Information provision device, information provision method, and information provision program

Abstract

To enable effective utilization of a language model specialized in a particular field.SOLUTION: An information provision device according to an embodiment comprises a discussion unit and a proposal unit. The discussion unit conducts a discussion regarding treatment policies of a patient among multiple doctor's accounts set in a language model generated by learning from data stored in a medical database and configured to generate an answer to an entered prompt. The proposal unit judges a result of the discussion conducted by the discussion unit using a judge account set in the language model, and proposes a treatment policy. The proposal unit generates minutes of the discussion that leads to the resultant treatment policy and provides the minutes together with the treatment policy.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The disclosed embodiments relate to an information providing device, an information providing method, and an information providing program. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there is known a system that uses a language model to generate an answer to a question input by a user (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2022-503838 Summary of the Invention [Problem to be solved by the invention]

[0004] While conventional technology generates answers based on news articles, it does not take into account the use of language models specialized for specific domains.

[0005] The present invention has been made in view of the above, and has an object to effectively utilize a language model specialized for a specific field. [Means for solving the problem]

[0006] An information providing device according to one aspect of an embodiment includes a discussion unit and a proposal unit. The discussion unit is a language model generated by learning data stored in a medical database, and holds a discussion on a treatment plan for a patient among a plurality of doctor accounts set in the language model that generates an answer to an input prompt. The proposal unit judges a result of the discussion by the discussion unit using a judge account set in the language model, and proposes a treatment plan. The proposal unit generates minutes of the discussion that led to the result of the treatment plan, and provides the minutes together with the treatment plan. Effect of the Invention

[0007] According to one aspect of the embodiment, a language model specialized for a specific field can be effectively utilized. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an overview of an information providing device according to an embodiment. [Diagram 2] FIG. 2 is a functional block diagram illustrating an example of the configuration of an information providing device according to the embodiment. [Diagram 3] FIG. 3 is a flowchart illustrating a processing procedure executed by the information providing device according to the embodiment. [Figure 4] FIG. 4 is a flowchart illustrating a processing procedure executed by the information providing device according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of a computer hardware configuration that functions as an information providing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The present invention will be described below through embodiments, but the following embodiments do not limit the scope of the invention according to the claims. In addition, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0010] A process flow of the information providing device according to the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram for explaining an overview of the information providing device according to the embodiment. Fig. 1 shows a configuration of an information providing system 1 including an information providing device 10 according to the embodiment.

[0011] 1, the information providing system 1 includes an information providing device 10 and a medical case database 20. The medical case database 20 is a database that stores various data related to medical care. For example, the medical case database 20 stores medical papers and knowledge, various medical records, various data related to medicines, various data related to insurance, various data related to medical equipment, and the like.

[0012] The information providing device 10 is a device to which various devices including a medical case database 20 are plugged in. In the information providing system 1, in which the various devices include various servers, vehicles, smartphones, and other devices, the information providing device 10 realizes the highest level of artificial intelligence by being plugged in to each device.

[0013] For example, in the information provision system 1, the information provision device 10 and each device including the medical case database 20 incorporate semiconductor chips manufactured by the same manufacturer. Each of these semiconductor chips may perform machine learning or deep learning by itself.

[0014] In addition, the semiconductor chips used in the information providing device 10 and the medical case database 20 are chips of a size suitable for each housing. For example, assuming that the chip size of the information providing device 10 is XL size, L size is used for servers and vehicles, and S or M size is used for user terminals. For terminals with housing sizes smaller than that of the user terminals, SS size is used and is put into a SoC (System on a chip) to form a single chip. Note that the number of tiles for each size is only an example and is not limited to the above. If it is desired to attach accessories such as a camera or a microphone to the user terminal, a chip dedicated to accessories other than the semiconductor chip may be set in an empty space near the semiconductor chip in the SoC.

[0015] In this way, in the information providing system 1, the use of semiconductor chips made by the same manufacturer in various devices connected to the information providing device 10 makes it possible to keep the information providing system 1 secure. In other words, in the information providing system 1, hacking, virus infection, and deep fakes can be avoided with a high degree of accuracy, and privacy can be ensured between the information providing device 10 and data source providers including the medical case database 20.

[0016] In this embodiment, the information providing device 10 learns a model based on a medical case database 20, which is a data source. The model is a language model. For example, ChatGPT by OpenAI is known as a language model (Reference: https: / / openai.com / blog / chatgpt). The language model may be a language model such as GAN (Generative Adversarial Networks) or VAE (Variational Autoencoder) that uses a neural network.

[0017] In this embodiment, the language model generates a text response to a text of a query (hereinafter, a prompt) input by a user. The language model is trained in advance using a data source. The language model may be trained by known machine learning techniques.

[0018] This enables the language model to generate answers based on the data source. In this embodiment, the information providing device 10 plugs in to the medical case database 20 (step S1), and learns the language model based on the data stored in the medical case database 20 (step S2).

[0019] In this way, the information providing device 10 trains the language model on various data stored in the medical case database 20. As a result, the language model generates a response to an input prompt based on the trained medical data. In other words, the information providing device 10 can generate a language model specialized for the medical field.

[0020] In this embodiment, a language model specialized for the medical field is used to provide advice on the patient's medical policy. Specifically, in this embodiment, accounts (roles) of "Doctor", "Patient", "Family", "Experts A to C" and "Judge System" are set in advance by system prompts of the language model.

[0021] "Doctor" is, for example, an account corresponding to a patient's primary physician. In FIG. 1, a case is shown in which "Doctor" is one account, but in reality, multiple accounts are set. Note that the multiple accounts may be, for example, doctors with different specialties (internal medicine, surgery, etc.).

[0022] "Patient" is an account corresponding to a patient. "Family" is an account corresponding to a patient's family. "Experts A to C" are expert accounts involved in the treatment of a patient.

[0023] Specific examples of expert accounts include medical researchers, hospitals, nurses, pharmacists, pharmaceutical manufacturers, and medical device manufacturers. The "judge system" is an account that judges the results of discussions by each account. For example, the judge system judges the merits of each account's arguments and proposes the content to an actual doctor (e.g., the patient's primary care physician).

[0024] In this embodiment, claims made by each of a plurality of doctor accounts are input as prompts into the language model, and the input prompts are set as system prompts so that other doctor accounts can debate the input prompts.

[0025] Such a setting enables each doctor account to hold a spontaneous discussion using the language model. As shown in Fig. 1, the information providing device 10 uses the language model to hold a discussion regarding a treatment plan for a patient by each account (step S3).

[0026] For example, a discussion begins by first entering various data such as the patient's age and gender, and the patient's medical records as prompts. To explain in more detail, the doctor account is further divided into multiple accounts such as Doctor 1, Doctor 2, and Doctor 3, and Doctor 1 is distinguished by personality, such as a conservative personality, Doctor 2 is proactive, and Doctor 3 is data-oriented, and each account debates using a language model. Doctor 1, for example, discusses medical policy in further subdivided accounts such as Doctor 1-1, 1-2, and 1-3, and Doctor 2, for example, Doctor 2-1, Doctor 2-2, and Doctor 2-3, and draws a conclusion as Doctor 1 or Doctor 2. The virtual experts debate billions of times per second. This enables verification and problem solving from all angles, and highly accurate diagnoses and treatment proposals based on past medical data can be made instantly. In addition, Doctor 1 performs multimodal data analysis and proposes the results of integrating and analyzing various types of medical data such as genetic data, blood data, MRI and X-ray images, Doctor 2 proposes using a real-time learning model to obtain new data from medical sites around the world in real time, and always incorporates the latest medical information and research results into treatment, and Doctor 3 proposes having advanced simulation functions to virtually simulate the progression of the disease and the effects of treatment, and predict the most appropriate treatment and its results in advance. Then, using the comprehensive judgment based on the output results of Doctor 1, Doctor 2, and Doctor 3, the doctor account may decide on a treatment policy. Other ideas that the doctor account may adopt include proposing individually optimized treatment plans based on the background of each patient's genetic information and lifestyle habits, such as promoting personalized medicine, and by linking with 3D bioprinting, it may propose identifying the structure of organs and tissues that are optimal for the patient and using 3D bioprinting technology to generate individually customized artificial organs and tissues.

[0027] For example, the family account draws a conclusion on the family's medical policy using multiple accounts set up within the family. Each expert account holds discussions with multiple accounts for each field and draws a conclusion on the medical policy for each field. Then, the doctor account holds a discussion on the patient's treatment policy based on the discussions in each field. Note that the final discussion may be held by all accounts including each account.

[0028] For example, the doctor accounts discuss the treatment method, cost, risk, cure rate, and the method and timing of informing the patient and family as a treatment policy for the patient. The "judgment system" then makes a judgment based on the content of the discussion (step S5). For example, the "judgment system" is made up of multiple accounts. The multiple accounts that make up the judgment system discuss the content of the discussions of the doctor accounts, etc., and judge the final medical policy. For example, when the discussion reaches a deadlock, or other specified termination conditions are met, the judgment system compares the claims of each account with the data stored in the medical case database 20 to determine the most appropriate medical policy.

[0029] In addition, in the present disclosure, the information providing device 10 may exclude the expert account when holding a discussion. This allows the discussion to be held between the doctor, the patient, and the family, so that the discussion can be held from the perspective of the parties involved (the doctor's perspective, the patient's perspective, and the family's perspective). That is, a language model specialized for the medical field can be effectively utilized. The information providing device 10 may allow some expert accounts from the expert accounts A to C to participate in the discussion. This allows the discussion to be held from the perspective of the parties involved while reflecting the opinions of the experts. The information providing device 10 may exclude either the family or the patient account from the discussion. This allows the discussion to be held from the perspective of the family or the patient among the parties involved. In addition, when a discussion is held using a language model individually for each expert account, the information providing device 10 may limit data that can be accessed when the language model generates a response. In addition, when a discussion is held using a language model individually for each doctor, patient, and family account, the information providing device 10 may limit data that can be accessed when the language model generates a response. In this way, by restricting access to data in the language model (including excluding expert accounts), it becomes possible to hold discussions from various perspectives.

[0030] The "judgment system" then proposes the determined medical policy to, for example, the patient's doctor. In other words, the information provision system 1 uses a language model to simulate a large-scale discussion between doctors and others regarding medical policies. In addition, because the language model is trained in advance with a large amount of data related to medical cases, it is possible to propose medical policies based on various past findings.

[0031] In this manner, the information providing device 10 according to the embodiment generates a language model based on various pieces of knowledge registered in the medical case database 20, and after holding a discussion using the language model, makes a proposal regarding a medical policy.

[0032] The information providing device 10 also generates minutes of the discussion that led to the decision of the medical policy. Specifically, the information providing device 10 generates minutes in which prompts input to the language model and answers output from the language model are arranged in chronological order for each account in the discussion. In this way, when proposing a medical policy to the parties (doctors, patients, and families), the minutes are also provided, so that the parties can understand what discussions were held by the parties to reach the decision of the medical policy, and therefore the parties can be more convinced of the medical policy. In other words, a language model specialized for the medical field can be effectively utilized. The information providing device 10 may also provide (display) the minutes by highlighting the contents (prompts or answers) that were decisive in deciding the medical policy. The information providing device 10 may also highlight the contents related to the medical policy that are different from the decided medical policy in the minutes.

[0033] Therefore, the information providing device 10 according to the embodiment can effectively utilize a language model specialized for a specific field.

[0034] The configuration of the information providing device 10 will be described with reference to Fig. 2. Fig. 2 is a functional block diagram showing an example of the configuration of the information providing device according to the embodiment.

[0035] As shown in FIG. 2, the information providing device 10 includes a communication unit 11, a storage unit 12, and a control unit 13.

[0036] The communication unit 11 transmits and receives information to and from the medical case database 20 and the like via the network.

[0037] The storage unit 12 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk drive (HDD), a solid state drive (SSD), an optical disk, etc. The storage unit 12 stores various programs and various data. The storage unit 12 stores language model information 121.

[0038] The language model information 121 is information about a language model. For example, the language model information 121 is information about parameters for constructing a language model. Such parameters are, for example, weights and biases of a neural network.

[0039] The control unit 13 is a controller, and includes, for example, a microcomputer having a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM, an input / output port, and various other circuits. The control unit 13 may also be configured with hardware such as an integrated circuit, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 13 includes an acquisition unit 31, a generation unit 32, a discussion unit 33, and a proposal unit 34.

[0040] The acquiring unit 31 acquires data from a data source provider. For example, the acquiring unit 31 acquires data related to medical cases from the medical case database 20 (see FIG. 1). For example, the acquiring unit 31 acquires data every time the medical case database 20 is updated.

[0041] The generation unit 32 generates a language model. Specifically, the generation unit 32 learns the language model based on a data source related to a medical case. The generation unit 32 also updates the parameters of the model through learning. The generation unit 32 may regenerate the model at each learning timing, or may reflect a data source difference from the previous learning in the model.

[0042] The discussion unit 33 uses a language model that is generated by learning data stored in a database related to medical cases and generates answers to input prompts, and holds discussions between multiple doctor accounts, each with different claims.

[0043] First, the discussion unit 33 sets accounts for each field according to the system prompts of the language model. For example, the discussion unit 33 sets a doctor account, a patient account, a family account, expert A to C accounts, and a judge system account.

[0044] After setting these accounts, the discussion unit 33 starts a discussion using the language model. For example, the discussion unit 33 holds a discussion for each field, and then holds a discussion using the doctor account based on the discussion results for each field. Specifically, the discussion unit 33 holds a discussion using the language model by the following steps 1 to 5.

[0045] Step 1: Set up a multi-functional virtual expert There will be hundreds of sets of virtual experts in different medical fields, for example, neurology will have specific characters such as "Neuro-Expert A", "Neuro-Expert B", and "Neuro-Expert C", and cardiology will have specific characters such as "Cardiologist A", "Cardiologist B", and "Cardiologist C".

[0046] Step 2: Speed ​​up information exchange These experts debate billions of times per second, exchanging information based on patient data, enabling them to make instantaneous decisions about a patient's condition, symptoms, and treatment options.

[0047] Step 3: Handling data The virtual experts discuss and share various types of information, such as MRI data, genetic data, and blood data, allowing them to quickly arrive at optimal solutions from multiple perspectives.

[0048] Step 4: Self-study and update If new knowledge, information, or treatment methods are discovered during the discussion, they will be immediately added to the database and used in future discussions, allowing doctors to provide diagnoses and treatments based on the latest medical information.

[0049] Step 5: Building consensus Discussion involves the formation of a final "consensus" through the clash of different opinions and perspectives. This allows information and knowledge from multiple sources to be comprehensively incorporated when making a diagnosis or treatment proposal to a patient. There may be multiple treatment proposals.

[0050] The above has been explained about the doctor account, but the patient account, family account, and Experts A to C accounts also have their own characters set, and each account is further divided into various characters. Through discussions between the two accounts, a consensus is formed as a patient, as a family, and as Experts A to C.

[0051] Furthermore, when the discussion unit 33 holds a discussion, it may exclude the expert account. This allows the discussion to be held between the doctor, the patient, and the family, so that the discussion can be held from the perspective of the parties involved (the doctor's perspective, the patient's perspective, and the family's perspective). That is, it is possible to effectively utilize a language model specialized for the medical field. The discussion unit 33 may allow some expert accounts from the expert accounts A to C to participate in the discussion. This allows the discussion to be held from the perspective of the parties involved while reflecting the opinions of the experts. The discussion unit 33 may exclude either the family or the patient account from the discussion. This allows the discussion to be held from the perspective of the family or the patient among the parties involved. Furthermore, when the discussion unit 33 holds a discussion using a language model individually for each expert account, it may limit data that can be accessed when the language model generates a response. Furthermore, when the discussion unit 33 holds a discussion using a language model individually for each doctor, patient, and family account, it may limit data that can be accessed when the language model generates a response. In this way, by restricting access to data in the language model (including excluding expert accounts), it becomes possible to hold discussions from various perspectives.

[0052] The suggestion unit 34 judges the discussion results by the discussion unit 33 using a judge account set in the language model, and proposes a treatment plan. For example, the suggestion unit 34 makes a judgment when a predetermined end condition is met, such as when the discussion by the discussion unit 33 reaches a deadlock.

[0053] For example, the proposing unit 34 refers to various data registered in the medical case database 20, judges the medical policy that is estimated to be the most appropriate among the medical policies discussed by each doctor account, and proposes that medical policy. Alternatively, a medical policy may be proposed by adding various consensuses such as a patient's consensus (do not want to be hospitalized as much as possible), a family's consensus (do not want to be left with scars as much as possible), and an expert A's consensus (up to what amount insurance will pay).

[0054] The proposing unit 34 also generates minutes of the discussion leading up to the decision of the medical policy, and provides the minutes together with the medical policy. Specifically, the proposing unit 34 generates minutes in which the prompts input to the language model and the answers outputted are arranged in chronological order for each account in the discussion. In this way, when proposing a medical policy to the parties (doctors, patients, and families), the minutes are also provided, so that the parties can understand what discussions the parties went through to decide on the medical policy, and therefore the parties can be more convinced of the medical policy. In other words, the language model specialized for the medical field can be effectively utilized. The proposing unit 34 may also provide (display) the minutes by highlighting the contents (prompts or answers) that were decisive in deciding the medical policy. The proposing unit 34 may also highlight the contents related to the medical policy that are different from the decided medical policy in the minutes.

[0055] Next, a process procedure executed by the information providing device 10 according to the embodiment will be described with reference to Fig. 3 and Fig. 4. Fig. 3 and Fig. 4 are flowcharts showing a process procedure executed by the information providing device 10 according to the embodiment.

[0056] First, the procedure for generating a language model will be described with reference to Fig. 3. As shown in Fig. 3, the information providing device 10 first acquires data related to a legal case from the medical case database 20 (step S101). Next, the information providing device 10 generates a language model based on the data acquired from the medical case database 20 (step S102), and ends the process.

[0057] Next, the discussion process using the language model will be described with reference to Fig. 4. As shown in Fig. 4, the information providing device 10 first holds a discussion on a medical policy in each field (step S111). Then, the information providing device 10 holds a discussion on a medical policy between fields (step S112).

[0058] Next, the information providing device 10 judges the results of the discussion (step S113), and then the information providing device 10 proposes a medical policy (step S114), and ends the process.

[0059] According to this embodiment, a medical policy can be proposed after discussing the medical policy using a language model trained based on data stored in the medical case database 20, so that a language model specialized for a specific field can be effectively utilized.

[0060] 5 is a diagram showing an example of a computer hardware configuration functioning as an information providing device. A program installed in the computer 1200 can cause the computer 1200 to function as one or more "parts" of the device according to the present embodiment, or cause the computer 1200 to execute operations or one or more "parts" associated with the device according to the present embodiment, and / or cause the computer 1200 to execute a process or steps of the process according to the present embodiment. Such a program can be executed by the CPU 1212 to cause the computer 1200 to execute specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0061] The computer 1200 according to this embodiment includes a CPU 1212, a RAM 1214, and a graphic controller 1216, which are connected to each other by a host controller 1210. The computer 1200 also includes input / output units such as a communication interface 1222, a storage device 1224, a DVD drive, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The DVD drive may be a DVD-ROM drive, a DVD-RAM drive, etc. The storage device 1224 may be a hard disk drive, a solid state drive, etc. The computer 1200 also includes input / output units such as a ROM 1230 and a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0062] The CPU 1212 operates according to a program stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphic controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into itself, and causes the image data to be displayed on the display device 1218.

[0063] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive reads programs or data from a DVD-ROM or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0064] The ROM 1230 stores therein a boot program or the like that is executed by the computer 1200 upon activation, and / or a program that depends on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via a USB port, a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0065] The programs are provided by a computer-readable storage medium such as a DVD-ROM or an IC card. The programs are read from the computer-readable storage medium, installed in the storage device 1224, the RAM 1214, or the ROM 1230, which are also examples of computer-readable storage media, and executed by the CPU 1212. Information processing described in these programs is read by the computer 1200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be constructed by implementing operations or processing of information according to the use of the computer 1200.

[0066] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 may execute a communication program loaded in the RAM 1214 and instruct the communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer area provided in the RAM 1214, the storage device 1224, a DVD-ROM, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer area or the like provided on the recording medium.

[0067] Furthermore, the CPU 1212 may cause all or a necessary portion of a file or database stored in an external recording medium such as the storage device 1224, a DVD drive (DVD-ROM), an IC card, etc. to be read into the RAM 1214, and perform various types of processing on the data on the RAM 1214. The CPU 1212 may then write back the processed data to the external recording medium.

[0068] Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and undergo information processing. The CPU 1212 may perform various types of processing on the data read from the RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequence of the program, and write back the results to the RAM 1214. The CPU 1212 may also search for information in a file, database, etc. in the recording medium. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 1212 may search for an entry whose attribute value of the first attribute matches a specified condition from among the plurality of entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0069] The above-mentioned programs or software modules may be stored in a computer-readable storage medium on the computer 1200 or in the vicinity of the computer 1200. In addition, a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the programs to the computer 1200 via the network.

[0070] The blocks in the flowcharts and block diagrams in the present embodiment may represent stages of a process in which an operation is performed or "parts" of an apparatus responsible for performing the operation. Particular stages and "parts" may be implemented by dedicated circuitry, programmable circuitry provided with computer readable instructions stored on a computer readable storage medium, and / or a processor provided with computer readable instructions stored on a computer readable storage medium. The dedicated circuitry may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. The programmable circuitry may include reconfigurable hardware circuits, such as, for example, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like, including AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, and memory elements.

[0071] A computer-readable storage medium may include any tangible device capable of storing instructions that are executed by a suitable device, such that a computer-readable storage medium having instructions stored thereon comprises an article of manufacture that includes instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), electrically erasable programmable read-only memories (EEPROMs), static random access memories (SRAMs), compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), Blu-ray disks, memory sticks, integrated circuit cards, and the like.

[0072] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0073] Computer readable instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, or to a programmable circuit, either locally or over a local area network (LAN), a wide area network (WAN), such as the Internet, etc., to cause the processor of the general purpose computer, special purpose computer, or other programmable data processing apparatus, or to a programmable circuit, to execute the computer readable instructions to generate means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0074] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is clear to those skilled in the art that various modifications or improvements can be made to the above embodiments. It is clear from the claims that such modifications or improvements can also be included in the technical scope of the present invention.

[0075] It should be noted that the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and may be realized in any order unless the output of a previous process is used in a later process. Even if the operational flow in the claims, specifications, and drawings is explained using "first," "next," etc. for convenience, it does not mean that it is essential to perform the process in this order. [Explanation of symbols]

[0076] 1 Information provision system 10 Information provision device 11 Communications Department 12 Storage section 13 Control section 20 Medical Case Database 31 Acquisition Department 32 Generation part 33 Discussion Department 34 Proposal Department 121 Language Model Information

Claims

1. A discussion section that holds discussions on treatment plans for patients among a plurality of doctor accounts set in a language model that is generated by learning data stored in a medical database and that generates answers to input prompts; a proposal unit that judges a result of the discussion by the discussion unit using a judge account set in the language model and proposes the treatment plan; Equipped with The suggestion unit, Generate a minutes of the discussion that resulted in the treatment course and provide the minutes together with the treatment course. Information provision device.

2. The discussion section: A discussion is held including expert accounts in each field involved in the treatment of the patient set in the language model.

2. The information providing device according to claim 1.

3. The discussion section: Discuss including the patient account set in the language model 2. The information providing device according to claim 1.

4. The discussion section: A discussion is conducted including the family account of the patient set in the language model.

2. The information providing device according to claim 1.

5. The discussion section: Discuss the treatment method, cure rate, cost and notification method as the treatment policy for the patient.

2. The information providing device according to claim 1.

6. An information providing method executed by an information providing device, comprising: A discussion process in which a language model is generated by learning data stored in a medical database, and a discussion process is performed on a treatment plan for a patient among a plurality of doctor accounts set in the language model that generates answers to input prompts; a proposal process for judging a result of the discussion by the discussion process using a judge account set in the language model and proposing the treatment plan; Including, The proposed process comprises: Generate a minutes of the discussion that resulted in the treatment course and provide the minutes together with the treatment course. Information provision method.

7. A discussion step of holding a discussion on a treatment plan for a patient among a plurality of doctor accounts set in a language model that is generated by learning data stored in a medical database and that generates answers to input prompts; a proposal step of judging a result of the discussion by the discussion step using a judge account set in the language model and proposing the treatment plan; Run the following on your computer: The proposing step includes: Generate a minutes of the discussion that resulted in the treatment course and provide the minutes together with the treatment course. Informational program.

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