system

Abstract

Provide a system. A means for receiving input product information; means for extracting related keywords based on the product information; means for generating product name candidates using the related keywords; A means for filtering the generated product name candidates; means for providing the filtered product name candidates; 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] In product marketing activities, it is extremely important to generate appropriate product names and catchy slogans that evoke emotions in order to differentiate a product from competing products. However, creating these manually takes time and effort, and requires specialized knowledge and experience, making it a significant hurdle for small and medium-sized enterprises and sole proprietors. As a result, there is a lack of effective product names and catchy slogans, leading to problems such as sluggish product recognition and sales. The present invention aims to solve these problems by providing a system that allows anyone to easily generate effective product names and catchy slogans. [Means for solving the problem]

[0005] The present invention first provides a means for receiving product information input by a user. The system also includes a means for extracting related keywords based on the product information and a means for generating product name candidates using the related keywords. The generated product name candidates are filtered by comparing them with preset criteria and a competing product database. The system then provides a means for providing the filtered product name candidates to the user. The system also provides a means for performing sentiment analysis using the input product information, a means for generating catchphrase candidates using the sentiment analysis results, and a means for evaluating the generated catchphrase candidates and providing the most appropriate one. This allows users to easily and quickly generate effective product names and catchphrases.

[0006] "Product information" refers to detailed data such as product-related features, target market, and usage scenarios.

[0007] "Related keywords" refer to important words and phrases extracted from product information that describe the product.

[0008] "Product Name Suggestions" refers to the list of product names suggested by the generation algorithm.

[0009] "Filtering" refers to the process of evaluating the generated product name candidates and eliminating duplicates and inappropriate ones.

[0010] "Competitive product database" refers to a database that stores information about competing products.

[0011] "Sentiment analysis" refers to the technology of analyzing emotions and tone from input text data.

[0012] A "catchphrase" is a short phrase that succinctly expresses the features and appeal of a product or service.

[0013] "Generation means" refers to a function that automatically generates product names and catchphrases using a specific algorithm or process.

[0014] "Evaluation method" refers to the process of evaluating the generated product names and catchphrases in terms of quality and effectiveness.

[0015] "Providing means" refers to the function of presenting product names and catch phrases to users.

[0016] "User" refers to an individual or company that uses the system to generate product names and catchphrases.

[0017] "Server" refers to a computer that processes information and stores and serves the data generated.

[0018] "Terminal" refers to the device through which a user connects to a server to input information and receive results. [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] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competing products based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[0041] Program processing

[0042] 1. User input

[0043] Users input detailed information about a product through the terminal, including the product's characteristics such as ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s."

[0044] 2. Sending data from the device to the server

[0045] The terminal converts the product information entered by the user into a certain format (e.g., JSON or XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0046] 3. Product information analysis on the server

[0047] The server receives the product information sent from the device and begins analyzing it. First, it uses a natural language processing (NLP) algorithm to extract related keywords. For example, keywords such as "natural," "moisture," and "sensuous" are extracted from the input information.

[0048] 4. Product name generation

[0049] The server generates product name candidates based on the extracted related keywords. The generation algorithm connects to a database and creates product name candidates based on past success stories and combinations of related keywords. For example, candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated.

[0050] 5. Filtering process

[0051] The server compares the generated product name candidates with a database of competing products to eliminate duplicates and names with legal issues, and cross-checks them with a patent database to select the final product name candidates that can be used.

[0052] 6. Generate a catchphrase

[0053] The server combines product information with sentiment analysis to generate catchy slogans. Using a sentiment analysis algorithm, it creates phrases that resonate with the target audience. For example, catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate your working life with organic products" are generated.

[0054] 7. Providing results to users

[0055] The filtered and evaluated list of product name and catchphrase candidates is sent to the terminal, which displays these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[0056] Specific examples

[0057] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." The server then compares the results with a database of competing products to select candidates that are legally acceptable.

[0058] Similarly, sentiment analysis is used to generate taglines, such as "Recharge your daily energy with the power of nature" or "Recharge your energy with organic products for the working woman" based on the target audience's attributes.

[0059] Users can check the list of product name and catchphrase candidates through their device and select the most suitable one, thereby enabling them to support their marketing activities quickly and effectively.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user enters detailed product information using the input form on the device. Specifically, the user enters the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events). For example, the user might enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[0063] Step 2:

[0064] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML). This conversion process unifies the format of the input data and makes it possible for the server to accurately parse it. After conversion, the data is sent to the server as encrypted data using the HTTPS protocol.

[0065] Step 3:

[0066] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0067] Step 4:

[0068] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0069] Step 5:

[0070] The server filters the generated product name candidates, using specific criteria and competitive product databases to eliminate duplicates and inappropriate names, and cross-checks them with patent databases to ensure legally sound product name candidates.

[0071] Step 6:

[0072] The server sends the filtered product name candidates to the terminal, and in the process encodes the candidate name metadata (e.g., keyword importance, popularity) and sends it back to the terminal via the network.

[0073] Step 7:

[0074] The terminal displays the list of product name candidates received from the server on the user interface, and the user checks the list of candidates presented through the terminal and selects the most suitable product name.

[0075] Step 8:

[0076] The server generates catchy copy using sentiment analysis and text generation algorithms. It generates emotionally appealing phrases based on product information and the attributes of the target market. For example, it generates catchy copy such as "Recharge your daily energy with the power of nature" or "Rejuvenate your vitality with organic products."

[0077] Step 9:

[0078] The server evaluates the generated tagline candidates and selects the most effective tagline through comparison with sentiment evaluation tools and past data.

[0079] Step 10:

[0080] The server transmits the evaluated catchphrase candidate list to the terminal, which displays the catchphrase candidates on a user interface, allowing the user to select the most suitable catchphrase.

[0081] This allows users to quickly and easily create excellent product names and catchy slogans, enabling them to effectively carry out marketing activities.

[0082] Example 1

[0083] 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."

[0084] In today's market, competition is intensifying, and to effectively promote new products, differentiated product names and catchy slogans that evoke emotions are required. However, creating these manually takes time and effort, and there is a high risk of duplication with competing products and legal issues. Given this background, there is a need for a system that allows users to easily generate attractive product names and catchy slogans, and reduce these risks by comparing them with competing products.

[0085] 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.

[0086] In this invention, the server includes means for receiving input product information, means for converting the product information into a certain format and encrypting the data before transmitting it, means for extracting related keywords based on the product information using a natural language processing algorithm, means for generating product name candidates using a generative AI model using the related keywords, means for comparing the generated product name candidates with a competing product database and filtering them, means for providing the filtered product name candidates, means for performing sentiment analysis based on the product information to generate catchy slogan candidates, means for generating catchy slogan candidates that will resonate with a target audience using the sentiment analysis algorithm, means for evaluating and providing the generated catchy slogan candidates, and means for comparing the related keywords extracted based on the product information with a competing product database and assessing patent infringement risk. This enables users to quickly and efficiently generate competitive product names and evocative catchy slogans, thereby reducing competition risk.

[0087] A "server" is a device that receives data input by a user and analyzes and processes it.

[0088] A "terminal" is an input device that allows a user to input information and transmit it to a server.

[0089] A "user" is someone who uses this system to input product information and obtain results.

[0090] "Product information" refers to detailed information about a specific product, such as ingredients, functions, design, target market, and usage scenarios.

[0091] A "natural language processing algorithm" is a computer algorithm for analyzing and understanding human language and extracting meaningful information.

[0092] "Related keywords" are important words or phrases extracted from product information.

[0093] A "generative AI model" is an artificial intelligence algorithm that generates new information based on past data and learned knowledge.

[0094] A "competitor product database" is a database that stores information on similar products and is used for comparison and matching.

[0095] "Filtering" is the process of eliminating inappropriate product names and catchphrases, including those with duplication or legal issues, from the generated product name and catchphrase candidates.

[0096] A "sentiment analysis algorithm" is an artificial intelligence algorithm that analyzes emotions and emotional responses contained in text data.

[0097] A "tagline" is a short phrase or slogan used to promote a product, intended to evoke an emotional response from the target audience.

[0098] "Patent infringement risk" refers to the possibility of infringing on the patent rights of others, in this case whether the product name or catchphrase overlaps with other existing patents.

[0099] "Evaluation" is the process of determining the appropriateness and effectiveness of the generated product names and catchphrases.

[0100] "Data transmission" refers to the process of transferring coded and encrypted data to send product information to the server.

[0101] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competitors based on product information entered by users. This system is composed of three parties: the user, a terminal, and a server.

[0102] First, the user enters detailed product information through a terminal with a dedicated interface. This information includes ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s."

[0103] Next, the terminal converts the product information entered by the user into JSON or XML format, encrypts the data using the HTTPS protocol, and sends it to the server, ensuring communication security.

[0104] The server analyzes the received product information. First, it uses a natural language processing (NLP) algorithm to extract relevant keywords from the product information. This process uses software such as SpaCy or NLTK. Extracted keywords include "natural," "moisture," and "sensuous."

[0105] Based on the related keywords, the server uses a generative AI model (such as GPT-3 (registered trademark)) to generate product name candidates. The generated product name candidates are compared with a database of competing products to eliminate duplicates and legally questionable names. This filtering process ultimately selects usable product name candidates.

[0106] The server then uses a sentiment analysis algorithm (such as VADER or BERT) to generate taglines that resonate with the target audience. For example, taglines like "Get your daily energy boost, recharge with the power of nature" or "Get organically recharged for the busy workforce" are generated.

[0107] After the generation and filtering are complete, the server sends the final product name and catchphrase candidates to the terminal, which then provides these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[0108] Examples:

[0109] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effects," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensuous," and generates product name candidates such as "Natural Moisture Cream," "Sensuous Day Cream," and "Pure Care Moisture." The server then compares the names with a database of competing products to select legally acceptable candidates. Sentiment analysis is also used to generate catchy slogans. Depending on the attributes of the target audience, suggested catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[0110] Examples of prompt statements

[0111] "Based on the information about a new skincare cream, please create a product name that differentiates it and a catchy copy that will evoke emotions. The product's features are 'natural ingredients,' 'moisturizing effect,' 'for sensitive skin,' 'for women in their 20s and 30s,' and 'daily skincare.'"

[0112] The system allows users to quickly and efficiently generate product names and catchphrases, reducing the risk of competition.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] Users input product information using a dedicated interface on the device. Specifically, they input product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The input information is sent to the device, and detailed information about the product (ingredients, functions, design, target market, usage scenarios, etc.) is prepared as input data.

[0116] Step 2:

[0117] The terminal converts the product information entered by the user into a specific format (e.g., JSON or XML). Specifically, the terminal interface receives the input data and uses a conversion algorithm to encode it in JSON or XML format. The converted data is then sent to the server using encrypted HTTPS protocol, where it is output as JSON format data.

[0118] Step 3:

[0119] The server receives the JSON data sent from the device. To analyze the received data, it first parses the JSON data. The parsed product information (ingredients, functions, design, target market, usage scenarios, etc.) is output.

[0120] Step 4:

[0121] The server uses a natural language processing (NLP) algorithm to extract related keywords based on the parsed product information. Specifically, it uses an NLP engine (e.g., SpaCy or NLTK) to analyze and extract important keywords from text data. The parsed product information is used as input data, and the extracted related keywords (e.g., "natural," "moisture," and "sensuous") are obtained as output data.

[0122] Step 5:

[0123] The server generates product name candidates using a generative AI model (such as GPT-3) based on the extracted related keywords. Specifically, the related keywords are input into the generative AI model, and the model runs a predictive generation algorithm. The related keywords are used as input data, and product name candidates (e.g., "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture") are generated as output data.

[0124] Step 6:

[0125] The server compares the generated product name candidates with a competing product database and performs filtering. Specifically, the server inputs the product name candidates into the competing product database as queries and receives the matching results. It also cross-checks with the patent database to eliminate duplication and legal issues. The generated product name candidates are used as input data, and filtered product name candidates are obtained as output data.

[0126] Step 7:

[0127] The server generates a catchy slogan by combining product information with a sentiment analysis algorithm. Specifically, it analyzes product information using a sentiment analysis engine (such as VADER or BERT) and generates phrases that resonate with the target audience. Product information is used as input data, and the generated catchy slogan (e.g., "Recharge your daily energy with the power of nature" or "Recharge your energy with organic products for the working man") is obtained as output data.

[0128] Step 8:

[0129] The server then compiles the final evaluated and filtered product name candidates and catchphrase candidates and sends them to the terminal. Specifically, the server compiles these candidates into a list and sends it to the terminal using the HTTPS protocol. The filtered product name candidates and generated catchphrases are used as input data, and the data to be sent is obtained as output data.

[0130] Step 9:

[0131] The terminal receives the data sent from the server and displays it to the user through a user interface. Specifically, the terminal analyzes the received data and presents it to the user in a visually easy-to-understand format. The data received from the server is used as input data, and display data that can be viewed by the user is obtained as output data. Based on this, the user can select the most suitable product name and catchphrase.

[0132] (Application example 1)

[0133] 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."

[0134] In conventional online shopping sites, creating product names and catchy slogans for new products is important for sales promotion, but creating them requires a great deal of time and effort, and it is difficult to differentiate a product from competing products. Furthermore, it is not easy to create an effective catchy slogan that stirs emotions. For this reason, a system that efficiently generates product names and catchy slogans is needed.

[0135] 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.

[0136] In this invention, the server includes means for receiving input product information, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for generating emotionally stirring catchphrase candidates based on the extracted keywords, means for filtering the generated product name candidates and catchphrase candidates, and means for providing the filtered product name candidates and catchphrase candidates, thereby enabling a user to quickly and effectively generate a product name and an emotionally stirring catchphrase that is differentiated from competing products.

[0137] "Product information" refers to detailed information entered by the user, such as product features, ingredients, functions, design, target market, and usage scenarios.

[0138] "Related keywords" are important words or phrases that represent the features or characteristics of a product and are extracted based on the product information.

[0139] "Product name candidates" are a set of suggested names for use as product names, generated using related keywords.

[0140] "Catchphrase candidates" are a series of promotional slogans or phrases created to evoke emotions based on product information and extracted keywords.

[0141] "Emotional appeal" means using expressions and words that strongly appeal to the emotions of the target audience, thereby attracting their interest and increasing their desire to purchase.

[0142] "Filtering" is the process of checking the generated product name and catchphrase candidates against a database of competing products, legal issues, and duplications to eliminate inappropriate ones.

[0143] "Providing" refers to presenting the generated product name and catchphrase candidates to the user via a user interface and providing them as information for selection or decision.

[0144] A "competitive product database" is a database that accumulates data on competing products that exist in the existing market, and is used to differentiate products and identify legal issues.

[0145] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competing products based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[0146] The server receives product information and uses the following hardware and software to perform the necessary processing: a natural language processing (NLP) algorithm, a sentiment analysis algorithm, the text generation model GPT-3, and the HTTPS protocol.

[0147] The user inputs detailed information about the product through the terminal. This includes the product's features such as ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s." The terminal converts the product information entered by the user into a specific format (e.g., JSON or XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0148] The server analyzes the received product information and extracts related keywords. First, it uses an NLP algorithm to extract important words and phrases from the information. For example, keywords such as "natural," "moisture," and "sensuous" are extracted. Based on these keywords, product name candidates are generated.

[0149] The server then uses the generated product name candidates, product information, and extracted keywords to generate emotionally appealing catchphrases. Using a sentiment analysis algorithm and a text generation model, the server creates phrases that resonate with the target audience. For example, suggested catchphrases include "Recharge your daily energy with the power of nature" and "Rejuvenate your working life with organic products."

[0150] The generated product name and catchphrase candidates are checked against a database of competing products to eliminate duplicates and legally questionable names and phrases. Finally, usable product name and catchphrase candidates are filtered. These candidate lists are sent to the terminal and displayed to the user through a user interface. The user can then select the most suitable candidate.

[0151] As a concrete example, let's consider a newly developed skin care cream. The user inputs product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," and "women in their 20s and 30s." The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." Sentiment analysis is also used to generate catchy slogans. Depending on the attributes of the target audience, suggested catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[0152] An example of a generated prompt is as follows:

[0153] Product Name Generation Prompt: "Generate product names using the following keywords: natural, moisturizing, sensuous"

[0154] Catchphrase generation prompt: "Generate catchy phrases for a product with the following attributes: {'ingredients': 'natural ingredients', 'function': 'moisturizing', 'design': 'simple', 'target market': 'women in their 20s and 30s'} and keywords: natural, moisturizing, sensuous"

[0155] This allows users to quickly and effectively create product names and emotionally appealing catchphrases that differentiate them from competing products.

[0156] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0157] Step 1:

[0158] The user enters product information.

[0159] The user uses the terminal to input detailed product information into the input screen. The information input includes the product's ingredients, functions, design, target market, and usage scenarios. For example, "Ingredients: Natural ingredients," "Function: Moisturizing effect," "Design: Simple," and "Target market: Women in their 20s and 30s."

[0160] Step 2:

[0161] The terminal transmits the product information to the server.

[0162] The terminal converts the entered product information into a certain format, such as JSON format, and sends it to the server as encrypted data using the HTTPS protocol.

[0163] Step 3:

[0164] The server analyzes the product information.

[0165] The server analyzes the received product information and uses NLP (Natural Language Processing) algorithms to extract relevant keywords, such as "natural," "moisture," and "sensuous," from the input data.

[0166] Input: Product information (JSON format)

[0167] Output: Extracted related keywords

[0168] Step 4:

[0169] The server generates product name candidates.

[0170] The server uses the extracted related keywords to generate product name candidates that take into account past success stories and related keyword combinations. Using a generative AI model such as GPT-3, the server generates text and generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0171] Input: Extracted related keywords

[0172] Output: Generated product name candidates

[0173] Step 5:

[0174] The server generates catchphrase candidates.

[0175] The server generates catchphrases based on product information and extracted keywords using a sentiment analysis algorithm and a generative AI model. By inputting a prompt, the server generates catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[0176] Input: Product information, extracted related keywords

[0177] Output: Generated tagline candidates

[0178] Step 6:

[0179] The server filters product name and tagline candidates.

[0180] The server compares the generated product name and catchphrase candidates with a database of competing products to check for duplication or legal issues, and eliminates inappropriate candidates.

[0181] Input: Generated product name candidates, generated tagline candidates

[0182] Output: Filtered product name candidates, filtered tagline candidates

[0183] Step 7:

[0184] The server provides the final candidate list to the terminal.

[0185] The filtered list of product name and catch phrase candidates is sent from the server to the terminal, which displays this information to the user through a user interface, allowing the user to select the product name and catch phrase that they think are most suitable.

[0186] Input: Filtered product name candidates, Filtered tagline candidates

[0187] Output: A list of product name and tagline suggestions provided to the user

[0188] 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.

[0189] This invention further improves the accuracy and effectiveness of the generated results by combining a system that automatically generates product names and emotionally appealing slogans that differentiate products from competing products based on product information entered by the user with an emotion engine that recognizes the user's emotions. This system is composed of three parties: the user, a terminal, and a server.

[0190] Program processing

[0191] 1. User input

[0192] The user uses the input form on the device to enter detailed product information, including the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events). For example, the user might enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[0193] 2. Sending data from the device to the server

[0194] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0195] 3. Product information analysis on the server

[0196] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0197] 4. Product name generation

[0198] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0199] 5. Filtering process

[0200] The server filters the generated product name candidates, using specific criteria and competitive product databases to eliminate duplicates and inappropriate names, and cross-checks them with patent databases to ensure legally sound product name candidates.

[0201] 6. Generate a catchphrase

[0202] The server generates catchphrases based on product information and the user's emotional information. The emotion engine analyzes the user's emotional state based on the information entered by the user and past behavioral data, and uses the results to generate phrases that stir emotions. For example, catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their working lives" are generated.

[0203] 7. User Emotion Recognition

[0204] While the user is using the device to input product information, the emotion engine analyzes the user's input pattern, time, and input content to infer the user's emotional state. For example, if the input speed is fast, it is inferred to be excited, and if the input is slow and careful, it is inferred to be attentive.

[0205] 8. Emotion Data Analysis

[0206] The server analyzes the emotion data obtained from the emotion engine and reflects it in the catchphrase generation process. The server incorporates the user's emotional state indicated by the emotion data into the catchphrase, generating phrases that are likely to resonate with the emotions of the target audience.

[0207] 9. Providing Results to Users

[0208] The filtered and evaluated list of product name and catchphrase candidates is sent to the terminal, which displays these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[0209] Specific examples

[0210] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." The server then compares the results with a database of competing products to select candidates that are legally acceptable.

[0211] The emotion engine analyzes the input pattern and speed while the user is entering information and estimates the user's emotional state. For example, if the user is typing quickly, it will determine that the user is excited.

[0212] The server generates catchy slogans while performing sentiment analysis using the emotional data obtained from the emotion engine. Phrases that suit the characteristics of the target audience are taken into consideration, and catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their working lives" are suggested.

[0213] Users can view a list of product name and catchphrase candidates via their device and select the most suitable one, enabling them to easily and quickly generate effective product names and catchphrases to support their marketing activities.

[0214] The processing flow will be explained below.

[0215] Step 1:

[0216] The user uses the input form on the device to enter detailed product information. Specifically, they enter the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events), and then press the send button. For example, they enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "everyday skin care."

[0217] Step 2:

[0218] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0219] Step 3:

[0220] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0221] Step 4:

[0222] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0223] Step 5:

[0224] The server filters the generated product name candidates, using a competitive product database and patent database to eliminate duplicates and inappropriate names and ensure that the product name candidates are legally sound.

[0225] Step 6:

[0226] The server uses an emotion engine to analyze the user's input patterns in real time and estimate the user's emotional state. For example, if the user types quickly, it may determine that the user is excited or nervous.

[0227] Step 7:

[0228] The server combines the emotional data obtained from the emotion engine with product information to generate catchy slogans. The emotional data reflects the user's psychological state, generating phrases that are likely to resonate with the target audience. For example, catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate yourself with organic products for those in the prime of their working lives" can be generated.

[0229] Step 8:

[0230] The server evaluates the generated tagline candidates and selects the most effective tagline through comparison with sentiment evaluation tools and past data.

[0231] Step 9:

[0232] The server sends the filtered and evaluated list of product name and catchphrase candidates to the terminal, and in the process encodes the metadata about the product names and catchphrases (e.g., importance and popularity of keywords) and sends it back to the terminal via the network.

[0233] Step 10:

[0234] The terminal displays the list of product name and catch phrase candidates received from the server on the user interface. The user can review the list of candidates presented through the terminal and select the most suitable product name and catch phrase. During this process, the candidates selected by the user are sent back to the server and saved as the final results.

[0235] This allows users to quickly and effectively generate product names and evocative catchphrases that differentiate their products from competitors, supporting their marketing efforts.

[0236] Example 2

[0237] 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."

[0238] In conventional systems, users had to manually input product information and come up with product names and catchy slogans themselves, which required time and effort, making it difficult to implement effective marketing activities. Furthermore, there were few methods for generating catchy slogans that took the user's emotional state into account, making it impossible to create slogans that resonated with the target audience. Furthermore, there was insufficient filtering of product names to avoid duplication with competing products, which increased the risk of legal issues.

[0239] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0240] In this invention, the server includes means for converting input product information into a specific format and transmitting it in encrypted form, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for cross-checking the generated product name candidates with specific criteria and a competing product database and filtering them, means for generating catch phrases based on the product information and user emotional information, and means for evaluating the generated catch phrase candidates. This enables effective marketing activities that reflect the user's emotional state while quickly generating product names and catch phrases based on product information and avoiding duplication with competing products.

[0241] "Inputted product information" refers to detailed information such as product features, target market, and usage scenarios that users provide through the system's input form.

[0242] "Format" refers to arranging data in a specific form, typically a data format such as JSON or XML.

[0243] "Encrypted form" means a form in which data has been converted using security technology to prevent unauthorized access by third parties.

[0244] "Related keywords" are specific important words or phrases extracted from the input product information using text analysis and natural language processing techniques.

[0245] "Product name candidates" are multiple product name suggestions automatically generated by the system based on related keywords.

[0246] "Filtering" is the process of removing duplicate, inappropriate, or legally questionable product name and tagline candidates from the generated product name and tagline candidates.

[0247] A "competitive product database" is a database that accumulates information on other companies' products that exist on the market, and is used for analyzing overlapping product names and competition.

[0248] "User emotion information" is data indicating the user's emotional state estimated based on the input pattern, speed, etc. when the user inputs product information.

[0249] "Catchphrase candidates" are marketing phrase ideas generated based on product information and user emotional information.

[0250] An "emotion engine" is an algorithm or system that analyzes the user's emotional state from input data and generates content based on that.

[0251] "Evaluation" is the process of evaluating the generated product name and catchphrase candidates based on specific criteria and selecting the most suitable candidate.

[0252] This invention relates to a system that automatically generates product names and evocative catchphrases to differentiate products from competitors based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[0253] The system operates using the following hardware and software.

[0254] Hardware

[0255] 1. Terminal: A device such as a PC, tablet, or smartphone. It provides an interface for users to input product information.

[0256] 2. Server: A high-performance computer (e.g., cloud server or on-premise server) that analyzes product information, generates product names and catchphrases, filters, and analyzes sentiment data.

[0257] software

[0258] 1. Input form: Implemented as a web application for users to input product features, target market, usage scenarios, etc. Specific formats include HTML, JavaScript (registered trademark), and CSS.

[0259] 2. Data transmission module: Converts the input product information into a specific format such as JSON or XML and sends it to the server in encrypted form using the HTTPS protocol.

[0260] 3. Text analysis tools: Using natural language processing (NLP) algorithms, related keywords are extracted from product information on the server side. Specific tools used include Python's NLTK and spaCy.

[0261] 4. Database management system: Used to store dictionary data for generated product names and catchphrases, as well as data on past success stories. Specifically, MySQL (registered trademark) or PostgreSQL is used.

[0262] 5. Emotion engine: This engine analyzes the user's input patterns and speed to estimate their emotional state. Specifically, it is implemented using Python libraries and machine learning models.

[0263] 6. AI Models: These include AI models for generating product names and emotive copy. These models are trained using frameworks such as TENSORFLOW® and PyTorch.

[0264] 7. UI / UX design: Provide an interface for users to view and select product name and tagline candidates using front-end frameworks such as React and Vue.js.

[0265] Specific examples

[0266] For example, let's say you're looking at a newly developed skin care cream. The user inputs the product information as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." Based on this information, the system operates as follows:

[0267] 1. The user enters information through the input form on the device.

[0268] 2. The terminal converts the entered information into JSON format and sends it to the server using the HTTPS protocol.

[0269] 3. The server receives the information and uses text analysis tools to extract keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0270] 4. Based on these keywords, product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated from the database.

[0271] 5. Filter the generated product name candidates by cross-checking them with competitor product databases and patent databases.

[0272] 6. The emotion engine analyzes the user's input patterns to infer their emotional state, and based on that generates catchy slogans such as "Recharge your daily energy with the power of nature" and "For those in the prime of their working lives, recharge with organic technology."

[0273] 7. The server sends the final filtered product name candidates and catchphrase candidates to the terminal.

[0274] 8. The user checks the list of candidates presented on the device and selects the most suitable one.

[0275] Prompt Sentence Examples

[0276] Create a product name and catchphrase based on the information "natural ingredients, moisturizing effect, for sensitive skin, for women in their 20s and 30s, daily skin care."

[0277] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0278] Step 1: User enters information

[0279] The user enters product information into an input form provided on the device. Specifically, the user enters information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." This input form is implemented using HTML and JavaScript, and once the user has completed their input, the device converts this information into JSON format. The input is detailed product information data, and the output is the product information data converted into JSON format.

[0280] Step 2: Send data from the device to the server

[0281] The terminal sends the product information data converted to JSON format to the server using the HTTPS protocol. This communication is encrypted to ensure data security. The input is JSON format product information data, and the output is encrypted data sent to the server. The terminal receives a response indicating successful transmission and notifies the user.

[0282] Step 3: Product information analysis on the server

[0283] The server analyzes the received JSON-formatted product information data. First, it uses a text analysis tool (e.g., Python's NLTK or spaCy) to extract related keywords from the product information. For example, keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin" are extracted. The input is the JSON-formatted product information data, and the output is a list of extracted keywords.

[0284] Step 4: Generate product name

[0285] The server generates product name candidates based on the extracted related keywords. It connects to a database management system (e.g., MySQL or PostgreSQL) and automatically generates product name candidates by referring to past success stories and related keyword combinations. For example, names such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated. The input is the extracted keyword list, and the output is the generated list of product name candidates.

[0286] Step 5: Filtering

[0287] The server filters the generated list of product name candidates. First, it eliminates duplicates and inappropriate names, and then cross-checks them with competitive product and patent databases to extract only legally sound product name candidates. This process is performed using Python scripts and SQL queries. The input is the generated list of product name candidates, and the output is the filtered list of product name candidates.

[0288] Step 6: Create a tagline

[0289] The server generates a catchy slogan based on product information and the user's emotional information. The emotion engine analyzes the information entered by the user and past behavioral data to estimate their emotional state. A generative AI model (e.g., TensorFlow or PyTorch) is used to generate a catchy slogan that will resonate with the target audience. For example, possible catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their lives." The input is product information and emotional information, and the output is a list of candidate catchy slogans.

[0290] Step 7: Recognizing User Emotions

[0291] While the user is entering product information, the device uses an emotion engine to analyze the input pattern and speed and estimate the user's emotional state. For example, if the input speed is fast, it is estimated to be an excited state, while if the input speed is slow and careful, it is estimated to be a cautious state. This emotion data is sent to the server in real time. The input is input pattern and speed data, and the output is emotional state data.

[0292] Step 8: Analyze the sentiment data

[0293] The server analyzes the emotional data obtained from the emotion engine and reflects it in the catchphrase generation process. It incorporates the user's emotional state indicated by the emotional data into the catchphrase to generate phrases that are likely to resonate with the target audience. The input is the emotional state data, and the output is an improved list of catchphrase candidates.

[0294] Step 9: Providing results to users

[0295] The server sends the filtered and evaluated product name candidate and catch phrase candidate list to the terminal. The terminal displays these candidates to the user through a user interface, allowing the user to select the most suitable product name and catch phrase. The input is the filtered product name candidate list and catch phrase candidate list, and the output is the candidate display on the user interface.

[0296] By following the steps above, the user can quickly and easily create a product name and catchy slogan, and effectively carry out marketing activities.

[0297] (Application example 2)

[0298] 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."

[0299] Conventional advertising generation systems were unable to consider the user's emotional state when generating product names and catchy slogans based on product information entered by the user. This made it difficult to generate effective product names and catchy slogans that would appeal to the target audience. Furthermore, filtering to differentiate from competing products was insufficient, limiting the accuracy and effectiveness of product names and catchy slogans.

[0300] The identification process by the identification 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 receiving input product information, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for filtering the generated product name candidates, means for providing the filtered product name candidates, means for analyzing the user's emotional state, means for generating catchphrase candidates based on the emotional state, and means for evaluating the generated catchphrase candidates. This makes it possible to generate effective product names and catchphrases that can differentiate from competing products, taking the user's emotional state into consideration.

[0301] "Product information" is information input by the user regarding the product's features, functions, target market, usage scenarios, etc.

[0302] "Related keywords" are important words and phrases extracted from product information that represent the characteristics and features of the product.

[0303] A "product name" is a unique, easy-to-remember combination of words used as the name of a product.

[0304] A "catchphrase" is a short phrase that succinctly conveys the features and value of a product and attracts consumer attention.

[0305] "Filtering" is the process of removing inappropriate or duplicated product names and catchphrases from the generated product name and catchphrase candidates.

[0306] The "emotional state" refers to the psychological state of the user when entering product information, and is analyzed based on facial expressions and voice data.

[0307] "Evaluation" is the process of determining whether the generated catchphrases and product name candidates are appropriate.

[0308] The "competitive product database" is a database that accumulates information about competing products that are already on the market.

[0309] A "smart device" is a device (e.g., smartphone, head-mounted display) that acquires a user's facial expressions and voice and performs emotional analysis.

[0310] To implement this invention, a system is required that links the terminal, server, and user. This system receives product information entered by the user, extracts related keywords based on that information, and then executes a series of processes to generate product names and catchphrases.

[0311] 1. Hardware and Software Configuration

[0312] Terminal

[0313] A smart device (smartphone or head-mounted display) is used as the terminal.

[0314] Input means: Provides an interface for users to input product information.

[0315] Emotion analysis means: A camera and microphone are used to capture the user's facial expressions and voice, and analyze the user's emotional state.

[0316] server

[0317] The server includes modules with the following functions:

[0318] Data receiving module: Receives product information sent from the terminal.

[0319] Keyword Extraction Module: Extracts relevant keywords from the received product information. This module uses natural language processing techniques (e.g., SpaCy, NLTK library).

[0320] Product name generation module: Generates product name candidates based on the extracted keywords. This is done using a generative AI model (e.g., GPT-3) while referencing a past database.

[0321] Filtering module: Filters the generated product name candidates and compares them with a competitor product database (e.g., SQL database).

[0322] Catchphrase generation module: Generates catchphrase candidates taking into account the user's emotional state. It uses a generative AI model and works in conjunction with the emotion engine.

[0323] Evaluation module: Evaluates the generated tagline candidates.

[0324] 2. Details of data processing and calculation

[0325] User input of product information: Users use their smart devices to input detailed information about the product, such as its features, target market, and usage scenarios. The input information is formatted in JSON or XML format.

[0326] Data reception and analysis: The smart device encrypts the product information entered by the user and the sentiment analysis results and sends them to the server. The server analyzes the received data and extracts keywords using text analysis tools and natural language processing algorithms.

[0327] Product Name and Slogan Generation: Using keywords, a generative AI model (e.g., GPT-3) generates product name and slogan candidates, taking into account the user's emotional state.

[0328] Filtering and Evaluation: The generated product name and tagline candidates are checked against a competitive product database to eliminate irrelevant and duplicated candidates, and tagline candidates are evaluated using data from the sentiment engine.

[0329] 3. Specific Examples

[0330] Example prompt sentence:

[0331] Your project is a new skincare cream. Its features include natural ingredients, moisturizing effects, and is suitable for sensitive skin. The target market is women in their 20s and 30s, and it is intended for daily skincare use. Based on this, please generate an emotionally appealing product name and tagline.

[0332] By implementing such a system, it becomes possible to generate effective product names and catchy slogans that take into account the user's emotional state and differentiate products from those of competitors.

[0333] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0334] Step 1:

[0335] Users enter product information using a smart device (smartphone or head-mounted display). In the input form, users enter details about the product's features, target market, and usage scenarios. The input information is formatted in JSON or XML. Specifically, users enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[0336] Step 2:

[0337] The terminal receives the product information entered by the user, encrypts it, and sends it to the server. The data is sent securely using the HTTPS protocol. Specifically, the terminal converts the information received from the user into JSON format and sends it to the server via HTTPS.

[0338] Step 3:

[0339] The server analyzes the received product information and extracts related keywords. This process uses natural language processing (NLP) tools (e.g., SpaCy, NLTK). Specifically, keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin" are extracted through text analysis.

[0340] Step 4:

[0341] The server generates product name candidates using a generative AI model (e.g., GPT-3) based on the extracted related keywords. Specifically, a prompt sentence is input into the generative AI model, and product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are output.

[0342] Step 5:

[0343] The server filters the generated product name candidates. This filtering uses a database of competing products to eliminate inappropriate names and duplicates. It also checks for legal issues. Specifically, it performs a database search and compares the candidates with existing product names to eliminate inappropriate candidates.

[0344] Step 6:

[0345] The server understands the user's emotional state by analyzing facial expression and tone of voice data from the device. To do this, it uses an emotion analysis engine to analyze facial expressions and voice patterns while the user is typing. Specifically, it analyzes camera and microphone data and determines that the user is in an excited state if they are typing quickly.

[0346] Step 7:

[0347] The server generates catchphrase candidates based on the results of the sentiment analysis. It uses a generative AI model to create emotionally appealing copy. Specifically, catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products" are generated.

[0348] Step 8:

[0349] The server evaluates the generated tagline candidates. It uses the sentiment analysis results and past data to select the best tagline for the target market. Specifically, it ranks the generated tagline candidates and selects the best candidate based on the evaluation criteria.

[0350] Step 9:

[0351] The server sends the filtered and evaluated product name and catchphrase candidates to the terminal. The user can check these candidate lists on the screen of the smart device and select the most suitable one. Specifically, the generated list is displayed on the user interface.

[0352] 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.

[0353] 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.

[0354] 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.

[0355] [Second embodiment]

[0356] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0357] 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.

[0358] 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).

[0359] 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.

[0360] 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.

[0361] 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).

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] In the smart glasses 214, 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.

[0367] 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."

[0368] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competing products based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[0369] Program processing

[0370] 1. User input

[0371] Users input detailed information about a product through the terminal, including the product's characteristics such as ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s."

[0372] 2. Sending data from the device to the server

[0373] The terminal converts the product information entered by the user into a certain format (e.g., JSON or XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0374] 3. Product information analysis on the server

[0375] The server receives the product information sent from the device and begins analyzing it. First, it uses a natural language processing (NLP) algorithm to extract related keywords. For example, keywords such as "natural," "moisture," and "sensuous" are extracted from the input information.

[0376] 4. Product name generation

[0377] The server generates product name candidates based on the extracted related keywords. The generation algorithm connects to a database and creates product name candidates based on past success stories and combinations of related keywords. For example, candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated.

[0378] 5. Filtering process

[0379] The server compares the generated product name candidates with a database of competing products to eliminate duplicates and names with legal issues, and cross-checks them with a patent database to select the final product name candidates that can be used.

[0380] 6. Generate a catchphrase

[0381] The server combines product information with sentiment analysis to generate catchy slogans. Using a sentiment analysis algorithm, it creates phrases that resonate with the target audience. For example, catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate your working life with organic products" are generated.

[0382] 7. Providing results to users

[0383] The filtered and evaluated list of product name and catchphrase candidates is sent to the terminal, which displays these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[0384] Specific examples

[0385] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." The server then compares the results with a database of competing products to select candidates that are legally acceptable.

[0386] Similarly, sentiment analysis is used to generate taglines, such as "Recharge your daily energy with the power of nature" or "Recharge your energy with organic products for the working woman" based on the target audience's attributes.

[0387] Users can check the list of product name and catchphrase candidates through their device and select the most suitable one, thereby enabling them to support their marketing activities quickly and effectively.

[0388] The processing flow will be explained below.

[0389] Step 1:

[0390] The user enters detailed product information using the input form on the device. Specifically, the user enters the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events). For example, the user might enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[0391] Step 2:

[0392] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML). This conversion process unifies the format of the input data and makes it possible for the server to accurately parse it. After conversion, the data is sent to the server as encrypted data using the HTTPS protocol.

[0393] Step 3:

[0394] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0395] Step 4:

[0396] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0397] Step 5:

[0398] The server filters the generated product name candidates, using specific criteria and competitive product databases to eliminate duplicates and inappropriate names, and cross-checks them with patent databases to ensure legally sound product name candidates.

[0399] Step 6:

[0400] The server sends the filtered product name candidates to the terminal, and in the process encodes the candidate name metadata (e.g., keyword importance, popularity) and sends it back to the terminal via the network.

[0401] Step 7:

[0402] The terminal displays the list of product name candidates received from the server on the user interface, and the user checks the list of candidates presented through the terminal and selects the most suitable product name.

[0403] Step 8:

[0404] The server generates catchy copy using sentiment analysis and text generation algorithms. It generates emotionally appealing phrases based on product information and the attributes of the target market. For example, it generates catchy copy such as "Recharge your daily energy with the power of nature" or "Rejuvenate your vitality with organic products."

[0405] Step 9:

[0406] The server evaluates the generated tagline candidates and selects the most effective tagline through comparison with sentiment evaluation tools and past data.

[0407] Step 10:

[0408] The server transmits the evaluated catchphrase candidate list to the terminal, which displays the catchphrase candidates on a user interface, allowing the user to select the most suitable catchphrase.

[0409] This allows users to quickly and easily create excellent product names and catchy slogans, enabling them to effectively carry out marketing activities.

[0410] Example 1

[0411] 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."

[0412] In today's market, competition is intensifying, and to effectively promote new products, differentiated product names and catchy slogans that evoke emotions are required. However, creating these manually takes time and effort, and there is a high risk of duplication with competing products and legal issues. Given this background, there is a need for a system that allows users to easily generate attractive product names and catchy slogans, and reduce these risks by comparing them with competing products.

[0413] 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.

[0414] In this invention, the server includes means for receiving input product information, means for converting the product information into a certain format and encrypting the data before transmitting it, means for extracting related keywords based on the product information using a natural language processing algorithm, means for generating product name candidates using a generative AI model using the related keywords, means for comparing the generated product name candidates with a competing product database and filtering them, means for providing the filtered product name candidates, means for performing sentiment analysis based on the product information to generate catchy slogan candidates, means for generating catchy slogan candidates that will resonate with a target audience using the sentiment analysis algorithm, means for evaluating and providing the generated catchy slogan candidates, and means for comparing the related keywords extracted based on the product information with a competing product database and assessing patent infringement risk. This enables users to quickly and efficiently generate competitive product names and evocative catchy slogans, thereby reducing competition risk.

[0415] A "server" is a device that receives data input by a user and analyzes and processes it.

[0416] A "terminal" is an input device that allows a user to input information and transmit it to a server.

[0417] A "user" is someone who uses this system to input product information and obtain results.

[0418] "Product information" refers to detailed information about a specific product, such as ingredients, functions, design, target market, and usage scenarios.

[0419] A "natural language processing algorithm" is a computer algorithm for analyzing and understanding human language and extracting meaningful information.

[0420] "Related keywords" are important words or phrases extracted from product information.

[0421] A "generative AI model" is an artificial intelligence algorithm that generates new information based on past data and learned knowledge.

[0422] A "competitor product database" is a database that stores information on similar products and is used for comparison and matching.

[0423] "Filtering" is the process of eliminating inappropriate product names and catchphrases, including those with duplication or legal issues, from the generated product name and catchphrase candidates.

[0424] A "sentiment analysis algorithm" is an artificial intelligence algorithm that analyzes emotions and emotional responses contained in text data.

[0425] A "tagline" is a short phrase or slogan used to promote a product, intended to evoke an emotional response from the target audience.

[0426] "Patent infringement risk" refers to the possibility of infringing on the patent rights of others, in this case whether the product name or catchphrase overlaps with other existing patents.

[0427] "Evaluation" is the process of determining the appropriateness and effectiveness of the generated product names and catchphrases.

[0428] "Data transmission" refers to the process of transferring coded and encrypted data to send product information to the server.

[0429] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competitors based on product information entered by users. This system is composed of three parties: the user, a terminal, and a server.

[0430] First, the user enters detailed product information through a terminal with a dedicated interface. This information includes ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s."

[0431] Next, the terminal converts the product information entered by the user into JSON or XML format, encrypts the data using the HTTPS protocol, and sends it to the server, ensuring communication security.

[0432] The server analyzes the received product information. First, it uses a natural language processing (NLP) algorithm to extract relevant keywords from the product information. This process uses software such as SpaCy or NLTK. Extracted keywords include "natural," "moisture," and "sensuous."

[0433] Based on related keywords, the server uses a generative AI model (such as GPT-3) to generate product name candidates. The generated product name candidates are compared with a database of competing products to eliminate duplicates and legally questionable names. This filtering process ultimately selects usable product name candidates.

[0434] The server then uses a sentiment analysis algorithm (such as VADER or BERT) to generate taglines that resonate with the target audience. For example, taglines like "Get your daily energy boost, recharge with the power of nature" or "Get organically recharged for the busy workforce" are generated.

[0435] After the generation and filtering are complete, the server sends the final product name and catchphrase candidates to the terminal, which then provides these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[0436] Examples:

[0437] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effects," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensuous," and generates product name candidates such as "Natural Moisture Cream," "Sensuous Day Cream," and "Pure Care Moisture." The server then compares the names with a database of competing products to select legally acceptable candidates. Sentiment analysis is also used to generate catchy slogans. Depending on the attributes of the target audience, suggested catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[0438] Examples of prompt statements

[0439] "Based on the information about a new skincare cream, please create a product name that differentiates it and a catchy copy that will evoke emotions. The product's features are 'natural ingredients,' 'moisturizing effect,' 'for sensitive skin,' 'for women in their 20s and 30s,' and 'daily skincare.'"

[0440] The system allows users to quickly and efficiently generate product names and catchphrases, reducing the risk of competition.

[0441] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0442] Step 1:

[0443] Users input product information using a dedicated interface on the device. Specifically, they input product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The input information is sent to the device, and detailed information about the product (ingredients, functions, design, target market, usage scenarios, etc.) is prepared as input data.

[0444] Step 2:

[0445] The terminal converts the product information entered by the user into a specific format (e.g., JSON or XML). Specifically, the terminal interface receives the input data and uses a conversion algorithm to encode it in JSON or XML format. The converted data is then sent to the server using encrypted HTTPS protocol, where it is output as JSON format data.

[0446] Step 3:

[0447] The server receives the JSON data sent from the device. To analyze the received data, it first parses the JSON data. The parsed product information (ingredients, functions, design, target market, usage scenarios, etc.) is output.

[0448] Step 4:

[0449] The server uses a natural language processing (NLP) algorithm to extract related keywords based on the parsed product information. Specifically, it uses an NLP engine (e.g., SpaCy or NLTK) to analyze and extract important keywords from text data. The parsed product information is used as input data, and the extracted related keywords (e.g., "natural," "moisture," and "sensuous") are obtained as output data.

[0450] Step 5:

[0451] The server generates product name candidates using a generative AI model (such as GPT-3) based on the extracted related keywords. Specifically, the related keywords are input into the generative AI model, and the model runs a predictive generation algorithm. The related keywords are used as input data, and product name candidates (e.g., "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture") are generated as output data.

[0452] Step 6:

[0453] The server compares the generated product name candidates with a competing product database and performs filtering. Specifically, the server inputs the product name candidates into the competing product database as queries and receives the matching results. It also cross-checks with the patent database to eliminate duplication and legal issues. The generated product name candidates are used as input data, and filtered product name candidates are obtained as output data.

[0454] Step 7:

[0455] The server generates a catchy slogan by combining product information with a sentiment analysis algorithm. Specifically, it analyzes product information using a sentiment analysis engine (such as VADER or BERT) and generates phrases that resonate with the target audience. Product information is used as input data, and the generated catchy slogan (e.g., "Recharge your daily energy with the power of nature" or "Recharge your energy with organic products for the working man") is obtained as output data.

[0456] Step 8:

[0457] The server then compiles the final evaluated and filtered product name candidates and catchphrase candidates and sends them to the terminal. Specifically, the server compiles these candidates into a list and sends it to the terminal using the HTTPS protocol. The filtered product name candidates and generated catchphrases are used as input data, and the data to be sent is obtained as output data.

[0458] Step 9:

[0459] The terminal receives the data sent from the server and displays it to the user through a user interface. Specifically, the terminal analyzes the received data and presents it to the user in a visually easy-to-understand format. The data received from the server is used as input data, and display data that can be viewed by the user is obtained as output data. Based on this, the user can select the most suitable product name and catchphrase.

[0460] (Application example 1)

[0461] 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."

[0462] In conventional online shopping sites, creating product names and catchy slogans for new products is important for sales promotion, but creating them requires a great deal of time and effort, and it is difficult to differentiate a product from competing products. Furthermore, it is not easy to create an effective catchy slogan that stirs emotions. For this reason, a system that efficiently generates product names and catchy slogans is needed.

[0463] 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.

[0464] In this invention, the server includes means for receiving input product information, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for generating emotionally stirring catchphrase candidates based on the extracted keywords, means for filtering the generated product name candidates and catchphrase candidates, and means for providing the filtered product name candidates and catchphrase candidates, thereby enabling a user to quickly and effectively generate a product name and an emotionally stirring catchphrase that is differentiated from competing products.

[0465] "Product information" refers to detailed information entered by the user, such as product features, ingredients, functions, design, target market, and usage scenarios.

[0466] "Related keywords" are important words or phrases that represent the features or characteristics of a product and are extracted based on the product information.

[0467] "Product name candidates" are a set of suggested names for use as product names, generated using related keywords.

[0468] "Catchphrase candidates" are a series of promotional slogans or phrases created to evoke emotions based on product information and extracted keywords.

[0469] "Emotional appeal" means using expressions and words that strongly appeal to the emotions of the target audience, thereby attracting their interest and increasing their desire to purchase.

[0470] "Filtering" is the process of checking the generated product name and catchphrase candidates against a database of competing products, legal issues, and duplications to eliminate inappropriate ones.

[0471] "Providing" refers to presenting the generated product name and catchphrase candidates to the user via a user interface and providing them as information for selection or decision.

[0472] A "competitive product database" is a database that accumulates data on competing products that exist in the existing market, and is used to differentiate products and identify legal issues.

[0473] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competing products based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[0474] The server receives product information and uses the following hardware and software to perform the necessary processing: a natural language processing (NLP) algorithm, a sentiment analysis algorithm, the text generation model GPT-3, and the HTTPS protocol.

[0475] The user inputs detailed information about the product through the terminal. This includes the product's features such as ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s." The terminal converts the product information entered by the user into a specific format (e.g., JSON or XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0476] The server analyzes the received product information and extracts related keywords. First, it uses an NLP algorithm to extract important words and phrases from the information. For example, keywords such as "natural," "moisture," and "sensuous" are extracted. Based on these keywords, product name candidates are generated.

[0477] The server then uses the generated product name candidates, product information, and extracted keywords to generate emotionally appealing catchphrases. Using a sentiment analysis algorithm and a text generation model, the server creates phrases that resonate with the target audience. For example, suggested catchphrases include "Recharge your daily energy with the power of nature" and "Rejuvenate your working life with organic products."

[0478] The generated product name and catchphrase candidates are checked against a database of competing products to eliminate duplicates and legally questionable names and phrases. Finally, usable product name and catchphrase candidates are filtered. These candidate lists are sent to the terminal and displayed to the user through a user interface. The user can then select the most suitable candidate.

[0479] As a concrete example, let's consider a newly developed skin care cream. The user inputs product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," and "women in their 20s and 30s." The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." Sentiment analysis is also used to generate catchy slogans. Depending on the attributes of the target audience, suggested catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[0480] An example of a generated prompt is as follows:

[0481] Product Name Generation Prompt: "Generate product names using the following keywords: natural, moisturizing, sensuous"

[0482] Catchphrase generation prompt: "Generate catchy phrases for a product with the following attributes: {'ingredients': 'natural ingredients', 'function': 'moisturizing', 'design': 'simple', 'target market': 'women in their 20s and 30s'} and keywords: natural, moisturizing, sensuous"

[0483] This allows users to quickly and effectively create product names and emotionally appealing catchphrases that differentiate them from competing products.

[0484] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0485] Step 1:

[0486] The user enters product information.

[0487] The user uses the terminal to input detailed product information into the input screen. The information input includes the product's ingredients, functions, design, target market, and usage scenarios. For example, "Ingredients: Natural ingredients," "Function: Moisturizing effect," "Design: Simple," and "Target market: Women in their 20s and 30s."

[0488] Step 2:

[0489] The terminal transmits the product information to the server.

[0490] The terminal converts the entered product information into a certain format, such as JSON format, and sends it to the server as encrypted data using the HTTPS protocol.

[0491] Step 3:

[0492] The server analyzes the product information.

[0493] The server analyzes the received product information and uses NLP (Natural Language Processing) algorithms to extract relevant keywords, such as "natural," "moisture," and "sensuous," from the input data.

[0494] Input: Product information (JSON format)

[0495] Output: Extracted related keywords

[0496] Step 4:

[0497] The server generates product name candidates.

[0498] The server uses the extracted related keywords to generate product name candidates that take into account past success stories and related keyword combinations. Using a generative AI model such as GPT-3, the server generates text and generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0499] Input: Extracted related keywords

[0500] Output: Generated product name candidates

[0501] Step 5:

[0502] The server generates catchphrase candidates.

[0503] The server generates catchphrases based on product information and extracted keywords using a sentiment analysis algorithm and a generative AI model. By inputting a prompt, the server generates catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[0504] Input: Product information, extracted related keywords

[0505] Output: Generated tagline candidates

[0506] Step 6:

[0507] The server filters product name and tagline candidates.

[0508] The server compares the generated product name and catchphrase candidates with a database of competing products to check for duplication or legal issues, and eliminates inappropriate candidates.

[0509] Input: Generated product name candidates, generated tagline candidates

[0510] Output: Filtered product name candidates, filtered tagline candidates

[0511] Step 7:

[0512] The server provides the final candidate list to the terminal.

[0513] The filtered list of product name and catch phrase candidates is sent from the server to the terminal, which displays this information to the user through a user interface, allowing the user to select the product name and catch phrase that they think are most suitable.

[0514] Input: Filtered product name candidates, Filtered tagline candidates

[0515] Output: A list of product name and tagline suggestions provided to the user

[0516] 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.

[0517] This invention further improves the accuracy and effectiveness of the generated results by combining a system that automatically generates product names and emotionally appealing slogans that differentiate products from competing products based on product information entered by the user with an emotion engine that recognizes the user's emotions. This system is composed of three parties: the user, a terminal, and a server.

[0518] Program processing

[0519] 1. User input

[0520] The user uses the input form on the device to enter detailed product information, including the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events). For example, the user might enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[0521] 2. Sending data from the device to the server

[0522] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0523] 3. Product information analysis on the server

[0524] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0525] 4. Product name generation

[0526] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0527] 5. Filtering process

[0528] The server filters the generated product name candidates, using specific criteria and competitive product databases to eliminate duplicates and inappropriate names, and cross-checks them with patent databases to ensure legally sound product name candidates.

[0529] 6. Generate a catchphrase

[0530] The server generates catchphrases based on product information and the user's emotional information. The emotion engine analyzes the user's emotional state based on the information entered by the user and past behavioral data, and uses the results to generate phrases that stir emotions. For example, catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their working lives" are generated.

[0531] 7. User Emotion Recognition

[0532] While the user is using the device to input product information, the emotion engine analyzes the user's input pattern, time, and input content to infer the user's emotional state. For example, if the input speed is fast, it is inferred to be excited, and if the input is slow and careful, it is inferred to be attentive.

[0533] 8. Emotion Data Analysis

[0534] The server analyzes the emotion data obtained from the emotion engine and reflects it in the catchphrase generation process. The server incorporates the user's emotional state indicated by the emotion data into the catchphrase, generating phrases that are likely to resonate with the emotions of the target audience.

[0535] 9. Providing Results to Users

[0536] The filtered and evaluated list of product name and catchphrase candidates is sent to the terminal, which displays these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[0537] Specific examples

[0538] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." The server then compares the results with a database of competing products to select candidates that are legally acceptable.

[0539] The emotion engine analyzes the input pattern and speed while the user is entering information and estimates the user's emotional state. For example, if the user is typing quickly, it will determine that the user is excited.

[0540] The server generates catchy slogans while performing sentiment analysis using the emotional data obtained from the emotion engine. Phrases that suit the characteristics of the target audience are taken into consideration, and catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their working lives" are suggested.

[0541] Users can view a list of product name and catchphrase candidates via their device and select the most suitable one, enabling them to easily and quickly generate effective product names and catchphrases to support their marketing activities.

[0542] The processing flow will be explained below.

[0543] Step 1:

[0544] The user uses the input form on the device to enter detailed product information. Specifically, they enter the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events), and then press the send button. For example, they enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "everyday skin care."

[0545] Step 2:

[0546] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0547] Step 3:

[0548] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0549] Step 4:

[0550] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0551] Step 5:

[0552] The server filters the generated product name candidates, using a competitive product database and patent database to eliminate duplicates and inappropriate names and ensure that the product name candidates are legally sound.

[0553] Step 6:

[0554] The server uses an emotion engine to analyze the user's input patterns in real time and estimate the user's emotional state. For example, if the user types quickly, it may determine that the user is excited or nervous.

[0555] Step 7:

[0556] The server combines the emotional data obtained from the emotion engine with product information to generate catchy slogans. The emotional data reflects the user's psychological state, generating phrases that are likely to resonate with the target audience. For example, catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate yourself with organic products for those in the prime of their working lives" can be generated.

[0557] Step 8:

[0558] The server evaluates the generated tagline candidates and selects the most effective tagline through comparison with sentiment evaluation tools and past data.

[0559] Step 9:

[0560] The server sends the filtered and evaluated list of product name and catchphrase candidates to the terminal, and in the process encodes the metadata about the product names and catchphrases (e.g., importance and popularity of keywords) and sends it back to the terminal via the network.

[0561] Step 10:

[0562] The terminal displays the list of product name and catch phrase candidates received from the server on the user interface. The user can review the list of candidates presented through the terminal and select the most suitable product name and catch phrase. During this process, the candidates selected by the user are sent back to the server and saved as the final results.

[0563] This allows users to quickly and effectively generate product names and evocative catchphrases that differentiate their products from competitors, supporting their marketing efforts.

[0564] Example 2

[0565] 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."

[0566] In conventional systems, users had to manually input product information and come up with product names and catchy slogans themselves, which required time and effort, making it difficult to implement effective marketing activities. Furthermore, there were few methods for generating catchy slogans that took the user's emotional state into account, making it impossible to create slogans that resonated with the target audience. Furthermore, there was insufficient filtering of product names to avoid duplication with competing products, which increased the risk of legal issues.

[0567] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0568] In this invention, the server includes means for converting input product information into a specific format and transmitting it in encrypted form, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for cross-checking the generated product name candidates with specific criteria and a competing product database and filtering them, means for generating catch phrases based on the product information and user emotional information, and means for evaluating the generated catch phrase candidates. This enables effective marketing activities that reflect the user's emotional state while quickly generating product names and catch phrases based on product information and avoiding duplication with competing products.

[0569] "Inputted product information" refers to detailed information such as product features, target market, and usage scenarios that users provide through the system's input form.

[0570] "Format" refers to arranging data in a specific form, typically a data format such as JSON or XML.

[0571] "Encrypted form" means a form in which data has been converted using security technology to prevent unauthorized access by third parties.

[0572] "Related keywords" are specific important words or phrases extracted from the input product information using text analysis and natural language processing techniques.

[0573] "Product name candidates" are multiple product name suggestions automatically generated by the system based on related keywords.

[0574] "Filtering" is the process of removing duplicate, inappropriate, or legally questionable product name and tagline candidates from the generated product name and tagline candidates.

[0575] A "competitive product database" is a database that accumulates information on other companies' products that exist on the market, and is used for analyzing overlapping product names and competition.

[0576] "User emotion information" is data indicating the user's emotional state estimated based on the input pattern, speed, etc. when the user inputs product information.

[0577] "Catchphrase candidates" are marketing phrase ideas generated based on product information and user emotional information.

[0578] An "emotion engine" is an algorithm or system that analyzes the user's emotional state from input data and generates content based on that.

[0579] "Evaluation" is the process of evaluating the generated product name and catchphrase candidates based on specific criteria and selecting the most suitable candidate.

[0580] This invention relates to a system that automatically generates product names and evocative catchphrases to differentiate products from competitors based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[0581] The system operates using the following hardware and software.

[0582] Hardware

[0583] 1. Terminal: A device such as a PC, tablet, or smartphone. It provides an interface for users to input product information.

[0584] 2. Server: A high-performance computer (e.g., cloud server or on-premise server) that analyzes product information, generates product names and catchphrases, filters, and analyzes sentiment data.

[0585] software

[0586] 1. Input form: This is implemented as a web application that allows users to input product features, target markets, usage scenarios, etc. The specific format uses HTML, JavaScript, CSS, etc.

[0587] 2. Data transmission module: Converts the input product information into a specific format such as JSON or XML and sends it to the server in encrypted form using the HTTPS protocol.

[0588] 3. Text analysis tools: Using natural language processing (NLP) algorithms, related keywords are extracted from product information on the server side. Specific tools used include Python's NLTK and spaCy.

[0589] 4. Database management system: Used to store dictionary data for generated product names and catchphrases, as well as data on past success stories. Specifically, MySQL or PostgreSQL is used.

[0590] 5. Emotion engine: This engine analyzes the user's input patterns and speed to estimate their emotional state. Specifically, it is implemented using Python libraries and machine learning models.

[0591] 6. AI Models: These include AI models for generating product names and emotive copy. These models are trained using frameworks such as TensorFlow and PyTorch.

[0592] 7. UI / UX design: Provide an interface for users to view and select product name and tagline candidates using front-end frameworks such as React and Vue.js.

[0593] Specific examples

[0594] For example, let's say you're looking at a newly developed skin care cream. The user inputs the product information as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." Based on this information, the system operates as follows:

[0595] 1. The user enters information through the input form on the device.

[0596] 2. The terminal converts the entered information into JSON format and sends it to the server using the HTTPS protocol.

[0597] 3. The server receives the information and uses text analysis tools to extract keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0598] 4. Based on these keywords, product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated from the database.

[0599] 5. Filter the generated product name candidates by cross-checking them with competitor product databases and patent databases.

[0600] 6. The emotion engine analyzes the user's input patterns to infer their emotional state, and based on that generates catchy slogans such as "Recharge your daily energy with the power of nature" and "For those in the prime of their working lives, recharge with organic technology."

[0601] 7. The server sends the final filtered product name candidates and catchphrase candidates to the terminal.

[0602] 8. The user checks the list of candidates presented on the device and selects the most suitable one.

[0603] Prompt Sentence Examples

[0604] Create a product name and catchphrase based on the information "natural ingredients, moisturizing effect, for sensitive skin, for women in their 20s and 30s, daily skin care."

[0605] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0606] Step 1: User enters information

[0607] The user enters product information into an input form provided on the device. Specifically, the user enters information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." This input form is implemented using HTML and JavaScript, and once the user has completed their input, the device converts this information into JSON format. The input is detailed product information data, and the output is the product information data converted into JSON format.

[0608] Step 2: Send data from the device to the server

[0609] The terminal sends the product information data converted to JSON format to the server using the HTTPS protocol. This communication is encrypted to ensure data security. The input is JSON format product information data, and the output is encrypted data sent to the server. The terminal receives a response indicating successful transmission and notifies the user.

[0610] Step 3: Product information analysis on the server

[0611] The server analyzes the received JSON-formatted product information data. First, it uses a text analysis tool (e.g., Python's NLTK or spaCy) to extract related keywords from the product information. For example, keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin" are extracted. The input is the JSON-formatted product information data, and the output is a list of extracted keywords.

[0612] Step 4: Generate product name

[0613] The server generates product name candidates based on the extracted related keywords. It connects to a database management system (e.g., MySQL or PostgreSQL) and automatically generates product name candidates by referring to past success stories and related keyword combinations. For example, names such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated. The input is the extracted keyword list, and the output is the generated list of product name candidates.

[0614] Step 5: Filtering

[0615] The server filters the generated list of product name candidates. First, it eliminates duplicates and inappropriate names, and then cross-checks them with competitive product and patent databases to extract only legally sound product name candidates. This process is performed using Python scripts and SQL queries. The input is the generated list of product name candidates, and the output is the filtered list of product name candidates.

[0616] Step 6: Create a tagline

[0617] The server generates a catchy slogan based on product information and the user's emotional information. The emotion engine analyzes the information entered by the user and past behavioral data to estimate their emotional state. A generative AI model (e.g., TensorFlow or PyTorch) is used to generate a catchy slogan that will resonate with the target audience. For example, possible catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their lives." The input is product information and emotional information, and the output is a list of candidate catchy slogans.

[0618] Step 7: Recognizing User Emotions

[0619] While the user is entering product information, the device uses an emotion engine to analyze the input pattern and speed and estimate the user's emotional state. For example, if the input speed is fast, it is estimated to be an excited state, while if the input speed is slow and careful, it is estimated to be a cautious state. This emotion data is sent to the server in real time. The input is input pattern and speed data, and the output is emotional state data.

[0620] Step 8: Analyze the sentiment data

[0621] The server analyzes the emotional data obtained from the emotion engine and reflects it in the catchphrase generation process. It incorporates the user's emotional state indicated by the emotional data into the catchphrase to generate phrases that are likely to resonate with the target audience. The input is the emotional state data, and the output is an improved list of catchphrase candidates.

[0622] Step 9: Providing results to users

[0623] The server sends the filtered and evaluated product name candidate and catch phrase candidate list to the terminal. The terminal displays these candidates to the user through a user interface, allowing the user to select the most suitable product name and catch phrase. The input is the filtered product name candidate list and catch phrase candidate list, and the output is the candidate display on the user interface.

[0624] By following the steps above, the user can quickly and easily create a product name and catchy slogan, and effectively carry out marketing activities.

[0625] (Application example 2)

[0626] 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."

[0627] Conventional advertising generation systems were unable to consider the user's emotional state when generating product names and catchy slogans based on product information entered by the user. This made it difficult to generate effective product names and catchy slogans that would appeal to the target audience. Furthermore, filtering to differentiate from competing products was insufficient, limiting the accuracy and effectiveness of product names and catchy slogans.

[0628] The identification process by the identification 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 receiving input product information, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for filtering the generated product name candidates, means for providing the filtered product name candidates, means for analyzing the user's emotional state, means for generating catchphrase candidates based on the emotional state, and means for evaluating the generated catchphrase candidates. This makes it possible to generate effective product names and catchphrases that can differentiate from competing products, taking the user's emotional state into consideration.

[0629] "Product information" is information input by the user regarding the product's features, functions, target market, usage scenarios, etc.

[0630] "Related keywords" are important words and phrases extracted from product information that represent the characteristics and features of the product.

[0631] A "product name" is a unique, easy-to-remember combination of words used as the name of a product.

[0632] A "catchphrase" is a short phrase that succinctly conveys the features and value of a product and attracts consumer attention.

[0633] "Filtering" is the process of removing inappropriate or duplicated product names and catchphrases from the generated product name and catchphrase candidates.

[0634] The "emotional state" refers to the psychological state of the user when entering product information, and is analyzed based on facial expressions and voice data.

[0635] "Evaluation" is the process of determining whether the generated catchphrases and product name candidates are appropriate.

[0636] The "competitive product database" is a database that accumulates information about competing products that are already on the market.

[0637] A "smart device" is a device (e.g., smartphone, head-mounted display) that acquires a user's facial expressions and voice and performs emotional analysis.

[0638] To implement this invention, a system is required that links the terminal, server, and user. This system receives product information entered by the user, extracts related keywords based on that information, and then executes a series of processes to generate product names and catchphrases.

[0639] 1. Hardware and Software Configuration

[0640] Terminal

[0641] A smart device (smartphone or head-mounted display) is used as the terminal.

[0642] Input means: Provides an interface for users to input product information.

[0643] Emotion analysis means: A camera and microphone are used to capture the user's facial expressions and voice, and analyze the user's emotional state.

[0644] server

[0645] The server includes modules with the following functions:

[0646] Data receiving module: Receives product information sent from the terminal.

[0647] Keyword Extraction Module: Extracts relevant keywords from the received product information. This module uses natural language processing techniques (e.g., SpaCy, NLTK library).

[0648] Product name generation module: Generates product name candidates based on the extracted keywords. This is done using a generative AI model (e.g., GPT-3) while referencing a past database.

[0649] Filtering module: Filters the generated product name candidates and compares them with a competitor product database (e.g., SQL database).

[0650] Catchphrase generation module: Generates catchphrase candidates taking into account the user's emotional state. It uses a generative AI model and works in conjunction with the emotion engine.

[0651] Evaluation module: Evaluates the generated tagline candidates.

[0652] 2. Details of data processing and calculation

[0653] User input of product information: Users use their smart devices to input detailed information about the product, such as its features, target market, and usage scenarios. The input information is formatted in JSON or XML format.

[0654] Data reception and analysis: The smart device encrypts the product information entered by the user and the sentiment analysis results and sends them to the server. The server analyzes the received data and extracts keywords using text analysis tools and natural language processing algorithms.

[0655] Product Name and Slogan Generation: Using keywords, a generative AI model (e.g., GPT-3) generates product name and slogan candidates, taking into account the user's emotional state.

[0656] Filtering and Evaluation: The generated product name and tagline candidates are checked against a competitive product database to eliminate irrelevant and duplicated candidates, and tagline candidates are evaluated using data from the sentiment engine.

[0657] 3. Specific Examples

[0658] Example prompt sentence:

[0659] Your project is a new skincare cream. Its features include natural ingredients, moisturizing effects, and is suitable for sensitive skin. The target market is women in their 20s and 30s, and it is intended for daily skincare use. Based on this, please generate an emotionally appealing product name and tagline.

[0660] By implementing such a system, it becomes possible to generate effective product names and catchy slogans that take into account the user's emotional state and differentiate products from those of competitors.

[0661] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0662] Step 1:

[0663] Users enter product information using a smart device (smartphone or head-mounted display). In the input form, users enter details about the product's features, target market, and usage scenarios. The input information is formatted in JSON or XML. Specifically, users enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[0664] Step 2:

[0665] The terminal receives the product information entered by the user, encrypts it, and sends it to the server. The data is sent securely using the HTTPS protocol. Specifically, the terminal converts the information received from the user into JSON format and sends it to the server via HTTPS.

[0666] Step 3:

[0667] The server analyzes the received product information and extracts related keywords. This process uses natural language processing (NLP) tools (e.g., SpaCy, NLTK). Specifically, keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin" are extracted through text analysis.

[0668] Step 4:

[0669] The server generates product name candidates using a generative AI model (e.g., GPT-3) based on the extracted related keywords. Specifically, a prompt sentence is input into the generative AI model, and product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are output.

[0670] Step 5:

[0671] The server filters the generated product name candidates. This filtering uses a database of competing products to eliminate inappropriate names and duplicates. It also checks for legal issues. Specifically, it performs a database search and compares the candidates with existing product names to eliminate inappropriate candidates.

[0672] Step 6:

[0673] The server understands the user's emotional state by analyzing facial expression and tone of voice data from the device. To do this, it uses an emotion analysis engine to analyze facial expressions and voice patterns while the user is typing. Specifically, it analyzes camera and microphone data and determines that the user is in an excited state if they are typing quickly.

[0674] Step 7:

[0675] The server generates catchphrase candidates based on the results of the sentiment analysis. It uses a generative AI model to create emotionally appealing copy. Specifically, catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products" are generated.

[0676] Step 8:

[0677] The server evaluates the generated tagline candidates. It uses the sentiment analysis results and past data to select the best tagline for the target market. Specifically, it ranks the generated tagline candidates and selects the best candidate based on the evaluation criteria.

[0678] Step 9:

[0679] The server sends the filtered and evaluated product name and catchphrase candidates to the terminal. The user can check these candidate lists on the screen of the smart device and select the most suitable one. Specifically, the generated list is displayed on the user interface.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] [Third embodiment]

[0684] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0685] 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.

[0686] 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).

[0687] 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.

[0688] 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.

[0689] 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).

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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."

[0696] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competing products based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[0697] Program processing

[0698] 1. User input

[0699] Users input detailed information about a product through the terminal, including the product's characteristics such as ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s."

[0700] 2. Sending data from the device to the server

[0701] The terminal converts the product information entered by the user into a certain format (e.g., JSON or XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0702] 3. Product information analysis on the server

[0703] The server receives the product information sent from the device and begins analyzing it. First, it uses a natural language processing (NLP) algorithm to extract related keywords. For example, keywords such as "natural," "moisture," and "sensuous" are extracted from the input information.

[0704] 4. Product name generation

[0705] The server generates product name candidates based on the extracted related keywords. The generation algorithm connects to a database and creates product name candidates based on past success stories and combinations of related keywords. For example, candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated.

[0706] 5. Filtering process

[0707] The server compares the generated product name candidates with a database of competing products to eliminate duplicates and names with legal issues, and cross-checks them with a patent database to select the final product name candidates that can be used.

[0708] 6. Generate a catchphrase

[0709] The server combines product information with sentiment analysis to generate catchy slogans. Using a sentiment analysis algorithm, it creates phrases that resonate with the target audience. For example, catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate your working life with organic products" are generated.

[0710] 7. Providing results to users

[0711] The filtered and evaluated list of product name and catchphrase candidates is sent to the terminal, which displays these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[0712] Specific examples

[0713] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." The server then compares the results with a database of competing products to select candidates that are legally acceptable.

[0714] Similarly, sentiment analysis is used to generate taglines, such as "Recharge your daily energy with the power of nature" or "Recharge your energy with organic products for the working woman" based on the target audience's attributes.

[0715] Users can check the list of product name and catchphrase candidates through their device and select the most suitable one, thereby enabling them to support their marketing activities quickly and effectively.

[0716] The processing flow will be explained below.

[0717] Step 1:

[0718] The user enters detailed product information using the input form on the device. Specifically, the user enters the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events). For example, the user might enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[0719] Step 2:

[0720] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML). This conversion process unifies the format of the input data and makes it possible for the server to accurately parse it. After conversion, the data is sent to the server as encrypted data using the HTTPS protocol.

[0721] Step 3:

[0722] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0723] Step 4:

[0724] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0725] Step 5:

[0726] The server filters the generated product name candidates, using specific criteria and competitive product databases to eliminate duplicates and inappropriate names, and cross-checks them with patent databases to ensure legally sound product name candidates.

[0727] Step 6:

[0728] The server sends the filtered product name candidates to the terminal, and in the process encodes the candidate name metadata (e.g., keyword importance, popularity) and sends it back to the terminal via the network.

[0729] Step 7:

[0730] The terminal displays the list of product name candidates received from the server on the user interface, and the user checks the list of candidates presented through the terminal and selects the most suitable product name.

[0731] Step 8:

[0732] The server generates catchy copy using sentiment analysis and text generation algorithms. It generates emotionally appealing phrases based on product information and the attributes of the target market. For example, it generates catchy copy such as "Recharge your daily energy with the power of nature" or "Rejuvenate your vitality with organic products."

[0733] Step 9:

[0734] The server evaluates the generated tagline candidates and selects the most effective tagline through comparison with sentiment evaluation tools and past data.

[0735] Step 10:

[0736] The server transmits the evaluated catchphrase candidate list to the terminal, which displays the catchphrase candidates on a user interface, allowing the user to select the most suitable catchphrase.

[0737] This allows users to quickly and easily create excellent product names and catchy slogans, enabling them to effectively carry out marketing activities.

[0738] Example 1

[0739] 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."

[0740] In today's market, competition is intensifying, and to effectively promote new products, differentiated product names and catchy slogans that evoke emotions are required. However, creating these manually takes time and effort, and there is a high risk of duplication with competing products and legal issues. Given this background, there is a need for a system that allows users to easily generate attractive product names and catchy slogans, and reduce these risks by comparing them with competing products.

[0741] 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.

[0742] In this invention, the server includes means for receiving input product information, means for converting the product information into a certain format and encrypting the data before transmitting it, means for extracting related keywords based on the product information using a natural language processing algorithm, means for generating product name candidates using a generative AI model using the related keywords, means for comparing the generated product name candidates with a competing product database and filtering them, means for providing the filtered product name candidates, means for performing sentiment analysis based on the product information to generate catchy slogan candidates, means for generating catchy slogan candidates that will resonate with a target audience using the sentiment analysis algorithm, means for evaluating and providing the generated catchy slogan candidates, and means for comparing the related keywords extracted based on the product information with a competing product database and assessing patent infringement risk. This enables users to quickly and efficiently generate competitive product names and evocative catchy slogans, thereby reducing competition risk.

[0743] A "server" is a device that receives data input by a user and analyzes and processes it.

[0744] A "terminal" is an input device that allows a user to input information and transmit it to a server.

[0745] A "user" is someone who uses this system to input product information and obtain results.

[0746] "Product information" refers to detailed information about a specific product, such as ingredients, functions, design, target market, and usage scenarios.

[0747] A "natural language processing algorithm" is a computer algorithm for analyzing and understanding human language and extracting meaningful information.

[0748] "Related keywords" are important words or phrases extracted from product information.

[0749] A "generative AI model" is an artificial intelligence algorithm that generates new information based on past data and learned knowledge.

[0750] A "competitor product database" is a database that stores information on similar products and is used for comparison and matching.

[0751] "Filtering" is the process of eliminating inappropriate product names and catchphrases, including those with duplication or legal issues, from the generated product name and catchphrase candidates.

[0752] A "sentiment analysis algorithm" is an artificial intelligence algorithm that analyzes emotions and emotional responses contained in text data.

[0753] A "tagline" is a short phrase or slogan used to promote a product, intended to evoke an emotional response from the target audience.

[0754] "Patent infringement risk" refers to the possibility of infringing on the patent rights of others, in this case whether the product name or catchphrase overlaps with other existing patents.

[0755] "Evaluation" is the process of determining the appropriateness and effectiveness of the generated product names and catchphrases.

[0756] "Data transmission" refers to the process of transferring coded and encrypted data to send product information to the server.

[0757] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competitors based on product information entered by users. This system is composed of three parties: the user, a terminal, and a server.

[0758] First, the user enters detailed product information through a terminal with a dedicated interface. This information includes ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s."

[0759] Next, the terminal converts the product information entered by the user into JSON or XML format, encrypts the data using the HTTPS protocol, and sends it to the server, ensuring communication security.

[0760] The server analyzes the received product information. First, it uses a natural language processing (NLP) algorithm to extract relevant keywords from the product information. This process uses software such as SpaCy or NLTK. Extracted keywords include "natural," "moisture," and "sensuous."

[0761] Based on related keywords, the server uses a generative AI model (such as GPT-3) to generate product name candidates. The generated product name candidates are compared with a database of competing products to eliminate duplicates and legally questionable names. This filtering process ultimately selects usable product name candidates.

[0762] The server then uses a sentiment analysis algorithm (such as VADER or BERT) to generate taglines that resonate with the target audience. For example, taglines like "Get your daily energy boost, recharge with the power of nature" or "Get organically recharged for the busy workforce" are generated.

[0763] After the generation and filtering are complete, the server sends the final product name and catchphrase candidates to the terminal, which then provides these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[0764] Examples:

[0765] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effects," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensuous," and generates product name candidates such as "Natural Moisture Cream," "Sensuous Day Cream," and "Pure Care Moisture." The server then compares the names with a database of competing products to select legally acceptable candidates. Sentiment analysis is also used to generate catchy slogans. Depending on the attributes of the target audience, suggested catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[0766] Examples of prompt statements

[0767] "Based on the information about a new skincare cream, please create a product name that differentiates it and a catchy copy that will evoke emotions. The product's features are 'natural ingredients,' 'moisturizing effect,' 'for sensitive skin,' 'for women in their 20s and 30s,' and 'daily skincare.'"

[0768] The system allows users to quickly and efficiently generate product names and catchphrases, reducing the risk of competition.

[0769] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0770] Step 1:

[0771] Users input product information using a dedicated interface on the device. Specifically, they input product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The input information is sent to the device, and detailed information about the product (ingredients, functions, design, target market, usage scenarios, etc.) is prepared as input data.

[0772] Step 2:

[0773] The terminal converts the product information entered by the user into a specific format (e.g., JSON or XML). Specifically, the terminal interface receives the input data and uses a conversion algorithm to encode it in JSON or XML format. The converted data is then sent to the server using encrypted HTTPS protocol, where it is output as JSON format data.

[0774] Step 3:

[0775] The server receives the JSON data sent from the device. To analyze the received data, it first parses the JSON data. The parsed product information (ingredients, functions, design, target market, usage scenarios, etc.) is output.

[0776] Step 4:

[0777] The server uses a natural language processing (NLP) algorithm to extract related keywords based on the parsed product information. Specifically, it uses an NLP engine (e.g., SpaCy or NLTK) to analyze and extract important keywords from text data. The parsed product information is used as input data, and the extracted related keywords (e.g., "natural," "moisture," and "sensuous") are obtained as output data.

[0778] Step 5:

[0779] The server generates product name candidates using a generative AI model (such as GPT-3) based on the extracted related keywords. Specifically, the related keywords are input into the generative AI model, and the model runs a predictive generation algorithm. The related keywords are used as input data, and product name candidates (e.g., "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture") are generated as output data.

[0780] Step 6:

[0781] The server compares the generated product name candidates with a competing product database and performs filtering. Specifically, the server inputs the product name candidates into the competing product database as queries and receives the matching results. It also cross-checks with the patent database to eliminate duplication and legal issues. The generated product name candidates are used as input data, and filtered product name candidates are obtained as output data.

[0782] Step 7:

[0783] The server generates a catchy slogan by combining product information with a sentiment analysis algorithm. Specifically, it analyzes product information using a sentiment analysis engine (such as VADER or BERT) and generates phrases that resonate with the target audience. Product information is used as input data, and the generated catchy slogan (e.g., "Recharge your daily energy with the power of nature" or "Recharge your energy with organic products for the working man") is obtained as output data.

[0784] Step 8:

[0785] The server then compiles the final evaluated and filtered product name candidates and catchphrase candidates and sends them to the terminal. Specifically, the server compiles these candidates into a list and sends it to the terminal using the HTTPS protocol. The filtered product name candidates and generated catchphrases are used as input data, and the data to be sent is obtained as output data.

[0786] Step 9:

[0787] The terminal receives the data sent from the server and displays it to the user through a user interface. Specifically, the terminal analyzes the received data and presents it to the user in a visually easy-to-understand format. The data received from the server is used as input data, and display data that can be viewed by the user is obtained as output data. Based on this, the user can select the most suitable product name and catchphrase.

[0788] (Application example 1)

[0789] 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."

[0790] In conventional online shopping sites, creating product names and catchy slogans for new products is important for sales promotion, but creating them requires a great deal of time and effort, and it is difficult to differentiate a product from competing products. Furthermore, it is not easy to create an effective catchy slogan that stirs emotions. For this reason, a system that efficiently generates product names and catchy slogans is needed.

[0791] 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.

[0792] In this invention, the server includes means for receiving input product information, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for generating emotionally stirring catchphrase candidates based on the extracted keywords, means for filtering the generated product name candidates and catchphrase candidates, and means for providing the filtered product name candidates and catchphrase candidates, thereby enabling a user to quickly and effectively generate a product name and an emotionally stirring catchphrase that is differentiated from competing products.

[0793] "Product information" refers to detailed information entered by the user, such as product features, ingredients, functions, design, target market, and usage scenarios.

[0794] "Related keywords" are important words or phrases that represent the features or characteristics of a product and are extracted based on the product information.

[0795] "Product name candidates" are a set of suggested names for use as product names, generated using related keywords.

[0796] "Catchphrase candidates" are a series of promotional slogans or phrases created to evoke emotions based on product information and extracted keywords.

[0797] "Emotional appeal" means using expressions and words that strongly appeal to the emotions of the target audience, thereby attracting their interest and increasing their desire to purchase.

[0798] "Filtering" is the process of checking the generated product name and catchphrase candidates against a database of competing products, legal issues, and duplications to eliminate inappropriate ones.

[0799] "Providing" refers to presenting the generated product name and catchphrase candidates to the user via a user interface and providing them as information for selection or decision.

[0800] A "competitive product database" is a database that accumulates data on competing products that exist in the existing market, and is used to differentiate products and identify legal issues.

[0801] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate a product from competing products based on product information entered by the user. This system is composed of three entities: a user, a terminal, and a server.

[0802] The server receives product information and uses the following hardware and software to perform the necessary processing: a natural language processing (NLP) algorithm, a sentiment analysis algorithm, the text generation model GPT-3, and the HTTPS protocol.

[0803] The user inputs detailed information about the product through the terminal. This includes the product's features such as ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s." The terminal converts the product information entered by the user into a specific format (e.g., JSON or XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0804] The server analyzes the received product information and extracts related keywords. First, it uses an NLP algorithm to extract important words and phrases from the information. For example, keywords such as "natural," "moisture," and "sensuous" are extracted. Based on these keywords, product name candidates are generated.

[0805] The server then uses the generated product name candidates, product information, and extracted keywords to generate emotionally appealing catchphrases. Using a sentiment analysis algorithm and a text generation model, the server creates phrases that resonate with the target audience. For example, suggested catchphrases include "Recharge your daily energy with the power of nature" and "Rejuvenate your working life with organic products."

[0806] The generated product name and catchphrase candidates are checked against a database of competing products to eliminate duplicates and legally questionable names and phrases. Finally, usable product name and catchphrase candidates are filtered. These candidate lists are sent to the terminal and displayed to the user through a user interface. The user can then select the most suitable candidate.

[0807] As a concrete example, let's consider a newly developed skin care cream. The user inputs product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," and "women in their 20s and 30s." The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." Sentiment analysis is also used to generate catchy slogans. Depending on the attributes of the target audience, suggested catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[0808] An example of a generated prompt is as follows:

[0809] Product Name Generation Prompt: "Generate product names using the following keywords: natural, moisturizing, sensuous"

[0810] Catchphrase generation prompt: "Generate catchy phrases for a product with the following attributes: {'ingredients': 'natural ingredients', 'function': 'moisturizing', 'design': 'simple', 'target market': 'women in their 20s and 30s'} and keywords: natural, moisturizing, sensuous"

[0811] This allows users to quickly and effectively create product names and emotionally appealing catchphrases that differentiate them from competing products.

[0812] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0813] Step 1:

[0814] The user enters product information.

[0815] The user uses the terminal to input detailed product information into the input screen. The information input includes the product's ingredients, functions, design, target market, and usage scenarios. For example, "Ingredients: Natural ingredients," "Function: Moisturizing effect," "Design: Simple," and "Target market: Women in their 20s and 30s."

[0816] Step 2:

[0817] The terminal transmits the product information to the server.

[0818] The terminal converts the entered product information into a certain format, such as JSON format, and sends it to the server as encrypted data using the HTTPS protocol.

[0819] Step 3:

[0820] The server analyzes the product information.

[0821] The server analyzes the received product information and uses NLP (Natural Language Processing) algorithms to extract relevant keywords, such as "natural," "moisture," and "sensuous," from the input data.

[0822] Input: Product information (JSON format)

[0823] Output: Extracted related keywords

[0824] Step 4:

[0825] The server generates product name candidates.

[0826] The server uses the extracted related keywords to generate product name candidates that take into account past success stories and related keyword combinations. Using a generative AI model such as GPT-3, the server generates text and generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0827] Input: Extracted related keywords

[0828] Output: Generated product name candidates

[0829] Step 5:

[0830] The server generates catchphrase candidates.

[0831] The server generates catchphrases based on product information and extracted keywords using a sentiment analysis algorithm and a generative AI model. By inputting a prompt, the server generates catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[0832] Input: Product information, extracted related keywords

[0833] Output: Generated tagline candidates

[0834] Step 6:

[0835] The server filters product name and tagline candidates.

[0836] The server compares the generated product name and catchphrase candidates with a database of competing products to check for duplication or legal issues, and eliminates inappropriate candidates.

[0837] Input: Generated product name candidates, generated tagline candidates

[0838] Output: Filtered product name candidates, filtered tagline candidates

[0839] Step 7:

[0840] The server provides the final candidate list to the terminal.

[0841] The filtered list of product name and catch phrase candidates is sent from the server to the terminal, which displays this information to the user through a user interface, allowing the user to select the product name and catch phrase that they think are most suitable.

[0842] Input: Filtered product name candidates, Filtered tagline candidates

[0843] Output: A list of product name and tagline suggestions provided to the user

[0844] 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.

[0845] This invention further improves the accuracy and effectiveness of the generated results by combining a system that automatically generates product names and emotionally appealing slogans that differentiate products from competing products based on product information entered by the user with an emotion engine that recognizes the user's emotions. This system is composed of three parties: the user, a terminal, and a server.

[0846] Program processing

[0847] 1. User input

[0848] The user uses the input form on the device to enter detailed product information, including the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events). For example, the user might enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[0849] 2. Sending data from the device to the server

[0850] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0851] 3. Product information analysis on the server

[0852] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0853] 4. Product name generation

[0854] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0855] 5. Filtering process

[0856] The server filters the generated product name candidates, using specific criteria and competitive product databases to eliminate duplicates and inappropriate names, and cross-checks them with patent databases to ensure legally sound product name candidates.

[0857] 6. Generate a catchphrase

[0858] The server generates catchphrases based on product information and the user's emotional information. The emotion engine analyzes the user's emotional state based on the information entered by the user and past behavioral data, and uses the results to generate phrases that stir emotions. For example, catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their working lives" are generated.

[0859] 7. User Emotion Recognition

[0860] While the user is using the device to input product information, the emotion engine analyzes the user's input pattern, time, and input content to infer the user's emotional state. For example, if the input speed is fast, it is inferred to be excited, and if the input is slow and careful, it is inferred to be attentive.

[0861] 8. Emotion Data Analysis

[0862] The server analyzes the emotion data obtained from the emotion engine and reflects it in the catchphrase generation process. The server incorporates the user's emotional state indicated by the emotion data into the catchphrase, generating phrases that are likely to resonate with the emotions of the target audience.

[0863] 9. Providing Results to Users

[0864] The filtered and evaluated list of product name and catchphrase candidates is sent to the terminal, which displays these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[0865] Specific examples

[0866] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." The server then compares the results with a database of competing products to select candidates that are legally acceptable.

[0867] The emotion engine analyzes the input pattern and speed while the user is entering information and estimates the user's emotional state. For example, if the user is typing quickly, it will determine that the user is excited.

[0868] The server generates catchy slogans while performing sentiment analysis using the emotional data obtained from the emotion engine. Phrases that suit the characteristics of the target audience are taken into consideration, and catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their working lives" are suggested.

[0869] Users can view a list of product name and catchphrase candidates via their device and select the most suitable one, enabling them to easily and quickly generate effective product names and catchphrases to support their marketing activities.

[0870] The processing flow will be explained below.

[0871] Step 1:

[0872] The user uses the input form on the device to enter detailed product information. Specifically, they enter the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events), and then press the send button. For example, they enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "everyday skin care."

[0873] Step 2:

[0874] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML) and sends it to the server as encrypted data using the HTTPS protocol.

[0875] Step 3:

[0876] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0877] Step 4:

[0878] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[0879] Step 5:

[0880] The server filters the generated product name candidates, using a competitive product database and patent database to eliminate duplicates and inappropriate names and ensure that the product name candidates are legally sound.

[0881] Step 6:

[0882] The server uses an emotion engine to analyze the user's input patterns in real time and estimate the user's emotional state. For example, if the user types quickly, it may determine that the user is excited or nervous.

[0883] Step 7:

[0884] The server combines the emotional data obtained from the emotion engine with product information to generate catchy slogans. The emotional data reflects the user's psychological state, generating phrases that are likely to resonate with the target audience. For example, catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate yourself with organic products for those in the prime of their working lives" can be generated.

[0885] Step 8:

[0886] The server evaluates the generated tagline candidates and selects the most effective tagline through comparison with sentiment evaluation tools and past data.

[0887] Step 9:

[0888] The server sends the filtered and evaluated list of product name and catchphrase candidates to the terminal, and in the process encodes the metadata about the product names and catchphrases (e.g., importance and popularity of keywords) and sends it back to the terminal via the network.

[0889] Step 10:

[0890] The terminal displays the list of product name and catch phrase candidates received from the server on the user interface. The user can review the list of candidates presented through the terminal and select the most suitable product name and catch phrase. During this process, the candidates selected by the user are sent back to the server and saved as the final results.

[0891] This allows users to quickly and effectively generate product names and evocative catchphrases that differentiate their products from competitors, supporting their marketing efforts.

[0892] Example 2

[0893] 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."

[0894] In conventional systems, users had to manually input product information and come up with product names and catchy slogans themselves, which required time and effort, making it difficult to implement effective marketing activities. Furthermore, there were few methods for generating catchy slogans that took the user's emotional state into account, making it impossible to create slogans that resonated with the target audience. Furthermore, there was insufficient filtering of product names to avoid duplication with competing products, which increased the risk of legal issues.

[0895] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0896] In this invention, the server includes means for converting input product information into a specific format and transmitting it in encrypted form, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for cross-checking the generated product name candidates with specific criteria and a competing product database and filtering them, means for generating catch phrases based on the product information and user emotional information, and means for evaluating the generated catch phrase candidates. This enables effective marketing activities that reflect the user's emotional state while quickly generating product names and catch phrases based on product information and avoiding duplication with competing products.

[0897] "Inputted product information" refers to detailed information such as product features, target market, and usage scenarios that users provide through the system's input form.

[0898] "Format" refers to arranging data in a specific form, typically a data format such as JSON or XML.

[0899] "Encrypted form" means a form in which data has been converted using security technology to prevent unauthorized access by third parties.

[0900] "Related keywords" are specific important words or phrases extracted from the input product information using text analysis and natural language processing techniques.

[0901] "Product name candidates" are multiple product name suggestions automatically generated by the system based on related keywords.

[0902] "Filtering" is the process of removing duplicate, inappropriate, or legally questionable product name and tagline candidates from the generated product name and tagline candidates.

[0903] A "competitive product database" is a database that accumulates information on other companies' products that exist on the market, and is used for analyzing overlapping product names and competition.

[0904] "User emotion information" is data indicating the user's emotional state estimated based on the input pattern, speed, etc. when the user inputs product information.

[0905] "Catchphrase candidates" are marketing phrase ideas generated based on product information and user emotional information.

[0906] An "emotion engine" is an algorithm or system that analyzes the user's emotional state from input data and generates content based on that.

[0907] "Evaluation" is the process of evaluating the generated product name and catchphrase candidates based on specific criteria and selecting the most suitable candidate.

[0908] This invention relates to a system that automatically generates product names and evocative catchphrases to differentiate products from competitors based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[0909] The system operates using the following hardware and software.

[0910] Hardware

[0911] 1. Terminal: A device such as a PC, tablet, or smartphone. It provides an interface for users to input product information.

[0912] 2. Server: A high-performance computer (e.g., cloud server or on-premise server) that analyzes product information, generates product names and catchphrases, filters, and analyzes sentiment data.

[0913] software

[0914] 1. Input form: This is implemented as a web application that allows users to input product features, target markets, usage scenarios, etc. The specific format uses HTML, JavaScript, CSS, etc.

[0915] 2. Data transmission module: Converts the input product information into a specific format such as JSON or XML and sends it to the server in encrypted form using the HTTPS protocol.

[0916] 3. Text analysis tools: Using natural language processing (NLP) algorithms, related keywords are extracted from product information on the server side. Specific tools used include Python's NLTK and spaCy.

[0917] 4. Database management system: Used to store dictionary data for generated product names and catchphrases, as well as data on past success stories. Specifically, MySQL or PostgreSQL is used.

[0918] 5. Emotion engine: This engine analyzes the user's input patterns and speed to estimate their emotional state. Specifically, it is implemented using Python libraries and machine learning models.

[0919] 6. AI Models: These include AI models for generating product names and emotive copy. These models are trained using frameworks such as TensorFlow and PyTorch.

[0920] 7. UI / UX design: Provide an interface for users to view and select product name and tagline candidates using front-end frameworks such as React and Vue.js.

[0921] Specific examples

[0922] For example, let's say you're looking at a newly developed skin care cream. The user inputs the product information as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." Based on this information, the system operates as follows:

[0923] 1. The user enters information through the input form on the device.

[0924] 2. The terminal converts the entered information into JSON format and sends it to the server using the HTTPS protocol.

[0925] 3. The server receives the information and uses text analysis tools to extract keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[0926] 4. Based on these keywords, product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated from the database.

[0927] 5. Filter the generated product name candidates by cross-checking them with competitor product databases and patent databases.

[0928] 6. The emotion engine analyzes the user's input patterns to infer their emotional state, and based on that generates catchy slogans such as "Recharge your daily energy with the power of nature" and "For those in the prime of their working lives, recharge with organic technology."

[0929] 7. The server sends the final filtered product name candidates and catchphrase candidates to the terminal.

[0930] 8. The user checks the list of candidates presented on the device and selects the most suitable one.

[0931] Prompt Sentence Examples

[0932] Create a product name and catchphrase based on the information "natural ingredients, moisturizing effect, for sensitive skin, for women in their 20s and 30s, daily skin care."

[0933] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0934] Step 1: User enters information

[0935] The user enters product information into an input form provided on the device. Specifically, the user enters information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." This input form is implemented using HTML and JavaScript, and once the user has completed their input, the device converts this information into JSON format. The input is detailed product information data, and the output is the product information data converted into JSON format.

[0936] Step 2: Send data from the device to the server

[0937] The terminal sends the product information data converted to JSON format to the server using the HTTPS protocol. This communication is encrypted to ensure data security. The input is JSON format product information data, and the output is encrypted data sent to the server. The terminal receives a response indicating successful transmission and notifies the user.

[0938] Step 3: Product information analysis on the server

[0939] The server analyzes the received JSON-formatted product information data. First, it uses a text analysis tool (e.g., Python's NLTK or spaCy) to extract related keywords from the product information. For example, keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin" are extracted. The input is the JSON-formatted product information data, and the output is a list of extracted keywords.

[0940] Step 4: Generate product name

[0941] The server generates product name candidates based on the extracted related keywords. It connects to a database management system (e.g., MySQL or PostgreSQL) and automatically generates product name candidates by referring to past success stories and related keyword combinations. For example, names such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated. The input is the extracted keyword list, and the output is the generated list of product name candidates.

[0942] Step 5: Filtering

[0943] The server filters the generated list of product name candidates. First, it eliminates duplicates and inappropriate names, and then cross-checks them with competitive product and patent databases to extract only legally sound product name candidates. This process is performed using Python scripts and SQL queries. The input is the generated list of product name candidates, and the output is the filtered list of product name candidates.

[0944] Step 6: Create a tagline

[0945] The server generates a catchy slogan based on product information and the user's emotional information. The emotion engine analyzes the information entered by the user and past behavioral data to estimate their emotional state. A generative AI model (e.g., TensorFlow or PyTorch) is used to generate a catchy slogan that will resonate with the target audience. For example, possible catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their lives." The input is product information and emotional information, and the output is a list of candidate catchy slogans.

[0946] Step 7: Recognizing User Emotions

[0947] While the user is entering product information, the device uses an emotion engine to analyze the input pattern and speed and estimate the user's emotional state. For example, if the input speed is fast, it is estimated to be an excited state, while if the input speed is slow and careful, it is estimated to be a cautious state. This emotion data is sent to the server in real time. The input is input pattern and speed data, and the output is emotional state data.

[0948] Step 8: Analyze the sentiment data

[0949] The server analyzes the emotional data obtained from the emotion engine and reflects it in the catchphrase generation process. It incorporates the user's emotional state indicated by the emotional data into the catchphrase to generate phrases that are likely to resonate with the target audience. The input is the emotional state data, and the output is an improved list of catchphrase candidates.

[0950] Step 9: Providing results to users

[0951] The server sends the filtered and evaluated product name candidate and catch phrase candidate list to the terminal. The terminal displays these candidates to the user through a user interface, allowing the user to select the most suitable product name and catch phrase. The input is the filtered product name candidate list and catch phrase candidate list, and the output is the candidate display on the user interface.

[0952] By following the steps above, the user can quickly and easily create a product name and catchy slogan, and effectively carry out marketing activities.

[0953] (Application example 2)

[0954] 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."

[0955] Conventional advertising generation systems were unable to consider the user's emotional state when generating product names and catchy slogans based on product information entered by the user. This made it difficult to generate effective product names and catchy slogans that would appeal to the target audience. Furthermore, filtering to differentiate from competing products was insufficient, limiting the accuracy and effectiveness of product names and catchy slogans.

[0956] The identification process by the identification 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 receiving input product information, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for filtering the generated product name candidates, means for providing the filtered product name candidates, means for analyzing the user's emotional state, means for generating catchphrase candidates based on the emotional state, and means for evaluating the generated catchphrase candidates. This makes it possible to generate effective product names and catchphrases that can differentiate from competing products, taking the user's emotional state into consideration.

[0957] "Product information" is information input by the user regarding the product's features, functions, target market, usage scenarios, etc.

[0958] "Related keywords" are important words and phrases extracted from product information that represent the characteristics and features of the product.

[0959] A "product name" is a unique, easy-to-remember combination of words used as the name of a product.

[0960] A "catchphrase" is a short phrase that succinctly conveys the features and value of a product and attracts consumer attention.

[0961] "Filtering" is the process of removing inappropriate or duplicated product names and catchphrases from the generated product name and catchphrase candidates.

[0962] The "emotional state" refers to the psychological state of the user when entering product information, and is analyzed based on facial expressions and voice data.

[0963] "Evaluation" is the process of determining whether the generated catchphrases and product name candidates are appropriate.

[0964] The "competitive product database" is a database that accumulates information about competing products that are already on the market.

[0965] A "smart device" is a device (e.g., smartphone, head-mounted display) that acquires a user's facial expressions and voice and performs emotional analysis.

[0966] To implement this invention, a system is required that links the terminal, server, and user. This system receives product information entered by the user, extracts related keywords based on that information, and then executes a series of processes to generate product names and catchphrases.

[0967] 1. Hardware and Software Configuration

[0968] Terminal

[0969] A smart device (smartphone or head-mounted display) is used as the terminal.

[0970] Input means: Provides an interface for users to input product information.

[0971] Emotion analysis means: A camera and microphone are used to capture the user's facial expressions and voice, and analyze the user's emotional state.

[0972] server

[0973] The server includes modules with the following functions:

[0974] Data receiving module: Receives product information sent from the terminal.

[0975] Keyword Extraction Module: Extracts relevant keywords from the received product information. This module uses natural language processing techniques (e.g., SpaCy, NLTK library).

[0976] Product name generation module: Generates product name candidates based on the extracted keywords. This is done using a generative AI model (e.g., GPT-3) while referencing a past database.

[0977] Filtering module: Filters the generated product name candidates and compares them with a competitor product database (e.g., SQL database).

[0978] Catchphrase generation module: Generates catchphrase candidates taking into account the user's emotional state. It uses a generative AI model and works in conjunction with the emotion engine.

[0979] Evaluation module: Evaluates the generated tagline candidates.

[0980] 2. Details of data processing and calculation

[0981] User input of product information: Users use their smart devices to input detailed information about the product, such as its features, target market, and usage scenarios. The input information is formatted in JSON or XML format.

[0982] Data reception and analysis: The smart device encrypts the product information entered by the user and the sentiment analysis results and sends them to the server. The server analyzes the received data and extracts keywords using text analysis tools and natural language processing algorithms.

[0983] Product Name and Slogan Generation: Using keywords, a generative AI model (e.g., GPT-3) generates product name and slogan candidates, taking into account the user's emotional state.

[0984] Filtering and Evaluation: The generated product name and tagline candidates are checked against a competitive product database to eliminate irrelevant and duplicated candidates, and tagline candidates are evaluated using data from the sentiment engine.

[0985] 3. Specific Examples

[0986] Example prompt sentence:

[0987] Your project is a new skincare cream. Its features include natural ingredients, moisturizing effects, and is suitable for sensitive skin. The target market is women in their 20s and 30s, and it is intended for daily skincare use. Based on this, please generate an emotionally appealing product name and tagline.

[0988] By implementing such a system, it becomes possible to generate effective product names and catchy slogans that take into account the user's emotional state and differentiate products from those of competitors.

[0989] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0990] Step 1:

[0991] Users enter product information using a smart device (smartphone or head-mounted display). In the input form, users enter details about the product's features, target market, and usage scenarios. The input information is formatted in JSON or XML. Specifically, users enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[0992] Step 2:

[0993] The terminal receives the product information entered by the user, encrypts it, and sends it to the server. The data is sent securely using the HTTPS protocol. Specifically, the terminal converts the information received from the user into JSON format and sends it to the server via HTTPS.

[0994] Step 3:

[0995] The server analyzes the received product information and extracts related keywords. This process uses natural language processing (NLP) tools (e.g., SpaCy, NLTK). Specifically, keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin" are extracted through text analysis.

[0996] Step 4:

[0997] The server generates product name candidates using a generative AI model (e.g., GPT-3) based on the extracted related keywords. Specifically, a prompt sentence is input into the generative AI model, and product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are output.

[0998] Step 5:

[0999] The server filters the generated product name candidates. This filtering uses a database of competing products to eliminate inappropriate names and duplicates. It also checks for legal issues. Specifically, it performs a database search and compares the candidates with existing product names to eliminate inappropriate candidates.

[1000] Step 6:

[1001] The server understands the user's emotional state by analyzing facial expression and tone of voice data from the device. To do this, it uses an emotion analysis engine to analyze facial expressions and voice patterns while the user is typing. Specifically, it analyzes camera and microphone data and determines that the user is in an excited state if they are typing quickly.

[1002] Step 7:

[1003] The server generates catchphrase candidates based on the results of the sentiment analysis. It uses a generative AI model to create emotionally appealing copy. Specifically, catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products" are generated.

[1004] Step 8:

[1005] The server evaluates the generated tagline candidates. It uses the sentiment analysis results and past data to select the best tagline for the target market. Specifically, it ranks the generated tagline candidates and selects the best candidate based on the evaluation criteria.

[1006] Step 9:

[1007] The server sends the filtered and evaluated product name and catchphrase candidates to the terminal. The user can check these candidate lists on the screen of the smart device and select the most suitable one. Specifically, the generated list is displayed on the user interface.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] [Fourth embodiment]

[1012] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1013] 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.

[1014] 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).

[1015] 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.

[1016] 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.

[1017] 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).

[1018] 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.

[1019] 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.

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] 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."

[1025] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competing products based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[1026] Program processing

[1027] 1. User input

[1028] Users input detailed information about a product through the terminal, including the product's characteristics such as ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s."

[1029] 2. Sending data from the device to the server

[1030] The terminal converts the product information entered by the user into a certain format (e.g., JSON or XML) and sends it to the server as encrypted data using the HTTPS protocol.

[1031] 3. Product information analysis on the server

[1032] The server receives the product information sent from the device and begins analyzing it. First, it uses a natural language processing (NLP) algorithm to extract related keywords. For example, keywords such as "natural," "moisture," and "sensuous" are extracted from the input information.

[1033] 4. Product name generation

[1034] The server generates product name candidates based on the extracted related keywords. The generation algorithm connects to a database and creates product name candidates based on past success stories and combinations of related keywords. For example, candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated.

[1035] 5. Filtering process

[1036] The server compares the generated product name candidates with a database of competing products to eliminate duplicates and names with legal issues, and cross-checks them with a patent database to select the final product name candidates that can be used.

[1037] 6. Generate a catchphrase

[1038] The server combines product information with sentiment analysis to generate catchy slogans. Using a sentiment analysis algorithm, it creates phrases that resonate with the target audience. For example, catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate your working life with organic products" are generated.

[1039] 7. Providing results to users

[1040] The filtered and evaluated list of product name and catchphrase candidates is sent to the terminal, which displays these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[1041] Specific examples

[1042] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." The server then compares the results with a database of competing products to select candidates that are legally acceptable.

[1043] Similarly, sentiment analysis is used to generate taglines, such as "Recharge your daily energy with the power of nature" or "Recharge your energy with organic products for the working woman" based on the target audience's attributes.

[1044] Users can check the list of product name and catchphrase candidates through their device and select the most suitable one, thereby enabling them to support their marketing activities quickly and effectively.

[1045] The processing flow will be explained below.

[1046] Step 1:

[1047] The user enters detailed product information using the input form on the device. Specifically, the user enters the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events). For example, the user might enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[1048] Step 2:

[1049] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML). This conversion process unifies the format of the input data and makes it possible for the server to accurately parse it. After conversion, the data is sent to the server as encrypted data using the HTTPS protocol.

[1050] Step 3:

[1051] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[1052] Step 4:

[1053] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[1054] Step 5:

[1055] The server filters the generated product name candidates, using specific criteria and competitive product databases to eliminate duplicates and inappropriate names, and cross-checks them with patent databases to ensure legally sound product name candidates.

[1056] Step 6:

[1057] The server sends the filtered product name candidates to the terminal, and in the process encodes the candidate name metadata (e.g., keyword importance, popularity) and sends it back to the terminal via the network.

[1058] Step 7:

[1059] The terminal displays the list of product name candidates received from the server on the user interface, and the user checks the list of candidates presented through the terminal and selects the most suitable product name.

[1060] Step 8:

[1061] The server generates catchy copy using sentiment analysis and text generation algorithms. It generates emotionally appealing phrases based on product information and the attributes of the target market. For example, it generates catchy copy such as "Recharge your daily energy with the power of nature" or "Rejuvenate your vitality with organic products."

[1062] Step 9:

[1063] The server evaluates the generated tagline candidates and selects the most effective tagline through comparison with sentiment evaluation tools and past data.

[1064] Step 10:

[1065] The server transmits the evaluated catchphrase candidate list to the terminal, which displays the catchphrase candidates on a user interface, allowing the user to select the most suitable catchphrase.

[1066] This allows users to quickly and easily create excellent product names and catchy slogans, enabling them to effectively carry out marketing activities.

[1067] Example 1

[1068] 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."

[1069] In today's market, competition is intensifying, and to effectively promote new products, differentiated product names and catchy slogans that evoke emotions are required. However, creating these manually takes time and effort, and there is a high risk of duplication with competing products and legal issues. Given this background, there is a need for a system that allows users to easily generate attractive product names and catchy slogans, and reduce these risks by comparing them with competing products.

[1070] 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.

[1071] In this invention, the server includes means for receiving input product information, means for converting the product information into a certain format and encrypting the data before transmitting it, means for extracting related keywords based on the product information using a natural language processing algorithm, means for generating product name candidates using a generative AI model using the related keywords, means for comparing the generated product name candidates with a competing product database and filtering them, means for providing the filtered product name candidates, means for performing sentiment analysis based on the product information to generate catchy slogan candidates, means for generating catchy slogan candidates that will resonate with a target audience using the sentiment analysis algorithm, means for evaluating and providing the generated catchy slogan candidates, and means for comparing the related keywords extracted based on the product information with a competing product database and assessing patent infringement risk. This enables users to quickly and efficiently generate competitive product names and evocative catchy slogans, thereby reducing competition risk.

[1072] A "server" is a device that receives data input by a user and analyzes and processes it.

[1073] A "terminal" is an input device that allows a user to input information and transmit it to a server.

[1074] A "user" is someone who uses this system to input product information and obtain results.

[1075] "Product information" refers to detailed information about a specific product, such as ingredients, functions, design, target market, and usage scenarios.

[1076] A "natural language processing algorithm" is a computer algorithm for analyzing and understanding human language and extracting meaningful information.

[1077] "Related keywords" are important words or phrases extracted from product information.

[1078] A "generative AI model" is an artificial intelligence algorithm that generates new information based on past data and learned knowledge.

[1079] A "competitor product database" is a database that stores information on similar products and is used for comparison and matching.

[1080] "Filtering" is the process of eliminating inappropriate product names and catchphrases, including those with duplication or legal issues, from the generated product name and catchphrase candidates.

[1081] A "sentiment analysis algorithm" is an artificial intelligence algorithm that analyzes emotions and emotional responses contained in text data.

[1082] A "tagline" is a short phrase or slogan used to promote a product, intended to evoke an emotional response from the target audience.

[1083] "Patent infringement risk" refers to the possibility of infringing on the patent rights of others, in this case whether the product name or catchphrase overlaps with other existing patents.

[1084] "Evaluation" is the process of determining the appropriateness and effectiveness of the generated product names and catchphrases.

[1085] "Data transmission" refers to the process of transferring coded and encrypted data to send product information to the server.

[1086] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate products from competitors based on product information entered by users. This system is composed of three parties: the user, a terminal, and a server.

[1087] First, the user enters detailed product information through a terminal with a dedicated interface. This information includes ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s."

[1088] Next, the terminal converts the product information entered by the user into JSON or XML format, encrypts the data using the HTTPS protocol, and sends it to the server, ensuring communication security.

[1089] The server analyzes the received product information. First, it uses a natural language processing (NLP) algorithm to extract relevant keywords from the product information. This process uses software such as SpaCy or NLTK. Extracted keywords include "natural," "moisture," and "sensuous."

[1090] Based on related keywords, the server uses a generative AI model (such as GPT-3) to generate product name candidates. The generated product name candidates are compared with a database of competing products to eliminate duplicates and legally questionable names. This filtering process ultimately selects usable product name candidates.

[1091] The server then uses a sentiment analysis algorithm (such as VADER or BERT) to generate taglines that resonate with the target audience. For example, taglines like "Get your daily energy boost, recharge with the power of nature" or "Get organically recharged for the busy workforce" are generated.

[1092] After the generation and filtering are complete, the server sends the final product name and catchphrase candidates to the terminal, which then provides these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[1093] Examples:

[1094] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effects," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensuous," and generates product name candidates such as "Natural Moisture Cream," "Sensuous Day Cream," and "Pure Care Moisture." The server then compares the names with a database of competing products to select legally acceptable candidates. Sentiment analysis is also used to generate catchy slogans. Depending on the attributes of the target audience, suggested catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[1095] Examples of prompt statements

[1096] "Based on the information about a new skincare cream, please create a product name that differentiates it and a catchy copy that will evoke emotions. The product's features are 'natural ingredients,' 'moisturizing effect,' 'for sensitive skin,' 'for women in their 20s and 30s,' and 'daily skincare.'"

[1097] The system allows users to quickly and efficiently generate product names and catchphrases, reducing the risk of competition.

[1098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1099] Step 1:

[1100] Users input product information using a dedicated interface on the device. Specifically, they input product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The input information is sent to the device, and detailed information about the product (ingredients, functions, design, target market, usage scenarios, etc.) is prepared as input data.

[1101] Step 2:

[1102] The terminal converts the product information entered by the user into a specific format (e.g., JSON or XML). Specifically, the terminal interface receives the input data and uses a conversion algorithm to encode it in JSON or XML format. The converted data is then sent to the server using encrypted HTTPS protocol, where it is output as JSON format data.

[1103] Step 3:

[1104] The server receives the JSON data sent from the device. To analyze the received data, it first parses the JSON data. The parsed product information (ingredients, functions, design, target market, usage scenarios, etc.) is output.

[1105] Step 4:

[1106] The server uses a natural language processing (NLP) algorithm to extract related keywords based on the parsed product information. Specifically, it uses an NLP engine (e.g., SpaCy or NLTK) to analyze and extract important keywords from text data. The parsed product information is used as input data, and the extracted related keywords (e.g., "natural," "moisture," and "sensuous") are obtained as output data.

[1107] Step 5:

[1108] The server generates product name candidates using a generative AI model (such as GPT-3) based on the extracted related keywords. Specifically, the related keywords are input into the generative AI model, and the model runs a predictive generation algorithm. The related keywords are used as input data, and product name candidates (e.g., "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture") are generated as output data.

[1109] Step 6:

[1110] The server compares the generated product name candidates with a competing product database and performs filtering. Specifically, the server inputs the product name candidates into the competing product database as queries and receives the matching results. It also cross-checks with the patent database to eliminate duplication and legal issues. The generated product name candidates are used as input data, and filtered product name candidates are obtained as output data.

[1111] Step 7:

[1112] The server generates a catchy slogan by combining product information with a sentiment analysis algorithm. Specifically, it analyzes product information using a sentiment analysis engine (such as VADER or BERT) and generates phrases that resonate with the target audience. Product information is used as input data, and the generated catchy slogan (e.g., "Recharge your daily energy with the power of nature" or "Recharge your energy with organic products for the working man") is obtained as output data.

[1113] Step 8:

[1114] The server then compiles the final evaluated and filtered product name candidates and catchphrase candidates and sends them to the terminal. Specifically, the server compiles these candidates into a list and sends it to the terminal using the HTTPS protocol. The filtered product name candidates and generated catchphrases are used as input data, and the data to be sent is obtained as output data.

[1115] Step 9:

[1116] The terminal receives the data sent from the server and displays it to the user through a user interface. Specifically, the terminal analyzes the received data and presents it to the user in a visually easy-to-understand format. The data received from the server is used as input data, and display data that can be viewed by the user is obtained as output data. Based on this, the user can select the most suitable product name and catchphrase.

[1117] (Application example 1)

[1118] 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."

[1119] In conventional online shopping sites, creating product names and catchy slogans for new products is important for sales promotion, but creating them requires a great deal of time and effort, and it is difficult to differentiate a product from competing products. Furthermore, it is not easy to create an effective catchy slogan that stirs emotions. For this reason, a system that efficiently generates product names and catchy slogans is needed.

[1120] 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.

[1121] In this invention, the server includes means for receiving input product information, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for generating emotionally stirring catchphrase candidates based on the extracted keywords, means for filtering the generated product name candidates and catchphrase candidates, and means for providing the filtered product name candidates and catchphrase candidates, thereby enabling a user to quickly and effectively generate a product name and an emotionally stirring catchphrase that is differentiated from competing products.

[1122] "Product information" refers to detailed information entered by the user, such as product features, ingredients, functions, design, target market, and usage scenarios.

[1123] "Related keywords" are important words or phrases that represent the features or characteristics of a product and are extracted based on the product information.

[1124] "Product name candidates" are a set of suggested names for use as product names, generated using related keywords.

[1125] "Catchphrase candidates" are a series of promotional slogans or phrases created to evoke emotions based on product information and extracted keywords.

[1126] "Emotional appeal" means using expressions and words that strongly appeal to the emotions of the target audience, thereby attracting their interest and increasing their desire to purchase.

[1127] "Filtering" is the process of checking the generated product name and catchphrase candidates against a database of competing products, legal issues, and duplications to eliminate inappropriate ones.

[1128] "Providing" refers to presenting the generated product name and catchphrase candidates to the user via a user interface and providing them as information for selection or decision.

[1129] A "competitive product database" is a database that accumulates data on competing products that exist in the existing market, and is used to differentiate products and identify legal issues.

[1130] This invention relates to a system that automatically generates product names and emotionally appealing catchphrases that differentiate a product from competing products based on product information entered by the user. This system is composed of three entities: a user, a terminal, and a server.

[1131] The server receives product information and uses the following hardware and software to perform the necessary processing: a natural language processing (NLP) algorithm, a sentiment analysis algorithm, the text generation model GPT-3, and the HTTPS protocol.

[1132] The user inputs detailed information about the product through the terminal. This includes the product's features such as ingredients, functions, design, target market, and usage scenarios. For example, information about a new skin care cream might include "natural ingredients," "moisturizing effects," "for sensitive skin," and "for women in their 20s and 30s." The terminal converts the product information entered by the user into a specific format (e.g., JSON or XML) and sends it to the server as encrypted data using the HTTPS protocol.

[1133] The server analyzes the received product information and extracts related keywords. First, it uses an NLP algorithm to extract important words and phrases from the information. For example, keywords such as "natural," "moisture," and "sensuous" are extracted. Based on these keywords, product name candidates are generated.

[1134] The server then uses the generated product name candidates, product information, and extracted keywords to generate emotionally appealing catchphrases. Using a sentiment analysis algorithm and a text generation model, the server creates phrases that resonate with the target audience. For example, suggested catchphrases include "Recharge your daily energy with the power of nature" and "Rejuvenate your working life with organic products."

[1135] The generated product name and catchphrase candidates are checked against a database of competing products to eliminate duplicates and legally questionable names and phrases. Finally, usable product name and catchphrase candidates are filtered. These candidate lists are sent to the terminal and displayed to the user through a user interface. The user can then select the most suitable candidate.

[1136] As a concrete example, let's consider a newly developed skin care cream. The user inputs product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," and "women in their 20s and 30s." The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." Sentiment analysis is also used to generate catchy slogans. Depending on the attributes of the target audience, suggested catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[1137] An example of a generated prompt is as follows:

[1138] Product Name Generation Prompt: "Generate product names using the following keywords: natural, moisturizing, sensuous"

[1139] Catchphrase generation prompt: "Generate catchy phrases for a product with the following attributes: {'ingredients': 'natural ingredients', 'function': 'moisturizing', 'design': 'simple', 'target market': 'women in their 20s and 30s'} and keywords: natural, moisturizing, sensuous"

[1140] This allows users to quickly and effectively create product names and emotionally appealing catchphrases that differentiate them from competing products.

[1141] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1142] Step 1:

[1143] The user enters product information.

[1144] The user uses the terminal to input detailed product information into the input screen. The information input includes the product's ingredients, functions, design, target market, and usage scenarios. For example, "Ingredients: Natural ingredients," "Function: Moisturizing effect," "Design: Simple," and "Target market: Women in their 20s and 30s."

[1145] Step 2:

[1146] The terminal transmits the product information to the server.

[1147] The terminal converts the entered product information into a certain format, such as JSON format, and sends it to the server as encrypted data using the HTTPS protocol.

[1148] Step 3:

[1149] The server analyzes the product information.

[1150] The server analyzes the received product information and uses NLP (Natural Language Processing) algorithms to extract relevant keywords, such as "natural," "moisture," and "sensuous," from the input data.

[1151] Input: Product information (JSON format)

[1152] Output: Extracted related keywords

[1153] Step 4:

[1154] The server generates product name candidates.

[1155] The server uses the extracted related keywords to generate product name candidates that take into account past success stories and related keyword combinations. Using a generative AI model such as GPT-3, the server generates text and generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[1156] Input: Extracted related keywords

[1157] Output: Generated product name candidates

[1158] Step 5:

[1159] The server generates catchphrase candidates.

[1160] The server generates catchphrases based on product information and extracted keywords using a sentiment analysis algorithm and a generative AI model. By inputting a prompt, the server generates catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products."

[1161] Input: Product information, extracted related keywords

[1162] Output: Generated tagline candidates

[1163] Step 6:

[1164] The server filters product name and tagline candidates.

[1165] The server compares the generated product name and catchphrase candidates with a database of competing products to check for duplication or legal issues, and eliminates inappropriate candidates.

[1166] Input: Generated product name candidates, generated tagline candidates

[1167] Output: Filtered product name candidates, filtered tagline candidates

[1168] Step 7:

[1169] The server provides the final candidate list to the terminal.

[1170] The filtered list of product name and catch phrase candidates is sent from the server to the terminal, which displays this information to the user through a user interface, allowing the user to select the product name and catch phrase that they think are most suitable.

[1171] Input: Filtered product name candidates, Filtered tagline candidates

[1172] Output: A list of product name and tagline suggestions provided to the user

[1173] 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.

[1174] This invention further improves the accuracy and effectiveness of the generated results by combining a system that automatically generates product names and emotionally appealing slogans that differentiate products from competing products based on product information entered by the user with an emotion engine that recognizes the user's emotions. This system is composed of three parties: the user, a terminal, and a server.

[1175] Program processing

[1176] 1. User input

[1177] The user uses the input form on the device to enter detailed product information, including the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events). For example, the user might enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[1178] 2. Sending data from the device to the server

[1179] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML) and sends it to the server as encrypted data using the HTTPS protocol.

[1180] 3. Product information analysis on the server

[1181] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[1182] 4. Product name generation

[1183] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[1184] 5. Filtering process

[1185] The server filters the generated product name candidates, using specific criteria and competitive product databases to eliminate duplicates and inappropriate names, and cross-checks them with patent databases to ensure legally sound product name candidates.

[1186] 6. Generate a catchphrase

[1187] The server generates catchphrases based on product information and the user's emotional information. The emotion engine analyzes the user's emotional state based on the information entered by the user and past behavioral data, and uses the results to generate phrases that stir emotions. For example, catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their working lives" are generated.

[1188] 7. User Emotion Recognition

[1189] While the user is using the device to input product information, the emotion engine analyzes the user's input pattern, time, and input content to infer the user's emotional state. For example, if the input speed is fast, it is inferred to be excited, and if the input is slow and careful, it is inferred to be attentive.

[1190] 8. Emotion Data Analysis

[1191] The server analyzes the emotion data obtained from the emotion engine and reflects it in the catchphrase generation process. The server incorporates the user's emotional state indicated by the emotion data into the catchphrase, generating phrases that are likely to resonate with the emotions of the target audience.

[1192] 9. Providing Results to Users

[1193] The filtered and evaluated list of product name and catchphrase candidates is sent to the terminal, which displays these candidates to the user through a user interface, allowing the user to select the most suitable candidate.

[1194] Specific examples

[1195] For example, consider a newly developed skin care cream. The user enters product information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." The device receives this information and sends it to the server. The server extracts keywords such as "natural," "moisture," and "sensitive," and generates product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture." The server then compares the results with a database of competing products to select candidates that are legally acceptable.

[1196] The emotion engine analyzes the input pattern and speed while the user is entering information and estimates the user's emotional state. For example, if the user is typing quickly, it will determine that the user is excited.

[1197] The server generates catchy slogans while performing sentiment analysis using the emotional data obtained from the emotion engine. Phrases that suit the characteristics of the target audience are taken into consideration, and catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their working lives" are suggested.

[1198] Users can view a list of product name and catchphrase candidates via their device and select the most suitable one, enabling them to easily and quickly generate effective product names and catchphrases to support their marketing activities.

[1199] The processing flow will be explained below.

[1200] Step 1:

[1201] The user uses the input form on the device to enter detailed product information. Specifically, they enter the product's features (e.g., ingredients, functions, design), target market (e.g., age group, gender), and usage scenario (e.g., everyday use, specific events), and then press the send button. For example, they enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "everyday skin care."

[1202] Step 2:

[1203] The terminal converts the product information entered by the user into a specific format (e.g., JSON, XML) and sends it to the server as encrypted data using the HTTPS protocol.

[1204] Step 3:

[1205] The server analyzes the product information received from the device. First, it uses text analysis tools and natural language processing (NLP) algorithms to extract relevant keywords, such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[1206] Step 4:

[1207] The server generates product name candidates based on the extracted related keywords. It connects to a database and automatically generates product name candidates using past success stories and related keyword combinations. For example, it generates candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture."

[1208] Step 5:

[1209] The server filters the generated product name candidates, using a competitive product database and patent database to eliminate duplicates and inappropriate names and ensure that the product name candidates are legally sound.

[1210] Step 6:

[1211] The server uses an emotion engine to analyze the user's input patterns in real time and estimate the user's emotional state. For example, if the user types quickly, it may determine that the user is excited or nervous.

[1212] Step 7:

[1213] The server combines the emotional data obtained from the emotion engine with product information to generate catchy slogans. The emotional data reflects the user's psychological state, generating phrases that are likely to resonate with the target audience. For example, catchy slogans such as "Recharge your daily energy with the power of nature" and "Rejuvenate yourself with organic products for those in the prime of their working lives" can be generated.

[1214] Step 8:

[1215] The server evaluates the generated tagline candidates and selects the most effective tagline through comparison with sentiment evaluation tools and past data.

[1216] Step 9:

[1217] The server sends the filtered and evaluated list of product name and catchphrase candidates to the terminal, and in the process encodes the metadata about the product names and catchphrases (e.g., importance and popularity of keywords) and sends it back to the terminal via the network.

[1218] Step 10:

[1219] The terminal displays the list of product name and catch phrase candidates received from the server on the user interface. The user can review the list of candidates presented through the terminal and select the most suitable product name and catch phrase. During this process, the candidates selected by the user are sent back to the server and saved as the final results.

[1220] This allows users to quickly and effectively generate product names and evocative catchphrases that differentiate their products from competitors, supporting their marketing efforts.

[1221] Example 2

[1222] 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."

[1223] In conventional systems, users had to manually input product information and come up with product names and catchy slogans themselves, which required time and effort, making it difficult to implement effective marketing activities. Furthermore, there were few methods for generating catchy slogans that took the user's emotional state into account, making it impossible to create slogans that resonated with the target audience. Furthermore, there was insufficient filtering of product names to avoid duplication with competing products, which increased the risk of legal issues.

[1224] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1225] In this invention, the server includes means for converting input product information into a specific format and transmitting it in encrypted form, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for cross-checking the generated product name candidates with specific criteria and a competing product database and filtering them, means for generating catch phrases based on the product information and user emotional information, and means for evaluating the generated catch phrase candidates. This enables effective marketing activities that reflect the user's emotional state while quickly generating product names and catch phrases based on product information and avoiding duplication with competing products.

[1226] "Inputted product information" refers to detailed information such as product features, target market, and usage scenarios that users provide through the system's input form.

[1227] "Format" refers to arranging data in a specific form, typically a data format such as JSON or XML.

[1228] "Encrypted form" means a form in which data has been converted using security technology to prevent unauthorized access by third parties.

[1229] "Related keywords" are specific important words or phrases extracted from the input product information using text analysis and natural language processing techniques.

[1230] "Product name candidates" are multiple product name suggestions automatically generated by the system based on related keywords.

[1231] "Filtering" is the process of removing duplicate, inappropriate, or legally questionable product name and tagline candidates from the generated product name and tagline candidates.

[1232] A "competitive product database" is a database that accumulates information on other companies' products that exist on the market, and is used for analyzing overlapping product names and competition.

[1233] "User emotion information" is data indicating the user's emotional state estimated based on the input pattern, speed, etc. when the user inputs product information.

[1234] "Catchphrase candidates" are marketing phrase ideas generated based on product information and user emotional information.

[1235] An "emotion engine" is an algorithm or system that analyzes the user's emotional state from input data and generates content based on that.

[1236] "Evaluation" is the process of evaluating the generated product name and catchphrase candidates based on specific criteria and selecting the most suitable candidate.

[1237] This invention relates to a system that automatically generates product names and evocative catchphrases to differentiate products from competitors based on product information entered by users. This system is composed of three entities: a user, a terminal, and a server.

[1238] The system operates using the following hardware and software.

[1239] Hardware

[1240] 1. Terminal: A device such as a PC, tablet, or smartphone. It provides an interface for users to input product information.

[1241] 2. Server: A high-performance computer (e.g., cloud server or on-premise server) that analyzes product information, generates product names and catchphrases, filters, and analyzes sentiment data.

[1242] software

[1243] 1. Input form: This is implemented as a web application that allows users to input product features, target markets, usage scenarios, etc. The specific format uses HTML, JavaScript, CSS, etc.

[1244] 2. Data transmission module: Converts the input product information into a specific format such as JSON or XML and sends it to the server in encrypted form using the HTTPS protocol.

[1245] 3. Text analysis tools: Using natural language processing (NLP) algorithms, related keywords are extracted from product information on the server side. Specific tools used include Python's NLTK and spaCy.

[1246] 4. Database management system: Used to store dictionary data for generated product names and catchphrases, as well as data on past success stories. Specifically, MySQL or PostgreSQL is used.

[1247] 5. Emotion engine: This engine analyzes the user's input patterns and speed to estimate their emotional state. Specifically, it is implemented using Python libraries and machine learning models.

[1248] 6. AI Models: These include AI models for generating product names and emotive copy. These models are trained using frameworks such as TensorFlow and PyTorch.

[1249] 7. UI / UX design: Provide an interface for users to view and select product name and tagline candidates using front-end frameworks such as React and Vue.js.

[1250] Specific examples

[1251] For example, let's say you're looking at a newly developed skin care cream. The user inputs the product information as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." Based on this information, the system operates as follows:

[1252] 1. The user enters information through the input form on the device.

[1253] 2. The terminal converts the entered information into JSON format and sends it to the server using the HTTPS protocol.

[1254] 3. The server receives the information and uses text analysis tools to extract keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin."

[1255] 4. Based on these keywords, product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated from the database.

[1256] 5. Filter the generated product name candidates by cross-checking them with competitor product databases and patent databases.

[1257] 6. The emotion engine analyzes the user's input patterns to infer their emotional state, and based on that generates catchy slogans such as "Recharge your daily energy with the power of nature" and "For those in the prime of their working lives, recharge with organic technology."

[1258] 7. The server sends the final filtered product name candidates and catchphrase candidates to the terminal.

[1259] 8. The user checks the list of candidates presented on the device and selects the most suitable one.

[1260] Prompt Sentence Examples

[1261] Create a product name and catchphrase based on the information "natural ingredients, moisturizing effect, for sensitive skin, for women in their 20s and 30s, daily skin care."

[1262] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1263] Step 1: User enters information

[1264] The user enters product information into an input form provided on the device. Specifically, the user enters information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care." This input form is implemented using HTML and JavaScript, and once the user has completed their input, the device converts this information into JSON format. The input is detailed product information data, and the output is the product information data converted into JSON format.

[1265] Step 2: Send data from the device to the server

[1266] The terminal sends the product information data converted to JSON format to the server using the HTTPS protocol. This communication is encrypted to ensure data security. The input is JSON format product information data, and the output is encrypted data sent to the server. The terminal receives a response indicating successful transmission and notifies the user.

[1267] Step 3: Product information analysis on the server

[1268] The server analyzes the received JSON-formatted product information data. First, it uses a text analysis tool (e.g., Python's NLTK or spaCy) to extract related keywords from the product information. For example, keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin" are extracted. The input is the JSON-formatted product information data, and the output is a list of extracted keywords.

[1269] Step 4: Generate product name

[1270] The server generates product name candidates based on the extracted related keywords. It connects to a database management system (e.g., MySQL or PostgreSQL) and automatically generates product name candidates by referring to past success stories and related keyword combinations. For example, names such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are generated. The input is the extracted keyword list, and the output is the generated list of product name candidates.

[1271] Step 5: Filtering

[1272] The server filters the generated list of product name candidates. First, it eliminates duplicates and inappropriate names, and then cross-checks them with competitive product and patent databases to extract only legally sound product name candidates. This process is performed using Python scripts and SQL queries. The input is the generated list of product name candidates, and the output is the filtered list of product name candidates.

[1273] Step 6: Create a tagline

[1274] The server generates a catchy slogan based on product information and the user's emotional information. The emotion engine analyzes the information entered by the user and past behavioral data to estimate their emotional state. A generative AI model (e.g., TensorFlow or PyTorch) is used to generate a catchy slogan that will resonate with the target audience. For example, possible catchy slogans include "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products for those in the prime of their lives." The input is product information and emotional information, and the output is a list of candidate catchy slogans.

[1275] Step 7: Recognizing User Emotions

[1276] While the user is entering product information, the device uses an emotion engine to analyze the input pattern and speed and estimate the user's emotional state. For example, if the input speed is fast, it is estimated to be an excited state, while if the input speed is slow and careful, it is estimated to be a cautious state. This emotion data is sent to the server in real time. The input is input pattern and speed data, and the output is emotional state data.

[1277] Step 8: Analyze the sentiment data

[1278] The server analyzes the emotional data obtained from the emotion engine and reflects it in the catchphrase generation process. It incorporates the user's emotional state indicated by the emotional data into the catchphrase to generate phrases that are likely to resonate with the target audience. The input is the emotional state data, and the output is an improved list of catchphrase candidates.

[1279] Step 9: Providing results to users

[1280] The server sends the filtered and evaluated product name candidate and catch phrase candidate list to the terminal. The terminal displays these candidates to the user through a user interface, allowing the user to select the most suitable product name and catch phrase. The input is the filtered product name candidate list and catch phrase candidate list, and the output is the candidate display on the user interface.

[1281] By following the steps above, the user can quickly and easily create a product name and catchy slogan, and effectively carry out marketing activities.

[1282] (Application example 2)

[1283] 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."

[1284] Conventional advertising generation systems were unable to consider the user's emotional state when generating product names and catchy slogans based on product information entered by the user. This made it difficult to generate effective product names and catchy slogans that would appeal to the target audience. Furthermore, filtering to differentiate from competing products was insufficient, limiting the accuracy and effectiveness of product names and catchy slogans.

[1285] The identification process by the identification 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 receiving input product information, means for extracting related keywords based on the product information, means for generating product name candidates using the related keywords, means for filtering the generated product name candidates, means for providing the filtered product name candidates, means for analyzing the user's emotional state, means for generating catchphrase candidates based on the emotional state, and means for evaluating the generated catchphrase candidates. This makes it possible to generate effective product names and catchphrases that can differentiate from competing products, taking the user's emotional state into consideration.

[1286] "Product information" is information input by the user regarding the product's features, functions, target market, usage scenarios, etc.

[1287] "Related keywords" are important words and phrases extracted from product information that represent the characteristics and features of the product.

[1288] A "product name" is a unique, easy-to-remember combination of words used as the name of a product.

[1289] A "catchphrase" is a short phrase that succinctly conveys the features and value of a product and attracts consumer attention.

[1290] "Filtering" is the process of removing inappropriate or duplicated product names and catchphrases from the generated product name and catchphrase candidates.

[1291] The "emotional state" refers to the psychological state of the user when entering product information, and is analyzed based on facial expressions and voice data.

[1292] "Evaluation" is the process of determining whether the generated catchphrases and product name candidates are appropriate.

[1293] The "competitive product database" is a database that accumulates information about competing products that are already on the market.

[1294] A "smart device" is a device (e.g., smartphone, head-mounted display) that acquires a user's facial expressions and voice and performs emotional analysis.

[1295] To implement this invention, a system is required that links the terminal, server, and user. This system receives product information entered by the user, extracts related keywords based on that information, and then executes a series of processes to generate product names and catchphrases.

[1296] 1. Hardware and Software Configuration

[1297] Terminal

[1298] A smart device (smartphone or head-mounted display) is used as the terminal.

[1299] Input means: Provides an interface for users to input product information.

[1300] Emotion analysis means: A camera and microphone are used to capture the user's facial expressions and voice, and analyze the user's emotional state.

[1301] server

[1302] The server includes modules with the following functions:

[1303] Data receiving module: Receives product information sent from the terminal.

[1304] Keyword Extraction Module: Extracts relevant keywords from the received product information. This module uses natural language processing techniques (e.g., SpaCy, NLTK library).

[1305] Product name generation module: Generates product name candidates based on the extracted keywords. This is done using a generative AI model (e.g., GPT-3) while referencing a past database.

[1306] Filtering module: Filters the generated product name candidates and compares them with a competitor product database (e.g., SQL database).

[1307] Catchphrase generation module: Generates catchphrase candidates taking into account the user's emotional state. It uses a generative AI model and works in conjunction with the emotion engine.

[1308] Evaluation module: Evaluates the generated tagline candidates.

[1309] 2. Details of data processing and calculation

[1310] User input of product information: Users use their smart devices to input detailed information about the product, such as its features, target market, and usage scenarios. The input information is formatted in JSON or XML format.

[1311] Data reception and analysis: The smart device encrypts the product information entered by the user and the sentiment analysis results and sends them to the server. The server analyzes the received data and extracts keywords using text analysis tools and natural language processing algorithms.

[1312] Product Name and Slogan Generation: Using keywords, a generative AI model (e.g., GPT-3) generates product name and slogan candidates, taking into account the user's emotional state.

[1313] Filtering and Evaluation: The generated product name and tagline candidates are checked against a competitive product database to eliminate irrelevant and duplicated candidates, and tagline candidates are evaluated using data from the sentiment engine.

[1314] 3. Specific Examples

[1315] Example prompt sentence:

[1316] Your project is a new skincare cream. Its features include natural ingredients, moisturizing effects, and is suitable for sensitive skin. The target market is women in their 20s and 30s, and it is intended for daily skincare use. Based on this, please generate an emotionally appealing product name and tagline.

[1317] By implementing such a system, it becomes possible to generate effective product names and catchy slogans that take into account the user's emotional state and differentiate products from those of competitors.

[1318] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1319] Step 1:

[1320] Users enter product information using a smart device (smartphone or head-mounted display). In the input form, users enter details about the product's features, target market, and usage scenarios. The input information is formatted in JSON or XML. Specifically, users enter information such as "natural ingredients," "moisturizing effect," "for sensitive skin," "for women in their 20s and 30s," and "daily skin care."

[1321] Step 2:

[1322] The terminal receives the product information entered by the user, encrypts it, and sends it to the server. The data is sent securely using the HTTPS protocol. Specifically, the terminal converts the information received from the user into JSON format and sends it to the server via HTTPS.

[1323] Step 3:

[1324] The server analyzes the received product information and extracts related keywords. This process uses natural language processing (NLP) tools (e.g., SpaCy, NLTK). Specifically, keywords such as "natural ingredients," "moisturizing effects," and "for sensitive skin" are extracted through text analysis.

[1325] Step 4:

[1326] The server generates product name candidates using a generative AI model (e.g., GPT-3) based on the extracted related keywords. Specifically, a prompt sentence is input into the generative AI model, and product name candidates such as "Natural Moisture Cream," "Sensitive Day Cream," and "Pure Care Moisture" are output.

[1327] Step 5:

[1328] The server filters the generated product name candidates. This filtering uses a database of competing products to eliminate inappropriate names and duplicates. It also checks for legal issues. Specifically, it performs a database search and compares the candidates with existing product names to eliminate inappropriate candidates.

[1329] Step 6:

[1330] The server understands the user's emotional state by analyzing facial expression and tone of voice data from the device. To do this, it uses an emotion analysis engine to analyze facial expressions and voice patterns while the user is typing. Specifically, it analyzes camera and microphone data and determines that the user is in an excited state if they are typing quickly.

[1331] Step 7:

[1332] The server generates catchphrase candidates based on the results of the sentiment analysis. It uses a generative AI model to create emotionally appealing copy. Specifically, catchphrases such as "Recharge your daily energy with the power of nature" and "Rejuvenate your vitality with organic products" are generated.

[1333] Step 8:

[1334] The server evaluates the generated tagline candidates. It uses the sentiment analysis results and past data to select the best tagline for the target market. Specifically, it ranks the generated tagline candidates and selects the best candidate based on the evaluation criteria.

[1335] Step 9:

[1336] The server sends the filtered and evaluated product name and catchphrase candidates to the terminal. The user can check these candidate lists on the screen of the smart device and select the most suitable one. Specifically, the generated list is displayed on the user interface.

[1337] 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.

[1338] 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.

[1339] 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 robot 414.

[1340] 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.

[1341] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1342] 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.

[1343] 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).

[1344] 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.

[1345] 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."

[1346] 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.

[1347] 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).

[1348] 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.

[1349] 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.

[1350] 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.

[1351] 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.

[1352] 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.

[1353] 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.

[1354] 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.

[1355] 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.

[1356] 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.

[1357] 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.

[1358] The following is further disclosed regarding the above embodiment.

[1359] (Claim 1)

[1360] A means for receiving input product information;

[1361] means for extracting related keywords based on the product information;

[1362] means for generating product name candidates using the related keywords;

[1363] A means for filtering the generated product name candidates;

[1364] means for providing the filtered product name candidates;

[1365] A system including:

[1366] (Claim 2)

[1367] A means for receiving input product information;

[1368] means for performing sentiment analysis based on the product information;

[1369] means for generating catchphrase candidates using the sentiment analysis results;

[1370] A means for evaluating the generated catchphrase candidates;

[1371] a means for providing the evaluated catchphrase candidates;

[1372] A system including:

[1373] (Claim 3)

[1374] 10. The system of claim 1, further comprising means for accessing a competitive product database to filter the candidate trade names.

[1375] "Example 1"

[1376] (Claim 1)

[1377] A means for receiving input product information;

[1378] means for converting the product information into a specific format, encrypting the data, and transmitting the data;

[1379] means for extracting related keywords based on the product information using a natural language processing algorithm;

[1380] A means for generating product name candidates using a generative AI model based on the related keywords;

[1381] A means for comparing the generated product name candidates with a competitive product database and filtering them;

[1382] means for providing the filtered product name candidates;

[1383] A system including:

[1384] (Claim 2)

[1385] means for performing sentiment analysis based on the product information and generating catchphrase candidates;

[1386] means for generating candidate catchy slogans that will resonate with a target audience using the sentiment analysis algorithm;

[1387] A means for evaluating and providing the generated tagline candidates;

[1388] 10. The system of claim 1, comprising:

[1389] (Claim 3)

[1390] 2. The system according to claim 1, further comprising means for comparing the product information with a database of competing products using related keywords extracted based on the product information to assess the risk of patent infringement.

[1391] "Application Example 1"

[1392] (Claim 1)

[1393] A means for receiving input product information;

[1394] means for extracting related keywords based on the product information;

[1395] means for generating product name candidates using the related keywords;

[1396] A means for generating candidate catchphrases that stir emotions based on the extracted keywords;

[1397] A means for filtering the generated product name candidates and catchphrase candidates;

[1398] a means for providing the filtered product name candidates and catchphrase candidates;

[1399] A system including:

[1400] (Claim 2)

[1401] A means for evaluating the generated catchphrase candidates;

[1402] a means for providing the evaluated catchphrase candidates;

[1403] The system of claim 1 further comprising:

[1404] (Claim 3)

[1405] 10. The system of claim 1, further comprising means for accessing a competitive product database to filter the product name candidates and catchphrase candidates.

[1406] "Example 2: Combining Emotion Engines"

[1407] (Claim 1)

[1408] A means for receiving input product information;

[1409] means for converting the product information into a specific format and transmitting it in an encrypted form;

[1410] means for extracting related keywords based on the product information;

[1411] means for generating product name candidates using the related keywords;

[1412] A means to filter the generated product name candidates by cross-checking them against specific criteria and a competitive product database;

[1413] means for providing the filtered product name candidates;

[1414] means for generating a catchphrase based on the product information and user emotion information;

[1415] A means for evaluating the generated catchphrase candidates;

[1416] a means for providing the evaluated catchphrase candidates;

[1417] A system including:

[1418] (Claim 2)

[1419] A means for receiving input product information;

[1420] means for analyzing emotion data using the product information and an emotion engine and estimating an emotional state;

[1421] means for generating catchphrase candidates using the sentiment analysis results;

[1422] A means for evaluating the generated catchphrase candidates;

[1423] a means for providing the evaluated catchphrase candidates;

[1424] 10. The system of claim 1, comprising:

[1425] (Claim 3)

[1426] 10. The system of claim 1, further comprising means for accessing a competitive product database to filter the candidate trade names.

[1427] "Application example 2 when combining emotion engines"

[1428] (Claim 1)

[1429] A means for receiving input product information;

[1430] means for extracting related keywords based on the product information;

[1431] means for generating product name candidates using the related keywords;

[1432] A means for filtering the generated product name candidates;

[1433] means for providing the filtered product name candidates;

[1434] means for analyzing the emotional state of a user;

[1435] means for generating catchphrase candidates based on the emotional state;

[1436] A means for evaluating the generated catchphrase candidates;

[1437] A system including:

[1438] (Claim 2)

[1439] The system of claim 1, wherein the system uses a smart device to acquire the user's facial expressions and voice and analyze the emotional state.

[1440] (Claim 3)

[1441] 10. The system of claim 1, further comprising means for accessing a competitive product database to filter the candidate trade names. [Explanation of symbols]

[1442] 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 receiving input product information; means for extracting related keywords based on the product information; means for generating product name candidates using the related keywords; A means for filtering the generated product name candidates; means for providing the filtered product name candidates; A system including:

2. A means for receiving input product information; means for performing sentiment analysis based on the product information; means for generating catchphrase candidates using the sentiment analysis results; A means for evaluating the generated catchphrase candidates; a means for providing the evaluated catchphrase candidates; A system including:

3. 10. The system of claim 1, further comprising means for accessing a competitive product database to filter the product name candidates.

Images