System

Abstract

To integrally analyze a data of a plurality of marketing channels, perform a trend analysis of an automatic specification and a customer behavior of a customer segment, and propose an advertising strategy in order to maximize an ROI, and thus improve an efficiency and an accuracy pf a marketing analysis.SOLUTION: A system contains: means of integrally analyzing a data of a plurality of marketing channels; means of performing a trend analysis of an automatic specification and a customer behavior of a customer segment; and means of proposing an advertising strategy in order to maximize an ROI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] This invention solves the problem of improving the efficiency and accuracy of marketing analysis by comprehensively analyzing data from multiple marketing channels, automatically identifying customer segments, analyzing trends in customer behavior, and proposing advertising strategies to maximize ROI. [Means for solving the problem]

[0005] A system that includes a means for comprehensively analyzing data from multiple marketing channels, a means for automatically identifying customer segments and analyzing trends in customer behavior, and a means for proposing advertising strategies to maximize ROI. [Brief explanation of the drawings]

[0006] [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. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0015] 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.

[0016] 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).

[0017] 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.

[0018] 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 (see FIG. 2) acquires the data indicating the user input.

[0019] The output device 40 is equipped with a display 40A, a speaker 40B, etc., and presents data to the user by outputting the data in a form of expression that the user 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.

[0020] 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.

[0021] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, or a home appliance). Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0026] (Example 1) One embodiment of the present invention is a system including a data processing module that collects and integrates data from multiple marketing channels, a means for classifying customer data and predicting trends using a machine learning algorithm, and a proposal module for optimizing advertising strategies. Specifically, the data processing module executes data processing procedures for collecting, integrating, and analyzing data from each marketing channel. The means for using a machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. The proposal module then proposes advertising strategies to maximize ROI based on the analysis results. (Example 2) An embodiment of the present invention is a system including a data processing module for comprehensively analyzing data from multiple marketing channels, a machine learning algorithm for classifying customer data and predicting trends, and a proposal module for proposing advertising strategies to maximize ROI. Specifically, the data processing module executes procedures for collecting and comprehensively analyzing data from multiple marketing channels. The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. The proposal module then proposes advertising strategies to maximize ROI based on the analysis results. (Example 3) An embodiment of the present invention is a system including a data processing module for comprehensively analyzing data from multiple marketing channels, a machine learning algorithm for automatically identifying customer segments and analyzing trends in customer behavior, and a proposal module for proposing advertising strategies to maximize ROI. Specifically, the data processing module provides means for collecting and comprehensively analyzing data from multiple marketing channels. The machine learning algorithm also provides means for learning from the collected customer data, automatically identifying customer segments, and analyzing trends in customer behavior. The proposal module also provides means for proposing advertising strategies to maximize ROI based on the analysis results.

[0027] The processing flow of each embodiment will be described below.

[0028] (Example 1) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. Specifically, the learning data is used to train the model and make predictions on unknown data. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment. (Example 2) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. Specifically, the learning data is used to train the model and make predictions on unknown data. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment. (Example 3) Step 1: The data processing module collects data from multiple marketing channels and provides a means for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: Machine learning algorithms provide a means to learn from collected customer data, automatically identify customer segments, and analyze trends in customer behavior. Specifically, the learning data is used to train a model and make predictions on unseen data. Step 3: The proposal module provides a means to propose advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment.

[0029] 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.

[0030] (Example 1) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine analyzes user voice and text data and uses machine learning algorithms to estimate the type and intensity of emotions. For example, if a user expresses positive emotions toward a product, the system can prioritize that product when proposing advertising strategies. (Example 2) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine uses computer vision technology to analyze the user's facial expressions and body movements and detect emotional changes and reactions. For example, if the user shows expressions of surprise or joy when viewing an advertisement, products or services related to the advertisement can be highlighted. (Example 3) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine analyzes user voice and text data and uses machine learning algorithms to estimate the type and intensity of emotions. For example, if a user expresses dissatisfaction or discomfort with an advertisement, the engine can suggest improvements to the advertisement or alternatives.

[0031] The processing flow of each embodiment will be described below.

[0032] (Example 1) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses machine learning algorithms to analyze the user's voice and text data and estimate the type and intensity of emotions. Specifically, it analyzes the user's speech and input text using text mining and speech recognition technology to extract emotional characteristics. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns tailored to the user's emotions. For example, if a user expresses joy, it will propose advertisements containing positive messages and images related to that product or service. (Example 2) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses computer vision technology to analyze the user's facial expressions and body movements and detect emotional changes and reactions. Specifically, it extracts the user's facial expressions and body movements from videos and images and analyzes their emotional features. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns tailored to the user's emotions. For example, if a user shows a surprised expression, it will propose an advertisement that highlights the features and benefits of a new product or service related to the advertisement. (Example 3) Step 1: The data processing module collects data from multiple marketing channels and provides a means for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses machine learning algorithms to analyze the user's voice and text data and estimate the type and intensity of emotions. Specifically, it analyzes the user's speech and input text using text mining and speech recognition technology to extract emotional characteristics. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns that match the user's emotions. For example, if a user expresses dissatisfaction or discomfort with an advertisement, it can suggest improvements or alternatives to the advertisement.

[0033] 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.

[0034] 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> ) and other generation AIs. The data generation model 58 is obtained by causing a neural network to perform deep learning. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58.

[0035] 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.

[0036] [Second embodiment]

[0037] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0038] 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.

[0039] 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).

[0040] 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.

[0041] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, 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.

[0042] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0048] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, or a home appliance). Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0049] (Example 1) One embodiment of the present invention is a system including a data processing module that collects and integrates data from multiple marketing channels, a means for classifying customer data and predicting trends using a machine learning algorithm, and a proposal module for optimizing advertising strategies. Specifically, the data processing module executes data processing procedures for collecting, integrating, and analyzing data from each marketing channel. The means for using a machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. The proposal module then proposes advertising strategies to maximize ROI based on the analysis results. (Example 2) An embodiment of the present invention is a system including a data processing module for comprehensively analyzing data from multiple marketing channels, a machine learning algorithm for classifying customer data and predicting trends, and a proposal module for proposing advertising strategies to maximize ROI. Specifically, the data processing module executes procedures for collecting and comprehensively analyzing data from multiple marketing channels. The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. The proposal module then proposes advertising strategies to maximize ROI based on the analysis results. (Example 3) An embodiment of the present invention is a system including a data processing module for comprehensively analyzing data from multiple marketing channels, a machine learning algorithm for automatically identifying customer segments and analyzing trends in customer behavior, and a proposal module for proposing advertising strategies to maximize ROI. Specifically, the data processing module provides means for collecting and comprehensively analyzing data from multiple marketing channels. The machine learning algorithm also provides means for learning from the collected customer data, automatically identifying customer segments, and analyzing trends in customer behavior. The proposal module also provides means for proposing advertising strategies to maximize ROI based on the analysis results.

[0050] The processing flow of each embodiment will be described below.

[0051] (Example 1) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. Specifically, the learning data is used to train the model and make predictions on unknown data. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment. (Example 2) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. Specifically, the learning data is used to train the model and make predictions on unknown data. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment. (Example 3) Step 1: The data processing module collects data from multiple marketing channels and provides a means for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: Machine learning algorithms provide a means to learn from collected customer data, automatically identify customer segments, and analyze trends in customer behavior. Specifically, the learning data is used to train a model and make predictions on unseen data. Step 3: The proposal module provides a means to propose advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment.

[0052] 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.

[0053] (Example 1) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine analyzes user voice and text data and uses machine learning algorithms to estimate the type and intensity of emotions. For example, if a user expresses positive emotions toward a product, the system can prioritize that product when proposing advertising strategies. (Example 2) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine uses computer vision technology to analyze the user's facial expressions and body movements and detect emotional changes and reactions. For example, if the user shows expressions of surprise or joy when viewing an advertisement, products or services related to the advertisement can be highlighted. (Example 3) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine analyzes user voice and text data and uses machine learning algorithms to estimate the type and intensity of emotions. For example, if a user expresses dissatisfaction or discomfort with an advertisement, the engine can suggest improvements to the advertisement or alternatives.

[0054] The processing flow of each embodiment will be described below.

[0055] (Example 1) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses machine learning algorithms to analyze the user's voice and text data and estimate the type and intensity of emotions. Specifically, it analyzes the user's speech and input text using text mining and speech recognition technology to extract emotional characteristics. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns tailored to the user's emotions. For example, if a user expresses joy, it will propose advertisements containing positive messages and images related to that product or service. (Example 2) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses computer vision technology to analyze the user's facial expressions and body movements and detect emotional changes and reactions. Specifically, it extracts the user's facial expressions and body movements from videos and images and analyzes their emotional features. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns tailored to the user's emotions. For example, if a user shows a surprised expression, it will propose an advertisement that highlights the features and benefits of a new product or service related to the advertisement. (Example 3) Step 1: The data processing module collects data from multiple marketing channels and provides a means for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses machine learning algorithms to analyze the user's voice and text data and estimate the type and intensity of emotions. Specifically, it analyzes the user's speech and input text using text mining and speech recognition technology to extract emotional characteristics. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns that match the user's emotions. For example, if a user expresses dissatisfaction or discomfort with an advertisement, it can suggest improvements or alternatives to the advertisement.

[0056] 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.

[0057] 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> ) and other generation AIs. The data generation model 58 is obtained by causing a neural network to perform deep learning. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58.

[0058] 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.

[0059] [Third embodiment]

[0060] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0061] 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.

[0062] 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).

[0063] 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.

[0064] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, 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.

[0065] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, or a home appliance). Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0072] (Example 1) One embodiment of the present invention is a system including a data processing module that collects and integrates data from multiple marketing channels, a means for classifying customer data and predicting trends using a machine learning algorithm, and a proposal module for optimizing advertising strategies. Specifically, the data processing module executes data processing procedures for collecting, integrating, and analyzing data from each marketing channel. The means for using a machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. The proposal module then proposes advertising strategies to maximize ROI based on the analysis results. (Example 2) An embodiment of the present invention is a system including a data processing module for comprehensively analyzing data from multiple marketing channels, a machine learning algorithm for classifying customer data and predicting trends, and a proposal module for proposing advertising strategies to maximize ROI. Specifically, the data processing module executes procedures for collecting and comprehensively analyzing data from multiple marketing channels. The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. The proposal module then proposes advertising strategies to maximize ROI based on the analysis results. (Example 3) An embodiment of the present invention is a system including a data processing module for comprehensively analyzing data from multiple marketing channels, a machine learning algorithm for automatically identifying customer segments and analyzing trends in customer behavior, and a proposal module for proposing advertising strategies to maximize ROI. Specifically, the data processing module provides means for collecting and comprehensively analyzing data from multiple marketing channels. The machine learning algorithm also provides means for learning from the collected customer data, automatically identifying customer segments, and analyzing trends in customer behavior. The proposal module also provides means for proposing advertising strategies to maximize ROI based on the analysis results.

[0073] The processing flow of each embodiment will be described below.

[0074] (Example 1) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. Specifically, the learning data is used to train the model and make predictions on unknown data. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment. (Example 2) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. Specifically, the learning data is used to train the model and make predictions on unknown data. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment. (Example 3) Step 1: The data processing module collects data from multiple marketing channels and provides a means for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: Machine learning algorithms provide a means to learn from collected customer data, automatically identify customer segments, and analyze trends in customer behavior. Specifically, the learning data is used to train a model and make predictions on unseen data. Step 3: The proposal module provides a means to propose advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment.

[0075] 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.

[0076] (Example 1) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine analyzes user voice and text data and uses machine learning algorithms to estimate the type and intensity of emotions. For example, if a user expresses positive emotions toward a product, the system can prioritize that product when proposing advertising strategies. (Example 2) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine uses computer vision technology to analyze the user's facial expressions and body movements and detect emotional changes and reactions. For example, if the user shows expressions of surprise or joy when viewing an advertisement, products or services related to the advertisement can be highlighted. (Example 3) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine analyzes user voice and text data and uses machine learning algorithms to estimate the type and intensity of emotions. For example, if a user expresses dissatisfaction or discomfort with an advertisement, the engine can suggest improvements to the advertisement or alternatives.

[0077] The processing flow of each embodiment will be described below.

[0078] (Example 1) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses machine learning algorithms to analyze the user's voice and text data and estimate the type and intensity of emotions. Specifically, it analyzes the user's speech and input text using text mining and speech recognition technology to extract emotional characteristics. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns tailored to the user's emotions. For example, if a user expresses joy, it will propose advertisements containing positive messages and images related to that product or service. (Example 2) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses computer vision technology to analyze the user's facial expressions and body movements and detect emotional changes and reactions. Specifically, it extracts the user's facial expressions and body movements from videos and images and analyzes their emotional features. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns tailored to the user's emotions. For example, if a user shows a surprised expression, it will propose an advertisement that highlights the features and benefits of a new product or service related to the advertisement. (Example 3) Step 1: The data processing module collects data from multiple marketing channels and provides a means for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses machine learning algorithms to analyze the user's voice and text data and estimate the type and intensity of emotions. Specifically, it analyzes the user's speech and input text using text mining and speech recognition technology to extract emotional characteristics. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns that match the user's emotions. For example, if a user expresses dissatisfaction or discomfort with an advertisement, it can suggest improvements or alternatives to the advertisement.

[0079] 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.

[0080] 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> ) and other generation AIs. The data generation model 58 is obtained by causing a neural network to perform deep learning. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58.

[0081] 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. [Fourth embodiment]

[0082] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, 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.

[0087] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, or a home appliance). Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0095] (Example 1) One embodiment of the present invention is a system including a data processing module that collects and integrates data from multiple marketing channels, a means for classifying customer data and predicting trends using a machine learning algorithm, and a proposal module for optimizing advertising strategies. Specifically, the data processing module executes data processing procedures for collecting, integrating, and analyzing data from each marketing channel. The means for using a machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. The proposal module then proposes advertising strategies to maximize ROI based on the analysis results. (Example 2) An embodiment of the present invention is a system including a data processing module for comprehensively analyzing data from multiple marketing channels, a machine learning algorithm for classifying customer data and predicting trends, and a proposal module for proposing advertising strategies to maximize ROI. Specifically, the data processing module executes procedures for collecting and comprehensively analyzing data from multiple marketing channels. The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. The proposal module then proposes advertising strategies to maximize ROI based on the analysis results. (Example 3) An embodiment of the present invention is a system including a data processing module for comprehensively analyzing data from multiple marketing channels, a machine learning algorithm for automatically identifying customer segments and analyzing trends in customer behavior, and a proposal module for proposing advertising strategies to maximize ROI. Specifically, the data processing module provides means for collecting and comprehensively analyzing data from multiple marketing channels. The machine learning algorithm also provides means for learning from the collected customer data, automatically identifying customer segments, and analyzing trends in customer behavior. The proposal module also provides means for proposing advertising strategies to maximize ROI based on the analysis results.

[0096] The processing flow of each embodiment will be described below.

[0097] (Example 1) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. Specifically, the learning data is used to train the model and make predictions on unknown data. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment. (Example 2) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The machine learning algorithm learns from the collected customer data, automatically identifies customer segments, and predicts trends in customer behavior. Specifically, the learning data is used to train the model and make predictions on unknown data. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment. (Example 3) Step 1: The data processing module collects data from multiple marketing channels and provides a means for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: Machine learning algorithms provide a means to learn from collected customer data, automatically identify customer segments, and analyze trends in customer behavior. Specifically, the learning data is used to train a model and make predictions on unseen data. Step 3: The proposal module provides a means to propose advertising strategies to maximize ROI based on the analysis results. Specifically, it calculates and proposes optimal advertising strategies for each customer segment.

[0098] 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.

[0099] (Example 1) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine analyzes user voice and text data and uses machine learning algorithms to estimate the type and intensity of emotions. For example, if a user expresses positive emotions toward a product, the system can prioritize that product when proposing advertising strategies. (Example 2) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine uses computer vision technology to analyze the user's facial expressions and body movements and detect emotional changes and reactions. For example, if the user shows expressions of surprise or joy when viewing an advertisement, products or services related to the advertisement can be highlighted. (Example 3) An embodiment of the present invention is an AI assistant for marketing analysis that comprehensively analyzes data from multiple marketing channels, automatically identifies customer segments, analyzes trends in customer behavior, and proposes advertising strategies to maximize ROI. The AI ​​assistant incorporates an emotion engine that recognizes user emotions. Specifically, the emotion engine analyzes user voice and text data and uses machine learning algorithms to estimate the type and intensity of emotions. For example, if a user expresses dissatisfaction or discomfort with an advertisement, the engine can suggest improvements to the advertisement or alternatives.

[0100] The processing flow of each embodiment will be described below.

[0101] (Example 1) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses machine learning algorithms to analyze the user's voice and text data and estimate the type and intensity of emotions. Specifically, it analyzes the user's speech and input text using text mining and speech recognition technology to extract emotional characteristics. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns tailored to the user's emotions. For example, if a user expresses joy, it will propose advertisements containing positive messages and images related to that product or service. (Example 2) Step 1: The data processing module collects data from multiple marketing channels and executes procedures for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses computer vision technology to analyze the user's facial expressions and body movements and detect emotional changes and reactions. Specifically, it extracts the user's facial expressions and body movements from videos and images and analyzes their emotional features. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns tailored to the user's emotions. For example, if a user shows a surprised expression, it will propose an advertisement that highlights the features and benefits of a new product or service related to the advertisement. (Example 3) Step 1: The data processing module collects data from multiple marketing channels and provides a means for integrated analysis. Specifically, it collects data from each marketing channel and formats and integrates the data. Step 2: The emotion engine uses machine learning algorithms to analyze the user's voice and text data and estimate the type and intensity of emotions. Specifically, it analyzes the user's speech and input text using text mining and speech recognition technology to extract emotional characteristics. Step 3: The proposal module proposes advertising strategies to maximize ROI based on the analysis results. Specifically, it takes into account the emotional information obtained from the emotion engine and proposes advertising content and campaigns that match the user's emotions. For example, if a user expresses dissatisfaction or discomfort with an advertisement, it can suggest improvements or alternatives to the advertisement.

[0102] 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.

[0103] 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> ) and other generation AIs. The data generation model 58 is obtained by causing a neural network to perform deep learning. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58.

[0104] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[0105] 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.

[0106] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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."

[0111] 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.

[0112] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The following is further disclosed regarding the above embodiment.

[0123] (Appendix 1) A system that includes a means for comprehensively analyzing data from multiple marketing channels, a means for automatically identifying customer segments and analyzing trends in customer behavior, and a means for proposing advertising strategies to maximize ROI. (Appendix 2) In the system described in Appendix 1, the means for comprehensively analyzing data from multiple marketing channels includes a data processing module for collecting, integrating, and analyzing data. (Appendix 3) In the system described in Appendix 1, the means for automatically identifying customer segments and analyzing trends in customer behavior is characterized by using machine learning algorithms to classify customer data and predict trends.

[0124] (Appendix 4) This AI assistant supports marketing analysis by comprehensively analyzing data from multiple marketing channels, automatically identifying customer segments, analyzing trends in customer behavior, and proposing advertising strategies to maximize ROI. It is characterized by its incorporation of an emotion engine that recognizes the user's emotions. (Appendix 5) In the system described in Supplementary Note 4, the emotion engine is characterized by using a machine learning algorithm to analyze the user's voice and text data and estimate the type and intensity of emotion. (Appendix 6) In the system described in Appendix 4, the emotion engine is characterized by using computer vision technology to analyze the user's facial expressions and body movements and detect emotional changes and reactions. [Explanation of symbols]

[0125] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system including a means for comprehensively analyzing data from multiple marketing channels, a means for automatically identifying customer segments and analyzing trends in customer behavior, and a means for proposing advertising strategies to maximize ROI.

2. 2. The system according to claim 1, wherein the means for comprehensively analyzing data from a plurality of marketing channels includes a data processing module for collecting, integrating, and analyzing the data.

3. In the system described in claim 1, the means for automatically identifying customer segments and analyzing trends in customer behavior is characterized by using machine learning algorithms to classify customer data and predict trends.

Images