting necessary information from the digital data and registering it in a database, means for comparing the extracted information with market prices, means for automatically negotiating prices with vendors, means for calculating the optimal price and recording it in a database, means for automatically generating approval documents based on the contents of the quotation and the results of the negotiations, means for sending the approval documents to users, means for scanning received quotations with a scanner and generating image data, means for converting the image data into text data using OCR software, means for the user to review the received approval documents and give final approval, and means for generating approval documents in PDF format. This automates the entire purchasing process, saving time and reducing errors.

[0127] "Vendor" refers to a company or entity that provides products or services.

[0128] "Quote" means a document that sets out the prices and terms for specific products or services.

[0129] "Digital data" is a general term for any data format that can be stored or transmitted electronically.

[0130] "Extraction" refers to the process of extracting necessary parts from data or information.

[0131] A "database" is a system that stores data in an organized manner and enables efficient access and management.

[0132] "Market Price" means the average price at which a commodity is generally traded in a particular market.

[0133] "Automatic" means that an operation or process is performed by a machine or computer without human intervention.

[0134] "Optical character recognition (OCR) technology" is a technology that converts printed or handwritten characters into digital data.

[0135] "Negotiation" refers to dialogue and discussion between the parties to adjust the terms of a transaction.

[0136] "Optimal price" refers to the price that is deemed most appropriate taking into account quality and market conditions.

[0137] A "requisition document" is a document used to obtain formal approval within an organization.

[0138] "Receiving" refers to the operation of taking in data or messages from the outside.

[0139] A "scanner" refers to a device that converts information written on paper into electronic data.

[0140] "Image data" refers to data stored in an image format.

[0141] "Text data" refers to a data format based on character information.

[0142] "Recognition" means to officially approve or permit.

[0143] "PDF format" is an abbreviation for Portable Document Format, a file format for electronically storing and sharing documents while preserving their format.

[0144] "Purchasing operations" refers to a series of business processes for procuring necessary goods and services.

[0145] The unmanned purchasing system of this invention is composed of a user terminal, a server, and peripheral devices as needed. The user processes the quotations received from the vendor on the terminal, and the system then automatically proceeds with the purchasing process.

[0146] Hardware and software used

[0147] User device: Used to scan the estimate and convert it into text data. Examples include a personal computer and a scanner (e.g., Fujitsu ScanSnap).

[0148] OCR software: Used to convert scanned image data into text data. Examples of OCR software include Adobe Acrobat and Tesseract OCR.

[0149] Server: Analyzes text data, registers it in a database, collects and compares market prices, negotiates prices, and automatically generates and sends approval documents. For example, SAP Ariba can be used as a purchasing system.

[0150] Explanation of program processing

[0151] 1. Quote Receipt:

[0152] A user receives a quote from a vendor. For example, a quote for "electrical wiring work."

[0153] The terminal scans this estimate with a scanner and generates image data (e.g., Fujitsu ScanSnap).

[0154] The terminal uses OCR software (e.g. Adobe Acrobat) to convert the image data into text data and send it to the server.

[0155] 2. Quotation Registration:

[0156] The server analyzes the received text data and divides it into items. For example, it analyzes it as "Vendor name: certain company", "Item: electric wire", "Quantity: 100m", "Unit price: 500 yen", "Total: 50,000 yen".

[0157] The server registers the analysis results in the database of the purchasing system (e.g., SAP Ariba).

[0158] 3. Market Price Matching:

[0159] The server accesses national databases (e.g., the National Tax Agency database) and market price databases on the Internet (e.g., Kakaku.com) to collect market prices.

[0160] The server evaluates the appropriateness of the estimated price based on the market price collected. For example, it determines that the market price is 450 yen per meter.

[0161] 4. Price Negotiation:

[0162] The server automatically generates a negotiation email and sends it to the vendor, proposing, for example, "Could you please set the unit price at 450 yen based on the market price?"

[0163] The server receives the vendor's response and negotiates further if necessary. For example, if the response is "How about 470 yen?", the server continues negotiations.

[0164] 5. Price Calculation:

[0165] The server calculates the optimal price based on the confirmed price and stores it in the database. For example, it records it based on "Confirmed price: 47,000 yen."

[0166] The server calculates the difference from the original estimated price (50,000 yen) and displays it to the user.

[0167] 6. Preparation of approval documents:

[0168] The server automatically generates a request for approval based on the estimate and negotiation results. Specific examples include information such as "electrical wiring work," "agreed price: 47,000 yen," and "negotiation details."

[0169] The server generates the request form in PDF format and sends it to the user's device.

[0170] 7. Final approval:

[0171] The user reviews the submitted request and logs in to the approval system (e.g., SharePoint) to make the final approval.

[0172] Specific examples

[0173] For example, if you receive a quote from a vendor for electrical installation work, you can proceed as follows:

[0174] Example prompt: "Please tell me how the system automatically processes electrical installation work quotes received from vendors."

[0175] This system automates the entire purchasing process, enabling a fast and accurate purchasing process.

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] Step 1:

[0178] Quote received

[0179] Specific behavior:

[0180] Input: Quote received from vendor

[0181] Action: A user receives a quote from a vendor.

[0182] Action: The device scans this quote with a scanner and generates image data. For example, it uses a Fujitsu ScanSnap to create high-resolution image data.

[0183] Output: Generated image data (JPEG or PDF format)

[0184] Step 2:

[0185] OCR conversion of image data

[0186] Specific behavior:

[0187] Input: Image data generated by a scanner

[0188] Operation: The device uses OCR software (e.g., Adobe Acrobat) to convert image data into text data.

[0189] How it works: OCR software analyzes image data and converts it into a string of characters that can be recognized as text.

[0190] Output: Converted text data

[0191] Step 3:

[0192] Sending text data

[0193] Specific behavior:

[0194] Input: Text data after OCR conversion

[0195] Operation: The terminal sends the converted text data to the server.

[0196] Output: Text data sent to the server

[0197] Step 4:

[0198] Quotation registration

[0199] Specific behavior:

[0200] Input: Text data received by the server

[0201] How it works: The server parses the text data and splits it into multiple fields (e.g., vendor name, item, quantity, unit price, total) using a natural language processing algorithm (e.g., SpaCy or Google® Cloud NLP API).

[0202] Operation: The server registers the analysis results in the database of the purchasing system (e.g., SAP Ariba).

[0203] Output: Quote information stored in the database

[0204] Step 5:

[0205] Market price comparison

[0206] Specific behavior:

[0207] Input: Quote information stored in the database

[0208] Operation: The server accesses national databases (e.g., the National Tax Agency database) and market price databases on the Internet (e.g., Kakaku.com) to collect market prices for the target item.

[0209] Operation: The server evaluates the appropriateness of the quoted price based on the market price collected. For example, the market price is 450 yen per meter, but the received quote is 500 yen per meter, so it is determined to be expensive.

[0210] Output: Comparison of market price and estimated price

[0211] Step 6:

[0212] Price negotiation

[0213] Specific behavior:

[0214] Input: Comparison result of market price and estimated price

[0215] How it works: The server automatically generates a negotiation email and sends it to the vendor. For example, it proposes, "Could you please set the unit price at 450 yen based on the market price?"

[0216] Operation: The server receives the vendor's response and negotiates further if necessary. For example, if the response is "How about 470 yen?", the server continues negotiations.

[0217] Output: Negotiation result data

[0218] Step 7:

[0219] Price calculation

[0220] Specific behavior:

[0221] Input: Negotiation result data

[0222] Operation: The server calculates the optimal price based on the confirmed price and saves it in the database. For example, it records it based on "Confirmed price: 47,000 yen."

[0223] Operation: Calculate the difference from the original estimated price (50,000 yen) and show it to the user. For example, notify the user that a cost reduction of 3,000 yen has been achieved.

[0224] Output: Optimal price calculation result

[0225] Step 8:

[0226] Creating approval documents

[0227] Specific behavior:

[0228] Input: Optimal price calculation results and negotiation history

[0229] Operation: The server automatically generates a request for approval based on the estimate and negotiation results. Specific examples include information such as "electrical wiring work," "agreed price: 47,000 yen," and "negotiation details."

[0230] Operation: The server generates a request form in PDF format and sends it to the user's device.

[0231] Output: Generated PDF of the approval document

[0232] Step 9:

[0233] Final approval

[0234] Specific behavior:

[0235] Input: Generated PDF format request form

[0236] What it does: A user reviews the submitted request and logs into an approval system (e.g., SharePoint) to make the final approval.

[0237] How it works: After logging in, check the contents of the request form, add comments if necessary, and finally press the approval button.

[0238] Output: Approved request

[0239] This system automates the entire purchasing process, enabling a fast and accurate purchasing process.

[0240] (Application example 1)

[0241] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0242] Current purchasing operations at logistics centers rely on many manual processes and time-consuming procedures, resulting in inefficiencies and a high risk of human error. There is a particular need for technology that can efficiently automate the entire process, from receiving quotes to matching market prices, negotiating prices, and creating and sending approval documents. This is expected to reduce labor costs, increase work speed, and improve accuracy.

[0243] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0244] In this invention, the server includes means for scanning estimates using a smartphone camera, means for converting image data into text data using optical character recognition technology, means for scraping price databases on the Internet to collect market prices, means for sending negotiation emails to vendors using automatic email generation technology, and means for notifying users using a push notification function, thereby enabling the automation and efficiency of the entire purchasing process.

[0245] "Quote" means a document that describes the price and terms of products or services provided by a vendor.

[0246] "Digital data" is data that has been converted into a format that can be processed by electronic devices such as computers.

[0247] A "database" is a system or software that systematically organizes information and allows it to be managed and searched centrally.

[0248] "Market price" is the price at which a particular product or service is generally traded in the market.

[0249] Optical character recognition (OCR) is a technology that recognizes characters from image data and converts them into text data.

[0250] "Scraping" is a technique for automatically extracting specific information from web pages.

[0251] "Push notification" is a technology that sends information directly from a server to a client device.

[0252] "Automatic email generation" is a technology in which a system automatically creates emails based on predefined conditions and formats.

[0253] A "rei-sho" is a document used to obtain the approval necessary to make decisions within an organization.

[0254] This invention relates to a system that fully automates purchasing operations at a logistics center. This system receives quotations from vendors, converts them into digital data, extracts necessary information, and registers it in a database. It then compares the extracted information with market prices and automatically negotiates prices with the vendor. It also calculates the optimal price, records it in a database, and automatically generates a request for approval based on the quotation and negotiation results and sends it to the user.

[0255] First, the user scans the quote using the camera on their smartphone. The scanned image data is converted into text data using optical character recognition (OCR) technology such as Google Cloud Vision API. This converted data is uploaded to AWS (registered trademark) S3 and transferred to a database using AWS Lambda.

[0256] The server then analyzes the received data and extracts the necessary information (e.g., item, quantity, unit price, total, etc.). This information is then registered in a relational database such as AWS RDS.

[0257] The server then scrapes the internet price database to collect market quotes, using AWS Lambda or other scraping tools. Based on the collected market quotes, the server evaluates the fairness of the quote.

[0258] If the price is not reasonable, the server will use automatic email generation technology to send a negotiation email to the vendor. This process uses SES to send emails. The reply from the vendor is also automatically received and the analysis and negotiation process will continue again.

[0259] Finally, the optimal price is calculated based on the finalized price and recorded in a database. The server automatically generates a request form based on this information and sends it in PDF format to the user's smartphone via push notification. Notification services such as social media are used for the push notification. The user checks the request form on their smartphone and digitally signs it using the DocuSign API or similar.

[0260] Specific examples

[0261] For example, when a user receives a quote for electrical equipment construction from a vendor, they scan the quote using their smartphone camera. This image data is converted into text data using Google Cloud Vision API and registered in a database via AWS Lambda. The server scrapes and collects market prices from the Internet and evaluates the fair price. After the evaluation, the server automatically sends a negotiation email to the vendor using SES, and the optimal price is confirmed. A request for approval is then automatically generated and sent to the user via push notification. The user then checks the request on their smartphone and electronically signs it using the DocuSign API.

[0262] Example prompts for generative AI models

[0263] "Write a program to convert the following text using OCR and register it in a purchasing system:

[0264] Quote image path:path / to / image.jpg

[0265] Database to register: AWS RDS

[0266] APIs and libraries used: Google Cloud Vision API, boto3, pdfkit

[0267] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0268] Step 1:

[0269] The user scans an estimate using the smartphone camera. The input is an estimate received from a vendor, which is captured as image data using the smartphone camera. The output is the image data of the scanned estimate. Specifically, the user launches the camera app and takes a picture of the estimate.

[0270] Step 2:

[0271] The device performs optical character recognition (OCR) on the scanned image data using the Google Cloud Vision API. The input is the image data acquired in step 1, and the output is text data converted by OCR. Specifically, the image data is sent to the Google Cloud Vision API and the text data is received as the result of the OCR processing.

[0272] Step 3:

[0273] The text data acquired by the device using OCR is uploaded to AWS S3. The input is the text data from step 2, and the output is the text data stored on AWS S3. Specifically, the process is performed to upload the text file to an Amazon S3 bucket.

[0274] Step 4:

[0275] The server uses AWS Lambda to retrieve text data from S3, analyze the information, and extract the required items. The input is the text data retrieved from AWS S3, and the output is the extracted information (e.g., item, quantity, unit price, total, etc.). Specifically, the script analyzes the text data and executes to extract the required information for each item.

[0276] Step 5:

[0277] The server registers the extracted information in AWS RDS. The input is the information extracted in step 4, and the output is the information registered in AWS RDS. Specifically, it generates an SQL statement to save the extracted information and inserts the data into AWS RDS.

[0278] Step 6:

[0279] The server scrapes price databases on the Internet to collect market price information. The input is a list of URLs for the price databases, and the output is the collected market price information. Specifically, a web scraping tool is used to access the price database and obtain market price information.

[0280] Step 7:

[0281] The server compares the market price collected with the registered quotation information to perform price evaluation. The input is the quotation information from step 5 and the market price information from step 6, and the output is the evaluation result regarding the appropriateness of the price. Specifically, it compares the estimated price with the market price and executes an algorithm to evaluate whether the estimated price is appropriate.

[0282] Step 8:

[0283] The server uses automatic email generation technology to send a negotiation email to the vendor. The input is the price evaluation result and vendor information, and the output is the sent negotiation email. Specifically, it uses SES to generate a negotiation email and sends it to the vendor's email address.

[0284] Step 9:

[0285] The server receives the reply from the vendor and re-analyzes and evaluates the negotiation results. The input is the reply email from the vendor, and the output is the updated negotiation results. Specifically, the server re-analyzes the contents of the received email and updates the negotiation results.

[0286] Step 10:

[0287] The server calculates the optimal price based on the confirmed price and records it in AWS RDS. The input is the updated negotiation result, and the output is the optimal price recorded in AWS RDS. Specifically, the optimal price is calculated and the information is saved in the database.

[0288] Step 11:

[0289] The server automatically generates a request for approval based on the quotation details and negotiation results, and generates it in PDF format. The input is the confirmed price information and negotiation results, and the output is a request for approval in PDF format. Specifically, the script that generates the request for approval is executed using PDFKit or similar.

[0290] Step 12:

[0291] The server uses the push notification function to send a request form to the user. The input is a PDF version of the request form, and the output is the notification sent to the user. Specifically, the server sends a push notification to the user's smartphone using a notification service such as SNS.

[0292] Step 13:

[0293] The user checks the approval document on their smartphone and digitally signs it using the DocuSign API. The input is a PDF version of the approval document, and the output is the signed version. Specifically, the process involves opening the approval document on the smartphone and digitally signing the required fields.

[0294] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0295] The unmanned purchasing system of this invention receives quotations from vendors and automates the purchasing process based on them. This system covers a series of processes, including quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval, and also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's behavior and interface accordingly.

[0296] The main components of this system are a user terminal, a server, an emotion engine, and peripheral devices as needed. The user terminal is used to input estimates, check approval documents, and recognize emotions, while the server is responsible for processing and storing various data. The emotion engine recognizes the user's emotions in real time and makes adjustments accordingly.

[0297] Program processing details

[0298] 1. Receipt of quotation

[0299] A user receives a quote from a vendor.

[0300] The terminal scans the estimate with a scanner and generates image data.

[0301] The device uses OCR software to convert the image data into text data.

[0302] The terminal transmits the text data to the server.

[0303] 2. Estimate registration

[0304] The server analyzes the received text data.

[0305] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[0306] The server registers the extracted information in the purchasing system database.

[0307] 3. Market price comparison

[0308] The server accesses national databases and internet market price databases to collect market prices for the target items.

[0309] The server evaluates the fairness of the received quote based on the market price collected by the server.

[0310] 4. Price Negotiation

[0311] The server generates a template for a price negotiation email.

[0312] The server automatically sends a negotiation email to the vendor, which includes specific negotiation details based on the market price.

[0313] After the server receives the vendor's response, it renegotiates if necessary.

[0314] The text of negotiation emails is automatically adjusted based on the user's emotions recognized by the emotion engine.

[0315] 5. Price Calculation

[0316] The server calculates the optimal price based on the finalized negotiation results.

[0317] The server records the determined price in a database.

[0318] The server calculates the difference between the original quoted price and the final price and displays it in a spreadsheet or report format.

[0319] 6. Creating approval documents

[0320] The server prepares a template for the approval document.

[0321] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[0322] The server generates the request form in PDF format and sends it to the user.

[0323] 7. Final approval

[0324] The user receives the request sent from the server.

[0325] The emotion engine recognizes the user's emotions while reviewing the request and encourages renegotiation or reconfirmation if the user expresses dissatisfaction.

[0326] The user logs in to the approval system and approves the contents of the request form.

[0327] Specific examples

[0328] For example, when an estimate for electrical equipment construction is received from a vendor, the process proceeds as follows:

[0329] 1. Receipt of quotation

[0330] The user receives a quote for "electrical wiring work" from a vendor.

[0331] The terminal scans the estimate and creates image data.

[0332] The device uses OCR technology to convert the image data into text and send it to the server.

[0333] 2. Estimate registration

[0334] The server analyzes the received text data and extracts information such as "Vendor name: ABC Company," "Item: Electric wire," "Quantity: 100m," "Unit price: 500 yen," and "Total: 50,000 yen."

[0335] The server registers this information in a database.

[0336] 3. Market price comparison

[0337] The server collects market price data from national databases and the Internet to check the market price for "electrical wiring work."

[0338] The server compares the market price with the quoted price and evaluates whether the quoted price is reasonable.

[0339] 4. Price Negotiation

[0340] The server generates a negotiation email saying, "Could you please set the unit price at 450 yen based on the market price?" and sends it to the vendor.

[0341] The server receives the vendor's response, "How about 470 yen?" and renegotiates if necessary.

[0342] The emotion engine recognizes the user's emotions and adjusts the content of the negotiation email.

[0343] 5. Price Calculation

[0344] The server determines the price based on the negotiation result of "470 yen, total 47,000 yen" and records it in the database.

[0345] The server calculates and displays the difference (3,000 yen) from the original quoted price (50,000 yen).

[0346] 6. Creating approval documents

[0347] The server creates a request form based on the estimate and negotiation results, and includes information such as "electrical wiring work," "agreed price: 47,000 yen," and "price negotiation details."

[0348] The server generates a request form and sends it to the user's device in PDF format.

[0349] 7. Final approval

[0350] The user receives the PDF request form and checks its contents.

[0351] The emotion engine recognizes user emotions in real time and prompts renegotiation or reaffirmation when dissatisfaction is expressed.

[0352] The user logs into the approval system and gives final approval.

[0353] In this way, the system is able to respond flexibly while taking into consideration the user's emotions, which not only makes purchasing operations more efficient but also improves the user experience.

[0354] The processing flow will be explained below.

[0355] Step 1:

[0356] A user receives a quote from a vendor.

[0357] The terminal scans the estimate with a scanner and generates image data.

[0358] The device uses OCR software to convert the image data into text data.

[0359] The terminal transmits the text data to the server.

[0360] Step 2:

[0361] The server analyzes the received text data.

[0362] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[0363] The server registers the extracted information in the purchasing system database.

[0364] Step 3:

[0365] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[0366] The server evaluates the fairness of the received quote based on the market price collected by the server.

[0367] Step 4:

[0368] The server generates a template for a price negotiation email.

[0369] The server automatically sends a negotiation email to the vendor, which contains specific negotiation terms based on the market price.

[0370] The server receives the response from the vendor.

[0371] The server uses an emotion engine to automatically adjust the text of the negotiation email according to the emotion of the user.

[0372] The server renegotiates with the vendor as needed.

[0373] Step 5:

[0374] The server calculates the optimal price based on the finalized negotiation results.

[0375] The server records the determined price in a database.

[0376] The server calculates the difference between the original quote and the confirmed price.

[0377] The server displays the differences in a spreadsheet or report format.

[0378] Step 6:

[0379] The server prepares a template for the approval document.

[0380] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[0381] The server generates the request form in PDF format and sends it to the user.

[0382] Step 7:

[0383] The user receives the request sent from the server.

[0384] The user checks the contents of the request form.

[0385] The server uses an emotion engine to prompt renegotiation or reconfirmation when the user expresses dissatisfaction.

[0386] The user logs in to the approval system and approves the contents of the request form.

[0387] Example 2

[0388] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0389] In traditional purchasing operations, processes such as receiving quotations from vendors, extracting and registering information, comparing prices with market rates, negotiating prices with vendors, and creating and approving approval documents are often fragmented, resulting in problems of time and effort. Furthermore, systems that do not consider user emotions can impair the user experience and prevent optimal purchasing decisions. This reduces the efficiency of the entire purchasing process and risks making purchases at inappropriate prices.

[0390] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0391] In this invention, the server includes means for receiving quotations from vendors and converting them into digital data, means for extracting necessary information from the digital data and registering it in a database, means for comparing the extracted information with market prices, means for automatically negotiating prices with vendors, means for calculating an optimal price and recording it in a database, means for automatically generating a request for purchase based on the contents of the quotation and the results of the negotiation, means for sending the request for purchase to the user, and means for adjusting the contents of the negotiation and the request for purchase based on the results of user emotion recognition. This makes it possible to improve the efficiency of purchasing operations and realize optimal decisions that take user emotions into consideration.

[0392] A "quote" is a document that describes the price, quantity, and terms of the goods provided by a vendor.

[0393] "Digital data" is data that has been converted from analog information into a numerical value and into a format that can be processed by a computer or the like.

[0394] Optical character recognition (OCR) technology is a technology that automatically identifies characters from images captured by a scanner or camera and converts them into digital text.

[0395] A "database" is a collection of information that is systematically organized so that it can be efficiently accessed, managed, and updated.

[0396] A "market price" is the general price at which a particular product or service is traded in the market.

[0397] "Price negotiation" is the process by which a buyer and seller decide on a mutually agreed-upon price in a transaction.

[0398] A "requisition document" is a document created to obtain approval within an organization, and is usually used to seek approval or opinions from superiors or related departments.

[0399] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, movements, etc. to determine their emotional state.

[0400] "Vendor" means a supplier or provider of goods or services.

[0401] A "server" is a computer system that provides services to other computers (clients) on a network.

[0402] A "user" is someone who uses the system to perform various tasks and operations.

[0403] The unmanned purchasing system of this invention receives quotations from vendors and automates the purchasing process based on them. This system covers a series of processes, including quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval, and also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's behavior and interface accordingly.

[0404] The main components of the system are a user terminal, a server, an emotion engine, and, if necessary, peripheral devices.

[0405] The user device performs operations such as inputting and scanning estimates, checking approval documents, and emotion recognition. Specific hardware options include scanners and high-performance cameras, such as the Canon DR-C240 scanner. Adobe Acrobat or other well-known OCR software is recommended for OCR technology.

[0406] The server is responsible for processing and storing various types of data. A relational database management system such as PostgreSQL is suitable as the database. General-purpose programming languages ​​such as Python are used for processes within the server, and data analysis, information extraction using regular expressions, and market price collection via APIs are performed. Furthermore, Python's smtplib and the Jinja2 template engine are used for automatically generating and sending emails.

[0407] The emotion engine uses advanced emotion recognition algorithms to analyze the user's facial expressions and voice, and automatically adjusts the system's behavior based on the results. Facial recognition technology can be used with libraries such as OpenCV and Dlib, enabling real-time emotion analysis.

[0408] Specifically, for example, when an estimate for electrical equipment construction is received from a vendor, the processing of this system proceeds as follows.

[0409] The user receives an estimate for "electrical wiring work" and scans it using the scanner or camera on the device.

[0410] The scanned image data is converted into text using OCR technology and sent to the server.

[0411] The server extracts the necessary information from the received text data and records it in a database.

[0412] The server collects market quotes from an internet database and evaluates the quotes.

[0413] The server automatically sends a negotiation email to the vendor and calculates the optimal price based on the results.

[0414] The server creates a request form based on the quotation details and negotiation results and sends it to the user in PDF format.

[0415] The user reviews the request, and the emotion engine prompts renegotiation if necessary. Finally, the user logs in to the system and approves the request.

[0416] An example of a prompt that can be used to adjust system behavior and message content using generative AI models is as follows:

[0417] "Please explain in natural language each process of the automated purchasing system, from receiving a quote to final approval. Please include specific hardware and software, as well as data processing and calculation methods. Also, please explain the function that recognizes user emotions and adjusts the system accordingly."

[0418] A system designed in this way can greatly contribute to improving the efficiency of purchasing operations and user satisfaction.

[0419] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0420] Step 1:

[0421] Quote received

[0422] A user receives a quote from a vendor.

[0423] Input: Quote provided by vendor (physical document or PDF).

[0424] Output: Physical copy or PDF file.

[0425] Specific action: The user receives the quote and prepares it for digitization using a scanner or camera.

[0426] The terminal uses a scanner to scan the estimate and generate image data.

[0427] Input: Physical quote.

[0428] Output: Scanned image data.

[0429] What it does: Scan the quote at high resolution using a Canon DR-C240 scanner.

[0430] The device uses OCR (Optical Character Recognition) technology to convert image data into text data.

[0431] Input: scanned image data.

[0432] Output: Text data.

[0433] Specific operation: Executes Adobe Acrobat's OCR function and extracts text information such as "100m of electric wire, unit price 500 yen" from the image data.

[0434] The terminal transmits the text data to the server.

[0435] Input: Text data generated by OCR.

[0436] Output: The text data sent to the server.

[0437] What it does: Issues a curl command to send text data to the server's API endpoint using HTTPS.

[0438] Step 2:

[0439] Quotation registration

[0440] The server analyzes the received text data.

[0441] Input: Text data sent from the terminal.

[0442] Output: A data object containing the analysis results.

[0443] Specific operation: Analyzes text data using Python's NLTK library and generates tokenized sentences.

[0444] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[0445] Input: A parsed data object.

[0446] Output: Data for each extracted item.

[0447] Specific operation: Using regular expression pattern matching, extract information such as "Vendor name: Vendor", "Item: Electric wire", "Quantity: 100m", "Unit price: 500 yen", and "Total: 50,000 yen".

[0448] The server registers the extracted information in the purchasing system database.

[0449] Input: Data for each extracted item.

[0450] Output: Information stored in a database.

[0451] Specific behavior: Connect to a PostgreSQL database and execute the INSERT INTO quote_table(...) VALUES(...) query.

[0452] Step 3:

[0453] Market price comparison

[0454] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[0455] Input: URL or API key of the market quote database from which the data is collected.

[0456] Output: Collected market quote price data.

[0457] Specific operation: Accesses the API of the Ministry of Economy, Trade and Industry, etc., and obtains current market price information for power cables.

[0458] The server evaluates the fairness of the received quote based on the market price collected by the server.

[0459] Input: Collected market quote data, quoted price data.

[0460] Output: Evaluation results on the fairness of the estimated price.

[0461] What it does: Runs an algorithm to verify that the quote is within ±10% of the market price.

[0462] Step 4:

[0463] Price negotiation

[0464] The server generates a template for a price negotiation email.

[0465] Input: Template data for negotiation, quote and market price data.

[0466] Output: The generated email template.

[0467] What it does: Uses the Jinja2 template engine to generate an email that says, "Would you mind adjusting the price based on market value?"

[0468] The server automatically sends a negotiation email to the vendor.

[0469] Input: The generated email template.

[0470] Output: Negotiation email sent to vendor.

[0471] Specific behavior: Uses the smtplib library to send the generated email to the vendor's email address.

[0472] After the server receives the vendor's response, it renegotiates if necessary.

[0473] Input: Response email from vendor.

[0474] Output: Renegotiation result.

[0475] Specific operation: Analyzes received emails and generates and sends renegotiation emails if necessary.

[0476] The text of negotiation emails is automatically adjusted based on the user's emotions recognized by the emotion engine.

[0477] Input: User emotion data, negotiation email template.

[0478] Output: Tailored negotiation email.

[0479] What it does: It uses facial recognition technology to analyze a user's emotions and adapts the tone and content of emails accordingly.

[0480] Step 5:

[0481] Price calculation

[0482] The server calculates the optimal price based on the finalized negotiation results.

[0483] Input: Negotiated price data.

[0484] Output: Optimal price.

[0485] Specific operation: Recalculate the total price based on the negotiated result of "470 yen".

[0486] The server records the determined price in a database.

[0487] Input: Best price data.

[0488] Output: The final price recorded in the database.

[0489] What it does: Runs an SQL query to insert the best price into a PostgreSQL database.

[0490] The server calculates the difference between the original quoted price and the final price and displays it in a spreadsheet or report format.

[0491] Input: Original Quoted Price Data, Final Price Data.

[0492] Output: Diff report.

[0493] Specific operation: Using Python's pandas and xlsxwriter, create a difference report in Excel format and provide it to the user.

[0494] Step 6:

[0495] Creating approval documents

[0496] The server prepares a template for the approval document.

[0497] Input: Request form template data.

[0498] Output: Prepared decision template.

[0499] Specific operation: Load an existing approval document template and pass it to the template engine.

[0500] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[0501] Input: Quotation data, negotiation data, final price data.

[0502] Output: Auto-filled approval request data.

[0503] Specific operation: Embed the necessary information into the approval form template using the python-docx library.

[0504] The server generates the request form in PDF format and sends it to the user.

[0505] Input: Automatically entered request data.

[0506] Output: A PDF version of the approval document.

[0507] Specific operation: Use reportlab to convert the request form into PDF format and send it to the user's device.

[0508] Step 7:

[0509] Final approval

[0510] The user receives the request sent from the server.

[0511] Input: PDF request form from the server.

[0512] Output: The received decision.

[0513] Specific operation: A user downloads a PDF request form via email or a portal site.

[0514] The emotion engine recognizes the user's emotions while reviewing the request and encourages renegotiation or reconfirmation if the user expresses dissatisfaction.

[0515] Input: User emotion data.

[0516] Output: Proposal for renegotiation or reaffirmation.

[0517] What it does: It uses facial recognition technology to analyze users' facial expressions in real time and displays an alert if dissatisfaction is detected.

[0518] The user logs in to the approval system and approves the contents of the request form.

[0519] Input: PDF request form, login information.

[0520] Output: Approval result.

[0521] Specific operation: A user logs in to the system through a web portal, checks the contents of the request form, and clicks the approval button.

[0522] By using this system, it is possible to streamline and optimize purchasing operations, while also expecting an improved user experience.

[0523] (Application example 2)

[0524] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0525] In current purchasing operations, the entire process from receiving quotations to final approval of the request form is often done manually, resulting in inefficiency. It is also difficult to respond flexibly and consider user feelings, which can lead to user dissatisfaction, especially during price negotiations and the request form approval process. This poses a challenge to the overall efficiency of operations and the user experience.

[0526] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0527] In this invention, the server includes a means for receiving quotations from vendors and converting them into digital data, a means for extracting necessary information from the digital data and registering it in a database, and an emotion recognition means for recognizing a user's emotion and adjusting the user's behavior. This makes it possible to automate the entire purchasing process while recognizing the user's emotion in real time and taking appropriate action.

[0528] "Vendor" refers to a company that provides products or services.

[0529] A "quotation" is a document that describes the price, quantity, terms, etc. of a product or service.

[0530] "Digital data" means data represented in electronic form.

[0531] "OCR technology" is an abbreviation for optical character recognition technology, a technology that extracts text information from images.

[0532] "Market price" refers to the price in the general market for a particular product or service.

[0533] A "database" is a system that is organized to efficiently manage large amounts of data and enable searching and updating.

[0534] "Emotion recognition means" refers to technology or devices for analyzing and recognizing a user's emotions in real time.

[0535] A "reisho" is a formal document created to obtain approval or settlement within an organization or company.

[0536] "Price negotiation" is the process by which a buyer and seller negotiate and agree on the price of a product or service.

[0537] "Final approval" refers to the final stage of the approval process, where all parties agree and formally approve the proposal.

[0538] This invention provides a system for automating purchasing processes and recognizing and flexibly responding to user emotions. This system is primarily composed of a server, a terminal, emotion recognition means, and a database. Specific embodiments of this system are described below.

[0539] System Components

[0540] 1. Server

[0541] The server has the following functions:

[0542] Receive and analyze digital estimate data.

[0543] Using OCR technology, necessary information is extracted from digital data and registered in a database.

[0544] The registered information is compared with market prices, and market prices are collected to assess the fairness of the prices.

[0545] Automatically generate price negotiation emails and negotiate prices with vendors.

[0546] A request form template is automatically generated based on the confirmed price and sent to the user in PDF format.

[0547] Recognize user sentiment and encourage renegotiation or reaffirmation when dissatisfaction is expressed.

[0548] 2. Terminal

[0549] The device can be a smartphone or a PC with certain functions:

[0550] Provides a function to scan estimates and generate scanned image data.

[0551] The generated image data is converted into text data using OCR technology and sent to the server.

[0552] The user takes an image for emotion recognition and sends it to the server.

[0553] 3. Emotion recognition means

[0554] Emotion recognition is a technology for recognizing a user's emotions in real time. This uses a machine learning model (e.g., using TENSORFLOW®) that analyzes the user's facial expressions from images captured by a camera.

[0555] 4. Database

[0556] The database is used to store information such as quotation information, negotiation results, market prices, and user emotional data.

[0557] Hardware and Software

[0558] Hardware: Scanner, smartphone or PC with camera, server.

[0559] Software: OCR software (e.g., Pytesseract), image processing libraries (e.g., OpenCV), machine learning models (e.g., TensorFlow), internet request libraries (e.g., Requests).

[0560] Example

[0561] For example, if a user receives an estimate for "electrical equipment construction," the process proceeds as follows:

[0562] 1. Scan the quote

[0563] The user takes a photo of the estimate using the smartphone camera and generates image data.

[0564] The device converts this image data into text using OCR technology and sends it to the server.

[0565] 2. Data analysis and registration

[0566] The server analyzes the received text data, extracts information such as "vendor name," "item," "quantity," "unit price," and "total," and registers it in a database.

[0567] 3. Comparison with market prices

[0568] The server collects market prices from a database on the Internet and compares them with the quoted prices to assess their suitability.

[0569] 4. Price Negotiation

[0570] The server automatically generates a negotiation email based on market prices and sends it to the vendor.

[0571] If the user expresses dissatisfaction, the server attempts to renegotiate.

[0572] 5. Preparation of approval documents

[0573] The server automatically generates a request form based on the negotiation results and sends it to the user in PDF format.

[0574] 6. Final Approval

[0575] The user reviews the submitted PDF request form and gives final approval.

[0576] Prompt Sentence Examples

[0577] As an example, the following prompt sentence is input into the generative AI model:

[0578] Scan the quote for "electrical wiring work" and convert it to text. Then generate and send a price negotiation email based on the market price. Use the camera to recognize the user's emotions and suggest renegotiation if there is dissatisfaction.

[0579] In this way, a system is realized that can improve the efficiency of purchasing operations and respond flexibly based on the user's emotions.

[0580] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0581] Step 1:

[0582] A user receives a quote from a vendor.

[0583] Input: Paper quote from vendor

[0584] How it works: The user takes a photo of a quote using their smartphone camera.

[0585] Output: The image data of the quotation is saved on the smartphone.

[0586] Step 2:

[0587] The terminal scans the estimate and converts it into text data using OCR technology.

[0588] Input: Image data of quotation

[0589] How it works: The device uses OpenCV to grayscale the image data and Pytesseract to perform OCR.

[0590] Output: Text data of the quotation

[0591] Step 3:

[0592] The terminal transmits the text data to the server.

[0593] Input: Text data of quotation

[0594] How it works: The device uses the Internet Requests library (Requests) to send text data to the server.

[0595] Output: Text data is saved on the server.

[0596] Step 4:

[0597] The server analyzes the received text data, extracts the necessary information, and registers it in a database.

[0598] Input: Text data of quotation

[0599] How it works: The server uses machine learning algorithms to analyze the text data and extract information such as "vendor name," "item," "quantity," "unit price," and "total." The extracted information is then registered in a database.

[0600] Output: Quote information stored in the database

[0601] Step 5:

[0602] The server collects market quotes and compares them with quote information.

[0603] Input: Quote information stored in the database

[0604] Operation: The server accesses a market price database on the Internet to collect the market price of the target item, and evaluates the fairness of the quoted price and the market price.

[0605] Output: Validity evaluation result

[0606] Step 6:

[0607] The server generates a price negotiation email template and automatically sends it to the vendor.

[0608] Input: Fairness assessment result, market price

[0609] How it works: The server generates a negotiation email based on a template and automatically sends it to the vendor.

[0610] Output: Price negotiation email sent

[0611] Step 7:

[0612] The server receives the vendor's response and renegotiates if necessary.

[0613] Input: Response email from vendor

[0614] Operation: The server analyzes the received response and, if renegotiation is necessary, generates and sends another negotiation email.

[0615] Output: Updated negotiation result

[0616] Step 8:

[0617] The server automatically generates a request form based on the finalized negotiation results and sends it to the user in PDF format.

[0618] Input: Updated negotiation results

[0619] Operation: The server automatically generates a request form template based on the negotiation results and sends it to the user in PDF format.

[0620] Output: PDF of the approval request sent to the user's device

[0621] Step 9:

[0622] The user reviews the request and gives final approval.

[0623] Input: PDF of the approval request

[0624] How it works: The user reviews the PDF request and gives final approval. The server records the approval process and renegotiates or reconfirms it if necessary.

[0625] Output: Approved request

[0626] Step 10:

[0627] An emotion recognition means recognizes the user's emotions in real time and adjusts the behavior.

[0628] Input: User facial expression image

[0629] How it works: The device's camera captures the user's facial expressions, and the TensorFlow model is used to recognize emotions. The server then adjusts its response based on the emotional data.

[0630] Output: Emotion recognition results and the system's response based on them

[0631] In this way, the entire purchasing process becomes more efficient and flexible responses based on the user's emotions become possible.

[0632] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0633] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0634] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0635] [Second embodiment]

[0636] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0637] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0638] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0639] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0640] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0641] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0642] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0643] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0644] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0645] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0646] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0647] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0648] The unmanned purchasing system of the present invention receives quotations from vendors and then automates a series of purchasing processes. This system can continuously execute each step of the process: quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval.

[0649] The main components of this system are the user terminal, a server, and peripheral devices as needed. The user processes the quotation received from the vendor on the terminal, and the system then proceeds automatically.

[0650] Program processing details

[0651] 1. Receipt of quotation

[0652] A user receives a quote from a vendor.

[0653] The terminal scans the estimate with a scanner and generates image data.

[0654] The device uses OCR software to convert the image data into text data.

[0655] The terminal transmits the text data to the server.

[0656] 2. Estimate registration

[0657] The server analyzes the received text data and divides it into items.

[0658] The server organizes the information extracted, such as "vendor name," "item," "quantity," "unit price," and "total."

[0659] The server registers the extracted information in the purchasing system database.

[0660] 3. Market price comparison

[0661] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[0662] The server evaluates the fairness of the received quote based on the market price collected by the server.

[0663] 4. Price Negotiation

[0664] The server automatically generates and sends a negotiation email to the vendor.

[0665] If the server receives a response from the vendor and adjusts the price again, it will conduct any necessary additional negotiations.

[0666] If the negotiations are deemed appropriate, the negotiations will be terminated.

[0667] 5. Price Calculation

[0668] The server calculates the optimal price based on the confirmed price and stores it in the database.

[0669] The server compares the original quoted price with the confirmed price and selects the best price.

[0670] 6. Creating approval documents

[0671] The server automatically generates a request form based on the estimate, negotiation details, and optimal price.

[0672] The server generates a request form and sends it to the user's device in PDF format.

[0673] 7. Final approval

[0674] The user checks the contents of the received request.

[0675] The user logs in to the approval system, checks the final approval request, and approves it.

[0676] Specific examples

[0677] For example, when an estimate for electrical equipment construction is received from a vendor, it is processed as follows:

[0678] 1. Receipt of quotation

[0679] The user receives a quote for "electrical wiring work" from a vendor.

[0680] The terminal scans the estimate and creates image data.

[0681] The device uses OCR technology to convert the image data into text and send it to the server.

[0682] 2. Estimate registration

[0683] The server analyzes the received text data and extracts information such as "Vendor name: ABC Company," "Item: Electric wire," "Quantity: 100m," "Unit price: 500 yen," and "Total: 50,000 yen."

[0684] The server registers the extracted information in a database.

[0685] 3. Market price comparison

[0686] The server collects the market price for "electrical wiring work" from a national database and market price data on the Internet, and determines that "the market price is 450 yen per meter."

[0687] The server evaluates the appropriateness of the estimated price (500 yen per meter) it received based on the market price and determines that it is too expensive.

[0688] 4. Price Negotiation

[0689] The server sends a negotiation email to the vendor saying, "Could you please set the unit price at 450 yen based on the market price?"

[0690] The server receives the vendor's reply, "How about 470 yen?" and continues negotiations if necessary.

[0691] 5. Price Calculation

[0692] The server records the information that was agreed upon as a result of negotiations with the vendor: "unit price 470 yen, total 47,000 yen."

[0693] The server calculates the difference (3,000 yen) from the original estimated price (50,000 yen) and displays this to the user.

[0694] 6. Creating approval documents

[0695] The server creates a request form based on the estimate and negotiation results, and includes information such as "electrical wiring work," "agreed price: 47,000 yen," and "price negotiation details."

[0696] The server generates the request form in PDF format and sends it to the user's device.

[0697] 7. Final approval

[0698] The user checks the submitted request and logs into the approval system.

[0699] The user approves the contents of the request and makes the final decision.

[0700] The above is a specific example of the program processing of the unmanned purchasing system based on the present invention. This system will improve the efficiency and speed of purchasing operations and reduce errors.

[0701] The processing flow will be explained below.

[0702] Step 1:

[0703] A user receives a quote from a vendor.

[0704] The terminal scans the estimate with a scanner and generates image data.

[0705] The device uses OCR software to convert the image data into text data.

[0706] The terminal transmits the text data to the server.

[0707] Step 2:

[0708] The server analyzes the received text data.

[0709] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[0710] The server registers the extracted information in the purchasing system database.

[0711] Step 3:

[0712] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[0713] The server evaluates the fairness of the received quote based on the market price collected by the server.

[0714] Step 4:

[0715] The server generates a template for a price negotiation email.

[0716] The server automatically sends a negotiation email to the vendor, which contains specific negotiation terms based on the market price.

[0717] The vendor responds to the negotiation email.

[0718] The server receives the vendor's response and renegotiates if necessary.

[0719] Step 5:

[0720] The server calculates the optimal price based on the finalized negotiation results.

[0721] The server records the determined price in a database.

[0722] The server calculates the difference between the original quote and the confirmed price.

[0723] The server displays the differences in a spreadsheet or report format.

[0724] Step 6:

[0725] The server prepares a template for the approval document.

[0726] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[0727] The server generates the request form in PDF format and sends it to the user.

[0728] Step 7:

[0729] The user receives the request sent from the server.

[0730] The user checks the contents of the approval request and logs in to the approval system.

[0731] The user clicks the confirmation button on the approval screen for the request form to approve it.

[0732] Example 1

[0733] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0734] In conventional purchasing operations, the entire process from receiving quotations to final approval is performed manually, which is time-consuming and prone to human error. Furthermore, because matching with market prices and price negotiations are also performed manually, efficiency is lacking. The present invention aims to solve these problems and improve the efficiency and accuracy of purchasing operations.

[0735] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0736] In this invention, the server includes means for receiving quotations from vendors and converting them into digital data, means for extracting necessary information from the digital data and registering it in a database, means for comparing the extracted information with market prices, means for automatically negotiating prices with vendors, means for calculating the optimal price and recording it in a database, means for automatically generating approval documents based on the contents of the quotation and the results of the negotiations, means for sending the approval documents to users, means for scanning received quotations with a scanner and generating image data, means for converting the image data into text data using OCR software, means for the user to review the received approval documents and give final approval, and means for generating approval documents in PDF format. This automates the entire purchasing process, saving time and reducing errors.

[0737] "Vendor" refers to a company or entity that provides products or services.

[0738] "Quote" means a document that sets out the prices and terms for specific products or services.

[0739] "Digital data" is a general term for any data format that can be stored or transmitted electronically.

[0740] "Extraction" refers to the process of extracting necessary parts from data or information.

[0741] A "database" is a system that stores data in an organized manner and enables efficient access and management.

[0742] "Market Price" means the average price at which a commodity is generally traded in a particular market.

[0743] "Automatic" means that an operation or process is performed by a machine or computer without human intervention.

[0744] "Optical character recognition (OCR) technology" is a technology that converts printed or handwritten characters into digital data.

[0745] "Negotiation" refers to dialogue and discussion between the parties to adjust the terms of a transaction.

[0746] "Optimal price" refers to the price that is deemed most appropriate taking into account quality and market conditions.

[0747] A "requisition document" is a document used to obtain formal approval within an organization.

[0748] "Receiving" refers to the operation of taking in data or messages from the outside.

[0749] A "scanner" refers to a device that converts information written on paper into electronic data.

[0750] "Image data" refers to data stored in an image format.

[0751] "Text data" refers to a data format based on character information.

[0752] "Recognition" means to officially approve or permit.

[0753] "PDF format" is an abbreviation for Portable Document Format, a file format for electronically storing and sharing documents while preserving their format.

[0754] "Purchasing operations" refers to a series of business processes for procuring necessary goods and services.

[0755] The unmanned purchasing system of this invention is composed of a user terminal, a server, and peripheral devices as needed. The user processes the quotations received from the vendor on the terminal, and the system then automatically proceeds with the purchasing process.

[0756] Hardware and software used

[0757] User device: Used to scan the estimate and convert it into text data. Examples include a personal computer and a scanner (e.g., Fujitsu ScanSnap).

[0758] OCR software: Used to convert scanned image data into text data. Examples of OCR software include Adobe Acrobat and Tesseract OCR.

[0759] Server: Analyzes text data, registers it in a database, collects and compares market prices, negotiates prices, and automatically generates and sends approval documents. For example, SAP Ariba can be used as a purchasing system.

[0760] Explanation of program processing

[0761] 1. Quote Receipt:

[0762] A user receives a quote from a vendor. For example, a quote for "electrical wiring work."

[0763] The terminal scans this estimate with a scanner and generates image data (e.g., Fujitsu ScanSnap).

[0764] The terminal uses OCR software (e.g. Adobe Acrobat) to convert the image data into text data and send it to the server.

[0765] 2. Quotation Registration:

[0766] The server analyzes the received text data and divides it into items. For example, it analyzes it as "Vendor name: certain company", "Item: electric wire", "Quantity: 100m", "Unit price: 500 yen", "Total: 50,000 yen".

[0767] The server registers the analysis results in the database of the purchasing system (e.g., SAP Ariba).

[0768] 3. Market Price Matching:

[0769] The server accesses national databases (e.g., the National Tax Agency database) and market price databases on the Internet (e.g., Kakaku.com) to collect market prices.

[0770] The server evaluates the appropriateness of the estimated price based on the market price collected. For example, it determines that the market price is 450 yen per meter.

[0771] 4. Price Negotiation:

[0772] The server automatically generates a negotiation email and sends it to the vendor, proposing, for example, "Could you please set the unit price at 450 yen based on the market price?"

[0773] The server receives the vendor's response and negotiates further if necessary. For example, if the response is "How about 470 yen?", the server continues negotiations.

[0774] 5. Price Calculation:

[0775] The server calculates the optimal price based on the confirmed price and stores it in the database. For example, it records it based on "Confirmed price: 47,000 yen."

[0776] The server calculates the difference from the original estimated price (50,000 yen) and displays it to the user.

[0777] 6. Preparation of approval documents:

[0778] The server automatically generates a request for approval based on the estimate and negotiation results. Specific examples include information such as "electrical wiring work," "agreed price: 47,000 yen," and "negotiation details."

[0779] The server generates the request form in PDF format and sends it to the user's device.

[0780] 7. Final approval:

[0781] The user reviews the submitted request and logs in to the approval system (e.g., SharePoint) to make the final approval.

[0782] Specific examples

[0783] For example, if you receive a quote from a vendor for electrical installation work, you can proceed as follows:

[0784] Example prompt: "Please tell me how the system automatically processes electrical installation work quotes received from vendors."

[0785] This system automates the entire purchasing process, enabling a fast and accurate purchasing process.

[0786] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0787] Step 1:

[0788] Quote received

[0789] Specific behavior:

[0790] Input: Quote received from vendor

[0791] Action: A user receives a quote from a vendor.

[0792] Action: The device scans this quote with a scanner and generates image data. For example, it uses a Fujitsu ScanSnap to create high-resolution image data.

[0793] Output: Generated image data (JPEG or PDF format)

[0794] Step 2:

[0795] OCR conversion of image data

[0796] Specific behavior:

[0797] Input: Image data generated by a scanner

[0798] Operation: The device uses OCR software (e.g., Adobe Acrobat) to convert image data into text data.

[0799] How it works: OCR software analyzes image data and converts it into a string of characters that can be recognized as text.

[0800] Output: Converted text data

[0801] Step 3:

[0802] Sending text data

[0803] Specific behavior:

[0804] Input: Text data after OCR conversion

[0805] Operation: The terminal sends the converted text data to the server.

[0806] Output: Text data sent to the server

[0807] Step 4:

[0808] Quotation registration

[0809] Specific behavior:

[0810] Input: Text data received by the server

[0811] How it works: The server parses the text data and splits it into multiple fields (e.g., vendor name, item, quantity, unit price, total) using a natural language processing algorithm (e.g., SpaCy or Google Cloud NLP API).

[0812] Operation: The server registers the analysis results in the database of the purchasing system (e.g., SAP Ariba).

[0813] Output: Quote information stored in the database

[0814] Step 5:

[0815] Market price comparison

[0816] Specific behavior:

[0817] Input: Quote information stored in the database

[0818] Operation: The server accesses national databases (e.g., the National Tax Agency database) and market price databases on the Internet (e.g., Kakaku.com) to collect market prices for the target item.

[0819] Operation: The server evaluates the appropriateness of the quoted price based on the market price collected. For example, the market price is 450 yen per meter, but the received quote is 500 yen per meter, so it is determined to be expensive.

[0820] Output: Comparison of market price and estimated price

[0821] Step 6:

[0822] Price negotiation

[0823] Specific behavior:

[0824] Input: Comparison result of market price and estimated price

[0825] How it works: The server automatically generates a negotiation email and sends it to the vendor. For example, it proposes, "Could you please set the unit price at 450 yen based on the market price?"

[0826] Operation: The server receives the vendor's response and negotiates further if necessary. For example, if the response is "How about 470 yen?", the server continues negotiations.

[0827] Output: Negotiation result data

[0828] Step 7:

[0829] Price calculation

[0830] Specific behavior:

[0831] Input: Negotiation result data

[0832] Operation: The server calculates the optimal price based on the confirmed price and saves it in the database. For example, it records it based on "Confirmed price: 47,000 yen."

[0833] Operation: Calculate the difference from the original estimated price (50,000 yen) and show it to the user. For example, notify the user that a cost reduction of 3,000 yen has been achieved.

[0834] Output: Optimal price calculation result

[0835] Step 8:

[0836] Creating approval documents

[0837] Specific behavior:

[0838] Input: Optimal price calculation results and negotiation history

[0839] Operation: The server automatically generates a request for approval based on the estimate and negotiation results. Specific examples include information such as "electrical wiring work," "agreed price: 47,000 yen," and "negotiation details."

[0840] Operation: The server generates a request form in PDF format and sends it to the user's device.

[0841] Output: Generated PDF of the approval document

[0842] Step 9:

[0843] Final approval

[0844] Specific behavior:

[0845] Input: Generated PDF format request form

[0846] What it does: A user reviews the submitted request and logs into an approval system (e.g., SharePoint) to make the final approval.

[0847] How it works: After logging in, check the contents of the request form, add comments if necessary, and finally press the approval button.

[0848] Output: Approved request

[0849] This system automates the entire purchasing process, enabling a fast and accurate purchasing process.

[0850] (Application example 1)

[0851] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0852] Current purchasing operations at logistics centers rely on many manual processes and time-consuming procedures, resulting in inefficiencies and a high risk of human error. There is a particular need for technology that can efficiently automate the entire process, from receiving quotes to matching market prices, negotiating prices, and creating and sending approval documents. This is expected to reduce labor costs, increase work speed, and improve accuracy.

[0853] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0854] In this invention, the server includes means for scanning estimates using a smartphone camera, means for converting image data into text data using optical character recognition technology, means for scraping price databases on the Internet to collect market prices, means for sending negotiation emails to vendors using automatic email generation technology, and means for notifying users using a push notification function, thereby enabling the automation and efficiency of the entire purchasing process.

[0855] "Quote" means a document that describes the price and terms of products or services provided by a vendor.

[0856] "Digital data" is data that has been converted into a format that can be processed by electronic devices such as computers.

[0857] A "database" is a system or software that systematically organizes information and allows it to be managed and searched centrally.

[0858] "Market price" is the price at which a particular product or service is generally traded in the market.

[0859] Optical character recognition (OCR) is a technology that recognizes characters from image data and converts them into text data.

[0860] "Scraping" is a technique for automatically extracting specific information from web pages.

[0861] "Push notification" is a technology that sends information directly from a server to a client device.

[0862] "Automatic email generation" is a technology in which a system automatically creates emails based on predefined conditions and formats.

[0863] A "rei-sho" is a document used to obtain the approval necessary to make decisions within an organization.

[0864] This invention relates to a system that fully automates purchasing operations at a logistics center. This system receives quotations from vendors, converts them into digital data, extracts necessary information, and registers it in a database. It then compares the extracted information with market prices and automatically negotiates prices with the vendor. It also calculates the optimal price, records it in a database, and automatically generates a request for approval based on the quotation and negotiation results and sends it to the user.

[0865] First, the user scans the quote using the camera on their smartphone. The scanned image data is converted into text data using optical character recognition (OCR) technology such as Google Cloud Vision API. This converted data is uploaded to AWS S3 and transferred to a database using AWS Lambda.

[0866] The server then analyzes the received data and extracts the necessary information (e.g., item, quantity, unit price, total, etc.). This information is then registered in a relational database such as AWS RDS.

[0867] The server then scrapes the internet price database to collect market quotes, using AWS Lambda or other scraping tools. Based on the collected market quotes, the server evaluates the fairness of the quote.

[0868] If the price is not reasonable, the server will use automatic email generation technology to send a negotiation email to the vendor. This process uses SES to send emails. The reply from the vendor is also automatically received and the analysis and negotiation process will continue again.

[0869] Finally, the optimal price is calculated based on the finalized price and recorded in a database. The server automatically generates a request form based on this information and sends it in PDF format to the user's smartphone via push notification. Notification services such as social media are used for the push notification. The user checks the request form on their smartphone and digitally signs it using the DocuSign API or similar.

[0870] Specific examples

[0871] For example, when a user receives a quote for electrical equipment construction from a vendor, they scan the quote using their smartphone camera. This image data is converted into text data using Google Cloud Vision API and registered in a database via AWS Lambda. The server scrapes and collects market prices from the Internet and evaluates the fair price. After the evaluation, the server automatically sends a negotiation email to the vendor using SES, and the optimal price is confirmed. A request for approval is then automatically generated and sent to the user via push notification. The user then checks the request on their smartphone and electronically signs it using the DocuSign API.

[0872] Example prompts for generative AI models

[0873] "Write a program to convert the following text using OCR and register it in a purchasing system:

[0874] Quote image path:path / to / image.jpg

[0875] Database to register: AWS RDS

[0876] APIs and libraries used: Google Cloud Vision API, boto3, pdfkit

[0877] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0878] Step 1:

[0879] The user scans an estimate using the smartphone camera. The input is an estimate received from a vendor, which is captured as image data using the smartphone camera. The output is the image data of the scanned estimate. Specifically, the user launches the camera app and takes a picture of the estimate.

[0880] Step 2:

[0881] The device performs optical character recognition (OCR) on the scanned image data using the Google Cloud Vision API. The input is the image data acquired in step 1, and the output is text data converted by OCR. Specifically, the image data is sent to the Google Cloud Vision API and the text data is received as the result of the OCR processing.

[0882] Step 3:

[0883] The text data acquired by the device using OCR is uploaded to AWS S3. The input is the text data from step 2, and the output is the text data stored on AWS S3. Specifically, the process is performed to upload the text file to an Amazon S3 bucket.

[0884] Step 4:

[0885] The server uses AWS Lambda to retrieve text data from S3, analyze the information, and extract the required items. The input is the text data retrieved from AWS S3, and the output is the extracted information (e.g., item, quantity, unit price, total, etc.). Specifically, the script analyzes the text data and executes to extract the required information for each item.

[0886] Step 5:

[0887] The server registers the extracted information in AWS RDS. The input is the information extracted in step 4, and the output is the information registered in AWS RDS. Specifically, it generates an SQL statement to save the extracted information and inserts the data into AWS RDS.

[0888] Step 6:

[0889] The server scrapes price databases on the Internet to collect market price information. The input is a list of URLs for the price databases, and the output is the collected market price information. Specifically, a web scraping tool is used to access the price database and obtain market price information.

[0890] Step 7:

[0891] The server compares the market price collected with the registered quotation information to perform price evaluation. The input is the quotation information from step 5 and the market price information from step 6, and the output is the evaluation result regarding the appropriateness of the price. Specifically, it compares the estimated price with the market price and executes an algorithm to evaluate whether the estimated price is appropriate.

[0892] Step 8:

[0893] The server uses automatic email generation technology to send a negotiation email to the vendor. The input is the price evaluation result and vendor information, and the output is the sent negotiation email. Specifically, it uses SES to generate a negotiation email and sends it to the vendor's email address.

[0894] Step 9:

[0895] The server receives the reply from the vendor and re-analyzes and evaluates the negotiation results. The input is the reply email from the vendor, and the output is the updated negotiation results. Specifically, the server re-analyzes the contents of the received email and updates the negotiation results.

[0896] Step 10:

[0897] The server calculates the optimal price based on the confirmed price and records it in AWS RDS. The input is the updated negotiation result, and the output is the optimal price recorded in AWS RDS. Specifically, the optimal price is calculated and the information is saved in the database.

[0898] Step 11:

[0899] The server automatically generates a request for approval based on the quotation details and negotiation results, and generates it in PDF format. The input is the confirmed price information and negotiation results, and the output is a request for approval in PDF format. Specifically, the script that generates the request for approval is executed using PDFKit or similar.

[0900] Step 12:

[0901] The server uses the push notification function to send a request form to the user. The input is a PDF version of the request form, and the output is the notification sent to the user. Specifically, the server sends a push notification to the user's smartphone using a notification service such as SNS.

[0902] Step 13:

[0903] The user checks the approval document on their smartphone and digitally signs it using the DocuSign API. The input is a PDF version of the approval document, and the output is the signed version. Specifically, the process involves opening the approval document on the smartphone and digitally signing the required fields.

[0904] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0905] The unmanned purchasing system of this invention receives quotations from vendors and automates the purchasing process based on them. This system covers a series of processes, including quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval, and also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's behavior and interface accordingly.

[0906] The main components of this system are a user terminal, a server, an emotion engine, and peripheral devices as needed. The user terminal is used to input estimates, check approval documents, and recognize emotions, while the server is responsible for processing and storing various data. The emotion engine recognizes the user's emotions in real time and makes adjustments accordingly.

[0907] Program processing details

[0908] 1. Receipt of quotation

[0909] A user receives a quote from a vendor.

[0910] The terminal scans the estimate with a scanner and generates image data.

[0911] The device uses OCR software to convert the image data into text data.

[0912] The terminal transmits the text data to the server.

[0913] 2. Estimate registration

[0914] The server analyzes the received text data.

[0915] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[0916] The server registers the extracted information in the purchasing system database.

[0917] 3. Market price comparison

[0918] The server accesses national databases and internet market price databases to collect market prices for the target items.

[0919] The server evaluates the fairness of the received quote based on the market price collected by the server.

[0920] 4. Price Negotiation

[0921] The server generates a template for a price negotiation email.

[0922] The server automatically sends a negotiation email to the vendor, which includes specific negotiation details based on the market price.

[0923] After the server receives the vendor's response, it renegotiates if necessary.

[0924] The text of negotiation emails is automatically adjusted based on the user's emotions recognized by the emotion engine.

[0925] 5. Price Calculation

[0926] The server calculates the optimal price based on the finalized negotiation results.

[0927] The server records the determined price in a database.

[0928] The server calculates the difference between the original quoted price and the final price and displays it in a spreadsheet or report format.

[0929] 6. Creating approval documents

[0930] The server prepares a template for the approval document.

[0931] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[0932] The server generates the request form in PDF format and sends it to the user.

[0933] 7. Final approval

[0934] The user receives the request sent from the server.

[0935] The emotion engine recognizes the user's emotions while reviewing the request and encourages renegotiation or reconfirmation if the user expresses dissatisfaction.

[0936] The user logs in to the approval system and approves the contents of the request form.

[0937] Specific examples

[0938] For example, when an estimate for electrical equipment construction is received from a vendor, the process proceeds as follows:

[0939] 1. Receipt of quotation

[0940] The user receives a quote for "electrical wiring work" from a vendor.

[0941] The terminal scans the estimate and creates image data.

[0942] The device uses OCR technology to convert the image data into text and send it to the server.

[0943] 2. Estimate registration

[0944] The server analyzes the received text data and extracts information such as "Vendor name: ABC Company," "Item: Electric wire," "Quantity: 100m," "Unit price: 500 yen," and "Total: 50,000 yen."

[0945] The server registers this information in a database.

[0946] 3. Market price comparison

[0947] The server collects market price data from national databases and the Internet to check the market price for "electrical wiring work."

[0948] The server compares the market price with the quoted price and evaluates whether the quoted price is reasonable.

[0949] 4. Price Negotiation

[0950] The server generates a negotiation email saying, "Could you please set the unit price at 450 yen based on the market price?" and sends it to the vendor.

[0951] The server receives the vendor's response, "How about 470 yen?" and renegotiates if necessary.

[0952] The emotion engine recognizes the user's emotions and adjusts the content of the negotiation email.

[0953] 5. Price Calculation

[0954] The server determines the price based on the negotiation result of "470 yen, total 47,000 yen" and records it in the database.

[0955] The server calculates and displays the difference (3,000 yen) from the original quoted price (50,000 yen).

[0956] 6. Creating approval documents

[0957] The server creates a request form based on the estimate and negotiation results, and includes information such as "electrical wiring work," "agreed price: 47,000 yen," and "price negotiation details."

[0958] The server generates a request form and sends it to the user's device in PDF format.

[0959] 7. Final approval

[0960] The user receives the PDF request form and checks its contents.

[0961] The emotion engine recognizes user emotions in real time and prompts renegotiation or reaffirmation when dissatisfaction is expressed.

[0962] The user logs into the approval system and gives final approval.

[0963] In this way, the system is able to respond flexibly while taking into consideration the user's emotions, which not only makes purchasing operations more efficient but also improves the user experience.

[0964] The processing flow will be explained below.

[0965] Step 1:

[0966] A user receives a quote from a vendor.

[0967] The terminal scans the estimate with a scanner and generates image data.

[0968] The device uses OCR software to convert the image data into text data.

[0969] The terminal transmits the text data to the server.

[0970] Step 2:

[0971] The server analyzes the received text data.

[0972] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[0973] The server registers the extracted information in the purchasing system database.

[0974] Step 3:

[0975] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[0976] The server evaluates the fairness of the received quote based on the market price collected by the server.

[0977] Step 4:

[0978] The server generates a template for a price negotiation email.

[0979] The server automatically sends a negotiation email to the vendor, which contains specific negotiation terms based on the market price.

[0980] The server receives the response from the vendor.

[0981] The server uses an emotion engine to automatically adjust the text of the negotiation email according to the emotion of the user.

[0982] The server renegotiates with the vendor as needed.

[0983] Step 5:

[0984] The server calculates the optimal price based on the finalized negotiation results.

[0985] The server records the determined price in a database.

[0986] The server calculates the difference between the original quote and the confirmed price.

[0987] The server displays the differences in a spreadsheet or report format.

[0988] Step 6:

[0989] The server prepares a template for the approval document.

[0990] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[0991] The server generates the request form in PDF format and sends it to the user.

[0992] Step 7:

[0993] The user receives the request sent from the server.

[0994] The user checks the contents of the request form.

[0995] The server uses an emotion engine to prompt renegotiation or reconfirmation when the user expresses dissatisfaction.

[0996] The user logs in to the approval system and approves the contents of the request form.

[0997] Example 2

[0998] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0999] In traditional purchasing operations, processes such as receiving quotations from vendors, extracting and registering information, comparing prices with market rates, negotiating prices with vendors, and creating and approving approval documents are often fragmented, resulting in problems of time and effort. Furthermore, systems that do not consider user emotions can impair the user experience and prevent optimal purchasing decisions. This reduces the efficiency of the entire purchasing process and risks making purchases at inappropriate prices.

[1000] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1001] In this invention, the server includes means for receiving quotations from vendors and converting them into digital data, means for extracting necessary information from the digital data and registering it in a database, means for comparing the extracted information with market prices, means for automatically negotiating prices with vendors, means for calculating an optimal price and recording it in a database, means for automatically generating a request for purchase based on the contents of the quotation and the results of the negotiation, means for sending the request for purchase to the user, and means for adjusting the contents of the negotiation and the request for purchase based on the results of user emotion recognition. This makes it possible to improve the efficiency of purchasing operations and realize optimal decisions that take user emotions into consideration.

[1002] A "quote" is a document that describes the price, quantity, and terms of the goods provided by a vendor.

[1003] "Digital data" is data that has been converted from analog information into a numerical value and into a format that can be processed by a computer or the like.

[1004] Optical character recognition (OCR) technology is a technology that automatically identifies characters from images captured by a scanner or camera and converts them into digital text.

[1005] A "database" is a collection of information that is systematically organized so that it can be efficiently accessed, managed, and updated.

[1006] A "market price" is the general price at which a particular product or service is traded in the market.

[1007] "Price negotiation" is the process by which a buyer and seller decide on a mutually agreed-upon price in a transaction.

[1008] A "requisition document" is a document created to obtain approval within an organization, and is usually used to seek approval or opinions from superiors or related departments.

[1009] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, movements, etc. to determine their emotional state.

[1010] "Vendor" means a supplier or provider of goods or services.

[1011] A "server" is a computer system that provides services to other computers (clients) on a network.

[1012] A "user" is someone who uses the system to perform various tasks and operations.

[1013] The unmanned purchasing system of this invention receives quotations from vendors and automates the purchasing process based on them. This system covers a series of processes, including quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval, and also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's behavior and interface accordingly.

[1014] The main components of the system are a user terminal, a server, an emotion engine, and, if necessary, peripheral devices.

[1015] The user device performs operations such as inputting and scanning estimates, checking approval documents, and emotion recognition. Specific hardware options include scanners and high-performance cameras, such as the Canon DR-C240 scanner. Adobe Acrobat or other well-known OCR software is recommended for OCR technology.

[1016] The server is responsible for processing and storing various types of data. A relational database management system such as PostgreSQL is suitable as the database. General-purpose programming languages ​​such as Python are used for processes within the server, and data analysis, information extraction using regular expressions, and market price collection via APIs are performed. Furthermore, Python's smtplib and the Jinja2 template engine are used for automatically generating and sending emails.

[1017] The emotion engine uses advanced emotion recognition algorithms to analyze the user's facial expressions and voice, and automatically adjusts the system's behavior based on the results. Facial recognition technology can be used with libraries such as OpenCV and Dlib, enabling real-time emotion analysis.

[1018] Specifically, for example, when an estimate for electrical equipment construction is received from a vendor, the processing of this system proceeds as follows.

[1019] The user receives an estimate for "electrical wiring work" and scans it using the scanner or camera on the device.

[1020] The scanned image data is converted into text using OCR technology and sent to the server.

[1021] The server extracts the necessary information from the received text data and records it in a database.

[1022] The server collects market quotes from an internet database and evaluates the quotes.

[1023] The server automatically sends a negotiation email to the vendor and calculates the optimal price based on the results.

[1024] The server creates a request form based on the quotation details and negotiation results and sends it to the user in PDF format.

[1025] The user reviews the request, and the emotion engine prompts renegotiation if necessary. Finally, the user logs in to the system and approves the request.

[1026] An example of a prompt that can be used to adjust system behavior and message content using generative AI models is as follows:

[1027] "Please explain in natural language each process of the automated purchasing system, from receiving a quote to final approval. Please include specific hardware and software, as well as data processing and calculation methods. Also, please explain the function that recognizes user emotions and adjusts the system accordingly."

[1028] A system designed in this way can greatly contribute to improving the efficiency of purchasing operations and user satisfaction.

[1029] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1030] Step 1:

[1031] Quote received

[1032] A user receives a quote from a vendor.

[1033] Input: Quote provided by vendor (physical document or PDF).

[1034] Output: Physical copy or PDF file.

[1035] Specific action: The user receives the quote and prepares it for digitization using a scanner or camera.

[1036] The terminal uses a scanner to scan the estimate and generate image data.

[1037] Input: Physical quote.

[1038] Output: Scanned image data.

[1039] What it does: Scan the quote at high resolution using a Canon DR-C240 scanner.

[1040] The device uses OCR (Optical Character Recognition) technology to convert image data into text data.

[1041] Input: scanned image data.

[1042] Output: Text data.

[1043] Specific operation: Executes Adobe Acrobat's OCR function and extracts text information such as "100m of electric wire, unit price 500 yen" from the image data.

[1044] The terminal transmits the text data to the server.

[1045] Input: Text data generated by OCR.

[1046] Output: The text data sent to the server.

[1047] What it does: Issues a curl command to send text data to the server's API endpoint using HTTPS.

[1048] Step 2:

[1049] Quotation registration

[1050] The server analyzes the received text data.

[1051] Input: Text data sent from the terminal.

[1052] Output: A data object containing the analysis results.

[1053] Specific operation: Analyzes text data using Python's NLTK library and generates tokenized sentences.

[1054] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[1055] Input: A parsed data object.

[1056] Output: Data for each extracted item.

[1057] Specific operation: Using regular expression pattern matching, extract information such as "Vendor name: Vendor", "Item: Electric wire", "Quantity: 100m", "Unit price: 500 yen", and "Total: 50,000 yen".

[1058] The server registers the extracted information in the purchasing system database.

[1059] Input: Data for each extracted item.

[1060] Output: Information stored in a database.

[1061] Specific behavior: Connect to a PostgreSQL database and execute the INSERT INTO quote_table(...) VALUES(...) query.

[1062] Step 3:

[1063] Market price comparison

[1064] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[1065] Input: URL or API key of the market quote database from which the data is collected.

[1066] Output: Collected market quote price data.

[1067] Specific operation: Accesses the API of the Ministry of Economy, Trade and Industry, etc., and obtains current market price information for power cables.

[1068] The server evaluates the fairness of the received quote based on the market price collected by the server.

[1069] Input: Collected market quote data, quoted price data.

[1070] Output: Evaluation results on the fairness of the estimated price.

[1071] What it does: Runs an algorithm to verify that the quote is within ±10% of the market price.

[1072] Step 4:

[1073] Price negotiation

[1074] The server generates a template for a price negotiation email.

[1075] Input: Template data for negotiation, quote and market price data.

[1076] Output: The generated email template.

[1077] What it does: Uses the Jinja2 template engine to generate an email that says, "Would you mind adjusting the price based on market value?"

[1078] The server automatically sends a negotiation email to the vendor.

[1079] Input: The generated email template.

[1080] Output: Negotiation email sent to vendor.

[1081] Specific behavior: Uses the smtplib library to send the generated email to the vendor's email address.

[1082] After the server receives the vendor's response, it renegotiates if necessary.

[1083] Input: Response email from vendor.

[1084] Output: Renegotiation result.

[1085] Specific operation: Analyzes received emails and generates and sends renegotiation emails if necessary.

[1086] The text of negotiation emails is automatically adjusted based on the user's emotions recognized by the emotion engine.

[1087] Input: User emotion data, negotiation email template.

[1088] Output: Tailored negotiation email.

[1089] What it does: It uses facial recognition technology to analyze a user's emotions and adapts the tone and content of emails accordingly.

[1090] Step 5:

[1091] Price calculation

[1092] The server calculates the optimal price based on the finalized negotiation results.

[1093] Input: Negotiated price data.

[1094] Output: Optimal price.

[1095] Specific operation: Recalculate the total price based on the negotiated result of "470 yen".

[1096] The server records the determined price in a database.

[1097] Input: Best price data.

[1098] Output: The final price recorded in the database.

[1099] What it does: Runs an SQL query to insert the best price into a PostgreSQL database.

[1100] The server calculates the difference between the original quoted price and the final price and displays it in a spreadsheet or report format.

[1101] Input: Original Quoted Price Data, Final Price Data.

[1102] Output: Diff report.

[1103] Specific operation: Using Python's pandas and xlsxwriter, create a difference report in Excel format and provide it to the user.

[1104] Step 6:

[1105] Creating approval documents

[1106] The server prepares a template for the approval document.

[1107] Input: Request form template data.

[1108] Output: Prepared decision template.

[1109] Specific operation: Load an existing approval document template and pass it to the template engine.

[1110] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[1111] Input: Quotation data, negotiation data, final price data.

[1112] Output: Auto-filled approval request data.

[1113] Specific operation: Embed the necessary information into the approval form template using the python-docx library.

[1114] The server generates the request form in PDF format and sends it to the user.

[1115] Input: Automatically entered request data.

[1116] Output: A PDF version of the approval document.

[1117] Specific operation: Use reportlab to convert the request form into PDF format and send it to the user's device.

[1118] Step 7:

[1119] Final approval

[1120] The user receives the request sent from the server.

[1121] Input: PDF request form from the server.

[1122] Output: The received decision.

[1123] Specific operation: A user downloads a PDF request form via email or a portal site.

[1124] The emotion engine recognizes the user's emotions while reviewing the request and encourages renegotiation or reconfirmation if the user expresses dissatisfaction.

[1125] Input: User emotion data.

[1126] Output: Proposal for renegotiation or reaffirmation.

[1127] What it does: It uses facial recognition technology to analyze users' facial expressions in real time and displays an alert if dissatisfaction is detected.

[1128] The user logs in to the approval system and approves the contents of the request form.

[1129] Input: PDF request form, login information.

[1130] Output: Approval result.

[1131] Specific operation: A user logs in to the system through a web portal, checks the contents of the request form, and clicks the approval button.

[1132] By using this system, it is possible to streamline and optimize purchasing operations, while also expecting an improved user experience.

[1133] (Application example 2)

[1134] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1135] In current purchasing operations, the entire process from receiving quotations to final approval of the request form is often done manually, resulting in inefficiency. It is also difficult to respond flexibly and consider user feelings, which can lead to user dissatisfaction, especially during price negotiations and the request form approval process. This poses a challenge to the overall efficiency of operations and the user experience.

[1136] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1137] In this invention, the server includes a means for receiving quotations from vendors and converting them into digital data, a means for extracting necessary information from the digital data and registering it in a database, and an emotion recognition means for recognizing a user's emotion and adjusting the user's behavior. This makes it possible to automate the entire purchasing process while recognizing the user's emotion in real time and taking appropriate action.

[1138] "Vendor" refers to a company that provides products or services.

[1139] A "quotation" is a document that describes the price, quantity, terms, etc. of a product or service.

[1140] "Digital data" means data represented in electronic form.

[1141] "OCR technology" is an abbreviation for optical character recognition technology, a technology that extracts text information from images.

[1142] "Market price" refers to the price in the general market for a particular product or service.

[1143] A "database" is a system that is organized to efficiently manage large amounts of data and enable searching and updating.

[1144] "Emotion recognition means" refers to technology or devices for analyzing and recognizing a user's emotions in real time.

[1145] A "reisho" is a formal document created to obtain approval or settlement within an organization or company.

[1146] "Price negotiation" is the process by which a buyer and seller negotiate and agree on the price of a product or service.

[1147] "Final approval" refers to the final stage of the approval process, where all parties agree and formally approve the proposal.

[1148] This invention provides a system for automating purchasing processes and recognizing and flexibly responding to user emotions. This system is primarily composed of a server, a terminal, emotion recognition means, and a database. Specific embodiments of this system are described below.

[1149] System Components

[1150] 1. Server

[1151] The server has the following functions:

[1152] Receive and analyze digital estimate data.

[1153] Using OCR technology, necessary information is extracted from digital data and registered in a database.

[1154] The registered information is compared with market prices, and market prices are collected to assess the fairness of the prices.

[1155] Automatically generate price negotiation emails and negotiate prices with vendors.

[1156] A request form template is automatically generated based on the confirmed price and sent to the user in PDF format.

[1157] Recognize user sentiment and encourage renegotiation or reaffirmation when dissatisfaction is expressed.

[1158] 2. Terminal

[1159] The device can be a smartphone or a PC with certain functions:

[1160] Provides a function to scan estimates and generate scanned image data.

[1161] The generated image data is converted into text data using OCR technology and sent to the server.

[1162] The user takes an image for emotion recognition and sends it to the server.

[1163] 3. Emotion recognition means

[1164] Emotion recognition is a technology for recognizing user emotions in real time, using a machine learning model (e.g., using TensorFlow) that analyzes the user's facial expressions from images captured by a camera.

[1165] 4. Database

[1166] The database is used to store information such as quotation information, negotiation results, market prices, and user emotional data.

[1167] Hardware and Software

[1168] Hardware: Scanner, smartphone or PC with camera, server.

[1169] Software: OCR software (e.g., Pytesseract), image processing libraries (e.g., OpenCV), machine learning models (e.g., TensorFlow), internet request libraries (e.g., Requests).

[1170] Example

[1171] For example, if a user receives an estimate for "electrical equipment construction," the process proceeds as follows:

[1172] 1. Scan the quote

[1173] The user takes a photo of the estimate using the smartphone camera and generates image data.

[1174] The device converts this image data into text using OCR technology and sends it to the server.

[1175] 2. Data analysis and registration

[1176] The server analyzes the received text data, extracts information such as "vendor name," "item," "quantity," "unit price," and "total," and registers it in a database.

[1177] 3. Comparison with market prices

[1178] The server collects market prices from a database on the Internet and compares them with the quoted prices to assess their suitability.

[1179] 4. Price Negotiation

[1180] The server automatically generates a negotiation email based on market prices and sends it to the vendor.

[1181] If the user expresses dissatisfaction, the server attempts to renegotiate.

[1182] 5. Preparation of approval documents

[1183] The server automatically generates a request form based on the negotiation results and sends it to the user in PDF format.

[1184] 6. Final Approval

[1185] The user reviews the submitted PDF request form and gives final approval.

[1186] Prompt Sentence Examples

[1187] As an example, the following prompt sentence is input into the generative AI model:

[1188] Scan the quote for "electrical wiring work" and convert it to text. Then generate and send a price negotiation email based on the market price. Use the camera to recognize the user's emotions and suggest renegotiation if there is dissatisfaction.

[1189] In this way, a system is realized that can improve the efficiency of purchasing operations and respond flexibly based on the user's emotions.

[1190] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1191] Step 1:

[1192] A user receives a quote from a vendor.

[1193] Input: Paper quote from vendor

[1194] How it works: The user takes a photo of a quote using their smartphone camera.

[1195] Output: The image data of the quotation is saved on the smartphone.

[1196] Step 2:

[1197] The terminal scans the estimate and converts it into text data using OCR technology.

[1198] Input: Image data of quotation

[1199] How it works: The device uses OpenCV to grayscale the image data and Pytesseract to perform OCR.

[1200] Output: Text data of the quotation

[1201] Step 3:

[1202] The terminal transmits the text data to the server.

[1203] Input: Text data of quotation

[1204] How it works: The device uses the Internet Requests library (Requests) to send text data to the server.

[1205] Output: Text data is saved on the server.

[1206] Step 4:

[1207] The server analyzes the received text data, extracts the necessary information, and registers it in a database.

[1208] Input: Text data of quotation

[1209] How it works: The server uses machine learning algorithms to analyze the text data and extract information such as "vendor name," "item," "quantity," "unit price," and "total." The extracted information is then registered in a database.

[1210] Output: Quote information stored in the database

[1211] Step 5:

[1212] The server collects market quotes and compares them with quote information.

[1213] Input: Quote information stored in the database

[1214] Operation: The server accesses a market price database on the Internet to collect the market price of the target item, and evaluates the fairness of the quoted price and the market price.

[1215] Output: Validity evaluation result

[1216] Step 6:

[1217] The server generates a price negotiation email template and automatically sends it to the vendor.

[1218] Input: Fairness assessment result, market price

[1219] How it works: The server generates a negotiation email based on a template and automatically sends it to the vendor.

[1220] Output: Price negotiation email sent

[1221] Step 7:

[1222] The server receives the vendor's response and renegotiates if necessary.

[1223] Input: Response email from vendor

[1224] Operation: The server analyzes the received response and, if renegotiation is necessary, generates and sends another negotiation email.

[1225] Output: Updated negotiation result

[1226] Step 8:

[1227] The server automatically generates a request form based on the finalized negotiation results and sends it to the user in PDF format.

[1228] Input: Updated negotiation results

[1229] Operation: The server automatically generates a request form template based on the negotiation results and sends it to the user in PDF format.

[1230] Output: PDF of the approval request sent to the user's device

[1231] Step 9:

[1232] The user reviews the request and gives final approval.

[1233] Input: PDF of the approval request

[1234] How it works: The user reviews the PDF request and gives final approval. The server records the approval process and renegotiates or reconfirms it if necessary.

[1235] Output: Approved request

[1236] Step 10:

[1237] An emotion recognition means recognizes the user's emotions in real time and adjusts the behavior.

[1238] Input: User facial expression image

[1239] How it works: The device's camera captures the user's facial expressions, and the TensorFlow model is used to recognize emotions. The server then adjusts its response based on the emotional data.

[1240] Output: Emotion recognition results and the system's response based on them

[1241] In this way, the entire purchasing process becomes more efficient and flexible responses based on the user's emotions become possible.

[1242] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1243] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1244] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1245] [Third embodiment]

[1246] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1247] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1248] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1249] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1250] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1251] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1252] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1253] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1254] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1255] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1256] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1257] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1258] The unmanned purchasing system of the present invention receives quotations from vendors and then automates a series of purchasing processes. This system can continuously execute each step of the process: quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval.

[1259] The main components of this system are the user terminal, a server, and peripheral devices as needed. The user processes the quotation received from the vendor on the terminal, and the system then proceeds automatically.

[1260] Program processing details

[1261] 1. Receipt of quotation

[1262] A user receives a quote from a vendor.

[1263] The terminal scans the estimate with a scanner and generates image data.

[1264] The device uses OCR software to convert the image data into text data.

[1265] The terminal transmits the text data to the server.

[1266] 2. Estimate registration

[1267] The server analyzes the received text data and divides it into items.

[1268] The server organizes the information extracted, such as "vendor name," "item," "quantity," "unit price," and "total."

[1269] The server registers the extracted information in the purchasing system database.

[1270] 3. Market price comparison

[1271] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[1272] The server evaluates the fairness of the received quote based on the market price collected by the server.

[1273] 4. Price Negotiation

[1274] The server automatically generates and sends a negotiation email to the vendor.

[1275] If the server receives a response from the vendor and adjusts the price again, it will conduct any necessary additional negotiations.

[1276] If the negotiations are deemed appropriate, the negotiations will be terminated.

[1277] 5. Price Calculation

[1278] The server calculates the optimal price based on the confirmed price and stores it in the database.

[1279] The server compares the original quoted price with the confirmed price and selects the best price.

[1280] 6. Creating approval documents

[1281] The server automatically generates a request form based on the estimate, negotiation details, and optimal price.

[1282] The server generates a request form and sends it to the user's device in PDF format.

[1283] 7. Final approval

[1284] The user checks the contents of the received request.

[1285] The user logs in to the approval system, checks the final approval request, and approves it.

[1286] Specific examples

[1287] For example, when an estimate for electrical equipment construction is received from a vendor, it is processed as follows:

[1288] 1. Receipt of quotation

[1289] The user receives a quote for "electrical wiring work" from a vendor.

[1290] The terminal scans the estimate and creates image data.

[1291] The device uses OCR technology to convert the image data into text and send it to the server.

[1292] 2. Estimate registration

[1293] The server analyzes the received text data and extracts information such as "Vendor name: ABC Company," "Item: Electric wire," "Quantity: 100m," "Unit price: 500 yen," and "Total: 50,000 yen."

[1294] The server registers the extracted information in a database.

[1295] 3. Market price comparison

[1296] The server collects the market price for "electrical wiring work" from a national database and market price data on the Internet, and determines that "the market price is 450 yen per meter."

[1297] The server evaluates the appropriateness of the estimated price (500 yen per meter) it received based on the market price and determines that it is too expensive.

[1298] 4. Price Negotiation

[1299] The server sends a negotiation email to the vendor saying, "Could you please set the unit price at 450 yen based on the market price?"

[1300] The server receives the vendor's reply, "How about 470 yen?" and continues negotiations if necessary.

[1301] 5. Price Calculation

[1302] The server records the information that was agreed upon as a result of negotiations with the vendor: "unit price 470 yen, total 47,000 yen."

[1303] The server calculates the difference (3,000 yen) from the original estimated price (50,000 yen) and displays this to the user.

[1304] 6. Creating approval documents

[1305] The server creates a request form based on the estimate and negotiation results, and includes information such as "electrical wiring work," "agreed price: 47,000 yen," and "price negotiation details."

[1306] The server generates the request form in PDF format and sends it to the user's device.

[1307] 7. Final approval

[1308] The user checks the submitted request and logs into the approval system.

[1309] The user approves the contents of the request and makes the final decision.

[1310] The above is a specific example of the program processing of the unmanned purchasing system based on the present invention. This system will improve the efficiency and speed of purchasing operations and reduce errors.

[1311] The processing flow will be explained below.

[1312] Step 1:

[1313] A user receives a quote from a vendor.

[1314] The terminal scans the estimate with a scanner and generates image data.

[1315] The device uses OCR software to convert the image data into text data.

[1316] The terminal transmits the text data to the server.

[1317] Step 2:

[1318] The server analyzes the received text data.

[1319] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[1320] The server registers the extracted information in the purchasing system database.

[1321] Step 3:

[1322] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[1323] The server evaluates the fairness of the received quote based on the market price collected by the server.

[1324] Step 4:

[1325] The server generates a template for a price negotiation email.

[1326] The server automatically sends a negotiation email to the vendor, which contains specific negotiation terms based on the market price.

[1327] The vendor responds to the negotiation email.

[1328] The server receives the vendor's response and renegotiates if necessary.

[1329] Step 5:

[1330] The server calculates the optimal price based on the finalized negotiation results.

[1331] The server records the determined price in a database.

[1332] The server calculates the difference between the original quote and the confirmed price.

[1333] The server displays the differences in a spreadsheet or report format.

[1334] Step 6:

[1335] The server prepares a template for the approval document.

[1336] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[1337] The server generates the request form in PDF format and sends it to the user.

[1338] Step 7:

[1339] The user receives the request sent from the server.

[1340] The user checks the contents of the approval request and logs in to the approval system.

[1341] The user clicks the confirmation button on the approval screen for the request form to approve it.

[1342] Example 1

[1343] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1344] In conventional purchasing operations, the entire process from receiving quotations to final approval is performed manually, which is time-consuming and prone to human error. Furthermore, because matching with market prices and price negotiations are also performed manually, efficiency is lacking. The present invention aims to solve these problems and improve the efficiency and accuracy of purchasing operations.

[1345] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1346] In this invention, the server includes means for receiving quotations from vendors and converting them into digital data, means for extracting necessary information from the digital data and registering it in a database, means for comparing the extracted information with market prices, means for automatically negotiating prices with vendors, means for calculating the optimal price and recording it in a database, means for automatically generating approval documents based on the contents of the quotation and the results of the negotiations, means for sending the approval documents to users, means for scanning received quotations with a scanner and generating image data, means for converting the image data into text data using OCR software, means for the user to review the received approval documents and give final approval, and means for generating approval documents in PDF format. This automates the entire purchasing process, saving time and reducing errors.

[1347] "Vendor" refers to a company or entity that provides products or services.

[1348] "Quote" means a document that sets out the prices and terms for specific products or services.

[1349] "Digital data" is a general term for any data format that can be stored or transmitted electronically.

[1350] "Extraction" refers to the process of extracting necessary parts from data or information.

[1351] A "database" is a system that stores data in an organized manner and enables efficient access and management.

[1352] "Market Price" means the average price at which a commodity is generally traded in a particular market.

[1353] "Automatic" means that an operation or process is performed by a machine or computer without human intervention.

[1354] "Optical character recognition (OCR) technology" is a technology that converts printed or handwritten characters into digital data.

[1355] "Negotiation" refers to dialogue and discussion between the parties to adjust the terms of a transaction.

[1356] "Optimal price" refers to the price that is deemed most appropriate taking into account quality and market conditions.

[1357] A "requisition document" is a document used to obtain formal approval within an organization.

[1358] "Receiving" refers to the operation of taking in data or messages from the outside.

[1359] A "scanner" refers to a device that converts information written on paper into electronic data.

[1360] "Image data" refers to data stored in an image format.

[1361] "Text data" refers to a data format based on character information.

[1362] "Recognition" means to officially approve or permit.

[1363] "PDF format" is an abbreviation for Portable Document Format, a file format for electronically storing and sharing documents while preserving their format.

[1364] "Purchasing operations" refers to a series of business processes for procuring necessary goods and services.

[1365] The unmanned purchasing system of this invention is composed of a user terminal, a server, and peripheral devices as needed. The user processes the quotations received from the vendor on the terminal, and the system then automatically proceeds with the purchasing process.

[1366] Hardware and software used

[1367] User device: Used to scan the estimate and convert it into text data. Examples include a personal computer and a scanner (e.g., Fujitsu ScanSnap).

[1368] OCR software: Used to convert scanned image data into text data. Examples of OCR software include Adobe Acrobat and Tesseract OCR.

[1369] Server: Analyzes text data, registers it in a database, collects and compares market prices, negotiates prices, and automatically generates and sends approval documents. For example, SAP Ariba can be used as a purchasing system.

[1370] Explanation of program processing

[1371] 1. Quote Receipt:

[1372] A user receives a quote from a vendor. For example, a quote for "electrical wiring work."

[1373] The terminal scans this estimate with a scanner and generates image data (e.g., Fujitsu ScanSnap).

[1374] The terminal uses OCR software (e.g. Adobe Acrobat) to convert the image data into text data and send it to the server.

[1375] 2. Quotation Registration:

[1376] The server analyzes the received text data and divides it into items. For example, it analyzes it as "Vendor name: certain company", "Item: electric wire", "Quantity: 100m", "Unit price: 500 yen", "Total: 50,000 yen".

[1377] The server registers the analysis results in the database of the purchasing system (e.g., SAP Ariba).

[1378] 3. Market Price Matching:

[1379] The server accesses national databases (e.g., the National Tax Agency database) and market price databases on the Internet (e.g., Kakaku.com) to collect market prices.

[1380] The server evaluates the appropriateness of the estimated price based on the market price collected. For example, it determines that the market price is 450 yen per meter.

[1381] 4. Price Negotiation:

[1382] The server automatically generates a negotiation email and sends it to the vendor, proposing, for example, "Could you please set the unit price at 450 yen based on the market price?"

[1383] The server receives the vendor's response and negotiates further if necessary. For example, if the response is "How about 470 yen?", the server continues negotiations.

[1384] 5. Price Calculation:

[1385] The server calculates the optimal price based on the confirmed price and stores it in the database. For example, it records it based on "Confirmed price: 47,000 yen."

[1386] The server calculates the difference from the original estimated price (50,000 yen) and displays it to the user.

[1387] 6. Preparation of approval documents:

[1388] The server automatically generates a request for approval based on the estimate and negotiation results. Specific examples include information such as "electrical wiring work," "agreed price: 47,000 yen," and "negotiation details."

[1389] The server generates the request form in PDF format and sends it to the user's device.

[1390] 7. Final approval:

[1391] The user reviews the submitted request and logs in to the approval system (e.g., SharePoint) to make the final approval.

[1392] Specific examples

[1393] For example, if you receive a quote from a vendor for electrical installation work, you can proceed as follows:

[1394] Example prompt: "Please tell me how the system automatically processes electrical installation work quotes received from vendors."

[1395] This system automates the entire purchasing process, enabling a fast and accurate purchasing process.

[1396] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1397] Step 1:

[1398] Quote received

[1399] Specific behavior:

[1400] Input: Quote received from vendor

[1401] Action: A user receives a quote from a vendor.

[1402] Action: The device scans this quote with a scanner and generates image data. For example, it uses a Fujitsu ScanSnap to create high-resolution image data.

[1403] Output: Generated image data (JPEG or PDF format)

[1404] Step 2:

[1405] OCR conversion of image data

[1406] Specific behavior:

[1407] Input: Image data generated by a scanner

[1408] Operation: The device uses OCR software (e.g., Adobe Acrobat) to convert image data into text data.

[1409] How it works: OCR software analyzes image data and converts it into a string of characters that can be recognized as text.

[1410] Output: Converted text data

[1411] Step 3:

[1412] Sending text data

[1413] Specific behavior:

[1414] Input: Text data after OCR conversion

[1415] Operation: The terminal sends the converted text data to the server.

[1416] Output: Text data sent to the server

[1417] Step 4:

[1418] Quotation registration

[1419] Specific behavior:

[1420] Input: Text data received by the server

[1421] How it works: The server parses the text data and splits it into multiple fields (e.g., vendor name, item, quantity, unit price, total) using a natural language processing algorithm (e.g., SpaCy or Google Cloud NLP API).

[1422] Operation: The server registers the analysis results in the database of the purchasing system (e.g., SAP Ariba).

[1423] Output: Quote information stored in the database

[1424] Step 5:

[1425] Market price comparison

[1426] Specific behavior:

[1427] Input: Quote information stored in the database

[1428] Operation: The server accesses national databases (e.g., the National Tax Agency database) and market price databases on the Internet (e.g., Kakaku.com) to collect market prices for the target item.

[1429] Operation: The server evaluates the appropriateness of the quoted price based on the market price collected. For example, the market price is 450 yen per meter, but the received quote is 500 yen per meter, so it is determined to be expensive.

[1430] Output: Comparison of market price and estimated price

[1431] Step 6:

[1432] Price negotiation

[1433] Specific behavior:

[1434] Input: Comparison result of market price and estimated price

[1435] How it works: The server automatically generates a negotiation email and sends it to the vendor. For example, it proposes, "Could you please set the unit price at 450 yen based on the market price?"

[1436] Operation: The server receives the vendor's response and negotiates further if necessary. For example, if the response is "How about 470 yen?", the server continues negotiations.

[1437] Output: Negotiation result data

[1438] Step 7:

[1439] Price calculation

[1440] Specific behavior:

[1441] Input: Negotiation result data

[1442] Operation: The server calculates the optimal price based on the confirmed price and saves it in the database. For example, it records it based on "Confirmed price: 47,000 yen."

[1443] Operation: Calculate the difference from the original estimated price (50,000 yen) and show it to the user. For example, notify the user that a cost reduction of 3,000 yen has been achieved.

[1444] Output: Optimal price calculation result

[1445] Step 8:

[1446] Creating approval documents

[1447] Specific behavior:

[1448] Input: Optimal price calculation results and negotiation history

[1449] Operation: The server automatically generates a request for approval based on the estimate and negotiation results. Specific examples include information such as "electrical wiring work," "agreed price: 47,000 yen," and "negotiation details."

[1450] Operation: The server generates a request form in PDF format and sends it to the user's device.

[1451] Output: Generated PDF of the approval document

[1452] Step 9:

[1453] Final approval

[1454] Specific behavior:

[1455] Input: Generated PDF format request form

[1456] What it does: A user reviews the submitted request and logs into an approval system (e.g., SharePoint) to make the final approval.

[1457] How it works: After logging in, check the contents of the request form, add comments if necessary, and finally press the approval button.

[1458] Output: Approved request

[1459] This system automates the entire purchasing process, enabling a fast and accurate purchasing process.

[1460] (Application example 1)

[1461] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1462] Current purchasing operations at logistics centers rely on many manual processes and time-consuming procedures, resulting in inefficiencies and a high risk of human error. There is a particular need for technology that can efficiently automate the entire process, from receiving quotes to matching market prices, negotiating prices, and creating and sending approval documents. This is expected to reduce labor costs, increase work speed, and improve accuracy.

[1463] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1464] In this invention, the server includes means for scanning estimates using a smartphone camera, means for converting image data into text data using optical character recognition technology, means for scraping price databases on the Internet to collect market prices, means for sending negotiation emails to vendors using automatic email generation technology, and means for notifying users using a push notification function, thereby enabling the automation and efficiency of the entire purchasing process.

[1465] "Quote" means a document that describes the price and terms of products or services provided by a vendor.

[1466] "Digital data" is data that has been converted into a format that can be processed by electronic devices such as computers.

[1467] A "database" is a system or software that systematically organizes information and allows it to be managed and searched centrally.

[1468] "Market price" is the price at which a particular product or service is generally traded in the market.

[1469] Optical character recognition (OCR) is a technology that recognizes characters from image data and converts them into text data.

[1470] "Scraping" is a technique for automatically extracting specific information from web pages.

[1471] "Push notification" is a technology that sends information directly from a server to a client device.

[1472] "Automatic email generation" is a technology in which a system automatically creates emails based on predefined conditions and formats.

[1473] A "rei-sho" is a document used to obtain the approval necessary to make decisions within an organization.

[1474] This invention relates to a system that fully automates purchasing operations at a logistics center. This system receives quotations from vendors, converts them into digital data, extracts necessary information, and registers it in a database. It then compares the extracted information with market prices and automatically negotiates prices with the vendor. It also calculates the optimal price, records it in a database, and automatically generates a request for approval based on the quotation and negotiation results and sends it to the user.

[1475] First, the user scans the quote using the camera on their smartphone. The scanned image data is converted into text data using optical character recognition (OCR) technology such as Google Cloud Vision API. This converted data is uploaded to AWS S3 and transferred to a database using AWS Lambda.

[1476] The server then analyzes the received data and extracts the necessary information (e.g., item, quantity, unit price, total, etc.). This information is then registered in a relational database such as AWS RDS.

[1477] The server then scrapes the internet price database to collect market quotes, using AWS Lambda or other scraping tools. Based on the collected market quotes, the server evaluates the fairness of the quote.

[1478] If the price is not reasonable, the server will use automatic email generation technology to send a negotiation email to the vendor. This process uses SES to send emails. The reply from the vendor is also automatically received and the analysis and negotiation process will continue again.

[1479] Finally, the optimal price is calculated based on the finalized price and recorded in a database. The server automatically generates a request form based on this information and sends it in PDF format to the user's smartphone via push notification. Notification services such as social media are used for the push notification. The user checks the request form on their smartphone and digitally signs it using the DocuSign API or similar.

[1480] Specific examples

[1481] For example, when a user receives a quote for electrical equipment construction from a vendor, they scan the quote using their smartphone camera. This image data is converted into text data using Google Cloud Vision API and registered in a database via AWS Lambda. The server scrapes and collects market prices from the Internet and evaluates the fair price. After the evaluation, the server automatically sends a negotiation email to the vendor using SES, and the optimal price is confirmed. A request for approval is then automatically generated and sent to the user via push notification. The user then checks the request on their smartphone and electronically signs it using the DocuSign API.

[1482] Example prompts for generative AI models

[1483] "Write a program to convert the following text using OCR and register it in a purchasing system:

[1484] Quote image path:path / to / image.jpg

[1485] Database to register: AWS RDS

[1486] APIs and libraries used: Google Cloud Vision API, boto3, pdfkit

[1487] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1488] Step 1:

[1489] The user scans an estimate using the smartphone camera. The input is an estimate received from a vendor, which is captured as image data using the smartphone camera. The output is the image data of the scanned estimate. Specifically, the user launches the camera app and takes a picture of the estimate.

[1490] Step 2:

[1491] The device performs optical character recognition (OCR) on the scanned image data using the Google Cloud Vision API. The input is the image data acquired in step 1, and the output is text data converted by OCR. Specifically, the image data is sent to the Google Cloud Vision API and the text data is received as the result of the OCR processing.

[1492] Step 3:

[1493] The text data acquired by the device using OCR is uploaded to AWS S3. The input is the text data from step 2, and the output is the text data stored on AWS S3. Specifically, the process is performed to upload the text file to an Amazon S3 bucket.

[1494] Step 4:

[1495] The server uses AWS Lambda to retrieve text data from S3, analyze the information, and extract the required items. The input is the text data retrieved from AWS S3, and the output is the extracted information (e.g., item, quantity, unit price, total, etc.). Specifically, the script analyzes the text data and executes to extract the required information for each item.

[1496] Step 5:

[1497] The server registers the extracted information in AWS RDS. The input is the information extracted in step 4, and the output is the information registered in AWS RDS. Specifically, it generates an SQL statement to save the extracted information and inserts the data into AWS RDS.

[1498] Step 6:

[1499] The server scrapes price databases on the Internet to collect market price information. The input is a list of URLs for the price databases, and the output is the collected market price information. Specifically, a web scraping tool is used to access the price database and obtain market price information.

[1500] Step 7:

[1501] The server compares the market price collected with the registered quotation information to perform price evaluation. The input is the quotation information from step 5 and the market price information from step 6, and the output is the evaluation result regarding the appropriateness of the price. Specifically, it compares the estimated price with the market price and executes an algorithm to evaluate whether the estimated price is appropriate.

[1502] Step 8:

[1503] The server uses automatic email generation technology to send a negotiation email to the vendor. The input is the price evaluation result and vendor information, and the output is the sent negotiation email. Specifically, it uses SES to generate a negotiation email and sends it to the vendor's email address.

[1504] Step 9:

[1505] The server receives the reply from the vendor and re-analyzes and evaluates the negotiation results. The input is the reply email from the vendor, and the output is the updated negotiation results. Specifically, the server re-analyzes the contents of the received email and updates the negotiation results.

[1506] Step 10:

[1507] The server calculates the optimal price based on the confirmed price and records it in AWS RDS. The input is the updated negotiation result, and the output is the optimal price recorded in AWS RDS. Specifically, the optimal price is calculated and the information is saved in the database.

[1508] Step 11:

[1509] The server automatically generates a request for approval based on the quotation details and negotiation results, and generates it in PDF format. The input is the confirmed price information and negotiation results, and the output is a request for approval in PDF format. Specifically, the script that generates the request for approval is executed using PDFKit or similar.

[1510] Step 12:

[1511] The server uses the push notification function to send a request form to the user. The input is a PDF version of the request form, and the output is the notification sent to the user. Specifically, the server sends a push notification to the user's smartphone using a notification service such as SNS.

[1512] Step 13:

[1513] The user checks the approval document on their smartphone and digitally signs it using the DocuSign API. The input is a PDF version of the approval document, and the output is the signed version. Specifically, the process involves opening the approval document on the smartphone and digitally signing the required fields.

[1514] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1515] The unmanned purchasing system of this invention receives quotations from vendors and automates the purchasing process based on them. This system covers a series of processes, including quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval, and also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's behavior and interface accordingly.

[1516] The main components of this system are a user terminal, a server, an emotion engine, and peripheral devices as needed. The user terminal is used to input estimates, check approval documents, and recognize emotions, while the server is responsible for processing and storing various data. The emotion engine recognizes the user's emotions in real time and makes adjustments accordingly.

[1517] Program processing details

[1518] 1. Receipt of quotation

[1519] A user receives a quote from a vendor.

[1520] The terminal scans the estimate with a scanner and generates image data.

[1521] The device uses OCR software to convert the image data into text data.

[1522] The terminal transmits the text data to the server.

[1523] 2. Estimate registration

[1524] The server analyzes the received text data.

[1525] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[1526] The server registers the extracted information in the purchasing system database.

[1527] 3. Market price comparison

[1528] The server accesses national databases and internet market price databases to collect market prices for the target items.

[1529] The server evaluates the fairness of the received quote based on the market price collected by the server.

[1530] 4. Price Negotiation

[1531] The server generates a template for a price negotiation email.

[1532] The server automatically sends a negotiation email to the vendor, which includes specific negotiation details based on the market price.

[1533] After the server receives the vendor's response, it renegotiates if necessary.

[1534] The text of negotiation emails is automatically adjusted based on the user's emotions recognized by the emotion engine.

[1535] 5. Price Calculation

[1536] The server calculates the optimal price based on the finalized negotiation results.

[1537] The server records the determined price in a database.

[1538] The server calculates the difference between the original quoted price and the final price and displays it in a spreadsheet or report format.

[1539] 6. Creating approval documents

[1540] The server prepares a template for the approval document.

[1541] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[1542] The server generates the request form in PDF format and sends it to the user.

[1543] 7. Final approval

[1544] The user receives the request sent from the server.

[1545] The emotion engine recognizes the user's emotions while reviewing the request and encourages renegotiation or reconfirmation if the user expresses dissatisfaction.

[1546] The user logs in to the approval system and approves the contents of the request form.

[1547] Specific examples

[1548] For example, when an estimate for electrical equipment construction is received from a vendor, the process proceeds as follows:

[1549] 1. Receipt of quotation

[1550] The user receives a quote for "electrical wiring work" from a vendor.

[1551] The terminal scans the estimate and creates image data.

[1552] The device uses OCR technology to convert the image data into text and send it to the server.

[1553] 2. Estimate registration

[1554] The server analyzes the received text data and extracts information such as "Vendor name: ABC Company," "Item: Electric wire," "Quantity: 100m," "Unit price: 500 yen," and "Total: 50,000 yen."

[1555] The server registers this information in a database.

[1556] 3. Market price comparison

[1557] The server collects market price data from national databases and the Internet to check the market price for "electrical wiring work."

[1558] The server compares the market price with the quoted price and evaluates whether the quoted price is reasonable.

[1559] 4. Price Negotiation

[1560] The server generates a negotiation email saying, "Could you please set the unit price at 450 yen based on the market price?" and sends it to the vendor.

[1561] The server receives the vendor's response, "How about 470 yen?" and renegotiates if necessary.

[1562] The emotion engine recognizes the user's emotions and adjusts the content of the negotiation email.

[1563] 5. Price Calculation

[1564] The server determines the price based on the negotiation result of "470 yen, total 47,000 yen" and records it in the database.

[1565] The server calculates and displays the difference (3,000 yen) from the original quoted price (50,000 yen).

[1566] 6. Creating approval documents

[1567] The server creates a request form based on the estimate and negotiation results, and includes information such as "electrical wiring work," "agreed price: 47,000 yen," and "price negotiation details."

[1568] The server generates a request form and sends it to the user's device in PDF format.

[1569] 7. Final approval

[1570] The user receives the PDF request form and checks its contents.

[1571] The emotion engine recognizes user emotions in real time and prompts renegotiation or reaffirmation when dissatisfaction is expressed.

[1572] The user logs into the approval system and gives final approval.

[1573] In this way, the system is able to respond flexibly while taking into consideration the user's emotions, which not only makes purchasing operations more efficient but also improves the user experience.

[1574] The processing flow will be explained below.

[1575] Step 1:

[1576] A user receives a quote from a vendor.

[1577] The terminal scans the estimate with a scanner and generates image data.

[1578] The device uses OCR software to convert the image data into text data.

[1579] The terminal transmits the text data to the server.

[1580] Step 2:

[1581] The server analyzes the received text data.

[1582] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[1583] The server registers the extracted information in the purchasing system database.

[1584] Step 3:

[1585] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[1586] The server evaluates the fairness of the received quote based on the market price collected by the server.

[1587] Step 4:

[1588] The server generates a template for a price negotiation email.

[1589] The server automatically sends a negotiation email to the vendor, which contains specific negotiation terms based on the market price.

[1590] The server receives the response from the vendor.

[1591] The server uses an emotion engine to automatically adjust the text of the negotiation email according to the emotion of the user.

[1592] The server renegotiates with the vendor as needed.

[1593] Step 5:

[1594] The server calculates the optimal price based on the finalized negotiation results.

[1595] The server records the determined price in a database.

[1596] The server calculates the difference between the original quote and the confirmed price.

[1597] The server displays the differences in a spreadsheet or report format.

[1598] Step 6:

[1599] The server prepares a template for the approval document.

[1600] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[1601] The server generates the request form in PDF format and sends it to the user.

[1602] Step 7:

[1603] The user receives the request sent from the server.

[1604] The user checks the contents of the request form.

[1605] The server uses an emotion engine to prompt renegotiation or reconfirmation when the user expresses dissatisfaction.

[1606] The user logs in to the approval system and approves the contents of the request form.

[1607] Example 2

[1608] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1609] In traditional purchasing operations, processes such as receiving quotations from vendors, extracting and registering information, comparing prices with market rates, negotiating prices with vendors, and creating and approving approval documents are often fragmented, resulting in problems of time and effort. Furthermore, systems that do not consider user emotions can impair the user experience and prevent optimal purchasing decisions. This reduces the efficiency of the entire purchasing process and risks making purchases at inappropriate prices.

[1610] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1611] In this invention, the server includes means for receiving quotations from vendors and converting them into digital data, means for extracting necessary information from the digital data and registering it in a database, means for comparing the extracted information with market prices, means for automatically negotiating prices with vendors, means for calculating an optimal price and recording it in a database, means for automatically generating a request for purchase based on the contents of the quotation and the results of the negotiation, means for sending the request for purchase to the user, and means for adjusting the contents of the negotiation and the request for purchase based on the results of user emotion recognition. This makes it possible to improve the efficiency of purchasing operations and realize optimal decisions that take user emotions into consideration.

[1612] A "quote" is a document that describes the price, quantity, and terms of the goods provided by a vendor.

[1613] "Digital data" is data that has been converted from analog information into a numerical value and into a format that can be processed by a computer or the like.

[1614] Optical character recognition (OCR) technology is a technology that automatically identifies characters from images captured by a scanner or camera and converts them into digital text.

[1615] A "database" is a collection of information that is systematically organized so that it can be efficiently accessed, managed, and updated.

[1616] A "market price" is the general price at which a particular product or service is traded in the market.

[1617] "Price negotiation" is the process by which a buyer and seller decide on a mutually agreed-upon price in a transaction.

[1618] A "requisition document" is a document created to obtain approval within an organization, and is usually used to seek approval or opinions from superiors or related departments.

[1619] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, movements, etc. to determine their emotional state.

[1620] "Vendor" means a supplier or provider of goods or services.

[1621] A "server" is a computer system that provides services to other computers (clients) on a network.

[1622] A "user" is someone who uses the system to perform various tasks and operations.

[1623] The unmanned purchasing system of this invention receives quotations from vendors and automates the purchasing process based on them. This system covers a series of processes, including quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval, and also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's behavior and interface accordingly.

[1624] The main components of the system are a user terminal, a server, an emotion engine, and, if necessary, peripheral devices.

[1625] The user device performs operations such as inputting and scanning estimates, checking approval documents, and emotion recognition. Specific hardware options include scanners and high-performance cameras, such as the Canon DR-C240 scanner. Adobe Acrobat or other well-known OCR software is recommended for OCR technology.

[1626] The server is responsible for processing and storing various types of data. A relational database management system such as PostgreSQL is suitable as the database. General-purpose programming languages ​​such as Python are used for processes within the server, and data analysis, information extraction using regular expressions, and market price collection via APIs are performed. Furthermore, Python's smtplib and the Jinja2 template engine are used for automatically generating and sending emails.

[1627] The emotion engine uses advanced emotion recognition algorithms to analyze the user's facial expressions and voice, and automatically adjusts the system's behavior based on the results. Facial recognition technology can be used with libraries such as OpenCV and Dlib, enabling real-time emotion analysis.

[1628] Specifically, for example, when an estimate for electrical equipment construction is received from a vendor, the processing of this system proceeds as follows.

[1629] The user receives an estimate for "electrical wiring work" and scans it using the scanner or camera on the device.

[1630] The scanned image data is converted into text using OCR technology and sent to the server.

[1631] The server extracts the necessary information from the received text data and records it in a database.

[1632] The server collects market quotes from an internet database and evaluates the quotes.

[1633] The server automatically sends a negotiation email to the vendor and calculates the optimal price based on the results.

[1634] The server creates a request form based on the quotation details and negotiation results and sends it to the user in PDF format.

[1635] The user reviews the request, and the emotion engine prompts renegotiation if necessary. Finally, the user logs in to the system and approves the request.

[1636] An example of a prompt that can be used to adjust system behavior and message content using generative AI models is as follows:

[1637] "Please explain in natural language each process of the automated purchasing system, from receiving a quote to final approval. Please include specific hardware and software, as well as data processing and calculation methods. Also, please explain the function that recognizes user emotions and adjusts the system accordingly."

[1638] A system designed in this way can greatly contribute to improving the efficiency of purchasing operations and user satisfaction.

[1639] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1640] Step 1:

[1641] Quote received

[1642] A user receives a quote from a vendor.

[1643] Input: Quote provided by vendor (physical document or PDF).

[1644] Output: Physical copy or PDF file.

[1645] Specific action: The user receives the quote and prepares it for digitization using a scanner or camera.

[1646] The terminal uses a scanner to scan the estimate and generate image data.

[1647] Input: Physical quote.

[1648] Output: Scanned image data.

[1649] What it does: Scan the quote at high resolution using a Canon DR-C240 scanner.

[1650] The device uses OCR (Optical Character Recognition) technology to convert image data into text data.

[1651] Input: scanned image data.

[1652] Output: Text data.

[1653] Specific operation: Executes Adobe Acrobat's OCR function and extracts text information such as "100m of electric wire, unit price 500 yen" from the image data.

[1654] The terminal transmits the text data to the server.

[1655] Input: Text data generated by OCR.

[1656] Output: The text data sent to the server.

[1657] What it does: Issues a curl command to send text data to the server's API endpoint using HTTPS.

[1658] Step 2:

[1659] Quotation registration

[1660] The server analyzes the received text data.

[1661] Input: Text data sent from the terminal.

[1662] Output: A data object containing the analysis results.

[1663] Specific operation: Analyzes text data using Python's NLTK library and generates tokenized sentences.

[1664] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[1665] Input: A parsed data object.

[1666] Output: Data for each extracted item.

[1667] Specific operation: Using regular expression pattern matching, extract information such as "Vendor name: Vendor", "Item: Electric wire", "Quantity: 100m", "Unit price: 500 yen", and "Total: 50,000 yen".

[1668] The server registers the extracted information in the purchasing system database.

[1669] Input: Data for each extracted item.

[1670] Output: Information stored in a database.

[1671] Specific behavior: Connect to a PostgreSQL database and execute the INSERT INTO quote_table(...) VALUES(...) query.

[1672] Step 3:

[1673] Market price comparison

[1674] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[1675] Input: URL or API key of the market quote database from which the data is collected.

[1676] Output: Collected market quote price data.

[1677] Specific operation: Accesses the API of the Ministry of Economy, Trade and Industry, etc., and obtains current market price information for power cables.

[1678] The server evaluates the fairness of the received quote based on the market price collected by the server.

[1679] Input: Collected market quote data, quoted price data.

[1680] Output: Evaluation results on the fairness of the estimated price.

[1681] What it does: Runs an algorithm to verify that the quote is within ±10% of the market price.

[1682] Step 4:

[1683] Price negotiation

[1684] The server generates a template for a price negotiation email.

[1685] Input: Template data for negotiation, quote and market price data.

[1686] Output: The generated email template.

[1687] What it does: Uses the Jinja2 template engine to generate an email that says, "Would you mind adjusting the price based on market value?"

[1688] The server automatically sends a negotiation email to the vendor.

[1689] Input: The generated email template.

[1690] Output: Negotiation email sent to vendor.

[1691] Specific behavior: Uses the smtplib library to send the generated email to the vendor's email address.

[1692] After the server receives the vendor's response, it renegotiates if necessary.

[1693] Input: Response email from vendor.

[1694] Output: Renegotiation result.

[1695] Specific operation: Analyzes received emails and generates and sends renegotiation emails if necessary.

[1696] The text of negotiation emails is automatically adjusted based on the user's emotions recognized by the emotion engine.

[1697] Input: User emotion data, negotiation email template.

[1698] Output: Tailored negotiation email.

[1699] What it does: It uses facial recognition technology to analyze a user's emotions and adapts the tone and content of emails accordingly.

[1700] Step 5:

[1701] Price calculation

[1702] The server calculates the optimal price based on the finalized negotiation results.

[1703] Input: Negotiated price data.

[1704] Output: Optimal price.

[1705] Specific operation: Recalculate the total price based on the negotiated result of "470 yen".

[1706] The server records the determined price in a database.

[1707] Input: Best price data.

[1708] Output: The final price recorded in the database.

[1709] What it does: Runs an SQL query to insert the best price into a PostgreSQL database.

[1710] The server calculates the difference between the original quoted price and the final price and displays it in a spreadsheet or report format.

[1711] Input: Original Quoted Price Data, Final Price Data.

[1712] Output: Diff report.

[1713] Specific operation: Using Python's pandas and xlsxwriter, create a difference report in Excel format and provide it to the user.

[1714] Step 6:

[1715] Creating approval documents

[1716] The server prepares a template for the approval document.

[1717] Input: Request form template data.

[1718] Output: Prepared decision template.

[1719] Specific operation: Load an existing approval document template and pass it to the template engine.

[1720] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[1721] Input: Quotation data, negotiation data, final price data.

[1722] Output: Auto-filled approval request data.

[1723] Specific operation: Embed the necessary information into the approval form template using the python-docx library.

[1724] The server generates the request form in PDF format and sends it to the user.

[1725] Input: Automatically entered request data.

[1726] Output: A PDF version of the approval document.

[1727] Specific operation: Use reportlab to convert the request form into PDF format and send it to the user's device.

[1728] Step 7:

[1729] Final approval

[1730] The user receives the request sent from the server.

[1731] Input: PDF request form from the server.

[1732] Output: The received decision.

[1733] Specific operation: A user downloads a PDF request form via email or a portal site.

[1734] The emotion engine recognizes the user's emotions while reviewing the request and encourages renegotiation or reconfirmation if the user expresses dissatisfaction.

[1735] Input: User emotion data.

[1736] Output: Proposal for renegotiation or reaffirmation.

[1737] What it does: It uses facial recognition technology to analyze users' facial expressions in real time and displays an alert if dissatisfaction is detected.

[1738] The user logs in to the approval system and approves the contents of the request form.

[1739] Input: PDF request form, login information.

[1740] Output: Approval result.

[1741] Specific operation: A user logs in to the system through a web portal, checks the contents of the request form, and clicks the approval button.

[1742] By using this system, it is possible to streamline and optimize purchasing operations, while also expecting an improved user experience.

[1743] (Application example 2)

[1744] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1745] In current purchasing operations, the entire process from receiving quotations to final approval of the request form is often done manually, resulting in inefficiency. It is also difficult to respond flexibly and consider user feelings, which can lead to user dissatisfaction, especially during price negotiations and the request form approval process. This poses a challenge to the overall efficiency of operations and the user experience.

[1746] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1747] In this invention, the server includes a means for receiving quotations from vendors and converting them into digital data, a means for extracting necessary information from the digital data and registering it in a database, and an emotion recognition means for recognizing a user's emotion and adjusting the user's behavior. This makes it possible to automate the entire purchasing process while recognizing the user's emotion in real time and taking appropriate action.

[1748] "Vendor" refers to a company that provides products or services.

[1749] A "quotation" is a document that describes the price, quantity, terms, etc. of a product or service.

[1750] "Digital data" means data represented in electronic form.

[1751] "OCR technology" is an abbreviation for optical character recognition technology, a technology that extracts text information from images.

[1752] "Market price" refers to the price in the general market for a particular product or service.

[1753] A "database" is a system that is organized to efficiently manage large amounts of data and enable searching and updating.

[1754] "Emotion recognition means" refers to technology or devices for analyzing and recognizing a user's emotions in real time.

[1755] A "reisho" is a formal document created to obtain approval or settlement within an organization or company.

[1756] "Price negotiation" is the process by which a buyer and seller negotiate and agree on the price of a product or service.

[1757] "Final approval" refers to the final stage of the approval process, where all parties agree and formally approve the proposal.

[1758] This invention provides a system for automating purchasing processes and recognizing and flexibly responding to user emotions. This system is primarily composed of a server, a terminal, emotion recognition means, and a database. Specific embodiments of this system are described below.

[1759] System Components

[1760] 1. Server

[1761] The server has the following functions:

[1762] Receive and analyze digital estimate data.

[1763] Using OCR technology, necessary information is extracted from digital data and registered in a database.

[1764] The registered information is compared with market prices, and market prices are collected to assess the fairness of the prices.

[1765] Automatically generate price negotiation emails and negotiate prices with vendors.

[1766] A request form template is automatically generated based on the confirmed price and sent to the user in PDF format.

[1767] Recognize user sentiment and encourage renegotiation or reaffirmation when dissatisfaction is expressed.

[1768] 2. Terminal

[1769] The device can be a smartphone or a PC with certain functions:

[1770] Provides a function to scan estimates and generate scanned image data.

[1771] The generated image data is converted into text data using OCR technology and sent to the server.

[1772] The user takes an image for emotion recognition and sends it to the server.

[1773] 3. Emotion recognition means

[1774] Emotion recognition is a technology for recognizing user emotions in real time, using a machine learning model (e.g., using TensorFlow) that analyzes the user's facial expressions from images captured by a camera.

[1775] 4. Database

[1776] The database is used to store information such as quotation information, negotiation results, market prices, and user emotional data.

[1777] Hardware and Software

[1778] Hardware: Scanner, smartphone or PC with camera, server.

[1779] Software: OCR software (e.g., Pytesseract), image processing libraries (e.g., OpenCV), machine learning models (e.g., TensorFlow), internet request libraries (e.g., Requests).

[1780] Example

[1781] For example, if a user receives an estimate for "electrical equipment construction," the process proceeds as follows:

[1782] 1. Scan the quote

[1783] The user takes a photo of the estimate using the smartphone camera and generates image data.

[1784] The device converts this image data into text using OCR technology and sends it to the server.

[1785] 2. Data analysis and registration

[1786] The server analyzes the received text data, extracts information such as "vendor name," "item," "quantity," "unit price," and "total," and registers it in a database.

[1787] 3. Comparison with market prices

[1788] The server collects market prices from a database on the Internet and compares them with the quoted prices to assess their suitability.

[1789] 4. Price Negotiation

[1790] The server automatically generates a negotiation email based on market prices and sends it to the vendor.

[1791] If the user expresses dissatisfaction, the server attempts to renegotiate.

[1792] 5. Preparation of approval documents

[1793] The server automatically generates a request form based on the negotiation results and sends it to the user in PDF format.

[1794] 6. Final Approval

[1795] The user reviews the submitted PDF request form and gives final approval.

[1796] Prompt Sentence Examples

[1797] As an example, the following prompt sentence is input into the generative AI model:

[1798] Scan the quote for "electrical wiring work" and convert it to text. Then generate and send a price negotiation email based on the market price. Use the camera to recognize the user's emotions and suggest renegotiation if there is dissatisfaction.

[1799] In this way, a system is realized that can improve the efficiency of purchasing operations and respond flexibly based on the user's emotions.

[1800] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1801] Step 1:

[1802] A user receives a quote from a vendor.

[1803] Input: Paper quote from vendor

[1804] How it works: The user takes a photo of a quote using their smartphone camera.

[1805] Output: The image data of the quotation is saved on the smartphone.

[1806] Step 2:

[1807] The terminal scans the estimate and converts it into text data using OCR technology.

[1808] Input: Image data of quotation

[1809] How it works: The device uses OpenCV to grayscale the image data and Pytesseract to perform OCR.

[1810] Output: Text data of the quotation

[1811] Step 3:

[1812] The terminal transmits the text data to the server.

[1813] Input: Text data of quotation

[1814] How it works: The device uses the Internet Requests library (Requests) to send text data to the server.

[1815] Output: Text data is saved on the server.

[1816] Step 4:

[1817] The server analyzes the received text data, extracts the necessary information, and registers it in a database.

[1818] Input: Text data of quotation

[1819] How it works: The server uses machine learning algorithms to analyze the text data and extract information such as "vendor name," "item," "quantity," "unit price," and "total." The extracted information is then registered in a database.

[1820] Output: Quote information stored in the database

[1821] Step 5:

[1822] The server collects market quotes and compares them with quote information.

[1823] Input: Quote information stored in the database

[1824] Operation: The server accesses a market price database on the Internet to collect the market price of the target item, and evaluates the fairness of the quoted price and the market price.

[1825] Output: Validity evaluation result

[1826] Step 6:

[1827] The server generates a price negotiation email template and automatically sends it to the vendor.

[1828] Input: Fairness assessment result, market price

[1829] How it works: The server generates a negotiation email based on a template and automatically sends it to the vendor.

[1830] Output: Price negotiation email sent

[1831] Step 7:

[1832] The server receives the vendor's response and renegotiates if necessary.

[1833] Input: Response email from vendor

[1834] Operation: The server analyzes the received response and, if renegotiation is necessary, generates and sends another negotiation email.

[1835] Output: Updated negotiation result

[1836] Step 8:

[1837] The server automatically generates a request form based on the finalized negotiation results and sends it to the user in PDF format.

[1838] Input: Updated negotiation results

[1839] Operation: The server automatically generates a request form template based on the negotiation results and sends it to the user in PDF format.

[1840] Output: PDF of the approval request sent to the user's device

[1841] Step 9:

[1842] The user reviews the request and gives final approval.

[1843] Input: PDF of the approval request

[1844] How it works: The user reviews the PDF request and gives final approval. The server records the approval process and renegotiates or reconfirms it if necessary.

[1845] Output: Approved request

[1846] Step 10:

[1847] An emotion recognition means recognizes the user's emotions in real time and adjusts the behavior.

[1848] Input: User facial expression image

[1849] How it works: The device's camera captures the user's facial expressions, and the TensorFlow model is used to recognize emotions. The server then adjusts its response based on the emotional data.

[1850] Output: Emotion recognition results and the system's response based on them

[1851] In this way, the entire purchasing process becomes more efficient and flexible responses based on the user's emotions become possible.

[1852] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1853] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1854] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1855] [Fourth embodiment]

[1856] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1857] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1858] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1859] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1860] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1861] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1862] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1863] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1864] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1865] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1866] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1867] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1868] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1869] The unmanned purchasing system of the present invention receives quotations from vendors and then automates a series of purchasing processes. This system can continuously execute each step of the process: quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval.

[1870] The main components of this system are the user terminal, a server, and peripheral devices as needed. The user processes the quotation received from the vendor on the terminal, and the system then proceeds automatically.

[1871] Program processing details

[1872] 1. Receipt of quotation

[1873] A user receives a quote from a vendor.

[1874] The terminal scans the estimate with a scanner and generates image data.

[1875] The device uses OCR software to convert the image data into text data.

[1876] The terminal transmits the text data to the server.

[1877] 2. Estimate registration

[1878] The server analyzes the received text data and divides it into items.

[1879] The server organizes the information extracted, such as "vendor name," "item," "quantity," "unit price," and "total."

[1880] The server registers the extracted information in the purchasing system database.

[1881] 3. Market price comparison

[1882] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[1883] The server evaluates the fairness of the received quote based on the market price collected by the server.

[1884] 4. Price Negotiation

[1885] The server automatically generates and sends a negotiation email to the vendor.

[1886] If the server receives a response from the vendor and adjusts the price again, it will conduct any necessary additional negotiations.

[1887] If the negotiations are deemed appropriate, the negotiations will be terminated.

[1888] 5. Price Calculation

[1889] The server calculates the optimal price based on the confirmed price and stores it in the database.

[1890] The server compares the original quoted price with the confirmed price and selects the best price.

[1891] 6. Creating approval documents

[1892] The server automatically generates a request form based on the estimate, negotiation details, and optimal price.

[1893] The server generates a request form and sends it to the user's device in PDF format.

[1894] 7. Final approval

[1895] The user checks the contents of the received request.

[1896] The user logs in to the approval system, checks the final approval request, and approves it.

[1897] Specific examples

[1898] For example, when an estimate for electrical equipment construction is received from a vendor, it is processed as follows:

[1899] 1. Receipt of quotation

[1900] The user receives a quote for "electrical wiring work" from a vendor.

[1901] The terminal scans the estimate and creates image data.

[1902] The device uses OCR technology to convert the image data into text and send it to the server.

[1903] 2. Estimate registration

[1904] The server analyzes the received text data and extracts information such as "Vendor name: ABC Company," "Item: Electric wire," "Quantity: 100m," "Unit price: 500 yen," and "Total: 50,000 yen."

[1905] The server registers the extracted information in a database.

[1906] 3. Market price comparison

[1907] The server collects the market price for "electrical wiring work" from a national database and market price data on the Internet, and determines that "the market price is 450 yen per meter."

[1908] The server evaluates the appropriateness of the estimated price (500 yen per meter) it received based on the market price and determines that it is too expensive.

[1909] 4. Price Negotiation

[1910] The server sends a negotiation email to the vendor saying, "Could you please set the unit price at 450 yen based on the market price?"

[1911] The server receives the vendor's reply, "How about 470 yen?" and continues negotiations if necessary.

[1912] 5. Price Calculation

[1913] The server records the information that was agreed upon as a result of negotiations with the vendor: "unit price 470 yen, total 47,000 yen."

[1914] The server calculates the difference (3,000 yen) from the original estimated price (50,000 yen) and displays this to the user.

[1915] 6. Creating approval documents

[1916] The server creates a request form based on the estimate and negotiation results, and includes information such as "electrical wiring work," "agreed price: 47,000 yen," and "price negotiation details."

[1917] The server generates the request form in PDF format and sends it to the user's device.

[1918] 7. Final approval

[1919] The user checks the submitted request and logs into the approval system.

[1920] The user approves the contents of the request and makes the final decision.

[1921] The above is a specific example of the program processing of the unmanned purchasing system based on the present invention. This system will improve the efficiency and speed of purchasing operations and reduce errors.

[1922] The processing flow will be explained below.

[1923] Step 1:

[1924] A user receives a quote from a vendor.

[1925] The terminal scans the estimate with a scanner and generates image data.

[1926] The device uses OCR software to convert the image data into text data.

[1927] The terminal transmits the text data to the server.

[1928] Step 2:

[1929] The server analyzes the received text data.

[1930] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[1931] The server registers the extracted information in the purchasing system database.

[1932] Step 3:

[1933] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[1934] The server evaluates the fairness of the received quote based on the market price collected by the server.

[1935] Step 4:

[1936] The server generates a template for a price negotiation email.

[1937] The server automatically sends a negotiation email to the vendor, which contains specific negotiation terms based on the market price.

[1938] The vendor responds to the negotiation email.

[1939] The server receives the vendor's response and renegotiates if necessary.

[1940] Step 5:

[1941] The server calculates the optimal price based on the finalized negotiation results.

[1942] The server records the determined price in a database.

[1943] The server calculates the difference between the original quote and the confirmed price.

[1944] The server displays the differences in a spreadsheet or report format.

[1945] Step 6:

[1946] The server prepares a template for the approval document.

[1947] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[1948] The server generates the request form in PDF format and sends it to the user.

[1949] Step 7:

[1950] The user receives the request sent from the server.

[1951] The user checks the contents of the approval request and logs in to the approval system.

[1952] The user clicks the confirmation button on the approval screen for the request form to approve it.

[1953] Example 1

[1954] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1955] In conventional purchasing operations, the entire process from receiving quotations to final approval is performed manually, which is time-consuming and prone to human error. Furthermore, because matching with market prices and price negotiations are also performed manually, efficiency is lacking. The present invention aims to solve these problems and improve the efficiency and accuracy of purchasing operations.

[1956] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1957] In this invention, the server includes means for receiving quotations from vendors and converting them into digital data, means for extracting necessary information from the digital data and registering it in a database, means for comparing the extracted information with market prices, means for automatically negotiating prices with vendors, means for calculating the optimal price and recording it in a database, means for automatically generating approval documents based on the contents of the quotation and the results of the negotiations, means for sending the approval documents to users, means for scanning received quotations with a scanner and generating image data, means for converting the image data into text data using OCR software, means for the user to review the received approval documents and give final approval, and means for generating approval documents in PDF format. This automates the entire purchasing process, saving time and reducing errors.

[1958] "Vendor" refers to a company or entity that provides products or services.

[1959] "Quote" means a document that sets out the prices and terms for specific products or services.

[1960] "Digital data" is a general term for any data format that can be stored or transmitted electronically.

[1961] "Extraction" refers to the process of extracting necessary parts from data or information.

[1962] A "database" is a system that stores data in an organized manner and enables efficient access and management.

[1963] "Market Price" means the average price at which a commodity is generally traded in a particular market.

[1964] "Automatic" means that an operation or process is performed by a machine or computer without human intervention.

[1965] "Optical character recognition (OCR) technology" is a technology that converts printed or handwritten characters into digital data.

[1966] "Negotiation" refers to dialogue and discussion between the parties to adjust the terms of a transaction.

[1967] "Optimal price" refers to the price that is deemed most appropriate taking into account quality and market conditions.

[1968] A "requisition document" is a document used to obtain formal approval within an organization.

[1969] "Receiving" refers to the operation of taking in data or messages from the outside.

[1970] A "scanner" refers to a device that converts information written on paper into electronic data.

[1971] "Image data" refers to data stored in an image format.

[1972] "Text data" refers to a data format based on character information.

[1973] "Recognition" means to officially approve or permit.

[1974] "PDF format" is an abbreviation for Portable Document Format, a file format for electronically storing and sharing documents while preserving their format.

[1975] "Purchasing operations" refers to a series of business processes for procuring necessary goods and services.

[1976] The unmanned purchasing system of this invention is composed of a user terminal, a server, and peripheral devices as needed. The user processes the quotations received from the vendor on the terminal, and the system then automatically proceeds with the purchasing process.

[1977] Hardware and software used

[1978] User device: Used to scan the estimate and convert it into text data. Examples include a personal computer and a scanner (e.g., Fujitsu ScanSnap).

[1979] OCR software: Used to convert scanned image data into text data. Examples of OCR software include Adobe Acrobat and Tesseract OCR.

[1980] Server: Analyzes text data, registers it in a database, collects and compares market prices, negotiates prices, and automatically generates and sends approval documents. For example, SAP Ariba can be used as a purchasing system.

[1981] Explanation of program processing

[1982] 1. Quote Receipt:

[1983] A user receives a quote from a vendor. For example, a quote for "electrical wiring work."

[1984] The terminal scans this estimate with a scanner and generates image data (e.g., Fujitsu ScanSnap).

[1985] The terminal uses OCR software (e.g. Adobe Acrobat) to convert the image data into text data and send it to the server.

[1986] 2. Quotation Registration:

[1987] The server analyzes the received text data and divides it into items. For example, it analyzes it as "Vendor name: certain company", "Item: electric wire", "Quantity: 100m", "Unit price: 500 yen", "Total: 50,000 yen".

[1988] The server registers the analysis results in the database of the purchasing system (e.g., SAP Ariba).

[1989] 3. Market Price Matching:

[1990] The server accesses national databases (e.g., the National Tax Agency database) and market price databases on the Internet (e.g., Kakaku.com) to collect market prices.

[1991] The server evaluates the appropriateness of the estimated price based on the market price collected. For example, it determines that the market price is 450 yen per meter.

[1992] 4. Price Negotiation:

[1993] The server automatically generates a negotiation email and sends it to the vendor, proposing, for example, "Could you please set the unit price at 450 yen based on the market price?"

[1994] The server receives the vendor's response and negotiates further if necessary. For example, if the response is "How about 470 yen?", the server continues negotiations.

[1995] 5. Price Calculation:

[1996] The server calculates the optimal price based on the confirmed price and stores it in the database. For example, it records it based on "Confirmed price: 47,000 yen."

[1997] The server calculates the difference from the original estimated price (50,000 yen) and displays it to the user.

[1998] 6. Preparation of approval documents:

[1999] The server automatically generates a request for approval based on the estimate and negotiation results. Specific examples include information such as "electrical wiring work," "agreed price: 47,000 yen," and "negotiation details."

[2000] The server generates the request form in PDF format and sends it to the user's device.

[2001] 7. Final approval:

[2002] The user reviews the submitted request and logs in to the approval system (e.g., SharePoint) to make the final approval.

[2003] Specific examples

[2004] For example, if you receive a quote from a vendor for electrical installation work, you can proceed as follows:

[2005] Example prompt: "Please tell me how the system automatically processes electrical installation work quotes received from vendors."

[2006] This system automates the entire purchasing process, enabling a fast and accurate purchasing process.

[2007] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2008] Step 1:

[2009] Quote received

[2010] Specific behavior:

[2011] Input: Quote received from vendor

[2012] Action: A user receives a quote from a vendor.

[2013] Action: The device scans this quote with a scanner and generates image data. For example, it uses a Fujitsu ScanSnap to create high-resolution image data.

[2014] Output: Generated image data (JPEG or PDF format)

[2015] Step 2:

[2016] OCR conversion of image data

[2017] Specific behavior:

[2018] Input: Image data generated by a scanner

[2019] Operation: The device uses OCR software (e.g., Adobe Acrobat) to convert image data into text data.

[2020] How it works: OCR software analyzes image data and converts it into a string of characters that can be recognized as text.

[2021] Output: Converted text data

[2022] Step 3:

[2023] Sending text data

[2024] Specific behavior:

[2025] Input: Text data after OCR conversion

[2026] Operation: The terminal sends the converted text data to the server.

[2027] Output: Text data sent to the server

[2028] Step 4:

[2029] Quotation registration

[2030] Specific behavior:

[2031] Input: Text data received by the server

[2032] How it works: The server parses the text data and splits it into multiple fields (e.g., vendor name, item, quantity, unit price, total) using a natural language processing algorithm (e.g., SpaCy or Google Cloud NLP API).

[2033] Operation: The server registers the analysis results in the database of the purchasing system (e.g., SAP Ariba).

[2034] Output: Quote information stored in the database

[2035] Step 5:

[2036] Market price comparison

[2037] Specific behavior:

[2038] Input: Quote information stored in the database

[2039] Operation: The server accesses national databases (e.g., the National Tax Agency database) and market price databases on the Internet (e.g., Kakaku.com) to collect market prices for the target item.

[2040] Operation: The server evaluates the appropriateness of the quoted price based on the market price collected. For example, the market price is 450 yen per meter, but the received quote is 500 yen per meter, so it is determined to be expensive.

[2041] Output: Comparison of market price and estimated price

[2042] Step 6:

[2043] Price negotiation

[2044] Specific behavior:

[2045] Input: Comparison result of market price and estimated price

[2046] How it works: The server automatically generates a negotiation email and sends it to the vendor. For example, it proposes, "Could you please set the unit price at 450 yen based on the market price?"

[2047] Operation: The server receives the vendor's response and negotiates further if necessary. For example, if the response is "How about 470 yen?", the server continues negotiations.

[2048] Output: Negotiation result data

[2049] Step 7:

[2050] Price calculation

[2051] Specific behavior:

[2052] Input: Negotiation result data

[2053] Operation: The server calculates the optimal price based on the confirmed price and saves it in the database. For example, it records it based on "Confirmed price: 47,000 yen."

[2054] Operation: Calculate the difference from the original estimated price (50,000 yen) and show it to the user. For example, notify the user that a cost reduction of 3,000 yen has been achieved.

[2055] Output: Optimal price calculation result

[2056] Step 8:

[2057] Creating approval documents

[2058] Specific behavior:

[2059] Input: Optimal price calculation results and negotiation history

[2060] Operation: The server automatically generates a request for approval based on the estimate and negotiation results. Specific examples include information such as "electrical wiring work," "agreed price: 47,000 yen," and "negotiation details."

[2061] Operation: The server generates a request form in PDF format and sends it to the user's device.

[2062] Output: Generated PDF of the approval document

[2063] Step 9:

[2064] Final approval

[2065] Specific behavior:

[2066] Input: Generated PDF format request form

[2067] What it does: A user reviews the submitted request and logs into an approval system (e.g., SharePoint) to make the final approval.

[2068] How it works: After logging in, check the contents of the request form, add comments if necessary, and finally press the approval button.

[2069] Output: Approved request

[2070] This system automates the entire purchasing process, enabling a fast and accurate purchasing process.

[2071] (Application example 1)

[2072] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2073] Current purchasing operations at logistics centers rely on many manual processes and time-consuming procedures, resulting in inefficiencies and a high risk of human error. There is a particular need for technology that can efficiently automate the entire process, from receiving quotes to matching market prices, negotiating prices, and creating and sending approval documents. This is expected to reduce labor costs, increase work speed, and improve accuracy.

[2074] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2075] In this invention, the server includes means for scanning estimates using a smartphone camera, means for converting image data into text data using optical character recognition technology, means for scraping price databases on the Internet to collect market prices, means for sending negotiation emails to vendors using automatic email generation technology, and means for notifying users using a push notification function, thereby enabling the automation and efficiency of the entire purchasing process.

[2076] "Quote" means a document that describes the price and terms of products or services provided by a vendor.

[2077] "Digital data" is data that has been converted into a format that can be processed by electronic devices such as computers.

[2078] A "database" is a system or software that systematically organizes information and allows it to be managed and searched centrally.

[2079] "Market price" is the price at which a particular product or service is generally traded in the market.

[2080] Optical character recognition (OCR) is a technology that recognizes characters from image data and converts them into text data.

[2081] "Scraping" is a technique for automatically extracting specific information from web pages.

[2082] "Push notification" is a technology that sends information directly from a server to a client device.

[2083] "Automatic email generation" is a technology in which a system automatically creates emails based on predefined conditions and formats.

[2084] A "rei-sho" is a document used to obtain the approval necessary to make decisions within an organization.

[2085] This invention relates to a system that fully automates purchasing operations at a logistics center. This system receives quotations from vendors, converts them into digital data, extracts necessary information, and registers it in a database. It then compares the extracted information with market prices and automatically negotiates prices with the vendor. It also calculates the optimal price, records it in a database, and automatically generates a request for approval based on the quotation and negotiation results and sends it to the user.

[2086] First, the user scans the quote using the camera on their smartphone. The scanned image data is converted into text data using optical character recognition (OCR) technology such as Google Cloud Vision API. This converted data is uploaded to AWS S3 and transferred to a database using AWS Lambda.

[2087] The server then analyzes the received data and extracts the necessary information (e.g., item, quantity, unit price, total, etc.). This information is then registered in a relational database such as AWS RDS.

[2088] The server then scrapes the internet price database to collect market quotes, using AWS Lambda or other scraping tools. Based on the collected market quotes, the server evaluates the fairness of the quote.

[2089] If the price is not reasonable, the server will use automatic email generation technology to send a negotiation email to the vendor. This process uses SES to send emails. The reply from the vendor is also automatically received and the analysis and negotiation process will continue again.

[2090] Finally, the optimal price is calculated based on the finalized price and recorded in a database. The server automatically generates a request form based on this information and sends it in PDF format to the user's smartphone via push notification. Notification services such as social media are used for the push notification. The user checks the request form on their smartphone and digitally signs it using the DocuSign API or similar.

[2091] Specific examples

[2092] For example, when a user receives a quote for electrical equipment construction from a vendor, they scan the quote using their smartphone camera. This image data is converted into text data using Google Cloud Vision API and registered in a database via AWS Lambda. The server scrapes and collects market prices from the Internet and evaluates the fair price. After the evaluation, the server automatically sends a negotiation email to the vendor using SES, and the optimal price is confirmed. A request for approval is then automatically generated and sent to the user via push notification. The user then checks the request on their smartphone and electronically signs it using the DocuSign API.

[2093] Example prompts for generative AI models

[2094] "Write a program to convert the following text using OCR and register it in a purchasing system:

[2095] Quote image path:path / to / image.jpg

[2096] Database to register: AWS RDS

[2097] APIs and libraries used: Google Cloud Vision API, boto3, pdfkit

[2098] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2099] Step 1:

[2100] The user scans an estimate using the smartphone camera. The input is an estimate received from a vendor, which is captured as image data using the smartphone camera. The output is the image data of the scanned estimate. Specifically, the user launches the camera app and takes a picture of the estimate.

[2101] Step 2:

[2102] The device performs optical character recognition (OCR) on the scanned image data using the Google Cloud Vision API. The input is the image data acquired in step 1, and the output is text data converted by OCR. Specifically, the image data is sent to the Google Cloud Vision API and the text data is received as the result of the OCR processing.

[2103] Step 3:

[2104] The text data acquired by the device using OCR is uploaded to AWS S3. The input is the text data from step 2, and the output is the text data stored on AWS S3. Specifically, the process is performed to upload the text file to an Amazon S3 bucket.

[2105] Step 4:

[2106] The server uses AWS Lambda to retrieve text data from S3, analyze the information, and extract the required items. The input is the text data retrieved from AWS S3, and the output is the extracted information (e.g., item, quantity, unit price, total, etc.). Specifically, the script analyzes the text data and executes to extract the required information for each item.

[2107] Step 5:

[2108] The server registers the extracted information in AWS RDS. The input is the information extracted in step 4, and the output is the information registered in AWS RDS. Specifically, it generates an SQL statement to save the extracted information and inserts the data into AWS RDS.

[2109] Step 6:

[2110] The server scrapes price databases on the Internet to collect market price information. The input is a list of URLs for the price databases, and the output is the collected market price information. Specifically, a web scraping tool is used to access the price database and obtain market price information.

[2111] Step 7:

[2112] The server compares the market price collected with the registered quotation information to perform price evaluation. The input is the quotation information from step 5 and the market price information from step 6, and the output is the evaluation result regarding the appropriateness of the price. Specifically, it compares the estimated price with the market price and executes an algorithm to evaluate whether the estimated price is appropriate.

[2113] Step 8:

[2114] The server uses automatic email generation technology to send a negotiation email to the vendor. The input is the price evaluation result and vendor information, and the output is the sent negotiation email. Specifically, it uses SES to generate a negotiation email and sends it to the vendor's email address.

[2115] Step 9:

[2116] The server receives the reply from the vendor and re-analyzes and evaluates the negotiation results. The input is the reply email from the vendor, and the output is the updated negotiation results. Specifically, the server re-analyzes the contents of the received email and updates the negotiation results.

[2117] Step 10:

[2118] The server calculates the optimal price based on the confirmed price and records it in AWS RDS. The input is the updated negotiation result, and the output is the optimal price recorded in AWS RDS. Specifically, the optimal price is calculated and the information is saved in the database.

[2119] Step 11:

[2120] The server automatically generates a request for approval based on the quotation details and negotiation results, and generates it in PDF format. The input is the confirmed price information and negotiation results, and the output is a request for approval in PDF format. Specifically, the script that generates the request for approval is executed using PDFKit or similar.

[2121] Step 12:

[2122] The server uses the push notification function to send a request form to the user. The input is a PDF version of the request form, and the output is the notification sent to the user. Specifically, the server sends a push notification to the user's smartphone using a notification service such as SNS.

[2123] Step 13:

[2124] The user checks the approval document on their smartphone and digitally signs it using the DocuSign API. The input is a PDF version of the approval document, and the output is the signed version. Specifically, the process involves opening the approval document on the smartphone and digitally signing the required fields.

[2125] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2126] The unmanned purchasing system of this invention receives quotations from vendors and automates the purchasing process based on them. This system covers a series of processes, including quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval, and also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's behavior and interface accordingly.

[2127] The main components of this system are a user terminal, a server, an emotion engine, and peripheral devices as needed. The user terminal is used to input estimates, check approval documents, and recognize emotions, while the server is responsible for processing and storing various data. The emotion engine recognizes the user's emotions in real time and makes adjustments accordingly.

[2128] Program processing details

[2129] 1. Receipt of quotation

[2130] A user receives a quote from a vendor.

[2131] The terminal scans the estimate with a scanner and generates image data.

[2132] The device uses OCR software to convert the image data into text data.

[2133] The terminal transmits the text data to the server.

[2134] 2. Estimate registration

[2135] The server analyzes the received text data.

[2136] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[2137] The server registers the extracted information in the purchasing system database.

[2138] 3. Market price comparison

[2139] The server accesses national databases and internet market price databases to collect market prices for the target items.

[2140] The server evaluates the fairness of the received quote based on the market price collected by the server.

[2141] 4. Price Negotiation

[2142] The server generates a template for a price negotiation email.

[2143] The server automatically sends a negotiation email to the vendor, which includes specific negotiation details based on the market price.

[2144] After the server receives the vendor's response, it renegotiates if necessary.

[2145] The text of negotiation emails is automatically adjusted based on the user's emotions recognized by the emotion engine.

[2146] 5. Price Calculation

[2147] The server calculates the optimal price based on the finalized negotiation results.

[2148] The server records the determined price in a database.

[2149] The server calculates the difference between the original quoted price and the final price and displays it in a spreadsheet or report format.

[2150] 6. Creating approval documents

[2151] The server prepares a template for the approval document.

[2152] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[2153] The server generates the request form in PDF format and sends it to the user.

[2154] 7. Final approval

[2155] The user receives the request sent from the server.

[2156] The emotion engine recognizes the user's emotions while reviewing the request and encourages renegotiation or reconfirmation if the user expresses dissatisfaction.

[2157] The user logs in to the approval system and approves the contents of the request form.

[2158] Specific examples

[2159] For example, when an estimate for electrical equipment construction is received from a vendor, the process proceeds as follows:

[2160] 1. Receipt of quotation

[2161] The user receives a quote for "electrical wiring work" from a vendor.

[2162] The terminal scans the estimate and creates image data.

[2163] The device uses OCR technology to convert the image data into text and send it to the server.

[2164] 2. Estimate registration

[2165] The server analyzes the received text data and extracts information such as "Vendor name: ABC Company," "Item: Electric wire," "Quantity: 100m," "Unit price: 500 yen," and "Total: 50,000 yen."

[2166] The server registers this information in a database.

[2167] 3. Market price comparison

[2168] The server collects market price data from national databases and the Internet to check the market price for "electrical wiring work."

[2169] The server compares the market price with the quoted price and evaluates whether the quoted price is reasonable.

[2170] 4. Price Negotiation

[2171] The server generates a negotiation email saying, "Could you please set the unit price at 450 yen based on the market price?" and sends it to the vendor.

[2172] The server receives the vendor's response, "How about 470 yen?" and renegotiates if necessary.

[2173] The emotion engine recognizes the user's emotions and adjusts the content of the negotiation email.

[2174] 5. Price Calculation

[2175] The server determines the price based on the negotiation result of "470 yen, total 47,000 yen" and records it in the database.

[2176] The server calculates and displays the difference (3,000 yen) from the original quoted price (50,000 yen).

[2177] 6. Creating approval documents

[2178] The server creates a request form based on the estimate and negotiation results, and includes information such as "electrical wiring work," "agreed price: 47,000 yen," and "price negotiation details."

[2179] The server generates a request form and sends it to the user's device in PDF format.

[2180] 7. Final approval

[2181] The user receives the PDF request form and checks its contents.

[2182] The emotion engine recognizes user emotions in real time and prompts renegotiation or reaffirmation when dissatisfaction is expressed.

[2183] The user logs into the approval system and gives final approval.

[2184] In this way, the system is able to respond flexibly while taking into consideration the user's emotions, which not only makes purchasing operations more efficient but also improves the user experience.

[2185] The processing flow will be explained below.

[2186] Step 1:

[2187] A user receives a quote from a vendor.

[2188] The terminal scans the estimate with a scanner and generates image data.

[2189] The device uses OCR software to convert the image data into text data.

[2190] The terminal transmits the text data to the server.

[2191] Step 2:

[2192] The server analyzes the received text data.

[2193] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[2194] The server registers the extracted information in the purchasing system database.

[2195] Step 3:

[2196] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[2197] The server evaluates the fairness of the received quote based on the market price collected by the server.

[2198] Step 4:

[2199] The server generates a template for a price negotiation email.

[2200] The server automatically sends a negotiation email to the vendor, which contains specific negotiation terms based on the market price.

[2201] The server receives the response from the vendor.

[2202] The server uses an emotion engine to automatically adjust the text of the negotiation email according to the emotion of the user.

[2203] The server renegotiates with the vendor as needed.

[2204] Step 5:

[2205] The server calculates the optimal price based on the finalized negotiation results.

[2206] The server records the determined price in a database.

[2207] The server calculates the difference between the original quote and the confirmed price.

[2208] The server displays the differences in a spreadsheet or report format.

[2209] Step 6:

[2210] The server prepares a template for the approval document.

[2211] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[2212] The server generates the request form in PDF format and sends it to the user.

[2213] Step 7:

[2214] The user receives the request sent from the server.

[2215] The user checks the contents of the request form.

[2216] The server uses an emotion engine to prompt renegotiation or reconfirmation when the user expresses dissatisfaction.

[2217] The user logs in to the approval system and approves the contents of the request form.

[2218] Example 2

[2219] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2220] In traditional purchasing operations, processes such as receiving quotations from vendors, extracting and registering information, comparing prices with market rates, negotiating prices with vendors, and creating and approving approval documents are often fragmented, resulting in problems of time and effort. Furthermore, systems that do not consider user emotions can impair the user experience and prevent optimal purchasing decisions. This reduces the efficiency of the entire purchasing process and risks making purchases at inappropriate prices.

[2221] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2222] In this invention, the server includes means for receiving quotations from vendors and converting them into digital data, means for extracting necessary information from the digital data and registering it in a database, means for comparing the extracted information with market prices, means for automatically negotiating prices with vendors, means for calculating an optimal price and recording it in a database, means for automatically generating a request for purchase based on the contents of the quotation and the results of the negotiation, means for sending the request for purchase to the user, and means for adjusting the contents of the negotiation and the request for purchase based on the results of user emotion recognition. This makes it possible to improve the efficiency of purchasing operations and realize optimal decisions that take user emotions into consideration.

[2223] A "quote" is a document that describes the price, quantity, and terms of the goods provided by a vendor.

[2224] "Digital data" is data that has been converted from analog information into a numerical value and into a format that can be processed by a computer or the like.

[2225] Optical character recognition (OCR) technology is a technology that automatically identifies characters from images captured by a scanner or camera and converts them into digital text.

[2226] A "database" is a collection of information that is systematically organized so that it can be efficiently accessed, managed, and updated.

[2227] A "market price" is the general price at which a particular product or service is traded in the market.

[2228] "Price negotiation" is the process by which a buyer and seller decide on a mutually agreed-upon price in a transaction.

[2229] A "requisition document" is a document created to obtain approval within an organization, and is usually used to seek approval or opinions from superiors or related departments.

[2230] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, movements, etc. to determine their emotional state.

[2231] "Vendor" means a supplier or provider of goods or services.

[2232] A "server" is a computer system that provides services to other computers (clients) on a network.

[2233] A "user" is someone who uses the system to perform various tasks and operations.

[2234] The unmanned purchasing system of this invention receives quotations from vendors and automates the purchasing process based on them. This system covers a series of processes, including quotation receipt, quotation registration, market price comparison, price negotiation, price calculation, approval document creation, and final approval, and also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's behavior and interface accordingly.

[2235] The main components of the system are a user terminal, a server, an emotion engine, and, if necessary, peripheral devices.

[2236] The user device performs operations such as inputting and scanning estimates, checking approval documents, and emotion recognition. Specific hardware options include scanners and high-performance cameras, such as the Canon DR-C240 scanner. Adobe Acrobat or other well-known OCR software is recommended for OCR technology.

[2237] The server is responsible for processing and storing various types of data. A relational database management system such as PostgreSQL is suitable as the database. General-purpose programming languages ​​such as Python are used for processes within the server, and data analysis, information extraction using regular expressions, and market price collection via APIs are performed. Furthermore, Python's smtplib and the Jinja2 template engine are used for automatically generating and sending emails.

[2238] The emotion engine uses advanced emotion recognition algorithms to analyze the user's facial expressions and voice, and automatically adjusts the system's behavior based on the results. Facial recognition technology can be used with libraries such as OpenCV and Dlib, enabling real-time emotion analysis.

[2239] Specifically, for example, when an estimate for electrical equipment construction is received from a vendor, the processing of this system proceeds as follows.

[2240] The user receives an estimate for "electrical wiring work" and scans it using the scanner or camera on the device.

[2241] The scanned image data is converted into text using OCR technology and sent to the server.

[2242] The server extracts the necessary information from the received text data and records it in a database.

[2243] The server collects market quotes from an internet database and evaluates the quotes.

[2244] The server automatically sends a negotiation email to the vendor and calculates the optimal price based on the results.

[2245] The server creates a request form based on the quotation details and negotiation results and sends it to the user in PDF format.

[2246] The user reviews the request, and the emotion engine prompts renegotiation if necessary. Finally, the user logs in to the system and approves the request.

[2247] An example of a prompt that can be used to adjust system behavior and message content using generative AI models is as follows:

[2248] "Please explain in natural language each process of the automated purchasing system, from receiving a quote to final approval. Please include specific hardware and software, as well as data processing and calculation methods. Also, please explain the function that recognizes user emotions and adjusts the system accordingly."

[2249] A system designed in this way can greatly contribute to improving the efficiency of purchasing operations and user satisfaction.

[2250] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2251] Step 1:

[2252] Quote received

[2253] A user receives a quote from a vendor.

[2254] Input: Quote provided by vendor (physical document or PDF).

[2255] Output: Physical copy or PDF file.

[2256] Specific action: The user receives the quote and prepares it for digitization using a scanner or camera.

[2257] The terminal uses a scanner to scan the estimate and generate image data.

[2258] Input: Physical quote.

[2259] Output: Scanned image data.

[2260] What it does: Scan the quote at high resolution using a Canon DR-C240 scanner.

[2261] The device uses OCR (Optical Character Recognition) technology to convert image data into text data.

[2262] Input: scanned image data.

[2263] Output: Text data.

[2264] Specific operation: Executes Adobe Acrobat's OCR function and extracts text information such as "100m of electric wire, unit price 500 yen" from the image data.

[2265] The terminal transmits the text data to the server.

[2266] Input: Text data generated by OCR.

[2267] Output: The text data sent to the server.

[2268] What it does: Issues a curl command to send text data to the server's API endpoint using HTTPS.

[2269] Step 2:

[2270] Quotation registration

[2271] The server analyzes the received text data.

[2272] Input: Text data sent from the terminal.

[2273] Output: A data object containing the analysis results.

[2274] Specific operation: Analyzes text data using Python's NLTK library and generates tokenized sentences.

[2275] The server extracts information such as "vendor name," "item," "quantity," "unit price," and "total" from the analyzed data.

[2276] Input: A parsed data object.

[2277] Output: Data for each extracted item.

[2278] Specific operation: Using regular expression pattern matching, extract information such as "Vendor name: Vendor", "Item: Electric wire", "Quantity: 100m", "Unit price: 500 yen", and "Total: 50,000 yen".

[2279] The server registers the extracted information in the purchasing system database.

[2280] Input: Data for each extracted item.

[2281] Output: Information stored in a database.

[2282] Specific behavior: Connect to a PostgreSQL database and execute the INSERT INTO quote_table(...) VALUES(...) query.

[2283] Step 3:

[2284] Market price comparison

[2285] The server accesses national databases and market price databases on the Internet to collect market prices for the target items.

[2286] Input: URL or API key of the market quote database from which the data is collected.

[2287] Output: Collected market quote price data.

[2288] Specific operation: Accesses the API of the Ministry of Economy, Trade and Industry, etc., and obtains current market price information for power cables.

[2289] The server evaluates the fairness of the received quote based on the market price collected by the server.

[2290] Input: Collected market quote data, quoted price data.

[2291] Output: Evaluation results on the fairness of the estimated price.

[2292] What it does: Runs an algorithm to verify that the quote is within ±10% of the market price.

[2293] Step 4:

[2294] Price negotiation

[2295] The server generates a template for a price negotiation email.

[2296] Input: Template data for negotiation, quote and market price data.

[2297] Output: The generated email template.

[2298] What it does: Uses the Jinja2 template engine to generate an email that says, "Would you mind adjusting the price based on market value?"

[2299] The server automatically sends a negotiation email to the vendor.

[2300] Input: The generated email template.

[2301] Output: Negotiation email sent to vendor.

[2302] Specific behavior: Uses the smtplib library to send the generated email to the vendor's email address.

[2303] After the server receives the vendor's response, it renegotiates if necessary.

[2304] Input: Response email from vendor.

[2305] Output: Renegotiation result.

[2306] Specific operation: Analyzes received emails and generates and sends renegotiation emails if necessary.

[2307] The text of negotiation emails is automatically adjusted based on the user's emotions recognized by the emotion engine.

[2308] Input: User emotion data, negotiation email template.

[2309] Output: Tailored negotiation email.

[2310] What it does: It uses facial recognition technology to analyze a user's emotions and adapts the tone and content of emails accordingly.

[2311] Step 5:

[2312] Price calculation

[2313] The server calculates the optimal price based on the finalized negotiation results.

[2314] Input: Negotiated price data.

[2315] Output: Optimal price.

[2316] Specific operation: Recalculate the total price based on the negotiated result of "470 yen".

[2317] The server records the determined price in a database.

[2318] Input: Best price data.

[2319] Output: The final price recorded in the database.

[2320] What it does: Runs an SQL query to insert the best price into a PostgreSQL database.

[2321] The server calculates the difference between the original quoted price and the final price and displays it in a spreadsheet or report format.

[2322] Input: Original Quoted Price Data, Final Price Data.

[2323] Output: Diff report.

[2324] Specific operation: Using Python's pandas and xlsxwriter, create a difference report in Excel format and provide it to the user.

[2325] Step 6:

[2326] Creating approval documents

[2327] The server prepares a template for the approval document.

[2328] Input: Request form template data.

[2329] Output: Prepared decision template.

[2330] Specific operation: Load an existing approval document template and pass it to the template engine.

[2331] The server automatically inputs the estimate details, negotiation details, and final price into the request form.

[2332] Input: Quotation data, negotiation data, final price data.

[2333] Output: Auto-filled approval request data.

[2334] Specific operation: Embed the necessary information into the approval form template using the python-docx library.

[2335] The server generates the request form in PDF format and sends it to the user.

[2336] Input: Automatically entered request data.

[2337] Output: A PDF version of the approval document.

[2338] Specific operation: Use reportlab to convert the request form into PDF format and send it to the user's device.

[2339] Step 7:

[2340] Final approval

[2341] The user receives the request sent from the server.

[2342] Input: PDF request form from the server.

[2343] Output: The received decision.

[2344] Specific operation: A user downloads a PDF request form via email or a portal site.

[2345] The emotion engine recognizes the user's emotions while reviewing the request and encourages renegotiation or reconfirmation if the user expresses dissatisfaction.

[2346] Input: User emotion data.

[2347] Output: Proposal for renegotiation or reaffirmation.

[2348] What it does: It uses facial recognition technology to analyze users' facial expressions in real time and displays an alert if dissatisfaction is detected.

[2349] The user logs in to the approval system and approves the contents of the request form.

[2350] Input: PDF request form, login information.

[2351] Output: Approval result.

[2352] Specific operation: A user logs in to the system through a web portal, checks the contents of the request form, and clicks the approval button.

[2353] By using this system, it is possible to streamline and optimize purchasing operations, while also expecting an improved user experience.

[2354] (Application example 2)

[2355] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2356] In current purchasing operations, the entire process from receiving quotations to final approval of the request form is often done manually, resulting in inefficiency. It is also difficult to respond flexibly and consider user feelings, which can lead to user dissatisfaction, especially during price negotiations and the request form approval process. This poses a challenge to the overall efficiency of operations and the user experience.

[2357] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2358] In this invention, the server includes a means for receiving quotations from vendors and converting them into digital data, a means for extracting necessary information from the digital data and registering it in a database, and an emotion recognition means for recognizing a user's emotion and adjusting the user's behavior. This makes it possible to automate the entire purchasing process while recognizing the user's emotion in real time and taking appropriate action.

[2359] "Vendor" refers to a company that provides products or services.

[2360] A "quotation" is a document that describes the price, quantity, terms, etc. of a product or service.

[2361] "Digital data" means data represented in electronic form.

[2362] "OCR technology" is an abbreviation for optical character recognition technology, a technology that extracts text information from images.

[2363] "Market price" refers to the price in the general market for a particular product or service.

[2364] A "database" is a system that is organized to efficiently manage large amounts of data and enable searching and updating.

[2365] "Emotion recognition means" refers to technology or devices for analyzing and recognizing a user's emotions in real time.

[2366] A "reisho" is a formal document created to obtain approval or settlement within an organization or company.

[2367] "Price negotiation" is the process by which a buyer and seller negotiate and agree on the price of a product or service.

[2368] "Final approval" refers to the final stage of the approval process, where all parties agree and formally approve the proposal.

[2369] This invention provides a system for automating purchasing processes and recognizing and flexibly responding to user emotions. This system is primarily composed of a server, a terminal, emotion recognition means, and a database. Specific embodiments of this system are described below.

[2370] System Components

[2371] 1. Server

[2372] The server has the following functions:

[2373] Receive and analyze digital estimate data.

[2374] Using OCR technology, necessary information is extracted from digital data and registered in a database.

[2375] The registered information is compared with market prices, and market prices are collected to assess the fairness of the prices.

[2376] Automatically generate price negotiation emails and negotiate prices with vendors.

[2377] A request form template is automatically generated based on the confirmed price and sent to the user in PDF format.

[2378] Recognize user sentiment and encourage renegotiation or reaffirmation when dissatisfaction is expressed.

[2379] 2. Terminal

[2380] The device can be a smartphone or a PC with certain functions:

[2381] Provides a function to scan estimates and generate scanned image data.

[2382] The generated image data is converted into text data using OCR technology and sent to the server.

[2383] The user takes an image for emotion recognition and sends it to the server.

[2384] 3. Emotion recognition means

[2385] Emotion recognition is a technology for recognizing user emotions in real time, using a machine learning model (e.g., using TensorFlow) that analyzes the user's facial expressions from images captured by a camera.

[2386] 4. Database

[2387] The database is used to store information such as quotation information, negotiation results, market prices, and user emotional data.

[2388] Hardware and Software

[2389] Hardware: Scanner, smartphone or PC with camera, server.

[2390] Software: OCR software (e.g., Pytesseract), image processing libraries (e.g., OpenCV), machine learning models (e.g., TensorFlow), internet request libraries (e.g., Requests).

[2391] Example

[2392] For example, if a user receives an estimate for "electrical equipment construction," the process proceeds as follows:

[2393] 1. Scan the quote

[2394] The user takes a photo of the estimate using the smartphone camera and generates image data.

[2395] The device converts this image data into text using OCR technology and sends it to the server.

[2396] 2. Data analysis and registration

[2397] The server analyzes the received text data, extracts information such as "vendor name," "item," "quantity," "unit price," and "total," and registers it in a database.

[2398] 3. Comparison with market prices

[2399] The server collects market prices from a database on the Internet and compares them with the quoted prices to assess their suitability.

[2400] 4. Price Negotiation

[2401] The server automatically generates a negotiation email based on market prices and sends it to the vendor.

[2402] If the user expresses dissatisfaction, the server attempts to renegotiate.

[2403] 5. Preparation of approval documents

[2404] The server automatically generates a request form based on the negotiation results and sends it to the user in PDF format.

[2405] 6. Final Approval

[2406] The user reviews the submitted PDF request form and gives final approval.

[2407] Prompt Sentence Examples

[2408] As an example, the following prompt sentence is input into the generative AI model:

[2409] Scan the quote for "electrical wiring work" and convert it to text. Then generate and send a price negotiation email based on the market price. Use the camera to recognize the user's emotions and suggest renegotiation if there is dissatisfaction.

[2410] In this way, a system is realized that can improve the efficiency of purchasing operations and respond flexibly based on the user's emotions.

[2411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2412] Step 1:

[2413] A user receives a quote from a vendor.

[2414] Input: Paper quote from vendor

[2415] How it works: The user takes a photo of a quote using their smartphone camera.

[2416] Output: The image data of the quotation is saved on the smartphone.

[2417] Step 2:

[2418] The terminal scans the estimate and converts it into text data using OCR technology.

[2419] Input: Image data of quotation

[2420] How it works: The device uses OpenCV to grayscale the image data and Pytesseract to perform OCR.

[2421] Output: Text data of the quotation

[2422] Step 3:

[2423] The terminal transmits the text data to the server.

[2424] Input: Text data of quotation

[2425] How it works: The device uses the Internet Requests library (Requests) to send text data to the server.

[2426] Output: Text data is saved on the server.

[2427] Step 4:

[2428] The server analyzes the received text data, extracts the necessary information, and registers it in a database.

[2429] Input: Text data of quotation

[2430] How it works: The server uses machine learning algorithms to analyze the text data and extract information such as "vendor name," "item," "quantity," "unit price," and "total." The extracted information is then registered in a database.

[2431] Output: Quote information stored in the database

[2432] Step 5:

[2433] The server collects market quotes and compares them with quote information.

[2434] Input: Quote information stored in the database

[2435] Operation: The server accesses a market price database on the Internet to collect the market price of the target item, and evaluates the fairness of the quoted price and the market price.

[2436] Output: Validity evaluation result

[2437] Step 6:

[2438] The server generates a price negotiation email template and automatically sends it to the vendor.

[2439] Input: Fairness assessment result, market price

[2440] How it works: The server generates a negotiation email based on a template and automatically sends it to the vendor.

[2441] Output: Price negotiation email sent

[2442] Step 7:

[2443] The server receives the vendor's response and renegotiates if necessary.

[2444] Input: Response email from vendor

[2445] Operation: The server analyzes the received response and, if renegotiation is necessary, generates and sends another negotiation email.

[2446] Output: Updated negotiation result

[2447] Step 8:

[2448] The server automatically generates a request form based on the finalized negotiation results and sends it to the user in PDF format.

[2449] Input: Updated negotiation results

[2450] Operation: The server automatically generates a request form template based on the negotiation results and sends it to the user in PDF format.

[2451] Output: PDF of the approval request sent to the user's device

[2452] Step 9:

[2453] The user reviews the request and gives final approval.

[2454] Input: PDF of the approval request

[2455] How it works: The user reviews the PDF request and gives final approval. The server records the approval process and renegotiates or reconfirms it if necessary.

[2456] Output: Approved request

[2457] Step 10:

[2458] An emotion recognition means recognizes the user's emotions in real time and adjusts the behavior.

[2459] Input: User facial expression image

[2460] How it works: The device's camera captures the user's facial expressions, and the TensorFlow model is used to recognize emotions. The server then adjusts its response based on the emotional data.

[2461] Output: Emotion recognition results and the system's response based on them

[2462] In this way, the entire purchasing process becomes more efficient and flexible responses based on the user's emotions become possible.

[2463] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2464] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2465] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2466] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2467] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2468] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2469] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2470] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recogni...

Claims

1. A means of receiving quotes from vendors and converting them into digital data; A means for extracting necessary information from digital data and registering it in a database; means for matching the extracted information with quoted market prices; A means to automatically negotiate prices with vendors, A means of calculating the best price and recording it in a database; A means to automatically generate approval documents based on the estimate contents and negotiation results, a means for sending the request to a user; A system including:

2. 2. The system of claim 1, wherein the means for extracting the required information from the digital data uses optical character recognition techniques.

3. 10. The system of claim 1, wherein said means for verifying market quotes utilizes market price data collected from national databases and the Internet.

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