system

Abstract

Provide a system. [Solution] A means for users to input information about new businesses and send it to a server; A means for analyzing the information received by the server and extracting related keywords; A means for searching past project information in a database using the extracted keywords; A means for extracting similar project information from the search results, as well as market data and competitive information related to the project; filtering means for eliminating redundant processes from the extracted information; a means for returning the filtered results to the user for display on the terminal; A system including:

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] When launching a new business, users often repeat many processes, such as market research and competitive analysis. Furthermore, because experience and data from past projects are not utilized, similar research and analysis is often repeated. This redundant process wastes time and resources, hindering efficient business launches. Another problem is that users cannot easily access accurate and useful information about past projects. To solve these issues, a system is needed that effectively utilizes past knowledge and reduces redundant processes. [Means for solving the problem]

[0005] The present invention provides a means for users to input information about new businesses and send it to a server. The server has means for analyzing the received information and extracting related keywords. It also has means for searching past project information in a database using the extracted keywords. It provides means for extracting highly similar project information from the search results, as well as market data and competitive information related to those projects. It also has filtering means for eliminating duplicate processes from the extracted information. Finally, it provides means for returning the filtered results to the user and displaying them on a terminal, thereby supporting the efficient launch of a business. This configuration provides a system that reduces duplicate processes and makes effective use of past knowledge.

[0006] "User" refers to an individual or corporation that uses the system to launch a new business.

[0007] "Device" refers to a device, such as a computer, smartphone, or tablet, that a user uses to enter information or view results.

[0008] "Server" refers to a computer system that receives information sent by users and performs processing such as analysis, searching, and filtering.

[0009] "Information about a new business" refers to information such as the content, goals, and required specifications of the business that the user is considering launching.

[0010] "Keywords" refer to words or phrases extracted from information about a new business that represent the characteristics or important elements of the business.

[0011] A "database" refers to a collection of data that centrally stores past project information, related market data, competitive information, etc., and manages them in a searchable manner.

[0012] "Highly similar project information" refers to data on past projects whose keywords match or are similar to the information about the new business.

[0013] "Market Data" refers to data that includes statistics, size, trends, etc., about a particular industry or market.

[0014] "Competitive Intelligence" means data concerning the information, products, services, and strategies of major competitors in the marketplace.

[0015] "Filtering methods" refer to techniques and algorithms that eliminate redundant processes and unnecessary information from extracted information, leaving only information that is useful to the user.

[0016] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and refers to a method of structuring data and representing it in a lightweight, readable format.

[0017] An "HTTP request" refers to a protocol-based communication method used by a user to send information from a terminal to a server.

[0018] "Cosine similarity" refers to an algorithm for calculating the angle between two vectors and assessing their similarity. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The system based on this invention allows users to input information about new projects, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, and a database. The specific operation of the system is described below.

[0041] First, the user uses the device to input information about the new business. This information might include a specific business plan, such as "launching an online education platform." The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0042] The server then analyzes the HTTP request received from the device to retrieve the business proposal details. This information is then analyzed and relevant keywords are extracted using natural language processing (NLP). For example, the keywords "online education," "platform," and "launch" are identified.

[0043] Based on the extracted keywords, the server searches for past project information in a database that stores project names, implementation details, market size, competitive information, etc. The server retrieves the relevant project data using SQL queries and full-text search engines.

[0044] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[0045] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, processes required for a new business can be automatically excluded if measures have already been taken in past projects.

[0046] Finally, the filtered results are formatted in JSON and sent back to the terminal, which then displays the results on its user interface, allowing users to obtain the information they need to efficiently launch their business without redundant processes.

[0047] As a concrete example, consider the case where a user types in "development of remote work tools." In this case, the server extracts keywords such as "remote work," "tools," and "development," and searches the database for past projects related to "remote work tools." It then extracts similar project information, market data, and competitor information, filtering out overlapping processes and providing them. By referencing this information, users can leverage past knowledge to launch new businesses quickly and effectively.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] The user inputs information about a new business into the device. For example, they enter a specific business plan, such as "launching an online education platform," into a form. The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0051] Step 2:

[0052] The server analyzes the HTTP request received from the terminal. It extracts JSON format data from the body of the HTTP request and obtains text information about the new business. For example, the following JSON data:

[0053] json

[0054] {

[0055] "project_idea": "Launching an online education platform"

[0056] }

[0057] Step 3:

[0058] The server uses an NLP (Natural Language Processing) module to extract relevant keywords from the retrieved text information, for example, identifying the keywords "online education," "platform," and "launch."

[0059] Step 4:

[0060] The server searches the database for past project information based on the extracted keywords. It uses SQL queries or ElasticSearch® to execute queries like the following:

[0061] SQL

[0062] SELECT FROM projects WHERE description LIKE '%online education%' OR description LIKE '%platform%' OR description LIKE '%launch%';

[0063] Step 5:

[0064] The server retrieves project information that matches or is similar to the keywords from the search results, including information on past projects such as "e-learning platforms," ​​as well as related market data and competitive information.

[0065] Step 6:

[0066] The server calculates the cosine similarity to evaluate the similarity of the acquired project information. The extracted keywords and the description of each project in the search results are vectorized, and the cosine similarity is calculated to score the similarity.

[0067] Step 7:

[0068] The server ranks the most relevant project information based on the similarity score, and then extracts information with particularly high scores.

[0069] Step 8:

[0070] The server filters the extracted information. For example, if market research has already been conducted, it eliminates duplicate processes in order to reuse the information. The filtered information leaves only the most appropriate data for the user to efficiently advance new business.

[0071] Step 9:

[0072] The server will format the filtered results in JSON, like this:

[0073] json

[0074] {

[0075] "similar_projects": [

[0076] {

[0077] "project_name": "E-learning platform",

[0078] "description": "Past launch examples",

[0079] "market_size": "2022: $2B",

[0080] "competitors": ["Company A", "Company B"]

[0081] }

[0082] ],

[0083] "unique_steps": [

[0084] "User Interface Design",

[0085] "User Testing"

[0086] ]

[0087] }

[0088] Step 10:

[0089] The server returns the formatted result to the terminal as an HTTP response, with the HTTP status code 200 OK.

[0090] Step 11:

[0091] The device analyzes the received data and displays it on a user interface. Users can refer to the information displayed on the device and use past project information, market data, and competitive information to help launch new businesses.

[0092] Example 1

[0093] 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."

[0094] Traditionally, launching a new business required manually collecting and analyzing information on past projects, market data, and competitive information, which was time-consuming and labor-intensive. It was also difficult to eliminate duplication of necessary information and redundant processes, making it difficult to launch a new business efficiently.

[0095] 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.

[0096] In this invention, the server includes means for inputting business-related information from a user and transmitting it to an information processor, means for analyzing the information received by the information processor and extracting related words and phrases, and means for searching past business information in a storage device using the extracted words and phrases. This makes it possible to efficiently collect and analyze past project information, market data, and competitive information, and provide it in a form that eliminates duplication and unnecessary processes.

[0097] "User" means a person or entity that operates the system and inputs information about a new business.

[0098] A "job" is a task or project related to commerce, service, manufacturing, or other economic activity.

[0099] An "information processor" is a computer or server that processes and analyzes received data.

[0100] "Terms" are important keywords and phrases extracted from text information using natural language processing technology.

[0101] "Storage device" means a digital data storage system, including a database, for storing historical business information.

[0102] "Market Data" means information about a particular industry or market, including market size, growth rates, trends, etc.

[0103] "Competitive information" is information about other companies or services that do similar business.

[0104] "Filtering" is the process of organizing collected information and eliminating duplication and unnecessary processes.

[0105] A "structured data format" is a format in which data is organized and stored in a certain structure, and is a method for making data handling easier.

[0106] A "display device" is a hardware device such as a monitor or display that visually displays information to a user.

[0107] "Cosine similarity" is a mathematical method for assessing the similarity of two vectors in a vector space.

[0108] This invention relates to a system that allows users to input business-related information, analyzes, searches, and filters it, and provides past business knowledge. This system is composed of a terminal, a server, and a storage device. The specific operation of the system is described below.

[0109] First, the user inputs information about the task using the device. This information might include a specific task proposal, such as "Developing a remote work tool." The device then converts the input information into JSON format and sends it to the server as an HTTP request using the HTTPS protocol.

[0110] Next, the server analyzes the HTTP request received from the device and obtains the details of the business proposal. This information is then used to extract related words and phrases using NLP (Natural Language Processing). For example, words such as "remote work," "tools," and "development" are identified. Python NLP libraries (e.g., NLTK, spaCy) are used for extraction.

[0111] Based on the extracted phrases, the server searches for past business information stored in a storage device. This storage device stores information such as business names, implementation details, market size, and competitive information. The server retrieves the relevant business data using SQL queries and full-text search engines.

[0112] The server then uses cosine similarity to extract similar business information from the acquired data. Scikit-learn's metrics.pairwise.cosine_similarity is used to evaluate similarity. The similarity evaluation identifies the past business information most relevant to the user's business proposal.

[0113] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, among the processes required for launching a business, it automatically excludes those for which countermeasures have already been taken in previous business operations.

[0114] Finally, the filtered results are formatted in JSON format and sent back to the terminal, which then displays the results on its user interface. This allows users to obtain the information they need to efficiently launch their business without redundant processes.

[0115] As a concrete example, consider the case where a user types in "development of remote work tools." In this case, the server extracts terms such as "remote work," "tools," and "development," and searches for past work related to "remote work tools" in the storage device. It then extracts similar work information, market data, and competitive information, filtering out overlapping processes and providing them. By referencing this information, users can utilize past knowledge to launch new work quickly and effectively.

[0116] Example prompt sentence:

[0117] "I've entered information about a new job: 'Developing remote work tools.' Please search for past jobs related to this job, rate and filter them by similarity, and provide the most relevant knowledge."

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] The user enters information about a new job on the device.

[0121] Input: The user enters a business proposal, such as "Development of remote work tools," into the input form on the device.

[0122] What it does: The device receives this information and converts the input into JSON format.

[0123] Output: The terminal generates business information converted into JSON format.

[0124] Step 2:

[0125] The device converts the information into JSON format and sends it to the server.

[0126] Input: Business information in JSON format in the terminal.

[0127] Operation: The terminal uses the HTTPS protocol to send business information in JSON format to the server as an HTTP request.

[0128] Output: The server receives the HTTP request.

[0129] Step 3:

[0130] The server analyzes the information it receives

[0131] Input: The HTTP request received by the server, and the business information contained within in JSON format.

[0132] How it works: The server parses the HTTP request and extracts the text information of the business proposal from the JSON data.

[0133] Output: Text information of the extracted business proposal.

[0134] Step 4:

[0135] The server extracts keywords using natural language processing (NLP) technology.

[0136] Input: Business proposal in text format.

[0137] How it works: The server uses Python NLP libraries (e.g., NLTK, spaCy) to extract relevant words such as "remote work," "tools," and "development" from the text information of the job proposal.

[0138] Output: A list of extracted terms.

[0139] Step 5:

[0140] The server searches for past business information in the storage device

[0141] Input: A list of extracted phrases.

[0142] How it works: The server uses SQL queries and a full-text search engine to search the database in the storage device and retrieve past business information.

[0143] Output: A list of the retrieved past business information.

[0144] Step 6:

[0145] The server identifies highly similar business information using cosine similarity.

[0146] Input: A list of retrieved past business information.

[0147] How it works: The server uses Scikit-learn's metrics.pairwise.cosine_similarity to identify information from past work information that is highly similar to "development of remote work tools."

[0148] Output: A list of business information with high similarity.

[0149] Step 7:

[0150] The server filters the information

[0151] Input: A list of business information with high similarity.

[0152] How it works: The server filters out duplicate processes and unnecessary information. For example, if the "market analysis" process is duplicated, it will exclude that part.

[0153] Output: A filtered list of business information.

[0154] Step 8:

[0155] The server formats the filtered information into JSON format.

[0156] Input: A filtered list of business information.

[0157] How it works: The server formats the filtering results into JSON format.

[0158] Output: Filtering information in JSON format.

[0159] Step 9:

[0160] The server returns filtering information in JSON format to the device.

[0161] Input: Filtering information in formatted JSON.

[0162] Operation: The server returns filtering information in JSON format to the device as an HTTP response.

[0163] Output: The device receives the HTTP response.

[0164] Step 10:

[0165] Displaying the information received by the device on the user interface

[0166] Input: Filtering information in JSON format.

[0167] Behavior: The device analyzes the information it receives and displays it on the user interface.

[0168] Output: Presents information in a user-readable format, such as relevant historical business information, market data, and competitive information.

[0169] This process allows users to quickly and effectively leverage past knowledge and smoothly launch new business operations.

[0170] (Application example 1)

[0171] 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."

[0172] Currently, when brick-and-mortar store operators launch new businesses, there are limited ways to efficiently utilize past project knowledge. Operators have to expend a lot of time and effort due to a lack of means to quickly and accurately obtain information, particularly using voice input. Another issue is that existing systems lack sufficient filtering and contain a lot of duplicate information.

[0173] 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.

[0174] In this invention, the server includes means for inputting information about a new business from a user and transmitting it to the server, means for analyzing the information received by the server and extracting related keywords, means for searching past project information in a database using the extracted keywords, means for inputting a new business plan by voice using a smart device and converting it into text using voice recognition technology, and means for returning the filtered results to the user and displaying them on a terminal. This enables operators of brick-and-mortar stores to quickly and accurately obtain project knowledge using voice input when launching a new business.

[0175] "User" refers to the operator of a physical store who inputs information about a new business and uses the system.

[0176] A "start-up" is a new commercial activity or project that a brick-and-mortar store operator is considering launching.

[0177] "Information" refers to specific data, proposals, ideas, plans, etc. related to new businesses.

[0178] "Server" refers to the computer system that analyzes, searches, and filters information submitted by users and returns relevant data.

[0179] "Parsing" refers to the process by which the server processes the information it receives and extracts meaningful elements and keywords.

[0180] "Keywords" refer to words or phrases that form the core of the information extracted as a result of analysis.

[0181] "Database" refers to an information accumulation device that stores past project information, market data, competitive information, and the like.

[0182] "Past Project Information" refers to detailed data about similar projects previously undertaken.

[0183] "Searching" refers to the process of using extracted keywords to find relevant information in a database.

[0184] "Similarity" refers to a criterion for evaluating the degree of commonality or relevance between the searched project information and the new business proposal.

[0185] "Market Data" refers to statistical information and analytical data relating to a particular industry or market.

[0186] "Competitive Information" refers to information about other competing companies or services in the marketplace.

[0187] "Filtering" refers to the process of removing duplicate processes and unnecessary information from search results and extracting only the necessary information.

[0188] "Smart device" refers to an advanced electronic device (e.g., smart glasses) that handles new business ideas through voice input.

[0189] "Voice recognition technology" refers to the technology that converts new business ideas input by users via voice into text.

[0190] "JSON format" refers to a text-based format that makes data lightweight and easy to handle.

[0191] "Terminal" refers to the electronic device that a user uses to view the filtering results sent from the server.

[0192] This invention provides a system for supporting operators of brick-and-mortar stores in launching new businesses, allowing users to input new business ideas by voice using a smart device and converting them into text using voice recognition technology. Specific embodiments for implementing this invention are described below.

[0193] Hardware and software used

[0194] Hardware:

[0195] Smart glasses: A device that allows physical store operators to input data using voice commands.

[0196] Server: A computer system that analyzes, searches, and filters the information it receives.

[0197] Terminal: A device (such as a smartphone or tablet) used to display information sent from the server.

[0198] software:

[0199] Speech recognition technology: Converts speech to text using the speech_recognition library.

[0200] Natural Language Processing (NLP): Extract relevant keywords from text using external NLP APIs.

[0201] Database Management System (DBMS): Uses sqlite3 to manage a database for storing past project information.

[0202] Similarity evaluation: Evaluate the similarity of search results using the TfidfVectorizer and cosine_similarity functions from the scikit-learn library.

[0203] Data processing and calculation

[0204] 1. Voice input and recognition:

[0205] The server records the user's voice through the smart glasses' microphone and converts it into text using voice recognition technology. For example, a user might say, "Open a supermarket in a new neighborhood."

[0206] 2. Text analysis and keyword extraction:

[0207] The converted text is sent to a server, which uses NLP technology to extract relevant keywords such as "new area," "supermarket," and "opening."

[0208] 3. Database Search:

[0209] Based on the extracted keywords, past project information in the database is searched for. For example, past project information related to "supermarket" is retrieved.

[0210] 4. Similarity Assessment:

[0211] The project information from the search results is vectorized using the user's input information and TfidfVectorizer, and the similarity is evaluated using cosine_similarity.

[0212] 5. Filtering and displaying information:

[0213] The system filters out duplicates and unnecessary information from the project information selected by similarity assessment, formats it in JSON format, and returns it to the device, where it displays it visually to the user.

[0214] Specific examples

[0215] For example, if a user speaks into the smart glasses, "Open a supermarket in a new area," the NLP API will extract keywords such as "new area," "supermarket," and "opening," and then search the database for past related projects, perform a similarity assessment, and provide the most relevant project knowledge.

[0216] Example prompt sentence:

[0217] When a user speaks into the smart glasses, "Open a supermarket in a new area," the NLP API will extract keywords such as "new area," "supermarket," and "opening," search the database for past related projects, and then perform a similarity assessment to provide the most relevant project knowledge.

[0218] This system will enable brick-and-mortar store operators to quickly and effectively utilize past project knowledge when launching new businesses.

[0219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0220] Step 1:

[0221] Voice input

[0222] Using the smart glasses, a user can voice-input a new business idea, such as "Open a supermarket in a new neighborhood." This voice is recorded through the smart glasses' microphone.

[0223] Input: User's voice

[0224] Output: Audio data

[0225] Step 2:

[0226] Voice Recognition

[0227] The server receives the recorded voice data and converts it into text using the speech_recognition library. The speech recognition technology generates the text "Open a supermarket in a new area."

[0228] Input: Audio data

[0229] Output: Text data

[0230] Step 3:

[0231] Text analysis and keyword extraction

[0232] The server sends the received text data to an external NLP API to extract relevant keywords, such as "new neighborhood," "supermarket," and "opening."

[0233] Input: Text data

[0234] Output: Extracted keywords

[0235] Step 4:

[0236] Database search

[0237] The server uses the extracted keywords to search for past project information in the database using SQLite3. Corresponding past project information is retrieved. For example, "Supermarket Opening Project 1" and "Supermarket Opening Project 2" are searched.

[0238] Input: Extracted keywords

[0239] Output: Past project information

[0240] Step 5:

[0241] Similarity assessment

[0242] The server vectorizes the search results and the user's input using TfidfVectorizer, and evaluates the similarity using cosine_similarity. The most similar project information is identified.

[0243] Input: User input information, past project information

[0244] Output: Similarity evaluation result

[0245] Step 6:

[0246] Information Filtering

[0247] The server filters out duplicates and unnecessary information from the similarity assessment results, for example, excluding unnecessary processes for which countermeasures have already been implemented.

[0248] Input: Similarity evaluation result

[0249] Output: Filtered information

[0250] Step 7:

[0251] Returning and displaying results

[0252] The server formats the filtered information into JSON format and returns it to the device. The device displays the returned information on a user interface, providing the user with the knowledge they need to launch a new business.

[0253] Input: Filtered information

[0254] Output: The displayed information

[0255] 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.

[0256] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, a database, and an emotion engine. The specific operation of the system is described below.

[0257] First, the user uses the device to input information about the new business. This information might include a specific business plan, such as "launching an online education platform." The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0258] The server then analyzes the HTTP request received from the device to retrieve the business proposal details. This information is then analyzed and relevant keywords are extracted using natural language processing (NLP). For example, the keywords "online education," "platform," and "launch" are identified.

[0259] Based on the extracted keywords, the server searches for past project information in a database that stores project names, implementation details, market size, competitive information, etc. The server retrieves the relevant project data using SQL queries and full-text search engines.

[0260] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[0261] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, processes required for a new business can be automatically excluded if measures have already been taken in past projects.

[0262] The emotion engine also recognizes user emotions based on user input, such as analyzing user comments and feedback to identify emotional states such as "positive" or "negative."

[0263] The emotion engine recognizes the user's emotions and adjusts the filtering results accordingly. For example, if the user's emotions are positive, it will highlight successful project examples, while if the emotions are negative, it will prioritize risk management methods.

[0264] The formatted results are then sent back to the device in JSON format, where they are displayed on the device's user interface. Users can refer to the information displayed on the device and use it to launch new businesses by leveraging past project information, market data, and competitive information.

[0265] For example, if a user types in "development of remote work tools" and has a positive emotional state, the system will prioritize providing data on past success stories and market size. On the other hand, if the user has a negative emotional state, the system will prioritize providing information on risk management methods and the problems of other competitors. As a result, users will receive information that best suits their emotional state, allowing them to make more effective business plans.

[0266] The processing flow will be explained below.

[0267] Step 1:

[0268] The user inputs information about a new business into the device. For example, they enter a specific business plan, such as "launching an online education platform," into a form. The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0269] Step 2:

[0270] The server analyzes the HTTP request received from the terminal. It extracts JSON format data from the body of the HTTP request and obtains text information about the new business. For example, the following JSON data:

[0271] json

[0272] {

[0273] "project_idea": "Launching an online education platform"

[0274] }

[0275] Step 3:

[0276] The server uses an NLP (Natural Language Processing) module to extract relevant keywords from the retrieved text information, for example, identifying the keywords "online education," "platform," and "launch."

[0277] Step 4:

[0278] The server uses an emotion engine to recognize emotions based on the information entered by the user. The emotion engine analyzes the content and context of the text and identifies emotion categories such as positive, negative, or neutral. For example, it identifies emotions such as "excited" or "anxious."

[0279] Step 5:

[0280] The server searches the database for past project information based on the extracted keywords, using SQL queries or Elasticsearch to execute queries like the following:

[0281] SQL

[0282] SELECT FROM projects WHERE description LIKE '%online education%' OR description LIKE '%platform%' OR description LIKE '%launch%';

[0283] Step 6:

[0284] The server retrieves project information that matches or is similar to the keywords from the search results, including information on past projects such as "e-learning platforms," ​​as well as related market data and competitive information.

[0285] Step 7:

[0286] The server calculates the cosine similarity to evaluate the similarity of the acquired project information. The extracted keywords and the description of each project in the search results are vectorized, and the cosine similarity is calculated to score the similarity.

[0287] Step 8:

[0288] The server ranks the most relevant project information based on the similarity score, and then extracts information with particularly high scores.

[0289] Step 9:

[0290] The server filters the extracted information. For example, if market research has already been conducted, it will exclude duplicate processes in order to reuse the information. The server also adjusts the filtering results according to the user's emotional state, as recognized by the emotion engine. For example, if the user's emotion is positive, it will prioritize displaying success stories, and if the emotion is negative, it will prioritize displaying risk management methods.

[0291] Step 10:

[0292] The server will format the filtered results in JSON, like this:

[0293] json

[0294] {

[0295] "similar_projects": [

[0296] {

[0297] "project_name": "E-learning platform",

[0298] "description": "Past launch examples",

[0299] "market_size": "2022: $2B",

[0300] "competitors": ["Company A", "Company B"]

[0301] }

[0302] ],

[0303] "unique_steps": [

[0304] "User Interface Design",

[0305] "User Testing"

[0306] ]

[0307] }

[0308] Step 11:

[0309] The server returns the formatted result to the terminal as an HTTP response, with the HTTP status code 200 OK.

[0310] Step 12:

[0311] The device analyzes the received data and displays it on a user interface. Users can refer to the information displayed on the device and utilize past project information, market data, and competitive information to help launch new businesses. For example, if positive emotions are recognized, success stories are highlighted, while if negative emotions are recognized, risk management information is emphasized.

[0312] Example 2

[0313] 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."

[0314] Conventional new business support systems have had the problem of overlooking important information before they can properly analyze the information entered by the user and provide relevant past project information. Furthermore, because they are unable to provide information that takes into account the user's emotional state, the information the user receives may not be optimal for launching a new business. To solve these problems, a system is needed that can perform highly accurate information analysis and filtering that takes into account the user's emotional state, providing optimal information for new businesses.

[0315] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information about a new business from a user and transmitting it to the server, a means for analyzing the information received by the server and extracting related keywords, a means for searching past project information in a database using the extracted keywords, a means for extracting highly similar project information from the search results, as well as market data and competitive information related to the project, a filtering means for eliminating redundant processes from the extracted information, a means for adjusting the filtered information based on the user's emotions, and a means for returning the filtered results to the user and displaying them on a terminal. This enables highly accurate information analysis and filtering that takes the user's emotional state into consideration, making it possible to quickly provide optimal information for launching a new business.

[0316] "User" refers to an individual or corporation that uses the system to input information about a new business and receives the results.

[0317] "Terminal" refers to a device such as a computer or smartphone used by a user to input information and display results.

[0318] "Server" means a central computer system that receives, analyzes, searches, and filters information submitted by users and returns the results.

[0319] "New Business" refers to any new business activity or project that you are planning or developing.

[0320] "Inputting information" refers to the act of a user using a terminal to provide detailed data and ideas about a new business to the system.

[0321] "Analysis" refers to the process by which the server understands and extracts meaning from the user's information received.

[0322] "Keywords" refer to important words or phrases that indicate a new business, identified from the analyzed information.

[0323] "Extraction" refers to the act of extracting only the necessary parts from analyzed information or search results.

[0324] A "database" refers to a collection of information that stores past projects and related information.

[0325] "Project Information" refers to data relating to specific past business activities or undertakings.

[0326] "Market Data" refers to statistics and trends relating to a particular industry or market.

[0327] "Competitive intelligence" refers to data about other companies and projects operating in the same market.

[0328] "Similarity" refers to the degree of relevance between a user's new business idea and past project information.

[0329] "Cosine similarity" refers to a mathematical measure used to quantify the similarity of information.

[0330] "Filtering" refers to the act of removing unnecessary or redundant parts from acquired information.

[0331] "Emotion" refers to a psychological state that is perceived based on user input and feedback.

[0332] "Adjustment" refers to the act of appropriately changing the filtering results based on the user's emotional state.

[0333] "JSON format" refers to a text format for structuring and expressing data, and is used when returning results to the terminal.

[0334] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, a database, and an emotion engine. The specific operation of the system is described below.

[0335] First, the user inputs information about the new business using the terminal. The terminal converts the information entered by the user into JSON format and sends it to the server as an HTTP request.

[0336] The server receives the HTTP request sent from the device and analyzes the content of the business proposal. Natural language processing (NLP) technology is used for the analysis. The server analyzes the information and extracts related keywords. For example, from a business proposal titled "Launch of an online education platform," the keywords "online education," "platform," and "launch" are extracted.

[0337] Next, the server searches the database for past project information based on the extracted keywords. This can be done using SQL queries or a full-text search engine (e.g., Elasticsearch). The server then uses a similarity evaluation algorithm, such as cosine similarity, to extract similar project information from the search results. This allows the server to identify the past project information that is most relevant to the new business proposal.

[0338] The server then filters this information, eliminating duplicate processes and unnecessary information, such as issues that have already been resolved in previous projects.

[0339] The server then uses an emotion engine to recognize the user's emotions. By analyzing the user's comments and feedback, the server identifies their emotional state, such as "positive" or "negative." The server then adjusts the filtering results based on the emotion recognized by the emotion engine. For example, if the user's emotion is positive, information highlighting success stories is provided, while if the emotion is negative, risk management methods are prioritized.

[0340] Finally, the formatted results are formatted in JSON and sent back to the device as an HTTP response. The device then parses the received JSON data and displays it in the user interface. Users can refer to the displayed information and use it to launch new businesses.

[0341] For example, if a user types in "development of remote work tools," and the user has a positive sentiment, the system will prioritize providing data on past success stories and market size. On the other hand, if the user has a negative sentiment, the system will prioritize providing information on risk management methods and the problems of other competitors.

[0342] An example of a prompt is:

[0343] New business idea: Development of remote work tools

[0344] Emotion: Positive

[0345] Information sought: Past success stories, market size data

[0346] This allows users to receive information that best suits their emotional state, allowing them to plan new business ventures more effectively.

[0347] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0348] Step 1:

[0349] A user uses a terminal to input information about a new business. The input information includes details of the business plan, such as "launching an online education platform." The input data is in text format.

[0350] Input: Information about the new business (text)

[0351] Output: Data converted to JSON format

[0352] Step 2:

[0353] The device converts the information entered by the user into JSON format and sends it to the server as an HTTP request. By converting it into JSON format, data integrity is maintained.

[0354] Input: Information about the new business (text)

[0355] Output: HTTP request (JSON format data)

[0356] Step 3:

[0357] The server analyzes the HTTP request received from the device, understands the content of the business proposal, and extracts related keywords using natural language processing (NLP) technology based on the input JSON data.

[0358] Input: HTTP request (JSON format data)

[0359] Output: Extracted keywords (e.g., "online education," "platform," "launch")

[0360] Step 4:

[0361] The server searches for past project information in the database based on the extracted keywords, using SQL queries or a full-text search engine (e.g., Elasticsearch).

[0362] Input: Extracted keywords

[0363] Output: Search results (past project information)

[0364] Step 5:

[0365] The server applies a similarity evaluation algorithm, such as cosine similarity, to identify similar project information from the search results, and the evaluation algorithm selects the most relevant information from the search results.

[0366] Input: Search results (past project information)

[0367] Output: Project information with high similarity (evaluated data)

[0368] Step 6:

[0369] The server performs filtering based on the information identified by the similarity assessment. The purpose of filtering is to remove redundant processes and unnecessary information, for example, by removing problems that have already been solved in the past.

[0370] Input: Similar project information

[0371] Output: Filtered information

[0372] Step 7:

[0373] The server uses an emotion engine to recognize the user's emotional state, analyzing the user's feedback and comments to identify emotions such as "positive" or "negative."

[0374] Input: User feedback and comments

[0375] Output: User's emotional state (e.g., positive, negative)

[0376] Step 8:

[0377] The server adjusts the filtering results based on the perceived sentiment: positive sentiment highlights success stories, while negative sentiment prioritizes risk management strategies.

[0378] Input: User's emotional state, filtered information

[0379] Output: Adjusted filtering result

[0380] Step 9:

[0381] The server formats the result in JSON format and sends it to the terminal as an HTTP response.

[0382] Input: Adjusted filtering results

[0383] Output: HTTP response (JSON format data)

[0384] Step 10:

[0385] The device analyzes the JSON data received from the server and displays the results on the user interface. Users can refer to the displayed information and use it to launch new businesses.

[0386] Input: HTTP response (JSON format data)

[0387] Output: Display on the user interface

[0388] (Application example 2)

[0389] 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."

[0390] Conventional new business support systems search past project data based on the business information entered by the user and provide results, but do not take the user's emotional state into consideration. As a result, appropriate information is not provided based on the user's emotions, making it difficult to formulate effective business plans. Furthermore, new manufacturing projects require past success stories and risk information, but appropriately filtering and providing this information has also been a challenge.

[0391] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting information about a new business from a user and transmitting it to the server; means for analyzing the information received by the server and extracting related keywords; means for searching for past project information in a database using the extracted keywords; means for extracting from the search results information about past activities with high similarity, as well as market data and competitive information related to those activities; filtering means for eliminating redundant processes from the extracted information; means for analyzing the user's emotions and adjusting the filtered results based on the emotions; and means for returning the filtered results to the user and displaying them on a terminal. This makes it possible to provide appropriate information that takes the user's emotional state into consideration, thereby supporting the development of effective business plans.

[0392] "New business information" is detailed data about new businesses or projects that users are planning.

[0393] A "server" is a computing device that receives and analyzes information sent by users.

[0394] "Related keywords" are important words and phrases extracted based on the information entered by the user.

[0395] A "database" is a collection of information that stores past project information, market data, and competitive information.

[0396] "Project Information" is details about business or work that has been carried out in the past.

[0397] The "search results" are past project information retrieved from a database using the extracted keywords.

[0398] "Highly similar past activity information" is data on past projects that have a high similarity to the information on the new business entered by the user.

[0399] "Market Data" means statistical data and trend information about a particular market.

[0400] "Competitive information" is data about other companies or projects operating in the same or similar field.

[0401] The "filtering means" is a part of the process for removing unnecessary data from the extracted information and organizing the necessary information.

[0402] "Emotion analysis means" refers to technology that identifies a user's emotional state based on their input and feedback.

[0403] "Means for adjusting filtering results" refers to a mechanism for appropriately changing filtered information based on the user's emotions.

[0404] A "terminal" is a device through which a user enters information and receives results.

[0405] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of terminals, servers, databases, and an emotion engine.

[0406] First, the user uses the terminal to input information about the new business. This information might include a specific business plan, such as a "project to introduce a new automated production line." The terminal converts this input information into JSON format and sends it to the server as an HTTP request.

[0407] The server then analyzes the HTTP request received from the device and retrieves the details of the business proposal. The server then uses a natural language processing (NLP) engine to extract relevant keywords from this retrieved information. For example, extracted keywords might include "automated line," "implementation," and "project."

[0408] Based on the extracted keywords, the server searches for past project information in a database that contains information on past projects, market data, competitive information, etc., and retrieves this information using SQL queries or a full-text search engine (e.g., Elasticsearch).

[0409] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[0410] Furthermore, the server uses an emotion engine to recognize the user's emotion based on the user's input information. For example, a text analysis engine (e.g., Google® Cloud Natural Language API) analyzes the user's comments and feedback to identify the user's emotional state, such as "positive" or "negative."

[0411] The server adjusts the filtered results according to the user's emotions recognized by the emotion engine. For example, if the user's emotions are positive, it highlights success stories, and if they are negative, it prioritizes risk management methods.

[0412] Finally, the formatted results are sent back to the device in JSON format, where they are displayed on a user interface. Users can refer to the information displayed on the device and use it to launch new businesses by leveraging past project information, market data, and competitive information.

[0413] Examples:

[0414] If a user inputs a plan for a "new automated line installation project" and has positive feelings about it, the system will prioritize providing data on past success stories and market size.

[0415] Example prompts for generative AI models:

[0416] New project for the user: Installation project for a new automated line

[0417] Emotional state: Positive

[0418] Information to provide: Similar success stories, implementation steps, and effective automation techniques

[0419] In this way, the system provides appropriate information that takes into account the user's emotional state, and supports the creation of effective business plans.

[0420] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0421] Step 1:

[0422] The user uses a terminal to input information about a new business. A specific business plan (e.g., "Project to introduce a new automated line") is entered, and the terminal converts this information into JSON format. The input data is text information about the new business, and data processing is performed to convert it into JSON format. The output is the converted JSON format data.

[0423] Step 2:

[0424] The terminal sends the converted JSON format information to the server as an HTTP request. Specifically, it creates a POST request using the requests library or similar and sends it to the server's endpoint. The input is the converted JSON data, and the output is sent to the server.

[0425] Step 3:

[0426] The server analyzes the HTTP request received from the terminal and obtains the details of the business proposal. At this time, it analyzes the received JSON data and extracts text information. The input is the JSON data as an HTTP request, and the output is the analyzed text information.

[0427] Step 4:

[0428] The server uses a natural language processing (NLP) engine to extract relevant keywords from the retrieved information. For example, it uses an NLP library (e.g., spaCy or NLTK) to perform text analysis and extract keywords such as "automation line," "implementation," and "project." The input is the parsed text information, and the output is the extracted keywords.

[0429] Step 5:

[0430] The server searches the database for past project information based on the extracted keywords, using SQL queries and Elasticsearch to retrieve past project data related to the keywords. The input is the extracted keywords, and the output is the searched project data.

[0431] Step 6:

[0432] The server uses an evaluation algorithm such as cosine similarity to extract similar project information from the acquired data. For example, it uses the Scikit-learn library to perform similarity evaluation. The input is the searched project data, and the output is similar project information.

[0433] Step 7:

[0434] The server uses an emotion engine to recognize the user's emotion based on the user's input information. The emotion engine (e.g., Google Cloud Natural Language API) is used to identify a positive or negative emotional state. The input is the user's input text, and the output is the recognized emotional state.

[0435] Step 8:

[0436] The server adjusts the filtering results based on the emotion. If the emotion is positive, it emphasizes successful cases, and if it is negative, it prioritizes risk management methods. The filtering uses project information that is highly similar to the emotional state, and the filtered information is output based on this.

[0437] Step 9:

[0438] The server formats the filtered results in JSON format and returns them to the terminal. The input is the filtered information, and the output is the JSON formatted data.

[0439] Step 10:

[0440] The terminal displays the received results on a user interface. The input is the filtering results formatted in JSON format, and the output is a screen display that can be viewed by the user. Users can refer to the information displayed on the terminal and use past project information, market data, and competitive information to help launch new businesses.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] [Second embodiment]

[0445] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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).

[0451] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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."

[0457] The system based on this invention allows users to input information about new projects, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, and a database. The specific operation of the system is described below.

[0458] First, the user uses the device to input information about the new business. This information might include a specific business plan, such as "launching an online education platform." The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0459] The server then analyzes the HTTP request received from the device to retrieve the business proposal details. This information is then analyzed and relevant keywords are extracted using natural language processing (NLP). For example, the keywords "online education," "platform," and "launch" are identified.

[0460] Based on the extracted keywords, the server searches for past project information in a database that stores project names, implementation details, market size, competitive information, etc. The server retrieves the relevant project data using SQL queries and full-text search engines.

[0461] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[0462] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, processes required for a new business can be automatically excluded if measures have already been taken in past projects.

[0463] Finally, the filtered results are formatted in JSON and sent back to the terminal, which then displays the results on its user interface, allowing users to obtain the information they need to efficiently launch their business without redundant processes.

[0464] As a concrete example, consider the case where a user types in "development of remote work tools." In this case, the server extracts keywords such as "remote work," "tools," and "development," and searches the database for past projects related to "remote work tools." It then extracts similar project information, market data, and competitor information, filtering out overlapping processes and providing them. By referencing this information, users can leverage past knowledge to launch new businesses quickly and effectively.

[0465] The processing flow will be explained below.

[0466] Step 1:

[0467] The user inputs information about a new business into the device. For example, they enter a specific business plan, such as "launching an online education platform," into a form. The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0468] Step 2:

[0469] The server analyzes the HTTP request received from the terminal. It extracts JSON format data from the body of the HTTP request and obtains text information about the new business. For example, the following JSON data:

[0470] json

[0471] {

[0472] "project_idea": "Launching an online education platform"

[0473] }

[0474] Step 3:

[0475] The server uses an NLP (Natural Language Processing) module to extract relevant keywords from the retrieved text information, for example, identifying the keywords "online education," "platform," and "launch."

[0476] Step 4:

[0477] The server searches the database for past project information based on the extracted keywords, using SQL queries or Elasticsearch to execute queries like the following:

[0478] SQL

[0479] SELECT FROM projects WHERE description LIKE '%online education%' OR description LIKE '%platform%' OR description LIKE '%launch%';

[0480] Step 5:

[0481] The server retrieves project information that matches or is similar to the keywords from the search results, including information on past projects such as "e-learning platforms," ​​as well as related market data and competitive information.

[0482] Step 6:

[0483] The server calculates the cosine similarity to evaluate the similarity of the acquired project information. The extracted keywords and the description of each project in the search results are vectorized, and the cosine similarity is calculated to score the similarity.

[0484] Step 7:

[0485] The server ranks the most relevant project information based on the similarity score, and then extracts information with particularly high scores.

[0486] Step 8:

[0487] The server filters the extracted information. For example, if market research has already been conducted, it eliminates duplicate processes in order to reuse the information. The filtered information leaves only the most appropriate data for the user to efficiently advance new business.

[0488] Step 9:

[0489] The server will format the filtered results in JSON, like this:

[0490] json

[0491] {

[0492] "similar_projects": [

[0493] {

[0494] "project_name": "E-learning platform",

[0495] "description": "Past launch examples",

[0496] "market_size": "2022: $2B",

[0497] "competitors": ["Company A", "Company B"]

[0498] }

[0499] ],

[0500] "unique_steps": [

[0501] "User Interface Design",

[0502] "User Testing"

[0503] ]

[0504] }

[0505] Step 10:

[0506] The server returns the formatted result to the terminal as an HTTP response, with the HTTP status code 200 OK.

[0507] Step 11:

[0508] The device analyzes the received data and displays it on a user interface. Users can refer to the information displayed on the device and use past project information, market data, and competitive information to help launch new businesses.

[0509] Example 1

[0510] 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."

[0511] Traditionally, launching a new business required manually collecting and analyzing information on past projects, market data, and competitive information, which was time-consuming and labor-intensive. It was also difficult to eliminate duplication of necessary information and redundant processes, making it difficult to launch a new business efficiently.

[0512] 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.

[0513] In this invention, the server includes means for inputting business-related information from a user and transmitting it to an information processor, means for analyzing the information received by the information processor and extracting related words and phrases, and means for searching past business information in a storage device using the extracted words and phrases. This makes it possible to efficiently collect and analyze past project information, market data, and competitive information, and provide it in a form that eliminates duplication and unnecessary processes.

[0514] "User" means a person or entity that operates the system and inputs information about a new business.

[0515] A "job" is a task or project related to commerce, service, manufacturing, or other economic activity.

[0516] An "information processor" is a computer or server that processes and analyzes received data.

[0517] "Terms" are important keywords and phrases extracted from text information using natural language processing technology.

[0518] "Storage device" means a digital data storage system, including a database, for storing historical business information.

[0519] "Market Data" means information about a particular industry or market, including market size, growth rates, trends, etc.

[0520] "Competitive information" is information about other companies or services that do similar business.

[0521] "Filtering" is the process of organizing collected information and eliminating duplication and unnecessary processes.

[0522] A "structured data format" is a format in which data is organized and stored in a certain structure, and is a method for making data handling easier.

[0523] A "display device" is a hardware device such as a monitor or display that visually displays information to a user.

[0524] "Cosine similarity" is a mathematical method for assessing the similarity of two vectors in a vector space.

[0525] This invention relates to a system that allows users to input business-related information, analyzes, searches, and filters it, and provides past business knowledge. This system is composed of a terminal, a server, and a storage device. The specific operation of the system is described below.

[0526] First, the user inputs information about the task using the device. This information might include a specific task proposal, such as "Developing a remote work tool." The device then converts the input information into JSON format and sends it to the server as an HTTP request using the HTTPS protocol.

[0527] Next, the server analyzes the HTTP request received from the device and obtains the details of the business proposal. This information is then used to extract related words and phrases using NLP (Natural Language Processing). For example, words such as "remote work," "tools," and "development" are identified. Python NLP libraries (e.g., NLTK, spaCy) are used for extraction.

[0528] Based on the extracted phrases, the server searches for past business information stored in a storage device. This storage device stores information such as business names, implementation details, market size, and competitive information. The server retrieves the relevant business data using SQL queries and full-text search engines.

[0529] The server then uses cosine similarity to extract similar business information from the acquired data. Scikit-learn's metrics.pairwise.cosine_similarity is used to evaluate similarity. The similarity evaluation identifies the past business information most relevant to the user's business proposal.

[0530] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, among the processes required for launching a business, it automatically excludes those for which countermeasures have already been taken in previous business operations.

[0531] Finally, the filtered results are formatted in JSON format and sent back to the terminal, which then displays the results on its user interface. This allows users to obtain the information they need to efficiently launch their business without redundant processes.

[0532] As a concrete example, consider the case where a user types in "development of remote work tools." In this case, the server extracts terms such as "remote work," "tools," and "development," and searches for past work related to "remote work tools" in the storage device. It then extracts similar work information, market data, and competitive information, filtering out overlapping processes and providing them. By referencing this information, users can utilize past knowledge to launch new work quickly and effectively.

[0533] Example prompt sentence:

[0534] "I've entered information about a new job: 'Developing remote work tools.' Please search for past jobs related to this job, rate and filter them by similarity, and provide the most relevant knowledge."

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1:

[0537] The user enters information about a new job on the device.

[0538] Input: The user enters a business proposal, such as "Development of remote work tools," into the input form on the device.

[0539] What it does: The device receives this information and converts the input into JSON format.

[0540] Output: The terminal generates business information converted into JSON format.

[0541] Step 2:

[0542] The device converts the information into JSON format and sends it to the server.

[0543] Input: Business information in JSON format in the terminal.

[0544] Operation: The terminal uses the HTTPS protocol to send business information in JSON format to the server as an HTTP request.

[0545] Output: The server receives the HTTP request.

[0546] Step 3:

[0547] The server analyzes the information it receives

[0548] Input: The HTTP request received by the server, and the business information contained within in JSON format.

[0549] How it works: The server parses the HTTP request and extracts the text information of the business proposal from the JSON data.

[0550] Output: Text information of the extracted business proposal.

[0551] Step 4:

[0552] The server extracts keywords using natural language processing (NLP) technology.

[0553] Input: Business proposal in text format.

[0554] How it works: The server uses Python NLP libraries (e.g., NLTK, spaCy) to extract relevant words such as "remote work," "tools," and "development" from the text information of the job proposal.

[0555] Output: A list of extracted terms.

[0556] Step 5:

[0557] The server searches for past business information in the storage device

[0558] Input: A list of extracted phrases.

[0559] How it works: The server uses SQL queries and a full-text search engine to search the database in the storage device and retrieve past business information.

[0560] Output: A list of the retrieved past business information.

[0561] Step 6:

[0562] The server identifies highly similar business information using cosine similarity.

[0563] Input: A list of retrieved past business information.

[0564] How it works: The server uses Scikit-learn's metrics.pairwise.cosine_similarity to identify information from past work information that is highly similar to "development of remote work tools."

[0565] Output: A list of business information with high similarity.

[0566] Step 7:

[0567] The server filters the information

[0568] Input: A list of business information with high similarity.

[0569] How it works: The server filters out duplicate processes and unnecessary information. For example, if the "market analysis" process is duplicated, it will exclude that part.

[0570] Output: A filtered list of business information.

[0571] Step 8:

[0572] The server formats the filtered information into JSON format.

[0573] Input: A filtered list of business information.

[0574] How it works: The server formats the filtering results into JSON format.

[0575] Output: Filtering information in JSON format.

[0576] Step 9:

[0577] The server returns filtering information in JSON format to the device.

[0578] Input: Filtering information in formatted JSON.

[0579] Operation: The server returns filtering information in JSON format to the device as an HTTP response.

[0580] Output: The device receives the HTTP response.

[0581] Step 10:

[0582] Displaying the information received by the device on the user interface

[0583] Input: Filtering information in JSON format.

[0584] Behavior: The device analyzes the information it receives and displays it on the user interface.

[0585] Output: Presents information in a user-readable format, such as relevant historical business information, market data, and competitive information.

[0586] This process allows users to quickly and effectively leverage past knowledge and smoothly launch new business operations.

[0587] (Application example 1)

[0588] 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."

[0589] Currently, when brick-and-mortar store operators launch new businesses, there are limited ways to efficiently utilize past project knowledge. Operators have to expend a lot of time and effort due to a lack of means to quickly and accurately obtain information, particularly using voice input. Another issue is that existing systems lack sufficient filtering and contain a lot of duplicate information.

[0590] 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.

[0591] In this invention, the server includes means for inputting information about a new business from a user and transmitting it to the server, means for analyzing the information received by the server and extracting related keywords, means for searching past project information in a database using the extracted keywords, means for inputting a new business plan by voice using a smart device and converting it into text using voice recognition technology, and means for returning the filtered results to the user and displaying them on a terminal. This enables operators of brick-and-mortar stores to quickly and accurately obtain project knowledge using voice input when launching a new business.

[0592] "User" refers to the operator of a physical store who inputs information about a new business and uses the system.

[0593] A "start-up" is a new commercial activity or project that a brick-and-mortar store operator is considering launching.

[0594] "Information" refers to specific data, proposals, ideas, plans, etc. related to new businesses.

[0595] "Server" refers to the computer system that analyzes, searches, and filters information submitted by users and returns relevant data.

[0596] "Parsing" refers to the process by which the server processes the information it receives and extracts meaningful elements and keywords.

[0597] "Keywords" refer to words or phrases that form the core of the information extracted as a result of analysis.

[0598] "Database" refers to an information accumulation device that stores past project information, market data, competitive information, and the like.

[0599] "Past Project Information" refers to detailed data about similar projects previously undertaken.

[0600] "Searching" refers to the process of using extracted keywords to find relevant information in a database.

[0601] "Similarity" refers to a criterion for evaluating the degree of commonality or relevance between the searched project information and the new business proposal.

[0602] "Market Data" refers to statistical information and analytical data relating to a particular industry or market.

[0603] "Competitive Information" refers to information about other competing companies or services in the marketplace.

[0604] "Filtering" refers to the process of removing duplicate processes and unnecessary information from search results and extracting only the necessary information.

[0605] "Smart device" refers to an advanced electronic device (e.g., smart glasses) that handles new business ideas through voice input.

[0606] "Voice recognition technology" refers to the technology that converts new business ideas input by users via voice into text.

[0607] "JSON format" refers to a text-based format that makes data lightweight and easy to handle.

[0608] "Terminal" refers to the electronic device that a user uses to view the filtering results sent from the server.

[0609] This invention provides a system for supporting operators of brick-and-mortar stores in launching new businesses, allowing users to input new business ideas by voice using a smart device and converting them into text using voice recognition technology. Specific embodiments for implementing this invention are described below.

[0610] Hardware and software used

[0611] Hardware:

[0612] Smart glasses: A device that allows physical store operators to input data using voice commands.

[0613] Server: A computer system that analyzes, searches, and filters the information it receives.

[0614] Terminal: A device (such as a smartphone or tablet) used to display information sent from the server.

[0615] software:

[0616] Speech recognition technology: Converts speech to text using the speech_recognition library.

[0617] Natural Language Processing (NLP): Extract relevant keywords from text using external NLP APIs.

[0618] Database Management System (DBMS): Uses sqlite3 to manage a database for storing past project information.

[0619] Similarity evaluation: Evaluate the similarity of search results using the TfidfVectorizer and cosine_similarity functions from the scikit-learn library.

[0620] Data processing and calculation

[0621] 1. Voice input and recognition:

[0622] The server records the user's voice through the smart glasses' microphone and converts it into text using voice recognition technology. For example, a user might say, "Open a supermarket in a new neighborhood."

[0623] 2. Text analysis and keyword extraction:

[0624] The converted text is sent to a server, which uses NLP technology to extract relevant keywords such as "new area," "supermarket," and "opening."

[0625] 3. Database Search:

[0626] Based on the extracted keywords, past project information in the database is searched for. For example, past project information related to "supermarket" is retrieved.

[0627] 4. Similarity Assessment:

[0628] The project information from the search results is vectorized using the user's input information and TfidfVectorizer, and the similarity is evaluated using cosine_similarity.

[0629] 5. Filtering and displaying information:

[0630] The system filters out duplicates and unnecessary information from the project information selected by similarity assessment, formats it in JSON format, and returns it to the device, where it displays it visually to the user.

[0631] Specific examples

[0632] For example, if a user speaks into the smart glasses, "Open a supermarket in a new area," the NLP API will extract keywords such as "new area," "supermarket," and "opening," and then search the database for past related projects, perform a similarity assessment, and provide the most relevant project knowledge.

[0633] Example prompt sentence:

[0634] When a user speaks into the smart glasses, "Open a supermarket in a new area," the NLP API will extract keywords such as "new area," "supermarket," and "opening," search the database for past related projects, and then perform a similarity assessment to provide the most relevant project knowledge.

[0635] This system will enable brick-and-mortar store operators to quickly and effectively utilize past project knowledge when launching new businesses.

[0636] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0637] Step 1:

[0638] Voice input

[0639] Using the smart glasses, a user can voice-input a new business idea, such as "Open a supermarket in a new neighborhood." This voice is recorded through the smart glasses' microphone.

[0640] Input: User's voice

[0641] Output: Audio data

[0642] Step 2:

[0643] Voice Recognition

[0644] The server receives the recorded voice data and converts it into text using the speech_recognition library. The speech recognition technology generates the text "Open a supermarket in a new area."

[0645] Input: Audio data

[0646] Output: Text data

[0647] Step 3:

[0648] Text analysis and keyword extraction

[0649] The server sends the received text data to an external NLP API to extract relevant keywords, such as "new neighborhood," "supermarket," and "opening."

[0650] Input: Text data

[0651] Output: Extracted keywords

[0652] Step 4:

[0653] Database search

[0654] The server uses the extracted keywords to search for past project information in the database using SQLite3. Corresponding past project information is retrieved. For example, "Supermarket Opening Project 1" and "Supermarket Opening Project 2" are searched.

[0655] Input: Extracted keywords

[0656] Output: Past project information

[0657] Step 5:

[0658] Similarity assessment

[0659] The server vectorizes the search results and the user's input using TfidfVectorizer, and evaluates the similarity using cosine_similarity. The most similar project information is identified.

[0660] Input: User input information, past project information

[0661] Output: Similarity evaluation result

[0662] Step 6:

[0663] Information Filtering

[0664] The server filters out duplicates and unnecessary information from the similarity assessment results, for example, excluding unnecessary processes for which countermeasures have already been implemented.

[0665] Input: Similarity evaluation result

[0666] Output: Filtered information

[0667] Step 7:

[0668] Returning and displaying results

[0669] The server formats the filtered information into JSON format and returns it to the device. The device displays the returned information on a user interface, providing the user with the knowledge they need to launch a new business.

[0670] Input: Filtered information

[0671] Output: The displayed information

[0672] 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.

[0673] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, a database, and an emotion engine. The specific operation of the system is described below.

[0674] First, the user uses the device to input information about the new business. This information might include a specific business plan, such as "launching an online education platform." The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0675] The server then analyzes the HTTP request received from the device to retrieve the business proposal details. This information is then analyzed and relevant keywords are extracted using natural language processing (NLP). For example, the keywords "online education," "platform," and "launch" are identified.

[0676] Based on the extracted keywords, the server searches for past project information in a database that stores project names, implementation details, market size, competitive information, etc. The server retrieves the relevant project data using SQL queries and full-text search engines.

[0677] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[0678] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, processes required for a new business can be automatically excluded if measures have already been taken in past projects.

[0679] The emotion engine also recognizes user emotions based on user input, such as analyzing user comments and feedback to identify emotional states such as "positive" or "negative."

[0680] The emotion engine recognizes the user's emotions and adjusts the filtering results accordingly. For example, if the user's emotions are positive, it will highlight successful project examples, while if the emotions are negative, it will prioritize risk management methods.

[0681] The formatted results are then sent back to the device in JSON format, where they are displayed on the device's user interface. Users can refer to the information displayed on the device and use it to launch new businesses by leveraging past project information, market data, and competitive information.

[0682] For example, if a user types in "development of remote work tools" and has a positive emotional state, the system will prioritize providing data on past success stories and market size. On the other hand, if the user has a negative emotional state, the system will prioritize providing information on risk management methods and the problems of other competitors. As a result, users will receive information that best suits their emotional state, allowing them to make more effective business plans.

[0683] The processing flow will be explained below.

[0684] Step 1:

[0685] The user inputs information about a new business into the device. For example, they enter a specific business plan, such as "launching an online education platform," into a form. The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0686] Step 2:

[0687] The server analyzes the HTTP request received from the terminal. It extracts JSON format data from the body of the HTTP request and obtains text information about the new business. For example, the following JSON data:

[0688] json

[0689] {

[0690] "project_idea": "Launching an online education platform"

[0691] }

[0692] Step 3:

[0693] The server uses an NLP (Natural Language Processing) module to extract relevant keywords from the retrieved text information, for example, identifying the keywords "online education," "platform," and "launch."

[0694] Step 4:

[0695] The server uses an emotion engine to recognize emotions based on the information entered by the user. The emotion engine analyzes the content and context of the text and identifies emotion categories such as positive, negative, or neutral. For example, it identifies emotions such as "excited" or "anxious."

[0696] Step 5:

[0697] The server searches the database for past project information based on the extracted keywords, using SQL queries or Elasticsearch to execute queries like the following:

[0698] SQL

[0699] SELECT FROM projects WHERE description LIKE '%online education%' OR description LIKE '%platform%' OR description LIKE '%launch%';

[0700] Step 6:

[0701] The server retrieves project information that matches or is similar to the keywords from the search results, including information on past projects such as "e-learning platforms," ​​as well as related market data and competitive information.

[0702] Step 7:

[0703] The server calculates the cosine similarity to evaluate the similarity of the acquired project information. The extracted keywords and the description of each project in the search results are vectorized, and the cosine similarity is calculated to score the similarity.

[0704] Step 8:

[0705] The server ranks the most relevant project information based on the similarity score, and then extracts information with particularly high scores.

[0706] Step 9:

[0707] The server filters the extracted information. For example, if market research has already been conducted, it will exclude duplicate processes in order to reuse the information. The server also adjusts the filtering results according to the user's emotional state, as recognized by the emotion engine. For example, if the user's emotion is positive, it will prioritize displaying success stories, and if the emotion is negative, it will prioritize displaying risk management methods.

[0708] Step 10:

[0709] The server will format the filtered results in JSON, like this:

[0710] json

[0711] {

[0712] "similar_projects": [

[0713] {

[0714] "project_name": "E-learning platform",

[0715] "description": "Past launch examples",

[0716] "market_size": "2022: $2B",

[0717] "competitors": ["Company A", "Company B"]

[0718] }

[0719] ],

[0720] "unique_steps": [

[0721] "User Interface Design",

[0722] "User Testing"

[0723] ]

[0724] }

[0725] Step 11:

[0726] The server returns the formatted result to the terminal as an HTTP response, with the HTTP status code 200 OK.

[0727] Step 12:

[0728] The device analyzes the received data and displays it on a user interface. Users can refer to the information displayed on the device and utilize past project information, market data, and competitive information to help launch new businesses. For example, if positive emotions are recognized, success stories are highlighted, while if negative emotions are recognized, risk management information is emphasized.

[0729] Example 2

[0730] 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."

[0731] Conventional new business support systems have had the problem of overlooking important information before they can properly analyze the information entered by the user and provide relevant past project information. Furthermore, because they are unable to provide information that takes into account the user's emotional state, the information the user receives may not be optimal for launching a new business. To solve these problems, a system is needed that can perform highly accurate information analysis and filtering that takes into account the user's emotional state, providing optimal information for new businesses.

[0732] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information about a new business from a user and transmitting it to the server, a means for analyzing the information received by the server and extracting related keywords, a means for searching past project information in a database using the extracted keywords, a means for extracting highly similar project information from the search results, as well as market data and competitive information related to the project, a filtering means for eliminating redundant processes from the extracted information, a means for adjusting the filtered information based on the user's emotions, and a means for returning the filtered results to the user and displaying them on a terminal. This enables highly accurate information analysis and filtering that takes the user's emotional state into consideration, making it possible to quickly provide optimal information for launching a new business.

[0733] "User" refers to an individual or corporation that uses the system to input information about a new business and receives the results.

[0734] "Terminal" refers to a device such as a computer or smartphone used by a user to input information and display results.

[0735] "Server" means a central computer system that receives, analyzes, searches, and filters information submitted by users and returns the results.

[0736] "New Business" refers to any new business activity or project that you are planning or developing.

[0737] "Inputting information" refers to the act of a user using a terminal to provide detailed data and ideas about a new business to the system.

[0738] "Analysis" refers to the process by which the server understands and extracts meaning from the user's information received.

[0739] "Keywords" refer to important words or phrases that indicate a new business, identified from the analyzed information.

[0740] "Extraction" refers to the act of extracting only the necessary parts from analyzed information or search results.

[0741] A "database" refers to a collection of information that stores past projects and related information.

[0742] "Project Information" refers to data relating to specific past business activities or undertakings.

[0743] "Market Data" refers to statistics and trends relating to a particular industry or market.

[0744] "Competitive intelligence" refers to data about other companies and projects operating in the same market.

[0745] "Similarity" refers to the degree of relevance between a user's new business idea and past project information.

[0746] "Cosine similarity" refers to a mathematical measure used to quantify the similarity of information.

[0747] "Filtering" refers to the act of removing unnecessary or redundant parts from acquired information.

[0748] "Emotion" refers to a psychological state that is perceived based on user input and feedback.

[0749] "Adjustment" refers to the act of appropriately changing the filtering results based on the user's emotional state.

[0750] "JSON format" refers to a text format for structuring and expressing data, and is used when returning results to the terminal.

[0751] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, a database, and an emotion engine. The specific operation of the system is described below.

[0752] First, the user inputs information about the new business using the terminal. The terminal converts the information entered by the user into JSON format and sends it to the server as an HTTP request.

[0753] The server receives the HTTP request sent from the device and analyzes the content of the business proposal. Natural language processing (NLP) technology is used for the analysis. The server analyzes the information and extracts related keywords. For example, from a business proposal titled "Launch of an online education platform," the keywords "online education," "platform," and "launch" are extracted.

[0754] Next, the server searches the database for past project information based on the extracted keywords. This can be done using SQL queries or a full-text search engine (e.g., Elasticsearch). The server then uses a similarity evaluation algorithm, such as cosine similarity, to extract similar project information from the search results. This allows the server to identify the past project information that is most relevant to the new business proposal.

[0755] The server then filters this information, eliminating duplicate processes and unnecessary information, such as issues that have already been resolved in previous projects.

[0756] The server then uses an emotion engine to recognize the user's emotions. By analyzing the user's comments and feedback, the server identifies their emotional state, such as "positive" or "negative." The server then adjusts the filtering results based on the emotion recognized by the emotion engine. For example, if the user's emotion is positive, information highlighting success stories is provided, while if the emotion is negative, risk management methods are prioritized.

[0757] Finally, the formatted results are formatted in JSON and sent back to the device as an HTTP response. The device then parses the received JSON data and displays it in the user interface. Users can refer to the displayed information and use it to launch new businesses.

[0758] For example, if a user types in "development of remote work tools," and the user has a positive sentiment, the system will prioritize providing data on past success stories and market size. On the other hand, if the user has a negative sentiment, the system will prioritize providing information on risk management methods and the problems of other competitors.

[0759] An example of a prompt is:

[0760] New business idea: Development of remote work tools

[0761] Emotion: Positive

[0762] Information sought: Past success stories, market size data

[0763] This allows users to receive information that best suits their emotional state, allowing them to plan new business ventures more effectively.

[0764] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0765] Step 1:

[0766] A user uses a terminal to input information about a new business. The input information includes details of the business plan, such as "launching an online education platform." The input data is in text format.

[0767] Input: Information about the new business (text)

[0768] Output: Data converted to JSON format

[0769] Step 2:

[0770] The device converts the information entered by the user into JSON format and sends it to the server as an HTTP request. By converting it into JSON format, data integrity is maintained.

[0771] Input: Information about the new business (text)

[0772] Output: HTTP request (JSON format data)

[0773] Step 3:

[0774] The server analyzes the HTTP request received from the device, understands the content of the business proposal, and extracts related keywords using natural language processing (NLP) technology based on the input JSON data.

[0775] Input: HTTP request (JSON format data)

[0776] Output: Extracted keywords (e.g., "online education," "platform," "launch")

[0777] Step 4:

[0778] The server searches for past project information in the database based on the extracted keywords, using SQL queries or a full-text search engine (e.g., Elasticsearch).

[0779] Input: Extracted keywords

[0780] Output: Search results (past project information)

[0781] Step 5:

[0782] The server applies a similarity evaluation algorithm, such as cosine similarity, to identify similar project information from the search results, and the evaluation algorithm selects the most relevant information from the search results.

[0783] Input: Search results (past project information)

[0784] Output: Project information with high similarity (evaluated data)

[0785] Step 6:

[0786] The server performs filtering based on the information identified by the similarity assessment. The purpose of filtering is to remove redundant processes and unnecessary information, for example, by removing problems that have already been solved in the past.

[0787] Input: Similar project information

[0788] Output: Filtered information

[0789] Step 7:

[0790] The server uses an emotion engine to recognize the user's emotional state, analyzing the user's feedback and comments to identify emotions such as "positive" or "negative."

[0791] Input: User feedback and comments

[0792] Output: User's emotional state (e.g., positive, negative)

[0793] Step 8:

[0794] The server adjusts the filtering results based on the perceived sentiment: positive sentiment highlights success stories, while negative sentiment prioritizes risk management strategies.

[0795] Input: User's emotional state, filtered information

[0796] Output: Adjusted filtering result

[0797] Step 9:

[0798] The server formats the result in JSON format and sends it to the terminal as an HTTP response.

[0799] Input: Adjusted filtering results

[0800] Output: HTTP response (JSON format data)

[0801] Step 10:

[0802] The device analyzes the JSON data received from the server and displays the results on the user interface. Users can refer to the displayed information and use it to launch new businesses.

[0803] Input: HTTP response (JSON format data)

[0804] Output: Display on the user interface

[0805] (Application example 2)

[0806] 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."

[0807] Conventional new business support systems search past project data based on the business information entered by the user and provide results, but do not take the user's emotional state into consideration. As a result, appropriate information is not provided based on the user's emotions, making it difficult to formulate effective business plans. Furthermore, new manufacturing projects require past success stories and risk information, but appropriately filtering and providing this information has also been a challenge.

[0808] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting information about a new business from a user and transmitting it to the server; means for analyzing the information received by the server and extracting related keywords; means for searching for past project information in a database using the extracted keywords; means for extracting from the search results information about past activities with high similarity, as well as market data and competitive information related to those activities; filtering means for eliminating redundant processes from the extracted information; means for analyzing the user's emotions and adjusting the filtered results based on the emotions; and means for returning the filtered results to the user and displaying them on a terminal. This makes it possible to provide appropriate information that takes the user's emotional state into consideration, thereby supporting the development of effective business plans.

[0809] "New business information" is detailed data about new businesses or projects that users are planning.

[0810] A "server" is a computing device that receives and analyzes information sent by users.

[0811] "Related keywords" are important words and phrases extracted based on the information entered by the user.

[0812] A "database" is a collection of information that stores past project information, market data, and competitive information.

[0813] "Project Information" is details about business or work that has been carried out in the past.

[0814] The "search results" are past project information retrieved from a database using the extracted keywords.

[0815] "Highly similar past activity information" is data on past projects that have a high similarity to the information on the new business entered by the user.

[0816] "Market Data" means statistical data and trend information about a particular market.

[0817] "Competitive information" is data about other companies or projects operating in the same or similar field.

[0818] The "filtering means" is a part of the process for removing unnecessary data from the extracted information and organizing the necessary information.

[0819] "Emotion analysis means" refers to technology that identifies a user's emotional state based on their input and feedback.

[0820] "Means for adjusting filtering results" refers to a mechanism for appropriately changing filtered information based on the user's emotions.

[0821] A "terminal" is a device through which a user enters information and receives results.

[0822] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of terminals, servers, databases, and an emotion engine.

[0823] First, the user uses the terminal to input information about the new business. This information might include a specific business plan, such as a "project to introduce a new automated production line." The terminal converts this input information into JSON format and sends it to the server as an HTTP request.

[0824] The server then analyzes the HTTP request received from the device and retrieves the details of the business proposal. The server then uses a natural language processing (NLP) engine to extract relevant keywords from this retrieved information. For example, extracted keywords might include "automated line," "implementation," and "project."

[0825] Based on the extracted keywords, the server searches for past project information in a database that contains information on past projects, market data, competitive information, etc., and retrieves this information using SQL queries or a full-text search engine (e.g., Elasticsearch).

[0826] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[0827] Furthermore, the server uses an emotion engine to recognize the user's emotion based on the user's input information. For example, a text analysis engine (e.g., Google Cloud Natural Language API) analyzes the user's comments and feedback to identify the user's emotional state, such as "positive" or "negative."

[0828] The server adjusts the filtered results according to the user's emotions recognized by the emotion engine. For example, if the user's emotions are positive, it highlights success stories, and if they are negative, it prioritizes risk management methods.

[0829] Finally, the formatted results are sent back to the device in JSON format, where they are displayed on a user interface. Users can refer to the information displayed on the device and use it to launch new businesses by leveraging past project information, market data, and competitive information.

[0830] Examples:

[0831] If a user inputs a plan for a "new automated line installation project" and has positive feelings about it, the system will prioritize providing data on past success stories and market size.

[0832] Example prompts for generative AI models:

[0833] New project for the user: Installation project for a new automated line

[0834] Emotional state: Positive

[0835] Information to provide: Similar success stories, implementation steps, and effective automation techniques

[0836] In this way, the system provides appropriate information that takes into account the user's emotional state, and supports the creation of effective business plans.

[0837] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0838] Step 1:

[0839] The user uses a terminal to input information about a new business. A specific business plan (e.g., "Project to introduce a new automated line") is entered, and the terminal converts this information into JSON format. The input data is text information about the new business, and data processing is performed to convert it into JSON format. The output is the converted JSON format data.

[0840] Step 2:

[0841] The terminal sends the converted JSON format information to the server as an HTTP request. Specifically, it creates a POST request using the requests library or similar and sends it to the server's endpoint. The input is the converted JSON data, and the output is sent to the server.

[0842] Step 3:

[0843] The server analyzes the HTTP request received from the terminal and obtains the details of the business proposal. At this time, it analyzes the received JSON data and extracts text information. The input is the JSON data as an HTTP request, and the output is the analyzed text information.

[0844] Step 4:

[0845] The server uses a natural language processing (NLP) engine to extract relevant keywords from the retrieved information. For example, it uses an NLP library (e.g., spaCy or NLTK) to perform text analysis and extract keywords such as "automation line," "implementation," and "project." The input is the parsed text information, and the output is the extracted keywords.

[0846] Step 5:

[0847] The server searches the database for past project information based on the extracted keywords, using SQL queries and Elasticsearch to retrieve past project data related to the keywords. The input is the extracted keywords, and the output is the searched project data.

[0848] Step 6:

[0849] The server uses an evaluation algorithm such as cosine similarity to extract similar project information from the acquired data. For example, it uses the Scikit-learn library to perform similarity evaluation. The input is the searched project data, and the output is similar project information.

[0850] Step 7:

[0851] The server uses an emotion engine to recognize the user's emotion based on the user's input information. The emotion engine (e.g., Google Cloud Natural Language API) is used to identify a positive or negative emotional state. The input is the user's input text, and the output is the recognized emotional state.

[0852] Step 8:

[0853] The server adjusts the filtering results based on the emotion. If the emotion is positive, it emphasizes successful cases, and if it is negative, it prioritizes risk management methods. The filtering uses project information that is highly similar to the emotional state, and the filtered information is output based on this.

[0854] Step 9:

[0855] The server formats the filtered results in JSON format and returns them to the terminal. The input is the filtered information, and the output is the JSON formatted data.

[0856] Step 10:

[0857] The terminal displays the received results on a user interface. The input is the filtering results formatted in JSON format, and the output is a screen display that can be viewed by the user. Users can refer to the information displayed on the terminal and use past project information, market data, and competitive information to help launch new businesses.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] [Third embodiment]

[0862] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0863] 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.

[0864] 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).

[0865] 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.

[0866] 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.

[0867] 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).

[0868] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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."

[0874] The system based on this invention allows users to input information about new projects, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, and a database. The specific operation of the system is described below.

[0875] First, the user uses the device to input information about the new business. This information might include a specific business plan, such as "launching an online education platform." The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0876] The server then analyzes the HTTP request received from the device to retrieve the business proposal details. This information is then analyzed and relevant keywords are extracted using natural language processing (NLP). For example, the keywords "online education," "platform," and "launch" are identified.

[0877] Based on the extracted keywords, the server searches for past project information in a database that stores project names, implementation details, market size, competitive information, etc. The server retrieves the relevant project data using SQL queries and full-text search engines.

[0878] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[0879] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, processes required for a new business can be automatically excluded if measures have already been taken in past projects.

[0880] Finally, the filtered results are formatted in JSON and sent back to the terminal, which then displays the results on its user interface, allowing users to obtain the information they need to efficiently launch their business without redundant processes.

[0881] As a concrete example, consider the case where a user types in "development of remote work tools." In this case, the server extracts keywords such as "remote work," "tools," and "development," and searches the database for past projects related to "remote work tools." It then extracts similar project information, market data, and competitor information, filtering out overlapping processes and providing them. By referencing this information, users can leverage past knowledge to launch new businesses quickly and effectively.

[0882] The processing flow will be explained below.

[0883] Step 1:

[0884] The user inputs information about a new business into the device. For example, they enter a specific business plan, such as "launching an online education platform," into a form. The device converts this input information into JSON format and sends it to the server as an HTTP request.

[0885] Step 2:

[0886] The server analyzes the HTTP request received from the terminal. It extracts JSON format data from the body of the HTTP request and obtains text information about the new business. For example, the following JSON data:

[0887] json

[0888] {

[0889] "project_idea": "Launching an online education platform"

[0890] }

[0891] Step 3:

[0892] The server uses an NLP (Natural Language Processing) module to extract relevant keywords from the retrieved text information, for example, identifying the keywords "online education," "platform," and "launch."

[0893] Step 4:

[0894] The server searches the database for past project information based on the extracted keywords, using SQL queries or Elasticsearch to execute queries like the following:

[0895] SQL

[0896] SELECT FROM projects WHERE description LIKE '%online education%' OR description LIKE '%platform%' OR description LIKE '%launch%';

[0897] Step 5:

[0898] The server retrieves project information that matches or is similar to the keywords from the search results, including information on past projects such as "e-learning platforms," ​​as well as related market data and competitive information.

[0899] Step 6:

[0900] The server calculates the cosine similarity to evaluate the similarity of the acquired project information. The extracted keywords and the description of each project in the search results are vectorized, and the cosine similarity is calculated to score the similarity.

[0901] Step 7:

[0902] The server ranks the most relevant project information based on the similarity score, and then extracts information with particularly high scores.

[0903] Step 8:

[0904] The server filters the extracted information. For example, if market research has already been conducted, it eliminates duplicate processes in order to reuse the information. The filtered information leaves only the most appropriate data for the user to efficiently advance new business.

[0905] Step 9:

[0906] The server will format the filtered results in JSON, like this:

[0907] json

[0908] {

[0909] "similar_projects": [

[0910] {

[0911] "project_name": "E-learning platform",

[0912] "description": "Past launch examples",

[0913] "market_size": "2022: $2B",

[0914] "competitors": ["Company A", "Company B"]

[0915] }

[0916] ],

[0917] "unique_steps": [

[0918] "User Interface Design",

[0919] "User Testing"

[0920] ]

[0921] }

[0922] Step 10:

[0923] The server returns the formatted result to the terminal as an HTTP response, with the HTTP status code 200 OK.

[0924] Step 11:

[0925] The device analyzes the received data and displays it on a user interface. Users can refer to the information displayed on the device and use past project information, market data, and competitive information to help launch new businesses.

[0926] Example 1

[0927] 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."

[0928] Traditionally, launching a new business required manually collecting and analyzing information on past projects, market data, and competitive information, which was time-consuming and labor-intensive. It was also difficult to eliminate duplication of necessary information and redundant processes, making it difficult to launch a new business efficiently.

[0929] 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.

[0930] In this invention, the server includes means for inputting business-related information from a user and transmitting it to an information processor, means for analyzing the information received by the information processor and extracting related words and phrases, and means for searching past business information in a storage device using the extracted words and phrases. This makes it possible to efficiently collect and analyze past project information, market data, and competitive information, and provide it in a form that eliminates duplication and unnecessary processes.

[0931] "User" means a person or entity that operates the system and inputs information about a new business.

[0932] A "job" is a task or project related to commerce, service, manufacturing, or other economic activity.

[0933] An "information processor" is a computer or server that processes and analyzes received data.

[0934] "Terms" are important keywords and phrases extracted from text information using natural language processing technology.

[0935] "Storage device" means a digital data storage system, including a database, for storing historical business information.

[0936] "Market Data" means information about a particular industry or market, including market size, growth rates, trends, etc.

[0937] "Competitive information" is information about other companies or services that do similar business.

[0938] "Filtering" is the process of organizing collected information and eliminating duplication and unnecessary processes.

[0939] A "structured data format" is a format in which data is organized and stored in a certain structure, and is a method for making data handling easier.

[0940] A "display device" is a hardware device such as a monitor or display that visually displays information to a user.

[0941] "Cosine similarity" is a mathematical method for assessing the similarity of two vectors in a vector space.

[0942] This invention relates to a system that allows users to input business-related information, analyzes, searches, and filters it, and provides past business knowledge. This system is composed of a terminal, a server, and a storage device. The specific operation of the system is described below.

[0943] First, the user inputs information about the task using the device. This information might include a specific task proposal, such as "Developing a remote work tool." The device then converts the input information into JSON format and sends it to the server as an HTTP request using the HTTPS protocol.

[0944] Next, the server analyzes the HTTP request received from the device and obtains the details of the business proposal. This information is then used to extract related words and phrases using NLP (Natural Language Processing). For example, words such as "remote work," "tools," and "development" are identified. Python NLP libraries (e.g., NLTK, spaCy) are used for extraction.

[0945] Based on the extracted phrases, the server searches for past business information stored in a storage device. This storage device stores information such as business names, implementation details, market size, and competitive information. The server retrieves the relevant business data using SQL queries and full-text search engines.

[0946] The server then uses cosine similarity to extract similar business information from the acquired data. Scikit-learn's metrics.pairwise.cosine_similarity is used to evaluate similarity. The similarity evaluation identifies the past business information most relevant to the user's business proposal.

[0947] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, among the processes required for launching a business, it automatically excludes those for which countermeasures have already been taken in previous business operations.

[0948] Finally, the filtered results are formatted in JSON format and sent back to the terminal, which then displays the results on its user interface. This allows users to obtain the information they need to efficiently launch their business without redundant processes.

[0949] As a concrete example, consider the case where a user types in "development of remote work tools." In this case, the server extracts terms such as "remote work," "tools," and "development," and searches for past work related to "remote work tools" in the storage device. It then extracts similar work information, market data, and competitive information, filtering out overlapping processes and providing them. By referencing this information, users can utilize past knowledge to launch new work quickly and effectively.

[0950] Example prompt sentence:

[0951] "I've entered information about a new job: 'Developing remote work tools.' Please search for past jobs related to this job, rate and filter them by similarity, and provide the most relevant knowledge."

[0952] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0953] Step 1:

[0954] The user enters information about a new job on the device.

[0955] Input: The user enters a business proposal, such as "Development of remote work tools," into the input form on the device.

[0956] What it does: The device receives this information and converts the input into JSON format.

[0957] Output: The terminal generates business information converted into JSON format.

[0958] Step 2:

[0959] The device converts the information into JSON format and sends it to the server.

[0960] Input: Business information in JSON format in the terminal.

[0961] Operation: The terminal uses the HTTPS protocol to send business information in JSON format to the server as an HTTP request.

[0962] Output: The server receives the HTTP request.

[0963] Step 3:

[0964] The server analyzes the information it receives

[0965] Input: The HTTP request received by the server, and the business information contained within in JSON format.

[0966] How it works: The server parses the HTTP request and extracts the text information of the business proposal from the JSON data.

[0967] Output: Text information of the extracted business proposal.

[0968] Step 4:

[0969] The server extracts keywords using natural language processing (NLP) technology.

[0970] Input: Business proposal in text format.

[0971] How it works: The server uses Python NLP libraries (e.g., NLTK, spaCy) to extract relevant words such as "remote work," "tools," and "development" from the text information of the job proposal.

[0972] Output: A list of extracted terms.

[0973] Step 5:

[0974] The server searches for past business information in the storage device

[0975] Input: A list of extracted phrases.

[0976] How it works: The server uses SQL queries and a full-text search engine to search the database in the storage device and retrieve past business information.

[0977] Output: A list of the retrieved past business information.

[0978] Step 6:

[0979] The server identifies highly similar business information using cosine similarity.

[0980] Input: A list of retrieved past business information.

[0981] How it works: The server uses Scikit-learn's metrics.pairwise.cosine_similarity to identify information from past work information that is highly similar to "development of remote work tools."

[0982] Output: A list of business information with high similarity.

[0983] Step 7:

[0984] The server filters the information

[0985] Input: A list of business information with high similarity.

[0986] How it works: The server filters out duplicate processes and unnecessary information. For example, if the "market analysis" process is duplicated, it will exclude that part.

[0987] Output: A filtered list of business information.

[0988] Step 8:

[0989] The server formats the filtered information into JSON format.

[0990] Input: A filtered list of business information.

[0991] How it works: The server formats the filtering results into JSON format.

[0992] Output: Filtering information in JSON format.

[0993] Step 9:

[0994] The server returns filtering information in JSON format to the device.

[0995] Input: Filtering information in formatted JSON.

[0996] Operation: The server returns filtering information in JSON format to the device as an HTTP response.

[0997] Output: The device receives the HTTP response.

[0998] Step 10:

[0999] Displaying the information received by the device on the user interface

[1000] Input: Filtering information in JSON format.

[1001] Behavior: The device analyzes the information it receives and displays it on the user interface.

[1002] Output: Presents information in a user-readable format, such as relevant historical business information, market data, and competitive information.

[1003] This process allows users to quickly and effectively leverage past knowledge and smoothly launch new business operations.

[1004] (Application example 1)

[1005] 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."

[1006] Currently, when brick-and-mortar store operators launch new businesses, there are limited ways to efficiently utilize past project knowledge. Operators have to expend a lot of time and effort due to a lack of means to quickly and accurately obtain information, particularly using voice input. Another issue is that existing systems lack sufficient filtering and contain a lot of duplicate information.

[1007] 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.

[1008] In this invention, the server includes means for inputting information about a new business from a user and transmitting it to the server, means for analyzing the information received by the server and extracting related keywords, means for searching past project information in a database using the extracted keywords, means for inputting a new business plan by voice using a smart device and converting it into text using voice recognition technology, and means for returning the filtered results to the user and displaying them on a terminal. This enables operators of brick-and-mortar stores to quickly and accurately obtain project knowledge using voice input when launching a new business.

[1009] "User" refers to the operator of a physical store who inputs information about a new business and uses the system.

[1010] A "start-up" is a new commercial activity or project that a brick-and-mortar store operator is considering launching.

[1011] "Information" refers to specific data, proposals, ideas, plans, etc. related to new businesses.

[1012] "Server" refers to the computer system that analyzes, searches, and filters information submitted by users and returns relevant data.

[1013] "Parsing" refers to the process by which the server processes the information it receives and extracts meaningful elements and keywords.

[1014] "Keywords" refer to words or phrases that form the core of the information extracted as a result of analysis.

[1015] "Database" refers to an information accumulation device that stores past project information, market data, competitive information, and the like.

[1016] "Past Project Information" refers to detailed data about similar projects previously undertaken.

[1017] "Searching" refers to the process of using extracted keywords to find relevant information in a database.

[1018] "Similarity" refers to a criterion for evaluating the degree of commonality or relevance between the searched project information and the new business proposal.

[1019] "Market Data" refers to statistical information and analytical data relating to a particular industry or market.

[1020] "Competitive Information" refers to information about other competing companies or services in the marketplace.

[1021] "Filtering" refers to the process of removing duplicate processes and unnecessary information from search results and extracting only the necessary information.

[1022] "Smart device" refers to an advanced electronic device (e.g., smart glasses) that handles new business ideas through voice input.

[1023] "Voice recognition technology" refers to the technology that converts new business ideas input by users via voice into text.

[1024] "JSON format" refers to a text-based format that makes data lightweight and easy to handle.

[1025] "Terminal" refers to the electronic device that a user uses to view the filtering results sent from the server.

[1026] This invention provides a system for supporting operators of brick-and-mortar stores in launching new businesses, allowing users to input new business ideas by voice using a smart device and converting them into text using voice recognition technology. Specific embodiments for implementing this invention are described below.

[1027] Hardware and software used

[1028] Hardware:

[1029] Smart glasses: A device that allows physical store operators to input data using voice commands.

[1030] Server: A computer system that analyzes, searches, and filters the information it receives.

[1031] Terminal: A device (such as a smartphone or tablet) used to display information sent from the server.

[1032] software:

[1033] Speech recognition technology: Converts speech to text using the speech_recognition library.

[1034] Natural Language Processing (NLP): Extract relevant keywords from text using external NLP APIs.

[1035] Database Management System (DBMS): Uses sqlite3 to manage a database for storing past project information.

[1036] Similarity evaluation: Evaluate the similarity of search results using the TfidfVectorizer and cosine_similarity functions from the scikit-learn library.

[1037] Data processing and calculation

[1038] 1. Voice input and recognition:

[1039] The server records the user's voice through the smart glasses' microphone and converts it into text using voice recognition technology. For example, a user might say, "Open a supermarket in a new neighborhood."

[1040] 2. Text analysis and keyword extraction:

[1041] The converted text is sent to a server, which uses NLP technology to extract relevant keywords such as "new area," "supermarket," and "opening."

[1042] 3. Database Search:

[1043] Based on the extracted keywords, past project information in the database is searched for. For example, past project information related to "supermarket" is retrieved.

[1044] 4. Similarity Assessment:

[1045] The project information from the search results is vectorized using the user's input information and TfidfVectorizer, and the similarity is evaluated using cosine_similarity.

[1046] 5. Filtering and displaying information:

[1047] The system filters out duplicates and unnecessary information from the project information selected by similarity assessment, formats it in JSON format, and returns it to the device, where it displays it visually to the user.

[1048] Specific examples

[1049] For example, if a user speaks into the smart glasses, "Open a supermarket in a new area," the NLP API will extract keywords such as "new area," "supermarket," and "opening," and then search the database for past related projects, perform a similarity assessment, and provide the most relevant project knowledge.

[1050] Example prompt sentence:

[1051] When a user speaks into the smart glasses, "Open a supermarket in a new area," the NLP API will extract keywords such as "new area," "supermarket," and "opening," search the database for past related projects, and then perform a similarity assessment to provide the most relevant project knowledge.

[1052] This system will enable brick-and-mortar store operators to quickly and effectively utilize past project knowledge when launching new businesses.

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] Voice input

[1056] Using the smart glasses, a user can voice-input a new business idea, such as "Open a supermarket in a new neighborhood." This voice is recorded through the smart glasses' microphone.

[1057] Input: User's voice

[1058] Output: Audio data

[1059] Step 2:

[1060] Voice Recognition

[1061] The server receives the recorded voice data and converts it into text using the speech_recognition library. The speech recognition technology generates the text "Open a supermarket in a new area."

[1062] Input: Audio data

[1063] Output: Text data

[1064] Step 3:

[1065] Text analysis and keyword extraction

[1066] The server sends the received text data to an external NLP API to extract relevant keywords, such as "new neighborhood," "supermarket," and "opening."

[1067] Input: Text data

[1068] Output: Extracted keywords

[1069] Step 4:

[1070] Database search

[1071] The server uses the extracted keywords to search for past project information in the database using SQLite3. Corresponding past project information is retrieved. For example, "Supermarket Opening Project 1" and "Supermarket Opening Project 2" are searched.

[1072] Input: Extracted keywords

[1073] Output: Past project information

[1074] Step 5:

[1075] Similarity assessment

[1076] The server vectorizes the search results and the user's input using TfidfVectorizer, and evaluates the similarity using cosine_similarity. The most similar project information is identified.

[1077] Input: User input information, past project information

[1078] Output: Similarity evaluation result

[1079] Step 6:

[1080] Information Filtering

[1081] The server filters out duplicates and unnecessary information from the similarity assessment results, for example, excluding unnecessary processes for which countermeasures have already been implemented.

[1082] Input: Similarity evaluation result

[1083] Output: Filtered information

[1084] Step 7:

[1085] Returning and displaying results

[1086] The server formats the filtered information into JSON format and returns it to the device. The device displays the returned information on a user interface, providing the user with the knowledge they need to launch a new business.

[1087] Input: Filtered information

[1088] Output: The displayed information

[1089] 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.

[1090] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, a database, and an emotion engine. The specific operation of the system is described below.

[1091] First, the user uses the device to input information about the new business. This information might include a specific business plan, such as "launching an online education platform." The device converts this input information into JSON format and sends it to the server as an HTTP request.

[1092] The server then analyzes the HTTP request received from the device to retrieve the business proposal details. This information is then analyzed and relevant keywords are extracted using natural language processing (NLP). For example, the keywords "online education," "platform," and "launch" are identified.

[1093] Based on the extracted keywords, the server searches for past project information in a database that stores project names, implementation details, market size, competitive information, etc. The server retrieves the relevant project data using SQL queries and full-text search engines.

[1094] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[1095] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, processes required for a new business can be automatically excluded if measures have already been taken in past projects.

[1096] The emotion engine also recognizes user emotions based on user input, such as analyzing user comments and feedback to identify emotional states such as "positive" or "negative."

[1097] The emotion engine recognizes the user's emotions and adjusts the filtering results accordingly. For example, if the user's emotions are positive, it will highlight successful project examples, while if the emotions are negative, it will prioritize risk management methods.

[1098] The formatted results are then sent back to the device in JSON format, where they are displayed on the device's user interface. Users can refer to the information displayed on the device and use it to launch new businesses by leveraging past project information, market data, and competitive information.

[1099] For example, if a user types in "development of remote work tools" and has a positive emotional state, the system will prioritize providing data on past success stories and market size. On the other hand, if the user has a negative emotional state, the system will prioritize providing information on risk management methods and the problems of other competitors. As a result, users will receive information that best suits their emotional state, allowing them to make more effective business plans.

[1100] The processing flow will be explained below.

[1101] Step 1:

[1102] The user inputs information about a new business into the device. For example, they enter a specific business plan, such as "launching an online education platform," into a form. The device converts this input information into JSON format and sends it to the server as an HTTP request.

[1103] Step 2:

[1104] The server analyzes the HTTP request received from the terminal. It extracts JSON format data from the body of the HTTP request and obtains text information about the new business. For example, the following JSON data:

[1105] json

[1106] {

[1107] "project_idea": "Launching an online education platform"

[1108] }

[1109] Step 3:

[1110] The server uses an NLP (Natural Language Processing) module to extract relevant keywords from the retrieved text information, for example, identifying the keywords "online education," "platform," and "launch."

[1111] Step 4:

[1112] The server uses an emotion engine to recognize emotions based on the information entered by the user. The emotion engine analyzes the content and context of the text and identifies emotion categories such as positive, negative, or neutral. For example, it identifies emotions such as "excited" or "anxious."

[1113] Step 5:

[1114] The server searches the database for past project information based on the extracted keywords, using SQL queries or Elasticsearch to execute queries like the following:

[1115] SQL

[1116] SELECT FROM projects WHERE description LIKE '%online education%' OR description LIKE '%platform%' OR description LIKE '%launch%';

[1117] Step 6:

[1118] The server retrieves project information that matches or is similar to the keywords from the search results, including information on past projects such as "e-learning platforms," ​​as well as related market data and competitive information.

[1119] Step 7:

[1120] The server calculates the cosine similarity to evaluate the similarity of the acquired project information. The extracted keywords and the description of each project in the search results are vectorized, and the cosine similarity is calculated to score the similarity.

[1121] Step 8:

[1122] The server ranks the most relevant project information based on the similarity score, and then extracts information with particularly high scores.

[1123] Step 9:

[1124] The server filters the extracted information. For example, if market research has already been conducted, it will exclude duplicate processes in order to reuse the information. The server also adjusts the filtering results according to the user's emotional state, as recognized by the emotion engine. For example, if the user's emotion is positive, it will prioritize displaying success stories, and if the emotion is negative, it will prioritize displaying risk management methods.

[1125] Step 10:

[1126] The server will format the filtered results in JSON, like this:

[1127] json

[1128] {

[1129] "similar_projects": [

[1130] {

[1131] "project_name": "E-learning platform",

[1132] "description": "Past launch examples",

[1133] "market_size": "2022: $2B",

[1134] "competitors": ["Company A", "Company B"]

[1135] }

[1136] ],

[1137] "unique_steps": [

[1138] "User Interface Design",

[1139] "User Testing"

[1140] ]

[1141] }

[1142] Step 11:

[1143] The server returns the formatted result to the terminal as an HTTP response, with the HTTP status code 200 OK.

[1144] Step 12:

[1145] The device analyzes the received data and displays it on a user interface. Users can refer to the information displayed on the device and utilize past project information, market data, and competitive information to help launch new businesses. For example, if positive emotions are recognized, success stories are highlighted, while if negative emotions are recognized, risk management information is emphasized.

[1146] Example 2

[1147] 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."

[1148] Conventional new business support systems have had the problem of overlooking important information before they can properly analyze the information entered by the user and provide relevant past project information. Furthermore, because they are unable to provide information that takes into account the user's emotional state, the information the user receives may not be optimal for launching a new business. To solve these problems, a system is needed that can perform highly accurate information analysis and filtering that takes into account the user's emotional state, providing optimal information for new businesses.

[1149] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information about a new business from a user and transmitting it to the server, a means for analyzing the information received by the server and extracting related keywords, a means for searching past project information in a database using the extracted keywords, a means for extracting highly similar project information from the search results, as well as market data and competitive information related to the project, a filtering means for eliminating redundant processes from the extracted information, a means for adjusting the filtered information based on the user's emotions, and a means for returning the filtered results to the user and displaying them on a terminal. This enables highly accurate information analysis and filtering that takes the user's emotional state into consideration, making it possible to quickly provide optimal information for launching a new business.

[1150] "User" refers to an individual or corporation that uses the system to input information about a new business and receives the results.

[1151] "Terminal" refers to a device such as a computer or smartphone used by a user to input information and display results.

[1152] "Server" means a central computer system that receives, analyzes, searches, and filters information submitted by users and returns the results.

[1153] "New Business" refers to any new business activity or project that you are planning or developing.

[1154] "Inputting information" refers to the act of a user using a terminal to provide detailed data and ideas about a new business to the system.

[1155] "Analysis" refers to the process by which the server understands and extracts meaning from the user's information received.

[1156] "Keywords" refer to important words or phrases that indicate a new business, identified from the analyzed information.

[1157] "Extraction" refers to the act of extracting only the necessary parts from analyzed information or search results.

[1158] A "database" refers to a collection of information that stores past projects and related information.

[1159] "Project Information" refers to data relating to specific past business activities or undertakings.

[1160] "Market Data" refers to statistics and trends relating to a particular industry or market.

[1161] "Competitive intelligence" refers to data about other companies and projects operating in the same market.

[1162] "Similarity" refers to the degree of relevance between a user's new business idea and past project information.

[1163] "Cosine similarity" refers to a mathematical measure used to quantify the similarity of information.

[1164] "Filtering" refers to the act of removing unnecessary or redundant parts from acquired information.

[1165] "Emotion" refers to a psychological state that is perceived based on user input and feedback.

[1166] "Adjustment" refers to the act of appropriately changing the filtering results based on the user's emotional state.

[1167] "JSON format" refers to a text format for structuring and expressing data, and is used when returning results to the terminal.

[1168] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, a database, and an emotion engine. The specific operation of the system is described below.

[1169] First, the user inputs information about the new business using the terminal. The terminal converts the information entered by the user into JSON format and sends it to the server as an HTTP request.

[1170] The server receives the HTTP request sent from the device and analyzes the content of the business proposal. Natural language processing (NLP) technology is used for the analysis. The server analyzes the information and extracts related keywords. For example, from a business proposal titled "Launch of an online education platform," the keywords "online education," "platform," and "launch" are extracted.

[1171] Next, the server searches the database for past project information based on the extracted keywords. This can be done using SQL queries or a full-text search engine (e.g., Elasticsearch). The server then uses a similarity evaluation algorithm, such as cosine similarity, to extract similar project information from the search results. This allows the server to identify the past project information that is most relevant to the new business proposal.

[1172] The server then filters this information, eliminating duplicate processes and unnecessary information, such as issues that have already been resolved in previous projects.

[1173] The server then uses an emotion engine to recognize the user's emotions. By analyzing the user's comments and feedback, the server identifies their emotional state, such as "positive" or "negative." The server then adjusts the filtering results based on the emotion recognized by the emotion engine. For example, if the user's emotion is positive, information highlighting success stories is provided, while if the emotion is negative, risk management methods are prioritized.

[1174] Finally, the formatted results are formatted in JSON and sent back to the device as an HTTP response. The device then parses the received JSON data and displays it in the user interface. Users can refer to the displayed information and use it to launch new businesses.

[1175] For example, if a user types in "development of remote work tools," and the user has a positive sentiment, the system will prioritize providing data on past success stories and market size. On the other hand, if the user has a negative sentiment, the system will prioritize providing information on risk management methods and the problems of other competitors.

[1176] An example of a prompt is:

[1177] New business idea: Development of remote work tools

[1178] Emotion: Positive

[1179] Information sought: Past success stories, market size data

[1180] This allows users to receive information that best suits their emotional state, allowing them to plan new business ventures more effectively.

[1181] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1182] Step 1:

[1183] A user uses a terminal to input information about a new business. The input information includes details of the business plan, such as "launching an online education platform." The input data is in text format.

[1184] Input: Information about the new business (text)

[1185] Output: Data converted to JSON format

[1186] Step 2:

[1187] The device converts the information entered by the user into JSON format and sends it to the server as an HTTP request. By converting it into JSON format, data integrity is maintained.

[1188] Input: Information about the new business (text)

[1189] Output: HTTP request (JSON format data)

[1190] Step 3:

[1191] The server analyzes the HTTP request received from the device, understands the content of the business proposal, and extracts related keywords using natural language processing (NLP) technology based on the input JSON data.

[1192] Input: HTTP request (JSON format data)

[1193] Output: Extracted keywords (e.g., "online education," "platform," "launch")

[1194] Step 4:

[1195] The server searches for past project information in the database based on the extracted keywords, using SQL queries or a full-text search engine (e.g., Elasticsearch).

[1196] Input: Extracted keywords

[1197] Output: Search results (past project information)

[1198] Step 5:

[1199] The server applies a similarity evaluation algorithm, such as cosine similarity, to identify similar project information from the search results, and the evaluation algorithm selects the most relevant information from the search results.

[1200] Input: Search results (past project information)

[1201] Output: Project information with high similarity (evaluated data)

[1202] Step 6:

[1203] The server performs filtering based on the information identified by the similarity assessment. The purpose of filtering is to remove redundant processes and unnecessary information, for example, by removing problems that have already been solved in the past.

[1204] Input: Similar project information

[1205] Output: Filtered information

[1206] Step 7:

[1207] The server uses an emotion engine to recognize the user's emotional state, analyzing the user's feedback and comments to identify emotions such as "positive" or "negative."

[1208] Input: User feedback and comments

[1209] Output: User's emotional state (e.g., positive, negative)

[1210] Step 8:

[1211] The server adjusts the filtering results based on the perceived sentiment: positive sentiment highlights success stories, while negative sentiment prioritizes risk management strategies.

[1212] Input: User's emotional state, filtered information

[1213] Output: Adjusted filtering result

[1214] Step 9:

[1215] The server formats the result in JSON format and sends it to the terminal as an HTTP response.

[1216] Input: Adjusted filtering results

[1217] Output: HTTP response (JSON format data)

[1218] Step 10:

[1219] The device analyzes the JSON data received from the server and displays the results on the user interface. Users can refer to the displayed information and use it to launch new businesses.

[1220] Input: HTTP response (JSON format data)

[1221] Output: Display on the user interface

[1222] (Application example 2)

[1223] 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."

[1224] Conventional new business support systems search past project data based on the business information entered by the user and provide results, but do not take the user's emotional state into consideration. As a result, appropriate information is not provided based on the user's emotions, making it difficult to formulate effective business plans. Furthermore, new manufacturing projects require past success stories and risk information, but appropriately filtering and providing this information has also been a challenge.

[1225] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting information about a new business from a user and transmitting it to the server; means for analyzing the information received by the server and extracting related keywords; means for searching for past project information in a database using the extracted keywords; means for extracting from the search results information about past activities with high similarity, as well as market data and competitive information related to those activities; filtering means for eliminating redundant processes from the extracted information; means for analyzing the user's emotions and adjusting the filtered results based on the emotions; and means for returning the filtered results to the user and displaying them on a terminal. This makes it possible to provide appropriate information that takes the user's emotional state into consideration, thereby supporting the development of effective business plans.

[1226] "New business information" is detailed data about new businesses or projects that users are planning.

[1227] A "server" is a computing device that receives and analyzes information sent by users.

[1228] "Related keywords" are important words and phrases extracted based on the information entered by the user.

[1229] A "database" is a collection of information that stores past project information, market data, and competitive information.

[1230] "Project Information" is details about business or work that has been carried out in the past.

[1231] The "search results" are past project information retrieved from a database using the extracted keywords.

[1232] "Highly similar past activity information" is data on past projects that have a high similarity to the information on the new business entered by the user.

[1233] "Market Data" means statistical data and trend information about a particular market.

[1234] "Competitive information" is data about other companies or projects operating in the same or similar field.

[1235] The "filtering means" is a part of the process for removing unnecessary data from the extracted information and organizing the necessary information.

[1236] "Emotion analysis means" refers to technology that identifies a user's emotional state based on their input and feedback.

[1237] "Means for adjusting filtering results" refers to a mechanism for appropriately changing filtered information based on the user's emotions.

[1238] A "terminal" is a device through which a user enters information and receives results.

[1239] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of terminals, servers, databases, and an emotion engine.

[1240] First, the user uses the terminal to input information about the new business. This information might include a specific business plan, such as a "project to introduce a new automated production line." The terminal converts this input information into JSON format and sends it to the server as an HTTP request.

[1241] The server then analyzes the HTTP request received from the device and retrieves the details of the business proposal. The server then uses a natural language processing (NLP) engine to extract relevant keywords from this retrieved information. For example, extracted keywords might include "automated line," "implementation," and "project."

[1242] Based on the extracted keywords, the server searches for past project information in a database that contains information on past projects, market data, competitive information, etc., and retrieves this information using SQL queries or a full-text search engine (e.g., Elasticsearch).

[1243] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[1244] Furthermore, the server uses an emotion engine to recognize the user's emotion based on the user's input information. For example, a text analysis engine (e.g., Google Cloud Natural Language API) analyzes the user's comments and feedback to identify the user's emotional state, such as "positive" or "negative."

[1245] The server adjusts the filtered results according to the user's emotions recognized by the emotion engine. For example, if the user's emotions are positive, it highlights success stories, and if they are negative, it prioritizes risk management methods.

[1246] Finally, the formatted results are sent back to the device in JSON format, where they are displayed on a user interface. Users can refer to the information displayed on the device and use it to launch new businesses by leveraging past project information, market data, and competitive information.

[1247] Examples:

[1248] If a user inputs a plan for a "new automated line installation project" and has positive feelings about it, the system will prioritize providing data on past success stories and market size.

[1249] Example prompts for generative AI models:

[1250] New project for the user: Installation project for a new automated line

[1251] Emotional state: Positive

[1252] Information to provide: Similar success stories, implementation steps, and effective automation techniques

[1253] In this way, the system provides appropriate information that takes into account the user's emotional state, and supports the creation of effective business plans.

[1254] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1255] Step 1:

[1256] The user uses a terminal to input information about a new business. A specific business plan (e.g., "Project to introduce a new automated line") is entered, and the terminal converts this information into JSON format. The input data is text information about the new business, and data processing is performed to convert it into JSON format. The output is the converted JSON format data.

[1257] Step 2:

[1258] The terminal sends the converted JSON format information to the server as an HTTP request. Specifically, it creates a POST request using the requests library or similar and sends it to the server's endpoint. The input is the converted JSON data, and the output is sent to the server.

[1259] Step 3:

[1260] The server analyzes the HTTP request received from the terminal and obtains the details of the business proposal. At this time, it analyzes the received JSON data and extracts text information. The input is the JSON data as an HTTP request, and the output is the analyzed text information.

[1261] Step 4:

[1262] The server uses a natural language processing (NLP) engine to extract relevant keywords from the retrieved information. For example, it uses an NLP library (e.g., spaCy or NLTK) to perform text analysis and extract keywords such as "automation line," "implementation," and "project." The input is the parsed text information, and the output is the extracted keywords.

[1263] Step 5:

[1264] The server searches the database for past project information based on the extracted keywords, using SQL queries and Elasticsearch to retrieve past project data related to the keywords. The input is the extracted keywords, and the output is the searched project data.

[1265] Step 6:

[1266] The server uses an evaluation algorithm such as cosine similarity to extract similar project information from the acquired data. For example, it uses the Scikit-learn library to perform similarity evaluation. The input is the searched project data, and the output is similar project information.

[1267] Step 7:

[1268] The server uses an emotion engine to recognize the user's emotion based on the user's input information. The emotion engine (e.g., Google Cloud Natural Language API) is used to identify a positive or negative emotional state. The input is the user's input text, and the output is the recognized emotional state.

[1269] Step 8:

[1270] The server adjusts the filtering results based on the emotion. If the emotion is positive, it emphasizes successful cases, and if it is negative, it prioritizes risk management methods. The filtering uses project information that is highly similar to the emotional state, and the filtered information is output based on this.

[1271] Step 9:

[1272] The server formats the filtered results in JSON format and returns them to the terminal. The input is the filtered information, and the output is the JSON formatted data.

[1273] Step 10:

[1274] The terminal displays the received results on a user interface. The input is the filtering results formatted in JSON format, and the output is a screen display that can be viewed by the user. Users can refer to the information displayed on the terminal and use past project information, market data, and competitive information to help launch new businesses.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] [Fourth embodiment]

[1279] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1280] 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.

[1281] 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).

[1282] 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.

[1283] 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.

[1284] 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).

[1285] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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."

[1292] The system based on this invention allows users to input information about new projects, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, and a database. The specific operation of the system is described below.

[1293] First, the user uses the device to input information about the new business. This information might include a specific business plan, such as "launching an online education platform." The device converts this input information into JSON format and sends it to the server as an HTTP request.

[1294] The server then analyzes the HTTP request received from the device to retrieve the business proposal details. This information is then analyzed and relevant keywords are extracted using natural language processing (NLP). For example, the keywords "online education," "platform," and "launch" are identified.

[1295] Based on the extracted keywords, the server searches for past project information in a database that stores project names, implementation details, market size, competitive information, etc. The server retrieves the relevant project data using SQL queries and full-text search engines.

[1296] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[1297] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, processes required for a new business can be automatically excluded if measures have already been taken in past projects.

[1298] Finally, the filtered results are formatted in JSON and sent back to the terminal, which then displays the results on its user interface, allowing users to obtain the information they need to efficiently launch their business without redundant processes.

[1299] As a concrete example, consider the case where a user types in "development of remote work tools." In this case, the server extracts keywords such as "remote work," "tools," and "development," and searches the database for past projects related to "remote work tools." It then extracts similar project information, market data, and competitor information, filtering out overlapping processes and providing them. By referencing this information, users can leverage past knowledge to launch new businesses quickly and effectively.

[1300] The processing flow will be explained below.

[1301] Step 1:

[1302] The user inputs information about a new business into the device. For example, they enter a specific business plan, such as "launching an online education platform," into a form. The device converts this input information into JSON format and sends it to the server as an HTTP request.

[1303] Step 2:

[1304] The server analyzes the HTTP request received from the terminal. It extracts JSON format data from the body of the HTTP request and obtains text information about the new business. For example, the following JSON data:

[1305] json

[1306] {

[1307] "project_idea": "Launching an online education platform"

[1308] }

[1309] Step 3:

[1310] The server uses an NLP (Natural Language Processing) module to extract relevant keywords from the retrieved text information, for example, identifying the keywords "online education," "platform," and "launch."

[1311] Step 4:

[1312] The server searches the database for past project information based on the extracted keywords, using SQL queries or Elasticsearch to execute queries like the following:

[1313] SQL

[1314] SELECT FROM projects WHERE description LIKE '%online education%' OR description LIKE '%platform%' OR description LIKE '%launch%';

[1315] Step 5:

[1316] The server retrieves project information that matches or is similar to the keywords from the search results, including information on past projects such as "e-learning platforms," ​​as well as related market data and competitive information.

[1317] Step 6:

[1318] The server calculates the cosine similarity to evaluate the similarity of the acquired project information. The extracted keywords and the description of each project in the search results are vectorized, and the cosine similarity is calculated to score the similarity.

[1319] Step 7:

[1320] The server ranks the most relevant project information based on the similarity score, and then extracts information with particularly high scores.

[1321] Step 8:

[1322] The server filters the extracted information. For example, if market research has already been conducted, it eliminates duplicate processes in order to reuse the information. The filtered information leaves only the most appropriate data for the user to efficiently advance new business.

[1323] Step 9:

[1324] The server will format the filtered results in JSON, like this:

[1325] json

[1326] {

[1327] "similar_projects": [

[1328] {

[1329] "project_name": "E-learning platform",

[1330] "description": "Past launch examples",

[1331] "market_size": "2022: $2B",

[1332] "competitors": ["Company A", "Company B"]

[1333] }

[1334] ],

[1335] "unique_steps": [

[1336] "User Interface Design",

[1337] "User Testing"

[1338] ]

[1339] }

[1340] Step 10:

[1341] The server returns the formatted result to the terminal as an HTTP response, with the HTTP status code 200 OK.

[1342] Step 11:

[1343] The device analyzes the received data and displays it on a user interface. Users can refer to the information displayed on the device and use past project information, market data, and competitive information to help launch new businesses.

[1344] Example 1

[1345] 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."

[1346] Traditionally, launching a new business required manually collecting and analyzing information on past projects, market data, and competitive information, which was time-consuming and labor-intensive. It was also difficult to eliminate duplication of necessary information and redundant processes, making it difficult to launch a new business efficiently.

[1347] 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.

[1348] In this invention, the server includes means for inputting business-related information from a user and transmitting it to an information processor, means for analyzing the information received by the information processor and extracting related words and phrases, and means for searching past business information in a storage device using the extracted words and phrases. This makes it possible to efficiently collect and analyze past project information, market data, and competitive information, and provide it in a form that eliminates duplication and unnecessary processes.

[1349] "User" means a person or entity that operates the system and inputs information about a new business.

[1350] A "job" is a task or project related to commerce, service, manufacturing, or other economic activity.

[1351] An "information processor" is a computer or server that processes and analyzes received data.

[1352] "Terms" are important keywords and phrases extracted from text information using natural language processing technology.

[1353] "Storage device" means a digital data storage system, including a database, for storing historical business information.

[1354] "Market Data" means information about a particular industry or market, including market size, growth rates, trends, etc.

[1355] "Competitive information" is information about other companies or services that do similar business.

[1356] "Filtering" is the process of organizing collected information and eliminating duplication and unnecessary processes.

[1357] A "structured data format" is a format in which data is organized and stored in a certain structure, and is a method for making data handling easier.

[1358] A "display device" is a hardware device such as a monitor or display that visually displays information to a user.

[1359] "Cosine similarity" is a mathematical method for assessing the similarity of two vectors in a vector space.

[1360] This invention relates to a system that allows users to input business-related information, analyzes, searches, and filters it, and provides past business knowledge. This system is composed of a terminal, a server, and a storage device. The specific operation of the system is described below.

[1361] First, the user inputs information about the task using the device. This information might include a specific task proposal, such as "Developing a remote work tool." The device then converts the input information into JSON format and sends it to the server as an HTTP request using the HTTPS protocol.

[1362] Next, the server analyzes the HTTP request received from the device and obtains the details of the business proposal. This information is then used to extract related words and phrases using NLP (Natural Language Processing). For example, words such as "remote work," "tools," and "development" are identified. Python NLP libraries (e.g., NLTK, spaCy) are used for extraction.

[1363] Based on the extracted phrases, the server searches for past business information stored in a storage device. This storage device stores information such as business names, implementation details, market size, and competitive information. The server retrieves the relevant business data using SQL queries and full-text search engines.

[1364] The server then uses cosine similarity to extract similar business information from the acquired data. Scikit-learn's metrics.pairwise.cosine_similarity is used to evaluate similarity. The similarity evaluation identifies the past business information most relevant to the user's business proposal.

[1365] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, among the processes required for launching a business, it automatically excludes those for which countermeasures have already been taken in previous business operations.

[1366] Finally, the filtered results are formatted in JSON format and sent back to the terminal, which then displays the results on its user interface. This allows users to obtain the information they need to efficiently launch their business without redundant processes.

[1367] As a concrete example, consider the case where a user types in "development of remote work tools." In this case, the server extracts terms such as "remote work," "tools," and "development," and searches for past work related to "remote work tools" in the storage device. It then extracts similar work information, market data, and competitive information, filtering out overlapping processes and providing them. By referencing this information, users can utilize past knowledge to launch new work quickly and effectively.

[1368] Example prompt sentence:

[1369] "I've entered information about a new job: 'Developing remote work tools.' Please search for past jobs related to this job, rate and filter them by similarity, and provide the most relevant knowledge."

[1370] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1371] Step 1:

[1372] The user enters information about a new job on the device.

[1373] Input: The user enters a business proposal, such as "Development of remote work tools," into the input form on the device.

[1374] What it does: The device receives this information and converts the input into JSON format.

[1375] Output: The terminal generates business information converted into JSON format.

[1376] Step 2:

[1377] The device converts the information into JSON format and sends it to the server.

[1378] Input: Business information in JSON format in the terminal.

[1379] Operation: The terminal uses the HTTPS protocol to send business information in JSON format to the server as an HTTP request.

[1380] Output: The server receives the HTTP request.

[1381] Step 3:

[1382] The server analyzes the information it receives

[1383] Input: The HTTP request received by the server, and the business information contained within in JSON format.

[1384] How it works: The server parses the HTTP request and extracts the text information of the business proposal from the JSON data.

[1385] Output: Text information of the extracted business proposal.

[1386] Step 4:

[1387] The server extracts keywords using natural language processing (NLP) technology.

[1388] Input: Business proposal in text format.

[1389] How it works: The server uses Python NLP libraries (e.g., NLTK, spaCy) to extract relevant words such as "remote work," "tools," and "development" from the text information of the job proposal.

[1390] Output: A list of extracted terms.

[1391] Step 5:

[1392] The server searches for past business information in the storage device

[1393] Input: A list of extracted phrases.

[1394] How it works: The server uses SQL queries and a full-text search engine to search the database in the storage device and retrieve past business information.

[1395] Output: A list of the retrieved past business information.

[1396] Step 6:

[1397] The server identifies highly similar business information using cosine similarity.

[1398] Input: A list of retrieved past business information.

[1399] How it works: The server uses Scikit-learn's metrics.pairwise.cosine_similarity to identify information from past work information that is highly similar to "development of remote work tools."

[1400] Output: A list of business information with high similarity.

[1401] Step 7:

[1402] The server filters the information

[1403] Input: A list of business information with high similarity.

[1404] How it works: The server filters out duplicate processes and unnecessary information. For example, if the "market analysis" process is duplicated, it will exclude that part.

[1405] Output: A filtered list of business information.

[1406] Step 8:

[1407] The server formats the filtered information into JSON format.

[1408] Input: A filtered list of business information.

[1409] How it works: The server formats the filtering results into JSON format.

[1410] Output: Filtering information in JSON format.

[1411] Step 9:

[1412] The server returns filtering information in JSON format to the device.

[1413] Input: Filtering information in formatted JSON.

[1414] Operation: The server returns filtering information in JSON format to the device as an HTTP response.

[1415] Output: The device receives the HTTP response.

[1416] Step 10:

[1417] Displaying the information received by the device on the user interface

[1418] Input: Filtering information in JSON format.

[1419] Behavior: The device analyzes the information it receives and displays it on the user interface.

[1420] Output: Presents information in a user-readable format, such as relevant historical business information, market data, and competitive information.

[1421] This process allows users to quickly and effectively leverage past knowledge and smoothly launch new business operations.

[1422] (Application example 1)

[1423] 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."

[1424] Currently, when brick-and-mortar store operators launch new businesses, there are limited ways to efficiently utilize past project knowledge. Operators have to expend a lot of time and effort due to a lack of means to quickly and accurately obtain information, particularly using voice input. Another issue is that existing systems lack sufficient filtering and contain a lot of duplicate information.

[1425] 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.

[1426] In this invention, the server includes means for inputting information about a new business from a user and transmitting it to the server, means for analyzing the information received by the server and extracting related keywords, means for searching past project information in a database using the extracted keywords, means for inputting a new business plan by voice using a smart device and converting it into text using voice recognition technology, and means for returning the filtered results to the user and displaying them on a terminal. This enables operators of brick-and-mortar stores to quickly and accurately obtain project knowledge using voice input when launching a new business.

[1427] "User" refers to the operator of a physical store who inputs information about a new business and uses the system.

[1428] A "start-up" is a new commercial activity or project that a brick-and-mortar store operator is considering launching.

[1429] "Information" refers to specific data, proposals, ideas, plans, etc. related to new businesses.

[1430] "Server" refers to the computer system that analyzes, searches, and filters information submitted by users and returns relevant data.

[1431] "Parsing" refers to the process by which the server processes the information it receives and extracts meaningful elements and keywords.

[1432] "Keywords" refer to words or phrases that form the core of the information extracted as a result of analysis.

[1433] "Database" refers to an information accumulation device that stores past project information, market data, competitive information, and the like.

[1434] "Past Project Information" refers to detailed data about similar projects previously undertaken.

[1435] "Searching" refers to the process of using extracted keywords to find relevant information in a database.

[1436] "Similarity" refers to a criterion for evaluating the degree of commonality or relevance between the searched project information and the new business proposal.

[1437] "Market Data" refers to statistical information and analytical data relating to a particular industry or market.

[1438] "Competitive Information" refers to information about other competing companies or services in the marketplace.

[1439] "Filtering" refers to the process of removing duplicate processes and unnecessary information from search results and extracting only the necessary information.

[1440] "Smart device" refers to an advanced electronic device (e.g., smart glasses) that handles new business ideas through voice input.

[1441] "Voice recognition technology" refers to the technology that converts new business ideas input by users via voice into text.

[1442] "JSON format" refers to a text-based format that makes data lightweight and easy to handle.

[1443] "Terminal" refers to the electronic device that a user uses to view the filtering results sent from the server.

[1444] This invention provides a system for supporting operators of brick-and-mortar stores in launching new businesses, allowing users to input new business ideas by voice using a smart device and converting them into text using voice recognition technology. Specific embodiments for implementing this invention are described below.

[1445] Hardware and software used

[1446] Hardware:

[1447] Smart glasses: A device that allows physical store operators to input data using voice commands.

[1448] Server: A computer system that analyzes, searches, and filters the information it receives.

[1449] Terminal: A device (such as a smartphone or tablet) used to display information sent from the server.

[1450] software:

[1451] Speech recognition technology: Converts speech to text using the speech_recognition library.

[1452] Natural Language Processing (NLP): Extract relevant keywords from text using external NLP APIs.

[1453] Database Management System (DBMS): Uses sqlite3 to manage a database for storing past project information.

[1454] Similarity evaluation: Evaluate the similarity of search results using the TfidfVectorizer and cosine_similarity functions from the scikit-learn library.

[1455] Data processing and calculation

[1456] 1. Voice input and recognition:

[1457] The server records the user's voice through the smart glasses' microphone and converts it into text using voice recognition technology. For example, a user might say, "Open a supermarket in a new neighborhood."

[1458] 2. Text analysis and keyword extraction:

[1459] The converted text is sent to a server, which uses NLP technology to extract relevant keywords such as "new area," "supermarket," and "opening."

[1460] 3. Database Search:

[1461] Based on the extracted keywords, past project information in the database is searched for. For example, past project information related to "supermarket" is retrieved.

[1462] 4. Similarity Assessment:

[1463] The project information from the search results is vectorized using the user's input information and TfidfVectorizer, and the similarity is evaluated using cosine_similarity.

[1464] 5. Filtering and displaying information:

[1465] The system filters out duplicates and unnecessary information from the project information selected by similarity assessment, formats it in JSON format, and returns it to the device, where it displays it visually to the user.

[1466] Specific examples

[1467] For example, if a user speaks into the smart glasses, "Open a supermarket in a new area," the NLP API will extract keywords such as "new area," "supermarket," and "opening," and then search the database for past related projects, perform a similarity assessment, and provide the most relevant project knowledge.

[1468] Example prompt sentence:

[1469] When a user speaks into the smart glasses, "Open a supermarket in a new area," the NLP API will extract keywords such as "new area," "supermarket," and "opening," search the database for past related projects, and then perform a similarity assessment to provide the most relevant project knowledge.

[1470] This system will enable brick-and-mortar store operators to quickly and effectively utilize past project knowledge when launching new businesses.

[1471] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1472] Step 1:

[1473] Voice input

[1474] Using the smart glasses, a user can voice-input a new business idea, such as "Open a supermarket in a new neighborhood." This voice is recorded through the smart glasses' microphone.

[1475] Input: User's voice

[1476] Output: Audio data

[1477] Step 2:

[1478] Voice Recognition

[1479] The server receives the recorded voice data and converts it into text using the speech_recognition library. The speech recognition technology generates the text "Open a supermarket in a new area."

[1480] Input: Audio data

[1481] Output: Text data

[1482] Step 3:

[1483] Text analysis and keyword extraction

[1484] The server sends the received text data to an external NLP API to extract relevant keywords, such as "new neighborhood," "supermarket," and "opening."

[1485] Input: Text data

[1486] Output: Extracted keywords

[1487] Step 4:

[1488] Database search

[1489] The server uses the extracted keywords to search for past project information in the database using SQLite3. Corresponding past project information is retrieved. For example, "Supermarket Opening Project 1" and "Supermarket Opening Project 2" are searched.

[1490] Input: Extracted keywords

[1491] Output: Past project information

[1492] Step 5:

[1493] Similarity assessment

[1494] The server vectorizes the search results and the user's input using TfidfVectorizer, and evaluates the similarity using cosine_similarity. The most similar project information is identified.

[1495] Input: User input information, past project information

[1496] Output: Similarity evaluation result

[1497] Step 6:

[1498] Information Filtering

[1499] The server filters out duplicates and unnecessary information from the similarity assessment results, for example, excluding unnecessary processes for which countermeasures have already been implemented.

[1500] Input: Similarity evaluation result

[1501] Output: Filtered information

[1502] Step 7:

[1503] Returning and displaying results

[1504] The server formats the filtered information into JSON format and returns it to the device. The device displays the returned information on a user interface, providing the user with the knowledge they need to launch a new business.

[1505] Input: Filtered information

[1506] Output: The displayed information

[1507] 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.

[1508] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, a database, and an emotion engine. The specific operation of the system is described below.

[1509] First, the user uses the device to input information about the new business. This information might include a specific business plan, such as "launching an online education platform." The device converts this input information into JSON format and sends it to the server as an HTTP request.

[1510] The server then analyzes the HTTP request received from the device to retrieve the business proposal details. This information is then analyzed and relevant keywords are extracted using natural language processing (NLP). For example, the keywords "online education," "platform," and "launch" are identified.

[1511] Based on the extracted keywords, the server searches for past project information in a database that stores project names, implementation details, market size, competitive information, etc. The server retrieves the relevant project data using SQL queries and full-text search engines.

[1512] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[1513] The server then filters the extracted information to eliminate redundant processes and unnecessary information. For example, processes required for a new business can be automatically excluded if measures have already been taken in past projects.

[1514] The emotion engine also recognizes user emotions based on user input, such as analyzing user comments and feedback to identify emotional states such as "positive" or "negative."

[1515] The emotion engine recognizes the user's emotions and adjusts the filtering results accordingly. For example, if the user's emotions are positive, it will highlight successful project examples, while if the emotions are negative, it will prioritize risk management methods.

[1516] The formatted results are then sent back to the device in JSON format, where they are displayed on the device's user interface. Users can refer to the information displayed on the device and use it to launch new businesses by leveraging past project information, market data, and competitive information.

[1517] For example, if a user types in "development of remote work tools" and has a positive emotional state, the system will prioritize providing data on past success stories and market size. On the other hand, if the user has a negative emotional state, the system will prioritize providing information on risk management methods and the problems of other competitors. As a result, users will receive information that best suits their emotional state, allowing them to make more effective business plans.

[1518] The processing flow will be explained below.

[1519] Step 1:

[1520] The user inputs information about a new business into the device. For example, they enter a specific business plan, such as "launching an online education platform," into a form. The device converts this input information into JSON format and sends it to the server as an HTTP request.

[1521] Step 2:

[1522] The server analyzes the HTTP request received from the terminal. It extracts JSON format data from the body of the HTTP request and obtains text information about the new business. For example, the following JSON data:

[1523] json

[1524] {

[1525] "project_idea": "Launching an online education platform"

[1526] }

[1527] Step 3:

[1528] The server uses an NLP (Natural Language Processing) module to extract relevant keywords from the retrieved text information, for example, identifying the keywords "online education," "platform," and "launch."

[1529] Step 4:

[1530] The server uses an emotion engine to recognize emotions based on the information entered by the user. The emotion engine analyzes the content and context of the text and identifies emotion categories such as positive, negative, or neutral. For example, it identifies emotions such as "excited" or "anxious."

[1531] Step 5:

[1532] The server searches the database for past project information based on the extracted keywords, using SQL queries or Elasticsearch to execute queries like the following:

[1533] SQL

[1534] SELECT FROM projects WHERE description LIKE '%online education%' OR description LIKE '%platform%' OR description LIKE '%launch%';

[1535] Step 6:

[1536] The server retrieves project information that matches or is similar to the keywords from the search results, including information on past projects such as "e-learning platforms," ​​as well as related market data and competitive information.

[1537] Step 7:

[1538] The server calculates the cosine similarity to evaluate the similarity of the acquired project information. The extracted keywords and the description of each project in the search results are vectorized, and the cosine similarity is calculated to score the similarity.

[1539] Step 8:

[1540] The server ranks the most relevant project information based on the similarity score, and then extracts information with particularly high scores.

[1541] Step 9:

[1542] The server filters the extracted information. For example, if market research has already been conducted, it will exclude duplicate processes in order to reuse the information. The server also adjusts the filtering results according to the user's emotional state, as recognized by the emotion engine. For example, if the user's emotion is positive, it will prioritize displaying success stories, and if the emotion is negative, it will prioritize displaying risk management methods.

[1543] Step 10:

[1544] The server will format the filtered results in JSON, like this:

[1545] json

[1546] {

[1547] "similar_projects": [

[1548] {

[1549] "project_name": "E-learning platform",

[1550] "description": "Past launch examples",

[1551] "market_size": "2022: $2B",

[1552] "competitors": ["Company A", "Company B"]

[1553] }

[1554] ],

[1555] "unique_steps": [

[1556] "User Interface Design",

[1557] "User Testing"

[1558] ]

[1559] }

[1560] Step 11:

[1561] The server returns the formatted result to the terminal as an HTTP response, with the HTTP status code 200 OK.

[1562] Step 12:

[1563] The device analyzes the received data and displays it on a user interface. Users can refer to the information displayed on the device and utilize past project information, market data, and competitive information to help launch new businesses. For example, if positive emotions are recognized, success stories are highlighted, while if negative emotions are recognized, risk management information is emphasized.

[1564] Example 2

[1565] 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."

[1566] Conventional new business support systems have had the problem of overlooking important information before they can properly analyze the information entered by the user and provide relevant past project information. Furthermore, because they are unable to provide information that takes into account the user's emotional state, the information the user receives may not be optimal for launching a new business. To solve these problems, a system is needed that can perform highly accurate information analysis and filtering that takes into account the user's emotional state, providing optimal information for new businesses.

[1567] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information about a new business from a user and transmitting it to the server, a means for analyzing the information received by the server and extracting related keywords, a means for searching past project information in a database using the extracted keywords, a means for extracting highly similar project information from the search results, as well as market data and competitive information related to the project, a filtering means for eliminating redundant processes from the extracted information, a means for adjusting the filtered information based on the user's emotions, and a means for returning the filtered results to the user and displaying them on a terminal. This enables highly accurate information analysis and filtering that takes the user's emotional state into consideration, making it possible to quickly provide optimal information for launching a new business.

[1568] "User" refers to an individual or corporation that uses the system to input information about a new business and receives the results.

[1569] "Terminal" refers to a device such as a computer or smartphone used by a user to input information and display results.

[1570] "Server" means a central computer system that receives, analyzes, searches, and filters information submitted by users and returns the results.

[1571] "New Business" refers to any new business activity or project that you are planning or developing.

[1572] "Inputting information" refers to the act of a user using a terminal to provide detailed data and ideas about a new business to the system.

[1573] "Analysis" refers to the process by which the server understands and extracts meaning from the user's information received.

[1574] "Keywords" refer to important words or phrases that indicate a new business, identified from the analyzed information.

[1575] "Extraction" refers to the act of extracting only the necessary parts from analyzed information or search results.

[1576] A "database" refers to a collection of information that stores past projects and related information.

[1577] "Project Information" refers to data relating to specific past business activities or undertakings.

[1578] "Market Data" refers to statistics and trends relating to a particular industry or market.

[1579] "Competitive intelligence" refers to data about other companies and projects operating in the same market.

[1580] "Similarity" refers to the degree of relevance between a user's new business idea and past project information.

[1581] "Cosine similarity" refers to a mathematical measure used to quantify the similarity of information.

[1582] "Filtering" refers to the act of removing unnecessary or redundant parts from acquired information.

[1583] "Emotion" refers to a psychological state that is perceived based on user input and feedback.

[1584] "Adjustment" refers to the act of appropriately changing the filtering results based on the user's emotional state.

[1585] "JSON format" refers to a text format for structuring and expressing data, and is used when returning results to the terminal.

[1586] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of a terminal, a server, a database, and an emotion engine. The specific operation of the system is described below.

[1587] First, the user inputs information about the new business using the terminal. The terminal converts the information entered by the user into JSON format and sends it to the server as an HTTP request.

[1588] The server receives the HTTP request sent from the device and analyzes the content of the business proposal. Natural language processing (NLP) technology is used for the analysis. The server analyzes the information and extracts related keywords. For example, from a business proposal titled "Launch of an online education platform," the keywords "online education," "platform," and "launch" are extracted.

[1589] Next, the server searches the database for past project information based on the extracted keywords. This can be done using SQL queries or a full-text search engine (e.g., Elasticsearch). The server then uses a similarity evaluation algorithm, such as cosine similarity, to extract similar project information from the search results. This allows the server to identify the past project information that is most relevant to the new business proposal.

[1590] The server then filters this information, eliminating duplicate processes and unnecessary information, such as issues that have already been resolved in previous projects.

[1591] The server then uses an emotion engine to recognize the user's emotions. By analyzing the user's comments and feedback, the server identifies their emotional state, such as "positive" or "negative." The server then adjusts the filtering results based on the emotion recognized by the emotion engine. For example, if the user's emotion is positive, information highlighting success stories is provided, while if the emotion is negative, risk management methods are prioritized.

[1592] Finally, the formatted results are formatted in JSON and sent back to the device as an HTTP response. The device then parses the received JSON data and displays it in the user interface. Users can refer to the displayed information and use it to launch new businesses.

[1593] For example, if a user types in "development of remote work tools," and the user has a positive sentiment, the system will prioritize providing data on past success stories and market size. On the other hand, if the user has a negative sentiment, the system will prioritize providing information on risk management methods and the problems of other competitors.

[1594] An example of a prompt is:

[1595] New business idea: Development of remote work tools

[1596] Emotion: Positive

[1597] Information sought: Past success stories, market size data

[1598] This allows users to receive information that best suits their emotional state, allowing them to plan new business ventures more effectively.

[1599] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1600] Step 1:

[1601] A user uses a terminal to input information about a new business. The input information includes details of the business plan, such as "launching an online education platform." The input data is in text format.

[1602] Input: Information about the new business (text)

[1603] Output: Data converted to JSON format

[1604] Step 2:

[1605] The device converts the information entered by the user into JSON format and sends it to the server as an HTTP request. By converting it into JSON format, data integrity is maintained.

[1606] Input: Information about the new business (text)

[1607] Output: HTTP request (JSON format data)

[1608] Step 3:

[1609] The server analyzes the HTTP request received from the device, understands the content of the business proposal, and extracts related keywords using natural language processing (NLP) technology based on the input JSON data.

[1610] Input: HTTP request (JSON format data)

[1611] Output: Extracted keywords (e.g., "online education," "platform," "launch")

[1612] Step 4:

[1613] The server searches for past project information in the database based on the extracted keywords, using SQL queries or a full-text search engine (e.g., Elasticsearch).

[1614] Input: Extracted keywords

[1615] Output: Search results (past project information)

[1616] Step 5:

[1617] The server applies a similarity evaluation algorithm, such as cosine similarity, to identify similar project information from the search results, and the evaluation algorithm selects the most relevant information from the search results.

[1618] Input: Search results (past project information)

[1619] Output: Project information with high similarity (evaluated data)

[1620] Step 6:

[1621] The server performs filtering based on the information identified by the similarity assessment. The purpose of filtering is to remove redundant processes and unnecessary information, for example, by removing problems that have already been solved in the past.

[1622] Input: Similar project information

[1623] Output: Filtered information

[1624] Step 7:

[1625] The server uses an emotion engine to recognize the user's emotional state, analyzing the user's feedback and comments to identify emotions such as "positive" or "negative."

[1626] Input: User feedback and comments

[1627] Output: User's emotional state (e.g., positive, negative)

[1628] Step 8:

[1629] The server adjusts the filtering results based on the perceived sentiment: positive sentiment highlights success stories, while negative sentiment prioritizes risk management strategies.

[1630] Input: User's emotional state, filtered information

[1631] Output: Adjusted filtering result

[1632] Step 9:

[1633] The server formats the result in JSON format and sends it to the terminal as an HTTP response.

[1634] Input: Adjusted filtering results

[1635] Output: HTTP response (JSON format data)

[1636] Step 10:

[1637] The device analyzes the JSON data received from the server and displays the results on the user interface. Users can refer to the displayed information and use it to launch new businesses.

[1638] Input: HTTP response (JSON format data)

[1639] Output: Display on the user interface

[1640] (Application example 2)

[1641] 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."

[1642] Conventional new business support systems search past project data based on the business information entered by the user and provide results, but do not take the user's emotional state into consideration. As a result, appropriate information is not provided based on the user's emotions, making it difficult to formulate effective business plans. Furthermore, new manufacturing projects require past success stories and risk information, but appropriately filtering and providing this information has also been a challenge.

[1643] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting information about a new business from a user and transmitting it to the server; means for analyzing the information received by the server and extracting related keywords; means for searching for past project information in a database using the extracted keywords; means for extracting from the search results information about past activities with high similarity, as well as market data and competitive information related to those activities; filtering means for eliminating redundant processes from the extracted information; means for analyzing the user's emotions and adjusting the filtered results based on the emotions; and means for returning the filtered results to the user and displaying them on a terminal. This makes it possible to provide appropriate information that takes the user's emotional state into consideration, thereby supporting the development of effective business plans.

[1644] "New business information" is detailed data about new businesses or projects that users are planning.

[1645] A "server" is a computing device that receives and analyzes information sent by users.

[1646] "Related keywords" are important words and phrases extracted based on the information entered by the user.

[1647] A "database" is a collection of information that stores past project information, market data, and competitive information.

[1648] "Project Information" is details about business or work that has been carried out in the past.

[1649] The "search results" are past project information retrieved from a database using the extracted keywords.

[1650] "Highly similar past activity information" is data on past projects that have a high similarity to the information on the new business entered by the user.

[1651] "Market Data" means statistical data and trend information about a particular market.

[1652] "Competitive information" is data about other companies or projects operating in the same or similar field.

[1653] The "filtering means" is a part of the process for removing unnecessary data from the extracted information and organizing the necessary information.

[1654] "Emotion analysis means" refers to technology that identifies a user's emotional state based on their input and feedback.

[1655] "Means for adjusting filtering results" refers to a mechanism for appropriately changing filtered information based on the user's emotions.

[1656] A "terminal" is a device through which a user enters information and receives results.

[1657] The system based on this invention allows users to input information about new businesses, and then analyzes, searches, and filters the information to provide past project knowledge. This system is composed of terminals, servers, databases, and an emotion engine.

[1658] First, the user uses the terminal to input information about the new business. This information might include a specific business plan, such as a "project to introduce a new automated production line." The terminal converts this input information into JSON format and sends it to the server as an HTTP request.

[1659] The server then analyzes the HTTP request received from the device and retrieves the details of the business proposal. The server then uses a natural language processing (NLP) engine to extract relevant keywords from this retrieved information. For example, extracted keywords might include "automated line," "implementation," and "project."

[1660] Based on the extracted keywords, the server searches for past project information in a database that contains information on past projects, market data, competitive information, etc., and retrieves this information using SQL queries or a full-text search engine (e.g., Elasticsearch).

[1661] The server then uses an evaluation algorithm, such as cosine similarity, to extract similar project information from the acquired data. The similarity evaluation identifies the past project information that is most relevant to the user's new business proposal.

[1662] Furthermore, the server uses an emotion engine to recognize the user's emotion based on the user's input information. For example, a text analysis engine (e.g., Google Cloud Natural Language API) analyzes the user's comments and feedback to identify the user's emotional state, such as "positive" or "negative."

[1663] The server adjusts the filtered results according to the user's emotions recognized by the emotion engine. For example, if the user's emotions are positive, it highlights success stories, and if they are negative, it prioritizes risk management methods.

[1664] Finally, the formatted results are sent back to the device in JSON format, where they are displayed on a user interface. Users can refer to the information displayed on the device and use it to launch new businesses by leveraging past project information, market data, and competitive information.

[1665] Examples:

[1666] If a user inputs a plan for a "new automated line installation project" and has positive feelings about it, the system will prioritize providing data on past success stories and market size.

[1667] Example prompts for generative AI models:

[1668] New project for the user: Installation project for a new automated line

[1669] Emotional state: Positive

[1670] Information to provide: Similar success stories, implementation steps, and effective automation techniques

[1671] In this way, the system provides appropriate information that takes into account the user's emotional state, and supports the creation of effective business plans.

[1672] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1673] Step 1:

[1674] The user uses a terminal to input information about a new business. A specific business plan (e.g., "Project to introduce a new automated line") is entered, and the terminal converts this information into JSON format. The input data is text information about the new business, and data processing is performed to convert it into JSON format. The output is the converted JSON format data.

[1675] Step 2:

[1676] The terminal sends the converted JSON format information to the server as an HTTP request. Specifically, it creates a POST request using the requests library or similar and sends it to the server's endpoint. The input is the converted JSON data, and the output is sent to the server.

[1677] Step 3:

[1678] The server analyzes the HTTP request received from the terminal and obtains the details of the business proposal. At this time, it analyzes the received JSON data and extracts text information. The input is the JSON data as an HTTP request, and the output is the analyzed text information.

[1679] Step 4:

[1680] The server uses a natural language processing (NLP) engine to extract relevant keywords from the retrieved information. For example, it uses an NLP library (e.g., spaCy or NLTK) to perform text analysis and extract keywords such as "automation line," "implementation," and "project." The input is the parsed text information, and the output is the extracted keywords.

[1681] Step 5:

[1682] The server searches the database for past project information based on the extracted keywords, using SQL queries and Elasticsearch to retrieve past project data related to the keywords. The input is the extracted keywords, and the output is the searched project data.

[1683] Step 6:

[1684] The server uses an evaluation algorithm such as cosine similarity to extract similar project information from the acquired data. For example, it uses the Scikit-learn library to perform similarity evaluation. The input is the searched project data, and the output is similar project information.

[1685] Step 7:

[1686] The server uses an emotion engine to recognize the user's emotion based on the user's input information. The emotion engine (e.g., Google Cloud Natural Language API) is used to identify a positive or negative emotional state. The input is the user's input text, and the output is the recognized emotional state.

[1687] Step 8:

[1688] The server adjusts the filtering results based on the emotion. If the emotion is positive, it emphasizes successful cases, and if it is negative, it prioritizes risk management methods. The filtering uses project information that is highly similar to the emotional state, and the filtered information is output based on this.

[1689] Step 9:

[1690] The server formats the filtered results in JSON format and returns them to the terminal. The input is the filtered information, and the output is the JSON formatted data.

[1691] Step 10:

[1692] The terminal displays the received results on a user interface. The input is the filtering results formatted in JSON format, and the output is a screen display that can be viewed by the user. Users can refer to the information displayed on the terminal and use past project information, market data, and competitive information to help launch new businesses.

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] FIG. 9 is a diagram illustrating 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 actions 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.

[1698] 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.

[1699] 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).

[1700] 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 Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1701] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1702] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1703] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1704] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1705] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1706] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1707] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1708] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1709] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1710] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1711] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1712] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1713] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1714] The following is further disclosed regarding the above embodiment.

[1715] (Claim 1)

[1716] A means for users to input information about new businesses and send it to a server;

[1717] A means for analyzing the information received by the server and extracting related keywords;

[1718] A means for searching past project information in a database using the extracted keywords;

[1719] A means for extracting similar project information from the search results, as well as market data and competitive information related to the project;

[1720] filtering means for eliminating redundant processes from the extracted information;

[1721] a means for returning the filtered results to the user for display on the terminal;

[1722] A system including:

[1723] (Claim 2)

[1724] 10. The system of claim 1, wherein cosine similarity is used to evaluate the similarity of search results.

[1725] (Claim 3)

[1726] 10. The system of claim 1, wherein the filtered information is formatted in JSON.

[1727] "Example 1"

[1728] (Claim 1)

[1729] A means for inputting business information from a user and transmitting the information to an information processing machine;

[1730] means for analyzing the received information by an information processor and extracting related words and phrases;

[1731] A means for searching past business information in a storage device using the extracted words and phrases;

[1732] A means for extracting highly similar business information from the search results, as well as market data and competitive information related to the business;

[1733] filtering means for eliminating redundant steps from the extracted information;

[1734] means for returning the filtered results to the user for display on a display device;

[1735] A system including:

[1736] (Claim 2)

[1737] 10. The system of claim 1, wherein cosine similarity is used to evaluate the similarity of search results.

[1738] (Claim 3)

[1739] 10. The system of claim 1, further comprising: shaping the filtered information into a structured data format.

[1740] "Application Example 1"

[1741] (Claim 1)

[1742] A means for users to input information about new businesses and send it to a server;

[1743] A means for analyzing the information received by the server and extracting related keywords;

[1744] A means for searching past project information in a database using the extracted keywords;

[1745] A means for extracting similar project information from the search results, as well as market data and competitive information related to the project;

[1746] filtering means for eliminating redundant processes from the extracted information;

[1747] A means to input new business ideas by voice using a smart device and convert them into text using voice recognition technology;

[1748] a means for returning the filtered results to the user for display on the terminal;

[1749] A system including:

[1750] (Claim 2)

[1751] 10. The system of claim 1, wherein cosine similarity is used to evaluate the similarity of search results.

[1752] (Claim 3)

[1753] 10. The system of claim 1, wherein the filtered information is formatted in JSON.

[1754] "Example 2: Combining Emotion Engines"

[1755] (Claim 1)

[1756] A means for users to input information about new businesses and send it to a server;

[1757] A means for analyzing the information received by the server and extracting related keywords;

[1758] A means for searching past project information in a database using the extracted keywords;

[1759] A means for extracting similar project information from the search results, as well as market data and competitive information related to the project;

[1760] filtering means for eliminating redundant processes from the extracted information;

[1761] a means for adjusting the filtered information based on the user's emotions;

[1762] a means for returning the filtered results to the user for display on the terminal;

[1763] A system including:

[1764] (Claim 2)

[1765] 10. The system of claim 1, wherein cosine similarity is used to evaluate the similarity of search results.

[1766] (Claim 3)

[1767] 10. The system of claim 1, wherein the filtered information is formatted in JSON.

[1768] "Application example 2 when combining emotion engines"

[1769] (Claim 1)

[1770] A means for users to input information about new businesses and send it to a server;

[1771] A means for analyzing the information received by the server and extracting related keywords;

[1772] A means for searching past project information in a database using the extracted keywords;

[1773] A means for extracting information on similar past activities from the search results, as well as market data and competitive information related to the activities;

[1774] filtering means for eliminating redundant processes from the extracted information;

[1775] means for analyzing a user's sentiment and adjusting the filtering results based on the sentiment;

[1776] a means for returning the filtered results to the user for display on the terminal;

[1777] A system including:

[1778] (Claim 2)

[1779] 10. The system of claim 1, wherein cosine similarity is used to evaluate the similarity of search results.

[1780] (Claim 3)

[1781] 10. The system of claim 1, wherein the filtered information is formatted in JSON. [Explanation of symbols]

[1782] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input information about new businesses and send it to a server; A means for analyzing the information received by the server and extracting related keywords; A means for searching past project information in a database using the extracted keywords; A means for extracting similar project information from the search results, as well as market data and competitive information related to the project; filtering means for eliminating redundant processes from the extracted information; a means for returning the filtered results to the user for display on the terminal; A system including:

2. The system of claim 1 , wherein cosine similarity is used to evaluate the similarity of search results.

3. The system of claim 1 , wherein the filtered information is formatted in JSON format.

Images