system

Abstract

The system according to the embodiment aims to provide a realistic understanding of customer psychology and selection criteria, and to provide useful hints for planning new businesses. [Solution] The system according to the embodiment includes a reception unit, a generation unit, a display unit, and an extraction unit. The reception unit receives information about products to be targeted. The generation unit generates candidate selection criteria based on the information received by the reception unit. The generation unit generates novels based on the selection criteria generated by the generation unit. The display unit displays the novels generated by the generation unit. The reception unit receives checks on the novels displayed by the display unit. The extraction unit extracts the checked novels received by the reception unit.

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] Conventional technology has made it difficult to realistically understand customer psychology and selection criteria, making it difficult to obtain effective hints when planning new businesses.

[0005] The system according to the embodiment aims to provide a realistic understanding of customer psychology and selection criteria, and to provide useful hints for planning new businesses. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a display unit, and an extraction unit. The reception unit receives information about products to be targeted. The generation unit generates candidate selection criteria based on the information received by the reception unit. The generation unit generates novels based on the selection criteria generated by the generation unit. The display unit displays the novels generated by the generation unit. The reception unit receives checks on the novels displayed by the display unit. The extraction unit extracts the checked novels received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can realistically understand the psychology and selection criteria of customers, and can provide useful hints for planning new businesses. [Brief explanation of the drawings]

[0008] [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

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] 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 generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) 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, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A new business planning support system according to an embodiment of the present invention allows users to easily understand the psychology and behavior of customers when selecting a product. The system allows users to specify a target product and generate candidate selection criteria using a generation AI. Based on the generated selection criteria, the generation AI then describes the customer's feelings in a realistic novel format. The novel is displayed in speech bubbles, allowing users to select and extract those they feel may provide clues for a new business. Furthermore, by utilizing the brain's tendency to easily transmit emotions from novels and other information, the system enables users to search for realistic and effective clues. For example, first, a user specifies the target product. To do this, the user simply enters the product name and category. For example, they might enter "new smartphone." This information is then input into the generation AI. The generation AI then analyzes the input information and generates candidate selection criteria. Based on past data and trends, the generation AI identifies the criteria customers use when selecting a product. For example, selection criteria such as "price," "function," and "design" are generated. Based on the generated selection criteria, the generation AI then describes the customer's feelings in a realistic novel format. For example, it provides a detailed description of the customer's psychology and behavior when choosing a smartphone. The novel is displayed in speech bubbles. Users can check the parts of the displayed novel speech bubbles that they feel could be hints for new businesses. Checked speech bubbles are extracted and displayed in a list. Furthermore, by utilizing the brain's tendency to easily transmit emotions from novels and other media, it is possible to search for realistic and effective hints. For example, realistic depictions of customers' feelings make it easier for users to empathize and obtain more specific hints. This allows the new business planning support system to easily understand the psychology and scenes of customers when selecting products, and effectively search for hints for new businesses.

[0029] A new business planning support system according to an embodiment includes a receiving unit, a generating unit, a display unit, and an extracting unit. The receiving unit receives information about a target product. The target product information includes, but is not limited to, electronic products, food products, and services. The receiving unit receives information, for example, by a user inputting a product name or category. The generating unit generates selection criteria candidates based on the information received by the receiving unit. The generating unit identifies criteria used by customers when selecting products based on, for example, past data and trends. For example, the generating unit generates selection criteria such as price, quality, and brand. The generating unit can generate selection criteria candidates using a generation AI. The generation AI analyzes past sales data, customer reviews, and the like to identify the selection criteria. The display unit generates a novel based on the selection criteria generated by the generating unit. The generated novel describes in detail the customer's feelings. For example, the generating unit generates a novel that describes in detail the customer's psychology and behavior when selecting a smartphone. The generated novel is displayed in a speech bubble format. The display unit displays the generated novel in a speech bubble format. Display in speech bubble format includes, but is not limited to, for example, the shape, color, and arrangement of the speech bubble. The display unit accepts speech bubbles checked by the user. The user can check parts of the displayed novel speech bubbles that the user feels may be hints for a new business. The reception unit accepts the speech bubbles checked by the user. The extraction unit extracts the checked speech bubbles accepted by the reception unit. The extraction unit displays a list of checked speech bubbles. This allows the new business planning support system according to the embodiment to easily understand the psychology and scenes of customers when selecting products and effectively search for hints for new businesses.

[0030] The generation unit can generate candidate selection criteria based on past data and trends. The generation unit identifies criteria that customers use when selecting products, for example, based on past sales data. For example, the generation unit analyzes past sales data and generates selection criteria such as price, quality, and brand. The generation unit can also generate candidate selection criteria based on trends. For example, the generation unit analyzes seasonal trends and industry trends and identifies selection criteria. This makes it possible to provide more realistic selection criteria by generating candidate selection criteria based on past data and trends. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past sales data and trend data into the generation AI and have the generation AI generate selection criteria.

[0031] The generation unit can generate a novel that depicts the customer's feelings in detail based on the selection criteria. The generation unit, for example, generates a novel that depicts the customer's feelings in detail based on the selection criteria. For example, the generation unit generates a novel that describes in detail the customer's psychology and behavior when choosing a smartphone. The generated novel realistically depicts the customer's feelings. This realistic portrayal of the customer's feelings makes it easier for the user to empathize. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the selection criteria into the generation AI and cause the generation AI to generate a novel that depicts the customer's feelings.

[0032] The display unit can display the novel in speech bubble format. The display unit, for example, displays the generated novel in speech bubble format. Display in speech bubble format includes, for example, but is not limited to, the shape, color, and arrangement of the speech bubble. By displaying in speech bubble format, the display unit makes it easier for the user to visually understand the novel. As a result, by displaying in speech bubble format, the display unit makes it easier for the user to visually understand the novel. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the generated novel into AI and have the AI ​​display it in speech bubble format.

[0033] The reception unit can receive speech bubbles checked by the user. For example, the reception unit can check parts of a displayed novel speech bubble that the user feels may be hints for a new business. The reception unit receives the speech bubbles checked by the user. This allows the user to check parts that may be hints for a new business. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the speech bubbles checked by the user into AI and have the AI ​​receive the checks.

[0034] The extraction unit can extract checked speech bubbles and display them as a list. The extraction unit, for example, extracts checked speech bubbles received by the reception unit. The extraction unit displays the checked speech bubbles as a list. This allows users to efficiently check hints for new businesses by displaying the checked speech bubbles as a list. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input the checked speech bubbles into AI and have the AI ​​perform the extraction and list display.

[0035] The generation unit can generate a realistic novel by utilizing the brain's characteristics that facilitate the propagation of emotions. The generation unit generates a realistic novel by utilizing, for example, the brain's characteristics that facilitate the propagation of emotions. The brain's characteristics that facilitate the propagation of emotions include, for example, scientific evidence and specific application methods, but are not limited to such examples. By utilizing the brain's characteristics that facilitate the propagation of emotions, the user can more easily obtain specific hints. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data regarding the brain's characteristics that facilitate the propagation of emotions into the generation AI and cause the generation AI to generate a realistic novel.

[0036] The new business planning support system includes a reception unit that analyzes a user's past input history and selects the optimal information reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal information reception method. For example, the reception unit prioritizes suggesting input methods that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. The reception unit can also select the most efficient reception method based on the user's past input history. In this way, by analyzing the past input history, the optimal information reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal information reception method.

[0037] The new business planning support system includes a reception unit that filters product information based on the user's current areas of interest when receiving the product information. The reception unit, for example, filters the product information based on the user's current areas of interest when receiving the product information. For example, the reception unit may preferentially receive product information in categories in which the user is currently interested. Related product information can also be filtered based on the user's current areas of interest. Unnecessary product information can also be excluded based on the user's areas of interest. This makes it possible to provide highly relevant information by filtering information based on the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's area of ​​interest data into the generation AI and have the generation AI perform information filtering.

[0038] The new business planning support system includes a reception unit that, when receiving product information, prioritizes receiving highly relevant information by taking into account the user's geographical location information. For example, when receiving product information, the reception unit prioritizes receiving highly relevant information by taking into account the user's geographical location information. For example, the reception unit prioritizes receiving information about nearby stores based on the user's current location. It is also possible to prioritize receiving regional product information based on the user's geographical location information. It is also possible to receive product information including optimal delivery options by taking into account the user's location information. This allows providing highly relevant information to the user by taking into account the geographical location information. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's geographical location information data into the generation AI and have the generation AI determine the priority of the information.

[0039] The new business planning support system includes a reception unit that analyzes a user's social media activity and receives related information when receiving product information. The reception unit, for example, analyzes the user's social media activity and receives related information when receiving product information. For example, the reception unit may receive related product information based on the user's social media interests. Trending product information may also be preferentially received based on the user's social media activity. Product information that is of interest to the user's social media followers and friends may also be received. This allows for analysis of social media activity to provide highly relevant information to the user. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's social media activity data into the generation AI and have the generation AI analyze and receive the information.

[0040] The generation unit can select optimal criteria by referring to past data and trends when generating selection criteria. For example, the generation unit selects optimal criteria by referring to past data and trends when generating selection criteria. For example, the generation unit generates the most popular selection criteria based on past sales data. The generation unit can also generate the latest selection criteria by referring to current market trends. The generation unit can also analyze customers' past selection history and generate optimal selection criteria. In this way, optimal selection criteria can be provided by referring to past data and trends. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs past data and trend data into the generation AI and causes the generation AI to generate selection criteria.

[0041] The generation unit can apply different generation algorithms depending on the product category when generating selection criteria. For example, the generation unit applies different generation algorithms depending on the product category when generating selection criteria. For example, in the case of electronic devices, technical selection criteria can be generated. In addition, in the case of fashion products, selection criteria based on design and trends can be generated. In addition, in the case of food, selection criteria based on health and nutrition can be generated. In this way, by applying different generation algorithms depending on the product category, more appropriate selection criteria can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product category data into the generation AI and cause the generation AI to generate selection criteria.

[0042] The generation unit can determine the priority of the criteria based on the time of submission of the product when generating the selection criteria. For example, the generation unit determines the priority of the criteria based on the time of submission of the product when generating the selection criteria. For example, for a new product, the latest selection criteria can be prioritized. Also, for seasonal products, selection criteria according to the season can be prioritized. Also, for sale products, selection criteria based on price can be prioritized. In this way, by determining the priority of the criteria based on the time of submission of the product, more appropriate selection criteria can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input product submission time data into the generation AI and cause the generation AI to determine the priority of the selection criteria.

[0043] The generation unit can adjust the order of the criteria based on the relevance of the products when generating the selection criteria. The generation unit, for example, adjusts the order of the criteria based on the relevance of the products when generating the selection criteria. For example, selection criteria for similar products can be displayed preferentially. Selection criteria for related products can also be displayed preferentially. Selection criteria with high relevance can also be displayed preferentially based on the user's interests. In this way, by adjusting the order of the criteria based on the relevance of the products, more appropriate selection criteria can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of the selection criteria.

[0044] The display unit can select the optimal display method by referring to the user's past browsing history when displaying a novel. For example, when displaying a novel, the display unit selects the optimal display method by referring to the user's past browsing history. For example, the display unit selects the optimal display method based on the style of novels the user has previously browsed. The display unit can also select a preferred display method from the user's past browsing history. The display unit can also analyze the user's past browsing history and select the most effective display method. In this way, the display unit can provide the user with the optimal display method by referring to the past browsing history. Some or all of the above-described processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the user's past browsing history data into the generation AI and have the generation AI select the optimal display method.

[0045] The display unit can customize the display content based on the user's current areas of interest when displaying a novel. For example, the display unit customizes the display content based on the user's current areas of interest when displaying a novel. For example, the display unit customizes the content of the novel based on the user's current areas of interest. It can also preferentially display related novels based on the user's areas of interest. It can also exclude unnecessary information based on the user's areas of interest. In this way, by customizing the display content based on the user's areas of interest, more relevant information can be provided. Some or all of the above-mentioned processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the user's area of ​​interest data into the generation AI and have the generation AI customize the display content.

[0046] The display unit can select the optimal display method by taking into consideration the user's device information when displaying a novel. For example, when displaying a novel, the display unit selects the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, by taking device information into consideration, the optimal display method can be provided to the user. Some or all of the above-mentioned processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the user's device information data into the generation AI and have the generation AI select the optimal display method.

[0047] The display unit can analyze the user's social media activity and display related content when displaying a novel. For example, the display unit can analyze the user's social media activity and display related content when displaying a novel. For example, the display unit can display related novels based on the user's social media interests. Trending novels can also be preferentially displayed based on the user's social media activity. Novels that the user's social media followers and friends are interested in can also be displayed. This makes it possible to provide the user with highly relevant information by analyzing social media activity. Some or all of the above-described processing in the display unit can be performed using or without the generation AI. For example, the display unit can input the user's social media activity data into the generation AI and have the generation AI analyze and display the information.

[0048] When accepting a check, the reception unit can select the optimal reception method by referring to the user's past check history. For example, when accepting a check, the reception unit selects the optimal reception method by referring to the user's past check history. For example, the reception unit may preferentially suggest check methods that the user has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past check history. The reception unit can also select the most efficient reception method based on the user's past check history. In this way, by referring to the past check history, the optimal reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past check history data into the generation AI and have the generation AI select the optimal reception method.

[0049] The reception unit can perform filtering based on the user's current areas of interest when receiving a check. For example, the reception unit performs filtering based on the user's current areas of interest when receiving a check. For example, the reception unit preferentially receives check items in categories in which the user is currently interested. Related check items can also be filtered based on the user's current areas of interest. Unnecessary check items can also be excluded based on the user's areas of interest. In this way, highly relevant information can be provided by filtering check items based on the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's area of ​​interest data into the generation AI and have the generation AI perform filtering of the check items.

[0050] When accepting checks, the reception unit can prioritize accepting highly relevant checks by taking into account the user's geographical location information. For example, when accepting checks, the reception unit prioritizes accepting highly relevant checks by taking into account the user's geographical location information. For example, based on the user's current location, the reception unit can prioritize accepting check items for nearby stores. Also, based on the user's geographical location information, the reception unit can prioritize accepting area-specific check items. Also, by taking into account the user's location information, the reception unit can accept check items including optimal delivery options. In this way, by taking into account the geographical location information, highly relevant information can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI determine the priority of the check items.

[0051] The reception unit can analyze the user's social media activity when receiving a check and receive related checks. For example, the reception unit can analyze the user's social media activity when receiving a check and receive related checks. For example, the reception unit can receive related check items based on the user's social media interests. It can also preferentially receive trending check items based on the user's social media activity. It can also receive check items that are of interest to the user's social media followers and friends. This makes it possible to provide the user with highly relevant information by analyzing social media activity. Some or all of the above-described processing in the reception unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI analyze and receive the check items.

[0052] The extraction unit can select the optimal extraction method by referring to the user's past check history when extracting a speech bubble. For example, the extraction unit can select the optimal extraction method by referring to the user's past check history when extracting a speech bubble. For example, the extraction unit can select the optimal extraction method based on speech bubbles that the user has checked in the past. The extraction unit can also prioritize extraction of highly relevant speech bubbles from the user's past check history. The extraction unit can also analyze the user's past check history and select the most effective extraction method. In this way, the extraction unit can provide the user with the optimal extraction method by referring to the past check history. Some or all of the above-mentioned processing in the extraction unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input the user's past check history data into the generation AI and have the generation AI select the optimal extraction method.

[0053] The extraction unit can perform filtering based on the user's current areas of interest when extracting speech bubbles. For example, the extraction unit performs filtering based on the user's current areas of interest when extracting speech bubbles. For example, the extraction unit preferentially extracts speech bubbles in categories in which the user is currently interested. The extraction unit can also filter related speech bubbles based on the user's current areas of interest. The extraction unit can also exclude unnecessary speech bubbles based on the user's areas of interest. In this way, highly relevant information can be provided by filtering speech bubbles based on the user's areas of interest. Some or all of the above-described processing in the extraction unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input user's area of ​​interest data into the generation AI and cause the generation AI to filter the speech bubbles.

[0054] The extraction unit can prioritize extracting highly relevant speech bubbles by taking into account the user's geographical location information when extracting speech bubbles. For example, the extraction unit prioritizes extracting highly relevant speech bubbles by taking into account the user's geographical location information when extracting speech bubbles. For example, the extraction unit can prioritize extracting speech bubbles for nearby stores based on the user's current location. The extraction unit can also prioritize extracting region-specific speech bubbles based on the user's geographical location information. The extraction unit can also extract speech bubbles containing optimal delivery options by taking into account the user's location information. This makes it possible to provide highly relevant information to the user by taking into account the geographical location information. Some or all of the above-described processing in the extraction unit may be performed using or without the generation AI. For example, the extraction unit can input the user's geographical location information data into the generation AI and have the generation AI determine the priority of the speech bubbles.

[0055] The extraction unit can analyze the user's social media activity and extract related speech bubbles when extracting speech bubbles. For example, the extraction unit can analyze the user's social media activity and extract related speech bubbles when extracting speech bubbles. For example, the extraction unit can extract related speech bubbles based on the user's social media interests. Trending speech bubbles can also be preferentially extracted from the user's social media activity. Speech bubbles that are of interest to the user's social media followers and friends can also be extracted. This makes it possible to provide the user with highly relevant information by analyzing social media activity. Some or all of the above-described processing in the extraction unit can be performed using or without the generation AI. For example, the extraction unit can input the user's social media activity data into the generation AI and cause the generation AI to extract speech bubbles.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The new business planning support system includes an analysis unit that analyzes a user's past purchase history and proposes optimal selection criteria. For example, it can generate selection criteria for similar products and services based on data on products and services purchased in the past by the user. It can also identify preferences for specific brands and price ranges from the user's purchase history and customize the selection criteria based on that. Furthermore, by analyzing the user's purchase history, it is possible to understand seasonal purchasing trends and propose selection criteria according to the season. This makes it possible to provide more personalized selection criteria by utilizing the user's past purchase history.

[0058] The new business planning support system includes a generation unit that customizes selection criteria based on the user's current areas of interest. For example, product information in categories in which the user is currently interested can be preferentially reflected in the selection criteria. It is also possible to generate relevant selection criteria based on the user's areas of interest. Furthermore, it is also possible to exclude unnecessary selection criteria based on the user's areas of interest. In this way, by customizing the selection criteria based on the user's areas of interest, it is possible to provide more relevant information.

[0059] The new business planning support system includes a display unit that analyzes a user's past browsing history and selects the optimal display method. For example, the optimal display method can be selected based on the style of novels the user has previously read. It is also possible to select a preferred display method from the user's past browsing history. Furthermore, the system can analyze the user's past browsing history and select the most effective display method. This makes it possible to provide the optimal display method to the user by referring to the past browsing history.

[0060] The new business planning support system includes a reception unit that selects the optimal reception method by referring to the user's past check history. For example, it is possible to preferentially suggest the check method that the user has used in the past. It is also possible to suggest the optimal reception method for a specific time period based on the user's past check history. Furthermore, it is also possible to select the most efficient reception method based on the user's past check history. In this way, it is possible to provide the optimal reception method to the user by referring to the past check history.

[0061] The new business planning support system includes an extraction unit that filters speech bubbles based on the user's current areas of interest. For example, speech bubbles in categories in which the user is currently interested can be preferentially extracted. It is also possible to filter related speech bubbles based on the user's areas of interest. Furthermore, it is also possible to exclude unnecessary speech bubbles based on the user's areas of interest. In this way, by filtering speech bubbles based on the user's areas of interest, it is possible to provide highly relevant information.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit receives information about the desired product. Information about the desired product may include, for example, electronic products, food, services, etc. The information is received by the user inputting the product name and category. Step 2: The generator generates candidate selection criteria based on the information received by the receiver. The generator identifies the criteria customers use when selecting products based on past data and trends. For example, it generates selection criteria such as price, quality, and brand. Candidate selection criteria can be generated using generative AI. Step 3: The display unit generates a story based on the selection criteria generated by the generation unit and displays it in speech bubble format. The generated story describes in detail the customer's feelings. For example, a story is generated that describes in detail the customer's psychology and behavior when selecting a smartphone. The speech bubble display includes the shape, color, and arrangement of the speech bubbles. Step 4: The reception unit receives the speech bubbles checked by the user. The user can check the parts of the displayed novel speech bubbles that they feel may be hints for a new business. Step 5: The extraction unit extracts the checked speech bubbles received by the reception unit, and displays a list of the checked speech bubbles.

[0064] (Example 2) A new business planning support system according to an embodiment of the present invention allows users to easily understand the psychology and behavior of customers when selecting a product. The system allows users to specify a target product and generate candidate selection criteria using a generation AI. Based on the generated selection criteria, the generation AI then describes the customer's feelings in a realistic novel format. The novel is displayed in speech bubbles, allowing users to select and extract those they feel may provide clues for a new business. Furthermore, by utilizing the brain's tendency to easily transmit emotions from novels and other information, the system enables users to search for realistic and effective clues. For example, first, a user specifies the target product. To do this, the user simply enters the product name and category. For example, they might enter "new smartphone." This information is then input into the generation AI. The generation AI then analyzes the input information and generates candidate selection criteria. Based on past data and trends, the generation AI identifies the criteria customers use when selecting a product. For example, selection criteria such as "price," "function," and "design" are generated. Based on the generated selection criteria, the generation AI then describes the customer's feelings in a realistic novel format. For example, it provides a detailed description of the customer's psychology and behavior when choosing a smartphone. The novel is displayed in speech bubbles. Users can check the parts of the displayed novel speech bubbles that they feel could be hints for new businesses. Checked speech bubbles are extracted and displayed in a list. Furthermore, by utilizing the brain's tendency to easily transmit emotions from novels and other media, it is possible to search for realistic and effective hints. For example, realistic depictions of customers' feelings make it easier for users to empathize and obtain more specific hints. This allows the new business planning support system to easily understand the psychology and scenes of customers when selecting products, and effectively search for hints for new businesses.

[0065] A new business planning support system according to an embodiment includes a receiving unit, a generating unit, a display unit, and an extracting unit. The receiving unit receives information about a target product. The target product information includes, but is not limited to, electronic products, food products, and services. The receiving unit receives information, for example, by a user inputting a product name or category. The generating unit generates selection criteria candidates based on the information received by the receiving unit. The generating unit identifies criteria used by customers when selecting products based on, for example, past data and trends. For example, the generating unit generates selection criteria such as price, quality, and brand. The generating unit can generate selection criteria candidates using a generation AI. The generation AI analyzes past sales data, customer reviews, and the like to identify the selection criteria. The display unit generates a novel based on the selection criteria generated by the generating unit. The generated novel describes in detail the customer's feelings. For example, the generating unit generates a novel that describes in detail the customer's psychology and behavior when selecting a smartphone. The generated novel is displayed in a speech bubble format. The display unit displays the generated novel in a speech bubble format. Display in speech bubble format includes, but is not limited to, for example, the shape, color, and arrangement of the speech bubble. The display unit accepts speech bubbles checked by the user. The user can check parts of the displayed novel speech bubbles that the user feels may be hints for a new business. The reception unit accepts the speech bubbles checked by the user. The extraction unit extracts the checked speech bubbles accepted by the reception unit. The extraction unit displays a list of checked speech bubbles. This allows the new business planning support system according to the embodiment to easily understand the psychology and scenes of customers when selecting products and effectively search for hints for new businesses.

[0066] The generation unit can generate candidate selection criteria based on past data and trends. The generation unit identifies criteria that customers use when selecting products, for example, based on past sales data. For example, the generation unit analyzes past sales data and generates selection criteria such as price, quality, and brand. The generation unit can also generate candidate selection criteria based on trends. For example, the generation unit analyzes seasonal trends and industry trends and identifies selection criteria. This makes it possible to provide more realistic selection criteria by generating candidate selection criteria based on past data and trends. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past sales data and trend data into the generation AI and have the generation AI generate selection criteria.

[0067] The generation unit can generate a novel that depicts the customer's feelings in detail based on the selection criteria. The generation unit, for example, generates a novel that depicts the customer's feelings in detail based on the selection criteria. For example, the generation unit generates a novel that describes in detail the customer's psychology and behavior when choosing a smartphone. The generated novel realistically depicts the customer's feelings. This realistic portrayal of the customer's feelings makes it easier for the user to empathize. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the selection criteria into the generation AI and cause the generation AI to generate a novel that depicts the customer's feelings.

[0068] The display unit can display the novel in speech bubble format. The display unit, for example, displays the generated novel in speech bubble format. Display in speech bubble format includes, for example, but is not limited to, the shape, color, and arrangement of the speech bubble. By displaying in speech bubble format, the display unit makes it easier for the user to visually understand the novel. As a result, by displaying in speech bubble format, the display unit makes it easier for the user to visually understand the novel. Some or all of the above-mentioned processing in the display unit may be performed using AI, or may be performed without using AI. For example, the display unit can input the generated novel into AI and have the AI ​​display it in speech bubble format.

[0069] The reception unit can receive speech bubbles checked by the user. For example, the reception unit can check parts of a displayed novel speech bubble that the user feels may be hints for a new business. The reception unit receives the speech bubbles checked by the user. This allows the user to check parts that may be hints for a new business. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the speech bubbles checked by the user into AI and have the AI ​​receive the checks.

[0070] The extraction unit can extract checked speech bubbles and display them as a list. The extraction unit, for example, extracts checked speech bubbles received by the reception unit. The extraction unit displays the checked speech bubbles as a list. This allows users to efficiently check hints for new businesses by displaying the checked speech bubbles as a list. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, or may be performed without using AI. For example, the extraction unit can input the checked speech bubbles into AI and have the AI ​​perform the extraction and list display.

[0071] The generation unit can generate a realistic novel by utilizing the brain's characteristics that facilitate the propagation of emotions. The generation unit generates a realistic novel by utilizing, for example, the brain's characteristics that facilitate the propagation of emotions. The brain's characteristics that facilitate the propagation of emotions include, for example, scientific evidence and specific application methods, but are not limited to such examples. By utilizing the brain's characteristics that facilitate the propagation of emotions, the user can more easily obtain specific hints. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data regarding the brain's characteristics that facilitate the propagation of emotions into the generation AI and cause the generation AI to generate a realistic novel.

[0072] The new business planning support system includes a reception unit that estimates a user's emotions and adjusts the timing of receiving product information based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of receiving product information based on the estimated user emotions. For example, if the user is stressed, the reception unit may receive product information at a time when the user is able to relax. Alternatively, if the user is excited, the reception unit may immediately receive product information. Alternatively, if the user is tired, the reception unit may receive product information after a break. This allows the timing of receiving information to be adjusted according to the user's emotions, thereby allowing the reception of information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to adjust the timing of receiving information.

[0073] The new business planning support system includes a reception unit that analyzes a user's past input history and selects the optimal information reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal information reception method. For example, the reception unit prioritizes suggesting input methods that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. The reception unit can also select the most efficient reception method based on the user's past input history. In this way, by analyzing the past input history, the optimal information reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal information reception method.

[0074] The new business planning support system includes a reception unit that filters product information based on the user's current areas of interest when receiving the product information. The reception unit, for example, filters the product information based on the user's current areas of interest when receiving the product information. For example, the reception unit may preferentially receive product information in categories in which the user is currently interested. Related product information can also be filtered based on the user's current areas of interest. Unnecessary product information can also be excluded based on the user's areas of interest. This makes it possible to provide highly relevant information by filtering information based on the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's area of ​​interest data into the generation AI and have the generation AI perform information filtering.

[0075] The new business planning support system includes a reception unit that estimates a user's emotions and determines the priority of products to be accepted based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of products to be accepted based on the estimated user emotions. For example, if the user is excited, the reception unit may prioritize the latest product information. Also, if the user is relaxed, the reception unit may prioritize detailed product information. Also, if the user is stressed, the reception unit may prioritize simple product information. This allows for more appropriate information to be provided by determining the priority of products according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and have the generation AI determine the priority of products.

[0076] The new business planning support system includes a reception unit that, when receiving product information, prioritizes receiving highly relevant information by taking into account the user's geographical location information. For example, when receiving product information, the reception unit prioritizes receiving highly relevant information by taking into account the user's geographical location information. For example, the reception unit prioritizes receiving information about nearby stores based on the user's current location. It is also possible to prioritize receiving regional product information based on the user's geographical location information. It is also possible to receive product information including optimal delivery options by taking into account the user's location information. This allows providing highly relevant information to the user by taking into account the geographical location information. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's geographical location information data into the generation AI and have the generation AI determine the priority of the information.

[0077] The new business planning support system includes a reception unit that analyzes a user's social media activity and receives related information when receiving product information. The reception unit, for example, analyzes the user's social media activity and receives related information when receiving product information. For example, the reception unit may receive related product information based on the user's social media interests. Trending product information may also be preferentially received based on the user's social media activity. Product information that is of interest to the user's social media followers and friends may also be received. This allows for analysis of social media activity to provide highly relevant information to the user. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's social media activity data into the generation AI and have the generation AI analyze and receive the information.

[0078] The new business planning support system includes a generation unit that estimates a user's emotions and generates candidate selection criteria based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and generates candidate selection criteria based on the estimated user emotions. For example, if the user is relaxed, detailed selection criteria can be generated. If the user is in a hurry, concise selection criteria can be generated. If the user is excited, visually appealing selection criteria can be generated. This allows for more appropriate selection criteria to be generated based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to generate selection criteria.

[0079] The generation unit can select optimal criteria by referring to past data and trends when generating selection criteria. For example, the generation unit selects optimal criteria by referring to past data and trends when generating selection criteria. For example, the generation unit generates the most popular selection criteria based on past sales data. The generation unit can also generate the latest selection criteria by referring to current market trends. The generation unit can also analyze customers' past selection history and generate optimal selection criteria. In this way, optimal selection criteria can be provided by referring to past data and trends. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs past data and trend data into the generation AI and causes the generation AI to generate selection criteria.

[0080] The generation unit can apply different generation algorithms depending on the product category when generating the selection criteria. For example, the generation unit applies different generation algorithms depending on the product category when generating the selection criteria. For example, in the case of electronic devices, technical selection criteria can be generated. In addition, in the case of fashion products, selection criteria based on design and trends can be generated. In addition, in the case of food, selection criteria based on health and nutrition can be generated. In this way, by applying different generation algorithms depending on the product category, more appropriate selection criteria can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product category data into the generation AI and cause the generation AI to generate the selection criteria.

[0081] The new business planning support system includes a generation unit that estimates a user's emotions and prioritizes selection criteria based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and prioritizes the selection criteria based on the estimated user emotions. For example, if the user is relaxed, detailed selection criteria may be prioritized. Also, if the user is in a hurry, concise selection criteria may be prioritized. Also, if the user is excited, visually appealing selection criteria may be prioritized. Thus, by prioritizing the selection criteria based on the user's emotions, more appropriate selection criteria can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and have the generation AI determine the priority of the selection criteria.

[0082] The generation unit can determine the priority of the criteria based on the time of submission of the product when generating the selection criteria. For example, the generation unit determines the priority of the criteria based on the time of submission of the product when generating the selection criteria. For example, for a new product, the latest selection criteria can be prioritized. Also, for seasonal products, selection criteria according to the season can be prioritized. Also, for sale products, selection criteria based on price can be prioritized. In this way, by determining the priority of the criteria based on the time of submission of the product, more appropriate selection criteria can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input product submission time data into the generation AI and cause the generation AI to determine the priority of the selection criteria.

[0083] The generation unit can adjust the order of the criteria based on the relevance of the products when generating the selection criteria. The generation unit, for example, adjusts the order of the criteria based on the relevance of the products when generating the selection criteria. For example, selection criteria for similar products can be displayed preferentially. Selection criteria for related products can also be displayed preferentially. Selection criteria with high relevance can also be displayed preferentially based on the user's interests. In this way, by adjusting the order of the criteria based on the relevance of the products, more appropriate selection criteria can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of the selection criteria.

[0084] The new business planning support system includes a display unit that estimates a user's emotions and adjusts the display method of a novel based on the estimated user emotions. The display unit, for example, estimates the user's emotions and adjusts the display method of the novel based on the estimated user emotions. For example, if the user is relaxed, a detailed novel can be displayed. Alternatively, if the user is in a hurry, a concise novel can be displayed. Alternatively, if the user is excited, a visually appealing novel can be displayed. This allows for more appropriate display by adjusting the display method of the novel based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using the generation AI, or may be performed without the generation AI. For example, the display unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the novel.

[0085] The display unit can select the optimal display method by referring to the user's past browsing history when displaying a novel. For example, when displaying a novel, the display unit selects the optimal display method by referring to the user's past browsing history. For example, the display unit selects the optimal display method based on the style of novels the user has previously browsed. The display unit can also select a preferred display method from the user's past browsing history. The display unit can also analyze the user's past browsing history and select the most effective display method. In this way, the display unit can provide the user with the optimal display method by referring to the past browsing history. Some or all of the above-described processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the user's past browsing history data into the generation AI and have the generation AI select the optimal display method.

[0086] The display unit can customize the display content based on the user's current areas of interest when displaying a novel. For example, the display unit customizes the display content based on the user's current areas of interest when displaying a novel. For example, the display unit customizes the content of the novel based on the user's current areas of interest. It can also preferentially display related novels based on the user's areas of interest. It can also exclude unnecessary information based on the user's areas of interest. In this way, by customizing the display content based on the user's areas of interest, more relevant information can be provided. Some or all of the above-mentioned processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the user's area of ​​interest data into the generation AI and have the generation AI customize the display content.

[0087] The new business planning support system includes a display unit that estimates a user's emotions and adjusts the display order of novels based on the estimated user emotions. The display unit, for example, estimates the user's emotions and adjusts the display order of novels based on the estimated user emotions. For example, if the user is relaxed, detailed novels may be displayed preferentially. Also, if the user is in a hurry, brief novels may be displayed preferentially. Also, if the user is excited, visually appealing novels may be displayed preferentially. By adjusting the display order of novels based on the user's emotions, information can be provided in a more appropriate order. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using the generation AI, or may be performed without the generation AI. For example, the display unit may input user emotion data into the generation AI and cause the generation AI to adjust the display order of novels.

[0088] The display unit can select the optimal display method by taking into consideration the user's device information when displaying a novel. For example, when displaying a novel, the display unit selects the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, by taking device information into consideration, the optimal display method can be provided to the user. Some or all of the above-mentioned processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the user's device information data into the generation AI and have the generation AI select the optimal display method.

[0089] The display unit can analyze the user's social media activity and display related content when displaying a novel. For example, the display unit can analyze the user's social media activity and display related content when displaying a novel. For example, the display unit can display related novels based on the user's social media interests. Trending novels can also be preferentially displayed based on the user's social media activity. Novels that the user's social media followers and friends are interested in can also be displayed. This makes it possible to provide the user with highly relevant information by analyzing social media activity. Some or all of the above-described processing in the display unit can be performed using or without the generation AI. For example, the display unit can input the user's social media activity data into the generation AI and have the generation AI analyze and display the information.

[0090] The new business planning support system includes a reception unit that estimates a user's emotions and adjusts the check acceptance method based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the check acceptance method based on the estimated user emotions. For example, if the user is relaxed, detailed check items can be provided. If the user is in a hurry, brief check items can be provided. If the user is excited, visually appealing check items can be provided. By adjusting the check acceptance method based on the user's emotions, checks can be accepted in a more appropriate manner. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input user emotion data into the generation AI and have the generation AI adjust the check acceptance method.

[0091] When accepting a check, the reception unit can select the optimal reception method by referring to the user's past check history. For example, when accepting a check, the reception unit selects the optimal reception method by referring to the user's past check history. For example, the reception unit may preferentially suggest check methods that the user has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past check history. The reception unit can also select the most efficient reception method based on the user's past check history. In this way, by referring to the past check history, the optimal reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past check history data into the generation AI and have the generation AI select the optimal reception method.

[0092] The reception unit can perform filtering based on the user's current areas of interest when receiving a check. For example, the reception unit performs filtering based on the user's current areas of interest when receiving a check. For example, the reception unit preferentially receives check items in categories in which the user is currently interested. Related check items can also be filtered based on the user's current areas of interest. Unnecessary check items can also be excluded based on the user's areas of interest. In this way, highly relevant information can be provided by filtering check items based on the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's area of ​​interest data into the generation AI and have the generation AI perform filtering of the check items.

[0093] The new business planning support system includes a reception unit that estimates a user's emotions and prioritizes check items based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes check items based on the estimated user emotions. For example, if the user is relaxed, detailed check items may be prioritized. Also, if the user is in a hurry, brief check items may be prioritized. Also, if the user is excited, visually appealing check items may be prioritized. By prioritizing check items based on the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without the generation AI. For example, the reception unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of check items.

[0094] When accepting checks, the reception unit can prioritize accepting highly relevant checks by taking into account the user's geographical location information. For example, when accepting checks, the reception unit prioritizes accepting highly relevant checks by taking into account the user's geographical location information. For example, based on the user's current location, the reception unit can prioritize accepting check items for nearby stores. Also, based on the user's geographical location information, the reception unit can prioritize accepting area-specific check items. Also, by taking into account the user's location information, the reception unit can accept check items including optimal delivery options. In this way, by taking into account the geographical location information, highly relevant information can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI determine the priority of the check items.

[0095] The reception unit can analyze the user's social media activity when receiving a check and receive related checks. For example, the reception unit can analyze the user's social media activity when receiving a check and receive related checks. For example, the reception unit can receive related check items based on the user's social media interests. It can also preferentially receive trending check items based on the user's social media activity. It can also receive check items that are of interest to the user's social media followers and friends. This makes it possible to provide the user with highly relevant information by analyzing social media activity. Some or all of the above-described processing in the reception unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI analyze and receive the check items.

[0096] The new business planning support system includes an extraction unit that estimates a user's emotions and prioritizes speech bubbles to be extracted based on the estimated user emotions. The extraction unit, for example, estimates the user's emotions and prioritizes the speech bubbles to be extracted based on the estimated user emotions. For example, if the user is relaxed, detailed speech bubbles may be preferentially extracted. Also, if the user is in a hurry, brief speech bubbles may be preferentially extracted. Also, if the user is excited, visually appealing speech bubbles may be preferentially extracted. This allows for more appropriate information to be provided by prioritizing speech bubbles based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the extraction unit may input user emotion data into the generation AI and have the generation AI determine the priority of the speech bubbles.

[0097] The extraction unit can select the optimal extraction method by referring to the user's past check history when extracting a speech bubble. For example, the extraction unit can select the optimal extraction method by referring to the user's past check history when extracting a speech bubble. For example, the extraction unit can select the optimal extraction method based on speech bubbles that the user has checked in the past. The extraction unit can also prioritize extraction of highly relevant speech bubbles from the user's past check history. The extraction unit can also analyze the user's past check history and select the most effective extraction method. In this way, the extraction unit can provide the user with the optimal extraction method by referring to the past check history. Some or all of the above-mentioned processing in the extraction unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input the user's past check history data into the generation AI and have the generation AI select the optimal extraction method.

[0098] The extraction unit can perform filtering based on the user's current areas of interest when extracting speech bubbles. For example, the extraction unit performs filtering based on the user's current areas of interest when extracting speech bubbles. For example, the extraction unit preferentially extracts speech bubbles in categories in which the user is currently interested. The extraction unit can also filter related speech bubbles based on the user's current areas of interest. The extraction unit can also exclude unnecessary speech bubbles based on the user's areas of interest. In this way, highly relevant information can be provided by filtering speech bubbles based on the user's areas of interest. Some or all of the above-described processing in the extraction unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input user's area of ​​interest data into the generation AI and cause the generation AI to filter the speech bubbles.

[0099] The new business planning support system includes an extraction unit that estimates a user's emotion and adjusts the display method of the extracted speech bubble based on the estimated user emotion. The extraction unit, for example, estimates the user's emotion and adjusts the display method of the extracted speech bubble based on the estimated user emotion. For example, if the user is relaxed, a detailed speech bubble can be displayed. If the user is in a hurry, a concise speech bubble can be displayed. If the user is excited, a visually appealing speech bubble can be displayed. This allows information to be provided in a more appropriate manner by adjusting the display method of the speech bubble based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the extraction unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the speech bubble.

[0100] The extraction unit can prioritize extracting highly relevant speech bubbles by taking into account the user's geographical location information when extracting speech bubbles. For example, the extraction unit prioritizes extracting highly relevant speech bubbles by taking into account the user's geographical location information when extracting speech bubbles. For example, the extraction unit can prioritize extracting speech bubbles for nearby stores based on the user's current location. The extraction unit can also prioritize extracting region-specific speech bubbles based on the user's geographical location information. The extraction unit can also extract speech bubbles containing optimal delivery options by taking into account the user's location information. This makes it possible to provide highly relevant information to the user by taking into account the geographical location information. Some or all of the above-described processing in the extraction unit may be performed using or without the generation AI. For example, the extraction unit can input the user's geographical location information data into the generation AI and have the generation AI determine the priority of the speech bubbles.

[0101] The extraction unit can analyze the user's social media activity and extract related speech bubbles when extracting speech bubbles. For example, the extraction unit can analyze the user's social media activity and extract related speech bubbles when extracting speech bubbles. For example, the extraction unit can extract related speech bubbles based on the user's social media interests. Trending speech bubbles can also be preferentially extracted from the user's social media activity. Speech bubbles that are of interest to the user's social media followers and friends can also be extracted. This makes it possible to provide the user with highly relevant information by analyzing social media activity. Some or all of the above-described processing in the extraction unit can be performed using or without the generation AI. For example, the extraction unit can input the user's social media activity data into the generation AI and cause the generation AI to extract speech bubbles. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and extraction unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives information when the user inputs a product name or category. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates selection criteria candidates based on past data and trends. The display unit is realized, for example, by the control unit 46A of the smart device 14 and displays the generated novel in speech bubble format. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts checked speech bubbles and displays them as a list. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and extraction unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives information by the user inputting a product name or category. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates selection criteria candidates based on past data and trends. The display unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays the generated novel in speech bubble format. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts checked speech bubbles and displays them as a list. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and extraction unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives information when the user inputs a product name or category. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates candidates for selection criteria based on past data and trends. The display unit is realized, for example, by the control unit 46A of the headset type terminal 314 and displays the generated novel in speech bubble format. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts checked speech bubbles and displays them as a list. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, display unit, and extraction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information when the user inputs a product name or category. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates candidates for selection criteria based on past data and trends. The display unit is realized, for example, by the control unit 46A of the robot 414 and displays the generated novel in speech bubble format. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts checked speech bubbles and displays them as a list.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The new business planning support system includes an analysis unit that analyzes a user's past purchase history and proposes optimal selection criteria. For example, it can generate selection criteria for similar products and services based on data on products and services purchased in the past by the user. It can also identify preferences for specific brands and price ranges from the user's purchase history and customize the selection criteria based on that. Furthermore, by analyzing the user's purchase history, it is possible to understand seasonal purchasing trends and propose selection criteria according to the season. This makes it possible to provide more personalized selection criteria by utilizing the user's past purchase history.

[0104] The new business planning support system includes a generation unit that estimates the user's emotions and generates candidate selection criteria based on the estimated emotions. For example, if the user is relaxed, detailed selection criteria can be generated. If the user is in a hurry, concise selection criteria can be generated. Furthermore, if the user is excited, visually appealing selection criteria can be generated. In this way, by generating candidate selection criteria based on the user's emotions, more appropriate selection criteria can be provided.

[0105] The new business planning support system includes a generation unit that customizes selection criteria based on the user's current areas of interest. For example, product information in categories in which the user is currently interested can be preferentially reflected in the selection criteria. It is also possible to generate relevant selection criteria based on the user's areas of interest. Furthermore, it is also possible to exclude unnecessary selection criteria based on the user's areas of interest. In this way, by customizing the selection criteria based on the user's areas of interest, it is possible to provide more relevant information.

[0106] The new business planning support system includes a display unit that estimates the user's emotions and adjusts the display method of the novel based on the estimated emotions. For example, if the user is relaxed, a detailed novel can be displayed. If the user is in a hurry, a concise novel can be displayed. Furthermore, if the user is excited, a visually appealing novel can be displayed. This allows for more appropriate display by adjusting the display method of the novel based on the user's emotions.

[0107] The new business planning support system includes a display unit that analyzes a user's past browsing history and selects the optimal display method. For example, the optimal display method can be selected based on the style of novels the user has previously read. It is also possible to select a preferred display method from the user's past browsing history. Furthermore, the system can analyze the user's past browsing history and select the most effective display method. This makes it possible to provide the optimal display method to the user by referring to the past browsing history.

[0108] The new business planning support system includes a reception unit that estimates the user's emotions and adjusts the check reception method based on the estimated emotions. For example, if the user is relaxed, detailed check items can be provided. If the user is in a hurry, simple check items can be provided. Furthermore, if the user is excited, visually appealing check items can be provided. In this way, by adjusting the check reception method based on the user's emotions, checks can be received in a more appropriate manner.

[0109] The new business planning support system includes a reception unit that selects the optimal reception method by referring to the user's past check history. For example, it is possible to preferentially suggest the check method that the user has used in the past. It is also possible to suggest the optimal reception method for a specific time period based on the user's past check history. Furthermore, it is also possible to select the most efficient reception method based on the user's past check history. In this way, it is possible to provide the optimal reception method to the user by referring to the past check history.

[0110] The new business planning support system includes an extraction unit that estimates the user's emotions and determines the priority of speech bubbles to be extracted based on the estimated emotions. For example, if the user is relaxed, detailed speech bubbles can be preferentially extracted. Also, if the user is in a hurry, concise speech bubbles can be preferentially extracted. Furthermore, if the user is excited, visually attractive speech bubbles can be preferentially extracted. Thus, by determining the priority of speech bubbles based on the user's emotions, more appropriate information can be provided.

[0111] The new business planning support system includes an extraction unit that filters speech bubbles based on the user's current areas of interest. For example, speech bubbles in categories in which the user is currently interested can be preferentially extracted. It is also possible to filter related speech bubbles based on the user's areas of interest. Furthermore, it is also possible to exclude unnecessary speech bubbles based on the user's areas of interest. In this way, by filtering speech bubbles based on the user's areas of interest, it is possible to provide highly relevant information.

[0112] The new business planning support system includes an extraction unit that estimates the user's emotions and adjusts the display method of the speech bubbles based on the estimated emotions. For example, if the user is relaxed, detailed speech bubbles can be displayed. If the user is in a hurry, concise speech bubbles can be displayed. Furthermore, if the user is excited, visually appealing speech bubbles can be displayed. In this way, by adjusting the display method of the speech bubbles based on the user's emotions, information can be provided in a more appropriate manner.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit receives information about the desired product. Information about the desired product may include, for example, electronic products, food, services, etc. The information is received by the user inputting the product name and category. Step 2: The generator generates candidate selection criteria based on the information received by the receiver. The generator identifies the criteria customers use when selecting products based on past data and trends. For example, it generates selection criteria such as price, quality, and brand. Candidate selection criteria can be generated using generative AI. Step 3: The display unit generates a story based on the selection criteria generated by the generation unit and displays it in speech bubble format. The generated story describes in detail the customer's feelings. For example, a story is generated that describes in detail the customer's psychology and behavior when selecting a smartphone. The speech bubble display includes the shape, color, and arrangement of the speech bubbles. Step 4: The reception unit receives the speech bubbles checked by the user. The user can check the parts of the displayed novel speech bubbles that they feel may be hints for a new business. Step 5: The extraction unit extracts the checked speech bubbles received by the reception unit, and displays a list of the checked speech bubbles.

[0115] 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.

[0116] 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> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0122] 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.

[0123] 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 according to instructions from the processor 46.

[0124] 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 a typical healthy person).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0138] 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.

[0139] 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 according to instructions from the processor 46.

[0140] 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 a typical healthy person).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0154] 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.

[0155] 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 according to instructions from the processor 46.

[0156] 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 image sensor or a CCD 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 a typical healthy person).

[0157] 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.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0159] 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.

[0160] 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.

[0161] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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 encompasses both emotions 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.

[0170] 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.

[0171] 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).

[0172] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process 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 process may be a single processor.

[0181] 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.

[0182] 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.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 reception unit that receives information about a desired product; a generating unit that generates candidates for selection criteria based on the information received by the receiving unit; a generation unit that generates a novel based on the selection criteria generated by the generation unit; a display unit that displays the novel generated by the generation unit; a reception unit that receives checks for the novels displayed by the display unit; an extraction unit that extracts the checked novels accepted by the acceptance unit; A system characterized by:

2. The generation unit Generate potential selection criteria based on historical data and trends The system of claim 1 .

3. The generation unit Generate detailed customer sentiment stories based on selection criteria The system of claim 1 .

4. The display unit Display the novel in speech bubble format The system of claim 1 .

5. The reception unit Accepts the speech bubble checked by the user The system of claim 1 .

6. The extraction unit Extract and list checked speech bubbles The system of claim 1 .

7. The generation unit Generating realistic novels by utilizing the brain's tendency to easily transmit emotions The system of claim 1 .

8. The reception unit Estimate the user's emotions and adjust the timing of receiving product information based on the estimated user emotions. The system of claim 1 .

Images