Method, apparatus and electronic device for obtaining trademark similarity

Abstract

The present application discloses a method, apparatus, and electronic device for obtaining trademark similarity. The method includes: acquiring character information of a first trademark and character information of a second trademark; constructing a feature information set according to the character information of the first trademark and the character information of the second trademark; and obtaining the similarity between the first trademark and the second trademark according to the feature information set. By automatically constructing multiple feature information for evaluating trademark similarity, this method can quickly and accurately obtain the similarity between trademarks. At the same time, it can also avoid the problems of manual rule design or inaccurate calculation of manually designed rules.

Description

Technical Field

[0001] The present disclosure relates to the field of natural language processing technology, and more particularly, to a method, apparatus, and electronic device for obtaining trademark similarity. This application also relates to a method for pre-judging trademark application conflicts. Background Art

[0002] A trademark is a mark used to distinguish the brand or service of one operator from the goods or services of other operators. In the increasingly fierce market competition, the value of trademarks is becoming more and more important.

[0003] Currently, for a trademark to be applied for, before an enterprise or an individual makes a formal application, or when a trademark examiner examines the trademark to be applied for, an electronic device can generally be used to retrieve in a registered trademark database to confirm whether there is a trademark similar to, that is, in conflict with, the trademark to be applied for, so as to generate a pre-judgment result or a review result for the trademark to be applied for.

[0004] However, when existing electronic devices retrieve trademarks similar to the trademark to be applied for, they generally only use simple rules for retrieval, and the retrieval results are not accurate. Often, it is necessary to spend manpower and time for secondary confirmation. Therefore, the existing methods for obtaining trademark similarity have the problem of low accuracy. Summary of the Invention

[0005] An object of an embodiment of the present disclosure is to provide a new technical solution for quickly and accurately obtaining trademark similarity.

[0006] In a first aspect of the present disclosure, there is provided a method for obtaining trademark similarity, the method including:

[0007] Obtaining character information of a first trademark and character information of a second trademark;

[0008] Constructing a feature information set according to the character information of the first trademark and the character information of the second trademark;

[0009] Obtaining the similarity between the first trademark and the second trademark according to the feature information set.

[0010] Optionally, the constructing a feature information set according to the character information of the first trademark and the character information of the second trademark includes:

[0011] Obtaining a first character set according to the character information of the first trademark, and obtaining a second character set according to the character information of the second trademark;

[0012] Constructing the feature information set according to the first character set and the second character set.

[0013] Optionally, constructing the feature information set according to the first character set and the second character set includes:

[0014] Calculating the union of the first character set and the second character set to obtain a target character set;

[0015] Obtaining an initial character vector according to the target character set, where each character of the initial character vector corresponds to the characters in the target character set in sequence, and the value of each character of the initial character vector is a first preset value;

[0016] Obtaining a first character vector according to the first character set and the initial character vector, and obtaining a second character vector according to the second character set and the initial character vector;

[0017] Constructing the feature information set according to the first character vector and the second character vector.

[0018] Optionally, obtaining the first character vector according to the first character set and the initial character vector includes:

[0019] Setting the character value at the corresponding position in the initial character vector to a second preset value according to the first character set and the correspondence between each character of the initial character vector and the characters in the target character set, to obtain the first character vector.

[0020] Optionally, constructing the feature information set according to the first character vector and the second character vector includes:

[0021] Constructing the feature information set by calculating the cosine similarity between the first character vector and the second character vector.

[0022] Optionally, constructing the feature information set according to the first character set and the second character set includes:

[0023] Calculating the Jaccard coefficient between the first character information and the second character information according to the first character set and the second character set;

[0024] Constructing the feature information set according to the Jaccard coefficient.

[0025] Optionally, constructing the feature information set according to the first character set and the second character set includes:

[0026] Constructing the feature information set by calculating the edit distance between the first character information and the second character information.

[0027] Optionally, constructing the feature information set according to the first character set and the second character set includes:

[0028] Obtaining a first length of the first character set and a second length of the second character set;

[0029] Constructing the feature information set by calculating the absolute value of the difference and the average value of the first length and the second length.

[0030] Optionally, obtaining the similarity between the first trademark and the second trademark according to the feature information set includes:

[0031] Inputting the feature information in the feature information set into a similarity calculation model to obtain the similarity.

[0032] Optionally, the first character information includes one or more of the Chinese character information, pinyin information, and phrase information corresponding to the first trademark; correspondingly, the second character information includes one or more of the Chinese character information, pinyin information, English information, and phrase information corresponding to the second trademark.

[0033] In a second aspect of the present disclosure, a method for pre-judging trademark application conflicts is further provided, including:

[0034] Obtaining a target trademark to be applied for;

[0035] Obtaining a set of similar trademarks corresponding to the target trademark;

[0036] Obtaining a similarity set according to the target trademark and the set of similar trademarks, where the similarity in the similarity set represents the similarity between the target trademark and the trademarks in the set of similar trademarks, and the similarity is obtained according to the method described in the first aspect of the present disclosure;

[0037] Obtaining a pre-judged application result of the target trademark according to the similarity set.

[0038] Optionally, the method further includes:

[0039] Generating a list of similar trademarks according to the set of similar trademarks and the similarity set, where the list of similar trademarks includes multiple data pairs, and the data pairs are composed of the trademarks in the set of similar trademarks and the similarities corresponding to the trademarks in the similarity set.

[0040] Optionally, when the method is applied to a server, the method further includes:

[0041] Providing the list of similar trademarks and the pre-judged application result to a terminal device;

[0042] Optionally, the method is applied to a terminal device, and the method further includes:

[0043] Display the list of similar trademarks and the predicted application result.

[0044] According to a third aspect of the present disclosure, there is also provided a device for obtaining trademark similarity, including:

[0045] A character information acquisition module, configured to acquire the character information of a first trademark and the character information of a second trademark;

[0046] A feature information set construction module, configured to construct a feature information set according to the character information of the first trademark and the character information of the second trademark;

[0047] A similarity acquisition module, configured to acquire the similarity between the first trademark and the second trademark according to the feature information set.

[0048] According to a fourth aspect of the present disclosure, there is also provided an electronic device, including the device according to the third aspect of the present disclosure; or, including:

[0049] A memory, configured to store executable instructions;

[0050] A processor, configured to operate the electronic device to execute the method according to the first aspect or the second aspect of the present disclosure under the control of the executable instructions.

[0051] According to a fifth aspect of the present disclosure, there is also provided a computer-readable storage medium, where the computer-readable storage medium stores a computer program that can be read and executed by a computer, and the computer program is configured to execute the method according to the first aspect or the second aspect of the present disclosure when being read and run by the computer.

[0052] According to an embodiment of the present disclosure, when an electronic device needs to obtain the similarity between two trademarks, for the first trademark and the second trademark, by respectively acquiring the character information of the first trademark and the character information of the second trademark, and constructing a feature information set according to the character information of the first trademark and the character information of the second trademark, the electronic device can acquire the similarity between the first trademark and the second trademark quickly and accurately. In the embodiment of the present disclosure, the electronic device automatically constructs multiple feature information for evaluating the trademark similarity by acquiring the character information of the trademark, can quickly and accurately obtain the similarity between trademarks, and at the same time, can also avoid the problems of manual rule design or inaccurate calculation of manual rules, thereby saving manpower and improving the user experience.

[0053] Through the following detailed description of the exemplary embodiments of the present disclosure with reference to the accompanying drawings, other features and advantages of the present disclosure will become clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0055] Figure 1 It is a schematic diagram of the scenario of the trademark similarity acquisition method provided by an embodiment of the present disclosure.

[0056] Figure 2 It is a structural diagram of the hardware configuration of an electronic device that can be used to implement the trademark similarity acquisition method provided by an embodiment of the present disclosure.

[0057] Figure 3 It is a schematic flowchart of the trademark similarity acquisition method provided by an embodiment of the present disclosure.

[0058] Figure 4 It is a schematic flowchart of the trademark application conflict prediction method provided by an embodiment of the present disclosure.

[0059] Figure 5 It is a schematic principle block diagram of the trademark similarity acquisition device provided by an embodiment of the present disclosure.

[0060] Figure 6a It is a schematic principle block diagram of an electronic device according to an embodiment of the present disclosure.

[0061] Figure 6b It is a schematic principle block diagram of an electronic device according to another embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] Now, various exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that: unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure.

[0063] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or its use.

[0064] Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be considered as part of the specification.

[0065] In all the examples shown and discussed herein, any specific values should be construed as merely exemplary and not as limitations. Therefore, other examples of the exemplary embodiments may have different values.

[0066] It should be noted that like reference numerals and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0067] In view of the problems of low accuracy in retrieving and obtaining similar trademarks by electronic devices in the prior art and the time-consuming problem caused by the need for manual secondary confirmation, an embodiment of the present disclosure provides a method for obtaining trademark similarity, so that an electronic device can quickly and accurately obtain the similarity between two trademarks, thereby improving the accuracy of trademark retrieval results.

[0068] Please refer to Figure 1 , which is a schematic diagram of an application scenario of a method for obtaining trademark similarity provided by an embodiment of the present disclosure. As Figure 1 shown, for a target trademark to be applied for, for example, the trademark "Cool Bar Shopping Website", when a user retrieves registered trademarks similar to the target trademark through an electronic device 1100, the target trademark can be sent to the electronic device 1100 through a terminal device 1200, for example, sent to a server; after the electronic device 1100 obtains the target trademark, it can first retrieve a set of similar trademarks corresponding to the target trademark according to simple rules, for example, under the trademark classification corresponding to the target trademark, according to Chinese character matching and / or pinyin matching rules; then, in order to improve the accuracy and reduce the time-consuming problem caused by manual secondary confirmation, the electronic device 1100 can regard the target trademark as the first trademark, and regard the trademarks in the set of similar trademarks as the second trademark in turn, obtain the character information of the first trademark and the character information of the second trademark, construct a feature information set including multiple feature information, and then obtain the similarity between the first trademark and the second trademark according to the feature information set; by obtaining the similarity between the target trademark and each similar trademark in the set of similar trademarks one by one, a similarity set can be obtained; then, according to the similarity set, the electronic device 1100 can quickly and accurately obtain a predicted application result of the target trademark. For example, it can output a predicted application result directly representing "success" or "failure" according to whether a statistical value such as the maximum value or average value in the similarity set is greater than a preset similarity threshold; and, the electronic device 1100 can also generate a list of similar trademarks according to the set of similar trademarks and the similarity set, and, for the convenience of the user to view, the electronic device 1100 can provide the list of similar trademarks and the predicted application result to the terminal device 1200 for the terminal device 1200 to display the list of similar trademarks and the predicted application result for the user to view.

[0069] <Hardware configuration>

[0070] Figure 2 It is a structural diagram of the hardware configuration of an alternative electronic device that can be used to implement the method for obtaining trademark similarity according to an embodiment of the present disclosure.

[0071] As Figure 2 shown, the method for obtaining trademark similarity provided in this embodiment can be applied to the electronic device 1100. In specific implementation, the electronic device 1100 can be a server or a terminal device, and no special limitation is made here.

[0072] When the electronic device 1100 is a server, the server can be a blade server, a rack server, etc., or the server can also be a server cluster deployed in the cloud, and no limitation is made here.

[0073] As Figure 2 shown, the electronic device 1100 may include a processor 1110, a memory 1120, an interface device 1130, a communication device 1140, a display device 1150, and an input device 1160. The processor 1110 can be, for example, a central processing unit CPU, etc. The memory 1120 includes, for example, a ROM (read-only memory), a RAM (random access memory), a non-volatile memory such as a hard disk, etc. The interface device 1130 includes, for example, a USB interface, a serial interface, etc. The communication device 1140 can perform wired or wireless communication, for example. The display device 1150 is, for example, a liquid crystal display screen. The input device 1160 can include, for example, a touch screen, a keyboard, etc.

[0074] In this embodiment, the electronic device 1100 can be used to participate in implementing the method for obtaining trademark similarity according to any embodiment of the present disclosure.

[0075] Applied to the embodiments of the present disclosure, the memory 1120 of the electronic device 1100 is used to store instructions for controlling the processor 1110 to operate to support implementing the method for obtaining trademark similarity according to any embodiment of the present disclosure. Those skilled in the art can design the instructions according to the solutions disclosed in the present disclosure. How the instructions control the processor to operate is well known in the art, so it will not be described in detail here.

[0076] Those skilled in the art should understand that although multiple devices of the electronic device 1100 are shown in Figure 2 , the electronic device 1100 in the embodiments of the present disclosure may only involve some of the devices, for example, only involve the processor 1110 and the memory 1120.

[0077] In addition, when the electronic device 1100 is a terminal device, the terminal device can be a smart phone, a portable computer, a desktop computer, a tablet computer, etc., and no special limitation is made here.

[0078] It should be noted that the method provided in this embodiment can be applied alone to the electronic device 1100, that is, the server or the terminal device, or can be applied to the interaction scenario between the terminal device and the server according to needs, and no special limitation is made here.

[0079] <First Method Embodiment>

[0080] Figure 3 It is a schematic flowchart of the method for obtaining trademark similarity provided by an embodiment of the present disclosure. The method provided in this embodiment can be applied to an electronic device. For example, it can be applied to Figure 2 the electronic device 1100 shown.

[0081] As Figure 3 shown, the method for obtaining trademark similarity in this embodiment may include the following steps S3100 - S3300, which will be described in detail below.

[0082] Step S3100, obtain the character information of the first trademark and the character information of the second trademark.

[0083] A trademark can include words, graphics, letters, numbers, three - dimensional marks, sounds, color combinations, or combinations of the above elements; in this embodiment, unless otherwise specified, the trademark is a character - type trademark, that is, a trademark composed of characters such as Chinese characters, numbers, letters, etc. is taken as an example for illustration; of course, in specific implementation, the method described in this embodiment can also process other types of trademarks. For example, for a trademark containing graphics or sounds, the characters in the graphics or the words in the sounds can be recognized to convert such trademarks into character - type trademarks, or the similarity between such trademarks can be obtained by combining methods such as image similarity acquisition methods and audio similarity acquisition methods, and no special limitation is made here.

[0084] The first trademark and the second trademark can be any two trademarks to be calculated for trademark similarity. For example, the first trademark can be "Cool Bar Shopping Website", and the second trademark can be "Ku Ba", and no special limitation is made here.

[0085] In this embodiment, the character information of the first trademark includes one or more of the Chinese character information, pinyin information, and phrase information corresponding to the first trademark; correspondingly, the character information of the second trademark includes one or more of the Chinese character information, pinyin information, English information, and phrase information corresponding to the second trademark.

[0086] For example, for the trademark "Cool Bar Shopping Website", its corresponding character information can be the Chinese character information "Cool, Bar, Purchase, Object, Network, Station"; or the pinyin information "ku, ba, gou, wu, wang, zhan"; or one or more of the phrase information "Cool Bar, Shopping, Website".

[0087] In this embodiment, to improve the accuracy of trademark similarity calculation, taking the character information of each trademark including Chinese character information, pinyin information, and phrase information as an example for illustration, that is, in this embodiment, the similarity feature information of two trademarks to be calculated for similarity can be extracted from three aspects: Chinese characters, pinyin, and phrases, so as to obtain the similarity of the two trademarks.

[0088] In specific implementation, the pinyin information of a trademark can be obtained by converting the Chinese characters in the trademark into pinyin, and the English remains unchanged; the phrase information of a trademark can be obtained by performing word segmentation on the Chinese characters in the trademark. Herein, the details of how to perform word segmentation on character-type texts are not elaborated.

[0089] Step S3200, construct a feature information set according to the character information of the first trademark and the character information of the second trademark.

[0090] In this embodiment, the feature information set includes multiple pieces of feature information, and the feature information includes information characterizing the similarity between the character information of the first trademark and the character information of the second trademark.

[0091] After obtaining the two trademarks to be calculated for similarity and the character information of the two trademarks through step S3100, the electronic device can construct multiple pieces of feature information characterizing the similarity between the two types of character information from multiple aspects according to the character information of the first trademark and the character information of the second trademark. For example, the feature information can be constructed from three aspects: Chinese characters, pinyin, and phrases, which are described in detail below.

[0092] In specific implementation, the constructing a feature information set according to the character information of the first trademark and the character information of the second trademark includes: obtaining a first character set according to the character information of the first trademark, and obtaining a second character set according to the character information of the second trademark; constructing the feature information set according to the first character set and the second character set.

[0093] In this embodiment, the first character set includes the characters corresponding to the first trademark, and the second character set includes the characters corresponding to the second trademark.

[0094] For example, when the first trademark is "CoolBar Shopping Website" and the second trademark is "KuBa", in terms of Chinese characters, the character information of the first trademark can be "Cool, Bar, Shopping, Website", and the character information of the second trademark can be "Trousers, Bar". Then the first character set can be {Cool, Bar, Shopping, Website}, and the second character set can be {Trousers, Bar}; in terms of pinyin, the character information of the first trademark can be "ku, ba, gou, wu, wang, zhan", and the character information of the second trademark can be "ku, ba". Then the first character set can be {ku, ba, gou, wu, wang, zhan}, and the second character set can be {ku, ba}; in terms of phrases, the character information of the first trademark can be "CoolBar, Shopping, Website", and the character information of the second trademark can be "KuBa". Then the first character set can be {CoolBar, Shopping, Website}, and the second character set can be {KuBa}.

[0095] After obtaining the first character set and the second character set, multiple feature information representing the similarity between the character information of the first and second trademarks can be automatically constructed based on the elements in the sets to obtain a feature information set.

[0096] Specifically, constructing the feature information set according to the first character set and the second character set includes: calculating the union of the first character set and the second character set to obtain a target character set; obtaining an initial character vector according to the target character set, where each character of the initial character vector corresponds to the characters in the target character set in sequence, and the value of each character of the initial character vector is a first preset value; obtaining a first character vector according to the first character set and the initial character vector, and obtaining a second character vector according to the second character set and the initial character vector; constructing the feature information set according to the first character vector and the second character vector.

[0097] Here, still taking the first trademark as "CoolBar Shopping Website" and the second trademark as "KuBa" as an example, in terms of Chinese characters, by calculating the union of the above first character set and the second character set, the target character set corresponding to the first trademark and the second trademark can be obtained as {Cool, Bar, Shopping, Website, Trousers}. Then, when the first preset value is "0", the initial character vector can be [0000000]; in terms of pinyin, the target character set can be {ku, ba, gou, wu, wang, zhan}, then the initial character vector can be [000000]; in terms of phrases, the target character set can be {CoolBar, Shopping, Website, KuBa}, then the initial character vector can be

[0000] .

[0098] In specific implementation, obtaining the first character vector according to the first character set and the initial character vector includes: setting the character values at corresponding positions in the initial character vector to a second preset value according to the first character set and the correspondence between each character of the initial character vector and the characters in the target character set, so as to obtain the first character vector.

[0099] For example, when the first trademark is "Cool Bar Shopping Website", according to the above description, in terms of Chinese characters, its first character set is {Cool, Bar, Shop, Website}, then, when the second preset value is "1", according to the correspondence between each character of the initial character vector and the characters in the target character set, that is, the first digit corresponds to "Cool", the second digit corresponds to "Bar", the third digit corresponds to "Shop", the fourth digit corresponds to "Website", the fifth digit corresponds to "Network", the sixth digit corresponds to "Station", and the seventh digit corresponds to "Trousers", the first character vector can be [1111110]; correspondingly, in terms of pinyin, the first character vector can be [111111]; and in terms of phrases, the first character vector can be

[1110] .

[0100] Another example is the second trademark "Trousers Bar". In terms of Chinese characters, the second character vector can be [0100001]; in terms of pinyin, the second character vector can be [110000]; and in terms of phrases, the second character vector can be

[0001] .

[0101] It should be noted that in specific implementation, the correspondence between each character of the initial character vector and the characters in the target character set, the first preset value, and the second preset value can also be set as needed, and no special limitation is made here.

[0102] After obtaining the first character vector of the first trademark and the second character vector of the second trademark through the above processing, the feature information set can be constructed according to the first character vector and the second character vector. Specifically, constructing the feature information set according to the first character vector and the second character vector includes: constructing the feature information set by calculating the cosine similarity between the first character vector and the second character vector.

[0103] Cosine similarity, also known as cosine similarity, is to evaluate the similarity between two adjacent vectors by calculating the cosine value of the included angle between the two vectors.

[0104] That is, the similarity between the two trademarks can be characterized by calculating the cosine similarity between the first character vector of the first trademark and the second character vector of the second trademark.

[0105] For example, in terms of Chinese characters, the cosine similarity between the first character vector [1111110] and the second character vector [0100001] can be calculated, so as to obtain, in terms of Chinese characters, the feature information characterizing the similarity between the character information of the two trademarks, that is, wordsimilarity; in terms of pinyin, the cosine similarity between the first character vector [111111] and the second character vector [110000] is calculated, so as to obtain, in terms of pinyin, the feature information characterizing the similarity between the character information of the two trademarks, that is, pinyinsimilarity; in terms of phrases, the cosine similarity between the first character vector

[1110] and the second character vector

[0001] is calculated, so as to obtain, in terms of phrases, the feature information characterizing the similarity between the character information of the two trademarks, that is, phrasesimilarity; after obtaining the above-mentioned feature information, one or more of the above-mentioned feature information can be used to construct the feature information in the feature information set corresponding to the first trademark "Cool Bar Shopping Website" and the second trademark "Ku Ba".

[0106] As described above, by separately obtaining the first and second character sets corresponding to the character information of the first trademark and the second trademark, respectively constructing their character vectors, and thus constructing the feature information in their corresponding feature information sets; in this embodiment, other feature information can also be constructed according to the first character set and the second character set, that is, constructing the feature information set according to the first character set and the second character set includes: calculating the Jaccard coefficient between the first character information and the second character information according to the first character set and the second character set; constructing the feature information set according to the Jaccard coefficient.

[0107] The Jaccard coefficient (Jaccardsimilaritycoefficient) can be used to compare the similarity and difference between finite character sets. Generally speaking, the larger the Jaccard coefficient value, the higher the similarity between the character sets. Among them, regarding how to calculate the Jaccard coefficient between two character sets, since it is described in detail in the prior art, it will not be elaborated here.

[0108] In addition, in specific implementation, constructing the feature information set according to the first character set and the second character set includes: constructing the feature information set by calculating the edit distance between the first character information and the second character information.

[0109] The edit distance, also known as the Levenshtein distance, is a quantitative measure of the difference between two strings. The measurement method is to see how many times of processing are required at least to change one string into another string. Regarding how to calculate the edit distance between two character sets, since it is described in detail in the prior art, it will not be elaborated here.

[0110] In specific implementation, the constructing of the feature information set according to the first character set and the second character set further includes: obtaining the first length of the first character set and the second length of the second character set; constructing the feature information set by calculating the absolute value of the difference and the average value between the first length and the second length.

[0111] That is, in order to extract the feature information characterizing the similarity between the first character information of the first trademark and the second character information of the second trademark from multiple aspects, the statistical data between the first character set and the second character set can also be obtained, such as the absolute value of the difference in character length, the average value, etc.

[0112] For example, for the first trademark "Cool Bar Shopping Website" and the second trademark "Ku Ba", according to the above description, in terms of Chinese characters, the first character set is {Cool, Bar, Shopping, Website}, the second character set is {Ku, Bar}, then the first length is 6, the second length is 2, the absolute value of the difference can be 4, and the average value can be 4; in terms of pinyin, according to the above description, the first character set is {ku, ba, gou, wu, wang, zhan}, the second character set is {ku, ba}, then the absolute value of the difference can also be 4, and the average value can be 4; in terms of phrases, according to the above description, the first character set is {Cool Bar, Shopping, Website}, the second character set is {Ku Ba}, then the absolute value of the difference is 2, and the average value is 2.

[0113] It should be noted that in specific implementation, one or a combination of the above methods can be used to obtain multiple feature information characterizing the character information of the first trademark and the second trademark to construct a feature information set; or, the above method can also be combined with other methods to construct a feature information set, and no special limitation is made here.

[0114] After step S3200, step S3300 is executed to obtain the similarity between the first trademark and the second trademark according to the feature information set.

[0115] After obtaining the feature information sets corresponding to the first trademark and the second trademark through the above steps, in the embodiments of the present disclosure, a machine learning algorithm can be used to automatically obtain the similarity between the first trademark and the second trademark according to the feature information in the feature information sets.

[0116] That is, in specific implementation, obtaining the similarity between the first trademark and the second trademark according to the feature information set includes: inputting the feature information in the feature information set into a similarity calculation model to obtain the similarity.

[0117] The similarity calculation model can be a neural network model that is pre-trained for calculating the similarity between at least two trademarks. For example, it can be a logistic regression model, a decision tree model, etc. The training method of the model is not elaborated here.

[0118] Through the above description, the similarity between the first trademark and the second trademark can be obtained quickly and accurately. In specific implementation, the method in this embodiment can be used in the trademark application pre-judgment scenario, that is, the applicant pre-searches for conflicting trademarks to modify or re-design the target trademark to be applied to avoid application failure. Also, it can be applied to the trademark application review scenario, that is, the examiner uses this method to search for registered trademarks similar to the target trademark submitted by the applicant to quickly and accurately make a review result.

[0119] For example, for the target trademark "Cool Bar Shopping Website" to be applied for, the applicant can send the target trademark to the server used to obtain the pre-judged application result through their terminal device. For example, by searching for the target trademark in a trademark query search engine and sending the target trademark to the corresponding server. Among them, the server corresponding to the search engine may contain pre-processed and structured index information of registered trademarks. For example, for each registered trademark, the engine data of the search engine may include all information constructed based on one or more of the Chinese character information, pinyin information, classification information, registrant information, etc. of the trademark; after the server obtains the target trademark, it can first perform a rough recall process, that is, first use simple rules, such as Chinese character matching or pinyin matching rules, etc., to retrieve similar trademarks that match the target trademark according to the pre-constructed index information of registered trademarks to obtain a set of similar trademarks. For example, the set of similar trademarks may be {"Coolpad Cube", "Ku Ba", "Cool Kids"}; then, for the target trademark and each trademark in the set of similar trademarks, the similarity obtaining method described in this embodiment can be used to obtain the similarity between the target trademark and each similar trademark to obtain a set of similarities. For example, it may be {5%, 43%, 34%}; then, according to the set of similarities, the server can return the pre-judged application result to the terminal device. For example, if the preset similarity threshold is set to 80%, then when the maximum value in the set of similarities is not greater than the similarity threshold, the server can determine that there are no similar trademarks among the registered trademarks, and then can return a "success" pre-judged application result to the terminal device.

[0120] Of course, for the convenience of users to view and conduct secondary confirmation, after the server obtains the set of similar trademarks and the set of similarities, it can also generate a list of similar trademarks according to the set of similar trademarks and the set of similarities. For example, generate a list of similar trademarks composed of data pairs {("Coolpad Cube", 5%), ("Ku Ba", 43%), ("Cool Kids", 34%)}, and provide the list of similar trademarks and the above-mentioned pre-judged application result to the terminal device for the terminal device to display the list of similar trademarks and the pre-judged application result for the user to view.

[0121] In summary, for the method for obtaining trademark similarity provided in this embodiment, for the first trademark and the second trademark, an electronic device, for example, a server for calculating trademark similarity, obtains the character information of the first trademark and the character information of the second trademark respectively, and constructs a feature information set according to the character information of the first trademark and the character information of the second trademark. Then, the electronic device can quickly and accurately obtain the similarity between the first trademark and the second trademark according to the feature information set. In the embodiments of the present disclosure, the electronic device automatically constructs multiple feature information for judging the similarity between two trademarks through the character information of the two trademarks to be obtained in similarity, and automatically obtains the similarity between trademarks quickly and accurately according to the feature information between the two trademarks by combining machine learning algorithms. This method can avoid the problems of manual rule design or inaccurate calculation of manually designed rules, thereby saving manpower and improving the user experience.

[0122] <Method Embodiment 2>

[0123] Corresponding to the above Method Embodiment 1, this embodiment further provides a method for pre-judging trademark application conflicts. Please refer to Figure 4 , which is a schematic flowchart of the method for pre-judging trademark application conflicts provided in the embodiments of the present disclosure. This method can be applied to an electronic device, for example, it can be applied to the electronic device 1100 shown in Figure 2 , and no special limitation is made here.

[0124] As shown in Figure 4 , the method provided in this embodiment may include steps S4100 - S4400.

[0125] Step S4100: Obtain a target trademark to be applied for.

[0126] Step S4200: Obtain a set of similar trademarks corresponding to the target trademark.

[0127] Step S4300: Obtain a similarity set according to the target trademark and the set of similar trademarks, where the similarity in the similarity set represents the similarity between the target trademark and the trademarks in the set of similar trademarks, and the similarity is obtained according to the method described in Method Embodiment 1.

[0128] Step S4400: Obtain a pre-judged application result of the target trademark according to the similarity set.

[0129] In one embodiment, the method further includes: generating a list of similar trademarks according to the set of similar trademarks and the similarity set, where the list of similar trademarks includes multiple data pairs, and each data pair is composed of a trademark in the set of similar trademarks and the similarity corresponding to the trademark in the similarity set.

[0130] In one embodiment, the method can be applied to a server. In this case, the method further includes: providing the list of similar trademarks and the predicted application result to a terminal device.

[0131] In one embodiment, the method can be applied to a terminal device. In this case, the method further includes: presenting the list of similar trademarks and the predicted application result.

[0132] For the target trademark to be applied for, the method provided in this embodiment can first perform a search using big data methods and simple rules to obtain a set of similar trademarks corresponding to the target trademark. At the same time, in order to avoid the time-consuming problem caused by secondary confirmation by the user and to improve the accuracy of the predicted application result, after obtaining the set of similar trademarks through a single search, this embodiment uses the trademark similarity obtaining method described in Method Embodiment 1 to obtain the similarity between the target trademark and each trademark in the set of similar trademarks, thereby obtaining a similarity set. According to this similarity set, the predicted application result for the target trademark can be accurately obtained.

[0133] <Device Embodiment>

[0134] Corresponding to the above embodiment, this embodiment further provides a device for obtaining trademark similarity, as Figure 5 shown, which is a schematic principle block diagram of the device for obtaining trademark similarity provided by an embodiment of the present disclosure.

[0135] According to Figure 5 shown, the device 5000 for obtaining trademark similarity in this embodiment includes a character information acquisition module 5100, a feature information set construction module 5200, and a similarity acquisition module 5300.

[0136] The character information acquisition module 5100 is configured to acquire the character information of a first trademark and the character information of a second trademark.

[0137] The feature information set construction module 5200 is configured to construct a feature information set according to the character information of the first trademark and the character information of the second trademark.

[0138] In one embodiment, when constructing the feature information set according to the character information of the first trademark and the character information of the second trademark, the feature information set construction module 5200 can be configured to: obtain a first character set according to the character information of the first trademark, and obtain a second character set according to the character information of the second trademark; construct the feature information set according to the first character set and the second character set.

[0139] In one embodiment, when constructing the feature information set according to the first character set and the second character set, the feature information set construction module 5200 can be used to: calculate the union of the first character set and the second character set to obtain a target character set; obtain an initial character vector according to the target character set, where each character of the initial character vector corresponds to the characters in the target character set in sequence, and the value of each character of the initial character vector is a first preset value; obtain a first character vector according to the first character set and the initial character vector, and obtain a second character vector according to the second character set and the initial character vector; construct the feature information set according to the first character vector and the second character vector.

[0140] In one embodiment, when obtaining the first character vector according to the first character set and the initial character vector, the feature information set construction module 5200 can be used to: set the character value at the corresponding position in the initial character vector to a second preset value according to the first character set and the correspondence between each character of the initial character vector and the characters in the target character set, so as to obtain the first character vector.

[0141] In one embodiment, when constructing the feature information set according to the first character vector and the second character vector, the feature information set construction module 5200 can be used to: construct the feature information set by calculating the cosine similarity between the first character vector and the second character vector.

[0142] In one embodiment, when constructing the feature information set according to the first character set and the second character set, the feature information set construction module 5200 can be used to: calculate the Jaccard coefficient between the first character information and the second character information according to the first character set and the second character set; construct the feature information set according to the Jaccard coefficient.

[0143] In one embodiment, when constructing the feature information set according to the first character set and the second character set, the feature information set construction module 5200 can be used to: construct the feature information set by calculating the edit distance between the first character information and the second character information.

[0144] In one embodiment, when constructing the feature information set according to the first character set and the second character set, the feature information set construction module 5200 can be used to: obtain the first length of the first character set and the second length of the second character set; construct the feature information set by calculating the absolute value of the difference and the average value between the first length and the second length.

[0145] The similarity obtaining module 5300 is used to obtain the similarity between the first trademark and the second trademark according to the feature information set.

[0146] In one embodiment, when obtaining the similarity between the first trademark and the second trademark according to the feature information set, the similarity obtaining module 5300 can be used to: input the feature information in the feature information set into a similarity calculation model to obtain the similarity.

[0147] <Device Embodiment>

[0148] Corresponding to the above embodiment, this embodiment provides an electronic device, as Figure 6a shown, the electronic device 100 includes a trademark similarity obtaining device 5000 according to any embodiment of the present disclosure.

[0149] In another embodiment, as Figure 6b shown, the electronic device 100 may include a memory 110 and a processor 120. The memory 110 is used to store executable instructions; the processor 120 is used to execute the method according to any method embodiment of the present disclosure under the control of the executable instructions.

[0150] <Medium Embodiment>

[0151] Corresponding to the above method embodiment, in this embodiment, a computer-readable storage medium is further provided. The computer-readable storage medium stores a computer program that can be read and run by a computer. The computer program is used to execute the method according to any of the above embodiments of the present disclosure when being read and run by the computer.

[0152] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0153] A computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example—but not limited to—an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed as being a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0154] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or can be downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0155] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present disclosure.

[0156] Aspects of the present disclosure are described herein with reference to the flowchart and / or block diagram of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer - readable program instructions.

[0157] These computer - readable program instructions can be provided to a processor of a general - purpose computer, a special - purpose computer, or other programmable data - processing apparatus to produce a machine such that when the instructions are executed by the processor of the computer or other programmable data - processing apparatus, a device is created that implements the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes a computer, a programmable data - processing apparatus, and / or other devices to operate in a particular manner, so that the computer - readable medium storing the instructions includes a manufacture that includes instructions for implementing various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0158] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to produce a computer-implemented process, such that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0159] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are equivalent.

[0160] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, the practical application, or the improvement of the technology in the market, or to enable other ordinary skilled persons in the technical field to understand the embodiments disclosed herein. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for obtaining trademark similarity, comprising: Obtaining the character information of a first trademark and the character information of a second trademark; Constructing a feature information set according to the character information of the first trademark and the character information of the second trademark; Obtaining the similarity between the first trademark and the second trademark according to the feature information set; Wherein, the constructing a feature information set according to the character information of the first trademark and the character information of the second trademark includes: obtaining a first character set according to the character information of the first trademark, and obtaining a second character set according to the character information of the second trademark; constructing the feature information set according to the first character set and the second character set; The constructing the feature information set according to the first character set and the second character set includes: calculating the union of the first character set and the second character set to obtain a target character set; obtaining an initial character vector according to the target character set, wherein each character of the initial character vector corresponds to the characters in the target character set in sequence, and the value of each character of the initial character vector is a first preset value; obtaining a first character vector according to the first character set and the initial character vector, and obtaining a second character vector according to the second character set and the initial character vector; constructing the feature information set according to the first character vector and the second character vector.

2. The method according to claim 1, wherein the obtaining a first character vector according to the first character set and the initial character vector includes: Setting the character values at the corresponding positions in the initial character vector to a second preset value according to the first character set and the correspondence between each character of the initial character vector and the characters in the target character set, to obtain the first character vector.

3. The method according to claim 1, wherein the constructing the feature information set according to the first character vector and the second character vector includes: Constructing the feature information set by calculating the cosine similarity between the first character vector and the second character vector.

4. The method according to claim 1, wherein The constructing the feature information set according to the first character set and the second character set includes: Calculating the Jaccard coefficient between the character information of the first trademark and the character information of the second trademark according to the first character set and the second character set; Constructing the feature information set according to the Jaccard coefficient.

5. The method according to claim 1, wherein The constructing the feature information set according to the first character set and the second character set includes: Constructing the feature information set by calculating the edit distance between the character information of the first trademark and the character information of the second trademark.

6. The method according to claim 1, wherein the constructing the feature information set according to the first character set and the second character set includes: Obtaining a first length of the first character set and a second length of the second character set; Construct the feature information set by calculating the absolute value of the difference and the average value between the first length and the second length.

7. The method according to claim 1, wherein obtaining the similarity between the first trademark and the second trademark according to the feature information set comprises: Input the feature information in the feature information set into a similarity calculation model to obtain the similarity.

8. The method according to claim 1, wherein the character information of the first trademark includes one or more of the Chinese character information, pinyin information, and phrase information corresponding to the first trademark; correspondingly, the character information of the second trademark includes one or more of the Chinese character information, pinyin information, English information, and phrase information corresponding to the second trademark.

9. A method for pre-judging trademark application conflicts, comprising: Obtain a target trademark to be applied for. Obtain a set of similar trademarks corresponding to the target trademark. Obtain a similarity set according to the target trademark and the set of similar trademarks, wherein the similarity in the similarity set represents the similarity between the target trademark and the trademarks in the set of similar trademarks, and the similarity is obtained according to the method described in any one of claims 1-8. Obtain a pre-judged application result of the target trademark according to the similarity set.

10. The method according to claim 9, further comprising: Generate a list of similar trademarks according to the set of similar trademarks and the similarity set, wherein the list of similar trademarks includes a plurality of data pairs, and each data pair consists of a trademark in the set of similar trademarks and the similarity corresponding to the trademark in the similarity set.

11. The method according to claim 10, wherein the method is applied to a server, and the method further comprises: Provide the list of similar trademarks and the pre-judged application result to a terminal device.

12. The method according to claim 10, wherein the method is applied to a terminal device, and the method further comprises: Display the list of similar trademarks and the pre-judged application result.

13. A device for obtaining trademark similarity, comprising: A character information acquisition module, configured to acquire the character information of a first trademark and the character information of a second trademark; A feature information set construction module, configured to construct a feature information set according to the character information of the first trademark and the character information of the second trademark. Wherein, constructing the feature information set according to the character information of the first trademark and the character information of the second trademark includes: obtaining a first character set according to the character information of the first trademark, and obtaining a second character set according to the character information of the second trademark; constructing the feature information set according to the first character set and the second character set, and constructing the feature information set according to the first character set and the second character set includes: calculating the union of the first character set and the second character set to obtain a target character set; obtaining an initial character vector according to the target character set, where each character of the initial character vector corresponds to the characters in the target character set in sequence, and the value of each character of the initial character vector is a first preset value; obtaining a first character vector according to the first character set and the initial character vector, and obtaining a second character vector according to the second character set and the initial character vector; constructing the feature information set according to the first character vector and the second character vector; A similarity obtaining module, configured to obtain the similarity between the first trademark and the second trademark according to the feature information set.

14. An electronic device, comprising the apparatus according to claim 13; or comprising: A memory, configured to store executable instructions; A processor, configured to run the electronic device to execute the method according to any one of claims 1-12 under the control of the executable instructions.

15. A computer-readable storage medium, wherein, The computer-readable storage medium stores a computer program that can be read and executed by a computer. The computer program is used to execute the method according to any one of claims 1-12 when being read and run by the computer.

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