Information processing system, information processing method, and information processing program

Abstract

This processor included in an information processing system receives input data input from a user, generates output data corresponding to the input data on the basis of data prepared in advance for each user and a generation system learned model, and outputs the output data.

Description

Information processing system, information processing method, and information processing program

[0001] This disclosure relates to an information processing system, an information processing method, and an information processing program.

[0002] Japanese Patent Publication No. 2024-052226 discloses a character modification system that can generate characters that accurately reflect user preferences while eliminating the complexity of character creation. This character modification system has a configuration that performs a component determination process to determine which components of a base character to change based on player-related information already stored or player input information entered by the player, and a modified character generation process that generates a modified character in which the components have been modified from the base character based on the determined components (see, for example, the [Abstract] of Japanese Patent Publication No. 2024-052226).

[0003] Furthermore, Japanese Patent Publication No. 7398723 discloses a technology that assists users in creating images. Specifically, Japanese Patent Publication No. 7398723 discloses an illustration background image generation system in which an illustration background image generation server and multiple user terminals are connected via a network. This illustration background image generation server includes a UI data transmission unit that transmits UI data for generating a UI including multiple tags to a user terminal, a tag receiving unit that receives one or more tags selected by the user from the user terminal, a prompt creation unit that creates a prompt including elements corresponding to the tags, an AI image generation unit that generates an image based on the prompt, and an image output unit that outputs the image.

[0004] Japanese Patent Publication No. 7329293 discloses a technology that assists in preparing visual content suitable as a record of a character's activities. Specifically, the program disclosed in Japanese Patent Publication No. 7329293 causes a computer to function as a means for obtaining feature information representing the characteristics of visual content predicted to be suitable for a specific character by providing a first model input based on profile information set for a specific character to a first model that has learned the relationship between the profile information of multiple reference characters and the characteristics of the visual content that each of the multiple reference characters has made publicly available; a means for obtaining candidate content that is visual content that features the appearance of a specific character and has characteristics corresponding to the second model input by providing a second model input based on the feature information to a second model that has learned to generate images that feature the appearance of a specific character as the subject; and a means for outputting the candidate content.

[0005] This disclosure aims to provide an information processing system, an information processing method, and an information processing program that can generate data corresponding to input data using a generative AI, in accordance with the data accessible to the user.

[0006] To achieve the above objective, the first aspect of the present disclosure is an information processing system comprising a processor, wherein the processor receives input data from a user, generates output data corresponding to the input data based on a generative trained model and data prepared in advance for each user, and outputs the output data.

[0007] Furthermore, the second aspect of this disclosure is an information processing method in which a computer performs processing to receive input data from a user, generate output data corresponding to the input data based on a generative trained model and data prepared in advance for each user, and output the output data.

[0008] Furthermore, the third aspect of this disclosure is an information processing program that causes a computer to execute a process that receives input data from a user, generates output data corresponding to the input data based on a pre-trained generative model and data prepared in advance for each user, and outputs the output data.

[0009] According to this disclosure, when generating data corresponding to input data using a generative AI, the effect is obtained that the data can be generated according to the data that the user has access to.

[0010] This figure shows an example of the schematic configuration of the information processing system of the first embodiment. This figure shows an example of the functional configuration of the server of the first embodiment. This figure is for explaining the learning content data of the first embodiment. This figure shows an example of a generative trained model of the first embodiment. This is a schematic block diagram of the computers that function as each device of the information processing system. This is an explanatory diagram for explaining the processing performed by the information processing system of the first embodiment. This is an explanatory diagram for explaining the processing performed by the information processing system of the first embodiment. This is an explanatory diagram for explaining the processing performed by the information processing system of the first embodiment. This is an explanatory diagram for explaining the processing performed by the information processing system of the second embodiment. This figure shows an example of the functional configuration of the server of the second embodiment. This is an explanatory diagram for explaining the processing performed by the information processing system of the third embodiment.

[0011] The embodiments will be described in detail below with reference to the drawings.

[0012] Incidentally, content is sometimes generated using so-called generative AI (Artificial Intelligence). In this case, when predetermined input data is provided to the generative AI, content corresponding to that input data is output from the generative AI. Examples of such content include text, images, or videos.

[0013] Generative AI is pre-generated based on collected training data. In this case, the generative AI may be trained using training data for which multiple copyright holders each hold copyright. However, if a generative AI is trained using training data provided by multiple different copyright holders, that generative AI may be undesirable from the perspective of copyright of the training data.

[0014] For example, user A owns the copyright to educational content C. Ａ And learning content C, whose copyright is held by another user B. Ｂ Based on this, let's consider the case where we train a single learning model to create a generative AI. In this case, the generative AI is the learning content C Ａ and learning content C Ｂ Because it is learned based on the content C Ａ The style and content C Ｂ The output may sometimes be a mix of different styles.

[0015] For example, if the generative AI is an image-generating AI or a video-generating AI, the AI ​​may output an anime character that is a mix of an anime character appearing in content copyrighted by User A and an anime character appearing in content copyrighted by User B.

[0016] However, since users A and B, who are copyright holders, own the copyright to their own content, it is conceivable that they would not like or would not permit the output of content from a generative AI that mixes their copyrighted content with distinctive content from others (e.g., anime characters), as mentioned above. On the other hand, for example, a copyright holder might tolerate the output of such content from a generative AI, even if their copyrighted content is mixed with general content (e.g., just a landscape image), because their own content would not be excessively affected.

[0017] While the above-mentioned documents disclose technologies for creating content such as anime characters or images, they do not disclose how to manage content used for training generative AI.

[0018] Therefore, even if the technologies disclosed in the above-mentioned documents are used, there is a problem in that when creating content using generative AI, it is not possible to manage the content used for training the generative AI.

[0019] Therefore, the objective of the first embodiment is to provide an information processing system, an information processing method, and an information processing program that can manage content used for learning a generative AI and generate content using a generative AI.

[0020] [First Embodiment] <System Configuration of Information Processing System> Figure 1 is a block diagram of the information processing system 10 of the first embodiment. As shown in Figure 1, the information processing system 10 of the first embodiment includes a server 12, a user terminal 14A operated by a first user, a user terminal 14B operated by a second user, and a user terminal 14C operated by a third user. The server 12, user terminals 14A, 14B, and 14C are connected by a network 15, such as the Internet. In the following, when referring to user terminals 14A, 14B, and 14C collectively, they will also simply be referred to as user terminal 14. User terminal 14 is, for example, a smartphone or a personal computer. Although three user terminals 14 are shown in Figure 1, there may be more user terminals 14.

[0021] Figure 2 is a diagram showing an example of the functional configuration of the server 12 in the first embodiment. As shown in Figure 2, the server 12 comprises a reception unit 20, a learning data storage unit 22, a control unit 24, a generative trained model storage unit 26, and an output unit 28. The server 12 is also an example of the information processing system disclosed herein. The server 12 receives input data from a user, generates output data corresponding to the input data based on the generative trained model and data prepared in advance for each user, and outputs the output data.

[0022] The reception unit 20 receives various data transmitted from the user terminal 14. The various data is an example of input data.

[0023] The learning data storage unit 22 stores learning data provided by a plurality of copyright holders (or a plurality of customers). FIG. 3 is a diagram for explaining the data stored in the learning data storage unit 22. As shown in FIG. 3, for example, learning content data C provided by copyright holder X Ｘ and learning content data C provided by copyright holder Y Ｙ and learning content data C provided by copyright holder Z Ｚ are stored in the learning data storage unit 22. Note that the learning content data C <> Ｘ and the learning content data C Ｙ and the learning content data C Ｚ are partitioned by a partition P so that their storage areas are separated and data contamination does not occur. Also, common learning content data C is stored in the learning data storage unit 22. This common learning content data C is content data for which consent of the copyright holder has been obtained regarding the learning and use of the generative trained model. The generative trained model is a generative AI.

[0024] Note that each of the copyright holders X, Y, and Z may not want a generative trained model to be generated based on the learning content data they own and the learning content data owned by others. As described above, for example, a copyright holder may not want content in which the content they own and the characteristic content owned by others are mixed to be generated by the generative trained model. Also, from the perspective of copyright management, it is not preferable for a generative trained model to be learned based on a plurality of contents for which consent of the copyright holder has not been obtained.

[0025] Therefore, in this embodiment, a generative pre-trained model is created for each copyright holder. Specifically, a generative pre-trained model is generated for each copyright holder based on the training content data provided by each copyright holder. For example, the above training content data C Ｘ Based on this, the pre-trained generative model M of copyright holder X Ｘ The above learning content data C is created. Ｙ Based on this, the pre-trained generative model M of copyright holder Y Ｙ The above learning content data C is created. Ｚ Based on this, the pre-trained generative model M of copyright holder Z Ｚ This is created. Furthermore, in this embodiment, when creating a generative trained model, the above generative trained model is trained based on common training content data C, which is general content for which consent has been obtained from other copyright holders. This results in a generative trained model M for each copyright holder. Ｘ , M Ｙ , M Ｚ This is generated. Note that copyright holders X, Y, and Z are so-called IP (Intellectual Property) holders.

[0026] The control unit 24 controls the operation of the server 12. Specifically, the control unit 24 generates a generative trained model for each copyright holder by training a training model using known machine learning techniques based on training data provided by multiple copyright holders stored in the training data storage unit 22. The generative trained model is a so-called generative AI. An example of a generative trained model is a generative AI that generates text, images, or videos. When some input data (e.g., a prompt) is input to this generative trained model, it outputs content data corresponding to that input data. Content data is an example of output data. The control unit 24 may also generate a generative trained model for each copyright holder by fine-tuning a generative AI that has already been trained (for example, the Llama (Language Large Models Meta AI) model provided as open source) using the training content data for each copyright holder as described above. The control unit 24 then stores each of the generative trained models for each copyright holder in the trained model storage unit 26. Furthermore, if the generative pre-trained model is an LLM (Large Language Model), the generative pre-trained model for each copyright holder in this embodiment is trained based on training content data separated by partitions, and therefore can also be called a partitioned LLM.

[0027] The trained model storage unit 26 stores each of the generative trained models for each copyright holder. Figure 4 shows an example of a generative trained model for each copyright holder. As shown in Figure 4, for example, the training content data C whose copyright is held by copyright holder X Ｘ A pre-trained generative model M learned based on this Ｘ And, the learning content data C, whose copyright is held by copyright holder Y. Ｙ A pre-trained generative model M learned based on this Ｙ And, the learning content data C, whose copyright is held by copyright holder Z. Ｚ A pre-trained generative model M learned based on this Ｚ and are stored.

[0028] The control unit 24 inputs the input data transmitted from the user terminal 14 into at least one of several pre-trained generative models, each pre-trained for each copyright holder, thereby acquiring content that corresponds to the input data and is output from the generative model.

[0029] For example, the input data transmitted from the user terminal 14 includes identification data and a prompt for selecting a generative trained model. In this case, the control unit 24 selects the target generative trained model from a plurality of generative trained models stored in the trained model storage unit 26 based on the identification data included in the input data.

[0030] For example, the identification data included in the input data is an ID for identifying a pre-trained generative model. Alternatively, for example, the identification data included in the input data may be text data. For example, if the input data is "Please output a townscape image in the style of Studio G," then "Studio G" represents the copyright holder, and the control unit 24 selects a pre-trained generative model corresponding to that copyright holder and generates content using that pre-trained generative model. For example, if the input data is "Please output a townscape image in the style of Anime R," then the control unit 24 selects a pre-trained generative model corresponding to the copyright holder of "Anime R" and generates content using that pre-trained generative model. To realize the above processing, for example, known natural language processing techniques and artificial intelligence techniques are used.

[0031] The control unit 24 then inputs the input data to the selected target generative trained model, thereby obtaining the content output from the generative trained model. The input data may include the input content and a prompt. In this case, the control unit 24 inputs the input content and the prompt to the generative trained model, thereby generating content that combines the input content and the training data used to train the generative trained model.

[0032] For example, when the input data includes an image and the prompt is "Please convert the attached image into an anime R-style image", the style of the learning content data when the generative pre-trained model is trained is reflected in the image that is the content to be input. Therefore, when the content to be input is input into the generative pre-trained model, content that is synthesized with the content to be input and the learning content data is output.

[0033] The output unit 28 transmits the content obtained by the control unit 24 to the user terminal 14.

[0034] In addition, each time the content generated by using the generative pre-trained model is used (for example, viewed), the fee paid to the copyright holder who provided the learning data of the used generative pre-trained model may be increased. In this case, for example, the control unit 24 may associate data for recording the number of times of use with each generative pre-trained model of each copyright holder stored in the pre-trained model storage unit 26 each time content is generated using the generative pre-trained model.

[0035] The server 12 and the user terminal 14 of the information processing system 10 can be realized by, for example, a computer 70 shown in FIG. 5. The computer 70 includes a CPU 71, a memory 72 as a temporary storage area, and a non-volatile storage unit 73. In addition, the computer 70 includes an input / output interface (I / F) 74 to which an input / output device etc. (not shown) is connected, and a read / write (R / W) unit 75 that controls reading and writing of data to and from a recording medium. In addition, the computer 70 includes a network I / F 76 connected to a network such as the Internet. The CPU 71, the memory 72, the storage unit 73, the input / output I / F 74, the R / W unit 75, and the network I / F 76 are connected to each other via a bus 77.

[0036] The storage unit 73 can be implemented using a Hard Disk Drive (HDD), Solid State Drive (SSD), flash memory, etc. The storage unit 73, as a storage medium, stores a program for making the computer 70 function. The CPU 71 reads the program from the storage unit 73, loads it into memory 72, and sequentially executes the processes contained in the program.

[0037] <Operation of Information Processing System 10> Next, the operation of the information processing system 10 in this embodiment will be described. The server 12 of the information processing system 10 executes the learning processing routine shown in Figure 6.

[0038] In step S100, the control unit 24 reads out a plurality of learning content data stored in the learning data storage unit 22.

[0039] In step S102, the control unit 24 creates a generative trained model for each copyright holder based on the training content data for each copyright holder read in step S100.

[0040] In step S104, the control unit 24 stores the pre-trained generation model for each copyright holder, which was created in step S102, into the pre-trained model storage unit 26.

[0041] The trained model storage unit 26 stores the trained model for each copyright holder, and when input data is output from the user terminal 14, the information processing system 10 executes the information processing routine shown in Figure 7.

[0042] In step S200, the reception unit 20 receives the input data output from the user terminal 14.

[0043] In step S202, the control unit 24 selects a target generative trained model from a plurality of generative trained models stored in the trained model storage unit 26 based on the identification data included in the input data received in step S200.

[0044] In step S204, the control unit 24 inputs the prompt portion of the input data received in step S200 to the generative trained model selected in step S202, thereby causing the generative trained model to output content.

[0045] In step S206, the output unit 28 outputs the content obtained in step S204 as a result.

[0046] As described above, the server, which is an example of an information processing system according to the first embodiment, inputs input data to at least one of several generative trained models, each pre-trained for each copyright holder, which are pre-trained based on training data provided by copyright holders. The server then acquires content corresponding to the input data and content output from the generative trained model, and outputs the content. This allows for the management of content used for training the generative AI while simultaneously generating content using the generative AI. Furthermore, when generating data corresponding to input data using the generative AI, the server can generate data according to data accessible to the user (data for which the user holds the copyright).

[0047] [Second Embodiment] Next, a second embodiment will be described. The second embodiment differs from the first embodiment in that it generates output data according to the input data without requiring a separate generation AI for each user.

[0048] Figure 8 is a diagram illustrating the overview of the information processing system of the first embodiment. In Figure 8, the user corresponding to the copyright holder in the first embodiment is referred to as a customer. As shown in Figure 8, if there are customers A to E, in the first embodiment, generative trained models PLM-A to PLM-E are generated for each of the multiple customers A to E. PLM is an abbreviation for Personalized Language Model. Specifically, as shown in Figure 8, generative trained models PLM-A for customer A, PLM-B for customer B, PLM-C for customer C, PLM-D for customer D, and PLM-E for customer E are generated in advance. Generative trained models PLM-A to PLM-E are pre-trained based on common data. DS1-B, DS2-B, and DS3-B shown in Figure 8 are data managed by customer B. Furthermore, DS1-C and DS2-C are data managed by customer C. Also, DS1-D is data managed by customer D. DS1-E, DS2-E, and DS3-B are data managed by customer B. Data managed by a customer refers to, for example, data for which the customer holds the copyright.

[0049] As shown in Figure 8, if a pre-trained generative model PLM-A to PLM-E is prepared for each customer, the pre-trained generative model must be further trained each time data managed by the customer is added. Specifically, as shown in the section for customer E in Figure 8, when data DS1-E is added to the database, the pre-trained generative model PLM-E must be further trained using data DS1-E; when data DS2-E is added to the database, the pre-trained generative model PLM-E must be further trained using data DS2-E; and when data DS3-E is added to the database, the pre-trained generative model PLM-E must be further trained using data DS3-E. However, the cost of such additional training is high. In particular, if a pre-trained generative model is trained for each customer, the cost becomes enormous.

[0050] Therefore, the information processing system of the second embodiment does not prepare a pre-trained generative model for each customer, but instead generates output data corresponding to input data using one or more pre-trained generative models. In this case, the information processing system of the second embodiment generates customer-specific data by utilizing a known RAG (Retrieval-Augmented Generation).

[0051] Figure 9 is a diagram illustrating the outline of the second embodiment. As shown in Figure 9, instead of preparing a generative pre-trained model for each customer, output data corresponding to the input data input by the customer is generated by combining one (or more) generative pre-trained models, LLM and RAG. This makes it possible to generate appropriate output data according to the customer without preparing a generative pre-trained model for each customer. Specifically, as shown in the section for customer E in Figure 9, it is no longer necessary to further train the generative pre-trained model PLM-E each time data DS1-E, data DS1-E, and data DS1-E are added, and it is simply necessary to add data DS1-E, data DS1-E, and data DS1-E to the database.

[0052] Figure 10 is a diagram showing an example of the functional configuration of the server 212 of the information processing system in the second embodiment. As shown in Figure 10, the server 212 includes a reception unit 20, a learning data storage unit 222, a control unit 224, a generator trained model storage unit 226, a database group 27, and an output unit 28.

[0053] The reception unit 20 receives input data entered by the customer, who is the user.

[0054] The training data storage unit 222 stores the common data shown in Figure 8. This common data is used for training the generative system trained model.

[0055] The generative trained model memory unit 226 stores one (or more) generative trained models, which are LLMs.

[0056] Database group 27 consists of databases for each customer. Specifically, as shown in Figure 8, database 27A stores data managed (or copyrighted) by customer A. Database 27B stores data managed (or copyrighted) by customer B. In addition, although not shown in the diagram, databases for customers C, D, and E are also provided.

[0057] When generating output data corresponding to input data, the control unit 224 retrieves related data to the input data by searching a database containing data managed by the user who entered the input data. For example, if the user who entered the input data is customer B, the control unit 224 retrieves related data to the input data by searching database 27B containing data managed by customer B.

[0058] The control unit 224 generates output data by inputting the input data and related data into the generative trained model stored in the generative trained model storage unit 226. Specifically, the control unit 224 generates output data corresponding to the input data by inputting a prompt containing part of the input data and related data into the generative trained model.

[0059] The processing performed by the control unit 224 is carried out using known RAG technology.

[0060] The output unit 28 outputs the output data generated by the control unit 224.

[0061] As explained above, the server, which is an example of an information processing system according to the second embodiment, retrieves related data to the input data by searching a database containing data managed by the user who input the input data when generating output data. The server then generates output data by inputting the input data and related data into a pre-trained generative model. This makes it possible to generate data corresponding to the input data using generative AI, according to the data that the user can access. Furthermore, it is possible to generate data according to the data that the user can access without having to prepare a pre-trained generative model for each user. Specifically, since it is no longer necessary to retrain a pre-trained generative model for each user each time new data is added, costs can be reduced.

[0062] [Third Embodiment] Next, a third embodiment will be described. In the third embodiment, if customer A has access to the data of another customer B, the output data corresponding to the input data entered by customer A is generated using customer B's data as well, which is different from the second embodiment.

[0063] In the second embodiment described above, for example, when generating output data corresponding to input data entered by customer A, the output data was generated using only the data stored in customer A's database 27A. In contrast, as shown in Figure 11, for example, a data usage agreement may have been concluded in advance between customer A and customer B, allowing customer A to use a portion of customer B's data (for example, data DS1-B).

[0064] Therefore, when generating output data, the server of the information processing system in the third embodiment uses data managed by a customer different from the customer who entered the input data. Since the server configuration of the third embodiment is the same as that of the second embodiment, a detailed explanation is omitted.

[0065] In the third embodiment, the control unit 224 of the server 212, when generating output data, searches a first database containing data managed by the first user who input the input data, and also searches a second database accessible to the first user and containing data managed by the second user, thereby obtaining related data related to the input data.

[0066] For example, consider a case where the first user is customer A and the second user is customer B. For example, as shown in Figure 11, customer A may have access to some of customer B's data. In such a case, the control unit 224 of the server 212 in the third embodiment searches database 27A, which stores data managed by customer A who entered the input data, and also searches database 27B, which is accessible to customer A and stores data managed by customer B, to obtain related data concerning the input data. At this time, the control unit 224 of the server 212 in the third embodiment refers to information regarding customer A's data access rights and determines which data customer A can access. For example, the information regarding customer A's data access rights indicates that they can access customer B's data (e.g., data DS1-B). In this case, the control unit 224 of the server 212 in the third embodiment refers to information regarding customer A's data access rights and determines that customer A can access customer B's data. Then, the control unit 224 of the server 212 in the third embodiment searches database 27A, which stores data managed by customer A, and also searches database 27B, which is accessible to customer A and stores data managed by customer B, thereby obtaining related data related to the input data.

[0067] Next, the control unit 224 of the server 212 in the third embodiment generates output data by inputting the input data and related data into the generative trained model stored in the trained model storage unit 226. Specifically, the control unit 224 generates output data corresponding to the input data by inputting a prompt including a part of the input data and related data into the generative trained model.

[0068] As described above, the server, which is an example of an information processing system according to the third embodiment, when generating output data, searches a first database containing data managed by the first user who input the input data, and also searches a second database accessible to the first user and containing data managed by the second user, thereby obtaining related data related to the input data. The server then inputs the input data and related data into a pre-trained generative model to generate output data. This allows the generative AI to generate data corresponding to the input data, according to the data accessible to the user. Specifically, it is possible to generate output data corresponding to the input data using data managed by other users that the user can access.

[0069] This disclosure is not limited to the embodiments described above, and various modifications and applications are possible without departing from the gist of this disclosure.

[0070] For example, the various processes that the CPU reads and executes in the above embodiment may be executed by various processors other than the CPU. Examples of such processors include dedicated electrical circuits, which are processors with circuit configurations specifically designed to execute particular processes, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices) whose circuit configuration can be changed after manufacturing, ASICs (Application Specific Integrated Circuits), and GPUs (Graphics Processing Units). Furthermore, the various processes may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (for example, multiple FPGAs, and a combination of a CPU, FPGA, and GPU). Furthermore, the hardware structure of these various processors is, more specifically, an electrical circuit composed of circuit elements such as semiconductor devices.

[0071] Furthermore, although the above embodiment describes a configuration in which the program is pre-stored (installed) on storage, the invention is not limited to this. The program may be provided in a form stored on a non-transitor storage medium such as a CD-ROM (Compact Disk Read Only Memory), DVD-ROM (Digital Versatile Disk Read Only Memory), or USB (Universal Serial Bus) memory. Alternatively, the program may be provided in a form downloaded from an external device via a network.

[0072] Although the processing performed by each device in the above embodiment has been described as software processing performed by executing a program, it may also be hardware processing. Alternatively, it may be a combination of both software and hardware processing. Furthermore, the program stored in the ROM may be stored and distributed on various storage media.

[0073] Furthermore, although the above embodiment describes a case where a server 12, which is an example of an information processing device, generates content, it is not limited to this. Some or all of the processing performed by the server 12 in the above embodiment may be performed by a terminal such as a smartphone carried by the user.

[0074] The disclosure of international application PCT / JP2024 / 035341, filed on 2 October 2024, is incorporated herein by reference in its entirety. All documents, patent applications, and technical standards described herein are incorporated herein by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated herein by reference.

[0075] (Note) The following is a note regarding the nature of this disclosure.

[0076] (Note 1) An information processing system comprising a processor, wherein the processor inputs input data to at least one of a plurality of generative trained models, each pre-trained based on training data provided by a copyright holder and pre-trained for each copyright holder, thereby acquiring content corresponding to the input data and outputting the content from the generative trained model, and outputting the content. (Note 2) The information processing system according to Note 1, wherein the input data is a prompt, and the generative trained model outputs the content corresponding to the input prompt. (Note 3) The information processing system according to Note 1 or Note 2, wherein the input data includes identification data for selecting the generative trained model, and the processor selects a target generative trained model from a plurality of generative trained models based on the identification data included in the input data, and acquires content outputting from the generative trained model by inputting the input data to the target generative trained model. (Note 4) The information processing system according to Note 3, wherein the input data is data including the content to be input and a prompt, and the processor inputs the content to be input and the prompt to the generative trained model to generate the content by synthesizing the content to be input and the training data used when the generative trained model was trained. (Note 5) The information processing system according to Note 2, wherein each time the content generated by using the generative trained model is used, the fee paid to the copyright holder who provided the training data for the generative trained model used is increased.(Note 6) An information processing method in which a computer performs a process to input input data into at least one of a plurality of generative trained models, each pre-trained for each copyright holder, which are pre-trained based on training data provided by the copyright holder, thereby acquiring content corresponding to the input data and outputting the content from the generative trained model. (Note 7) An information processing program for causing a computer to perform a process to input input data into at least one of a plurality of generative trained models, each pre-trained for each copyright holder, which are pre-trained based on training data provided by the copyright holder, thereby acquiring content corresponding to the input data and outputting the content from the generative trained model. (Note 8) An information processing system comprising a processor, wherein the processor receives input data from a user, generates output data corresponding to the input data based on a generative trained model and data prepared in advance for each user, and outputs the output data. (Note 9) The information processing system according to Note 1, wherein when generating the output data, it retrieves related data to the input data by searching a database containing data managed by the user who entered the input data, and generates the output data by inputting the input data and the related data into the generative trained model. (Note 10) The information processing system according to Note 1, wherein when generating the output data, it retrieves related data to the input data by searching a first database containing data managed by the first user who entered the input data, and also searches a second database accessible to the first user and containing data managed by the second user, and generates the output data by inputting the input data and the related data into the generative trained model.(Note 11) The generative trained model is prepared in advance for each copyright holder who is the user, and each of the multiple generative trained models is pre-trained based on training content data provided by the copyright holder, and the information processing system according to Note 1 obtains the content which is the output data and has been output from the generative trained model by inputting the input data to the generative trained model, and outputs the content. (Note 12) The information processing system according to Note 11, wherein the input data is a prompt, and the generative trained model outputs the content corresponding to the input prompt. (Note 13) The information processing system according to Note 11 or Note 12, wherein the input data includes identification data for selecting the generative trained model, and the processor selects a target generative trained model from the multiple generative trained models based on the identification data included in the input data, and obtains the content output from the generative trained model by inputting the input data to the target generative trained model. (Note 14) The information processing system according to Note 13, wherein the input data is data including the content to be input and a prompt, and the processor generates the content by inputting the content to be input and the prompt to the generative trained model, thereby synthesizing the content to be input and the training content data used when the generative trained model was trained. (Note 15) The information processing system according to Note 12, wherein each time the content generated by using the generative trained model is used, the fee paid to the copyright holder who provided the training content data for the used generative trained model is increased. (Note 16) An information processing method in which a computer receives input data from a user, generates output data corresponding to the input data based on the generative trained model and data prepared in advance for each user, and outputs the output data.(Note 17) An information processing program that causes a computer to execute a process that receives input data from a user, generates output data corresponding to the input data based on a pre-trained generative model and data prepared in advance for each user, and outputs the output data.

Claims

1. An information processing system comprising a processor, wherein the processor receives input data from a user, generates output data corresponding to the input data based on a pre-trained generative model and data prepared in advance for each user, and outputs the output data.

2. The information processing system according to claim 1, wherein, when generating the output data, it retrieves related data related to the input data by searching a database containing data managed by the user who entered the input data, and generates the output data by inputting the input data and the related data into the generative trained model.

3. The information processing system according to claim 1, wherein, when generating the output data, the system searches a first database containing data managed by the first user who input the input data, and also searches a second database accessible to the first user and containing data managed by the second user to obtain related data related to the input data, and generates the output data by inputting the input data and the related data into the generative trained model.

4. The information processing system according to claim 1, wherein the generative trained model is prepared in advance for each copyright holder who is a user, each of the multiple generative trained models is pre-trained based on training content data provided by the copyright holder, and by inputting the input data into the generative trained model, the system obtains the content which is the output data and is output from the generative trained model, and outputs the content.

5. The information processing system according to claim 4, wherein the input data is a prompt, and the generative trained model outputs the content corresponding to the input prompt.

6. The information processing system according to claim 4 or 5, wherein the input data includes identification data for selecting the generative trained model, the processor selects a target generative trained model from a plurality of generative trained models based on the identification data included in the input data, and obtains content output from the generative trained model by inputting the input data to the target generative trained model.

7. The information processing system according to claim 6, wherein the input data is data including content to be input and a prompt, and the processor generates content by inputting the content to be input and the prompt to the generative trained model, thereby synthesizing the content to be input and the training content data used when the generative trained model was trained.

8. The information processing system according to claim 5, wherein each time the content generated by using the generative trained model is used, the fee paid to the copyright holder who provided the training content data for the generative trained model used is increased.

9. An information processing method in which a computer performs a process that receives input data from a user, generates output data corresponding to the input data based on a pre-trained generative model and data prepared in advance for each user, and outputs the output data.

10. An information processing program that causes a computer to execute a process that receives input data from a user, generates output data corresponding to the input data based on a pre-trained generative model and data prepared in advance for each user, and outputs the output data.

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