KR20190084145A - Artificial intelligence system for finding promising technologies using online patent auction system - Google Patents

Abstract

The present invention provides a system allowing an artificial intelligence system to discover a promising technology by using patent bidding information and completed sales information collected from an online patent auction system. The system comprises: an online patent auction system which outputs quantity information of tokens wagered by users, causal model information of patents with completed sales, and profiles of a user and a buyer; a knowledge reflecting unit which identifies an area of expertise of the user and the buyer based on the profiles; a natural science knowledge database which is an aggregate of numerous causal relationships extracted from natural science papers and natural science databases; a causal model generating unit which continuously combines millions of sets of causal models from causal relationships within the natural science knowledge databases; and a causal model suitability determination unit which judges and extracts only suitable causal models of the combined causal model sets.

Description

{Artificial intelligence system for finding promising technologies using online patent auction system}

The present invention relates to an artificial intelligence system capable of finding promising technologies using an online patent auction system.

Modern knowledge - based industrial society recognizes the competitiveness of national science and technology as a source of national competitiveness because knowledge and information influence the competitiveness of the country.

As a result, the number of publications of scientific and technological literatures has been steadily increasing due to the intensive research and development by eliciting and selecting core technologies and research tasks for survival of the countries of the world in the future. In addition, Is also increasing.

However, the qualitative method as a way to find promising research areas that will lead the future national science and technology is problematic in that objectivity is not obtained when experts intuitively get promising technologies with personal experiences, There is a problem that it takes a lot of time and cost to reflect opinions.

In addition, it takes a lot of cost and time to analyze the cost of data acquisition, analysis, and long-term analysis to derive citation-related papers and patents into promising technologies (research areas) using quantitative methods using patent papers There is a problem that it is administered.

In order to derive promising technologies by existing methods, at least 4 months work time is required, and it is difficult to select promising technologies by reflecting the change of situation in the last 2 months in real time.

Prior art relating to the present invention is disclosed in Korean Patent Laid-Open No. 10-2010-0132289 (Promising Commercialized Item Discovery System and Method), Korean Patent No. 10-1528481 (Extraction of Element Function, Classification and Functional Relation Analysis , And Korean Patent No. 10-1259417 (Hybrid type promising technology extraction system and method using collective intelligence), and the like.

Disclosure of Invention Technical Problem [8] The present invention has been proposed in order to solve the above-mentioned problems, and provides a system for allowing an artificial intelligence system to discover promising technologies by using patent bidding information and bargaining information collected in an online patent auction system It has its purpose.

In order to accomplish the above object, the artificial intelligence system for discovering a promising technology using an online patent auction system according to a preferred embodiment of the present invention is characterized in that a quantity information of tokens bet on users, causal model information of traded patents, An online patent auction system that outputs the buyer's profile; A knowledge reflector for recognizing the expert field of the user and the buyer based on the profile; A database of natural science knowledge, which is the aggregate of a number of causal relationships extracted from natural science papers and natural science databases; A causal model generating unit continuously combining millions of causal model sets from causal relations within the natural science knowledge database; And a causality model suitability judging unit which judges and eliminates only appropriate causality models among the combined causality model sets.

According to the present invention having such a configuration, it is possible to easily discover a promising technology by using the patent bidding information and the bargaining information collected in the online patent auction system.

1 is a configuration diagram of a promising artificial intelligence system using an online patent auction system according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating an operation of the artificial intelligence system for discovering promising technologies using an online patent auction system according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In order to facilitate the understanding of the present invention, the same reference numerals are used for the same constituent elements in the drawings and redundant explanations for the same constituent elements are omitted.

FIG. 1 is a block diagram of an artificial intelligence system for discovering a promising technology using an online patent auction system according to an embodiment of the present invention. FIG. 2 is a block diagram of an artificial intelligence system for discovering promising technologies using an online patent auction system according to an embodiment of the present invention. Fig.

1 and 2, users can view the titles of the patents displayed in the online patent auction system and request the auction system to view the summary information of the patent. When the system is requested to browse the patent summary information, the system displays the keywords summarizing the description of the patent according to the information structure based on the causal model, and displays related papers in a list format. At this time, the causal model is a class consisting of 10 types of instances (objects) already defined, and causality between each instance (object) within the causal model (class) is already fixed and defined. Values indexed within 10 instances may be different. One causal model (class) is suitable for summarizing one product system. The causal models for each patent are already stored when the patent information is initially stored in the database. The list of papers related to each patent is also stored together when the patent information is initially stored in the database.

Users may be interested in the patent by betting a certain number of tokens they have on the patent (a willingness to bid). Users can pre-purchase available tokens in this online patent auction system.

Once the sale is established, a certain percentage of the platform proceeds of the sale amount will be reimbursed to users who have bet on the patent. Divide a certain amount of money (total amount) by the total number of tokens bet, and distribute them to betting users.

If a patent is traded on a regular basis, the betting will not be terminated. If the same patent is traded again for the same patent, the users who have received the compensation before can receive the reward again. However, if the transaction is a positive or exclusive license transaction, the betting ends when the transaction ends. The patents displayed in this online auction system display the high-betting patents at the top.

Users can also purchase licenses for the displayed patents. The license price of the patent is entered from the manager or the seller who registers the patent information. When registering patent information in the system, you can choose the type of form in which the patent is sold (amniotic fluid, exclusive license, normal license, etc.). It is also possible to set the desired selling price as a range. When registering patent information in the system, you can decide whether or not to display the desired price to sell. When the user indicates a willingness to buy a particular patent, he can also set the desired price range. The buyer must pay the maximum amount of the buyer's desired price range before final transfer of the buyer's intention. The buyer's bid price range is displayed at the top of the buyer's price range, which is the highest price range, and is provided to the seller. The seller may indicate an intention to sell to the displayed buyers. If the buyer who wishes to sell the offer is a purchaser and the exclusive license, the seller may only sell to one buyer. In the case of other commercial licenses, the intention to sell may be transmitted to a plurality of buyers. If the buyer finally approves the sale, a final approval message is sent to the seller, and the sale is automatically established if the seller has final approval of the sale or refusal within five days. PURPOSE OR DEDICATED RIGHTS Patents sold do no longer display on this online patent auction system.

This online patent auction system transmits the quantity information of tokens wagered by users, causal and model information of established patents, users (betting users) and buyer's profile to the technology discovery system. The information summarizing the description of the patent is displayed according to the information structure based on the causal model as described above, and the related papers are displayed in a list format. The keywords indexed in the causal model are vocabularies stored in the vocabulary database within the technology discovery system. The Knowledge Reflection Department of the technology discovery system grasps the expertise of the subject (user and buyer) based on the profile (the user and the buyer profile, and the "profile" in the future). Depending on the number of tokens, causal relationships within the patent causation model are weighted. And transmits the weight to the knowledge reflecting unit. The technology discovery system has a natural science knowledge database inside the system. The Natural Science Knowledge Database is the aggregate (ontology) of a number of causal relationships extracted from natural science papers and natural science databases. The causal modeling department of the technology discovery system continuously combines millions of causal model sets from causal relationships within the natural science knowledge database. The Causality and Models Appropriateness Determination Department within the technology discovery system judges and decides only the causal models of the set of combined causal models by themselves. The knowledge reflection department plays a role of complementing the rule used when judging the conformity. The number (or percentage) of tokens granted to a patent is weighted and assigned to the causal relationships that make up the causal model. In addition, the number (or percentage) of tokens assigned to a patent is weighted, and the corresponding vocabulary database corresponding to the user field information (professional fields such as biotechnology, genetic engineering, and mechanical engineering) And the causal model. In order to construct a new causal model, the technology discovery system assumes a specific field and constructs a causal model of the invention. At this time, it can be supplemented by the weight given to the causal relations and the weight given to the field-specific vocabulary database .

The causal models derived from the technology discovery system are displayed on the online technology recommendation system. Users of the online technology recommendation system can write feedback on the causal models generated through the input unit.

The system consists of (1) 'server', (2) 'online patent auction system', (3) 'technology discovery system', and (4) 'online technology recommendation system'.

The server comprises (1-1) a patent database, (1-2) a user database, (1-2) a control system, and (1-3) a communication system.

The online patent auction system consists of (2-1) an input unit and (2-2) a terminal communication unit.

(3-1) Natural Science Knowledge Database, (3-2) Vocabulary Database, (3-3) Knowledge Reflection Department, (3-4) Causality Model Conformity Assessment Department, (3-5) Causality Model And a generating unit.

The online technology recommendation system comprises (4-1) an input unit and (4-2) a terminal communication unit.

Claims (1)

An online patent auction system for outputting quantity information of tokens bet on users, causal model information of established patents, user and buyer profile;
A knowledge reflector for recognizing the expert field of the user and the buyer based on the profile;
A database of natural science knowledge, which is the aggregate of a number of causal relationships extracted from natural science papers and natural science databases;
A causal model generating unit continuously combining millions of causal model sets from causal relations within the natural science knowledge database; And
And a causality model suitability judging unit for judging and solving only suitable causal models among the combined causal model sets.

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