Methods, apparatus, equipment, media, and products for analyzing the relevance of technical cooperation.

Abstract

This disclosure provides a method, apparatus, device, medium, and product for analyzing the relevance of technological cooperation. The method includes: identifying two regions to be analyzed; obtaining cooperative patents in the two regions; identifying target patents from the cooperative patents in the two regions; obtaining the importance of each target patent; obtaining the target importance based on the importance of all target patents and the distribution of the number of cooperative patents in the two regions; obtaining the similarity between the two sets of technical fields based on the sets of technical fields in the two regions; and obtaining the relevance of technological cooperation between the two regions based on the target importance and the similarity. This method can accurately reflect the degree of technical correlation and the depth of cooperation between the cooperating regions through the relevance of technological cooperation between the two regions.

Description

Technical Field

[0001] This disclosure relates to the field of data mining technology, and more specifically, this application relates to a method, apparatus, device, medium, and product for analyzing the relevance of technical cooperation. Background Technology

[0002] Analyzing the relevance of technological cooperation can reveal the synergistic effects of technological innovation across different regions, organizations, or countries. Currently, the strength of cooperation is primarily assessed by counting the number of collaborative patents. While this method is simple and intuitive, it overlooks the technical details and depth of the patents.

[0003] Some technical solutions attempt to enhance the assessment of cooperation strength by analyzing patent citation relationships and cooperation network structures. These methods can reveal the connection patterns between cooperating entities, but they often require a lot of data cleaning and complex network analysis, and do not take into account the technical content of the patents, so they cannot accurately reflect the degree of technical relevance and the depth of cooperation between the cooperating parties. Summary of the Invention

[0004] This disclosure provides a method, apparatus, device, medium, and product for analyzing the relevance of technical cooperation, addressing the technical problem that existing methods for analyzing the relevance of technical cooperation do not consider the technical content of patents, resulting in inaccurate relevance analysis results.

[0005] According to one aspect of the present disclosure, a method for analyzing the relevance of technological cooperation is provided, including:

[0006] Two regions to be analyzed are identified, and the collaborative patents of the two regions are obtained respectively. The target patent is then identified from the collaborative patents of the two regions. The collaborative patent is a patent with multiple patent holders and at least one patent holder located in the corresponding region. The target patent includes at least one first patent holder and at least one second patent holder. The first patent holder is located in one of the two regions, and the second patent holder is located in the other of the two regions.

[0007] The importance of each target patent is obtained. Based on the importance of all target patents and the distribution of the number of collaborative patents in the two regions, the target importance is obtained. The importance is used to measure the importance of the feature information of the target patent among the feature information of all collaborative patents. The target importance is used to represent the common importance of all target patents in the two regions.

[0008] Based on the technology sets of the two regions, the degree of similarity between the two technology sets is obtained. Each technology set of the region contains the technology fields of the various collaborative patents in the corresponding region.

[0009] The relevance of technological cooperation between the two regions is determined based on the importance and similarity of the objectives.

[0010] According to another aspect of the present disclosure, a device for analyzing the relevance of technological cooperation is provided, comprising:

[0011] The target patent determination module is used to determine two regions to be analyzed, obtain the collaborative patents in the two regions respectively, and determine the target patent from the collaborative patents in the two regions. The collaborative patent is a patent with multiple patentees and at least one patentee located in the corresponding region. The target patent includes at least one first patentee and at least one second patentee. The first patentee is located in one of the two regions, and the second patentee is located in the other of the two regions.

[0012] The importance determination module is used to obtain the importance of each target patent. Based on the importance of all target patents and the distribution of the number of collaborative patents in the two regions, the target importance is obtained. The importance is used to measure the importance of the feature information of the target patent among the feature information of all collaborative patents. The target importance is used to represent the common importance of all target patents in the two regions.

[0013] The similarity determination module is used to obtain the similarity between two sets of technical fields based on the sets of technical fields of two regions. Each set of technical fields of a region contains the technical fields of each collaborative patent in the corresponding region.

[0014] The technology cooperation analysis module is used to determine the relevance of technology cooperation between two regions based on the importance and similarity of the objectives.

[0015] According to another aspect of the present disclosure, an electronic device is provided, the electronic device including a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the steps of the method provided in any of the above embodiments.

[0016] According to another aspect of the present disclosure, a computer-readable storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the steps of the method provided in any of the above embodiments.

[0017] According to one aspect of the present disclosure, a computer program product is provided, including a computer program that is executed by a processor to perform the steps of the method provided in any of the above embodiments.

[0018] The beneficial effects of the technical solutions provided in this disclosure are:

[0019] By analyzing relevant data on collaborative patents in the regions under analysis, including the number and distribution of collaborative patents in the two regions and the target patents with collaborative relationships between them, this study delves into the importance of the target patent's characteristic information among all collaborative patents' characteristic information. It determines the importance of the target patent and assesses the similarity between the corresponding technology sets of the two regions to measure the technological agglomeration effect of collaborative patents within the regions. By combining the characteristic information of individual patents and the overall technological fields of the regions, the study determines the degree of correlation between the two regions' technological collaboration. This approach more comprehensively captures technological details, accurately reflects the degree of technological connection and collaboration between the collaborating regions, and more directly and accurately quantifies the intensity of collaboration between the two regions, thus improving the accuracy of the correlation analysis results. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments of this disclosure will be briefly introduced below.

[0021] Figure 1 A flowchart illustrating a method for analyzing the relevance of technical cooperation provided in this embodiment of the disclosure;

[0022] Figure 2 This is a schematic diagram of a method for obtaining first information about a region provided in an embodiment of this disclosure;

[0023] Figure 3 This is a schematic diagram of a method for obtaining second information about a region provided in an embodiment of this disclosure;

[0024] Figure 4 A schematic diagram of a device for analyzing the relevance of technical cooperation provided in this embodiment of the disclosure;

[0025] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0026] The embodiments of this disclosure are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this disclosure, and do not constitute a limitation on the technical solutions of the embodiments of this disclosure.

[0027] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this disclosure mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element are connected through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.”

[0028] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0029] The following description of several exemplary embodiments illustrates the technical solutions of this disclosure and the technical effects produced by these solutions. It should be noted that the following embodiments can be referenced, learned from, or combined with each other. Identical terms, similar features, and similar implementation steps in different embodiments will not be repeated.

[0030] The relevant technologies involved in this application are described below:

[0031] Related Technology 1: Currently, the main method for analyzing collaboration is based on the number of patents. This method assesses the intensity of collaboration by counting the number of collaborative patents. While this single-dimensional approach is simple and intuitive, it does not involve in-depth analysis of the technical details of the collaborative patents. It ignores the importance and relevance of the collaborative patents in terms of their technical content, which can easily lead to a one-sided understanding of the relevance of the technical collaboration and fail to fully reflect the actual value of the collaborative relationship.

[0032] For example, patent holders in two regions may jointly hold multiple collaborative patents, but if the technical content of these collaborative patents is not closely related to the overall technological direction of the region, then the actual significance and influence of these collaborations are relatively limited.

[0033] Related technology 2: This approach employs collaborative network analysis, assessing collaboration strength by analyzing patent citation relationships, constructing collaborative networks, and analyzing network structure. However, these methods often require extensive data cleaning and complex network analysis, and struggle to quantify the specific importance of individual patents within the collaboration. Furthermore, related technologies rarely consider the technical content and classification information of patents, thus failing to accurately reflect the technical relevance and depth of collaboration between the collaborating parties.

[0034] Based on the shortcomings of the above-mentioned related technologies in analyzing the degree of relevance of technological cooperation, this disclosure provides a more comprehensive and accurate method for analyzing the degree of relevance of technological cooperation, in order to solve or partially solve the above problems.

[0035] It is understood that in the method for analyzing the relevance of technical cooperation provided in this disclosure, any step of the method can be executed by the terminal and / or the server, and all steps in the method can be executed independently by the terminal or the server, or jointly by the terminal and the server.

[0036] The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services. The terminal can be a smartphone, tablet, laptop, desktop computer, smart voice interaction device (such as a smart speaker), wearable electronic device (such as a smartwatch), in-vehicle terminal, smart home appliance (such as a smart TV), AR / VR device, etc., but is not limited to these.

[0037] The embodiments of this disclosure will be described below with the server as the execution subject; however, this does not constitute a limitation on the embodiments of this disclosure.

[0038] Figure 1 A flowchart illustrating a method for analyzing the relevance of technical cooperation provided in this embodiment of the disclosure is shown below. Figure 1 As shown, the technical solution provided by the embodiments of this disclosure includes the following steps:

[0039] Step S101: Determine the two regions to be analyzed, obtain the collaborative patents of the two regions respectively, and determine the target patent from the collaborative patents of the two regions.

[0040] Among them, the collaborative patent is a patent with multiple patent holders and at least one patent holder located in the corresponding region. The target patent includes at least one first patent holder and at least one second patent holder, with the first patent holder located in one of the two regions and the second patent holder located in the other of the two regions.

[0041] Specifically, this disclosure provides a technical solution for analyzing the relevance of regional technological cooperation, wherein a region refers to a unit with clear geographical, administrative, economic or socio-cultural boundaries, and the rules for dividing the region and the selection of the region to be analyzed can be determined according to actual needs.

[0042] For example, regions can be divided into Europe, Asia, the Yangtze River Delta, the Pearl River Delta, etc., based on natural geographical features; regions can be divided into different countries, provinces, cities, and smaller administrative units, based on administrative regions; regions can be divided into special economic zones or economic alliances, based on economic features; and regions can be divided into different organizations, which can be composed of multiple administrative regions, enterprises, universities, research institutions, or individuals, based on socio-cultural features.

[0043] In this embodiment of the disclosure, the two regions to be analyzed can be the same or different regions. When the two regions are the same, the correlation of technical cooperation within the region can be analyzed. When the two regions are different, the correlation of technical cooperation between the two regions can be analyzed.

[0044] In step S101, two regions to be analyzed are determined, and the two regions are denoted as follows: and ,area and They can be the same or different.

[0045] For the region and Each patent obtains a collaborative patent for a corresponding region. A collaborative patent is a patent with multiple patent holders, at least one of whom is located in the corresponding region. Let p be the collaborative patent, and the corresponding region be the region. Let the collaborative patents be denoted as p1, and we obtain the set of collaborative patents P1, in the region. Let p2 be the collaborative patent, and we get the set of collaborative patents P2.

[0046] Identify the target patent from the collaborative patents of the two regions, and denote the target patent as... Target patent It includes at least one first patentee and at least one second patentee, with the first patentee located in one of the two regions and the second patentee located in the other of the two regions.

[0047] Understandably, when two regions are identical, a target patent refers to a patent where at least two patent holders reside in that region; when the regions are identical... and At the same time, the target patent refers to a patent with two patent holders located in different regions. and .

[0048] Additionally, it should be noted that when analyzing the relevance of technological cooperation between regions, one can conduct a comprehensive analysis of technological cooperation between regions across all technological fields, or one can analyze technological cooperation between regions within a specific technological field.

[0049] When determining collaborative patents, the technical fields to which the collaborative patents belong can be screened. For example, only collaborative patents related to the field of communication technology can be selected, or a narrower technical field such as wireless communication technology can be chosen. Whether it is necessary to select a technical field, and the specific type of technical field selected, can be determined based on actual needs.

[0050] Step S102: Obtain the importance of each target patent. Based on the importance of all target patents and the distribution of the number of collaborative patents in the two regions, obtain the target importance.

[0051] Among them, importance is used to measure the importance of the feature information of the target patent among the feature information of all collaborative patents, and target importance is used to represent the common importance of all target patents in both regions.

[0052] Specifically, after identifying the target patents with a cooperative relationship in the two regions, step S102 obtains the importance of each target patent. The importance is used to measure the significance of the feature information of the target patent among the feature information of all cooperative patents. That is, the importance is used to reflect the extent to which the target patent represents the technological cooperation between the two regions, and to quantify the relative importance or influence of a single target patent on the technological cooperation between the two regions.

[0053] Among them, feature information refers to information that can be extracted from the patent text and can represent certain attributes or characteristics of the patent, including but not limited to keywords, classification numbers, citation relationships, patent family size, application year, etc., which can be determined according to actual needs.

[0054] For example, it can be assumed that the more keyword frequency, the more times the classification number appears, the more times it is cited, the more patent families there are, and the more recent the application year of the patent is, the higher its importance. The specific type of feature information used to measure importance and the method of calculating importance can be determined according to actual needs.

[0055] The target importance is determined based on the significance of all target patents and the distribution of the number of collaborative patents between the two regions. The target importance is used to represent the common importance of all target patents in the two regions, that is, to measure the degree of relevance of all target patents to the overall technological direction of the two regions with a collaborative relationship. The target importance can be used to assess the relative importance or influence of all target patents in the collaboration between the regions among all collaborative patents in the two regions, so as to obtain an overall measure.

[0056] It is understood that the number of collaborative patents in different regions may be different, that is, the proportion of target patents in different regions may be different. This disclosure embodiment considers the distribution of the number of collaborative patents in two regions, summarizes or averages the importance of all target patents, and obtains the target importance level to measure the overall importance of all target patents. This can effectively avoid the impact of too many or too few regional collaborative patents on the target importance level.

[0057] The method for calculating the importance of a target can be determined according to actual needs. The methods for calculating the importance of a target include, but are not limited to, weighted summation, arithmetic mean, or harmonic mean.

[0058] For example, the target importance can be obtained by using the ratio of the number of target patents to the number of corresponding collaborative patents in the region as a weight, and then summing the weights of the importance of each target patent; or, the target importance can be obtained by taking the arithmetic mean of the number of collaborative patents in the two regions, and then dividing the sum of the importance of each target patent by the arithmetic mean; or, the target importance can be obtained by taking the harmonic mean of the number of collaborative patents in the two regions, and then dividing the sum of the importance of each target patent by the harmonic mean.

[0059] It is understandable that the harmonic mean method is used to calculate the importance of a target. All target patents The set formed is denoted as P. o Target patent The degree of importance is denoted as Weight ( ), abbreviated ,area The number of corresponding collaborative patents is denoted as .

[0060] When two regions are identical, the region is denoted as The harmonic mean is equal to the region The number of corresponding collaborative patents .

[0061] Importance of the target Calculated using the following formula:

[0062]

[0063] When the area and At the same time, the region and The number of collaborative patents are respectively denoted as and The harmonic mean is denoted as .

[0064] Harmonic Mean Calculated using the following formula:

[0065]

[0066] Importance of the target Calculated using the following formula:

[0067]

[0068] Step S103: Based on the technical field sets of the two regions, obtain the degree of similarity between the two technical field sets.

[0069] The technology set for each region includes the technology fields of the various collaborative patents in that region.

[0070] Specifically, considering the characteristics of regional cooperation, patent holders within a region may be concentrated in specific technological fields, forming a technology agglomeration effect; there may be cross-regional technology cooperation between patent holders in different regions, which may involve multiple technological fields; regional cooperation may include various forms of cooperation such as joint research and development, technology transfer, and technology licensing. Based on these characteristics, this disclosure embodiment considers the technology agglomeration effect within the region and measures the technological relevance and importance of inter-regional cooperation by the degree of similarity between two sets of technological fields.

[0071] It is understandable that each collaborative patent corresponds to a technical field. The technical field can be determined based on information such as the classification number, keywords, and semantics of the patent text. The specific method of obtaining the technical field can be determined according to actual needs.

[0072] In addition, the granularity and rules for dividing technical fields can be determined according to actual needs. For example, when conducting a comprehensive analysis of technical cooperation between regions across all technical fields, the technical fields can be divided into coarse-grained categories. When analyzing technical cooperation between regions within a specific technical field, a finer-grained category can be defined within that specific technical field.

[0073] For example, patent classification numbers can be determined according to classification methods such as the International Patent Classification (IPC), the Chinese Library Classification (CLC), and the Classification of Instructional Programs (CIP).

[0074] Taking the IPC classification method as an example, the IPC classification method divides all technical content related to invention patents into multiple levels such as parts, divisions, major categories, minor categories, main groups, and subgroups, forming a complete hierarchical classification system. Different granularities of technical fields can be determined according to each level. For example, the IPC subclass can be used as the classification granularity of technical fields, and each IPC subclass can be regarded as a technical field dimension.

[0075] After determining the technical field corresponding to each collaborative patent, the technical field sets of the two technical fields are obtained respectively. It can be understood that the technical fields of the collaborative patents corresponding to the region may be the same or different, and the technical field set of each region contains the technical fields of each collaborative patent in the corresponding region.

[0076] Considering that patent holders and related collaborative patents within the region may be concentrated in one or more technical fields, forming a technology agglomeration effect, in step S103, the similarity between the two sets of technical fields is obtained based on the sets of technical fields in the two regions. The similarity between the two sets of technical fields is called the technology concentration, which is used to measure the technology agglomeration effect of collaborative patents within the region. It reflects the degree of agglomeration of collaborative patents in the two regions in terms of technology field distribution, and more comprehensively reflects the agglomeration effect of technology cooperation within the region and technology exchange between regions.

[0077] It is understandable that the specific methods for measuring the similarity between two sets of technical fields may include, but are not limited to, using the Jaccard similarity coefficient, cosine similarity, bulldozer distance, edit distance, or corresponding variations.

[0078] For example, the Wasserstein Distance is used to measure the similarity of the distribution of technical fields between two regions, in order to calculate the degree of similarity between two sets of technical fields, dividing the regions... and The degree of similarity between sets of technical fields is denoted as .

[0079] Similarity Calculated using the following formula:

[0080]

[0081] In the formula, and Representing regions and Distribution vector of technical fields; distribution vector of technical fields Through statistical regions The distribution vector of the IPC classification codes of all patents within the patent is obtained. Each IPC subclass is regarded as a technical field dimension, and the corresponding regional technical field distribution vector is obtained through multi-hot vector encoding. This stands for Wasserstein Distance, used to measure the similarity of the distribution of technical fields between two regions.

[0082] It is understandable that when two regions are identical, the region is denoted as... The degree of similarity within a region is denoted as , Take 1.

[0083] Step S104: Based on the importance and similarity of the targets, obtain the degree of relevance of technological cooperation between the two regions.

[0084] Specifically, after obtaining the importance and similarity of the targets, in step S104, the degree of relevance of technical cooperation between the two regions is obtained based on the importance and similarity of the targets.

[0085] It is understandable that when two regions are the same, the degree of correlation (i.e., the intensity of cooperation) measures the technical connection and importance of cooperation within the region. When two regions are different, the degree of correlation (i.e., the intensity of cooperation) measures the technical connection and importance of cooperation between the two regions. The specific calculation method of the degree of correlation can be determined according to actual needs, including but not limited to weighted summation and multiplication.

[0086] For example, the product of the importance of the target and the similarity can be used as the degree of relevance, and the degree of relevance can be denoted as... abbreviation .

[0087] Relevance Calculated using the following formula:

[0088]

[0089] It is understandable that when two regions are identical, the region is denoted as... similarity within the region Take 1, correlation level This refers to the importance of the target. .

[0090] After obtaining the degree of relevance of technological cooperation between two regions, the degree of relevance can be compared with a preset relevance threshold. If the degree of relevance is not less than the threshold, it indicates that the technological cooperation between the regions is close. If the degree of relevance is less than the threshold, it indicates that the technological cooperation between the regions is distant, lacks effective communication and resource sharing, and that technological exchange and cooperation are at a low level.

[0091] Once it is determined that the degree of relevance is less than the relevance threshold, pre-set measures can be taken to strengthen technological cooperation between regions. These pre-set measures include, but are not limited to, periodically or irregularly pushing relevant patents of patent holders in the same technical field in the other region to patent holders in one of the two regions (or, for the same region, pushing relevant patents of other patent holders to patent holders in the same technical field), and carrying out regional development projects, such as joint research between regions and joint development of industrial parks.

[0092] The technical solution provided in this disclosure uses relevant data on collaborative patents in the region to be analyzed, along with the number and distribution of collaborative patents in the two regions and the target patents with collaborative relationships in the two regions. It deeply analyzes the importance of the target patent's characteristic information among all the characteristic information of collaborative patents, determines the importance of the target patent, and determines the similarity between the corresponding sets of technical fields in the two regions to measure the technological agglomeration effect of collaborative patents within the region. By combining the characteristic information of a single patent and the overall technical field of the region, the correlation between the two regions' technological collaboration is determined. This allows for a more comprehensive capture of technical details, accurately reflects the degree of technical correlation and the depth of collaboration between the collaborating regions, and enables a more direct and precise quantification of the intensity of collaboration between the two regions, thus improving the accuracy of the correlation analysis results.

[0093] Compared to techniques that only analyze cooperation intensity using information such as the number of collaborations, the technical solution provided in this disclosure quantifies the cooperation intensity of each regional collaboration at a fine-grained level, based on the importance of each target patent, providing a more substantial measure of cooperation intensity. Furthermore, it considers not only the total number of target patents and collaborative patents, but also the distribution of collaborative patents and target patents across regions, using the importance of each target patent to jointly determine the target cooperation intensity. This makes the target importance more direct and accurate, better reflecting the intensity and depth of cooperation, and revealing the complexity and distribution characteristics of technological cooperation.

[0094] In one possible implementation, the characteristic information of the target patent includes keywords and classification numbers;

[0095] Accordingly, the importance of acquiring each target patent includes:

[0096] For each target patent, obtain the first information and the second information of the target patent. The first information is used to measure the importance of the keywords of the target patent among the keywords of all the cooperative patents, and the second information is used to measure the importance of the classification number of the target patent among the classification numbers of all the cooperative patents.

[0097] For each target patent, the first and second information of the target patent are weighted and summed to obtain the importance of the target patent.

[0098] Specifically, in this embodiment of the disclosure, the feature information of the target patent includes keywords and classification numbers. Keywords can be used to measure the similarity of patents in terms of technical topics and research fields, while classification numbers can be used to measure the similarity of patents in terms of technical classification. By comprehensively considering the two dimensions of keywords and classification numbers, the importance of the target patent can be measured more comprehensively and accurately.

[0099] From the perspective of keywords, for each target patent The first information of the target patent is obtained. This first information is used to measure the importance of the keywords of the target patent among the keywords of all cooperating patents. The similarity and importance of the keywords of the target patent and the keywords of all cooperating patents are positively correlated. The first information can be determined by the frequency of the keywords of the target patent appearing in all cooperating patents. The specific method can be determined according to actual needs.

[0100] From the perspective of classification number, for each target patent The second information of the target patent is obtained. The second information is used to measure the importance of the classification number of the target patent among the classification numbers of all cooperative patents. The similarity between the classification number of the target patent and the classification numbers of all cooperative patents is positively correlated with the importance. The second information can be determined by the structural similarity between the classification number of the target patent and the classification numbers of all cooperative patents. The specific method can be determined according to actual needs.

[0101] It is understandable that a classification number is a numbering system. The classification number of a patent can directly reflect the technical classification and application scenario of the patent. The higher the structural similarity of the classification numbers of two patents, the closer the technical classification of the patents is.

[0102] For each target patent, after obtaining the first and second information of the target patent, the first and second information are weighted and summed to obtain the importance of the target patent.

[0103] Target Patent Importance Calculated using the following formula:

[0104]

[0105] In the formula, The target patent The first information; The target patent The second information; and The weight parameters for the first and second information are used to balance the contributions of the two dimensions of keywords and classification numbers. The specific values ​​of the weight parameters can be determined according to actual needs. For example, the weight parameters can be determined by human experience, cross-validation, or principal component analysis.

[0106] The feature information of the collaborative patents (including target patents) in the technical solutions provided in this disclosure includes keywords and classification numbers. From the perspective of keywords, the similarity of patents in technical description can be directly reflected. From the perspective of classification numbers, the similarity of patents in technical classification can be reflected. By comprehensively considering the importance of the keywords and classification numbers of each target patent among all collaborative patents, the importance of the target patent can be determined together. This can more comprehensively capture technical details and the depth of cooperation, and comprehensively evaluate the technical association between the target patent and collaborative patents in various regions.

[0107] In one possible implementation, the first information of the target patent is obtained in the following way:

[0108] Extract keywords from the target patent;

[0109] For each region, the average value of the regional keyword weights of each keyword in the target patent in the region is calculated based on the regional keyword weights of each keyword in the region, and this value is used as the first information of the target patent in the region.

[0110] The sum of the first information of the target patent in the two regions is taken as the first information of the target patent;

[0111] Specifically, for each region, the regional keyword weight in the region is positively correlated with the number of first patents in the region, where the first patent is a collaborative patent with the corresponding keyword.

[0112] Specifically, in order to more accurately reflect the importance of the target patent in the regional cooperation relationship, in terms of keywords, this disclosure not only considers whether the keywords existing in the corresponding regional cooperative patents exist in the target patent, but also considers the importance of each keyword of the target patent in each regional cooperative patent. It proposes an extended calculation method based on the basic idea of ​​Jaccard similarity, and introduces regional keyword weights to more accurately reflect the similarity between patents in technical description.

[0113] Jaccard similarity is a statistical method for comparing the similarity of two sets, particularly suitable for comparing binary features (presence / absence). Its calculation formula is:

[0114]

[0115] In the formula, A ∩ B Represents a set A and set B The size of the intersection (i.e., the number of elements that exist in both sets). A ∪ B Represents a set A and set B The size of the union of the two sets (i.e., the number of all distinct elements in the two sets), the Jaccard similarity range is [0,1], where 0 means that the two sets have no common elements and 1 means that the two sets are completely identical.

[0116] By introducing regional keyword weights, the importance of keywords within a region is considered. For each target patent, based on the keywords in the target patent and the corresponding regional keyword weights, for each region, the primary information of the target patent in that region needs to be obtained. The primary information of the target patent is determined by jointly using the primary information of the two regions.

[0117] Figure 2 This is a schematic diagram of a method for obtaining first information about a region provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, for the target patent Extract target patent The keyword k in the target patent The text includes at least one keyword k, targeting the patent. All keywords in the text are recorded as The total number of keywords is denoted as The method and number of keywords extracted are determined based on actual needs.

[0118] It is understandable that the number of keywords affects the complexity of the calculation. In this embodiment, a keyword list can be constructed based on the high-frequency technical terms of the technical fields involved in the collaborative patents of the two regions to be analyzed. Keywords in the target patent can be extracted based on the keyword list to reduce the number of keywords, reduce the computational complexity, and improve the computational efficiency.

[0119] With any region For example, for the target patent For each keyword k in the region, determine the keyword k in the region. Regional keyword weight .

[0120] For each region Keyword k in the region Regional keyword weight With the region The number of first patents is positively correlated with the number of first patents, where the first patent is a collaborative patent with the corresponding keyword k, i.e., for the region The more collaborative patents with the corresponding keyword k, the stronger the keyword k becomes in this region. Regional keyword weight The larger the value, the higher the weight of the regional keyword. The specific values ​​and methods for determining them can be determined based on actual needs.

[0121] For the target patent All keywords Calculate the average weight of the corresponding regional keywords, and use the average weight of the regional keywords as the target patent. In the region First information of the region .

[0122] First information in the region Calculated using the following formula:

[0123]

[0124] Using the above calculation method, for the region If i is 1 or 2, the target patent can be calculated. In the region First information of the region and target patents In the region First information of the region .

[0125] target patent First information in both regions and The sum serves as the first piece of information for the target patent. .

[0126] First Information Calculated using the following formula:

[0127]

[0128] It is understandable that when two regions are identical, the region is denoted as... The first information of the target patent For target patent In the region First information of the region Twice as much.

[0129] The technical solution provided in this disclosure considers the importance of keywords within each region by introducing regional keyword weights for each region. For each target patent, the average of the regional keyword weights of each keyword of the target patent in the two regions is obtained as the first information of the region. The first information of the region can reflect the similarity of the keyword set of the target patent and all cooperating patents in the corresponding region, and can directly reflect the similarity of the technical description between the target patent and the cooperating patents in the region, and can more accurately assess the importance of the target patent in the corresponding region from the perspective of keywords.

[0130] In one possible implementation, the regional keyword weight of a keyword in a region is obtained in the following way:

[0131] Obtain keywords from various collaborative patents in the region and construct a regional keyword set;

[0132] Obtain the first number of first patents corresponding to each keyword in the regional keyword set;

[0133] Based on the first number of first patents corresponding to each keyword in the regional keyword set, the first number of first patents corresponding to each keyword is normalized to obtain the regional keyword weight of the keyword in the region.

[0134] Specifically, in any region For example, the region The collaborative patent is recorded as ,area All collaborative patent construction areas The collection of collaborative patents is recorded as .

[0135] Extract each collaborative patent The keyword k in the text is based on the collaborative patent set. All collaborative patents Using keyword k, construct a set of regional keywords. The same keyword is recorded only once in the regional keyword set.

[0136] For regional keyword sets For each keyword k, the collaborative patents containing that keyword k are denoted as the first patent, and the number of first patents is taken as the first quantity. .

[0137] Given the first quantity for each keyword, we can determine the region keyword set for each keyword. The total count in is denoted as The first number of first patents corresponding to the keywords Normalization is performed to obtain the keyword k in the region. Regional keyword weight .

[0138] Regional keyword weights take into account the importance of keywords within each region. Through normalization, it ensures that when there are many keywords, the corresponding regional keyword weights will not be too large, thus guaranteeing the consistency of the calculation metrics.

[0139] Regional keyword weight Calculated using the following formula:

[0140]

[0141] Using the above calculation method, for the region If i is 1 or 2, the keyword k in the region can be calculated. Regional keyword weight Keyword k in the region Regional keyword weight It is understandable that when two regions are identical, the keyword k has the same weight as the regional keyword in both regions.

[0142] The technical solution provided in this disclosure can measure the importance of keywords within a corresponding region by using regional keyword weights. For each keyword of a target patent, the average value of the corresponding regional keyword weights of each keyword of the target patent is calculated in two regions as the first information of the region, which can accurately quantify the importance of the target patent in the corresponding region.

[0143] In one possible implementation, the second information of the target patent is obtained in the following manner:

[0144] Determine the classification number of the target patent;

[0145] For each region, obtain the classification similarity between the classification number of the target patent and the classification numbers of the various cooperating patents in the region;

[0146] For each region, based on the regional classification weight of each cooperative patent in the region, the similarity between the classification number of the target patent and the classification number of each cooperative patent in the region is weighted and summed to obtain the second regional information of the target patent in the region.

[0147] The sum of the second information of the target patent in the two regions is taken as the second information of the target patent;

[0148] Among them, the similarity between two classification numbers is positively correlated with the length of the common prefix of the two classification numbers;

[0149] For each region, the weight of the regional classification number in the region is positively correlated with the number of second patents in the region, where the second patents are collaborative patents with the corresponding classification numbers.

[0150] Specifically, in order to more accurately reflect the importance of the target patent in the regional cooperation relationship, in terms of classification number, this disclosure embodiment not only considers whether the classification number of the target patent is the same as the classification numbers of the corresponding cooperative patents in the region, but also considers the importance of the classification numbers of each cooperative patent in the region's cooperative patents. By introducing regional classification number weights, the similarity between patents in technical classification is reflected more accurately.

[0151] For each target patent, based on the similarity between the classification number in the target patent and the classification numbers of each cooperating patent in the region, and the corresponding regional classification number weight, for each region, the second information of the target patent in that region needs to be obtained, and the second information of the target patent is determined by jointly using the second information of the two regions.

[0152] Figure 3 This is a schematic diagram of a method for obtaining second information about a region provided in an embodiment of this disclosure, as shown below. Figure 3 As shown, for the target patent Identify target patents The classification number is m.

[0153] With any region For example, the region The collaborative patent is recorded as ,area All collaborative patent construction areas The collection of collaborative patents is recorded as Determine each collaborative patent Using classification number c, construct a set of regional classification numbers. The same classification number is recorded only once in the regional classification number set. The classification system used to determine the classification number of each patent in this embodiment can be determined according to actual needs.

[0154] For the set of regional classification numbers For each classification number c in the database, obtain the target patent. The classification similarity between classification number m and each classification number c is defined as the structural similarity between two classification numbers, denoted as [missing information]. Abbreviated as .

[0155] Patents with similar technical classifications may have significant differences in text descriptions and keywords. Considering only the keyword dimension is insufficient to capture such patents, and analysis based on a single dimension may lead to bias or omissions, adversely affecting the calculation of the target patent's importance and resulting in inaccurate results. Classification number structural similarity, however, is suitable for measuring patents that are close in technical classification. Combining both keyword and classification number dimensions can effectively and comprehensively measure the importance of the target patent, improving the accuracy of importance calculations.

[0156] It is understandable that the similarity of classification numbers is positively correlated with the length of the common prefix of the two classification numbers. That is, the longer the length of the common prefix of the two classification numbers, the greater the similarity of the classification numbers. The specific value and determination method of classification number similarity can be determined according to actual needs.

[0157] With any region For example, for the set of regional classification numbers For each classification number c in the data, determine the region where classification number c is located. Regional classification number weight .

[0158] For each region Classification number C in region Classification number weight With the region The number of second patents is positively correlated with the number of second patents, which are collaborative patents with the corresponding classification number c, i.e., for the region. The more collaborative patents with the corresponding classification number c, the more effective the classification number c is in that region. Regional classification number weight The larger the value, the higher the weight of the regional classification number. The specific values ​​and methods for determining them can be determined based on actual needs.

[0159] For the set of regional classification numbers All classification numbers c in the region, based on each classification number c in the region Regional classification number weight For the target patent Classification similarity between classification number m and classification numbers c of various collaborative patents in the region Perform a weighted summation to obtain the target patent. In the region Second information of the region .

[0160] Second information of the region Calculated using the following formula:

[0161]

[0162] Using the above calculation method, for the region If i is 1 or 2, the target patent can be calculated. In the region Second information of the region ( ), and the target patent In the region Second information of the region ( ).

[0163] target patent Second information in both regions ( )and The sum serves as the second piece of information in the target patent. .

[0164] Second Information Calculated using the following formula:

[0165]

[0166] It is understandable that when two regions are identical, the region is denoted as... Second information of the target patent region For target patent In the region Second information of the region Twice as much.

[0167] The technical solution provided in this disclosure considers the importance of a classification number within a region by introducing a regional classification number weight for each region. For each target patent, in each region, the similarity between the target's classification number and the classification numbers of the corresponding region and collaborating patents is calculated. The corresponding regional classification number weights are then used to weight and sum the similarities of each classification number to obtain the second information of the region. The second information of the region can reflect the similarity between the target patent and the set of classification numbers of all collaborating patents in the corresponding region, and can directly reflect the similarity between the target patent and the collaborating patents in the region in terms of technical classification. This allows for a more accurate assessment of the importance of the target patent in the corresponding region from the perspective of classification numbers.

[0168] In one possible implementation, the regional classification number weight within a region is obtained as follows:

[0169] Obtain the classification numbers of each collaborative patent in the region and construct a set of regional classification numbers;

[0170] Obtain the second number of second patents corresponding to each classification number in the set of regional classification numbers;

[0171] Based on the second number of second patents corresponding to each classification number in the regional classification number set, the second number of second patents corresponding to each classification number is normalized to obtain the regional classification number weight of the classification number in the region.

[0172] Specifically, in any region For example, the region The collaborative patent is recorded as ,area All collaborative patent construction areas The collection of collaborative patents is recorded as Determine each collaborative patent Using classification number c, construct a set of regional classification numbers. .

[0173] For the set of regional classification numbers For each classification number c, the collaborative patent with classification number c is recorded as the second patent, and the number of second patents is used as the second quantity. .

[0174] For the second quantity corresponding to each classification number, we can determine the region classification number set for each classification number. The total count in is denoted as The second number of second patents corresponding to the classification number Normalization is performed to obtain the classification number c in the region. Regional classification number weight .

[0175] The regional classification number weight takes into account the importance of the classification number within each region. The normalization process ensures that when there are many classification numbers, the corresponding regional classification number weight will not be too large, thus guaranteeing the consistency of the calculation metric.

[0176] Regional classification number weight Calculated using the following formula:

[0177]

[0178] Using the above calculation method, for the region If i is 1 or 2, the classification number c in the region can be calculated. Regional classification number weight Classification number C in region Regional classification number weight It is understandable that when two regions are identical, the weight of classification number c in the regional classification numbers of the two regions is also the same.

[0179] The technical solution provided in this disclosure can measure the importance of a classification number within a corresponding region by using regional classification number weights. For each target patent's classification number, the similarity between the target's classification number and the classification numbers of the corresponding region and the cooperative patents is calculated in two regions. The similarity of each classification number is then weighted and summed according to the corresponding regional classification number weights to obtain the second information of the region. This can accurately quantify the importance of the target patent's classification number in the corresponding region.

[0180] In one possible implementation, the similarity between two classification numbers is obtained in the following ways:

[0181] Construct a similarity calculation function, which is the reciprocal of an exponential function with a base greater than 1;

[0182] For any two classification numbers, determine the length of the difference in the difference between the two classification numbers based on the length of the common prefix of the two classification numbers;

[0183] Substitute the difference length as the independent variable into the similarity calculation function, and use the calculated dependent variable as the classification number similarity.

[0184] Specifically, for classification numbers c and m, the length of the common prefix of the two classification numbers is denoted as... Abbreviated as Let the maximum length of classification number c and classification number m be denoted as Abbreviated as That is, the difference length of the difference portion between the two classification numbers is .

[0185] In this embodiment, a similarity calculation function is constructed using the reciprocal of an exponential function with a base greater than 1. The specific size of the base can be determined according to actual needs.

[0186] Difference length Substitute the independent variable into the similarity calculation function, and use the calculated dependent variable as the classification number similarity. .

[0187] For example, similarity calculation function The calculation formula is:

[0188]

[0189] Taking the IPC classification system as an example, the IPC code of the classification number is 5 digits, which represent the department, major category, minor category, major group and minor group respectively from front to back. The granularity of the corresponding technical classification is from coarse to fine.

[0190] When the common prefix length of two classification numbers is 5, every coding bit of the two classification numbers is the same. 1; When the common prefix length of two classification numbers is 4, the two classification numbers are identical up to the major group code position and before that. When the common prefix length of two classification numbers is 3, the two classification numbers are identical up to the subclass code position and before that. When the common prefix length of two classification numbers is 2, the two classification numbers are identical up to the major category code position and before that. When the common prefix length of two classification numbers is 1, the two classification numbers only have the same partial code bits. .

[0191] The technical solution provided in this disclosure constructs a similarity calculation formula using an exponential function, which enables classification numbers with longer common prefixes to reflect higher classification number similarity. The longer the common prefix, the closer the calculated similarity value is to 1. For classification numbers with shorter common prefixes, the classification number similarity will be lower, but will not drop to zero, which is consistent with the actual situation. Even if classification numbers belong to different major categories, there may be some similarity, which can provide good distinguishability, so that even classification numbers with slight differences can have obvious differences in similarity.

[0192] Compared with existing related technologies 1 and 2, the method for analyzing the relevance of technical cooperation provided in this disclosure has the following advantages:

[0193] The technical solution provided in this disclosure provides a more efficient analysis approach by comprehensively considering multiple dimensions, such as the number of collaborative patents in each region, the number of target patents with collaborative relationships between two regions, the importance of keywords and classification numbers of each target patent in all collaborative patents, and the degree of similarity (technology concentration) between two sets of technical fields. Compared with the traditional methods that require a lot of data cleaning and complex network analysis in existing related technologies, this approach reduces the complexity and time cost of data processing.

[0194] Furthermore, compared to existing technologies, our enhanced analytical depth allows for a more in-depth and accurate analysis of the relevance of technological cooperation between regions, quantifying the intensity of regional cooperation and thus helping users gain a more comprehensive view of collaborative relationships. By revealing the patterns and intensity of technological cooperation between different entities, our technical solutions can promote cross-domain and cross-regional technological collaboration, driving technological innovation and knowledge sharing. We provide users with data-driven insights, helping them understand the technological dynamics and trends behind cooperation, thereby maintaining a competitive edge.

[0195] The technical solutions provided in this disclosure offer customized analysis services based on different user needs. For example, they can analyze the relevance of technological cooperation in specific industries (technical fields) or regions, such as analyzing the relevance of technological cooperation between patent holders, domestic regions, or international cities (cross-border), providing users with greater flexibility and value. These solutions can be applied to patent analysis tools, enterprise cooperation decision support systems, regional innovation cooperation evaluation platforms, and other products or projects, exhibiting a significant competitive advantage, especially in areas requiring in-depth analysis of technological cooperation and knowledge flow.

[0196] It can provide more accurate and detailed analyses of the relevance of technological cooperation, offering scientific evidence and decision support to policymakers, business managers, and research institutions. This enables more data-driven decision-making, improving the quality and efficiency of decision-making. It also helps promote technological innovation, foster cross-sectoral, cross-regional, and transnational technological cooperation, and achieve more efficient resource allocation and broader knowledge sharing.

[0197] It is understood that the embodiments disclosed herein are merely specific examples to illustrate the present invention in detail, and the described embodiments are intended only to facilitate the understanding of the present invention, and do not constitute any limitation thereof.

[0198] Figure 4 A schematic diagram of a device for analyzing the relevance of technical cooperation provided in this embodiment of the disclosure, as shown below. Figure 4 As shown, the device 400 for analyzing the relevance of technological cooperation includes:

[0199] The target patent determination module 401 is used to determine two regions to be analyzed, obtain the cooperative patents in the two regions respectively, and determine the target patent from the cooperative patents in the two regions. The cooperative patent is a patent with multiple patentees and at least one patentee located in the corresponding region. The target patent includes at least one first patentee and at least one second patentee. The first patentee is located in one of the two regions, and the second patentee is located in the other of the two regions.

[0200] The importance determination module 402 is used to obtain the importance of each target patent. Based on the importance of all target patents and the distribution of the number of cooperative patents in the two regions, the target importance is obtained. The importance is used to measure the importance of the feature information of the target patent among the feature information of all cooperative patents. The target importance is used to represent the common importance of all target patents in the two regions.

[0201] The similarity determination module 403 is used to obtain the similarity between two sets of technical fields based on the sets of technical fields of the two regions. Each set of technical fields of the region contains the technical fields of each cooperative patent in the corresponding region.

[0202] The technology cooperation analysis module 404 is used to obtain the degree of correlation of technology cooperation between two regions based on the importance and similarity of the objectives.

[0203] In one possible implementation, the characteristic information of the target patent includes keywords and classification numbers;

[0204] Accordingly, the importance of acquiring each target patent includes:

[0205] For each target patent, obtain the first information and the second information of the target patent. The first information is used to measure the importance of the keywords of the target patent among the keywords of all the cooperative patents, and the second information is used to measure the importance of the classification number of the target patent among the classification numbers of all the cooperative patents.

[0206] For each target patent, the first and second information of the target patent are weighted and summed to obtain the importance of the target patent.

[0207] In one possible implementation, the first information of the target patent is obtained in the following way:

[0208] Extract keywords from the target patent;

[0209] For each region, the average value of the regional keyword weights of each keyword in the target patent in the region is calculated based on the regional keyword weights of each keyword in the region, and this value is used as the first information of the target patent in the region.

[0210] The sum of the first information of the target patent in the two regions is taken as the first information of the target patent;

[0211] Specifically, for each region, the regional keyword weight in the region is positively correlated with the number of first patents in the region, where the first patent is a collaborative patent with the corresponding keyword.

[0212] In one possible implementation, the regional keyword weight of a keyword in a region is obtained in the following way:

[0213] Obtain keywords from various collaborative patents in the region and construct a regional keyword set;

[0214] Obtain the first number of first patents corresponding to each keyword in the regional keyword set;

[0215] Based on the first number of first patents corresponding to each keyword in the regional keyword set, the first number of first patents corresponding to each keyword is normalized to obtain the regional keyword weight of the keyword in the region.

[0216] In one possible implementation, the second information of the target patent is obtained in the following manner:

[0217] Determine the classification number of the target patent;

[0218] For each region, obtain the classification similarity between the classification number of the target patent and the classification numbers of the various cooperating patents in the region;

[0219] For each region, based on the regional classification weight of each cooperative patent in the region, the similarity between the classification number of the target patent and the classification number of each cooperative patent in the region is weighted and summed to obtain the second regional information of the target patent in the region.

[0220] The sum of the second information of the target patent in the two regions is taken as the second information of the target patent;

[0221] Among them, the similarity between two classification numbers is positively correlated with the length of the common prefix of the two classification numbers;

[0222] For each region, the weight of the regional classification number in the region is positively correlated with the number of second patents in the region, where the second patents are collaborative patents with the corresponding classification numbers.

[0223] In one possible implementation, the regional classification number weight within a region is obtained as follows:

[0224] Obtain the classification numbers of each collaborative patent in the region and construct a set of regional classification numbers;

[0225] Obtain the second number of second patents corresponding to each classification number in the set of regional classification numbers;

[0226] Based on the second number of second patents corresponding to each classification number in the regional classification number set, the second number of second patents corresponding to each classification number is normalized to obtain the regional classification number weight of the classification number in the region.

[0227] In one possible implementation, the similarity between two classification numbers is obtained in the following ways:

[0228] Construct a similarity calculation function, which is the reciprocal of an exponential function with a base greater than 1;

[0229] For any two classification numbers, determine the length of the difference in the difference between the two classification numbers based on the length of the common prefix of the two classification numbers;

[0230] Substitute the difference length as the independent variable into the similarity calculation function, and use the calculated dependent variable as the classification number similarity.

[0231] The apparatus of this disclosure embodiment can execute the method provided in this disclosure embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each disclosure embodiment correspond to the steps in the method of each disclosure embodiment. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0232] Furthermore, in this disclosure, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Moreover, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0233] This disclosure provides an electronic device (computer device / equipment / system) including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the method provided in any optional embodiment of this disclosure and achieve the corresponding technical effects.

[0234] In one alternative embodiment, an electronic device is provided. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure, such as... Figure 5 As shown, the electronic device 500 includes a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, for example, via a bus 502. Optionally, the electronic device 500 may further include a transceiver 504, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 504 is not limited to one type, and the structure of the electronic device 500 does not constitute a limitation on the embodiments of this disclosure.

[0235] Processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure. Processor 501 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0236] Bus 502 may include a pathway for transmitting information between the aforementioned components. Bus 502 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 502 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0237] The memory 503 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium capable of carrying or storing computer programs and capable of being read by a computer, without limitation.

[0238] The memory 503 is used to store computer programs that execute embodiments of the present disclosure, and the execution is controlled by the processor 501. The processor 501 is used to execute the computer programs stored in the memory 503 to implement the steps shown in the foregoing method embodiments.

[0239] The electronic devices in this disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), wearable devices, and fixed terminals such as digital TVs and desktop computers.

[0240] This disclosure provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0241] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0242] It should be noted that the computer-readable storage medium described above in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0243] In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0244] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0245] The terms “first,” “second,” “third,” “fourth,” “1,” “2,” etc. (if present) in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in a sequence other than that shown in the figures or text.

[0246] It should be understood that although arrows indicate various operation steps in the flowcharts of the embodiments of this disclosure, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of the embodiments of this disclosure, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured as required, and the embodiments of this disclosure do not limit this.

[0247] The above description is only an optional implementation method for some implementation scenarios of this disclosure. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this disclosure without departing from the technical concept of this disclosure also fall within the protection scope of the embodiments of this disclosure.

Claims

1. A method for analyzing the relevance of technological cooperation, characterized in that, include: Two regions to be analyzed are identified, and the collaborative patents of the two regions are obtained respectively. The target patent is determined from the collaborative patents of the two regions. The collaborative patent is a patent with multiple patentees and at least one patentee located in the corresponding region. The target patent includes at least one first patentee and at least one second patentee. The first patentee is located in one of the two regions, and the second patentee is located in the other of the two regions. The importance of each target patent is obtained. Based on the importance of all target patents and the distribution of the number of collaborative patents in the two regions, the target importance is obtained. The importance of the target patent is used to measure the importance of the feature information of the target patent among the feature information of all collaborative patents. The target importance is used to represent the common importance of all target patents in the two regions. Based on the technology sets of the two regions, the degree of similarity between the two technology sets is obtained. Each technology set of the region contains the technology fields of the various collaborative patents in the corresponding region. Based on the importance of the objectives and the similarity between the sets of technical fields, the degree of relevance of technological cooperation between the two regions is obtained; The characteristic information of the target patent includes keywords and classification numbers; Accordingly, the importance of acquiring each target patent includes: For each target patent, obtain the first information and the second information of the target patent; For each target patent, the first and second information of the target patent are weighted and summed to obtain the importance of the target patent; The first information is used to measure the importance of the keywords of the target patent among the keywords of all cooperating patents. The first information is determined by the frequency or frequency of the occurrence of the keywords of the target patent among all cooperating patents. The second information is used to measure the importance of the classification number of the target patent among the classification numbers of all collaborative patents. The second information is determined by the structural similarity between the classification number of the target patent and the classification numbers of all collaborative patents.

2. The method for analyzing the relevance of technological cooperation according to claim 1, characterized in that, The first information of the target patent is obtained through the following methods: Extract keywords from the target patent; For each region, based on the regional keyword weight of each keyword in the region, the average regional keyword weight of each keyword in the target patent in the region is calculated, and used as the first regional information of the target patent in the region; The sum of the first information of the target patent in the two regions is taken as the first information of the target patent; Specifically, for each region, the regional keyword weight of the keyword in that region is positively correlated with the number of first patents in that region, where the first patents are collaborative patents with the corresponding keywords.

3. The method for analyzing the relevance of technological cooperation according to claim 2, characterized in that, The regional keyword weight of the keyword in the region is obtained through the following methods: Obtain keywords from various collaborative patents in the region and construct a regional keyword set; Obtain the first number of first patents corresponding to each keyword in the set of regional keywords; Based on the first number of first patents corresponding to each keyword in the regional keyword set, the first number of first patents corresponding to each keyword is normalized to obtain the regional keyword weight of the keyword in the region.

4. The method for analyzing the relevance of technological cooperation according to claim 2 or 3, characterized in that, The second information of the target patent is obtained through the following methods: Determine the classification number of the target patent; For each region, obtain the classification similarity between the classification number of the target patent and the classification numbers of the various cooperating patents in that region; For each region, based on the regional classification number weight of each cooperative patent in the region, the classification number similarity between the classification number of the target patent and the classification number of each cooperative patent in the region is weighted and summed to obtain the regional second information of the target patent in the region. The sum of the second information of the target patent in the two regions is taken as the second information of the target patent; Among them, the similarity between two classification numbers is positively correlated with the length of the common prefix of the two classification numbers; For each region, the weight of the regional classification number in that region is positively correlated with the number of second patents in that region, where the second patents are collaborative patents with the corresponding classification number.

5. The method for analyzing the relevance of technological cooperation according to claim 4, characterized in that, The regional classification weight of the classification number in the region is obtained in the following way: Obtain the classification numbers of each collaborative patent in the region and construct a regional classification number set; Obtain the second number of second patents corresponding to each classification number in the set of regional classification numbers; Based on the second number of second patents corresponding to each classification number in the set of regional classification numbers, the second number of second patents corresponding to each classification number is normalized to obtain the regional classification number weight of the classification number in the region.

6. The method for analyzing the relevance of technological cooperation according to claim 4, characterized in that, The similarity between two classification numbers is obtained through the following methods: Construct a similarity calculation function, wherein the similarity calculation function is the reciprocal of an exponential function with a base greater than 1; For any two classification numbers, determine the length of the difference in the difference between the two classification numbers based on the length of the common prefix of the two classification numbers; The difference length is substituted as the independent variable into the similarity calculation function, and the calculated dependent variable is used as the classification number similarity.

7. A device for analyzing the degree of relevance of technological cooperation, characterized in that, include: The target patent determination module is used to determine two regions to be analyzed, obtain the collaborative patents in the two regions respectively, and determine the target patent from the collaborative patents in the two regions. The collaborative patent is a patent with multiple patentees and at least one patentee located in the corresponding region. The target patent includes at least one first patentee and at least one second patentee. The first patentee is located in one of the two regions, and the second patentee is located in the other of the two regions. The importance determination module is used to obtain the importance of each target patent. Based on the importance of all target patents and the distribution of the number of collaborative patents in the two regions, the target importance is obtained. The importance of the target patent is used to measure the importance of the feature information of the target patent among the feature information of all collaborative patents. The target importance is used to represent the common importance of all target patents in the two regions. The similarity determination module is used to obtain the similarity between two sets of technical fields based on the sets of technical fields of two regions. Each set of technical fields of a region contains the technical fields of each collaborative patent in the corresponding region. The technology cooperation analysis module is used to obtain the degree of correlation of technology cooperation between two regions based on the importance of the target and the similarity between the sets of technology fields. The characteristic information of the target patent includes keywords and classification numbers; Accordingly, the importance of acquiring each target patent includes: For each target patent, obtain the first information and the second information of the target patent; For each target patent, the first and second information of the target patent are weighted and summed to obtain the importance of the target patent; The first information is used to measure the importance of the keywords of the target patent among the keywords of all cooperating patents. The first information is determined by the frequency or frequency of the occurrence of the keywords of the target patent among all cooperating patents. The second information is used to measure the importance of the classification number of the target patent among the classification numbers of all collaborative patents. The second information is determined by the structural similarity between the classification number of the target patent and the classification numbers of all collaborative patents.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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