JP2020046738A - History storage system of block chain and history storage method of block chain - Google Patents

Abstract

To provide a history storage system of a block chain and a history storage method of a block chain to record a learnt model generated by a neural network or a deep learning in the block chain.SOLUTION: A solution concept is introduced by generating a learnt model, the learnt model makes the solution concept solve and a database records training data for making the learnt model generate. The system determines whether the learnt model to solve the introduced solution concept exists or not, and stores all data related to the solved solution concept as a new block chain and couples them, if the learnt model to solve the introduced solution concept exists.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a blockchain history accumulation system and a blockchain history accumulation recording method for recording a solution concept presented by AI (Artificial Intelligence) in blocks connected by a blockchain constructed in a network. is there.

  Neural networks and deep learning are known as the most successful machine learning techniques that allow machines and devices to perform tasks and executions such as translating a wide variety of languages, classifying a vast amount of images, or generating images. . In neural networks and deep learning, it is possible to obtain a problem-solving result by first creating a trained model using a large amount of teacher data and inputting the data to be evaluated to the trained model. .

  The search for problem solving using these neural networks and deep learning can efficiently and quickly support innovation creation for problem solving by using a large amount of teacher data. In this case, for example, a patent management database can be used as the teacher data. The patent management database is a structured technical document that describes what is an issue for a technology, how to solve it, how it is specifically realized, what effect it is, who invented it, Details such as the difference from the existing technology are all recorded and made public. Therefore, if the patent management database is used as teacher data, an appropriate result of problem solving can be obtained.

  For example, Patent Literature 1 and Patent Literature 2 have relevance between a reference character string controlled by an AI and extracted by mining means and a solution concept (eg, equivalent transformation theory, TRIZ, ARIZ, QFD, Taguchi method, etc.). Is learned in advance by artificial intelligence to search for a relevant solution concept.

  Further, Patent Literature 1 and Patent Literature 2 show that the relationship between a combination of reference character strings and a solution concept can be updated at any time by re-learning an AI-trained model. You can choose. The result selected by the user makes the reference string and the solution concept more relevant, the reference string that was not selected by the user and the solution concept less relevant, more user-friendly, and the solution Discloses the provision of the shortest solution concept.

  Also, for example, Non-Patent Document 1 is designed so that a neural network or deep learning can execute processing only when a large amount of teacher data is introduced at once. Therefore, if a large amount of teacher data is received in the occasional update of the learned model as described above, the previously set various parameters, comparison data, their weights, etc. will be overwritten to solve the new machine learning solution concept. There is a problem of catastrophic oblivion.

Patent No. 6019303 Patent No. 6019304

James Kirkpatricka, Razvan Pascanua, Neil Rabinowitza, Joel Venessa, Guillaume Desjardinsa, Andrei A. Rusua, Kieran Milana, John Quana, Tiago Ramalhoa, Agnieszka Grabska-Barwinska a, Demis Hassabisa, Claudia Clopathb, Dharshan Kumarph forgetting in neuralnetworks '', arXiv: 1612.00796v2, DeepMind, December 2, 2016

  By the way, Patent Documents 1 and 2 described above disclose that a learned model such as a combination of reference character strings and a solution concept is updated as needed by re-learning. No consideration was given to the reuse of such materials.

  Also, in Non-Patent Document 1, when trying to learn a new solution concept, information and data for extracting the solution concept previously learned are overwritten and cannot be protected. It was difficult to reuse the results, various set parameters, comparison data, weights, etc. to provide efficient idea creation support.

  Therefore, the present invention has been devised in view of the above-described problems, and an object of the present invention is to provide information and data for extracting a previously learned solution concept by using blocks connected by a block chain. Another object of the present invention is to provide a blockchain history storage system that can protect information by recording the information in a document and efficiently support idea creation.

  The blockchain history accumulation system according to the first invention includes a mining unit for extracting a character string from information on a newly created creation, a reference character string acquired in advance, and each of the solution concepts classified into two or more types. A search for searching for one or more solution concepts based on three or more levels of association between a reference character string corresponding to the character string extracted by the mining means and a solution concept, with reference to three or more levels of association. An idea creation support learning unit having means and learning means for acquiring a relationship between a character string and a solution concept based on newly provided information, and learning so as to reflect the relationship in the degree of association to be referred to. When the learning means obtains the relationship between the new character string and the solution concept, these and the reflected degree of association are stored in the network. And recording means for recording the blocks are connected by Built driving block chain, the recording means, each for obtaining the above relationship, accumulating a history of the contents recorded on the block, characterized by.

  The blockchain history accumulating system according to a second aspect of the present invention is the grouping of the information on the creation based on the attribute information added to the information on the creation newly created and / or the extracted character string in the first invention. And the recording means records in each block in the block chain for each group created by the grouping means.

  In a blockchain history accumulation system according to a third aspect, in the second aspect, the grouping means performs the grouping based on a character string extracted from a specific area.

  In a blockchain history accumulation system according to a fourth aspect, in the second aspect, the grouping means performs the grouping based on a history of the content accumulated in each block.

  In a blockchain history accumulation system according to a fifth invention, in the second invention, the grouping means is configured based on topic information on a social network acquired based on the attribute information and / or the extracted character string. The above grouping is performed.

  In the blockchain history accumulation system according to a sixth aspect, in the second aspect, the grouping means performs grouping excluding the prohibition information when the character string extracted from the specific area includes the prohibition information. The recording means records each block in the block chain in a group unit excluding the prohibition information created by the grouping means.

  In the blockchain history accumulation system according to a seventh invention, in the second invention, the attribute information includes information on a participant involved in the creation of the creation and / or information on a degree of involvement of the participant; It is characterized by.

  The blockchain history accumulation method according to the eighth invention includes an extraction step of extracting a character string from information on a newly created creation, a reference character string acquired in advance, and each of the solution concepts classified into two or more types. A search for searching for one or more solution concepts based on three or more levels of association between a reference character string and a solution concept corresponding to the character string extracted in the above extraction step, with reference to the three or more levels of association. A learning step of acquiring a relationship between a character string and a solution concept based on newly provided information, and learning to reflect the relationship in the degree of association to be referred to; and a new character in the learning step. When the relationship between the column and the solution concept is acquired, these and the reflected degree of relevance are linked in a blockchain built in the network. And a recording step of recording the blocks, said recording step, each time of acquiring the above relationship, accumulating a history of the contents recorded on the block, characterized by.

  According to the first to seventh aspects, the learning means acquires the relationship between the character string and the solution concept based on the newly provided information, and performs learning so as to reflect the relationship in the degree of association. Therefore, the solution concept and the reflected degree of association can be recorded in blocks connected by a block chain constructed in the network. As a result, information and data for extracting a solution concept learned previously can be protected, and the results learned in the past, various parameters, comparison data, weights, and the like can be reused to provide efficient idea creation support. It becomes possible.

  In particular, according to the second aspect, the grouping means extracts the attribute information and / or the character string added to the information on the newly created creation. For this reason, it is possible to group information relating to creations, and to record each created group in each block in the block chain. This makes it possible to reuse the results learned in the past, set various parameters, comparison data, weights, etc., and to perform efficient idea creation support.

  In particular, according to the third aspect, the grouping means performs grouping based on a character string extracted from a specific area. For this reason, character strings extracted from a specific area can be recorded in blocks in groups. This eliminates the need to check the information recorded in all blocks, and enables efficient idea creation support by reusing the results learned in the past, various parameters set, comparison data, weights, etc. Becomes

  In particular, according to the fourth aspect, the grouping means performs grouping based on the history of the contents stored in each block. For this reason, the stored contents can be recorded in each block in the block chain on a group-by-group basis based on the degree of reference of the stored contents, the number of times of use, and the like. As a result, a distinguished group can be protected, and the results of learning in the past, various set parameters, comparison data, weights, and the like can be reused to support idea creation with an incentive.

  In particular, according to the fifth aspect, the grouping means extracts topic information on the social network acquired based on the attribute information and / or the extracted character string. For this reason, it is possible to perform grouping in association with externally topical information. As a result, it is possible to reuse the results learned in the past, set various parameters, comparison data, weights, and the like, take into account the latest topics and trends, and provide efficient idea creation support. Furthermore, in addition to the document information, it can be taken in as teacher data with high freshness. This makes it possible to efficiently present a solution concept using a neural network or deep learning.

  In particular, according to the sixth aspect, when the character string extracted from the specific area includes the prohibition information, the grouping unit performs the grouping excluding the prohibition information. For this reason, it is possible to provide protection by eliminating harmful information. As a result, information and data having no problem can be reused from the results learned in the past, various set parameters, comparison data, weights, and the like, and efficient idea creation support can be performed.

  In particular, according to the seventh aspect, the attribute information has information on a participant involved in creation of a creation and / or information on a degree of involvement of the participant. For this reason, it is possible to provide an incentive according to a creator or a provider of each creation or registered content. As a result, correspondence between old and new teacher data and the like becomes clear, and efficient idea creation support can be performed.

  According to the ninth invention, the learning step acquires a relationship between the character string and the solution concept based on the newly provided information, and performs learning so as to reflect the relationship in the degree of association. Therefore, the solution concept and the reflected degree of association can be recorded in blocks connected by a block chain constructed in the network. As a result, information and data for extracting a solution concept learned previously can be protected, and the results learned in the past, various parameters, comparison data, weights, and the like can be reused to provide efficient idea creation support. It becomes possible.

FIG. 1 is a block diagram illustrating an example of the entire configuration of the blockchain history accumulation system according to the first embodiment. FIG. 2 is a schematic diagram illustrating an example of a configuration of the content distribution management device according to the first embodiment. FIG. 3 is a schematic diagram illustrating an example of functions of the content distribution management device according to the first embodiment. FIG. 2 is a schematic diagram illustrating an example of a learning model of the blockchain history accumulation system according to the first embodiment. FIG. 5 is a content management table illustrating an example of the management database according to the first embodiment. FIG. 6 is a related information table illustrating an example of the management database according to the first embodiment. FIG. 7 is a content participant table showing an example of the management database in the first embodiment. FIG. 8 is a learned model update table illustrating an example of the reference database according to the first embodiment. FIG. 9 is a content determination management table illustrating an example of a database of the blockchain history accumulation system according to the first embodiment. FIG. 10 is a content utilization history table showing an example of a database of the blockchain history accumulation system according to the first embodiment. FIG. 11 is a learned model management table illustrating an example of the reference database according to the first embodiment. FIG. 12 is a flowchart illustrating an example of the operation of the blockchain history accumulation system according to the first embodiment. FIG. 13 is a schematic diagram illustrating an example of history accumulation in the first embodiment. FIG. 14 is a schematic diagram illustrating an example of a creation history according to the second embodiment. FIG. 15 is a schematic diagram illustrating an example of a character string history according to the third embodiment. FIG. 16 is a schematic diagram illustrating an example of a content history according to the fourth embodiment. FIG. 17 is a schematic diagram illustrating an example of a topic history according to the fifth embodiment. FIG. 18 is a schematic diagram illustrating an example of an exclusion history according to the sixth embodiment.

  Hereinafter, an example of a blockchain history storage system and a blockchain history storage method according to an embodiment of the present invention will be described with reference to the drawings.

(First Embodiment: Configuration of Content Distribution Management Apparatus 1)
An example of the configuration of the content distribution management device 1 according to the first embodiment will be described with reference to FIGS. FIG. 1 is a block diagram illustrating the overall configuration of the content distribution management device 1 according to the present embodiment.

  As shown in FIG. 1, the blockchain history accumulation system 100 includes, for example, a content distribution management device 1. The content distribution management device 1 includes, for example, a user terminal 5 (5a to 5c, etc.) via a public communication network 4 (Internet 4a, content distribution network 4b, blockchain network 4c), and a database 6 (management database 6a, It may be connected to at least one of the reference databases 6b).

<Content distribution management device 1>
The content distribution management device 1 according to the present embodiment is mainly provided on a network. For example, researchers, developers, and the like of an R & D business unit and a product development business unit related to problem solving and idea generation in a company or a research institution. Used by engineers and others. The content distribution management device 1 uses a patent management database and a huge amount of technical information and information and data for extracting a solution concept held by a company or a research institution to support efficient idea creation. Things.

  FIG. 2 is a schematic diagram illustrating an example of a configuration of the content distribution management device 1 in the blockchain history accumulation system 100. As the content distribution management device 1, electronic devices such as a mainframe, a midrange, a workstation, and a supercomputer are used. The content distribution management device 1 includes a housing 10, a CPU 11, a ROM 12, a RAM 13, a storage unit 14, and I / Fs 15 to 17. The I / Fs 15 to 17 are connected by an internal bus 18.

  A CPU (Central Processing Unit) 11 controls the entire content distribution management device 1. A ROM (Read Only Memory) 12 stores an operation code of the CPU 11. A RAM (Random Access Memory) 13 is a work area used when the CPU 11 operates. The storage unit 14 stores various information such as application programs, analysis logic, vocabulary / syntax programs, and the like. As the storage unit 14, for example, a data storage device such as a solid state drive (SSD) is used in addition to a hard disk drive (HDD). For example, the content distribution management device 1 may include a GPU (Graphics Processing Unit) not shown. By having a GPU, higher-speed arithmetic processing can be performed than usual.

  The I / F 15 is an interface for transmitting and receiving various information to and from the user terminal 5 and an external database (not shown) via the public communication network 4. The I / F 16 is an interface for transmitting and receiving information to and from the input unit 20. For example, a keyboard is used as the input unit 20, and an administrator, an operator, or the like of the blockchain history storage system 100 inputs various information or control commands of the content distribution management device 1 via the input unit 20. The I / F 17 is an interface for transmitting and receiving various information to and from the output unit 19. The output unit 19 outputs various information stored in the storage unit 14, the processing status of the content distribution management device 1, and the like. A display is used as the output unit 19, and may be, for example, a touch panel type.

  FIG. 3 is a schematic diagram illustrating an example of a function of the content distribution management device 1 in the blockchain history accumulation system 100. The content distribution management device 1 includes at least an idea creation support learning unit 2, a block chain unit 3, a storage unit 24, a grouping unit 25, a transmission / reception unit 26, a determination unit 27, and a permission unit 28, and further includes a management database 6a Connect to reference database 6b. Note that the functions illustrated in FIG. 3 are realized by the CPU 11 executing a program stored in the storage unit 14 or the like using the RAM 13 as a work area. The I / Fs 15 to 17 may be controlled by, for example, artificial intelligence. Here, “artificial intelligence” may be based on any known artificial intelligence technology.

<Idea creation support learning section 2>
FIG. 4 shows an example of the idea creation support learning unit 2 of the learned model. The idea creation support learning unit 2 includes, for example, a mining unit 21 that extracts a character string from information on a newly created creation, and a reference character string acquired in advance and a solution concept classified into two or more types. A search unit that searches for one or more solution concepts based on three or more levels of association between a reference character string corresponding to the character string extracted by the mining unit 21 and a solution concept, with reference to three or more levels of association. 22 and learning means 23 for acquiring the relationship between the character string and the solution concept based on the newly provided information and learning so as to reflect the relationship in the degree of association to be referred to.

  The idea creation support learning unit 2 uses, for example, a database stored in the reference database 6b as teacher data. The database used is, for example, a database such as a “patent gazette DB”, a “research report DB”, a “technical report DB”, an “idea memo”, and the like. These databases may be downloaded via an external public communication network 4, for example. From each database, predetermined data is extracted as teacher data as a learning target range. Thereby, for example, it is possible to generate a learning model based on highly accurate teacher data.

  The idea creation support learning unit 2 may have, for example, an algorithm that can calculate the degree of association, and uses a function (classifier) optimized based on the past evaluation target information, the reference character string, and the degree of association. You may be. The learning model is constructed using, for example, machine learning. As a method of machine learning, for example, deep learning is used, and as another method, a neural network may be used. In that case, the degree of association may be indicated by a hidden layer and a weight variable.

<Mining means 21>
The mining unit 21 extracts a character string from information on a newly created creation. The mining unit 21 extracts a character string from information such as a document, for example, as basic information. For the extraction of the character string, for example, the information to be evaluated may be extracted from the importance of the combination of each character string included in the information group A to be evaluated and the other information group B.

<Search means 22>
The search unit 22 refers to the three or more levels of association between each reference character string acquired in advance by the mining unit 21 and each of the solution concepts classified into two or more types, and determines the character string extracted by the mining unit 21. At least one solution concept is searched for based on three or more levels of association between the corresponding reference character string and the solution concept.

  The search means 22 indicates that, for example, “A11” included in the past evaluation target information indicates the degree of association “72%” with “A1” included in the reference information, and “A3” with “A3”. "15%" is shown. In this case, since “A1” has a stronger connection with “A11” than “A3”, the relationship between “A11” and “A1” is acquired.

<Learning means 23>
The learning unit 23 learns a learning model based on the information newly provided by the mining unit 21 and based on the relationship between the character string acquired by the searching unit 22 and the solution concept so that the learning model is reflected in the degree of association to be referred to. Let it.

<Recording means 24>
For example, when the learning unit 23 acquires a relationship between a new character string and a solution concept in the learning unit 23, the storage unit 24 stores the relationship between the new character string and the degree of association already reflected in the blockchain unit 3 constructed in the network. Record in each block connected by. Further, the storage unit 14 accumulates, for example, every time the learning unit 23 acquires a relationship between a new character string and a solution concept, a history of contents already recorded on the block. The storage unit 24 may store the information in units of information, or for each group, or may store the information at predetermined intervals.

<Grouping unit 25>
The grouping unit 25 performs grouping on the information on the creation based on, for example, the attribute information attached to the information on the newly created creation and / or the character string extracted by the learning unit 23. The storage unit 24 records each block in the block chain unit 3 in units of each group created by the grouping unit 25.

<Transmitting / receiving means 26>
The transmission / reception means 26 includes, for example, a user terminal 5 (5a to 5c, etc.) connected to the content distribution management device 1 via the public communication network 4 (Internet 4a, content distribution network 4b, blockchain network 4c), and a database. 6 (the management database 6a and the reference database 6b). Each history exchanged via the transmission / reception means 26 is recorded on each corresponding block.

<Determination means 27>
The determination unit 27 includes, for example, a learned model that solves the relationship between the acquired character string and the solution concept or the introduced solution concept in the idea creation support learning unit 2 based on the newly provided information. It is determined whether or not this is the case with reference to the management database 6a and the reference database 6b.

<Permitting means 28>
The permission unit 28 permits the block in which the information on the solution concept extracted by the learning unit 23 is recorded to be overwritten with the learned model thereafter. For example, based on the result of the determination by the determination unit 27, the learning unit 23 permits the recording of the relationship between the acquired new character string and the solution concept, the learning model, and the like on the corresponding block. This prevents the data and information already recorded from being accidentally overwritten.

<Block chain part 3>
The block chain unit 3 in the content distribution management device 1 includes, for example, at least a block chain control unit 31, a metadata generation unit 32, an encryption unit 33, and an electronic signature unit 34 related to block chain technology. The block chain unit 3 forms, for example, a block chain network 4c on the content distribution network 4b of the public communication network 4, and stores and manages all records exchanged on the block chain network 4c via the transmission / reception means 26. It is stored in the database 6a.

  Among the user terminals 5, for example, the user terminals 5b to 5c registered as members are connected to the block chain network 4c. The terminals such as the user terminals 5b to 5c are connected to each other by a blockchain network 4c, and are configured in a “peer-to-peer type” management without a terminal that plays a centralized management role.

<Block chain control means 31>
The block chain control unit 31 connects the content distribution management device 1 and the terminals such as the user terminals 5b to 5c between the content distribution management device 1 and the terminals such as the user terminals 5b to 5c connected by the block chain network 4c. When a certain time interval or a certain number is reached, an attempt is made to group a series of information such as transactions, transactions, etc., as a block, and if the block is successfully generated, or a new block is generated from another block generation device. When it is received, control is performed to link it to the current block chain as a new block chain.

  When a new block is received by the content distribution management device 1 or a terminal such as the user terminal 5b to the user terminal 5c, the block chain control unit 31 checks the block and each information and transaction included in the block. After verifying the above, it is linked to the following block only when it is justified.

  In the exchange in the block chain control means 31, for example, an application is executed by the distributed ledger technology, and continuity of each stored history is guaranteed. As a result, it becomes possible for the content distribution management device 1 and each node to jointly hold blockchain data, various contents, history, and the like, and to transmit and receive data, information, and various contents via the blockchain network 4c.

<Metadata generation means 32>
The metadata generating means 32 generates metadata relating to various contents exchanged between the content distribution management device 1 and the terminals such as the user terminals 5b to 5c in the blockchain history storage system 100. Various contents are stored in the management database 6a. The metadata generation unit 32 generates content such as a generation date of each content, a creator (user terminal ID), and information on software and hardware used for generation of each content, based on metadata of various contents. Include relevant software and hardware information. As a result, for example, it is possible to ensure the authenticity of various contents, and it is possible to have information recorded in all blocks as the only absolute block chains.

  The encryption unit 33 encrypts various contents exchanged by the content distribution management device 1 based on a license (public / limited release) given to various contents. Management information relating to encryption of various contents is stored in the management database 6a. The encryption unit 33 may encrypt various contents in accordance with, for example, policies and regulations of the blockchain history storage system 100 and the content distribution management device 1. Furthermore, encryption may be performed according to the instruction of the right holder (right holder) of various contents. For example, among the various contents registered in the management database 6a, the encrypting unit 33 performs unencryption without encrypting the contents that allow free access or free distribution. It may be set as the content of the conversion.

<Electronic signature means 34>
The electronic signature unit 34 performs, for example, an electronic signature on a hash value and metadata of various contents. The encryption unit 33 encrypts various contents, calculates a hash value of the encrypted content, and attaches an electronic signature together with the hash value and metadata of the original content. If the encoder chip supports the digital signature, the encryption unit 33 obtains the hash value of the content and the digital signature of the encoder chip for the metadata in the content. The encryption unit 33 includes, in addition to the metadata described in the various contents, the metadata generated and assigned by the metadata generation unit 32 and, when the content is encrypted, the hash value of the encrypted content. And a digital signature.

<Public communication network 4 (Internet 4a)>
The public communication network 4 (Internet 4a) is an Internet network or the like connected via a communication circuit, and may be configured by an optical fiber communication network. The public communication network 4 is not limited to a wired communication network, but may be realized by a wireless communication network. The public communication network 4 includes a content distribution network 4b, and a blockchain network 4c is further configured on the content distribution network 4b.

<Content distribution network 4b>
The content distribution network 4b is a network that can be browsed by, for example, the user terminal 5 registered as a member in the content distribution management device 1 via the blockchain network 4c. The content distribution network 4b may allow the registered user terminal 5 to receive various content services through the blockchain network 4c with predetermined authentication.

<Blockchain network 4c>
The blockchain network 4c is realized by blockchain technology (distributed ledger technology). Each block has a number of transactions, a special value called Nonce, and a hash of the previous block. Only transactions (transactions) included in the block are recognized as “correct transactions”. This makes it possible to have a “single block chain” as a whole connected by the content distribution network 4b.

  In the blockchain network 4c, the content distribution management device 1 connected thereto and the user terminal 5 connected by the blockchain network 4c can distribute and share a consistent exchange history throughout the blockchain network 4c. Further, the blockchain network 4c has, for example, an electronic (content data) presence proof function while being connected by the content distribution network 4b. The presence proof function has a function of proving that certain electronic data was present on a device connected by the content distribution network 4b and on the content distribution network 4b at a specific point in time.

  In the blockchain network 4c, as electronic data to be a target of existence proof, for example, contents registered in a content management table 200 described later are targeted, and various types of data such as text messages (character strings), documents, still images, and moving images are used. Data and files are registered. It also includes a large amount of teacher data (not shown) for AI to learn to present the solution concept.

<User terminal 5>
The user terminal 5 is connected to a plurality of user terminals 5a to 5c and the like via the public communication network 4. The user terminals 5 include, for example, a user terminal 5a that does not receive a service provided by the content distribution management device 1, and a user terminal 5b to a user terminal 5c that receives a service provided by the content distribution management device 1. The user terminals 5b to 5c can receive content distribution services provided by the content distribution management device 1 by performing user registration with the content distribution management device 1. Further, the user terminals 5b to 5c register the content held by each user in the content distribution management device 1 so that another registered user can register the registered content via the content distribution management device 1. Can be provided.

<Database 6>
The database 6 includes, for example, at least a management database 6a and a reference database 6b.

<Management database 6a>
The management database 6a stores, for example, a content management table 200, a related information table 201, a content participant table 202, a learned model update table 203, a content determination management table 204, and a content utilization history table 205, respectively. The management database 6a stores, in addition to information on users registered in the content distribution management device 1, management information on each content provided by the content distribution management device 1, management information on the history of linked block chains, and the like. Is done.

<Content management table 200>
As shown in FIG. 5, information related to registration of each content provided by the content distribution management device 1 and the like are stored as a content management table 200.

  In the content management table 200, all data, content, and related information registered in the present service are stored in association with each other. In the content management table 200, for example, items such as “content ID”, “content title”, “registered content information”, “registration date”, and “registrant ID” are associated with each other. Stored in a file.

  The “content ID” is, for example, identification information given to identify each content, and is stored as a unique identifier.

  The “content title” is, for example, the name of the content to be registered. For a patent document, “patent specification” is used. For a report such as a report or report on technology or research, the “technical report” is used. And "research report".

  In the “registered content information”, for example, in the case of a patent specification of a patent document, “name”, “abstract”, “text”, “drawing”, “related ID” and the like are respectively stored in association with each other. Is done. For example, a detailed description (text) of the content may be registered in the record area of each registered content.

  The “related ID” stores an identifier of information registered in a related information table 201 of FIG. 6 described later. For example, when there is related information related to the registered content, the identifier may be linked to the registered content ID and the related ID of the related information. In the “registration date”, for example, information on the date and time when each content is registered in the database 6 is stored.

  The “registration date” may be sequentially recorded in each content based on a time stamp function (not shown) provided in the block chain control unit 31 of the block chain unit 3.

  The “registrant ID” is identification information relating to a registrant of the content distribution management device 1, and is stored in association with each registered content.

  In the content management table 200, for example, in the data record of “No. 1”, “00001” is set as the content ID in “content ID”, “patent specification” is set in the content title, and the name “AAA1” is set in the registered content. , "AAA2" for the abstract, "AAA3" for the main text, "10" for the number of drawings in the drawing, and "001" for the identification ID of the related information in the related information. Is done.

  For example, when the content to be registered is a patent specification, the “content title” stores the format of the patent specification, the configuration, and the whole or each part of the registered content. This allows the user to read a large amount of the specified portion as teacher data by specifying a specific range and specifying as a search focused on each specified portion or input information on a newly created creation. It becomes possible. This makes it possible to generate a learned model with the designated part as a nucleus.

  For registration of each content registered in the content management table 200, for example, a large number of patent specifications may be registered collectively, or limited to a certain field, and only a target portion thereof may be registered individually. Although the patent specification has been described as an example, the registered content may be other content, for example, a technical report and a research report are also the same. The registered content may be registered in accordance with the characteristics of the registered content, such as the document structure of each registered content, the features of each chapter, the language, the data amount, the degree of attention, and the like. In this case, a flag indicating the identification may be registered and stored together with the record of each registered content (not shown).

  In addition to the registration of the text information of the registered content, the content management table 200 may be partially specified and registered as, for example, a drawing or a graph, and stored in the content management table 200. Documents, chapters, contents, written information, and the like of the content registered in the content management table 200 are managed by the content ID of each registered content, and may be the administrator of the content distribution management device 1 or the registrant of each content. For example, if there is additional or insufficient information, it may be possible to appropriately add the information.

  In the content management table 200, the related new content is associated with the original content, and is registered by adding an additional number to the content ID. For example, the content IDs “00001-1” and “00001-2” are stored as the content related to the already registered content ID “00001”, respectively.

  The content ID “00001-1” is, for example, a content registered by the registrant 0001, and is stored as a registration from the same registrant as the registrant 0001 with the content ID “00001”. The content ID “00001-2” indicates that the content is registered by the registrant 1002, and in this case, is stored as another registrant.

  In the content management table 200, for example, the original registrant of each content and the registrant of the related information are separately registered and stored. In this way, the content is registered and stored in association with the attribute of each content. This makes it possible to acquire teacher data by grouping and generate a learned model based on the teacher data.

  The content management table 200 stores information on registered contents, confirmation of use and use, incentives related to registered contents, and the like, by a registrant identified by a registered registrant ID. As the registrant ID of each content, for example, a unique mail address or telephone number may be registered and stored in addition to the above-described member ID. Furthermore, the configuration, content, degree of update, update date, updater information, and the like of each content may be registered and stored as history information. With such a configuration, differences such as updates and changes can be clearly distinguished.

<Related information table 201>
The related information table 201 stores, for example, a “content ID” associated with the “related ID” of the content management table 200. In the related information table 201, “registered content information”, “publication date”, “possibility of disclosure”, “teacher data ID”, and the like correspond to registered content information of each content ID grouped into “related ID”. It is stored along with it.

  The “registered content information” further includes “name”, “abstract”, “text”, “drawing”, and the like. The related information table 201 stores, for example, related content and information on grouping in each item based on the recorded information. The grouping may be given by a registrant or a participant, for example, according to the characteristics or state of the registered content information. Further, the grouping is performed by a certain item or keyword, and the result of the grouping is taken in as teacher data and solved. Information generated as a trained model for the concept may be stored.

  The “related ID” in the related information table 201 is, for example, when the content information to be registered is “patent specification”, a technical report, an idea memo, a competitive evaluation report referred to in creating the patent specification. (Secret) and the like are stored together as related information.

  The “registered content information” stores, for example, “name”, “abstract”, “text”, “drawing”, and the like. The “name” and the “abstract” are stored as, for example, text information, and the “text” may be a unit of each sentence or a subdivision into each paragraph for each chapter constituting the text. . “Drawings” are stored in association with all drawings and the like used in each content. In the “drawing”, for example, a file in a general graphic format may be registered, or a page of each registered content information may be stored in one file format.

  In the related information table 201, for example, “publication date” of each piece of registered content information and information about whether or not disclosure is possible in “publication availability” are registered and stored. As the “publication date”, for example, the date when each registered content information is published is registered. The “publication date” and “publication availability” manage whether the stored registered content information is public or not.

  Each content of the related information table 201 stores, for example, information on whether or not the content can be disclosed by a manager of each registered content information or a registrant of each content information. In the related information table 201, for example, when the registered content information can be made public, information indicating that the content can be made public (for example, “OK”), and when the registered content information is private, information showing that the content is private (for example, “ NG ") is stored.

  In the related information table 201, for example, “teacher data” is also stored. By storing the “teacher data” in the related information table 201, a large number of learned models can be held by the same neural network or deep learning. A plurality of learned models are stored in the related information table 201, and a learned model that is most suitable for a solution concept (problem) given by the user terminal 5b to the user terminal 5c is determined based on attribute information and parameters of registered content information. The information is stored.

  For example, the related information table 201 may be stored in association with a period so that the related information table 201 is disclosed to a designated party or a specific target only during a certain period in conjunction with the disclosure period of each registered content information. . Whether the registered content information can be disclosed or not can be determined by, for example, the apparatus determining text or status information such as management information (confidential, confidential, CONFIDENTIAL) assigned to each registered content information (not shown). Based on the result of the determination, it is determined whether or not disclosure is possible, and the result is registered and stored.

  The “teacher data ID” stores identification information adopted as a large amount of teacher data used for generating a learned model. The population of the learned model differs depending on the solution concept to be solved, and the content to be extracted also differs depending on what original data is used. Such information is registered and stored. For example, assuming that a patent publication is used as teacher data, a large amount of information with a content ID of “00001” or later is used. When the patent gazette is used as the material of the teacher data, the same identification information is stored in the “teacher data” column of each content ID.

  For example, when the patent data of the content IDs “00001” to “00004” is used in the teacher data ID “A001”, the association is stored. In the generation of teacher data, for example, referring to attribute information of each content and narrowing down the range, target, period, amount, etc. used as teacher data according to the conditions of the solution concept to be solved and the result to be obtained. Good. By performing such narrowing down, a more appropriate learning diagram can be generated in advance as a model.

  Each registered content information is, for example, each registered content information associated with the related ID, and according to the configuration information such as the name, the summary, and the body of the registered content information, it is possible to determine which configuration information is related to the other registered content information. They are stored in association with each other so that they can be determined. This makes it possible to collectively search and refer to related registered content information when searching for desired registered content information.

  As a result, the registered content information allows the member to efficiently refer to the reference information. Further, for example, it is possible to grasp a series of flows from the initial information of idea idea (idea memo) to intermediate examination information (technical report, competitive evaluation report, etc.) to the patent specification as a completed form. As a result, in addition to the content of each registered content information, a mechanism such as a procedure, a method of summarizing, and a scheme can be grasped.

  From the related information table 201, it is possible to understand a more efficient way of compiling ideas and a process up to patent application. Further, by utilizing the process, it is possible to use it as a material for supporting the generation of completely different ideas. The information registered in the related information table 201, the content and date and time of the change, and the information of the registrant and the changer may be recorded by, for example, the above-described blockchain technology.

  The related information table 201 stores, as a summary of each process (transaction) of the content to be exchanged, together with a hash value of a previous block, such as a hash value and a time stamp. The stored information is chained, and is held, distributed and managed in a storage area (ledger) on a plurality of predetermined computers. The plurality of computers may be configured so that each computer is synchronized via a network (not shown). With such a configuration of the related information table 201, it becomes difficult to falsify registered content information, setting information, and the like. This makes it possible to clearly distinguish, for example, differences from registered content information, such as differences and changes.

<Content participant table 202>
As shown in FIG. 7, for example, information related to the ratio of the involvement of the participants of each content provided by the content distribution management device 1 is registered as the content participant table 202, and the registered contents are stored. .

  The content participant table 202 is associated with the “related ID” of the content management table 200 and stores “content ID”, “content title”, “participant information”, “public update availability”, “update date”, and the like. Is done. The “participant information” is stored together with identification information (for example, participant information: 0001 to 0020, etc.) for identifying members involved in creation or examination of each content. Each content participant information is registered by the registrant, for example, as each member related to each content title. This information is referred to in the process of providing incentives to the participants.

  In the “participant information”, for example, information for identifying each participant is stored as “participant information: 0001” to “0003”. In the “participant information”, the degree of involvement of members in each content is stored. As the ratio of the participant information, for example, the work time or the amount of work for each content related to the creation of each participant, the ratio of contribution related to the content of the content, and the like are registered and stored in association with each other. As the numerical value such as the ratio, for example, a predetermined contribution ratio or the like may be stored for each participant involved in the creation of the substance.

  The “participant information” of each content is registered when the target content of the content distribution management device 1 is registered, and is stored in the content participant table 202. For each content registered in the management database 6a, information on the theme of each content and a participant (creator) is set at the start of creation of each content after registration by, for example, blockchain technology, and each is associated with each other. And may be stored. These processes may be collected by, for example, an aggregation program provided in the content distribution management device 1 and recorded in the content participant table 202 (not shown).

  The content participant table 202 is set as information to be blocked, and history information such as work content and time of each participant related to the theme of each content is recorded by blockchain technology. The information is synchronized and managed by the ledger on the computer of the administrator, the registrant or the participant, and is synchronized and managed by the blockchain history storage / recording server and the ledger on the user terminal 5, and stored in association with each other. The ledgers may refer to the information managed synchronously, calculate the contribution ratio for each chapter and part constituting each content, and register and store the contribution ratio for each participant. .

  With this configuration, the content participant table 202 reads each exchange by the blockchain network 4c as teacher data as an activity log, generates a trained model from the result, and executes the learning. The completed model is registered and stored. By recording such information together, for example, the involvement of a member who has merely spoken or a member who has not performed any work may be set not to be stored as a history of the activity and involvement.

  The content participant table 202 stores, for example, a history of original members who were actually involved in the creation of each content. The content participant table 202 further records the degree of contribution of each participant's work. By being stored in the content participant table 202, for example, the activity of the participant related to generation of each content can be accurately grasped. According to the information stored in the content participant table 202, for example, the participant involved in the creation or change of each content determines the number and time of browsing the content after registration, or the number of derived contents, etc. Appropriate incentives can be given according to the proportion of participants.

  “Update availability” stores whether the number of participants of each content is increased or decreased and whether the percentage of participation of each content is updated. The storage of “update permission / prohibition” may be set, for example, by an administrator or a registrant when each content is registered, and the setting result may be stored. In addition, in the storage of update availability, setting information according to the characteristics of each content is stored. In the content participant table 202, for example, among the registered contents, in the case of “patent specification”, the participant is stored as a person involved in the creation of the invention. If a patent has already been filed or a patent has been granted, the registered information cannot be updated, and the registered information cannot be stored and updated. Also, the "research paper" may not be updated in the case of, for example, post-publishing, publication, or issuance of the research paper, and only the addition may be stored as related information.

  Furthermore, among the registered content information stored in the content participant table 202, for example, information such as “idea memo” or “competition evaluation report (secret)” indicates a user of the registered content (content distribution). If the user is a registered user of the management apparatus 1), it is stored that the idea is additionally added to the original registered content information. For example, when performing an additional evaluation, the person who performed those operations is registered as a participant, and the calculated degree of involvement is registered, and the registered contents are stored. If the user is already registered in the content participant table 202 as a participant, the stored percentage information and the like are updated and overwritten according to the work time or work amount for each piece of registered content information involved in the creation. Alternatively, it may be stored.

  Regarding the storage of the information regarding the registrant's involvement in the content participant table 202, for example, the involvement with the registrant already registered by the registrant of the registered content information is compared, and the comparison result is stored. In the content participant table 202, for example, a new participant and the ratio of the participant may be registered and stored in association with other related information. If the participant information stored in the content participant table 202 is a participant or a registrant of each content, the percentage of involvement, additional registration of a new participant, and the percentage of involvement are set and stored. Become.

  In the “update date”, the information of the participant registered in each content and the date and time of the participation ratio are registered. As for the update date, for example, for a content whose update has been registered as NG by an administrator or a registrant, the registration date of the content may be registered. In addition, for content whose update has been registered as OK, an update date of the content may be registered. The “update date” may be stored according to a time stamp function (not shown) managed by the block chain unit 3, such as the content distribution management device 1 or the user terminals 5b to 5c. Good. Each piece of information registered in the content participant table 202 is synchronously managed by a management ledger on a computer of an administrator, a registrant, or a participant through the blockchain network 4c using blockchain technology.

  In the content participant table 202, when the content constituting each content is updated by the registrant or the participant, the content is additionally registered as new content according to the scope of the update and the content of the update, and the additional registration is performed. The information is stored. For example, the added new registered content information may inherit the configuration information such as the name, abstract, and text of the original registered content information, and may be registered and stored as an additional number of the already registered content ID.

<Learned model update table 203>
As shown in FIG. 8, for example, information on learning of each learned model provided by the content distribution management device 1 and the like are stored as the learned model update table 203.

  In the learned model update table 203, learned models generated in the past include “learned model ID”, “learned model history information”, “teacher data ID”, “whether or not to be released”, “last update date”, A “registrant ID”, a “storage destination block ID”, and the like are stored in association with each other. In the “learned model history information”, a “registration date” indicating a first registration date and an “update date” indicating a subsequent update date are registered together and stored in association with each other.

  The learned model update table 203 stores information for determining whether the learned model has been generated previously by the determination unit 27 of the content distribution management device 1. In the learned model management table 206, for example, if a certain learned model does not exist, a learned model is generated using a large amount of teacher data, a learned model ID is newly assigned, and “registration date” The used “teacher data ID”, the “learned” of the trained model, the “last updated date (the initial registration is the same as the registration date)”, the “registrant ID”, and the “storage block ID” are respectively associated with each other. Stored.

  When the learning means 23 acquires a relationship between a new character string and a solution concept, the learned model update table 203 associates these with the reflected association degree as a “learned model”. Remember. The block chain unit 3 also records each block connected by the block chain constructed in the block chain network 4c. When the storage unit 24 obtains the relationship between the character string obtained by the learning unit 23 and the solution concept, the storage unit 24 stores the teacher data according to the conditions of the introduced solution concept.

In “No. 1” of the learned model update table 203, for example, the learned model “K00
01 "," K0001-1 "," K0001-2 "and the like are stored in association with each other. For example, the learned model ID “K0001” indicates that information related to the storage destination block “111” is stored. As the learned model ID “K0001-1”, for example, a learned model that has been overwritten (updated) twice based on the original learned model ID “K0001” is stored in the storage destination block “112”. Indicates that

  When a new solution concept is introduced into the learned model update table 203, all data of the previously created learned model “K0001” is associated with the learned model ID “K0001”, and a new storage destination is stored. A block is generated, and related information for identifying the block is stored in association with the block. In the learned model update table 203, all data stored before the overwriting of “K0001” is stored in “111” that identifies the block that is the storage destination. After being stored in the learned model update table 203, for example, after the block chaining process by the block chain control unit 31, the metadata generation unit 32, the encryption unit 33, and the electronic signature unit 34, the transmission and reception unit 26 It is transmitted on the chain network 4c. Thereafter, processing is performed at the user terminals 5b to 5c connected to the block chain network 4c of the content distribution network 4b.

The learned model update table 203 is referred to by the content distribution management device 1 and, for example, based on the record recorded in the learned model ID “K0001” of “No. The disclosure based on the stored condition is performed with reference to the condition of “disclosure permission”. For example, if "learned or not: OK" is stored in the learned model update table 203, the use is permitted with another solution concept. The availability may be determined in advance based on the registrant or the importance, evaluation, or the like of each learned model, and may be stored. If "disclosure possible: OK", it is limited or open to the public, and is effectively referred to for solving similar problems. On the other hand, if, for example, “disclosure availability: NG” is stored in the learned model update table 203, the corresponding learning model will not be disclosed. Thus, for example, it is possible to appropriately select whether or not to disclose based on the importance of each learning model.

  Further, based on each registered information and the like recorded in the learned model update table 203, for example, depending on the update history of the generated learned model, the presence or absence of disclosure, the degree of utilization, etc. Some incentive may be given to the registrant (creator).

<Content determination management table 204>
As shown in FIG. 9, for example, information relating to the determination and correspondence of each registered content is stored as the content determination management table 204.

  The content determination management table 204 stores, for example, “determination”, “correspondence”, and “determination date” in association with “content ID” and “registered content information” of the content management table 200.

  The content determination management table 204 stores, for example, the result of the determination unit 27 and the correspondence. In the content management table 200, “judgment”, “correspondence”, and “judgment / correspondence date” of each part of the registered content or the entire content are recorded in association with each other. The “judgment” is, for example, a check result (not shown) of the virus check program when registered in the content management table 200, and the information is stored. The “judgment” may be determined before overwriting the learned model before storing it in a new block, and the result may be stored.

  In the content determination management table 204, for example, a determination result by the determination unit 27 is stored as code information. As this code information, for example, information such as “Error XX” as an error determination result is stored as an error message and an error number (code) corresponding to the message. The content determination management table 204 stores the corresponding content according to the result of the determination by the determination unit 27. The content determination management table 204 includes, for example, a grouping unit 25, a transmission / reception unit 26, a block chain control unit 31, a metadata generation unit 32, an encryption unit 33, and an electronic signature unit 34 as correspondences to the determination results of the determination unit 27. Are stored in association with, for example, “correspondence A” and the determination result. Further, the content determination management table 204 stores the determination result by the determination unit 27, the correspondence performed by an administrator or the like according to the determination result, and each date together.

<Content utilization history table 205>
As shown in FIG. 10, for example, information on the usage status of the entire registered content or each part is stored as the content usage history table 205.

  The content use history table 205 stores, for example, the “name”, “summary”, and “body” of each content in association with the “content ID” of the content management table 200.

  The content utilization history table 205 includes, for example, the number of times the registered content has been utilized, the number of times the content has been updated, the percentage of utilization in a certain field, and the like, the percentage and number of times, the period of time registered as evaluation data, and the like. It is memorized. Based on the record of the utilization status stored in the content utilization history table 205, for example, a charge or a point is given as an incentive to a registrant of each evaluation data, a creator who generated the evaluation data, a registrant of the content, and the like. You may.

<Reference database 6b>
In the reference database 6b, for example, in addition to technical literature information such as patent publications that are patent documents, information on technology-related fields such as research reports, R & D reports, technology evaluation reports, and idea memos of each company, etc. are stored. The content is registered by the content distribution management device 1 or the user terminals 5b to 5c, and the registered content is stored.

  In the reference database 6b, for example, specific external information, information that has been talked about for a certain period of time in SNS, or the like is recorded in association with each other. The reference database 6b stores, for example, a result of the content distribution management apparatus 1 periodically accessing a specific institution or the like in advance and collecting information related to a specific keyword or topic. In the reference database 6b, information such as the link destination and the browsing history of each collected information is also registered, and a related information source, each information, an acquisition date, and the like may be stored in association with each other. .

  The reference database 6b stores a large amount of teacher data for making AI learn. The reference database 6b also stores, for example, information obtained by accessing an external content providing server (not shown) connected via the Internet 4a. In this case, a large amount of teacher data can be obtained (downloaded) via, for example, an information providing database connected to the Internet 4a or the user terminal 5. The reference database 6b stores a large amount of teacher data and information such as a history of acquisition.

<Learned model management table 206>
As shown in FIG. 11, for example, information on the utilization status of the whole or each part of each content registered in each learned model is stored as the learned model management table 206.

  In the learned model management table 206, for example, the learned model management table 206 acquires evaluation data with reference to past registered content information, and stores an evaluation result generated based on the evaluation data. In the learned model management table 206, for example, “evaluation target information” and “learned input / output result information” regarding “patent specification” and “patent specification” which are patent information as information groups of registered content information are described. It is memorized. The “evaluation target information” includes, for example, “information group A” and “information B”, which are a group of a certain unit of information, and “importance” indicating their relationship. The “output information” includes, for example, a “character string for reference”, a “solution concept”, and a “degree of association” indicating a relationship between them, and are stored in association with each other. In the learned model management table 206, for example, information on the date and time when each learned model is registered is also stored.

  The information stored in the learned model management table 206 can be used to evaluate various learned models by changing numerical values and parameters set by the learning means 23 and the like of the idea creation support learning unit 2. Verification can be confirmed. For example, by confirming the registration date, a new learned model can be generated under exactly the same conditions, and the difference between the registered date and the current trend may be confirmed.

  The “learned model input / output information” stored in the learned model management table 206 is information that is stored as past evaluation target information acquired in advance. The degree of association between the reference character string input based on the character string extracted as the evaluation target information and the corresponding solution concept is stored. The association degree indicates, for example, the degree of connection between the past evaluation target information and the reference character string, and is indicated in three or more stages, such as a percentage, ten stages, or five stages.

(Embodiment: Operation of Blockchain History Storage System 100)
Next, an example of the operation of the blockchain history accumulation system 100 according to the present embodiment will be described. FIG. 12 is a flowchart illustrating an example of the operation of the blockchain history accumulation system 100 according to the present embodiment.

<Mining means: S110>
First, a character string is extracted from information on a new creation to be created (mining means S110). The mining means 21 includes a user terminal 5a connected to the content distribution management device 1 via the public communication network 4 (the Internet 4a, the content distribution network 4b, and the blockchain network 4c), or a user terminal 5b to a user registered as a member. From the terminal 5c, information (character string) on a newly created creation is acquired. For example, since the user terminal 5a is not a member, its use is restricted, and for example, it may be possible to refer to only information that is publicly disclosed by the content distribution management device 1. On the other hand, since the user terminals 5b to 5c are members, they can be used without restriction.

  The mining means 21, for example, the user terminal 5b to the user terminal 5c acquire information on a new creation from the basic information (document information) registered in the reference database 6b, and evaluate based on the acquired information. A character string is extracted based on the information group A and the information group B of the target information and the importance. Information such as the extracted character string may be stored in the management database 6a.

<Searching means: S120>
Next, the solution concept is acquired by referring to the learned model management table 206 of the reference database 6b (search means S120). The search unit 22 refers to the degree of association of each of the reference character strings obtained in advance and the solution concepts classified into two or more types in three or more stages, and refers to the reference character string corresponding to the character string extracted by the mining unit 21. One or more solution concepts are searched for based on three or more degrees of association between the character string and the solution concept (search means S120).

  The search unit 22 can calculate the first degree of association between the reference character string and the face determination concept by referring to the reference database 6b. The search unit 22 selects, for example, a past reference character string that matches, partially matches, or is similar to the reference character string, and calculates a first association degree based on the corresponding association degree. The search unit 22 stores the calculated first association degree, the acquired evaluation data, and the like in a corresponding information table of the management database 6a or the reference database 6b. Further, the search unit 22 calculates the first degree of association between the combination of each character string included in the reference character string and the solution concept with reference to, for example, the information tables shown in FIGS. You may.

<Learning means: S130>
Next, a relationship between the past reference character string and the solution concept is newly acquired, and the relationship is reflected on the degree of association (learning means S130). The learning means 23 may acquire the solution concept from each database of the reference database 6b, and may acquire, for example, a format for displaying the solution concept from the management database 6a or the reference database 6b. Further, the learning means 23 may generate the latest solution concept as an additional idea based on the solution concept, for example, by referring to a format.

  Thereafter, the learning means 23 outputs the result of the solution concept. For example, a solution concept result may be output based on a result of comparing a preset threshold value with the first degree of association. In this case, for example, when the threshold is set to “70% or more”, the evaluation result is output only when the first degree of association is 70% or more. The condition of the threshold can be set arbitrarily. The learning unit 23 calculates and updates the degree of association using, for example, machine learning, and for example, deep learning may be used for machine learning.

<Storage means: S140>
Thereafter, for example, when the learning unit 23 acquires a relationship between a new character string and a solution concept, these are connected to the reflected association degree by a block chain constructed in the block chain network 4c. Record in each block. Thereafter, when a new character string and a solution concept are obtained, the new character string and the solution concept are recorded in each block of the block chain for each group, and the history of each block is accumulated (storage means S140). When the relationship between the new character string and the solution concept is acquired by the processing by the mining unit 21 and the search unit 22 and the learning unit 23, the storage unit 24 stores the relationship between the new string and the reflected association degree with the block chain. The data is recorded in each block connected by a block chain constructed in the network 4c. The storage unit 24 accumulates a history of the contents recorded on the immediately preceding block each time a relationship between a new character string and a solution concept is acquired in steps 110 to 130.

  As a result, even if a new character string and a solution concept are acquired, the relationship between the previous string and the solution concept can be maintained, and the previously learned solution concept can be extracted without being overwritten or lost. Information and data can be protected by recording them in blocks connected by a block chain.

  FIG. 13 shows an example of a schematic diagram of the blockchain history accumulation system 100 in the present embodiment. It is assumed that the blocks 35 and 36 to be connected exist in the block chain network 4c, and the learning model, registered content information, and related information up to that point are recorded in the blocks 35 and 36. In the blocks 36 and 37, a block 37 for storing a record of acquiring a new character string and a solution concept as an additional history is newly formed into a block 37 by the block chain unit 3 and connected.

  Here, when the relationship between the past reference character string and the solution concept is newly acquired by the user terminal 5b to the user terminal 5c or the like, the storage unit 24 stores the relationship between the character string and the reflected degree of association. After storing the recorded contents in the block 37 connected by a block chain constructed in the block chain network 4c, the block 36 is updated as the block 37. The block 37 shows an example in which the data and each information related to the previously learned solution concept stored in the block 36 are stored as, for example, “A”, “B”, “C”, and the like.

  For example, a user who wants to create a new creation performs a creation activity or the like based on the information recorded in the block 36. At this point, the information to be referred to is stored in the block 36, and is stored in association with a group of information such as "A", "B", "C", and the like. This information group includes, for example, “content ID”, “content title”, “registered content”, “registration date”, “registrant ID” stored in the content management table 200, or the learned model management table 206. Is stored for each of the user terminals 5b to 5c in the management database 6a and is managed.

  For example, when the idea creation support learning unit 2 acquires the relationship between a new character string and a solution concept based on the information stored in the block 36, the information up to that point and the information on the new acquisition are combined into a block. The block 37 is generated by the chain unit 3, and the generated block 37 stores data and information on the acupuncture point concept previously learned, and is distributed in the block chain network 4c.

  Thus, the operation of the blockchain history accumulation system 100 according to the present embodiment ends. The above operation may be executed by a computer as the blockchain history accumulation program in the present embodiment.

  According to the present embodiment, a mining unit 21 is provided. For this reason, the mining means 21 extracts a character string from various types of document information such as a patent publication, a research report, a technical report, an idea memo, and SNS as basic information. This makes it possible to extract character strings from past publications and published documents in a wide range.

  Further, according to the present embodiment, the learning unit 23 acquires the relationship between the character string and the solution concept based on the newly provided information, and performs learning so as to reflect the relationship in the degree of association. Therefore, the solution concept and the reflected degree of association can be recorded in blocks connected by a block chain constructed in the network. As a result, information and data for extracting a solution concept learned previously can be protected, and the results learned in the past, various parameters, comparison data, weights, and the like can be reused to provide efficient idea creation support. It becomes possible.

  Further, according to the present embodiment, when the relationship between the past reference character string and the solution concept is newly acquired, the search unit 22 reflects the relationship in the association degree. Therefore, the degree of association can be easily updated, and the accuracy of the evaluation result can be further increased.

  Further, according to the present embodiment, the management database 6a and the reference database 6b are provided. By referring to these databases 6, evaluation data including three or more levels of first association between the reference character string and the solution concept is acquired. The reference character string includes information on a new creation to be created from the user terminals 5b to 5c. For this reason, based on ideas, texts, keywords, etc. of newly created creations, quantitatively evaluate the degree of importance and / or the tendency of solution concepts with novelty, inventive step, etc. of creations Can be. This makes it possible to reuse the results learned in the past, set various parameters, comparison data, weights, etc., and to perform efficient idea creation support.

  Further, according to the present embodiment, the learning unit 23 newly acquires the relationship between the past reference character string and the solution concept, and reflects the relationship in the degree of association. For this reason, it is possible to reuse the results learned in the past, the set various parameters, the comparison data, the weights, and the like. As a result, it is possible to repeatedly provide efficient idea creation support. Furthermore, in addition to the document information, it can be taken in as teacher data with high freshness, and a solution concept by an efficient neural network or deep learning can be presented.

  Further, according to the present embodiment, when a new character string and a solution concept are acquired, the storage unit 24 connects the character string and the reflected association degree with a block chain constructed in the block chain network 4c. Each time the relationship is obtained, the history of the content recorded on each block is accumulated. For this reason, even if a large amount of teacher data is received in the occasional update of the learned model, the previously set various parameters, comparison data, and the like are not overwritten or lost. As a result, services using features common to the AI and the blockchain can be provided, and a seamless relationship can be formed. Further, since the information on the blockchain network 4c is open and decentralized, anyone who participates in the blockchain network 4c can access the data, and it is possible to prepare rich registered content information. .

  Further, according to the present embodiment, the storage unit 24 records the blocks connected by the block chains constructed on the block chain network 4c. For this reason, a large amount of data is accumulated in the distributed system. As a result, as compared with the conventional central management server, even if an unexpected situation such as a natural disaster occurs and is damaged, the data is kept stored. Furthermore, the risk of hacking can be eliminated and the resistance to data tampering can be kept high.

(Second to Sixth Embodiments: Configuration of Blockchain History Storage System 100)
Next, an example of the content distribution management device 1 of the blockchain history accumulation system 100 according to the second to sixth embodiments will be described with reference to FIGS.

  The difference between the second to sixth embodiments and the first embodiment is that the content distribution management device 1 is provided with a grouping unit 25, and the grouping unit 25 uses the result of each determination described later by the determination unit 27 and newly created Based on the attribute information attached to the information on the creation to be created and / or the extracted character strings, the information on the creation is grouped, and the blockchain unit 3 and the storage unit 24 are created by the grouping unit 25. The point is that a record is stored as a history in each block in the block chain for each group. Other points are the same as those of the above-described embodiment, and the description will be appropriately omitted.

(Second Embodiment: Configuration of Blockchain History Storage System 100)
FIG. 14 shows an example of a schematic diagram of a blockchain history accumulation system 100 according to the second embodiment.

  A user who intends to create a new creation performs, for example, a creation activity based on the information recorded in the block 36. At this point, information to be referred to is stored in the block 36, and is stored in association with a group of information such as "A", "B", "C", and the like.

  The grouping means 25, for example, from the information group of the block 36 to be referred to, or from the range of the information group (“A”, “B”, “C”, etc.) processed for the newly created creation, A specific portion of data and information relating to a solution concept previously learned is extracted in units of groups (for example, “A”, “C”, etc.), and the group units of the extracted specific portions are registered as new blocks 37. The specific portion is, for example, a designation related to registered content, and may be a title, a range, a period, a keyword, a degree of importance, or the like. The information group other than the specific portion may be stored in the block 37, for example, as an area different from the information group of the specific portion. Then, the block chain unit 3 distributes the new block 35 in the block chain network 4c as a new block 35 storing the specific part.

  In addition, the grouping unit 25 may, for example, select an information group (“A”, “B”, “C”) processed from the information group of the block 36 to be referred to or a newly created creation. )), Character strings extracted from specific areas, or specific tendency parts extracted from data or information on previously learned solution concepts in units of groups (for example, “A”, “B”, etc.) Then, the group unit of the extracted specific tendency part is registered as a new block 37. The specific tendency part is, for example, a specification related to registered content, and may be the number of registrations, the number of references, the number of remarks, the number of downloads, or the like. The information group other than the group unit of the information group having the specific tendency may be stored in the same block 37 as an area different from the information group of the specific tendency part, for example. Then, the block chain unit 3 distributes the new block 35 in the block chain network 4c as a new block 35 storing the specific part.

  Further, the grouping means 25 may, for example, select from among the information group of the block 36 to be referred to, or the information group (“A-1”, “A-2”, “A- 3)), a minimum part of data and information relating to a solution concept previously learned is extracted in units of a group (for example, “A-1”), and the extracted minimum group is extracted as a new block. Register as 37. The minimum part is, for example, a specification related to registered content, and may be the number of characters, the amount of information, a period, the number of cases, or the like. The information group other than the minimum part may be stored in the block 35, for example, as an area different from the information group of the minimum part. Then, the block chain unit 3 distributes the new block 37 in the block chain network 4c as a new block 37 storing the specific part.

  According to the present embodiment, the grouping unit 25 can perform a desired search or recording for each user. As a result, it is possible to reuse the results learned in the past, set various parameters, comparison data, weights, and the like to provide efficient idea creation support. Further, the grouping means 25 may prepare a large amount of data having a specific tendency and use it as teacher data for finding a new solution concept. Machine learning may also be performed using the teacher data to generate a learned model, and the generated learned model may be registered in the learned model management table 206 or the like.

(Third Embodiment: Configuration of Blockchain History Storage System 100)
Next, FIG. 15 shows an example of a schematic diagram of a blockchain history accumulation system 100 according to the third embodiment.

  A user who intends to create a new creation performs, for example, a creation activity based on information recorded in the block 35 from blocks in a block chain, and the record is newly created as a history. It is stored in the block 35 and distributed in the blockchain network 4c.

  The grouping means 25 generates, for example, a block 35 and a block 37 as one new block 38 from the list of blocks distributed in the block chain network 4c. In the block 38, specific blocks 35 and blocks 37 among the data and information related to the solution concept previously learned are grouped and stored, and the block chain unit 3 stores the specific blocks 35 and 37 as new blocks 35 storing the specific portions. Distributed in the blockchain network 4c.

  Each block is stored in a decentralized manner in the blockchain network 4c. For example, which block is to be a specific block is determined by the user according to the result of the determination by the determination unit 27. -You may make it select from the user terminal 5c etc. The grouping unit 25 determines a desired block based on the result of the selection, and blocks the target block stored in the database 6 (the management database 6a and the reference database 6b) by the block chain unit 3. Is also good.

  By the grouping by the grouping means 25 as described above, recording can be performed for each block which is a large unit. As a result, it is possible to reuse the results learned in the past, set various parameters, comparison data, weights, and the like to provide efficient idea creation support.

  According to the present embodiment, if there is data or a transaction indicating a minimum item or a minimum range based on the determination result of the determination unit 27, the storage unit 24 uses the minimum item or range. After the grouping, identification information is added, and related data is stored in a new block. This makes it possible to block, for example, only the summary portion or the problem or effect portion. As a result, the blockchain network 4c can be focused on a specific technology or event, and can be studied with a wider range of applications and developments.

(Fourth Embodiment: Configuration of Blockchain History Storage System 100)
Next, FIG. 16 shows an example of a schematic diagram of a blockchain history accumulation system 100 according to the third embodiment.

  The grouping unit 25 refers to, for example, the content management table 200, the content participant table 202, the content utilization history table 205, and the like by the determination unit 27, and according to the content history, the participants, registrants, And the degree of involvement of each subject such as the creator of the learning model is calculated. Then, corresponding registered content information and predetermined incentive information are given to the target person according to the calculated degree of involvement.

  The grouping unit 25, for example, the determination unit 27 determines data such as data on a previously learned solution concept stored in the management database 6a and information such as a history of utilization of each information, and determines whether or not the target person in each information table is involved. The degree is calculated as the degree of utilization of the registered content. A new block 35 is generated after the participation of the target person or the incentive according to the history of utilization is performed, and grouping is performed for each of the target person or the registered content information.

  Block 35 covers the degree of involvement and incentive information corresponding to a series of operations such as browsing, citation, production, addition, etc. recorded for creation of a new creation, together with data and information on the solution concept previously learned. Is given to registered contents, participants, etc. The block 37 is distributed by the block chain unit 3 as a block linked to the block 36 in the block chain network 4c.

  According to the present embodiment, the degree of involvement or the incentive information is, for example, based on the determination result of the determination unit 27, based on each target person, the registered content reference count, the cooperation count, and the reference time included in the storage target data. In accordance with one of the data, incentive information corresponding to the usage history is given to the target registered content and the target person. For this reason, the degree of involvement or the incentive can be given on the basis of numerical information such as the number of times of reference, the time, the degree of use, the degree of involvement, and the like, and the log. As a result, it is possible to calculate an accurate degree of involvement and an incentive, and to provide an appropriate incentive according to the registered content information of the target and the target person.

  Further, according to the present embodiment, the incentive information may be, for example, points or money. The incentive information may be stored in the management database 6a, and points or amounts corresponding to the incentive information may be paid to each subject according to a predetermined period, time, and number of times.

(Fifth Embodiment: Configuration of Blockchain History Storage System 100)
Next, FIG. 17 shows an example of a schematic diagram of a blockchain history accumulation system 100 according to the fifth embodiment.

  A user who wants to create a new creation performs a creation activity or the like based on, for example, keywords of the SNS information recorded in the block 36. At this point, the keyword or the like of the SNS information to be referred to is stored in the block 36, and is stored as, for example, “001”, “002”, “003”, etc. in association with other related information groups. .

  According to the determination result of the grouping unit 25, for example, the determination unit 27, a keyword or the like (“001”, “ 002 ”,“ 003 ”, etc.), keywords having a high appearance rate or high topicality are extracted and grouped (for example, keywords“ 001 ”and“ 002 ”are grouped into group“ A ”and keyword“ 001 ”). And “003” as a group “B”), and a group unit of keywords having a high appearance rate and a wide topicality is registered as a new block 37. Then, the block chain unit 3 distributes the grouped portions as a new block 37 in the block chain network 4c.

  By the determination and grouping by the grouping unit 25 and the determination unit 27 as described above, it is possible to record each block by a certain appearance rate or topicality. As a result, it is possible to reuse the results learned in the past, set various parameters, comparison data, weights, and the like to provide efficient idea creation support.

  According to the present embodiment, the grouping unit 25 extracts keywords and comment data extracted from the SNS information based on the determination of the determination unit 27. Therefore, a learned model can be generated by taking in the extracted information as teacher data. This makes it possible to match the related information recorded as registered content information based on the current information appearing on the SNS. For example, the registered content information and the real-time information It is possible to generate creative activities, learning models, etc. by combining and matching.

(Sixth Embodiment: Configuration of Blockchain History Storage System 100)
Next, FIG. 18 shows an example of a schematic diagram of a blockchain history accumulation system 100 according to the sixth embodiment.

  The grouping unit 25 determines the newly registered content or the like by the determination unit 27, and if it is determined that the harmful information is included, the permission unit 28 cancels the generation of the block 37 and does not include the harmful information. A block 38 is generated, and a block 39 is subsequently linked to the generated block 36.

  The grouping unit 25 performs grouping based on registered or registered content information based on the content determination management table 204 registered according to the result of the determination unit 27. The prohibition information is, for example, “slandering”, “discriminatory term”, “prohibition phrase”, “harmful image”, “virus code” and the like.

  When the prohibited information is included in the registered content information or the specific area of the registered content information, the grouping unit 25 determines whether the determination result is minor, such as an error, according to the determination result registered in the content determination management table 204. It is also possible to perform considerable correspondence and perform grouping. For example, if it is determined that the grouping unit 25 includes harmful information (for example, a virus program), the block chain unit 3 generates a new block 36, and the storage unit 24 stores the prohibited block created by the grouping unit 25. The information is registered as a new block 38 configured in a group unit from which information is excluded. Then, the block chain unit 3 disperses the new blocks 36 in the block chain network 4c as new blocks 36 excluding the grouped prohibition times.

  According to the present embodiment, the grouping unit 25 uses the determination unit 27 to determine the registration and mixing of the prohibition information. Therefore, if the stored registered content information includes data, transactions, and blocks determined to be harmful, the data, transactions, and blocks determined to be harmful are not connected. As a result, a blockchain network 4c that can safely reference data while avoiding harmful data becomes possible.

  In addition, the blockchain history accumulation recording method according to the present embodiment includes a mining unit that extracts a character string from information on a newly created creation, a reference character string acquired in advance, and each solution classified into two or more types. One or more solution concepts are searched based on three or more degrees of association between the concept and the solution character, based on three or more degrees of association between the reference character string corresponding to the character string extracted by the mining means and the solution concept. Search means, learning means for acquiring the relationship between the character string and the solution concept based on the newly provided information, and learning to reflect this in the degree of association to be referred to, and new character string in the learning means When the relationship between the information and the solution concept is obtained, these and the reflected degree of relevance are linked by a blockchain constructed in the network. Recording means for recording the click, recording step, each time of acquiring a relationship is realized by storing a history of the content recorded on the block.

  In the blockchain history accumulation recording method according to the present embodiment, parameters and related information necessary for making the previously acquired knowledge and solution concept work well are stored in a new block. Therefore, it is possible to protect data and information related to the learned model that has been learned before, and it is not lost due to catastrophic forgetting.

  Further, it is possible to realize a problem solution by a highly versatile and efficient neural network or deep learning.

  Although the embodiments of the present invention have been described, each embodiment is presented by way of example and is not intended to limit the scope of the invention. These new embodiments can be implemented in other various forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and their equivalents.

1: Content distribution management device 10: Housing 11: CPU
12: ROM
13: RAM
14: storage unit 15: I / F
16: I / F
17: I / F
18: Internal bus 19: Output part 100: Blockchain history storage system 2: Idea creation support learning part 20: Input part 21: Mining means 22: Search means 23: Learning means 24: Storage means 25: Grouping means 26: Transmission / reception means 27: determination means 28: permission means 200: content management table 201: related information table 202: content participant table 203: learned model update table 204: content determination management table 205: content utilization history table 206: learned model management table 3: Block chain unit 31: Block chain control unit 32: Metadata generation unit 33: Encryption unit 34: Electronic signature unit 35: Block 36: Block 37: Block 38: Block 39: Block 4: Public Communication network 4a: Internet 4b: Content distribution network 4c: Blockchain network 5: User terminal 5a: User terminal 5b: User terminal 5c: User terminal 6: Database 6a: Management database 6b: Reference database S110: Mining means S120: Search means S130: learning means S140: storage means

The blockchain history accumulation system according to the first invention includes a mining unit for extracting a character string from information on a newly created creation, a reference character string acquired in advance, and each of the solution concepts classified into two or more types. A search for searching for one or more solution concepts based on three or more levels of association between a reference character string corresponding to the character string extracted by the mining means and a solution concept, with reference to three or more levels of association. An idea creation support learning unit having means and learning means for acquiring a relationship between a character string and a solution concept based on newly provided information, and learning so as to reflect the relationship in the degree of association to be referred to. If, based on the newly attribute information attached to the information about the creations of creation and / or the extracted character string, the grouping of information on the creations A grouping means for performing, when acquiring the relationship between the new string and resolve concept in the learning means, with these, the association degree which is the reflected information regarding creations which are grouped by the grouping unit each Recording means for recording , in group units, blocks connected by a block chain constructed in a network, wherein the recording means accumulates a history of the contents recorded on the blocks each time the relationship is obtained. It is characterized by the following.

Block Chain history storage system according to the second invention, in the first invention, the grouping means, based on the character string extracted from a specific area, to perform the grouping, characterized by.

Block Chain history storage system according to the third invention, in the first invention, the grouping means, based on the history of the contents stored in each block, and performs the grouping.

Block Chain history storage system according to the fourth light, in the first invention, the grouping means, based on the attribute information and / or the extracted character topic information on social networks obtained based on the column, The above grouping is performed.

Block Chain history storage system according to a fifth invention, in the first invention, the grouping means, if it contains prohibited information to the character string extracted from a particular region, performs a grouping which eliminated the inhibition information, the The recording means records each block in the block chain in a group unit excluding the prohibition information created by the grouping means.

In the blockchain history accumulation system according to a sixth invention, in the first invention, the attribute information includes information on a participant involved in the creation of the creation and / or information on a degree of involvement of the participant. It is characterized by.

The blockchain history accumulation method according to the seventh invention includes an extraction step of extracting a character string from information on a newly created creation, a reference character string acquired in advance and each of the solution concepts classified into two or more types. A search for searching for one or more solution concepts based on three or more levels of association between a reference character string and a solution concept corresponding to the character string extracted in the above extraction step, with reference to the three or more levels of association. a step to obtain the relationship between the character string and solution concept based on the newly provided information, which the learning step of learning to reflect the association degree to be referred above relates creations of creation new based on the assigned attribute information and / or the extracted character string information, and grouping step of performing grouping of information related to the creative work, the When acquiring the relationship between the new string and resolve concept in learning step, with these, a degree of association above reflect the information on creations which are grouped by the grouping step on each group unit, the network A computer that executes a recording step of recording the history of the contents recorded on the block each time the relation is acquired , while recording the blocks in the blocks connected by the block chain constructed in the above.

According to the first invention to the sixth invention, the learning means acquires the relationship between the character string and the solution concept based on the newly provided information, and performs learning so as to reflect the relationship in the degree of association. Therefore, the solution concept and the reflected degree of association can be recorded in blocks connected by a block chain constructed in the network. As a result, information and data for extracting a solution concept learned previously can be protected, and the results learned in the past, various parameters, comparison data, weights, and the like can be reused to provide efficient idea creation support. It becomes possible.

In particular, according to the first to sixth inventions , the grouping means extracts attribute information and / or a character string added to information on a newly created creation. For this reason, it is possible to group information relating to creations, and to record each created group in each block in the block chain. This makes it possible to reuse the results learned in the past, set various parameters, comparison data, weights, etc., and to perform efficient idea creation support.

In particular, according to the second aspect , the grouping means performs grouping based on a character string extracted from a specific area. For this reason, character strings extracted from a specific area can be recorded in blocks in groups. This eliminates the need to check the information recorded in all blocks, and enables efficient idea creation support by reusing the results learned in the past, various parameters set, comparison data, weights, etc. Becomes

In particular, according to the third aspect , the grouping means performs grouping based on the history of the contents stored in each block. For this reason, the stored contents can be recorded in each block in the block chain on a group-by-group basis based on the degree of reference of the stored contents, the number of times of use, and the like. As a result, a distinguished group can be protected, and the results of learning in the past, various set parameters, comparison data, weights, and the like can be reused to support idea creation with an incentive.

In particular, according to the fourth aspect , the grouping means extracts topic information on the social network obtained based on the attribute information and / or the extracted character string. For this reason, it is possible to perform grouping in association with externally topical information. As a result, it is possible to reuse the results learned in the past, set various parameters, comparison data, weights, and the like, take into account the latest topics and trends, and provide efficient idea creation support. Furthermore, in addition to the document information, it can be taken in as teacher data with high freshness. This makes it possible to efficiently present a solution concept using a neural network or deep learning.

In particular, according to the fifth aspect , when the character string extracted from the specific area includes the prohibition information, the grouping unit performs the grouping excluding the prohibition information. For this reason, it is possible to provide protection by eliminating harmful information. As a result, information and data having no problem can be reused from the results learned in the past, various set parameters, comparison data, weights, and the like, and efficient idea creation support can be performed.

In particular, according to the sixth aspect , the attribute information includes information on a participant involved in creation of the creation and / or information on a degree of involvement of the participant. For this reason, it is possible to provide an incentive according to a creator or a provider of each creation or registered content. As a result, correspondence between old and new teacher data and the like becomes clear, and efficient idea creation support can be performed.

According to the seventh invention, the computer is, the learning step to obtain the relationship between the character string and solution concept based on the newly provided information, performs learning so as to reflect the association degree. In the grouping step, the grouping of the information on the creation is performed based on the attribute information attached to the information on the creation to be newly created and / or the extracted character string. For this reason, the solution concept, the reflected association degree, and the information on the creations grouped by the grouping step can be recorded for each group in blocks connected by a block chain constructed in the network. As a result, information and data for extracting a solution concept learned previously can be protected, and the results learned in the past, various parameters, comparison data, weights, and the like can be reused to provide efficient idea creation support. It becomes possible.

Claims (8)

Mining means for extracting character strings from information on newly created creations,
The reference character string and the solution concept according to the character string extracted by the mining means are referred to by referring to the degree of association of three or more stages between each reference character string acquired in advance and each of the solution concepts classified into two or more types. Searching means for searching for one or more solution concepts based on three or more degrees of association with
Learning means for acquiring a relationship between the character string and the solution concept based on the newly provided information, and learning to reflect this in the degree of association to be referred to;
Idea creation support learning department with
Recording means for recording the relationship between the new character string and the solution concept in the learning means and the reflected degree of association in blocks connected by a block chain constructed in the network With
The recording means accumulates a history of the contents recorded on the block each time the relation is acquired,
A blockchain history accumulation system characterized by the following. Grouping means for grouping the information on the creation based on the attribute information attached to the information on the creation to be newly created and / or the extracted character string,
The recording unit records in each block in the block chain in units of each group created by the grouping unit;
The blockchain history accumulation system according to claim 1, wherein: The grouping unit performs the grouping based on a character string extracted from a specific area;
The blockchain history accumulation system according to claim 2, wherein: The grouping means performs the grouping based on a history of the content stored in each of the blocks;
The blockchain history accumulation system according to claim 2, wherein: The grouping unit performs the grouping based on topic information on a social network acquired based on the attribute information and / or the extracted character string;
The blockchain history accumulation system according to claim 2, wherein: The grouping means, when a character string extracted from a specific area includes prohibition information, performs grouping excluding the prohibition information,
The recording unit records each block in the block chain in a group unit excluding the prohibition information created by the grouping unit;
The blockchain history accumulation system according to claim 2, wherein: The attribute information includes information on a participant involved in the creation of the creation and / or information on a degree of involvement of the participant;
The blockchain history accumulation system according to claim 2, wherein: An extraction step of extracting a character string from information on a newly created creation;
The reference character string and the solution concept corresponding to the character string extracted in the above-described extraction step are referred to by referring to three or more levels of association between each reference character string acquired in advance and each of the solution concepts classified into two or more types. A search step for searching for one or more solution concepts based on three or more degrees of association with
A learning step of acquiring a relationship between the character string and the solution concept based on the newly provided information, and learning to reflect this in the association degree to be referred to;
A recording step of recording the relationship between the new character string and the solution concept in the learning step and the reflected degree of relevance in a block connected by a block chain constructed in a network; And
The method according to claim 1, wherein the recording step stores a history of contents recorded on the block each time the relationship is acquired.

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