CN108665182B - Patent litigation risk prediction method - Google Patents

Patent litigation risk prediction method Download PDF

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CN108665182B
CN108665182B CN201810482063.3A CN201810482063A CN108665182B CN 108665182 B CN108665182 B CN 108665182B CN 201810482063 A CN201810482063 A CN 201810482063A CN 108665182 B CN108665182 B CN 108665182B
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刘淇
陈恩红
武晗
叶雨扬
杜东舫
赵洪科
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University of Science and Technology of China USTC
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Abstract

The invention discloses a patent litigation risk prediction method, which is characterized in that litigation factors serving as original reports, litigation factors serving as rejected and litigation factors of each company and litigation factors of each patent are obtained by a method of combining tensor decomposition and a convolutional neural network according to heterogeneous patent data (meta-features, text features and patent citation network) and patent litigation case records, and then the patent litigation risk prediction is carried out by utilizing the three litigation factors (all vector representations).

Description

Patent litigation risk prediction method
Technical Field
The invention relates to the technical field of machine learning and patent data mining, in particular to a method for predicting risk of patent litigation.
Background
The patent is an important means for intellectual property protection. In recent years, with the development of science and technology and the innovation of technology, the number of patent applications and grants in various regions of the world is rapidly increasing, and the patent litigation cases caused by patent infringement are also remarkably increasing. Patent litigation cases are complex in cause, complex in procedure and high in cost, once the cases are greatly influenced by both litigation parties, early warning of patent litigation can provide more time for both litigation parties to make development strategies, potential litigation patents are negotiated and solved, and accordingly risks are dredged and avoided in time, and resources are saved.
In current research work and patents, the following methods are the main methods for patent litigation prediction:
1) patent litigation factor analysis based on statistics.
At present, the analysis of patent litigation factors based on statistics mainly focuses on analyzing the relationship between patent features and patent litigation, and patents meeting specific features are potential litigation patents. Through analysis by predecessors, factors that are known to affect patent litigation include patent forward citation, patent backward citation, patent family size, patent review process, patentee, and the like.
2) Corporate litigation risk prediction based on collaborative filtering.
The company litigation risk prediction based on collaborative filtering combines with a collaborative filtering (such as matrix decomposition) algorithm commonly used in the traditional recommendation system, and a learner uses the method to predict an industry or a company of which litigation is possible.
Both of the above approaches do not solve the problem of two companies regarding whether litigation can occur for a patent. In addition, the above methods do not utilize the heterogeneous data including text in patents, nor do they consider modeling relationships between companies, and between companies and litigation patents.
Disclosure of Invention
The invention aims to provide a method for predicting risk of patent litigation, which can improve the accuracy of a prediction result.
The purpose of the invention is realized by the following technical scheme:
a method for predicting risk of patent litigation, comprising:
obtaining patent data in an authorized heterogeneous form, and crawling patent litigation case data;
transforming patent data in a heterogeneous form of each granted patent into comprehensive patent vector representation by using a convolutional neural network and a network embedding method;
establishing an original notice-defendant-patent third-order tensor and a tensor decomposition model by using patent litigation case data;
combining the comprehensive patent vector representation with a tensor decomposition model to obtain a mixed model;
training the mixed model by using a sequencing learning method;
calculating litigation factors of each company as original reports, litigation factors as defended reports and litigation factors of each patent by using the mixed model obtained by training;
the three litigation factors are used for predicting the litigation risk of a patent among companies.
According to the technical scheme provided by the invention, the litigation factors of each company as the original report, the litigation factors as the advised litigation factors and the litigation factors of each patent are obtained by combining tensor decomposition and a convolutional neural network according to heterogeneous patent data (meta features, text features and patent citation network) and patent litigation case records, and then the patent litigation risk prediction is carried out by utilizing the three litigation factors (all vector representations), so that the accuracy of the prediction result is greatly improved compared with the prior art.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a flowchart of a method for predicting risk of patent litigation according to an embodiment of the present invention;
FIG. 2 is a diagram of a neural network structure combined with network characterization for a method for predicting risk of litigation according to an embodiment of the present invention;
FIG. 3 is a conceptual diagram of modeling a method for predicting risk of litigation according to an embodiment of the present invention;
FIG. 4 is a probability model diagram of a method for predicting risk of litigation according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for predicting risk of patent litigation, which mainly comprises the following steps as shown in figure 1:
and step 11, obtaining authorized patent data in a heterogeneous form, and crawling patent litigation case data.
In embodiments of the present invention, authorized patent data in heterogeneous form and patent litigation case data may be obtained from the internet.
And step 12, converting patent data in a heterogeneous form of each granted patent into a patent vector representation by using a convolutional neural network and a network embedding method.
In the embodiment of the present invention, after obtaining the patent data in the authorized heterogeneous form, the patent data in the authorized heterogeneous form is represented in a unified mathematical form, where the patent data in the authorized heterogeneous form includes: meta-features, text features, and patent citation networks; using S p1, { k | k ═ 1,2,3, …, N } represents a patent set, and N is the number of patents; establishing a patent citation network G according to a patent citation relation, wherein each node of the patent citation network G represents a patent; each patent has certain characteristics, and thus the patent citation network G is an attribute network.
For patent k in the patent citation network G (i.e. the patent with the serial number k in the patent set), X is usedkRepresenting the characteristics of the characters, including meta characteristics and text characteristics; the meta-features refer to basic features that can be directly extracted from patent documents, and include: forward reference, backward reference, claim number, picture number, table number, patent classification information, grant gap, change in number of patents in patent category, locationThe number of patents in the company varies; text features refer to textual descriptions in patent documents, including: patent titles, patent abstract and patent claims.
The contents and descriptions of meta-features and text features are shown in table 1:
Figure BDA0001665764360000031
Figure BDA0001665764360000041
TABLE 1 contents and description of Meta-features and text features
In the embodiment of the invention, the patent data is processed by using a convolutional neural network and a network embedding method in the following way:
1. and processing the meta-characteristics and the patent citation network G in a network embedding learning mode. First, the meta-features of each patent node in the patent citation network G are spliced into a meta-feature vector (e.g., the meta-feature vector of patent k can be written as
Figure BDA0001665764360000042
) Training the attributes as patent node attributes of network embedded learning; then, through network embedding learning of the patent citation network G, the high-dimensional patent meta-feature vector can be converted into the low-dimensional patent representation, and meanwhile, the patent citation relation is embedded into the patent representation, so that the patent features are accurately depicted. The treatment process is as follows:
firstly, the element characteristic vectors of all patents are spliced to form a characteristic matrix FN×QWherein Q is the dimension of the characteristic vector of the patent element; feature matrix FN×QIs denoted as fk(equivalent to
Figure BDA0001665764360000043
) (ii) a Definition of input characterization of patent k as ek＝ETfkIn which EIs a transformation matrix to be trained, obviously the dimension of E is Qxd1(d1Can be set by oneself);
secondly, for each node in the patent citation network G, taking it as a root node root, randomly sampling its neighbor nodes to generate different paths:
＜root,neighborhood1,neighborhood2,…＞；
wherein, the neighbor nodes are represented by neighbor 1 and 2;
for each path, a set of neighbor nodes (referred to as a scenario) for patent k is given:
context(k)＝{k-l,…,k+l}\{k}；
that is, consider 2l neighbor nodes of patent k;
the following objective function, i.e. the probability of predicting the central node by the neighbor nodes, is maximized:
Figure BDA0001665764360000044
e 'in the formula'kAnd econtext(k)Respectively represents an output representation and a scene representation of a patent k, wherein the patent m is a neighbor node of the patent k, e'mAn output characterization representing patent m;
econtext(k)is defined as:
Figure BDA0001665764360000045
in the above formula emThe input characterization of the patent m;
finally, by approximating the objective function by negative sampling, an output representation is obtained, i.e. a vector representation of the meta-features, whose output representation is e 'for patent k'k。
2. The method adopts a convolutional neural network to process text features, and comprises the following steps:
firstly, words in the text features after the stop words are removed are converted into Word vectors with the dimension d through the Word2Vec technology0(ii) a Each sentence can be considered as a matrix and the sentences can form a tensor.
Secondly, dividing the patent titles, the abstract of the patent specification and the patent claims into a plurality of patent claims, wherein C-2 patent claims are taken before (0 is supplemented if the patent claims are not enough), and a patent title and the abstract of the patent specification are added to form C pieces; each piece is a word sequence consisting of word vectors, each piece takes the first H words (if the H words are not enough, 0 is complemented), and the text characteristics of the patent k are converted into tensors
Figure BDA0001665764360000051
Wherein, the value of C can be set according to the actual need.
Then, a two-layered convolutional neural network pair tensor as shown in FIG. 2 is used
Figure BDA0001665764360000052
Carrying out treatment; the first layer is convolution and pooling of word level, and the second layer is convolution and pooling of sentence level; the convolution and pooling operations of the first layer are as follows:
and (3) convolution operation: the convolution kernel shape of the convolution operation is c' × d0Where c' is the dimension of the convolution kernel, the goal is to pass each slice s of patent k through a convolution operation as w1,w2,…,wHAll are converted into a new hidden layer sequence by ecAre shown here
Figure BDA0001665764360000053
Each item in the new hidden layer sequence
Figure BDA0001665764360000054
(1. ltoreq. n. ltoreq.H) satisfies:
Figure BDA0001665764360000055
wherein the content of the first and second substances,
Figure BDA0001665764360000056
and
Figure BDA0001665764360000057
is the parameter of convolution operation, d is the dimension of output, n-c' +1 < 0 means the boundary, needs to complement 0; relu (x) is a non-linear activation function, where relu (x) is max (0, x);
Figure BDA0001665764360000058
is a join operation to join vectors; w is a word sequence, and subscripts are sequence numbers of the word sequence;
and (3) pooling operation: the step size of the pooling operation is u. The new hidden layer sequence e we obtained for the above convolution operationcPerforming u max pooling, converting into new global hidden layer sequence, and using ecuAre shown here
Figure BDA0001665764360000059
Each of the new global hidden layer sequences
Figure BDA00016657643600000510
All satisfy
Figure BDA00016657643600000511
Wherein, r-u +1 < 0 means that the boundary is a boundary, and 0 needs to be supplemented:
the convolution and pooling operations of the second layer are the same as the first layer, except that the input is changed from a sequence of words to a sequence of sentences.
Vector representation of text features is obtained through a convolutional neural network, and vector representation of meta-features is obtained through a network embedding learning mode; and splicing the vector representation of the text features and the vector representation of the meta features, and then obtaining the comprehensive patent vector representation through the full-connection layer.
And step 13, establishing an original notice-defended-patent third-order tensor and establishing a tensor decomposition model by using patent litigation case data.
A patent litigation case includes three elements, namely a source company, a defendant company and related patents. Given M companies and N patents, assume the original company set is S U1,2,3, …, M, the advertised company set is S V1,2,3, …, M, patent setIs synthesized into SpAs shown in fig. 3, a third-order tensor is formed with the three axes as axes, and each value of the third-order tensor represents whether a litigation has occurred to a patent by a company.
Suppose the litigation case set is R ═ { Rijk|i∈SU,j∈SV,k∈SpGiven a grandfather i and a defendant j, called a company pair (i, j), if the grandfather i calls the defendant j about the patent k, a litigation record r is obtainedijk1 is ═ 1; if the company does not have litigation for k for (i, j) at present, the litigation record rijk＝0；
Referring also to FIG. 3, prediction of risk of litigation for a patent refers to predicting the likelihood that a company will litigation for patent k for patent i, j given company pair (i, j) and patent k, with the prediction result being denoted as r'ijk(ii) a We break down litigation possibilities into the following forms:
Figure BDA0001665764360000061
wherein, Ui、VjAnd PkThese three litigation factor solving methods will be described in detail later, each of which represents a litigation factor of the original reporting company i, a litigation factor of the reported company j, and a litigation factor of the patent k.
And 14, combining the comprehensive vector representation with a tensor decomposition model to obtain a mixed model.
From the viewpoint of data mining, companies have been classified into the following 4 causes of litigation on patent k for (i, j): 1) the value of patent k itself; 2) degree of importance of original company i to patent k; 3) degree of importance of the advertised company j to the patent k; 4) competitive relationship between the original company i and the defendant company j;
the comprehensive vector representation is obtained by splicing the vector representation of the text features obtained by the convolutional neural network and the vector representation of the meta-features obtained by a network embedding learning mode, the reason 1 can be described by utilizing the vector representation of the text features obtained by the convolutional neural network, and the reasons 2) -4 can be described by a tensor decomposition model); the combined vector representation and the patent litigation factors obtained from the tensor decomposition model are unified to form a hybrid model, and the details are shown in fig. 4 and in step 15.
And step 15, training the hybrid model by using a sequencing learning method.
Considering that litigation records are discrete data, and only litigation and non-litigation are the two cases, the tensors in the commonly used tensor decomposition store continuous values, and the direct use of the tensor decomposition is not feasible. The embodiment of the invention adopts a sequencing learning method to solve the problem. Mainly as follows:
the possibility of patent litigation between companies is similar to user preferences in traditional recommendation systems, namely: if it is not
Figure BDA0001665764360000062
Figure BDA0001665764360000063
Then relative patent k-In other words, company prefers patent k to (i, j)+Then the problem translates into a prediction
Figure BDA0001665764360000071
Here, the
Figure BDA0001665764360000072
The formula means that: company i about litigation patent k+Litigation of company j, company i regarding non-litigation patent k-No litigation company j. Wherein k is+Indicating that patent k is a litigation patent, k-Patent k is denoted as a non-litigation patent,
Figure BDA0001665764360000073
represents a company pair (i, j) and is related to k+The occurrence of litigation is caused,
Figure BDA0001665764360000074
represents a company pair (i, j) and is related to k-No litigation occurs.
For patent k, it contains k+And k is-In both forms, our target pairsA series of unknown patents are predicted by a probability ordering method, and both lawsuited patents and unlawned patents are required during training, so that both patents participate in the model.
Given a company pair (i, j), k is used+＞i,j k-To indicate that the company is paired with (i, j) for patent k+And k-Bias-order relationship of preference degrees; then company pairs (i, j) in patent k+Litigation occurred in the above, but not in patent k-The probability of litigation occurring is expressed as:
Figure BDA00016657643600000712
here, the
Figure BDA0001665764360000075
Namely the commonly known sigmoid function.
From the perspective of probability theory, finding the best patent sequence
Figure BDA0001665764360000076
Herei,jAll the partial order relations representing possible patent disputes between the original company i and the reported company j are represented, so it is SP×SPThe problem can be achieved by maximizing the following a posteriori distribution: p (U, V, P, W | >)i,j)∝p(＞i,jL U, V, P, W) P (U, V, P, W), where U, V, P, W represent the prosecutor matrix, the defendant litigation factor matrix, the patent litigation factor matrix, and all parameter matrices in the convolutional neural network, respectively.
Assuming that all company pairs are independent of each other, there are:
Figure BDA0001665764360000077
the conditional distribution of the above formula is:
Figure BDA0001665764360000078
wherein the content of the first and second substances,
Figure BDA0001665764360000079
the partial order relation of all company litigation patents is stored;
as a generative model, assuming that litigation factors of the original agent all obey a 0-mean Gaussian distribution, the variance can be expressed as
Figure BDA00016657643600000710
Wherein deltaUI is an indication matrix, which is a diagonal matrix, and is 0 except for the diagonal.
The litigation factor matrix U of the original trailer also follows a 0-mean gaussian distribution and can be expressed as:
Figure BDA00016657643600000711
wherein N represents a gaussian distribution.
Similarly, suppose that the litigation factor matrix V of the defendant company is also subject to 0-mean Gaussian distribution with a variance of
Figure BDA0001665764360000081
Figure BDA0001665764360000082
Wherein, deltaVIs the standard deviation;
in order to better represent the patent hidden vector, on one hand, patent litigation record information is combined, and on the other hand, the meta-feature and the text feature of the patent are extracted. It is assumed that the patent hidden vector is determined by two factors, one is the weight in the convolutional neural network, and the other is the input patent feature. Assume the implicit vector of patent k is of the form:
Figure BDA0001665764360000083
wherein, PkIs the litigation factor, O, of the patent k that we finally expect to obtainkIs the comprehensive vector characterization of the final patent k in step 13, i.e., Ok＝NCNN(W,Xk) Wherein X iskDenotes the characteristic of patent k, ∈kIs PkAnd OkDifference between, we assume εkObedience mean 0 and variance
Figure BDA0001665764360000084
Of a Gaussian distribution ofPIs the standard deviation.
The probability of a patent latent vector is thus expressed as:
Figure BDA0001665764360000085
wherein X is a characteristic of all patents;
for each weight W in WqAssuming they also obey a mean of 0 and a variance of
Figure BDA0001665764360000086
Gaussian distribution of (a):
Figure BDA0001665764360000087
in the above formula, δwIs the standard deviation.
Through the method, the output (namely the comprehensive vector representation) of the NCNN can be used as the mean value of the Gaussian distribution of the hidden vector of the patent, and the mean value is connected with the convolutional neural network and tensor decomposition to play a role of a bridge.
Finally, the parameters U, V, P, W and the variances corresponding to these parameters in the hybrid model can be trained.
It will be appreciated by those skilled in the art that the above mentions various forms of standard deviation δU、δV、δP、δwWhich in turn correspond to the parameters U, V, P, W.
And step 16, calculating litigation factors of each company as original reports, as told litigation factors and as patent litigation factors by using the trained mixed model.
When calculating litigation factors as original reports, as rejected litigation factors and for each patent for each company using the trained hybrid model, the following objective functions are maximized:
Figure BDA0001665764360000091
taking the logarithm of the posterior distribution and taking the negative to obtain:
Figure BDA0001665764360000092
in the above formula, λU、λV、λW、λPAre all parameters that need to be adjusted to minimize the objective function.
In order to minimize the objective function, Adadelta optimizer is adopted to iteratively update parameters in the model, and the Adadelta optimizer can automatically derive parameters through Tensorflow so as to calculate litigation factor U of the original reporting company iiLitigation factor V for the defendant company jjAnd litigation factor P of patent kkAll three litigation factors are characterized by vectors.
And step 17, predicting the litigation risk of a patent among companies by using the three litigation factors.
The prediction result of the possibility that original carrier i and defendant j will litigation about patent k is denoted as r'ijkThe calculation formula is as follows:
Figure BDA0001665764360000093
wherein, Ui、VjAnd PkRespectively generation by generationLitigation factors for the indictor i, litigation factors for the indictor j, and litigation factors for the patent k.
According to the technical scheme of the embodiment of the invention, the litigation factors of each company as the original notice, the told litigation factors and the litigation factors of each patent are obtained by combining tensor decomposition and a convolutional neural network according to heterogeneous patent data (meta-features, text features and patent citation network) and patent litigation case records, and then the patent litigation risk prediction is carried out by using the three litigation factors (all vector representations), so that the accuracy of the prediction result is greatly improved compared with the prior art.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. A method for predicting risk of patent litigation, comprising:
obtaining patent data in an authorized heterogeneous form, and crawling patent litigation case data; wherein the granted heterogeneous forms of patent data include: meta-features, text features, and patent citation networks; using SpWhere { k | k ═ 1,2,3, …, N } denotes a patent set, N isThe number of patents; establishing a patent citation network G according to a patent citation relation, wherein each node of the patent citation network G represents a patent; for patent k in patent citation network G, XkRepresenting the characteristics of the characters, including meta characteristics and text characteristics; the meta-features refer to basic features that can be directly extracted from patent documents, and include: forward reference, backward reference, claim quantity, picture quantity, table quantity, patent classification information, authorization gap, change of patent quantity in the patent category, change of patent quantity in the company; text features refer to textual descriptions in patent documents, including: patent titles, abstract of the specification of the patent and patent claims;
transforming patent data in a heterogeneous form of each granted patent into comprehensive patent vector representation by using a convolutional neural network and a network embedding learning method; processing the meta-features and the patent citation network G by adopting a network embedding learning mode, and processing the text features by adopting a convolutional neural network; vector representation of text features is obtained through a convolutional neural network, and vector representation of meta-features is obtained through a network embedding learning mode; splicing the vector representation of the text features and the vector representation of the meta features, and then obtaining comprehensive patent vector representation through a full connection layer;
establishing an original notice-defendant-patent third-order tensor and a tensor decomposition model by using patent litigation case data;
combining the comprehensive patent vector characterization with a tensor decomposition model to obtain a hybrid model, comprising: the reason why litigation occurs for patent k in (i, j) is divided into the following 4 aspects: 1) the value of patent k itself; 2) degree of importance of original company i to patent k; 3) degree of importance of the advertised company j to the patent k; 4) competitive relationship between the original company i and the defendant company j; the comprehensive vector representation is obtained by splicing the vector representation of the text features obtained by the convolutional neural network and the vector representation of the meta-features obtained by a network embedding learning mode, the aspect 1 can be described by utilizing the vector representation of the text features obtained by the convolutional neural network, and the aspects 2) to 4 can be described by a tensor decomposition model); unifying the patent litigation factors obtained by the comprehensive patent vector representation and the tensor decomposition model to form a mixed model;
training the mixed model by using a sequencing learning method;
calculating litigation factors of each company as original reports, litigation factors as defended reports and litigation factors of each patent by using the mixed model obtained by training;
the three litigation factors are used for predicting the litigation risk of a patent among companies.
2. The method of claim 1, wherein the patent data in the authorized heterogeneous form is represented in a unified mathematical form after being obtained.
3. The method for predicting risk of patent litigation according to claim 2, wherein the meta-features and the patent citation network G are processed by network embedding learning: splicing the meta-features of each patent node in the patent citation network G into a meta-feature vector, and training the meta-features as the attributes of the patent nodes for network embedding learning; then, by carrying out network embedding learning on the patent citation network G, the patent element feature vector can be converted into a patent representation, and meanwhile, the patent citation relation is embedded into the patent representation, so that the patent feature is described; the treatment process comprises the following steps:
firstly, the element characteristic vectors of all patents are spliced to form a characteristic matrix FN×QWherein Q is the dimension of the characteristic vector of the patent element; feature matrix FN×QIs denoted as fkRepresenting the meta feature vector of patent k; definition of input characterization of patent k as ek＝ETfkWhere E is the transformation matrix to be trained;
secondly, for each node in the patent citation network G, taking it as a root node root, randomly sampling its neighbor nodes to generate different paths:
＜root,neighborhood1,neighborhood2,…＞；
wherein, the neighbor nodes are represented by neighbor 1 and 2;
for each path, the set of neighbor nodes for patent k is given:
context(k)＝{k-l,…,k+l}\{k}；
the following objective function, i.e. the probability of predicting the central node by the neighbor nodes, is maximized:
Figure FDA0003319008380000021
e 'in the formula'kAnd econtext(k)Respectively representing the output representation and scene representation, e 'of patent k'mRepresenting the output representation of a patent m, wherein the patent m is a neighbor node of a patent k;
econtext(k)is defined as:
Figure FDA0003319008380000022
in the above formula emThe input characterization of the patent m;
finally, by approximating the objective function by negative sampling, an output representation is obtained, i.e. a vector representation of the meta-features, whose output representation is e 'for patent k'k。
4. A method for predicting risk of patent litigation according to claim 2 or 3, wherein a convolutional neural network is used to process the text features as follows:
firstly, words in the text features after the stop words are removed are converted into Word vectors with the dimension d through the Word2Vec technology0；
Secondly, dividing the patent titles, the abstract of the patent specification and the patent claims into a plurality of pieces, wherein C-2 pieces are taken before the patent claims, and a patent title and an abstract of the patent specification are added to form C pieces; each one from word to wordQuantity-formed word sequences, each of which takes the first H word sequences, then the text features of the patent k are converted into tensors
Figure FDA0003319008380000031
Then, a two-layer convolutional neural network pair tensor is used
Figure FDA0003319008380000032
Carrying out treatment; the first layer is convolution and pooling of word level, and the second layer is convolution and pooling of sentence level; the convolution and pooling operations of the first layer are as follows:
and (3) convolution operation: the convolution kernel shape of the convolution operation is c' × d0Where c' is the dimension of the convolution kernel, the goal is to pass each slice s of patent k through a convolution operation as w1,w2,…,wHAll are converted into a new hidden layer sequence ec：
Figure FDA0003319008380000033
Wherein:
Figure FDA0003319008380000034
Figure FDA0003319008380000035
and
Figure FDA0003319008380000036
is a parameter of the convolution operation, d is the dimension of the output, relu (x) is a non-linear activation function,
Figure FDA0003319008380000037
is a join operation to join vectors; w is a word sequence, and subscripts are sequence numbers of the word sequence;
and (3) pooling operation: step size of pooling operation is u, new hidden layer sequence e obtained for the above convolution operationcCarrying out uMax pooling and converting into a new global hidden layer sequence ecu：
Figure FDA0003319008380000038
Wherein the content of the first and second substances,
Figure FDA0003319008380000039
satisfy the requirement of
Figure FDA00033190083800000310
The convolution and pooling operations of the second layer are the same as the first layer, except that the input is changed from a sequence of words to a sequence of sentences.
5. The method of claim 1, wherein the step of establishing a source-defendant-patent third-order tensor using patent litigation case data to form a tensor resolution model comprises:
given M companies and N patents, assume the original company set is SU1,2,3, …, M, the advertised company set is SV1,2,3, …, M, patent set SpForming a third-order tensor by taking the three as coordinate axes, wherein each value of the third-order tensor represents whether litigation occurs to a certain patent by a company;
suppose the litigation case set is R ═ { Rijk|i∈SU,j∈SV,k∈SpGiven a grandfather i and a defendant j, called a company pair (i, j), if the grandfather i calls the defendant j about the patent k, a litigation record r is obtainedijk1 is ═ 1; if the company does not have litigation for k for (i, j) at present, the litigation record rijk＝0；
The prediction of patent litigation risk refers to a prediction of the possibility that a company will litigation for patent k in relation to patent (i, j) given company pair (i, j) and patent k, and the prediction result is denoted as r'ijk(ii) a The litigation probability is decomposed into the following forms:
Figure FDA0003319008380000041
wherein, Ui、VjAnd PkRepresenting litigation factors of the original reporting company i, litigation factors of the reported company j, and litigation factors of the patent k, respectively.
6. The method for predicting risk of patent litigation according to claim 5, wherein the training of the mixture model by using the method of ranking learning is as follows:
the possibility of patent litigation between companies is similar to user preferences in traditional recommendation systems, namely: if patent k is litigation patent, it is marked as k+If patent k is a non-litigation patent, it is recorded as k-(ii) a If it is not
Figure FDA0003319008380000042
Then relative patent k-In other words, company prefers patent k to (i, j)+Then the problem translates into a prediction
Figure FDA0003319008380000043
Here, the
Figure FDA0003319008380000044
Wherein k is+Indicating that patent k is a litigation patent, k-Patent k is denoted as a non-litigant patent;
Figure FDA0003319008380000045
represents a company pair (i, j) and is related to k+The occurrence of litigation is caused,
Figure FDA0003319008380000046
represents a company pair (i, j) and is related to k-No litigation occurs;
given a company pair (i, j), k is used+＞i,jk-To indicate that the company is paired with (i, j) for patent k+And k-Bias-order relationship of preference degrees; then company pairs (i, j) in patent k+Litigation occurred in the above, but not in patent k-The probability of litigation occurring is expressed as:
Figure FDA0003319008380000047
wherein σ (x) is a sigmoid function;
from the perspective of probability theory, finding the best patent sequence
Figure FDA00033190083800000411
The problem can be solved by maximizing the following posterior distribution:
p(U,V,P,W|＞i,j)∝p(＞i,j|U,V,P,W)p(U,V,P,W)；
wherein >i,jAll partial order relations which represent possible patent disputes between the original company i and the reported company j; u, V, P and W respectively represent a litigation factor matrix of the original reporting company, a litigation factor matrix of the reported company, a patent litigation factor matrix and all parameter matrices in the convolutional neural network;
assuming that all company pairs are independent of each other, there are:
Figure FDA0003319008380000048
the conditional distribution of the above formula is:
Figure FDA0003319008380000049
wherein the content of the first and second substances,
Figure FDA00033190083800000410
the partial order relation of all company litigation patents is stored;
as a generative model, suppose the litigation factors of the original agent obey a 0-mean Gaussian distribution with a variance of
Figure FDA0003319008380000056
The litigation factor matrix U of the original trailer also follows a 0-mean gaussian distribution, expressed as:
Figure FDA0003319008380000051
wherein N represents a Gaussian distribution;
suppose that litigation factor matrix V of the defendant company also obeys a 0-mean gaussian distribution with a variance of
Figure FDA0003319008380000057
Figure FDA0003319008380000052
Wherein, deltaVIs the standard deviation;
assume the implicit vector of patent k is of the form:
Figure FDA0003319008380000053
in the above formula, PkIs litigation factor, O, of patent kkIs a comprehensive vector characterization of patent k, i.e. Ok＝NCNN(W,Xk) Wherein X iskDenotes the characteristic of patent k, ∈kIs PkAnd OkDifference between, let us assume ∈kObedience mean 0 and variance
Figure FDA0003319008380000058
Of a Gaussian distribution ofPIs the standard deviation;
the probability of a patent latent vector is thus expressed as:
Figure FDA0003319008380000054
in the above formula, X is the characteristic of all patents;
for each weight W in WqAssuming they also obey a mean of 0 and a variance of
Figure FDA0003319008380000059
The mean gaussian distribution of (a), as follows:
Figure FDA0003319008380000055
in the above formula, δwIs the standard deviation;
finally, the parameters U, V, P, W and the variances corresponding to these parameters in the hybrid model can be trained.
7. The method of claim 6, wherein the following objective functions are maximized when the litigation factors as a source, the litigation factors as a destination, and the litigation factors for each patent are calculated using the trained mixture model:
Figure FDA0003319008380000061
taking the logarithm of the posterior distribution and taking the negative to obtain:
Figure FDA0003319008380000062
in the above formula, λU、λV、λW、λPAll parameters need to be adjusted when the objective function is minimized;
iterative updating of parameters in the model by using Adadelta optimizer to calculate litigation factor U of original trailer company iiLitigation factor V for the defendant company jjAnd litigation factor P of patent kk。
8. The method for predicting risk of litigation for a patent according to claim 1, wherein the prediction of risk of litigation for a patent between companies using the three litigation factors comprises:
the prediction result of the possibility that original carrier i and defendant j will litigation about patent k is denoted as r'ijkThe calculation formula is as follows:
Figure FDA0003319008380000063
wherein, Ui、VjAnd PkRepresenting litigation factors of the original reporting company i, litigation factors of the reported company j, and litigation factors of the patent k, respectively.
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