Intelligent recommendation system for patent conversion application objects

Abstract

The invention relates to an intelligent recommendation system for patent conversion application objects, belongs to the technical field of intelligent recommendation, and solves the problem that an existing patent conversion application object recommendation mode is low in accuracy. The system comprises a multi-dimensional technical summary module for acquiring technical summary of a to-be-converted patent in different dimensions by using a large model, and checking the technical summary to obtain a technical summary result of the to-be-converted patent in the corresponding dimension; the multi-dimensional candidate patent retrieval module is used for performing feature extraction on technical summary results of the to-be-converted patent in different dimensions, and performing intelligent retrieval on the basis of extracted technical features to obtain a candidate patent set of the to-be-converted patent in the corresponding dimension; the intelligent patent conversion application object recommendation module is used for acquiring the co-occurrence degree, the average similarity and the operation degree of co-occurrence patents of the to-be-converted patent among the candidate patent sets of different dimensions so as to determine the recommendation degree of each co-occurrence patent; and taking the co-occurrence patent with the maximum recommendation degree as a patent conversion application object.

Description

Technical Field

[0001] The present invention relates to the field of intelligent recommendation technology, and in particular to an intelligent recommendation system for patent transformation and application objects. Background Art

[0002] Patent transformation and application are of great significance in promoting the practical application of technological innovation, optimizing the overall resource allocation of society, and promoting industrial upgrading. However, due to the information barriers between patent holders and potential users, a large number of valuable technological achievements are difficult to be discovered and effectively applied. Therefore, it is necessary to find possible patent transformation and application objects for the patents to be transformed through certain means.

[0003] At present, there are some scientific and technological achievements or patent transformation service systems and patent recommendation systems on the market, and the implementation methods mainly include the following:

[0004] (1) Seek partners with relevant technical capabilities and resources through market research, visits, negotiations, etc.;

[0005] (2) Establishing publishing channels and communication connections between the receiving entity and the supply entity;

[0006] (3) Recommendations based on the same IPC code; however, the number of patents recommended based on IPC is large, and it is difficult for users to quickly find highly relevant target patents from the massive amount of patent information;

[0007] (4) After obtaining the pre-recommended patent data set based on the data features of bibliographic item dimensions such as international classification numbers, keyword sets, applicant sets, and inventor sets, the feature vectors of the recommended patents and each patent in the pre-recommended patent data set are compared, and the recommended patent is determined based on the relevance;

[0008] (5) Directly calculate the similarity between patent texts through methods such as keyword extraction and word shift distance, and make recommendations based on this.

[0009] Among these existing technologies, it is a general trend to determine the objects of patent transformation through intelligent matching by means of information technology. However, the premise of intelligent matching is to obtain candidate patents with relatively high accuracy, and on this basis, screen out more suitable recommended patents through various control strategies. At present, the methods of obtaining candidate patents through data feature matching of recorded items or calculation based on full-text semantic similarity have problems with accuracy, which seriously restricts the scope of subsequent discovery of patent transformation objects. Therefore, how to effectively improve the accuracy of the recommendation of patent transformation and application objects is a technical problem that urgently needs to be solved. Summary of the invention

[0010] In view of the above analysis, an embodiment of the present invention aims to provide an intelligent recommendation system for patent transformation and application objects, so as to solve the problem of low accuracy of existing patent transformation and application object recommendation methods.

[0011] The present invention discloses an intelligent recommendation system for patent transformation and application objects, the system comprising:

[0012] The multi-dimensional technical summary module uses a large model to obtain technical summaries of the patents to be converted in different dimensions, and obtains technical summary results of the patents to be converted in the corresponding dimensions after reviewing the technical summaries;

[0013] The multi-dimensional candidate patent search module extracts features from the technical summary results of the patents to be converted in different dimensions, and performs intelligent searches based on the extracted technical features to obtain a candidate patent set in the corresponding dimensions of the patents to be converted;

[0014] The intelligent recommendation module for patent conversion and application objects obtains the co-occurrence degree, average similarity and operation degree of co-occurring patents among the candidate patent sets of different dimensions of the patents to be converted, so as to determine the recommendation degree of each co-occurring patent; and selects the co-occurring patent with the highest recommendation degree as the patent conversion and application object.

[0015] On the basis of the above scheme, the present invention also makes the following improvements:

[0016] Furthermore, the multi-dimensional candidate patent search module includes: a technical feature extraction unit for each dimension, a search expression generation unit, and an intelligent search unit; wherein,

[0017] The technical feature extraction unit of each dimension extracts features from the technical summary results of the patent to be converted in the corresponding dimension, and obtains the technical features of the patent to be converted in the corresponding dimension;

[0018] The search expression generation unit comprehensively analyzes the technical features of the patent to be converted in different dimensions and obtains multiple intelligent search expressions of the patent to be converted in different dimensions;

[0019] The intelligent retrieval unit uses a variety of intelligent retrieval expressions of the patent to be converted in different dimensions to perform patent retrieval in the intelligent retrieval system to obtain a candidate patent set of the patent to be converted in different dimensions.

[0020] Further, in the search expression generation unit, execute:

[0021] The technical features of the patent to be converted that appear simultaneously in the technical problem dimension, technical means dimension and technical effect dimension are regarded as key technical features;

[0022] Determine the similarity of multiple technical features of the patent to be converted in the technical problem dimension, and take multiple technical features whose similarity exceeds a certain similarity threshold as similar features in the technical problem dimension, and connect them with an or relationship;

[0023] Determine the similarity of multiple technical features in the technical means dimension of the patent to be converted, and take multiple technical features whose similarity exceeds a certain similarity threshold as similar features in the technical means dimension, and connect them with an or relationship;

[0024] Determine the similarity of multiple technical features of the patent to be converted in the technical effect dimension, and take multiple technical features whose similarity exceeds a certain similarity threshold as similar features in the technical effect dimension, and connect them with an or relationship;

[0025] Use key technical features as necessary keywords for retrieval, and connect similar features of the technical problem dimension connected by the relationship of "or", and the remaining technical features of the technical problem dimension using multiple forms of logical relationships to form multiple intelligent retrieval expressions of the patent to be converted in the technical problem dimension;

[0026] Use key technical features as necessary keywords for retrieval, and connect similar features in the technical means dimension connected by the relationship of "or", and the remaining technical features in the technical means dimension using a variety of different forms of logical relationships to form a variety of intelligent retrieval expressions in the technical means dimension of the patent to be converted;

[0027] The key technical features are used as necessary keywords for retrieval, and similar features in the technical effect dimension connected by the relationship of "or" and the remaining technical features in the technical effect dimension are connected by a variety of different forms of logical relationships to form a variety of intelligent retrieval expressions for the patent to be converted in the technical effect dimension.

[0028] Further, in the intelligent retrieval unit, execute:

[0029] Use each intelligent search expression of the patent to be converted in the corresponding dimension to perform patent search in the intelligent search system, and obtain the top M related patents with the highest similarity corresponding to each intelligent search expression;

[0030] The related patents that appear repeatedly in the top M related patents with similarity corresponding to different types of intelligent search expressions are taken as the preliminary candidate patents of the corresponding dimension, and the top N preliminary candidate patents with similarity to the technical content of the patent to be converted in the corresponding dimension are selected from them as the candidate patent set of the patent to be converted in the corresponding dimension.

[0031] Furthermore, in the intelligent recommendation module for patent transformation and application objects, execute:

[0032] Arrange the similarity of each candidate patent in the candidate patent set of different dimensions of the patent to be converted in descending order and assign corresponding similarity scores;

[0033] Select candidate patents that appear in at least two candidate patent sets of different dimensions as co-occurring patents, add up the similarity scores of each co-occurring patent in each candidate patent set, and normalize them to obtain the co-occurrence degree of each co-occurring patent;

[0034] The similarity scores of each co-occurring patent in each candidate patent set are averaged to obtain the average similarity of each co-occurring patent;

[0035] According to the number of different types of operating behaviors implemented by the patentee in each co-occurring patent, the operating degree of the corresponding co-occurring patent is obtained.

[0036] Furthermore, in the intelligent recommendation module for patent transformation and application objects, it is assumed that the weights of co-occurrence, average similarity, and operation degree are α, β, and γ, respectively, and α+β+γ=1;

[0037] The recommendation degree R of each co-occurring patent is calculated according to the formula: R = α*DC + β*DS + γ*DO;

[0038] Among them, DC, DS, and DO represent the co-occurrence degree, average similarity, and operation degree of co-occurring patents, respectively.

[0039] Furthermore, different types of operational activities carried out by patent owners include: transfer, licensing, and pledge.

[0040] Furthermore, the weights of the number of transfers, the number of licenses, and the number of pledges are set to a, b, and c respectively, and a+b+c=1;

[0041] For each co-occurring patent, assuming that the number of transfers is N1, the number of licenses is N2, and the number of pledges is N3, then a*N1+b*N2+c*N3 is normalized to a value of [1,100] as the operational degree of the current co-occurring patent.

[0042] Furthermore, the multi-dimensional technical summary module includes a preliminary technical summary unit and an output review unit for each dimension; wherein,

[0043] The preliminary technical summary unit of each dimension is used to summarize the technical content of the corresponding dimension in the patent to be converted based on the macro model of the corresponding dimension, and obtain the preliminary technical summary results of the patent to be converted in the corresponding dimension;

[0044] The output review unit of each dimension is used to output and review the preliminary technical summary results of the patent to be transformed in the corresponding dimension, and obtain the technical summary results of the patent to be transformed in the corresponding dimension.

[0045] Furthermore, the dimensions include technical problem dimension, technical means dimension and technical effect dimension.

[0046] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0047] The patent transformation and application object intelligent recommendation system provided by the present invention is realized by the cooperation of a multi-dimensional technology summary module, a multi-dimensional candidate patent search module and a patent transformation and application object intelligent recommendation module. In the multi-dimensional technology summary module, by using a large model to obtain the technical summary of the patent to be transformed in different dimensions, and after reviewing the technical summary, the technical summary results of the patent to be transformed in the corresponding dimensions are obtained, and the technical summary results of the patent to be transformed in different dimensions with high accuracy can be obtained. Afterwards, in the multi-dimensional candidate patent search module, by extracting features from the technical summary results of the patent to be transformed in different dimensions, and intelligently searching based on the extracted technical features, the candidate patent set of the patent to be transformed in the corresponding dimension is obtained, which effectively improves the accuracy of the selected candidate patents. Finally, in the patent transformation and application object intelligent recommendation module, by comprehensively considering the quantitative data of co-occurring patents from different angles between different dimensions, the recommendation degree of each co-occurring patent is quantified; finally, the co-occurring patent with the largest recommendation degree is selected as the patent transformation and application object. In summary, the present invention can effectively improve the accuracy of the obtained candidate patent set by improving the multi-dimensional technology summary and multi-dimensional patent search process. On this basis, we creatively proposed a quantitative calculation method for the recommendation degree of co-occurring patents, and ultimately successfully selected the objects of patent transformation and application, effectively solving the problem of low accuracy of the existing methods for recommending objects of patent transformation and application.

[0048] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0050] Figure 1 A schematic diagram of the structure of the intelligent recommendation system for patent transformation and application objects provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of the structure of another patent conversion application object intelligent recommendation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0053] A specific embodiment of the present invention discloses an intelligent recommendation system for patent transformation and application objects, the structural diagram of which is shown in FIG. Figure 1 and 2 shown.

[0054] (1) Multi-dimensional technical summary module

[0055] The multi-dimensional technical summary module uses a large model to obtain the technical summary of the patent to be converted in different dimensions, and obtains the technical summary results of the patent to be converted in the corresponding dimensions after reviewing the technical summary.

[0056] Specifically, in this embodiment, the dimensions involved include technical problem dimension, technical means dimension and technical effect dimension. Therefore, in the multi-dimensional technical summary module, the technical summary of the patent to be converted is performed in the technical problem dimension, technical means dimension and technical effect dimension respectively, so as to obtain the technical summary results of the patent to be converted in the technical problem dimension, technical means dimension and technical effect dimension.

[0057] Preferably, in this embodiment, the multi-dimensional technology summary module includes a preliminary technology summary unit and an output review unit for each dimension. The functions of each unit are specifically introduced below.

[0058] 1) Preliminary technical summary unit for each dimension

[0059] The preliminary technical summary unit of each dimension is used to make a technical summary of the technical content of the corresponding dimension in the patent to be transformed based on the big model of the corresponding dimension, and obtain the preliminary technical summary results of the patent to be transformed in the corresponding dimension.

[0060] Preferably, in this embodiment, the technical contents of different dimensions in the patent to be converted are not the same. Specifically, the technical contents of the technical problem dimension include the background technology of the patent to be converted, the technical contents of the technical means dimension include the claims of the patent to be converted, and the technical contents of the technical effect dimension include the beneficial effects of the patent to be converted.

[0061] During the specific implementation process, large models of different dimensions may be implemented in the form of models of the same or different categories. In this embodiment, the large model is preferably implemented by a generative artificial intelligence interaction system. Currently, the more common generative artificial intelligence interaction systems include ChatGPT, Wenxinyiyan or chatGLM. Before using the large model to predict the output of the actual preliminary technical summary results, it is necessary to train the parameters of the large model through a large amount of training corpus to fit the correspondence between input and output. During the training process, large models of different dimensions need to use training expectations of corresponding dimensions for model training. For example, the large model of the technical problem dimension needs to use the background technology in multiple patent applications as training expectations for training; the large model of the technical means dimension needs to use the claims in multiple patent applications as training expectations for training; the large model of the technical effect dimension uses the beneficial effects in multiple patent applications as training expectations for training.

[0062] At the same time, in this embodiment, a variety of interactive input templates of the corresponding dimension are provided in the big model of each dimension; when making a technical summary of the technical content of the corresponding dimension in the patent to be transformed, the technical content of the corresponding dimension in the patent to be transformed is placed in an interactive input template of the corresponding dimension as the input information of the big model of the corresponding dimension, and the big model of the corresponding dimension processes the input information to obtain a preliminary technical summary result of the corresponding dimension.

[0063] In a certain example, the following method can be adopted to set the functions of the preliminary technical summary unit of the technical problem dimension, the technical means dimension and the technical effect dimension respectively.

[0064] a) Preliminary technical summary unit of technical problem dimension

[0065] The preliminary technical summary unit of the technical problem dimension is used to perform a technical summary of the background technology in the patent to be converted based on the large model of the technical problem dimension, and obtain the preliminary technical summary results of the patent to be converted in the technical problem dimension.

[0066] It should be noted that the background technology of the patent to be converted contains technical problems. In order to ensure the accuracy of subsequent feature extraction and intelligent retrieval and reduce the workload of subsequent processes, it is necessary to summarize the technical problems of the background technology.

[0067] Preferably, in the preliminary technical summary unit of the technical problem dimension, a large model of the technical problem dimension is built in. Various forms of interactive input templates of the technical problem dimension are provided in the large model of the technical problem dimension. By setting various forms of interactive input templates of the technical problem dimension, the output results of the large model of the technical problem dimension can be controlled to a certain extent. Exemplarily, an interactive input template of the technical problem dimension can be expressed as: summarizing the technical problems of "background technology". Here, "background technology" refers to the background technology in the patent to be converted. In addition, in other interactive input templates of the technical problem dimension, further limitations can be made on the basis of the above-mentioned interactive input templates in terms of the number of words in the technical problem summary, the included keywords, etc., so as to obtain various forms of interactive input templates of the technical problem dimension.

[0068] During the specific implementation process, the background technology of the patent to be converted is placed in an interactive input template in the technical problem dimension as input information of the big model of the technical problem dimension. After the big model of the technical problem dimension processes the input information, a preliminary technical summary result of the technical problem dimension is obtained.

[0069] b) Preliminary technical summary unit of technical means dimension

[0070] The preliminary technical summary unit of the technical means dimension is used to make a technical summary of the claims in the patent to be transformed based on the big model of the technical means dimension, and obtain the preliminary technical summary results of the patent to be transformed in the technical means dimension.

[0071] Preferably, in the preliminary technical summary unit of the technical means dimension, a large model of the technical means dimension is built in. Various forms of interactive input templates of the technical means dimension are provided in the large model of the technical means dimension. By setting various forms of interactive input templates of the technical means dimension, the output results of the large model of the technical means dimension can be controlled to a certain extent. Exemplarily, an interactive input template of the technical means dimension can be expressed as: summarizing the technical means of the "claims". Here, "claims" refers to the claims in the patent to be converted. In addition, in other interactive input templates of the technical means dimension, further limitations can be made on the basis of the above-mentioned interactive input templates in terms of the number of words in the technical means summary, the included keywords, etc., so as to obtain various forms of interactive input templates of the technical means dimension.

[0072] During the specific implementation process, the claims of the patent to be converted are placed in an interactive input template in the technical means dimension as input information of the big model of the technical means dimension. The big model of the technical means dimension processes the input information to obtain the preliminary technical summary results of the technical means dimension.

[0073] c) Preliminary technical summary unit of technical effect dimension

[0074] The preliminary technical summary unit of the technical effect dimension is used to make a technical summary of the beneficial effects in the patent to be transformed based on the large model of the technical effect dimension, and obtain the preliminary technical summary results of the patent to be transformed in the technical effect dimension.

[0075] It should be noted that, in the specific implementation process, the beneficial effects of the patent to be transformed (and / or specific phrases containing vocabulary representing technical effects) can be summarized into technical effects to obtain preliminary technical summary results of the patent to be transformed in the technical effect dimension.

[0076] Preferably, in the preliminary technical summary unit of the technical effect dimension, a large model of the technical effect dimension is built in. Various forms of interactive input templates of the technical effect dimension are provided in the large model of the technical effect dimension. By setting various forms of interactive input templates of the technical effect dimension, the output results of the large model of the technical effect dimension can be controlled to a certain extent. Exemplarily, an interactive input template of the technical effect dimension can be expressed as: summarizing the technical effects of "beneficial effects", where "beneficial effects" refer to the beneficial effects in the patent to be converted. In addition, in other interactive input templates of the technical effect dimension, further limitations can be made on the basis of the above-mentioned interactive input templates in terms of the number of words in the technical effect summary, the included keywords, etc., so as to obtain various forms of interactive input templates of the technical effect dimension.

[0077] During the specific implementation process, the beneficial effects of the patent to be converted are placed in an interactive input template in the technical effect dimension as input information of the big model of the technical effect dimension. After the big model of the technical effect dimension processes the input information, a preliminary technical summary result of the technical effect dimension is obtained.

[0078] 2) Output review unit for each dimension

[0079] The output review unit of each dimension is used to output and review the preliminary technical summary results of the patent to be transformed in the corresponding dimension, and obtain the technical summary results of the patent to be transformed in the corresponding dimension.

[0080] Preferably, in this embodiment, the output review unit of each dimension is used to review whether the technical summary result of the corresponding dimension is the technical-related information of the patent to be converted. If so, the output review is passed, and the preliminary technical summary result of the corresponding dimension is used as the technical summary result of the patent to be converted in the corresponding dimension; otherwise, the output review fails, and the preliminary technical summary unit of the corresponding dimension is returned, and the interactive input template of the corresponding dimension is replaced, and the technical content of the corresponding dimension in the patent to be converted is placed in the replaced interactive input template of the corresponding dimension, and again used as the input information of the large model of the corresponding dimension. The large model of the corresponding dimension processes the input information to obtain the preliminary technical summary result of the corresponding dimension, and then the output review unit of the corresponding dimension performs output review until the output review is passed.

[0081] During the specific implementation process, the output review unit of each dimension has a built-in output review controller and multiple output reviewers of the corresponding dimension; wherein the output review controller is used to control whether each output reviewer is effective and to control the logical relationship between the multiple effective output reviewers. Each output reviewer, from its own review perspective, reviews whether the preliminary technical summary results of the corresponding dimension are technical-related information of the patent to be converted. Exemplarily, the output reviewer can be one of a technology classifier, a tendency classifier, an output approval keyword whitelist, and an output approval keyword blacklist. During the specific implementation process, the output review controller adjusts the review intensity of the output review unit of the corresponding dimension by controlling the effectiveness of different output reviewers and controlling the logical relationship between the effective output reviewers.

[0082] Below, the technical classifier, tendency classifier, output approval keyword whitelist and output approval keyword blacklist are explained in detail.

[0083] Preferably, in this embodiment, both the technical classifier and the tendency classifier can be obtained based on neural network training. Among them, the technical classifier is a binary classification judgment of "yes / no is it a technical statement" for the preliminary technical summary results. And the tendency classifier is a binary classification judgment of "yes / no contains bad tendencies" for the preliminary technical summary results. Exemplarily, the technical classifier and the tendency classifier can be obtained based on a general CNN neural network model. In the specific implementation process, for the technical classifier, a large amount of input information and its technical labels can be made in advance. Exemplarily, 0 and 1 labels can be designed, where 1 represents that the input information is a technical statement and 0 represents that the input information is not a technical statement, thereby forming a technical training sample set. And based on the technical training sample set, the neural network corresponding to the technical classifier is trained for multiple rounds. After the training is passed, a technical classifier that can be used for binary classification judgment of actual technical statements is obtained. Similar to the technical classifier, the tendency classifier can also make a large amount of input information and its tendency labels in advance during the training process. Exemplarily, 0 and 1 labels can be designed, where 1 represents that the input information does not contain bad tendencies and 0 represents that the input information contains bad tendencies, thereby forming a tendency training sample set. Based on the tendency training sample set, the neural network corresponding to the tendency classifier is trained for multiple rounds. After the training, a tendency classifier that can be used for actual tendency binary classification is obtained. In the specific implementation process, in order to improve the efficiency of training and model application, the same technical classifier and tendency classifier can be selected for output audit units of different dimensions.

[0084] Preferably, considering that the output approval keyword whitelist and output approval keyword blacklist of different dimensions have different focuses, it is necessary to specifically set the output approval keyword whitelist and output approval keyword blacklist of different dimensions according to the content of the patent to be converted. Exemplarily, the output approval keyword whitelist of the technical problem dimension may include the core vocabulary in the technical problem solved by the patent to be converted. The output approval keyword blacklist of the technical problem dimension may include the relevant vocabulary of common technical problems that the patent to be converted does not involve in the relevant technical field. The output approval keyword whitelist of the technical means dimension may include the core vocabulary in the technical means of the patent to be converted. The output approval keyword blacklist of the technical means dimension may include the relevant vocabulary of common technical means that the patent to be converted does not involve in the relevant technical field. In addition, the output approval keyword whitelist of the technical effect dimension may include the core vocabulary in the technical effect of the patent to be converted. The output approval keyword blacklist of the technical effect dimension may include the relevant vocabulary of common technical effects that the patent to be converted does not involve in the relevant technical field.

[0085] Based on the above description, it can be known that for each output auditor, it is possible to audit whether the technical summary results of the corresponding dimension are technical related information of the patent to be converted from the corresponding audit perspective. That is, for the technical classifier, it can be judged whether the preliminary technical summary results are technical statements. For example, 1 is a technical statement, that is, technical related information of the patent to be converted; 0 is not a technical statement, that is, it is not technical related information of the patent to be converted. For the tendency classifier, it can be judged whether the preliminary technical summary results contain bad tendencies. 1 is not containing bad tendencies, that is, technical related information of the patent to be converted; 0 is containing bad tendencies, that is, it is not technical related information of the patent to be converted. For the output approval keyword whitelist, it can be judged whether the preliminary technical summary results contain words in the output approval keyword whitelist. For example, 1 is included, that is, technical related information of the patent to be converted; 0 is not included, that is, it is not technical related information of the patent to be converted. For the output approval keyword blacklist, it can be judged whether the preliminary technical summary results contain words in the output approval keyword blacklist. For example, 1 is not included, that is, technical related information of the patent to be converted; 0 is included, that is, it is not technical related information of the patent to be converted.

[0086] The output audit controller can control different output auditors to be effective, and only the effective output auditor can audit whether the technical summary results of the corresponding dimension are technical related information of the patent to be converted. In addition, the output audit controller can also adjust the audit strength of the output audit unit of the corresponding dimension by controlling the logical relationship between the effective output auditors. Exemplarily, when the output audit controller controls the above four output auditors to be effective and the logical relationship is "and", only when the judgment results of the above four output auditors are all 1, the output audit passes; otherwise, the output audit fails. When the output audit controller controls the above four output auditors to be effective and the logical relationship is "or", the judgment result of any output auditor is 1, and the output audit passes; only when the judgment results of the four output auditors are all 0, the output audit fails. Therefore, the purpose of adjusting the audit strength of the output audit unit can be achieved by controlling the effectiveness and logical relationship of the output auditor by the output audit controller.

[0087] (2) Multi-dimensional candidate patent search module

[0088] The multi-dimensional candidate patent search module extracts features from the technical summary results of the patents to be converted in different dimensions, and performs intelligent searches based on the extracted technical features to obtain a set of candidate patents for the patents to be converted in the corresponding dimensions.

[0089] That is, the multi-dimensional candidate patent search module extracts features from the technical summary results of the patents to be transformed in the technical problem dimension, technical means dimension, and technical effect dimension, and performs intelligent searches based on the extracted technical features to obtain a set of candidate patents for the patents to be transformed in the technical problem dimension, technical means dimension, and technical effect dimension.

[0090] Preferably, the multi-dimensional candidate patent search module includes: a technical feature extraction unit for each dimension, a search expression generation unit, and an intelligent search unit. The specific functions are described as follows.

[0091] 1) Technical feature extraction unit for each dimension

[0092] The technical feature extraction unit of each dimension extracts features from the technical summary results of the patent to be transformed in the corresponding dimension, and obtains the technical features of the patent to be transformed in the corresponding dimension.

[0093] Specifically, in the technical feature extraction unit of the technical problem dimension, feature extraction is performed on the technical summary results of the patent to be transformed in the technical problem dimension to obtain the technical features of the patent to be transformed in the technical problem dimension. In the technical feature extraction unit of the technical means dimension, feature extraction is performed on the technical summary results of the patent to be transformed in the technical means dimension to obtain the technical features of the patent to be transformed in the technical means dimension. In the technical feature extraction unit of the technical effect dimension, feature extraction is performed on the technical summary results of the patent to be transformed in the technical effect dimension to obtain the technical features of the patent to be transformed in the technical effect dimension.

[0094] Preferably, in this embodiment, the TF-IDF algorithm can be combined with a key dictionary to extract features from the technical summary results of the patent to be transformed in the technical problem dimension, technical means dimension and technical effect dimension, so as to obtain the technical features of the patent to be transformed in the technical problem dimension, technical means dimension and technical effect dimension; or, feature extraction is performed on the technical summary results of the patent to be transformed in the technical problem dimension, technical means dimension and technical effect dimension, and after obtaining the corresponding feature extraction results, the corresponding feature extraction results are also associated and inferred with hypernym elements to summarize the technical feature hypernyms of the corresponding feature extraction results, and the feature extraction results and technical feature hypernyms of the patent to be transformed in the technical problem dimension, technical means dimension and technical effect dimension are respectively summarized to form the technical features of the patent to be transformed in the technical problem dimension, technical means dimension and technical effect dimension. It should be noted that in this embodiment, the technical features of the patent to be transformed in the technical problem dimension, technical means dimension and technical effect dimension are embodied in the form of feature words.

[0095] In the specific implementation process, a deep learning model, such as a convolutional neural network (CNN), can be used to associate and infer hypernym elements of each technical feature extraction result to obtain the technical feature hypernym of the corresponding feature extraction result. Through the above deep learning model, intelligent retrieval based on the summarized technical feature hypernyms can effectively identify certain patents with different patent expressions but the same hypernym concepts (that is, the same essential content).

[0096] 2) Retrieval expression generation unit

[0097] The search expression generation unit comprehensively analyzes the technical features of the patents to be converted in different dimensions and obtains a variety of intelligent search expressions for the patents to be converted in different dimensions.

[0098] Preferably, the search expression generation unit can combine the format requirements of the intelligent search engine for the search expression and use the following method to obtain the intelligent search expression of the patent to be converted in the technical problem dimension, technical means dimension and technical effect dimension:

[0099] The technical features of the patent to be converted that appear simultaneously in the technical problem dimension, technical means dimension and technical effect dimension are regarded as key technical features;

[0100] Determine the similarity of multiple technical features of the patent to be converted in the technical problem dimension, and take multiple technical features whose similarity exceeds a certain similarity threshold as similar features in the technical problem dimension, and connect them with an or relationship;

[0101] Determine the similarity of multiple technical features in the technical means dimension of the patent to be converted, and take multiple technical features whose similarity exceeds a certain similarity threshold as similar features in the technical means dimension, and connect them with an or relationship;

[0102] Determine the similarity of multiple technical features of the patent to be converted in the technical effect dimension, and take multiple technical features whose similarity exceeds a certain similarity threshold as similar features in the technical effect dimension, and connect them with an or relationship;

[0103] Use key technical features as necessary keywords for retrieval, and connect similar features of the technical problem dimension connected by the relationship of "or", and the remaining technical features of the technical problem dimension using multiple forms of logical relationships to form multiple intelligent retrieval expressions of the patent to be converted in the technical problem dimension;

[0104] Use key technical features as necessary keywords for retrieval, and connect similar features in the technical means dimension connected by the relationship of "or", and the remaining technical features in the technical means dimension using a variety of different forms of logical relationships to form a variety of intelligent retrieval expressions in the technical means dimension of the patent to be converted;

[0105] The key technical features are used as necessary keywords for retrieval, and similar features in the technical effect dimension connected by the relationship of "or" and the remaining technical features in the technical effect dimension are connected by a variety of different forms of logical relationships to form a variety of intelligent retrieval expressions for the patent to be converted in the technical effect dimension.

[0106] Exemplarily, the various forms of logical relationship connections used here can be logical relationships such as and, or, etc., thereby enriching the form of intelligent search expressions and being closer to the actual search habits of technicians. In the above manner, a variety of intelligent search expressions can be formed for the patents to be converted in the dimensions of technical problems, technical means, and technical effects, which can ensure that the intelligent search expressions contain key technical features and the richness of the retrieved patents.

[0107] 3) Intelligent retrieval unit

[0108] The intelligent retrieval unit uses a variety of intelligent retrieval expressions of the patent to be converted in different dimensions to perform patent retrieval in the intelligent retrieval system to obtain a candidate patent set of the patent to be converted in different dimensions.

[0109] Preferably, in the intelligent retrieval unit, a plurality of intelligent retrieval expressions of the patent to be transformed in the technical problem dimension, technical means dimension and technical effect dimension are used to perform patent retrieval in the intelligent retrieval system, and the retrieved similar patents of the patent to be transformed in the technical problem dimension, technical means dimension and technical effect dimension are used as the candidate patent sets of the patent to be transformed in the technical problem dimension, technical means dimension and technical effect dimension.

[0110] Specifically, in the intelligent search unit, for each dimension, the candidate patent set of the patent to be converted in the corresponding dimension is obtained in the following way:

[0111] Use each intelligent search expression of the patent to be converted in the corresponding dimension to perform patent search in the intelligent search system respectively, and obtain M related patents with the highest similarity corresponding to each intelligent search expression.

[0112] The related patents that appear repeatedly in the top M related patents with similarity corresponding to different types of intelligent search expressions are taken as the preliminary candidate patents of the corresponding dimension, and the top N preliminary candidate patents with similarity to the technical content of the patent to be converted in the corresponding dimension are selected from them as the candidate patent set of the patent to be converted in the corresponding dimension.

[0113] In the specific implementation process, for the technical problem dimension, technical means dimension and technical effect dimension, by performing the above operations respectively, the candidate patent sets of the patent to be converted in the technical problem dimension, technical means dimension and technical effect dimension can be obtained. Exemplarily, M is greater than N. In the specific implementation process, considering that the number of patent conversion and application objects finally selected is small, or even only one. The value of N should not be too large. Exemplarily, N is 50. The value of N can also be set adaptively according to the number of preliminary candidate patents.

[0114] (3) Intelligent recommendation module for patent transformation and application objects

[0115] The intelligent recommendation module for patent conversion and application objects obtains the co-occurrence degree, average similarity and operation degree of co-occurring patents among the candidate patent sets of different dimensions of the patents to be converted, so as to determine the recommendation degree of each co-occurring patent; and selects the co-occurring patent with the highest recommendation degree as the patent conversion and application object.

[0116] In the patent conversion application object intelligent recommendation module, the candidate patent sets of the patents to be converted in different dimensions are processed as follows:

[0117] 1) Calculate the co-occurrence DC of co-occurring patents

[0118] The similarity of each candidate patent in the candidate patent set of different dimensions of the patent to be converted (i.e., the similarity of the technical content of each candidate patent with the patent to be converted in the corresponding dimension) is arranged in descending order, and a corresponding similarity score is assigned. For example, in each candidate patent set, the similarity score of the candidate patent with the highest similarity is 50 points, and so on, the similarity score of the candidate patent with the lowest similarity is 1 point.

[0119] Candidate patents that appear in at least two candidate patent sets in different dimensions are selected as co-occurring patents. The similarity scores of each co-occurring patent in each candidate patent set are summed up and normalized to obtain the co-occurrence degree DC of each co-occurring patent.

[0120] Exemplarily, the maximum value of the sum of similarity scores of co-occurring patents in each candidate patent set is 150, and is normalized to a value of [1,100], thereby obtaining the co-occurrence degree of each co-occurring patent.

[0121] 2) Calculate the average similarity DS of co-occurring patents

[0122] The similarity scores of each co-occurring patent in each candidate patent set are averaged to obtain the average similarity of each co-occurring patent.

[0123] It should be noted that only co-occurring patents are included in the calculation. That is, if a candidate patent only appears in two candidate patent sets, only the average value of its similarity in the two candidate patent sets is calculated.

[0124] The maximum value of the similarity score is 100, and the maximum value of the average similarity DS is also 100.

[0125] 3) Calculate the operational degree DO of co-occurring patents

[0126] According to the number of different types of operating behaviors implemented by the patentee in each co-occurring patent, the operating degree of the corresponding co-occurring patent is obtained.

[0127] Specifically, different types of operations implemented by patent owners include: transfer, license, and pledge. The weights of the number of transfers, licenses, and pledges are set to a, b, and c, respectively, and a+b+c=1. For each co-occurring patent, assume that the number of transfers is N1, the number of licenses is N2, and the number of pledges is N3. At this time, a*N1+b*N2+c*N3 is normalized to a value of [1,100] to obtain the operation degree DO of the current co-occurring patent.

[0128] 4) Calculate the recommendation degree R of co-occurring patents

[0129] Assume that the weights of co-occurrence, average similarity, and operation degree are α, β, and γ respectively, and satisfy α+β+γ=1.

[0130] The recommendation degree R of each co-occurring patent is calculated according to the formula: R = α*DC + β*DS + γ*DO.

[0131] Afterwards, the co-occurring patents with the highest recommendation degree are selected as the objects of patent transformation and application.

[0132] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0133] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. An intelligent recommendation system for patent transformation and application objects, characterized in that: The system comprises: The multi-dimensional technical summary module uses a large model to obtain technical summaries of the patents to be converted in different dimensions, and obtains technical summary results of the patents to be converted in the corresponding dimensions after reviewing the technical summaries; The multi-dimensional candidate patent search module extracts features from the technical summary results of the patents to be converted in different dimensions, and performs intelligent searches based on the extracted technical features to obtain a candidate patent set in the corresponding dimensions of the patents to be converted; The intelligent recommendation module for patent conversion and application objects obtains the co-occurrence degree, average similarity and operation degree of co-occurring patents among the candidate patent sets of different dimensions of the patents to be converted, so as to determine the recommendation degree of each co-occurring patent; and selects the co-occurring patent with the highest recommendation degree as the patent conversion and application object.

2. The patent transformation and application object intelligent recommendation system according to claim 1 is characterized in that: The multi-dimensional candidate patent search module includes: a technical feature extraction unit for each dimension, a search expression generation unit, and an intelligent search unit; The technical feature extraction unit of each dimension extracts features from the technical summary results of the patent to be converted in the corresponding dimension, and obtains the technical features of the patent to be converted in the corresponding dimension; The search expression generation unit comprehensively analyzes the technical features of the patent to be converted in different dimensions and obtains multiple intelligent search expressions of the patent to be converted in different dimensions; The intelligent retrieval unit uses a variety of intelligent retrieval expressions of the patent to be converted in different dimensions to perform patent retrieval in the intelligent retrieval system to obtain a candidate patent set of the patent to be converted in different dimensions.

3. The patent transformation and application object intelligent recommendation system according to claim 2 is characterized in that: In the search expression generation unit, execute: The technical features of the patent to be converted that appear simultaneously in the technical problem dimension, technical means dimension and technical effect dimension are regarded as key technical features; Determine the similarity of multiple technical features of the patent to be converted in the technical problem dimension, and take multiple technical features whose similarity exceeds a certain similarity threshold as similar features in the technical problem dimension, and connect them with an or relationship; Determine the similarity of multiple technical features in the technical means dimension of the patent to be converted, and take multiple technical features whose similarity exceeds a certain similarity threshold as similar features in the technical means dimension, and connect them with an or relationship; Determine the similarity of multiple technical features of the patent to be converted in the technical effect dimension, and take multiple technical features whose similarity exceeds a certain similarity threshold as similar features in the technical effect dimension, and connect them with an or relationship; Use key technical features as necessary keywords for retrieval, and connect similar features of the technical problem dimension connected by the relationship of "or", and the remaining technical features of the technical problem dimension using multiple forms of logical relationships to form multiple intelligent retrieval expressions of the patent to be converted in the technical problem dimension; Use key technical features as necessary keywords for retrieval, and connect similar features in the technical means dimension connected by the relationship of "or", and the remaining technical features in the technical means dimension using a variety of different forms of logical relationships to form a variety of intelligent retrieval expressions in the technical means dimension of the patent to be converted; The key technical features are used as necessary keywords for retrieval, and similar features in the technical effect dimension connected by the relationship of "or" and the remaining technical features in the technical effect dimension are connected by a variety of different forms of logical relationships to form a variety of intelligent retrieval expressions for the patent to be converted in the technical effect dimension.

4. The patent transformation and application object intelligent recommendation system according to claim 2 is characterized in that: In the smart search unit, execute: Use each intelligent search expression of the patent to be converted in the corresponding dimension to perform patent search in the intelligent search system, and obtain the top M related patents with the highest similarity corresponding to each intelligent search expression; The related patents that appear repeatedly in the top M related patents with similarity corresponding to different types of intelligent search expressions are taken as the preliminary candidate patents of the corresponding dimension, and the top N preliminary candidate patents with similarity to the technical content of the patent to be converted in the corresponding dimension are selected from them as the candidate patent set of the patent to be converted in the corresponding dimension.

5. The patent transformation and application object intelligent recommendation system according to any one of claims 1 to 4, characterized in that: In the patent transformation application object intelligent recommendation module, execute: Arrange the similarity of each candidate patent in the candidate patent set of different dimensions of the patent to be converted in descending order and assign corresponding similarity scores; Select candidate patents that appear in at least two candidate patent sets of different dimensions as co-occurring patents, add up the similarity scores of each co-occurring patent in each candidate patent set, and normalize them to obtain the co-occurrence degree of each co-occurring patent; The similarity scores of each co-occurring patent in each candidate patent set are averaged to obtain the average similarity of each co-occurring patent; According to the number of different types of operating behaviors implemented by the patentee in each co-occurring patent, the operating degree of the corresponding co-occurring patent is obtained.

6. The patent transformation and application object intelligent recommendation system according to claim 5 is characterized in that: In the patent transformation and application object intelligent recommendation module, it is assumed that the weights of co-occurrence, average similarity, and operation degree are α, β, and γ respectively, and α+β+γ=1; The recommendation degree R of each co-occurring patent is calculated according to the formula: R = α*DC + β*DS + γ*DO; Among them, DC, DS, and DO represent the co-occurrence degree, average similarity, and operation degree of co-occurring patents, respectively.

7. The patent transformation and application object intelligent recommendation system according to claim 5 is characterized in that: Different types of operations implemented by patent owners include: transfer, licensing, and pledge.

8. The patent transformation and application object intelligent recommendation system according to claim 7 is characterized in that: Set the weights of the number of transfers, the number of licenses, and the number of pledges to be a, b, and c respectively, and satisfy a+b+c=1; For each co-occurring patent, assuming that the number of transfers is N1, the number of licenses is N2, and the number of pledges is N3, then a*N1+b*N2+c*N3 is normalized to a value of [1,100] as the operational degree of the current co-occurring patent.

9. The patent transformation and application object intelligent recommendation system according to claim 5 is characterized in that: The multi-dimensional technical summary module includes a preliminary technical summary unit and an output review unit for each dimension; wherein, The preliminary technical summary unit of each dimension is used to summarize the technical content of the corresponding dimension in the patent to be converted based on the macro model of the corresponding dimension, and obtain the preliminary technical summary results of the patent to be converted in the corresponding dimension; The output review unit of each dimension is used to output and review the preliminary technical summary results of the patent to be transformed in the corresponding dimension, and obtain the technical summary results of the patent to be transformed in the corresponding dimension.

10. The patent transformation and application object intelligent recommendation system according to claim 1 is characterized in that: The dimensions include technical problem dimension, technical means dimension and technical effect dimension.

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