JP2018169854A - Patent requirement propriety prediction device and patent requirement propriety prediction program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To predict propriety of patent requirements so as to match a judgement practical business, and effectively reduce a load for preparing application documents.SOLUTION: A patent requirement propriety prediction server includes: summary data storage means which stores summary data that is data where the invention to be predicted is described in one or more claims and is extracted from proposal document data constituting a proposal document including detailed description where the invention to be predicted is described and indicates terms capable of specifying the summary of the invention to be predicted; specified prior art data storage means which stores specified prior art data; an advance prediction processing section which generates invention specified advance prediction data on advance requirement propriety of the invention to be predicted by a relationship with the specified prior art invention; and patent requirement propriety prediction processing means having a prediction result file generation section which generates a prediction result file indicating the patent requirement propriety of the invention to be predicted using the invention specified advance prediction data.SELECTED DRAWING: Figure 4

Description

  The present invention relates to a patent requirement suitability prediction apparatus and a patent requirement suitability prediction program for predicting suitability of whether or not an invention described in a draft document of an application document conforms to patent requirements such as novelty and inventive step.

  Conventionally, prediction is performed in various scenes such as prediction of power demand and stock price, prediction of purchase of goods, and prediction of future price of real estate, and many apparatuses and methods have been proposed. A technology for predicting patentability (also referred to as patentability or patentability) relating to an applied invention has also been proposed with respect to a right-giving operation for applying a patent for an invention (see, for example, Patent Document 1). Patent Document 1 describes the following patentability prediction apparatus. This device obtains a patent application (notified application) that has been notified of examination results from the patent database, detects the amount of information about the claims of the already-notified application, and the number of similar prior applications, and The target regression analysis is executed, and the predicted value of the patentability of the application for which the examination result is not notified is calculated according to the registration prediction formula calculated from these.

  Conventionally, in addition to prediction of patentability, devices and methods for evaluating the patentability and quality of an invention, the value of patent applications and patent rights have also been proposed (for example, Patent Documents 2, 3, 4, and 4). 5 and 6).

JP 2009-238074 A JP2015-207194A JP 2000-181966 A JP 2000-132606 A Japanese Patent Laying-Open No. 2015-187883 JP 2007-108803 A

  As described above, according to the prior art, it is possible to predict the patentability and evaluate the patent right for a patent application.

  However, in the above-described conventional technology, for example, the patentability prediction apparatus described in Patent Document 1, the patentability prediction is calculated based on information such as the amount of information about the claims of a previously notified application and the number of similar prior applications. This is done according to the registration preview formula. This prediction was made based on a statistical correlation between the breadth of claims, the density of technical fields, and patentability, and was not based on patent law or patent / utility model examination standards. Therefore, the patentability prediction apparatus described in Patent Document 1 has a problem that there is a high possibility that a prediction result in accordance with the patent practice cannot be obtained.

  By the way, when applying for a patent application, a prior art search is performed on the invention to be applied, and the results are compared with the prior art described in the documents found as a result. It is desirable to prepare the application documents by clarifying the differences between the invention to be attempted and the prior art.

  However, the examination of patent requirements is performed by the JPO examiner in accordance with the Patent Law and Patent / Utility Model Examination Standard (Patent Law Article 47). It is essential to understand patent law and patent / utility model examination standards. In particular, in order to prepare application documents so as to satisfy the requirements (inventive step) of Article 29, Paragraph 2 of the Patent Law, A sufficient understanding of patent / utility model examination standards and more experienced experience and skills are required. Therefore, it is very time consuming and time consuming to prepare the application documents so as to satisfy the patent requirements.

  In this regard, since the patentability of the invention is predicted by the prior art described in Patent Document 1, it may be possible to reduce the burden required for preparing the application documents by utilizing this.

  However, in the prior art described in Patent Document 1, there is a possibility that the prediction result may not be suitable for examination practice. Therefore, even if such a prediction result is used, there is a possibility that the reduction of the preparation burden of the application documents may not be effective. is there.

  Therefore, it is hoped that the prediction of the propriety of the patent requirements will be made with the content that suits the examination practice so that the burden of preparing the application documents that satisfy the patent requirements will be effective. It was rare.

  The present invention has been made to solve the above-mentioned problems, and the prediction regarding the propriety of the patent requirement is performed with the content suitable for the examination practice, and the patent requirement propriety prediction device capable of effectively reducing the preparation burden of the application documents and It is an object of the present invention to provide a patent requirement conformity prediction program.

  In order to solve the above-mentioned problems, the present invention includes a detailed description in which the invention to be predicted that is the object to be predicted for the suitability of patent requirements is described in one or more claims, and the invention to be predicted is described. Data extracted from proposed document data constituting the document, term data indicating terms that can specify the gist of the invention to be predicted, and at least feature part data and details extracted from feature parts of each claim Summary data storage means for storing data including problem data extracted from the problem part included in the description as summary data, and designated prior art data constituting the designated prior art document describing the designated prior art invention Invention-designated inventive step prediction data relating to the suitability of the inventive step to be predicted based on the relationship between the designated prior art data storage means to be stored and the designated prior art invention Patent requirement suitability prediction processing means comprising: an inventive step prediction processing unit to generate; and a prediction result file generation unit that generates a prediction result file indicating the patent requirement suitability of the invention to be predicted using the invention-designated inventive step prediction data. The inventive step predictive processing unit has a document classification unit for classifying document vectors, and the document classification unit is input by machine learning using a plurality of training data including a learning document vector and a teacher vector. The abstract movement vector is classified into whether or not it conforms to the requirement of inventive step, and the requirement conformity document vector is output according to the classification result, and the abstract movement vector is defined in each claim. Characterized by a patent requirement conformity prediction device which is a vector corresponding to a difference between a gist vector of a prediction target invention according to the reference prior art data and a citation candidate vector according to designated prior art data

  Further, the present invention provides a draft document that includes a detailed description in which one or two or more claims include a prediction target invention that is a prediction target of suitability for patent requirements and includes the prediction target invention. It is data extracted from document data, and is term data indicating terms that can specify the gist of the invention to be predicted, and is included in at least the feature part data extracted from the feature part of each claim and the detailed description Abstract data storage means for storing data including problem data extracted from the problem part as abstract data, and specified prior art for storing specified prior art data constituting the specified prior art document describing the specified prior art invention Using the data storage means and the summary data stored in the summary data storage means, the public gazette data, which is the electronic data of the public gazette, is searched for and verified. According to the result, a novelty prediction processing unit that generates novelty prediction data indicating whether the novelty requirement of the prediction target invention is appropriate, and an inventive step that generates inventive step prediction data indicating whether the inventive step is appropriate Patent requirement adequacy prediction processing means comprising: a prediction processing unit; and a prediction result file generation unit that generates a prediction result file indicating whether the patent requirement of the invention to be predicted is appropriate using the novelty prediction data and the inventive step prediction data. The inventive step prediction processing unit includes a main citation invention search unit that searches for a main citation invention that is closest to the prediction target invention among prior art inventions specified by the publication gazette data, and a document classification unit that classifies document vectors. The main citation invention search unit uses the feature part data and the problem data of each claim of the summary data stored in the summary data storage means as the main search document data. A concept search is performed on public gazette data. As a result of the concept search, a plurality of documents including the most similar document with the highest similarity in descending order of similarity are extracted as similar documents, and each similar document is extracted. The document categorizing unit sets the input abstract movement vector as a requirement for inventive step by machine learning using a plurality of training data including a learning document vector and a teacher vector. It is constructed so that it can be classified into a class that is very unlikely to be matched, a high class, or a class that meets the requirements of the inventive step, and a requirement pass / fail document vector corresponding to the classification result is output, and its summary movement vector Is a plurality of vectors according to the difference between the gist vector of the invention to be predicted according to each claim and each citation candidate vector according to each similar document. The suitability prediction processing means calculates a non-conformance rate calculation that calculates a non-conformance rate indicating a possibility that the invention to be predicted does not conform to the inventive step requirement according to a plurality of requirement suitability document vectors output from the document classification unit. And the designated prior art data storage means stores the specified prior art data, the inventive step predictive processing unit uses the inventive step of the prediction target invention in relation to the designated prior art invention instead of the inventive step predictive data. A patent requirement suitability prediction apparatus further comprising a prediction processing control unit that controls to generate invention specified inventive step prediction data relating to the suitability of requirements.

  In the case of the above-mentioned patent requirement conformity prediction apparatus, the document classification unit uses training data as a combination of a learning document vector as a first learning document vector and a teacher vector as a vector indicating that there is a reason for rejection of novelty and inventive step, The combination of the document vector is the second learning document vector and the teacher vector is a vector indicating that there is a reason for refusal of inventive step and there is no reason for refusal of novelty, and the learning document vector is the third learning document vector and the teacher vector is inventive. Is used in combination with a vector indicating that there is no reason for refusal, and the first learning document vector is an application for which a notice of reason for refusal has been issued among already published applications, The reason for refusal of a novelty / inventive step rejection application was pointed out by citing the same document and the reason for refusal without novelty and inventive step was pointed out A first moving document vector corresponding to a difference between a document vector corresponding to a request and a first cited document vector corresponding to a first main cited publication which is a document cited for the reason for refusal The second learning document vector is an application for which a notice of reason for refusal has been issued in the published application, and the reason for refusal of novelty is not pointed out in the notice of reason for refusal, but the reason for refusal of inventive step is pointed out A document vector corresponding to the claim in which the reason for refusal of the inventive step rejection application was pointed out, and a second corresponding to the second main cited publication cited as the main publication for the reason for refusal A second moving document vector corresponding to a difference from the cited document vector, and the third learning document vector is a non-rejection application in which a patent decision is issued without a notice of reason for refusal being issued in the published application or Reason for refusal issued A document vector corresponding to claim 1 of the non-inventive refusal application for which the reason for refusal of refusal was not pointed out in the notice of reasons for refusal, and an application without refusal or an application without inventive step refusal As a result of the concept search, the patent requirement suitability prediction apparatus which is the third moving document vector obtained from the difference from the non-cited document vector corresponding to the learning most similar document having the highest similarity can be obtained. .

  Further, the present invention provides a draft document that includes a detailed description in which one or two or more claims include a prediction target invention that is a prediction target of whether or not patent requirements are appropriate, and the prediction target invention is described. It is data extracted from document data, and is term data indicating terms that can specify the gist of the invention to be predicted, and is included in at least the feature part data extracted from the feature part of each claim and the detailed description Abstract data storage means for storing data including assignment data extracted from the assignment portion as abstract data, and abstract data stored in the abstract data storage means, using the abstract data stored in the abstract data, A novelty prediction processing unit that generates novelty prediction data indicating whether or not the novelty requirement of the prediction target invention is appropriate according to the search result, and the progress of the prediction target invention A prediction result generation unit that generates the inventive step prediction data indicating whether or not the requirements of the invention are appropriate, and a prediction result file that generates the prediction result file indicating the suitability of the patent requirements of the invention to be predicted using the novelty prediction data and the inventive step prediction data A patent requirement suitability prediction processing unit having a file generation unit, and a prediction result storage unit for storing a prediction result file generated by the prediction result file generation unit. Among the specified prior art inventions, the invention includes a main citation invention search unit that searches for a main citation invention that is closest to the prediction target invention, and a document classification unit that classifies document vectors. Of the summary data stored in the data storage means, the feature part data and the subject data of each claim are used as main search document data, and the publication publication data is targeted. As a result of the concept search, a plurality of documents including the most similar document having the highest similarity in descending order of similarity are extracted as similar documents, and the main cited invention is disclosed for each similar document. The document classification unit is highly likely that the input abstract movement vector does not meet the inventive step requirement by machine learning using a plurality of training data including a learning document vector and a teacher vector. It is constructed to classify into either a high class, a high class, or a class that meets the requirements of the inventive step and output a requirement conformity document vector according to the classification result, and the summary movement vector is included in each claim. Provided is a patent requirement propriety prediction device which is a plurality of vectors corresponding to differences between a gist vector of a corresponding invention to be predicted and a citation candidate vector corresponding to each similar document.

  Furthermore, the present invention is a patent requirement suitability prediction program for causing a computer to function as a patent requirement suitability prediction device, wherein the invention to be predicted is one or more claims. Is a data extracted from the draft document data constituting the draft document including the detailed explanation in which the invention to be predicted is described, and indicates a term that can identify the gist of the invention to be predicted Summary data storage control means for storing, as summary data, data including at least feature part data extracted from the feature part of each claim and problem data extracted from the problem part included in the detailed description; Designated prior art data for storing designated prior art data constituting the designated prior art document describing the designated prior art invention An inventive step prediction processing unit for generating invention-designated inventive step prediction data relating to the suitability of the inventive step to be predicted based on the relationship between the storage control means and the designated prior art invention, and prediction using the invention-designated inventive step prediction data A prediction result file generation unit that generates a prediction result file indicating whether or not the patent requirement of the subject invention is generated, and a document classification unit that classifies the document vectors by the inventive step prediction processing unit. The document classification unit classifies the input abstract movement vector as to whether it meets the requirement of inventive step by machine learning using a plurality of training data including a learning document vector and a teacher vector. The document is constructed so as to output a requirement conformity document vector corresponding to the classification result, and the summary movement vector is the summary vector of the invention to be predicted according to each claim. And torr, providing a patent requirements appropriateness prediction program is a vector corresponding to the difference between the reference candidate vector corresponding to the designated prior art data.

  As described above in detail, according to the present invention, the prediction regarding the propriety of the patent requirement is performed with the content suitable for the examination practice, and the patent requirement propriety prediction device and the patent requirement propriety that can effectively reduce the preparation burden of the application documents. A prediction program is obtained.

1 is a system configuration diagram of a patent requirement suitability prediction system including a patent requirement suitability prediction server according to a first embodiment of the present invention. It is a block diagram which mainly shows the internal structure of a patent requirement suitability prediction server. It is a block diagram which mainly shows the internal structure of a user terminal device. It is a functional block diagram which shows the main structures of the patent requirement suitability prediction server which concerns on the 1st Embodiment of this invention. It is a functional block diagram which shows the main structures of an abstract data extraction part. It is a functional block diagram which shows the main structures of the patent requirement suitability prediction process part which concerns on the 1st Embodiment of this invention. It is a functional block diagram which shows the main structures of an input vector production | generation part. It is a figure which shows an example of the record layout of a summary data storage part. It is a figure which shows an example of the record layout of CT data storage part. It is a figure which shows an example of the record layout of a prediction result memory | storage part. It is a figure which shows an example of the network structure of a machine learning part. It is a flowchart which shows an example of the operation | movement procedure of a patent requirement suitability prediction process. It is a flowchart which shows an example of the operation | movement procedure of a patent requirement suitability prediction routine. It is a flowchart which shows an example of the operation | movement procedure of a novelty and expansion prior application prediction routine. It is a flowchart which shows an example of the operation | movement procedure of an expansion prior application prediction routine. It is a flowchart which shows an example of the operation | movement procedure of an inventive step prediction routine. It is a flowchart which shows an example of a single independent term routine. It is a flowchart which shows an example of the operation | movement procedure of a multiple independent term routine. It is a flowchart which shows an example of an independent term search process. It is a flowchart which shows an example of main citation invention search processing. It is a flowchart which shows an example of a subcitation invention search process. It is a flowchart which shows an example of a dependent term search process. It is the figure which showed typically the relationship between the patent application used as examination object, and several patent publications. It is a figure which shows an example of the record layout of an independent item table. It is a figure which shows an example of a patent requirement suitability prediction list. It is a functional block diagram which shows the main structures of the patent requirement suitability prediction server which concerns on the 2nd Embodiment of this invention. It is a functional block diagram which shows the main structures of the patent requirement suitability prediction process part which concerns on the 2nd Embodiment of this invention. It is a functional block diagram which similarly shows the main structures of an input vector production | generation part. It is a figure which similarly shows an example of the record layout of a prediction result memory | storage part. It is a figure which similarly shows an example of a patent requirement suitability prediction list. It is a functional block diagram which shows the main structures of the patent requirement suitability prediction process part which concerns on a modification. It is a flowchart which shows an example of the operation | movement procedure of a patent requirement adequacy prediction routine without a search. It is a flowchart which shows an example of the operation | movement procedure of a search-less progress prediction routine. It is a flowchart which shows an example of a single independent term routine without a search. It is a flowchart which shows an example of the operation | movement procedure of a search independent multiple independent term routine.

  Embodiments of the present invention will be described below with reference to the drawings. In addition, the same code | symbol is used for the same element and the overlapping description is abbreviate | omitted.

First embodiment
(Overall structure of patent requirement conformity prediction system)
First, the configuration of the patent requirement suitability prediction system 1 including the patent requirement suitability prediction server 10 according to the first embodiment of the present invention will be described.

  FIG. 1 is a system configuration diagram of a patent requirement suitability prediction system 1. As shown in FIG. 1, the patent requirement suitability prediction system 1 has a patent requirement suitability prediction server 10 and a plurality of user terminal devices 30 (in FIG. 1, fixed terminal devices 30A, 30B, and 30C) operated by the user. These are connected to each other via the Internet N1.

  The patent requirement suitability prediction server 10 performs data processing according to the patent requirement suitability prediction program. The patent requirement suitability prediction server 10 provides a patent requirement (in this embodiment, novelty (Patent Act Article 29, Paragraph 1, Item 3), an extended prior application (Patent Act Article 29 2) and the inventive step (Patent Act Article 29, Paragraph 2)). The user terminal device 30 receives or transmits data with the patent requirement suitability prediction server 10.

  In the patent requirement suitability prediction system 1, the invention that the user intends to apply for is subject to the prediction of whether or not the patent requirement is met (patent requirement suitability), and therefore corresponds to the prediction target invention according to the present embodiment. . Then, the patent requirement suitability prediction server 10 predicts the suitability of the patent requirement for the prediction target invention in two types of prediction modes (either a mode with search or a mode without search described later). In any mode, predict whether the reason for refusal of violating the inventive step is high or low by using an artificial intelligence program that has performed machine learning with multiple training data based on examination results, and predict the suitability of patent requirements. Is done. Since the patent requirement conformity prediction server 10 predicts the suitability of the patent requirement with the content suitable for the examination practice, the preparation burden of the application documents can be effectively reduced.

  Here, the search mode refers to a search for public gazette data (main citation invention search and sub-citation invention search described later), which is electronic data of a public gazette (mainly a published patent gazette), with respect to the prediction target invention. In this mode, the propriety of patent requirements is predicted according to the result. The no-search mode is a mode in which the propriety of patent requirements is predicted based on the relationship with the designated prior art invention described later without searching the publication gazette data for the prediction target invention.

(Configuration of Patent Requirements Compliance Predicting Server 10)
Next, the configuration of the patent requirement suitability prediction server 10 will be described with reference to FIG. FIG. 2 is a block diagram centering on the internal configuration of the patent requirement suitability prediction server 10. The patent requirement propriety prediction server 10 is a server that is operated by a specialized business operator that provides a service related to prediction of patent requirement propriety of the invention to be predicted.

  The patent requirement conformity prediction server 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, and a RAM (Random Access Memory) 13. The CPU 11 operates in accordance with a program stored in the ROM 12 and inputs input data obtained by operation input of the keyboard 19 and the mouse 20 via the KBC (Key board controller) 17 via the main bus 19A. Input / output of signals to / from other components is performed to control the operation of the patent requirement conformity prediction server 10 as a whole. The CPU 11 follows a later-described patent requirement suitability prediction program, a later-described draft sentence data generation unit 101, a gist data extraction unit 102, a patent requirement suitability prediction processing unit 103, a target gazette extraction unit 104, a prediction result edit processing unit 105, a designated preceding The operation as the technical data generation unit 106 is performed. The ROM 12 stores a control program executed by the CPU 11 such as a patent requirement suitability prediction program and permanent data. The RAM 13 stores data and programs used when the CPU 11 operates.

  In addition, the patent requirement compliance prediction server 10 includes a hard disk drive (HDD) 14, a communication control unit 15, a communication processing unit 16, and a video controller 18.

  The hard disk device 14 is formed with various storage units or DBs (database) shown in FIG. 4 and other storage units or DBs necessary for executing the patent requirement suitability prediction program. The hard disk device 14 includes a designated prior art data storage unit 151, a prediction target transaction storage unit (prediction target TR storage unit) 152, a gist data storage unit 153, a claim tree data storage unit 154, and a target publication storage unit 155. And a prediction result file storage unit 156 is formed. Each storage unit or DB will be described later.

  The communication control unit 15 operates in accordance with an instruction from the CPU 11 to control connection and disconnection of a line for performing communication with the user terminal device 30 and a patent office server (not shown). The communication processing unit 16 operates according to an instruction from the communication control unit 15 and executes data transmission / reception performed via the Internet N1.

  The video controller 18 controls image display on a display device (not shown) to display a screen used for various settings.

  The various storage units or DBs of the hard disk device 14 will be described as follows. The designated prior art data storage unit 151 stores data (for example, a member ID) necessary for user identification transmitted from the user terminal device 30 and a designated prior art document in which the designated prior art invention designated by the user is described. The designated prior art data to be configured is stored. The designated prior art invention is a prior art invention designated by the user in the prediction of patent requirement suitability by the patent requirement suitability prediction server 10, for example, the prior art described in the published patent publication found by the user's own search Corresponds to the invention.

  The case where the designated prior art data is stored in the designated prior art data storage unit 151 corresponds to the case where the user designates the prior art invention. In this case, in the no-search mode, the suitability of the inventive step to be predicted is predicted in relation to the designated prior art invention (whether there is an inventive step in view of the designated prior art invention).

  The prediction target TR storage unit 152 stores the plan document data generated by the plan sentence data generation unit 101 (electronic data constituting a plan document described later). The public gazette DB 150 stores the electronic data of the published patent gazette as public gazette data. As the public gazette DB 150, FIG. 4 assumes a database of a patent information platform (J-PlatPat) operated by the industrial property information / training hall or a database storing electronic data downloaded therefrom. The latter database can be stored in a server (not shown) or may be stored in the HDD 14.

  A draft document is a document in which the invention to be predicted is described, and the invention to be predicted is one or more claims (in this embodiment, it is assumed that the invention is described in an independent form). , Which may be a subordinate form) (a document corresponding to the claims attached to the application) and a part where the invention to be predicted is described in detail (in this embodiment, “details”) And a document corresponding to “Detailed Description of the Invention” in the specification attached to the application. The “detailed description” includes at least a portion where the name of the prediction target invention is described (a portion corresponding to the “name of the invention”) and a problem portion (the problem to be solved by the prediction target invention). In the present embodiment, a portion corresponding to “a mode for carrying out the invention” and a technical field are included. Part is included. The draft document data is transmitted from the user terminal device 30 to the patent requirement conformity prediction server 10 by encrypted communication (for example, encrypted communication using SSL).

  The summary data storage unit 153 stores summary data extracted and generated by the summary data extraction unit 102. The summary data is term data indicating terms that can specify the summary of the invention to be predicted, and includes at least feature portion data and task data described later.

  For example, as shown in FIG. 8, the abstract data storage unit 153 stores a record having a data type area 153a, an item number area 153b, and a term storage unit 153c. The data type area 153a stores data (referred to as “data type”) indicating which part of the draft document the data stored in each record is. In the present embodiment, four types, “C”, “P”, “T”, and “D”, are set as data types. “C” indicates data of claims, “P” indicates data of a problem part, “T” indicates data of a technical field part, and “D” indicates data of an embodiment part of the invention. The number of the claim is stored in the item number area 153b. The term storage unit 153c has a term area 153c1, a development degree area 153c2, and an essential flag area 153c3. In FIG. 8, 15 combinations of these are prepared, but there may be more or less than 15 combinations. In the term area 153c1, the development degree area 153c2, and the essential flag area 153c3, terms used for specifying the gist, a later-described development degree (Ed), and an essential flag (Ef) are stored.

  In FIG. 8, as an example, summary data (upper half data) when the invention described in Japanese Patent Application Laid-Open No. 2008-62282 is the invention to be predicted and described in Japanese Patent Application Laid-Open No. 2011-186735. Summary data (lower half data) in the case where the present invention is the invention to be predicted is described. The former is an example when there is one claim (independent claim) described in an independent form, and the latter is an example when there are a plurality of independent claims.

  The claim tree data storage unit (CT data storage unit) 154 stores claim tree data (also referred to as claim tree data or CT data) to be described later. In the CT data storage unit 154, for example, as shown in FIG. 9A, a record including an independent division area 154a, a number area 154b, a MAX division area 154c, a dependent item area 154d, and a search flag area 154e. It is remembered.

  In the independent division area 154a, each claim described in the draft document is either a claim described in an independent form (independent claim) or a claim described in a dependent form (dependent claim) An independent section (independent term is space, dependent term is “D”) is stored. In the number area 154b, the number of each claim (claim number) is stored. The MAX division area 154c stores a MAX division. In the MAX section, “M” is set in the claim with the largest number (maximum dependent claim) when there are a plurality of dependent claims that refer to the same independent term, and a space is set in addition to that.

  FIG. 9A shows CT data when the invention described in Japanese Patent Application Laid-Open No. 2008-62282 is the invention to be predicted. Since it is the maximum dependent term, “M” is set in the MAX section of the record whose claim number is “9”, and a space is set in the MAX section of the other claim numbers. FIG. 9B shows CT data when the invention described in Japanese Patent Application Laid-Open No. 2011-186735 is the invention to be predicted. In this publication, claims 1 and 6 are shown. Is an independent term, and claims 5 and 7 are the maximum dependent claims, so the record number of the claim number is "5", "M" in the MAX section of the record of "7", and space in the other MAX sections Is set.

  In the dependent claim area 154d, the number of the claim cited by the dependent claim is stored. The search flag area 154e stores a search flag, that is, a classification indicating whether or not a later-described main citation invention search has been executed. A space indicates before execution of the main cited invention search, and “9” indicates execution completed.

  The target publication storage unit 155 stores public gazette data (search target publication data) to be searched for the main citation invention search and the sub-citation invention search. The prediction result storage unit 156 stores a prediction result file described later as shown in FIG.

  Next, the summary data extraction unit 102 and the patent requirement suitability prediction processing unit 103 will be described. As shown in FIG. 5, the summary data extraction unit 102 includes a candidate extraction unit 111, a main data extraction unit 112, a CT data generation unit 113, a text analysis / term extraction unit 114, a development level / essential requirement analysis unit 115, A receiving analysis unit 116, a pattern data extraction unit 117, and a file generation unit 118 are included. For convenience of illustration, in FIG. 5, a detailed explanation data storage unit (detailed description TR) 160, a claim data storage unit (claims TR) 161, a main data storage unit (main data TR) 162 are included in the summary data extraction unit 102, and these are provided in the HDD 14 serving as data storage means.

  The candidate extraction unit 111 reads the draft document data stored in the prediction target TR storage unit 152, skips unnecessary data from the draft document data, extracts data necessary for creating the summary data, The data is divided into detailed explanation data (specification data) and claim scope data, and stored in the detailed explanation TR160 and claim scope TR161, respectively. Here, “the above”, “the”, “the” and the paragraph numbers are skipped.

  The main part data extraction unit 112 reads the draft document data stored in the prediction target TR storage unit 152, extracts data corresponding to the main part of the draft document (main part data), and extracts the extracted key data. The part data is stored in the main part data storage unit 162. Here, as the main part data, the data of the part (name of the invention) in which the name of the invention to be predicted is described, the data corresponding to the technical field, and the “present invention” or “this invention” of the problem part Extract one sentence data including the character string.

  The CT data generation unit 113 reads the draft document data stored in the prediction target TR storage unit 152, reads the data described in the claims, and reads the above-described claim tree data (CT data). It is generated and stored in the CT data storage unit 154.

  The text analysis / term extraction unit 114 inputs text data from the detailed description TR160 and the claims TR161, extracts feature words for each of them (the claims are for each claim), and each feature word Are output in the important order. In this case, for example, index character string extraction processing such as morphological analysis or N-Gram is executed to check the appearance frequency of each word and the co-occurrence frequency of each word, and output each feature word according to the result.

  The development degree / required requirement analysis unit 115 reads the claim range data from the claim range TR161, and for each feature word extracted by the text analysis / term extraction unit 114, the development degree and whether or not it meets the essential requirements Check the heel and output the result. Here, the degree of expansion (Ed) is data indicating how many claims each feature word is expanded (used) and the number of claims expanded. In general, in an application for a patent application, more important matters are set forth in claim 1 (or other independent claims) in a broad range so that the broadest possible inventive concept is covered, and claims subordinate thereto. In many cases, it is described while the range is reduced (embraced) step by step. Therefore, it is considered that the greater the degree of development (Ed), the higher the degree of importance. Therefore, the degree of development is considered to be useful information for grasping the gist of the invention. For example, the term “punch” is set in the area of “term 1” in the term area 153c1 of data type “C” and item number “1” shown in FIG. In the scope of claims of Japanese Patent Application Laid-Open No. 2008-62282, since it is described in claims 1, 2, 3, 4, and 5, "5" is set in the expansion degree area 153c2.

  Whether or not it meets the essential requirements is whether or not it is described in the characteristic part of each claim and is indicated by an essential flag (Ef). In the embodiment of the invention according to the present application, the last paragraph in each claim or a sentence including the character string of “characteristic” is used as the feature part of each claim, and the data is the feature part data. , The terms included here shall meet the essential requirements. For the term extracted from the feature portion, “X” indicating that the essential requirement is satisfied is set in the essential flag (Ef).

  The dependency analysis unit 116 performs dependency analysis on the main data stored in the main data storage unit 162 and outputs the result to the pattern data extraction unit 117.

  The pattern data extraction unit 117 inputs the analysis result of the dependency analysis unit 116, and the noun immediately before the hiragana character “O”, the character string that is the combination of the corresponding verb, and the beginning of the name of the invention (First name) is output. For example, in a sentence including the character string “present invention” in the column “Problem to be Solved by” of Japanese Patent Application Laid-Open No. 2008-62282, the noun immediately before “O” in hiragana and the corresponding Verb combinations include “adjustment” and “do not do”, “concentricity” and “obtain”, “precision punching” and “provide”. These are output from the pattern data extraction unit 117. In the present embodiment, among the data output from the pattern data extraction unit 117, the data extracted from the task portion corresponds to the task data, for example, a record of the data type “P” in FIG. It can be data.

  The file generation unit 118 generates summary data using data output from the text analysis / term extraction unit 114, the development level / essential requirement analysis unit 115, and the pattern data extraction unit 117, and stores the summary data in the summary data storage unit 153. In this case, data of the data types “C” and “D” is generated using the output data of the text analysis / term extraction unit 114 and the development level / essential requirement analysis unit 115, and the output data of the pattern data extraction unit 117 is used as the output data. As a result, data of data types “P” and “T” is generated.

  As shown in FIG. 6, the patent requirement suitability prediction processing unit 103 includes a novelty / expansion prior application prediction processing unit 125 and an inventive step prediction processing unit 126. The novelty / expanded prior application prediction processing unit 125 uses the summary data stored in the summary data storage unit 153 as a search term to perform a full text search of the search target publication data in the target publication storage unit 155, and according to the result. The novelty / expansion prior application prediction data Nd is output to the prediction result file generation unit 127. The function and operation procedure of the novelty / expansion earlier application prediction processing unit 125 will be described in detail later.

  The inventive step prediction processing unit 126 includes a cited invention search unit 131, an input vector generation unit 132, a machine learning unit 133, and a designated prediction data generation unit 134. The cited invention search unit 131 has a main cited invention search unit that performs a main cited invention search and a subcited invention search unit that performs a subcited invention search, which will be described later. In addition, the cited invention search unit 131 outputs the inventive step prediction data Vd1 to the prediction result file generation unit 127 according to the results of the main cited invention search and the sub-cited invention search, and responds to the claim that is the target of the search. Claim summary data ied and concept search data Vd 2 are output to input vector generation section 132. The concept search data Vd2 includes publication gazette data of a document (the most similar document) that is determined to have the highest similarity as a result of the concept search. The function and operation procedure of the cited invention search unit 131 will be described in detail later.

  As shown in FIG. 7, the input vector generation unit 132 includes a summary vector generation unit 132a, a citation candidate vector generation unit 132b, and a movement vector generation unit 132c.

  The summary vector generation unit 132a receives the claim summary data ied, extracts the feature words, performs weighting according to each word, and generates the summary vector EV according to the description of each claim. The citation candidate vector generation unit 132b inputs public gazette data of the most similar document included in the concept search data Vd2 in the search with mode described later, extracts the feature word, performs weighting according to each word, and performs the weighting. A document vector (citation candidate vector) RfV corresponding to similar documents is generated. In the no-search mode, the designated prior art data Vs2 is input to extract the feature word, and the document vector (citation candidate vector) RfV corresponding to the designated prior art data is generated by performing weighting according to each word. . The movement vector generation unit 132c calculates a difference between the abstract vector EV and the citation candidate vector RfV, and generates an abstract movement vector V3 according to the difference between the two document vectors.

  Since the most similar document is a document having the highest similarity as a result of the concept search by the main citation invention search unit, the probability of being cited as a disclosure document of the main citation invention in the inventive step examination of the prediction target invention is high. Presumed to be the highest. Therefore, if the citation candidate vector RfV is obtained using the most similar document as a citation candidate and the difference between the citation candidate vector RfV is calculated and the gist movement vector V3 is obtained, the invention to be predicted and the invention disclosed in the most similar document A document vector (corresponding to the abstract movement vector V3) corresponding to the difference is generated.

  The machine learning unit 133 is a document classification unit according to the embodiment of the present invention, and a summary movement vector described later by machine learning (supervised learning) using the following training data (also referred to as a learning pattern). It is constructed to classify V3 into a class that conforms to the requirement of inventive step and a class that does not conform (class that has no reason for refusal) and output an output signal (requirement conformity document vector) V4 according to the classification result. ing. In the embodiment of the present invention, the learning pattern can be an HL pattern described below.

  In the HL pattern, the learning document vector is the first learning document vector, and the teacher vector is a vector indicating that there is a reason for rejection of the inventive step (for example, only the dimension corresponding to the correct class is “1” and the others are “0”). Vector), the learning document vector is the second learning document vector, and the teacher vector is a vector indicating that there is no reason for rejection of the inventive step (for example, only a dimension different from the above is “1” and the others are “0”). Is a combination pattern with "vector".

  The first learning document vector is an application for which the first notice of reasons for refusal (1st action) has been issued as a result of examination by the Patent Office in a published application, and the reason for refusal of inventive step violation ( Claims (at the time of issuance of the notice of reasons for refusal) that pointed out the reasons for refusal of the application (reasonable refusal application) for which the reason for refusal that the requirement of Article 29 (2) of the Patent Act was not satisfied was pointed out Is a first moving document vector corresponding to a difference between a document vector corresponding to the document vector and a document vector (cited document vector) corresponding to the cited document 1 (main cited document cited as the main publication) at that time. .

  The second learning document vector is the result of examination in the published application that the patent action is issued without the first action being issued (the application without refusal) or the first action is issued but the reason for refusal is advanced Document vectors according to claim 1 (at the time of the notice of reasons for refusal) of applications for which no reason for refusal of sex violation has been pointed out (applications without inventive refusal), and those applications without refusal or inventive refusal As a result of the concept search for the application, the second moving document vector corresponding to the difference from the document vector (non-cited document vector) corresponding to the document (the most similar document for learning) that has the highest similarity is used. is there.

  The machine learning unit 133 classifies the abstract movement vector V3 into either a class with or without a reason for refusal of inventive step by repeatedly performing learning with the learning pattern of the HL pattern as described above, and the class is classified into the classified class. A corresponding requirement conformity document vector V4 is output. The former corresponds to a case where the reason for refusal of inventive step violation is likely to be issued and the latter is low when the invention to be predicted is filed.

  The machine learning unit 133 may classify the inputted abstract movement vector V3 into a class having a reason for refusal of inventive step and a class having no reason for refusal of inventive step, and output a requirement conformity document vector V4 according to the classification result. A learning algorithm called a support vector machine (SVM) can be applied to the unit 133. According to the support vector machine (SVM), the decision boundary can be obtained so that the distance (margin) from the decision boundary is maximized.

  In addition, a neural network modeled on the brain neural network can be applied to the information processing of the machine learning unit 133. Neural networks include hierarchical neural networks and interconnected neural networks. For example, a perceptron of a hierarchical neural network can be applied as a learning algorithm of the machine learning unit 133.

  The perceptron is composed of a three-layer hierarchical network called S layer, A layer, and R layer (not shown), and there is only one-way coupling from S layer to A layer and from A layer to R layer. . Given the learning pattern of the HL pattern described above, when the output vector when the first learning document vector or the second learning document vector is input is different from each teacher vector, according to the error. The learning ends when the weight of the connection is corrected and the error between the output vector and the teacher vector becomes a certain value or less.

  However, the perceptron may not stop the algorithm when the learning pattern cannot be linearly separated. Therefore, in order for the machine learning unit 133 to acquire a non-linear decision boundary by learning, there are many application examples in a hierarchical neural network, and a BP (back propagation) network that can learn a non-linear discriminant plane with few misidentifications. Is preferably applied.

  As shown in FIG. 11, the BP network has an input layer and an output layer, and an intermediate layer therebetween, and a learning algorithm called an error back-propagation algorithm makes it possible to learn the weights of all the connections between units. ing. In the error back-propagation algorithm, the input signal is transmitted to the input layer, the intermediate layer, and the output layer, while the error signal is transmitted in the reverse direction, whereby weight adjustment is performed.

Then, when a learning pattern x _p (x ₀ , x ₁ ... X _n ) is input to the BP network shown in FIG. 11, the j (0 ≦ j ≦ n) -th unit in a certain hierarchy A weighted signal is input from the unit one layer before having a connection with the unit j. Therefore, if the signal from the i-th unit (0 ≦ i ≦ n) of the previous layer is t _ip and the weight is w _ij , the input to the unit j is given by Equation 1, and the output of the unit j is Suppose f is a threshold function and Equation 2 is obtained.

Formula 1

Formula 2

Error D _p for learning pattern x _p is the output of unit k of output layer, since is defined by the sum of squares of the difference between the teacher signal b _kp, so equation 3 below. The error D _p 3GS sum for all learning patterns sought D of formula 4, as its D is minimized, is adjusted connection weights between units is learning in machine learning unit 133 is carried out. In this case, each time an individual learning pattern is input, the weight is adjusted by Expression 5. w _ij is a weight before update, w ′ _ij is a weight after update, and ρ is a learning coefficient. This is called stochastic steepest descent. Note that the sigmoid function shown in Equation 6 is used as the unit input / output function.

Formula 3

Formula 4

Formula 5

Equation 6

  The designated prediction data generation unit 134 inputs the summary data stored in the summary data storage unit 153 and the designated prior art data stored in the designated prior art data storage unit 151 in the no-search mode, The summary data ied of the invention to be predicted according to the claims and the designated prior art data Vs2 are output to the input vector generation unit 132, and the inventive step prediction data Vs1 is output to the prediction result file generation unit 127. The inventive step predictive data Vs1 includes a search flag “VY” indicating that the main cited invention has been found together with the designated prior art data Vs2 for each claim, and a requirement suitability document vector V4 described later. This corresponds to the invention-designated inventive step prediction data according to the present embodiment.

(Configuration of the user terminal device 30)
As shown in FIG. 1, the user terminal device 30 includes a connection environment to the Internet N <b> 1 and can communicate with the patent requirement suitability prediction server 10. The user terminal device 30 is assumed to be a stationary (or portable notebook type) personal computer, but may be a tablet-type terminal device.

  As illustrated in FIG. 3, the user terminal device 30 includes a CPU 31, a ROM 32, a RAM 33, a data storage unit 34, and a liquid crystal display unit 35. In addition, the user terminal device 30 includes a voice conversion processing unit 36, a communication control unit 37, a communication processing unit 38a, a wireless communication unit 38b, a speaker 39, and a microphone 40.

  The CPU 31 operates according to a program stored in the ROM 32 and controls operation of the entire user terminal device 30. The ROM 32 stores a program executed by the CPU 31, for example, a communication control program for performing data communication. The RAM 33 stores data necessary for the CPU 31 to execute the program.

  Various data are stored in the data storage unit 34. The liquid crystal display unit 35 includes an LCD (Liquid Crystal Display) and its driving unit, and is an image display unit that displays images of characters, figures, symbols, and the like. The voice conversion processing unit 36 decompresses the voice data and outputs it to the speaker 39, while converting and compressing the analog voice signal input from the microphone 40 into digital voice data and inputs the digital voice data to the communication processing unit 38a. The communication control unit 37 operates in response to an instruction from the CPU 31 and controls connection and disconnection of a line for performing data communication. The communication processing unit 38a operates according to an instruction from the communication control unit 37, and executes transmission / reception of data performed via the Internet N1. The wireless communication unit 38b is a wireless communication unit that performs wireless data transmission / reception under the control of the communication control unit 37. The speaker 39 is an audio output means for outputting audio, and the microphone 40 inputs audio such as the user's conversation content and converts it into an electrical signal.

(Operations of the patent requirement conformity prediction system)
Next, referring to FIGS. 12 to 22 and FIGS. 32 to 35 together with FIG. 4, the operation content of the patent requirement suitability prediction process by the patent requirement suitability prediction server 10 will be described.

  Here, FIG. 4 is a functional block diagram showing a main configuration of the patent requirement suitability prediction server 10 for realizing the patent requirement suitability prediction process. In the patent requirement suitability prediction server 10, the CPU 11 accesses various files and DBs stored in the publication gazette DB 150, the designated prior art data storage unit 151, the abstract data storage unit 153 according to the patent requirement suitability prediction program, The draft sentence data generation unit 101, the summary data extraction unit 102, the patent requirement suitability prediction processing unit 103, the prediction result editing processing unit 105, and the designated prior art data generation unit 106 are operated. Thereby, a patent requirement suitability prediction process is executed. The patent requirement suitability prediction program includes a patent requirement suitability prediction server 10 that includes a draft sentence data generation unit 101, a gist data extraction unit 102, a patent requirement suitability prediction processing unit 103, a prediction result editing processing unit 105, and a designated prior art data generation unit 106. It is a program to make it function as.

  When the patent requirement suitability prediction server 10 performs the patent requirement suitability prediction process, the CPU 11 performs an operation according to the flowchart shown in FIG. 12 according to the patent requirement suitability prediction program. FIG. 12 is a flowchart showing an example of the operation procedure of the patent requirement suitability prediction process of the CPU 11 according to the patent requirement suitability prediction program. In FIG. 12, FIG. 13, etc., “S” is an abbreviation of step.

  When starting the operation according to the patent requirement suitability prediction program, the CPU 11 proceeds to step 1 and performs user authentication processing. Here, the user authentication process is performed by confirming the user ID and password input by the user using the user terminal device 30. Next, the CPU 11 proceeds to step 2 to perform a point balance confirmation process. In the point balance confirmation process, the CPU 11 confirms whether or not the user's point balance is equal to or greater than a certain value. If the point balance is insufficient, the patent requirement suitability prediction process is terminated, or a message notifying that the point balance is insufficient is transmitted. Do.

  When the CPU 11 proceeds with the process to step 3, the invention data reception / decryption is executed. In step 3, the CPU 11 activates the communication processing unit 16 to receive the invention data by encrypted communication (for example, encrypted communication using SSL) from the user terminal device 30, and the draft sentence data generating unit 101. The draft document data received by the invention data reception is decoded, and the machine date is set to the filing date and stored in the prediction target TR storage unit 152. Further, when the designated prior art data is included in the received data of the invention data reception, the CPU 11 decodes the designated prior art data and further performs the operation as the designated prior art data storage control means and designates it. The prior art data generation unit 106 is operated to store the designated prior art data that has been decrypted in the designated prior art data storage unit 151. Subsequently, when the process proceeds to step 4, the number of received draft document data is set in the draft sentence counter MAX, and “0” is set in the draft sentence counter.

  Next, the CPU 11 advances the process to step 5 to determine whether the prediction mode is the search mode or the search-free mode depending on whether the specified prior art data is stored in the specified prior art data storage unit 151. When the designated prior art data storage unit 151 stores the designated prior art data, the CPU 11 operates as a prediction process control unit, and performs the process in step 6A in order to predict the requirement suitability in the no-search mode. On the other hand, if the designated prior art data storage unit 151 does not store the designated prior art data, the CPU 11 advances the process to step 6 in order to predict the suitability of the requirement in the search mode.

  Then, when the process proceeds to step 6, the CPU 11 determines whether or not a prediction end condition is satisfied. Here, the CPU 11 proceeds to step 8 when the prediction end condition is satisfied, but proceeds to step 7 otherwise. When proceeding to step 7, the CPU 11 executes a patent requirement suitability prediction routine which will be described later. When proceeding to step 8, the CPU 11 executes a termination process and terminates the patent requirement conformity prediction process for the user. By doing so, in the search mode, the patent requirement suitability prediction process is automatically and continuously executed until the prediction end condition is satisfied.

  Also, when the process proceeds to step 6A, the success or failure of the prediction end condition is determined as in step 6, and if the prediction end condition is satisfied, the process proceeds to step 8; otherwise, the process proceeds to step 9, and the CPU 11 will be described later. A search-less patent requirement suitability prediction routine is executed, and then the process returns to step 6A. By doing so, in the no-search mode, the non-search patent requirement suitability prediction process is automatically and continuously executed until the prediction end condition is satisfied.

  And when CPU11 progresses to step 7, it performs a patent requirement suitability prediction routine along the flowchart shown in FIG.

(Patent requirement compliance routine)
When the CPU 11 starts the patent requirement suitability prediction routine, the CPU 11 proceeds to step 11 and adds “1” to the draft sentence counter. In the following step 12, it is determined whether or not the draft counter is larger than the draft counter MAX. If not larger, the process proceeds to step 13. If not (if the draft counter is larger than the draft counter MAX), step 16 is executed. Proceed with the process.

  When the CPU 11 proceeds with the process to step 13, it performs an operation as the target publication extracting unit 104 to extract the publication publication data based on the application date of the draft document data (the machine date is set in step 3). The extracted data is stored in the target publication storage unit 155 as search target publication data. Further, the CPU 11 performs the operation as the summary data extraction unit 102 to generate the above-described summary data and CT data, and stores them in the summary data storage unit 153 and the CT data storage unit 154, respectively. The target publication extraction unit 104 extracts public publication data whose application date is earlier than the application date of the draft document data.

  Subsequently, the CPU 11 proceeds to step 14 to execute a novelty / expansion prior application prediction routine described later, and then proceeds to step 15 to execute the inventive step prediction routine. Thereafter, the CPU 11 returns to step 11 and repeats the same processing as described above. In step 16, the CPU 11 performs an operation as the prediction result editing processing unit 105 to edit and output a prediction result list L <b> 1 described later. Thereafter, the point consumption process of step 17 is executed, and the point balance is reduced according to the number of proposal document data for which the propriety of patent requirement is predicted. Thereafter, the patent requirement suitability prediction routine ends.

(No-search patent requirement suitability prediction routine)
On the other hand, as shown in FIG. 32, when starting the no-search patent requirement suitability prediction routine, the CPU 11 executes step 11 and step 12 in the same manner as the patent requirement suitability prediction routine, and then executes step 13A. In step 13A, the CPU 11 operates as the gist data extraction unit 102 to generate the gist data and the CT data described above, and stores them in the gist data storage unit 153 and the CT data storage unit 154, respectively. Further, the CPU 11 then proceeds to step 125 to execute a search-less inventive step prediction routine described later, and then returns to step 11. When the process proceeds from step 12 to steps 16 and 17, the steps 16 and 17 are executed in the same manner as the patent requirement suitability prediction routine.

(Novelty / expanded application prediction routine)
Returning to FIG. 13, when the CPU 11 proceeds the process to step 14, the CPU 11 operates as the novelty / expansion prior application prediction processing unit 125 described above, and follows the flowcharts shown in FIGS. 14 and 15.・ Extended application prediction routine is executed.

  In this case, when the CPU 11 starts the novelty / expansion prior application prediction routine, the CPU 11 proceeds to step 21, sets “0” to the document counter (document ct) and document MAX, and sets the item number counter (item ct). Set “1”. Subsequently, the process proceeds to step 22, and a full-text search is performed on the search target publication data in the target publication storage section 155 using data corresponding to the item number ct of the summary data stored in the summary data storage section 153 as a search term. The number of hit documents is set in the document MAX. In this case, since “1” is set in the item number counter in step 21, the search term is set using the data in which the item number area 153b is “1”, that is, the summary data of claim 1.

  Subsequently, the process proceeds to step 23, where it is determined whether or not there is a document hit in step 22 (whether or not document MAX is 1 or more). If there is a hit document, the process proceeds to step 24; The novelty / expansion prior application prediction routine ends.

  In step 24, “1” is added to the document counter, and in subsequent step 25, it is determined whether or not the document counter is equal to or less than document MAX. If this is true, the process proceeds to step 26, and so on. If not, the process proceeds to step 29. In step 26, whether the application publication date of the hit document (hit document publication date) is smaller than the filing date (target application date) of the draft document data (whether hit document publication date <subject application date is satisfied). ) Is determined, and if this is true, the process proceeds to step 27; otherwise, the process proceeds to step 28. In step 27, novelty / expansion prior application prediction data Nd is generated so as to include a novelty flag “N1” indicating no novelty. Thereafter, the process returns to step 24 and the same processing as described above is repeated.

  In step 28, an enlargement-first application prediction routine, which will be described later, is executed. In step 29, it is determined whether or not there is another independent term by referring to the CT data storage unit 154. If there is another independent term, the process proceeds to step 30; After the sex / expansion prior application prediction data Nd is output, the novelty / expansion prior application prediction routine ends. In step 30, the CPU 11 sets "0" in the document counter and document MAX, and a claim number larger than "1" is set in the item number counter. Thereafter, the process returns to step 22 and the same process as described above is repeated.

  On the other hand, the CPU 11 executes an enlargement-first application prediction routine according to the flowchart shown in FIG. When the extended earlier application prediction routine starts, the process proceeds to step 41, and whether or not the filing date of the hit document (document filing date) is earlier than the target filing date (reference filing date <subject filing date is satisfied) If this is true, the process proceeds to step 42; otherwise, the enlargement earlier application prediction routine is terminated. In step 42, it is determined whether or not the inventor is the same between the invention to be predicted and the hit document, and if this is not established, the process proceeds to step 43. End the routine. In step 43, it is determined whether or not the applicant is the same for the prediction target invention and the hit document. If this is not the case, the process proceeds to step 44. End the routine. Then, when the CPU 11 proceeds the process to step 44, the CPU 11 includes an enlarged first application flag “F1” indicating that the requirement of the enlarged first application (required in Article 29-2 of the Patent Law) is not satisfied. The novelty / expansion prior application prediction data Nd is generated. Thereafter, the enlargement destination application prediction routine ends.

  When the novelty / expansion prior application prediction routine is completed as described above, the process proceeds from step 14 to step 15 in FIG. 13, and the CPU 11 performs the operation as the inventive step prediction processing unit 126 to execute the inventive step prediction routine. The CPU 11 executes an inventive step prediction routine according to the flowcharts shown in FIGS.

(Inventive step prediction routine)
When starting the inventive step predictive routine, the CPU 11 advances the process to step 51, obtains the claim number of the number area 154b for the record of the independent section area 154a from the CT data storage unit 154, and It is set in the number area (No area) 165b of the independent item table 165 described later. In the subsequent step 52, the claim number of the number area 154b is obtained from the CT data storage unit 154 from a record in which the search flag of the search flag area 154e is a space and the MAX section of the MAX section area 154c is "M". Then, the minimum value (MIN) of the obtained claim number is obtained and set in the MAX counter. As shown in FIG. 9B, when there are a plurality of records whose MAX classification is “M”, the smallest claim number is set in the MAX counter.

  Then, the CPU 11 proceeds to step 53 to search the No area 165b of the independent item table 165, and in the subsequent step 54, determines whether there is a claim number larger than “1”, and “1”. Processing branches depending on whether there is a claim number larger than that. In this case, if there is no claim number greater than “1”, the process proceeds to step 55, and if it is, the process proceeds to step 56. Step 55 is processing when there is only one independent term among claims included in the draft document of the invention to be predicted (single independent term routine), and step 56 is processing when there are a plurality of independent terms (multiple independent terms). Routine). For example, the former corresponds to the case where the prediction target invention is disclosed in Japanese Patent Application Laid-Open No. 2008-62282, and the latter corresponds to the case where the prediction target invention is disclosed in Japanese Patent Application Laid-Open No. 2011-186735, for example. .

  As shown in FIG. 24, the independent term table 165 has a counter area 165a, a No area 165b, and a search flag area 165c. The counter area 165a stores a numerical value corresponding to the number of stored data. In the No area 165b, the number of the independent term is stored. A search flag is stored in the search flag area 165c. FIG. 24 shows, as an example, a case where the invention to be predicted is an invention disclosed in Japanese Patent Application Laid-Open No. 2011-186735.

  Then, when the CPU 11 proceeds with the process to step 55, the CPU 11 executes a single independent term routine according to the flowchart shown in FIG. When the CPU 11 starts the single independent term routine, the process proceeds to step 61, and the claim number is acquired from the number area 154b for the record in which the search flag in the search flag area 154e is space from the CT data storage unit 154. Then, the minimum value (MIN) is set in the item number counter. In the subsequent step 62, an independent term search process to be described later is performed. In subsequent step 63, it is determined whether or not the search flag (search flag) is “VX” or “VY”. If the search flag is “VX” or “VY”, the process proceeds to step 64; The single independent term routine is terminated.

  When the CPU 11 advances the process to step 64, it adds “1” to the item number counter. In the next step 65, it is determined whether or not the item number counter is equal to or less than the MAX counter set in step 52. If the item number counter is equal to or less than the MAX counter, the process proceeds to step 66 to execute a dependent item search process described later. Otherwise, the independent term search process ends.

  Then, the CPU 11 executes a plurality of independent term routines according to the flowchart shown in FIG. When the CPU 11 starts the process, the process proceeds to step 52, executes the same process as described above, and then proceeds to step 55 to execute the single independent term routine in the same manner as described above. Thereafter, the CPU 11 advances the process to step 67 to determine whether or not there is a record in the CT data storage unit 154 where the search flag in the search flag area 154e is a space (that is, whether or not there is a record for which search processing has not been performed). ) Is determined, and if it is determined, the process returns to step 52 and the same processing as described above is executed. Otherwise, the plural independent term routine is terminated.

  Further, the CPU 11 executes an independent term search process according to the flowchart shown in FIG. In the independent term search processing, the CPU 11 performs the operation as the cited invention search unit 131, and performs the main cited invention search and the sub cited invention search for the independent term.

  When starting the independent term search process, the CPU 11 advances the process to step 71 to execute a main citation invention search process to be described later. In the following step 72, it is determined whether or not there is a main citation invention in the main citation invention search process (whether or not a main citation document described later is set). If there is a main citation invention, the process proceeds to step 73. If there is no main cited invention, the process proceeds to step 76. In the subsequent step 73, a sub-invention search process to be described later is executed, and in a subsequent step 74, whether or not there has been a sub-citation invention in the sub-citation invention search process (whether a sub-citation document to be described later is set). Is determined. If there is a secondary citation invention, the process proceeds to step 75, and if there is no secondary citation invention, the process proceeds to step 77.

  When the processing proceeds to step 75, the CPU 11 sets “VX” to the search flag (search flag) of the corresponding claim number, and sets “VY” to the search flag (search flag) in step 77. Further, when the CPU 11 advances the process to step 76, among the records stored in the CT data storage unit 154, the records in which the claim number in the number area 154b matches the item number counter are stored in the search flag area 154e. A search flag Ef is set to “9” indicating that the search has been completed (searched). Further, the CPU 11 generates the inventive step prediction data Vd1 so as to include the set search flag, and outputs it to the prediction result file generation unit 127. Further, the CPU 11 outputs the claim summary data ied and the concept search data Vd2 corresponding to the search result to the input vector generation unit 132. In this case, the claim summary data ied can be the summary data of the claim that is the target of the search, but the claim that is the target of the search of the prediction target invention stored in the prediction target TR storage unit 152. It may be term data. The search flag is set to “VX” or “VY” when the main cited invention is found, but if the main cited invention is found, it is likely that the search flag does not satisfy the inventive step requirement. Therefore, whether or not the reason for refusal of the inventive step violation is found depends greatly on whether or not the main cited invention is found. By including such a search flag, the inventive step prediction data Vd1 indicates the suitability of the inventive step requirement.

  Then, the CPU 11 executes main citation invention search processing according to the flowchart shown in FIG. The main citation invention search process is a process of searching for the main citation invention closest to the prediction target invention.

  When the CPU 11 starts the main cited invention search process, the CPU 11 proceeds to step 81 and sets “0” in the development degree counter tc. Subsequently, the CPU 11 advances the processing to step 82, reads the next data from the summary data storage unit 153, and sets main search document data (document data when searching the main cited invention by concept search). One is a mandatory flag from a record in which the data type is “C” and the number of the item number area 153b corresponds to the item number counter (the item number counter is set with the minimum value of the independent item in step 61). Ef is “X”, and the degree of development Ed is the maximum value of the degree of development (expansion degree MAX) −the term of the degree of development counter tc (for example, if the degree of development MAX is “5”, the degree of development Ed is “5” −tc The other is problem data, that is, data of a record whose data type is “P”.

  In the following step 83, the CPU 11 performs a concept search for the search target publication data stored in the target publication storage unit 155 using the search process of the main cited invention, that is, the main search document data as the input document. In this conceptual search, a vector (document vector) corresponding to each document is generated by extracting and weighting the main search document data and each document to be searched, and the inner product of each vector is obtained. Thus, the similarity is calculated. Next, in step 84, it is determined from the result of the concept search in step 83 whether or not the largest similarity is a certain value or more, and if it is more than a certain value, the process proceeds to step 85. If so, the process proceeds to step 87. In step 85, it is determined whether or not there are a plurality of documents whose similarity is equal to or greater than a certain value. If there are no documents, the process proceeds to step 86, and if there is, the process proceeds to step 89. In the main citation invention search process, the main citation is set only when there is a document whose similarity is a certain value or more.

  In step 86, the hit document is set as the main cited document (prior art document in which the main cited invention is disclosed), and the main cited invention search process ends. In step 87, “1” is added to the expansion degree counter tc, and then in step 88, it is determined whether or not the expansion degree MAX−expansion degree counter tc is “0” or less. The cited invention search process is terminated. If not, the process returns to step 82 and the above process is repeated.

  In this way, first, a concept search is performed with terms that have a degree of expansion Ed more important than the degree of expansion MAX. In the concept search, a plurality of documents can be extracted according to the similarity of documents, but when the highest similarity does not reach a certain value, there is a high possibility that the document does not correspond to the main cited document. For this reason, when a document with a similarity equal to or greater than a certain value is not found, the concept search is executed again including a term whose expansion degree Ed is smaller than the expansion degree MAX.

  In step 89, the document with the highest degree of similarity (also referred to as the most similar document) is set as the main cited document, and then the main cited invention search process ends.

  Then, the CPU 11 executes the secondary citation invention search process according to the flowchart shown in FIG. The sub-cited invention search process is a process for searching a sub-cited invention including a difference between the prediction target invention and the main-cited invention, and is executed only when the main-cited invention is found in the main-cited invention search process.

  When the CPU 11 starts the sub-cited invention search process, the process proceeds to step 91, and from the summary data storage unit 153, the data type is “C” and the number of the item number area 153b corresponds to the item number counter. Terms that are not included in the main search document data (unused search data) and data of records whose data type is “P” are read out, and they are sub-search terms (keywords for searching the sub-cited invention by full-text search) ).

  In the subsequent step 92, the CPU 11 performs a full-text search on the search target publication data stored in the target publication storage section 155 using the search process of the subcitation invention, that is, the sub search term as a search keyword. In the following step 93, it is determined whether or not there is a document hit in step 92. If there is a hit document, the process proceeds to step 94, and if not, the process proceeds to step 96. In step 94, it is determined whether or not there are a plurality of hit documents. If there are no hit documents, the process proceeds to step 95. If there are a plurality of hit documents, the process proceeds to step 98.

  In step 95, the hit document is set in the sub-cited document (prior art document in which the sub-cited invention is disclosed), and the sub-cited invention search process ends. In step 96, the sub-search term is changed and the full-text search is performed again. Here, the sub-search term is the data type “C”, the search number unused data not included in the main search document data of the record corresponding to the item number counter with the data type “C”, and the data type “ It is changed to the data of the record of “T”. In step 97, it is determined whether or not there is a hit document. If there is a hit document, the process proceeds to step 94. If not, the sub-cited invention search process is terminated. Further, in step 98, for each of the hit documents, the number of matches with the term having the data type “C” and the number of the item number area 153b corresponding to the item number counter is “X”. It is counted and the number of matches is set in the subcitation file. In the next step 99, the sub-cited files are sorted in descending order of the number of matches, and in the subsequent step 100, three sub-cited files from the top of the sub-cited file are set in the sub-cited document, and then the sub-cited invention search process ends. When a plurality of documents are hit in the sub-cited invention search, the extent to which the feature part of the prediction target invention is disclosed is examined for each of them by the number of matches with the term whose essential flag Ef is “X”. A document with a large number of matches is set as a sub-cited document.

  And CPU11 performs a dependent term search process according to the flowchart shown in FIG. The dependent term search process is executed only when the search flag (search flag) is “VX” or “VY” (when the main cited invention is found). When the CPU 11 starts the subordinate item search process, the process proceeds to step 111, and a record (item number counter) from the summary data storage unit 153 corresponding to the item number counter with the data type “C” and the item number area 153b number. In step 64, the term having the required flag Ef of “X” is read from the minimum value of the independent term in order at step 64, and this is the dependent search term (invention described in the dependent term). Is set as a keyword when searching in full-text search. In the next step 112, the CPU 11 performs a full-text search for the main cited document using the subordinate search term as a search keyword, and checks whether the invention described in the dependent claim is disclosed in the main cited document.

  In step 113, it is determined whether there is a hit document. If there is a hit document, the process proceeds to step 114. If not, the process proceeds to step 116. In step 114, "VX" is set in the search flag (search flag) of the corresponding claim number, and the hit document is set as the main cited reference of the corresponding claim number. Thereafter, the process proceeds to step 115, and among the records stored in the CT data storage unit 154, the record whose claim number in the number area 154b matches the item number counter is set in the search flag Ef in the search flag area 154e. 9 "is set, and then the dependent term search process ends. Further, in step 116, a full-text search is performed for the sub-cited document by the subordinate search term, and in the next step 117, it is determined whether or not there is a hit document. If there is a hit document, step 118 is executed and then the process proceeds to step 115. If not, the dependent term search process is terminated. In step 118, “VY” is set in the search flag (search flag) of the corresponding claim number, and the hit document is set in the sub-citation document of the corresponding claim number.

  As described above, when the novelty / expansion prior application prediction routine and the inventive step prediction routine are executed, the novelty / expansion prior application prediction data Nd and the inventive step prediction data Vd1 according to the respective results. Is output to the prediction result file generation unit 127. Further, since the requirement adequacy document vector V4 is output from the machine learning unit 133, the prediction result file generation unit 127 generates a prediction result file illustrated in FIG. 10 using these, and stores the prediction result file in the prediction result storage unit 156.

  As shown in FIG. 10, the prediction result file stores the data of each item of the draft document number, claim, main search document data, sub search term, search flag, hit document, and machine prediction for each prediction target invention. Yes. The machine prediction is data corresponding to the requirement conformity document vector V4 from the machine learning storage unit 133, and the reason for refusal of the inventive step violation that cited the main cited document and the sub cited document found in the inventive step prediction routine is found. The possibility is high or low (“H” when high, “L” when low).

  Further, the prediction result editing unit 105 reads the prediction result file, edits and outputs a patent requirement suitability prediction list L 1 as shown in FIG. 25, and transmits it to the user terminal device 30. The patent requirement propriety prediction list L1 includes the document number of the invention to be predicted and the number of the claim, as well as the documents on which the suitability of the requirements of novelty (expanded prior application) and inventive step is based (main cited document, sub cited document) ). “X” is described when it is predicted that the requirement of novelty (expanded prior application) and inventive step will not be met (does not meet), and “A” is recorded when it is predicted that it will meet (satisfy). These are determined by the search flag of the prediction result file.

  “H” and “L” are also written in “X” (main citation and sub-citation) and “Y” (only main citation) when predicted not to meet the inventive step requirement. (In FIG. 25, only when “H” is written together) This is in accordance with the requirement adequacy document vector V4 of the machine learning unit 133, and “H” is a violation of inventive step in the main cited document. When the possibility of refusal is likely to be issued, “L” indicates a low case.

(No search progress routine without search)
When the CPU 11 starts the search-less inventive step prediction routine, it executes steps 51, 52, 53, and 54 in the same manner as the inventive step prediction routine, as shown in FIG. In step 54, the process branches depending on whether there is a claim number larger than “1”. In this case, if there is no claim number greater than “1”, the process proceeds to step 126, and if there is, the process proceeds to step 127. Step 126 is a single independent term routine without search, and step 127 is a multiple independent term routine without search. The former is executed along FIG. 34, while the latter is executed along FIG.

(Single independent term routine without search)
When the CPU 11 starts the single independent term routine without search, as shown in FIG. 34, after executing step 61 as in FIG. 17, the CPU 11 performs the operation as the designated prediction data generation unit 134, and performs steps 130 and 131. Run. When the CPU 11 proceeds with the process to step 130, it reads out the record corresponding to the minimum value MIN in the item number 153 b and the record whose data type is “P” from the abstract data storage unit 153 and generates the claimed abstract data ied. In the following step 131, the CPU 11 reads the designated prior art data stored in the designated prior art data storage unit 151, generates the inventive step prediction data Vs1 and the designated prior art data Vs2s, and each of the prediction result file generation unit 127. And output to the input vector generation unit 132. In subsequent step 132, when "9" is set in the search flag Ef of the search flag area 154e, the single independent term routine without search is terminated.

(Multiple independent term routines without search)
When starting the multiple independent term routine without search, the CPU 11 proceeds to step 126 and executes the single independent term routine without search as described above, as shown in FIG. Thereafter, the process proceeds to the same step 67 as in FIG. 18, and it is determined whether or not there is a record whose search flag in the search flag area 154e is a space in the CT data storage unit 154. If there is a record, the process returns to step 126. A single independent term routine without search is executed in the same manner as described above. If there is no single independent term routine without search, the multiple independent term routine without search ends.

  As described above, in the patent requirement suitability prediction server 10 according to the embodiment of the present invention, in the search mode, summary data is generated from the draft document data of the draft document in which the invention to be predicted is described. Is used to perform a main citation invention search and a sub-citation invention search. The main citation invention search is common to the prediction target invention and its skeleton, that is, the process of finding the main citation invention closest to the prediction target invention among the prior art inventions in the summary data. Patent law and patent / utility model examination Done in line with standards. The sub-cited invention search is performed by a full-text search using a term specifying a problem to be solved by the invention or a technical field when the main cited invention is found by the main cited invention search. And patent and utility model examination standards. Therefore, in the patent requirement conformity prediction server 10 according to the embodiment of the present invention, the prediction regarding the propriety of the patent requirement is performed with the content suitable for the examination practice, so that the burden of preparing the application documents for the patent application is effectively reduced. Can do.

  On the other hand, as described above, the patent requirement suitability prediction processing unit 103 has the machine learning unit 133, and the machine learning unit 133 is constructed by an artificial intelligence program trained with learning data based on past examination results. ing.

  By the way, about 320,000 or more patent applications are filed annually on a performance basis in 2014, and the 1st action has already been issued for some or many patent applications before that. Among them, there are many applications (progressive rejection applications) in which the reasons for refusal of violation of inventive step are pointed out in the notice of reasons for refusal.

  In the inventive step rejection application, the examination results differed between the invention described in the claim at the time of examination and the main cited invention. However, the examiner determined that there was an inventive step based only on the difference. It means that there was no. On the other hand, in patent applications, applications for which a patent assessment was issued without the first action being issued, or a reason for refusal was issued, but the reasons for refusal of the inventive step violation were pointed out. There are applications that did not exist (applications without inventive step rejection).

Then, for example, as shown in FIG. 23, if there is a patent application Pd to be examined and its filing date is t ₀ , the main cited invention or sub-cited invention for the patent application Pd can be known or publicly used. are known from the literature and the Internet known invention is primarily (in FIG. 23, rf1~rf6) filed t ₀ Patent Laid-open application previously have been published from the invention disclosed in.

  Here, it is assumed that, as a result of examination, it is determined that the invention disclosed in the publication rf6 corresponds to the main cited invention. In this case, although there is a difference between the invention according to the patent application Pd and the invention disclosed in the publication rf6, the distance dp corresponding to the difference is the inventive step of the invention according to the patent application Pd. It is thought that it was not large enough to affirm. On the other hand, if it is determined that the invention disclosed in the publication rf6 does not fall under the main cited invention, it is considered that the distance dp is large enough to affirm the inventive step of the application related to the application Pd. It is done.

  If it is possible to determine how much the difference is when the inventive step is affirmed and what is the difference when the invention is denied, it can be considered as an objective judgment material for whether or not the patent requirement is met. In view of the above, for that purpose, it is considered effective to determine the distance dp according to the difference between the two inventions. The machine learning unit 133 determines this by learning the training data based on the past examination results, and gives a measure of whether the inventive step is highly likely to be denied or low.

  In the learning of the machine learning unit 133, in the invention according to the present application (the invention of the present application), the distance dp is regarded as a difference between two document vectors, and the distance dp is learned when there is a reason for refusal of inventive step and when there is no reason for rejection. Therefore, learning is performed using the training data based on the above-described HL pattern.

  When predicting the suitability of the patent requirement, the gist data is obtained for the invention to be predicted, and the most similar document is searched by using the concept data. Since the most similar document has the document vector having the highest similarity to the document vector of the invention to be predicted (to be exact, the document vector obtained from the description of the independent item), it is the main document in the published application. It is recognized that it is most likely to be cited.

  A difference between the citation candidate vector RfV obtained from the most similar document and the summary vector EV obtained from the summary data of the prediction target invention is obtained to generate a summary movement vector V3, which is input to the machine learning unit 133, Whether or not there is a reason for refusal of inventive step violation that cites the main cited document found in the main cited invention search is output. As a result, it is possible to give an indication of whether the reason for refusal of violating the inventive step is high or low.

  As described above, the patent requirement suitability prediction server 10 includes the machine learning unit 133, so that the patent requirement suitability is predicted in a manner that reflects the examination results of the Patent Office. Since the judgment results of artificial intelligence can be used for judgment results that must rely solely on the experience and intuition of experts such as examiners and patent attorneys as before, it is possible to make the prediction results objective Therefore, it can be expected that the efficiency of the rights acquisition work will be improved by reducing the burden of preparing the application documents.

  In particular, in the no-search mode, the propriety of the patent requirement is predicted based on the relationship with the designated prior art invention. Therefore, the invention to be predicted has the inventive step requirement in view of the prior art invention designated by the user himself / herself. It is predicted whether or not. When the application documents are prepared, the difference between the prior art described in the documents found in the prior art search and the invention to be filed should be clarified. There are many cases where one cannot be sure that the invention has an inventive step. In addition, although the application documents are prepared to have inventive step, the reason for refusal of violating the inventive step may be pointed out. In this respect, in the patent requirement suitability prediction server 10, since the machine learning unit 133 can provide an indication of suitability for inventive step, it can be used at the stage of preparing the application documents to reduce the preparation burden of the application documents. In addition, improvement in the quality of application documents can be expected.

  Further, the inventive step prediction processing unit 126 performs a concept search in the main citation invention search and performs a full text search in the sub citation invention search. Whether or not there is a reason for refusal to violate the inventive step largely depends on whether or not the main cited invention is found, but if a full text search is performed in the main cited invention search to find the main cited invention, there is a possibility that multiple documents will be hit In some cases, the main cited invention (main cited document) cannot be specified. In this regard, in the concept search, similar documents are ordered according to the similarity obtained from the inner product of the document vectors. Therefore, the main cited document can be specified by selecting the document having the highest similarity. By combining the prediction by the machine learning unit 133 with the main citation document found in this way, it is possible to predict whether the reason for refusal of the inventive step violation that cited the main citation document is high or low. In addition, it is possible to clarify whether there is a sub-cited document by performing a full text search by sub-cited invention search.

  In the above description, as a more preferred embodiment, a case where the cited invention search unit 131 of the inventive step prediction processing unit 126 has a main cited invention search unit and a sub-cited invention search unit is shown. As described above, when the main cited invention is found by the main cited invention search, there is a high possibility that the reason for refusal without inventive step will be found. The generation unit 132 and the machine learning unit 133 may be operated to output the requirement suitability document vector V4. Even in this case, by using together the inventive step prediction data Vd1 related to the main cited document set in step 89 and the requirement suitability document vector V4 generated by the machine learning unit 133, the content suitable for the examination practice can be obtained. Since the prediction can be performed and the prediction uses the judgment result of the artificial intelligence, the prediction result can have objectivity. Therefore, it is sufficient that the inventive step prediction processing unit 126 has the main citation invention search unit and does not have to have the sub citation invention search unit, but as described above, the progress having the sub citation invention search unit. The sex prediction processing unit 126 is more preferable.

Second Embodiment Subsequently, a patent requirement suitability prediction server 200 according to a second embodiment will be described with reference to FIGS. 26 to 30. As shown in FIG. 26, the patent requirement suitability prediction server 200 is replaced with the patent requirement suitability prediction processing unit 103, the prediction result editing processing unit 105, and the prediction result storage unit 156, as compared with the patent requirement suitability prediction server 10 described above. 3 further includes a patent requirement suitability prediction processing unit 203, a prediction result editing processing unit 205, and a prediction result storage unit 256, and a point that the prediction result list L2 is output instead of the prediction result list L1.

  As shown in FIG. 27, the patent requirement suitability prediction processing unit 203 is different from the patent requirement suitability prediction processing unit 103 in that an inventive step prediction processing unit 226 is used instead of the inventive step prediction processing unit 126 and the prediction result file generation unit 127. And the prediction result file generation unit 227 is different.

  And the inventive step prediction processing unit 226 is different from the inventive step prediction processing unit 126 in that it has a cited invention search unit 231 and a machine learning unit 233 instead of the cited invention search unit 131 and the machine learning unit 133, and an input The difference is that the operation of the vector generation unit 132 is different.

In the inventive step predictive processing unit 126 according to the first embodiment described above, only the most similar document is set as the main cited document by performing the main cited invention search (step 89). In the inventive step predictive processing unit 226, n documents including the most similar documents in descending order of similarity (n is an integer of 2 or more) are extracted as similar documents, and these similar documents are used as main cited documents. It is set. In addition, the machine learning unit 233 classifies the _n summary movement vectors V3 _{1 to} V3 _n into three classes of S class, H class, and L class, which will be described later.

The cited invention search unit 231 has a main cited invention search unit and a sub-cited invention search unit similar to the cited invention search unit 131, but the operation of the main cited invention search unit compared to the cited invention search unit 131. Are different, and output data is also different. The cited invention search unit 131 sets the most similar document as the main cited document by the main cited invention search unit performing the main cited invention search (step 89 of the above-described main cited invention search process). In step 89, the main citation invention search unit of the search unit 231 extracts n documents including the most similar documents in descending order of similarity as similar documents, and sets them as the main cited documents (doc _{1 to} doc _n ). To do. The main cited invention with the operation of the search part vary, and generates n matter of inventive step prediction data Vd1 ₁ ~Vd1 _n corresponding the CPU11 at step 76 in independent claim retrieval processing described above to each similar document, They are output to the prediction result file generation unit 227. Further, in the independent claims search process, CPU 11 is output to the claims subject matter data ied to the input vector generating unit 132, a concept search data _Vd2 1 _{~Vd2 n} of n matter in accordance with the similar documents to the input vector generator 132 Output.

Compared with the machine learning unit 133, the machine learning unit 233 performs n pieces of abstract movement vectors V3 ₁ to V3 ₁ corresponding to each similar document by machine learning (supervised learning) using the SHL pattern described below as a learning pattern. V3 _n is classified into one of S class, H class, and L class, respectively, and n output signals (requirement conformity document vectors V4 _{1 to} V4 _n ) corresponding to the classification result are output. . The S class, the H class, and the L class are respectively a class that is very likely not to meet the inventive step requirement, a class that is highly likely not to match, and a conforming class (for the invention to be predicted, there is a reason for refusal of inventive step violation). Class that is very likely to be found, a high class, a class that is not found).

The SHL pattern is a combination of the following three patterns S, H, and L.
Pattern S: The learning document vector is the first learning document vector, and the teacher vector is a vector indicating that there is a reason for rejection of novelty and inventive step (for example, only the dimension corresponding to the correct class is "1" and the others are "0") Combination pattern H with “vector” of: the learning document vector is the second learning document vector, the teacher vector is a vector indicating the reason for refusal of inventive step and no reason for refusal of novelty (for example, the above corresponding to the correct class) A combination pattern L: a vector in which the learning document vector is the third learning document vector and the teacher vector has no reason for refusal of inventive step. For example, it is a combination pattern with a vector whose only dimension different from the above two is “1” and the other is “0”.

  The first learning document vector is an application for which the first notice of reasons for refusal (1st action) has been issued as a result of examination by the Patent Office in a published application, and the same document is cited in the 1st action. An application (novelty / inventive step) in which the reason for refusal of violation of novelty or inventive step (the reason for refusal that the requirements of Article 29 (1) (iii) and Article 2 (2) of the Patent Act are not met) was pointed out The document vector corresponding to the claim (at the time of issuance of the reason for refusal) where the reason for refusal of the application for refusal was pointed out, and the cited reference 1 at that time (the first main cited publication cited as the main publication) The first moving document vector corresponding to the difference from the document vector corresponding to the object (first quoted document vector).

  The second learning document vector is an application in which the first notice of reasons for refusal (1st action) is issued as a result of examination by the Patent Office in the published application, and the reason for refusal of novelty ( The reason for refusal to violate the inventive step (rejection not satisfying the requirement of paragraph 2 of the same Article) is not pointed out, but the reason for refusal of violating the inventive step is not pointed out The document vector corresponding to the claim (at the time of issuance of the reason for refusal) and the cited reference 1 (main publication) where the reason for refusal of the application (reasonable refusal application) was pointed out The second moving document vector corresponding to the difference from the document vector (second cited document vector) corresponding to the second main cited publication cited as.

  The third learning document vector is the result of examination in the published application, where the first action is not issued and the patent decision is issued (non-rejection application) or the first action is issued but progresses to the reason for refusal Document vectors according to claim 1 (at the time of the notice of reasons for refusal) of applications for which no reason for refusal of sex violation has been pointed out (applications without inventive refusal), and those applications without refusal or inventive refusal As a result of the concept search for the application, a third moving document vector corresponding to the difference from the document vector (non-cited document vector) corresponding to the document (the most similar document for learning) that is considered to have the highest similarity. is there.

  As with the machine learning unit 133, the machine learning unit 233 can apply a neural network modeled on a cranial nerve network for information processing, and preferably uses a BP (back propagation) network among them.

As shown in FIG. 28, the input vector generation unit 132 of the inventive step prediction processing unit 126 includes a gist vector generation unit 132a, a citation candidate vector generation unit 132b, and a movement vector generation unit 132c. Among them, the operations of the citation candidate vector generation unit 132b and the movement vector generation unit 132c are different. That is, reference candidate vector generation unit 132b, since the concept search data Vd2 ₁ ~Vd2 _n of n matter from the cited invention search unit 231 is input, and inputs the publication data of each similar documents included in that each of which Feature words are extracted and weighted according to each word to generate _n document vectors (citation candidate vectors) RfV _{1 to} RfV _n corresponding to each similar document. Movement vector generation unit 132c includes a summary vector EV, the difference between the reference candidate vectors _RfV 1 _{~RfV n} by calculating the n matter gist movement vector _V3 1 to V3 _n for corresponding to the difference between both the document vector Generate.

As described above, since each similar document is a document extracted in descending order from the highest similarity by the concept search by the main cited invention search unit, all of them are the examination of the prediction target invention, and the disclosure of the main cited invention is disclosed. It is estimated that the probability of being cited as a document is high. Therefore, if the candidate documents RfV _{1 to} RfV _n are obtained by using each similar document as a citation candidate and the difference between the citation candidate vectors RfV _{1 to} RfV _n is calculated to obtain the gist movement vectors V3 _{1 to} V3 _n , the prediction target invention, Abstract movement vectors V3 _{1 to} V3 _n corresponding to differences from the invention disclosed in similar documents are generated.

  Compared with the prediction result file generation unit 127, the prediction result file generation unit 227 generates a prediction result file having the layout shown in FIG. 29 and stores the prediction result file in the prediction result storage unit 256. The difference is that the operation as the non-conformance rate calculation unit according to the embodiment is performed to calculate the non-conformance rate Vr related to the prediction target invention.

The non-conformance rate Vr indicates the possibility of not meeting the inventive step requirement for the prediction target invention, and indicates the possibility of finding the reason for refusal of the inventive step violation for the prediction target invention. The requirement adequacy document vectors V4 _{1 to} V4 _n output from the machine learning unit 233 are generated with contents that are highly likely to be incompatible with the requirement of inventive step, are high, and are not included. It shows the possibility of finding reasons for refusal of violation of inventive step in relation to the literature. Therefore, using these, the prediction result file generation unit 227 calculates the non-conformance rate Vr related to the prediction target invention. In this case, the prediction result file generation unit 227 calculates the non-conformance rate Vr according to the non-conformance rate calculation rule. The non-conformance rate calculation rule is a rule by which the prediction result file generation unit 227 calculates the non-conformance rate Vr. In the present embodiment, the S class included in the requirement conformity document vectors V4 _{1 to} V4 _n , The calculation rule table (not shown) is set so that the numerical value of the non-conformance rate Vr is determined according to the number of cases of the H class and the M class. The non-conformance rate calculation rule can be set as follows, for example.

2 or more S class: Vr ≧ 85%
There is one S class and the number of H class is 50% or more: Vr ≧ 75%
One S class and less than 50% H class: Vr ≧ 65%
S class is 0, H class is more than 50%: Vr ≧ 50%
S class is 0, H class is less than 50%: Vr ≧ 40%
0 for both S and H classes: Vr ≧ 15%

  According to the non-conformance rate calculation rule, for example, when there are five similar documents (the above-mentioned integer n is “5”) and there are two S classes, Vr ≧ 85%. Also, if there is one S class and three H classes, Vr ≧ 75%, but if there are two H classes, Vr ≧ 65%, both S class and H class are zero (all cases are L class) ) Vr ≧ 15%.

  29, the prediction result file generated by the prediction result file generation unit 227 includes a non-conformance rate Vr as compared to the prediction result file generated by the prediction result file generation unit 127. Is different.

  Further, the prediction result editing processing unit 205 is different from the prediction result editing processing unit 105 in that it reads a prediction result file, and edits and outputs a patent requirement suitability prediction list L2 as shown in FIG. . The patent requirement suitability prediction list L2 is different from the patent requirement suitability prediction list L1 in that an OA rate is added. The OA rate indicates the possibility that a reason for refusal that violates patent requirements (novelty or inventive step) will be found in the examination process and a notice of reason for refusal indicating this will be issued. A numerical value corresponding to the non-conformity rate Vr is shown.

As described above, in the patent requirement suitability prediction server 200 according to the second embodiment, the cited invention search unit 231 includes the most similar documents in the inventive step predictive processing unit 226 of the patent requirement suitability prediction processing unit 203. Is set as the main cited document. In addition, the machine learning unit 233 classifies the abstract movement vectors V3 _{1 to} V3 _n corresponding to each similar document into three classes of S class, H class, and L class, and the requirement conformity document vector V4 ₁ to V class according to the classification result. V4 _n is output.

  In the patent requirement conformity prediction server 10 according to the first embodiment, the prediction regarding patent requirement conformity is performed in a form that conforms to examination practice and reflects examination results of the Patent Office. This also applies to the patent requirement suitability prediction server 200.

  However, in the patent requirement suitability prediction server 10, only the most similar document is extracted as the main cited document in the main cited invention search. Since the most similar document is a document having the highest similarity to the prediction target invention as a result of the concept search, it is considered that the most similar document is most likely cited as the main cited document as a result of the actual examination. However, the most similar document is not always cited in the actual examination, and there is very little difference in similarity between the most similar document and the next similar document (next similar document) It is fully conceivable that the next similar document is more appropriate as the main cited document than the most similar document. Therefore, if a plurality of documents including the most similar documents are extracted in the main cited invention search, and the document vectors are classified by the machine learning unit 233 for these documents, whether or not the patent requirement is met in consideration of the next similar documents. Predictions are made. Therefore, the prediction accuracy of the patent requirement suitability prediction server 200 is improved more than the prediction accuracy of the patent requirement suitability prediction server 10.

In actual examination practice, when a reason for refusal of inventive step violation is found for a patent application, the reason for refusal of inventive step violation and the reason for refusal of novelty violation is the same document (the document cited in this case is newly May also be pointed out by quoting refusal documents of sex / inventive step rejection). In such a case, since the examiner determines that there is no difference between the invention according to the claims of the patent application and the invention disclosed in the novelty / inventive step rejection citation, the summary movement vector V3 _When document vectors classified into the S class are included in _{1 to} V3 _n , it is considered that the possibility of finding the reason for refusal of the inventive step violation is higher than the case where it is not. Therefore, by constructing the machine learning unit 233 by performing machine learning using the SHL pattern so that the distance dp viewed from the novelty / inventive rejection application and the distance dp viewed from the inventive rejection application can be distinguished, It is possible to make a prediction that distinguishes between cases where the reason for refusal of an inventive step violation is very likely and cases where it is not. By doing so, it is possible to expect improvement in prediction accuracy by the patent requirement suitability prediction server 200 and further improvement in business efficiency.

(Modification 1)
In the inventive step prediction processing unit 226, one machine learning unit 233 classifies _n summary movement vectors V3 _{1 to} V3 _n . However, the progress in the patent requirement suitability prediction processing unit 213 shown in FIG. as sex prediction processing unit 227 includes a plurality of machine learning unit ₂₃₃ 1 _{~233 n} corresponding to the subject matter movement vector _V3 1 to V3 _n, each of which is adapted to classify the subject matter movement vector _V3 1 to V3 _n May be. Further, although not shown, a plurality of input vector generation units 132 may be provided according to the number of concept search data Vd2 _{1 to} Vd2 _n . By doing so, each of the machine learning units 233 _{1 to} 233 _n or each of the input vector generation units 132 executes the processing in parallel, so that the processing time can be shortened. Although not shown, when a plurality of designated prior art data are stored in the designated prior art data storage unit 151 (when a plurality of designated prior art data are received), the designated predicted data generation unit 134 includes them. Accordingly, a plurality of inventive step prediction data Vs1 and designated prior art data Vs2 may be generated.

(Modification 2)
In each of the embodiments described above, an example in which the patent requirement suitability prediction server 10 or 200 functions as a patent requirement suitability prediction device by installing the patent requirement suitability prediction program in the patent requirement suitability prediction server 10 or 200 will be described. To explain. In addition, the present invention is also applicable to the case where the user terminal device 30 functions as a patent requirement suitability prediction device. In this case, the above-described patent requirement suitability prediction program is changed according to at least the following changes 1) and 2), and the changed patent requirement suitability prediction program is transferred from the patent requirement suitability prediction servers 10 and 200 to the user terminal device. It may be downloaded to 30 and installed in the user terminal device 30.

Modification 1) Image data for performing an input operation such as a designated number is displayed on the user terminal device 30 without being transmitted from the patent requirement suitability prediction servers 10 and 200 to the user terminal device 30.
Modification 2) The user terminal device 30 outputs a patent requirement suitability prediction list.

  The above description is the description of the embodiment of the present invention, and does not limit the apparatus and method of the present invention, and various modifications can be easily implemented. In addition, an apparatus or a method configured by appropriately combining components, functions, features, or method steps in each embodiment is also included in the present invention.

  For example, the user terminal device may be a notebook computer or a PDA instead of a high-function mobile phone or a tablet-type terminal device. The patent requirement suitability prediction program executed by the CPU 11 can be recorded on various recording media such as a magnetic recording medium, a CD-ROM, and a DVD, and can also be downloaded from a server (not shown) via a network.

  In the above-described embodiment, the user terminal device 30 transmits the draft document data to the patent requirement suitability prediction server 10. The prediction target invention specified by the draft document data has not been filed yet, so it is necessary not to make it publicly known. For this purpose, a gist data extracting means (corresponding to the gist data extraction unit 102) for extracting gist data from the draft document data is provided in the user terminal device 30, the user terminal device 30 generates gist data, and the user terminal device 30. The summary data is preferably transmitted to the patent requirement suitability prediction server 10 by encrypted communication. In this case, the patent requirement suitability prediction server 10 only needs to include the summary data storage unit 153 that stores the received summary data, and does not need to include the summary data extraction unit 102.

  By applying the present invention, the prediction regarding the suitability of the patent requirement is made in a content that conforms to the examination practice, and the preparation burden of the application documents can be effectively reduced. The present invention can be used in the field of patent requirement suitability prediction apparatus and patent requirement suitability prediction program.

  DESCRIPTION OF SYMBOLS 1 ... Patent requirement suitability prediction system, 10,200 ... Patent requirement suitability prediction server, 11, 31 ... CPU, 30 ... User terminal device, 101 ... Proposed sentence data generation unit, 102 ... Abstract data extraction unit, 103, 203, 213 ... Patent requirement suitability prediction processing unit, 105 ... Prediction result editing processing unit, 106 ... Designated prior art data generation unit, 125 ... Novelty / expansion prior application prediction processing unit, 126, 226, 227 ... Inventive step prediction processing unit, 132 ... Input vector generation unit, 132a ... abstract vector generation unit, 132b ... citation candidate vector generation unit, 132c ... movement vector generation unit, 133, 233 ... machine learning unit, 134 ... designated prediction data generation unit, 151 ... designated prior art data storage 153 ... summary data storage unit, 154 ... CT data storage unit, 156, 256 ... prediction result storage unit, L1, L2 ... prediction result List.

Claims (4)

The invention to be predicted, which is subject to prediction of whether or not the patent requirement is satisfied, is described in one or more claims and extracted from the proposal document data constituting the proposal document including the detailed description in which the invention to be predicted is described Data, term data indicating terms that can specify the gist of the invention to be predicted, from at least feature part data extracted from the feature part of each of the claims and the problem part included in the detailed description Summary data storage means for storing data including the extracted task data as summary data;
Designated prior art data storage means for storing designated prior art data constituting the designated prior art document in which the designated prior art invention is described;
An inventive step prediction processing unit for generating invention-designated inventive step prediction data relating to the suitability of the inventive step of the prediction target invention based on the relationship with the designated prior art invention, and the prediction target invention using the invention-designated inventive step prediction data A patent result suitability prediction processing means having a prediction result file generation unit for generating a prediction result file indicating whether or not the patent requirement is suitable,
The inventive step predictive processing unit has a document classification unit for classifying document vectors,
The document classifying unit classifies an input summary movement vector as to whether or not it meets the requirement of inventive step by machine learning using a plurality of training data including a learning document vector and a teacher vector. It is constructed so as to output a requirement conformity document vector according to the classification result, and the summary movement vector includes the summary vector of the invention to be predicted according to each of the claims and the citation candidate according to the designated prior art data A patent requirement conformity prediction apparatus which is a vector corresponding to a difference from a vector. The invention to be predicted, which is subject to prediction of whether or not the patent requirement is satisfied, is described in one or more claims and extracted from the proposal document data constituting the proposal document including the detailed description in which the invention to be predicted is described Data, term data indicating terms that can specify the gist of the invention to be predicted, from at least feature part data extracted from the feature part of each of the claims and the problem part included in the detailed description Summary data storage means for storing data including the extracted task data as summary data;
Designated prior art data storage means for storing designated prior art data constituting the designated prior art document in which the designated prior art invention is described;
Using the gist data stored in the gist data storage means, search public gazette data which is electronic data of a public gazette, and indicate whether the novelty requirement of the prediction target invention is appropriate according to the search result. Novelty prediction processing unit for generating novelty prediction data, inventive step prediction processing unit for generating inventive step prediction data indicating whether or not the inventive step requirements are appropriate, and the novelty prediction data and the inventive step prediction data Patent requirement suitability prediction processing means having a prediction result file generation unit that generates a prediction result file indicating the suitability of patent requirements of the invention to be predicted using
The inventive step prediction processing unit includes a main citation invention search unit that searches for a main citation invention closest to the prediction target invention among prior art inventions specified by the publication gazette data, and a document classification that classifies document vectors And
The main citation invention search unit targets the publication gazette data with the feature portion data and the problem data of each claim out of the summary data stored in the summary data storage means as main search document data. A plurality of documents including the most similar document having the highest similarity in descending order of the similarity as a result of the concept search, and the main cited invention discloses each similar document. As the main cited reference,
The document classification unit is a class that has a high possibility that the input summary movement vector does not conform to the requirement of inventive step by machine learning using a plurality of training data including a learning document vector and a teacher vector, a high class, It is constructed so as to be classified into any of the classes that meet the requirement of the inventive step and to output a requirement conformity document vector according to the classification result, and the summary movement vector is the prediction target according to each claim A plurality of vectors according to the difference between the gist vector of the invention and each citation candidate vector according to each of the similar documents,
The patent requirement conformity prediction processing means includes:
A non-conformance rate calculating unit that calculates a non-conformance rate indicating the possibility of not conforming to the inventive step requirement for the prediction target invention according to the plurality of requirement conformity document vectors output from the document classification unit;
When the designated prior art data is stored in the designated prior art data storage means, the inventive step predictive processing unit uses the prediction subject invention in relation to the designated prior art invention instead of the inventive step predictive data. A patent requirement conformity prediction apparatus further comprising: a prediction processing control unit that performs control so as to generate invention-designated inventive step prediction data relating to the suitability of the inventive step. The invention to be predicted, which is subject to prediction of whether or not the patent requirement is satisfied, is described in one or more claims and extracted from the proposal document data constituting the proposal document including the detailed description in which the invention to be predicted is described Data, term data indicating terms that can specify the gist of the invention to be predicted, from at least feature part data extracted from the feature part of each of the claims and the problem part included in the detailed description Summary data storage means for storing data including the extracted task data as summary data;
Using the gist data stored in the gist data storage means, search public gazette data which is electronic data of a public gazette, and indicate whether the novelty requirement of the prediction target invention is appropriate according to the search result. Novelty prediction processing unit for generating novelty prediction data, inventive step prediction processing unit for generating inventive step prediction data indicating whether or not the inventive step requirements are appropriate, and the novelty prediction data and the inventive step prediction data Patent requirement suitability prediction processing means having a prediction result file generation unit for generating a prediction result file indicating the suitability of patent requirements of the invention to be predicted using
Prediction result storage means for storing the prediction result file generated by the prediction result file generation unit;
The inventive step prediction processing unit includes a main citation invention search unit that searches for a main citation invention closest to the prediction target invention among prior art inventions specified by the publication gazette data, and a document classification that classifies document vectors And
The main citation invention search unit targets the publication gazette data with the feature portion data and the problem data of each claim out of the summary data stored in the summary data storage means as main search document data. A plurality of documents including the most similar document having the highest similarity in descending order of the similarity as a result of the concept search, and the main cited invention discloses each similar document. As the main cited reference,
The document classification unit is a class that has a high possibility that the input summary movement vector does not conform to the requirement of inventive step by machine learning using a plurality of training data including a learning document vector and a teacher vector, a high class, It is constructed so as to be classified into any of the classes that meet the requirement of the inventive step and to output a requirement conformity document vector according to the classification result, and the summary movement vector is the prediction target according to each claim A patent requirement propriety prediction device which is a plurality of vectors corresponding to differences between a gist vector of an invention and each citation candidate vector corresponding to each similar document. A patent requirement propriety prediction program for causing a computer to function as a patent requirement propriety prediction device, wherein the prediction target invention for which the computer is subject to patent requirement propriety prediction is described in one or more claims, and the prediction Data extracted from the draft document data constituting the draft document including the detailed description in which the subject invention is described, and is term data indicating terms that can specify the gist of the invention to be predicted, Gist data storage control means for storing, as gist data, characteristic part data extracted from the characteristic part of claim and data including problem data extracted from the problem part included in the detailed description;
Designated prior art data storage control means for storing designated prior art data constituting the designated prior art document in which the designated prior art invention is described;
An inventive step prediction processing unit for generating invention-designated inventive step prediction data relating to the suitability of the inventive step of the prediction target invention based on the relationship with the designated prior art invention, and the prediction target invention using the invention-designated inventive step prediction data A prediction result file generation unit that generates a prediction result file indicating whether or not the patent requirement is appropriate, and function as a patent requirement appropriateness prediction processing means,
The inventive step predictive processing unit has a document classification unit for classifying document vectors;
The document classification unit classifies the input summary movement vector as to whether it meets the requirement of inventive step by machine learning using a plurality of training data including a learning document vector and a teacher vector. It is constructed so as to output a requirement conformity document vector according to the classification result, and the summary movement vector includes the summary vector of the invention to be predicted according to each of the claims and the citation candidate according to the designated prior art data Patent requirement conformity prediction program which is a vector corresponding to a difference from a vector.

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