CN116628245B - Intelligent trademark recommendation method and system based on artificial intelligence - Google Patents
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Abstract
The invention discloses an intelligent trademark recommending method and system based on artificial intelligence, which are implemented by acquiring mass trademark data of a trademark library; the trademark data at least comprises trademark categories and trademark names under each trademark category; inputting trademark data into an initial artificial intelligent model for training to obtain a first recommended model; acquiring user design requirements, inputting the user design requirements into a first recommendation model, and outputting to obtain a target trademark name; inputting the design requirement of the user and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph; generating a target trademark according to the target trademark name and the target trademark graph; the method can intelligently help the user to screen the trademark name, avoid repeated names and influence the auditing period, automatically generate trademark graphs (logo) according to the user requirements and the generated target trademark name, conveniently and rapidly generate trademarks for the user, and promote user experience.
Description
Technical Field
The invention relates to the technical field of data prediction, in particular to an intelligent trademark recommendation method and system based on artificial intelligence.
Background
Trademark (trade mark) is a specific legal term, which is a logo used to identify and distinguish a source of goods or services. Any sign that can distinguish the merchandise of a natural person, legal person, or other organization from the merchandise of another person, including text, graphics, letters, numbers, three-dimensional signs, color combinations, sounds, etc., and combinations of the above elements, can be registered as a trademark application; brands or portions of brands are known as "brands" after legal registration by government authorities. The trademark is protected by law, and the registrant has exclusive rights.
Along with the annual increase of the trademark application amount, the name and the graph of the trademark can play a decisive role in examination, but the user generally wants to apply for a trademark by himself or herself to want some trademark name intention words and design graphs, which is difficult for users without design experience, time and effort are wasted, and how to automatically generate the trademark for the users according to the design requirements of the users is a technical problem to be solved urgently.
Disclosure of Invention
The present invention has been made in view of the above-mentioned problems, and it is an object of the present invention to provide an artificial intelligence based trademark intelligence recommendation method and system which overcomes or at least partially solves the above-mentioned problems.
According to one aspect of the present invention, there is provided an artificial intelligence based trademark intelligence recommendation method, including:
acquiring mass trademark data of a trademark library; the trademark data at least comprises trademark categories and trademark names under each trademark category;
inputting trademark data into an initial artificial intelligent model for training to obtain a first recommended model;
acquiring user design requirements, inputting the user design requirements into a first recommendation model, and outputting to obtain a target trademark name;
inputting the design requirement of the user and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph; wherein, at least, the second recommendation model comprises: a mass design template database;
generating a target trademark according to the target trademark name and the target trademark graph;
wherein, the user design requirement includes at least: the user's nominated intention word, the user's required trademark font, trademark category, trademark color.
According to another aspect of the present invention, there is provided an artificial intelligence based trademark intelligence recommendation system, including:
the name generation module is used for acquiring mass trademark data of the trademark library; wherein the trademark data at least comprises trademark categories and trademark names under each trademark category; inputting the trademark data into an initial artificial intelligent model for training to obtain a first recommended model; acquiring user design requirements, inputting the user design requirements into the first recommendation model, and outputting to obtain a target trademark name;
the graph generating module is used for inputting the user design requirement and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph; wherein, at least, the second recommendation model comprises: a mass design template database;
the target trademark generation module is used for generating a target trademark according to the target trademark name and the target trademark graph;
wherein the user design requirements include at least: the user's nominated intention word, the user's required trademark font, trademark category, trademark color.
According to yet another aspect of the present invention, there is provided a computing device comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface are communicated with each other through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the trademark intelligent recommendation method based on the artificial intelligence.
According to still another aspect of the present invention, there is provided a computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to an artificial intelligence based trademark intelligence recommendation method as described above.
According to the trademark intelligent recommending method and system based on artificial intelligence, mass trademark data of a trademark library are obtained; the trademark data at least comprises trademark categories and trademark names under each trademark category; inputting trademark data into an initial artificial intelligent model for training to obtain a first recommended model; acquiring user design requirements, inputting the user design requirements into a first recommendation model, and outputting to obtain a target trademark name; inputting the design requirement of the user and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph; generating a target trademark according to the target trademark name and the target trademark graph; the method can intelligently help the user to screen the trademark name, avoid repeated names and influence the auditing period, automatically generate trademark graphs (logo) according to the user requirements and the generated target trademark name, conveniently and rapidly generate trademarks for the user, and promote user experience.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 shows a flowchart of a trademark intelligent recommendation method based on artificial intelligence, which is provided by an embodiment of the invention;
fig. 2 is a schematic structural diagram of a trademark intelligent recommendation system based on artificial intelligence according to an embodiment of the present invention;
FIG. 3 illustrates a schematic diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 1 shows a flowchart of an embodiment of an artificial intelligence based trademark intelligence recommendation method, as shown in FIG. 1, comprising the steps of:
step S110: acquiring mass trademark data of a trademark library, inputting the trademark data into an initial artificial intelligent model for training to obtain a first recommended model;
wherein the trademark data includes at least a trademark category and a trademark name under each trademark category. Step S110 further includes: aiming at each trademark category, arranging trademark names under the trademark category, inputting an initial artificial intelligent model, applying a deduplication algorithm, and obtaining a plurality of trademark categories, wherein each trademark category corresponds to a plurality of trademark names; determining a plurality of trademark categories and a plurality of trademark names as comparison databases, and constructing a first recommendation model according to the comparison databases;
specifically, this step requires pre-building a resource database for brand categories established by the trademark office and applied and approved brand names under each brand category; in general, the brand name is not repeatedly registered, so that a duplicate name needs to be avoided before a user applies for the application, in order to save the application time, the embodiment may store the brand name set by the trademark office and the applied and audited brand name under each brand category, input an initial artificial intelligence model to apply a duplicate removal algorithm, and then store all the brand names in a classified manner according to the brand category. And these multiple brand categories and multiple brand names may be used as a comparison database. It should be noted that, the first recommendation model of the present embodiment is used to recommend the target trademark name.
Step S120: acquiring user design requirements, inputting the user design requirements into a first recommendation model, and outputting to obtain a target trademark name;
wherein, the user design requirement includes at least: the method comprises the steps of enabling a user to take up intention words, and enabling a trademark to be required by the user to be in fonts, trademark categories and trademark colors;
generally, the trademark includes two parts, namely a trademark name and a trademark graphic (logo), and the trademark logo generally should embody or include the trademark name, so in this embodiment, the target trademark name is generated first, and then the logo is generated through artificial intelligence according to the target trademark name.
In an alternative way, the user design requirements include at least: the user's nominated intent word; step S120 further includes: inputting the nominated meaning words into a first recommendation model, and sequentially performing similarity calculation on brand names in a database by comparing the nominated meaning words with the brand names in the first recommendation model; if the similarity between the nominated meaning word and any trademark name is equal to a preset threshold value, outputting a non-passing word sample by the first recommendation model to prompt a user to re-input the nominated meaning word until the first recommendation model outputs the passing word sample; determining the passed nominated meaning word as a target trademark name; wherein the target brand name is one or more.
In an alternative manner, step S120 further includes: inputting the nominated intention words into a first recommendation model to generate feature vectors which are expressed as%) The method comprises the steps of carrying out a first treatment on the surface of the Selecting trademark names with the same number of feature vectors as the famous intention words in the comparison database as a target comparison database; generating a feature vector representation for each brand name in the target alignment library as (++>) The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Splitting words in the nominated intention words; />Splitting words in trade name; sequentially calculating +.>And->Is a similarity of (3).
For example, if the nominated intention word is "Donghua animation design", six feature vectors (Donghua, hua, dynamic, drawing, design) can be generated; that is, one character generates one feature vector, and it should be noted that if the nominated meaning word or the trademark name contains english, one letter generates one feature vector, for example, if the nominated meaning word is "hobby", five feature vectors may be generated.
In order to reduce the calculation amount of the similarity calculation, only the brand names having the same number of feature vectors as the nominated meaning words in the comparison database are selected as the target comparison database in this step, for example, if six feature vectors are generated by the nominated meaning words, only the brand names including the six feature vectors may be selected as the target comparison database.
In an alternative manner, step S120 further includes: if it is=/>Then the similarity is processed by adding 1; when the preset threshold is taken as n, namely the similarity is equal to n, the first recommendation model outputs a word which does not pass through; the preset threshold value is equal to the number of feature vectors of the nominated meaning words.
That is, since the brand name identical to the nominated meaning word is confirmed only when each corresponding feature vector is identical, the nominated meaning word feature vector at the corresponding position is compared with only the feature vector of the brand name at the corresponding position, that isOnly need to be +.>Comparing, if->=/>Then the similarity is added by 1, and it should be noted that the initial value of the similarity is 0, so that only when the similarity=n, the target comparison library is described as having the identical brand name, and therefore, the named intention word should be output without prompting the user to reenter until passing.
It should be noted that, the user may input one or more nominated intention words, if both pass through, one or more target trademark names may be generated, and if there are multiple target trademark names, the user may perform screening to determine the unique one as the final target trademark name.
Step S130: inputting the design requirement of the user and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph;
wherein, at least, the second recommendation model comprises: a mass design template database;
specifically, in this step, the second recommendation model may be completed using an open source artificial intelligence (Artificial Intelligence, AI) logo generator. These AI logo generators can quickly create trademarks. Finding a proper AI logo generator, inputting some text, and waiting for one minute. Generally, the AI logo generator at least includes: the AI Logo generator can create trademarks of any size or format from a mass design template database.
In an alternative manner, step S130 further includes: inputting the trademark fonts, trademark categories, trademark colors and target trademark names required by the users into a second pre-created recommended model, and generating target trademark graphs according to corresponding templates in the trademark fonts, trademark categories, trademark colors and target trademark names application design template database required by the users; wherein the target trademark pattern is one or more.
It should be noted that, for one target trademark name, multiple target trademark patterns may be generated, and the user may screen according to his own needs.
Step S140: generating a target trademark according to the target trademark name and the target trademark graph;
in an alternative, the method further comprises: the user selects one target trademark name and one target trademark graph from the target trademark names and the target trademark graphs as a final target trademark name and a final target trademark graph; or, determining the target trademark name and the target trademark pattern as the final target trademark name and the final target trademark pattern;
specifically, if only one target trademark name and one target trademark pattern are generated, the target trademark name and the target trademark pattern are determined to be the final target trademark name and the final target trademark pattern, if a plurality of target trademark names and a plurality of target trademark patterns are generated, the user first needs to select one target trademark name to input the second recommendation model, and if the second recommendation model generates a plurality of target trademark patterns, the user needs to select one target trademark pattern as the final selection of the target trademark.
By adopting the method of the embodiment, massive trademark data of a trademark library are obtained; the trademark data at least comprises trademark categories and trademark names under each trademark category; inputting trademark data into an initial artificial intelligent model for training to obtain a first recommended model; acquiring user design requirements, inputting the user design requirements into a first recommendation model, and outputting to obtain a target trademark name; inputting the design requirement of the user and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph; generating a target trademark according to the target trademark name and the target trademark graph; the system can intelligently help users to screen trademark names, avoid repeated names and influence auditing deadlines, automatically generate trademark graphs (logo) according to user demands and generated target trademark names, conveniently and rapidly generate trademarks for the users, promote user experience, further automatically recommend designed trademarks to the users according to the user demands, realize intellectualization and automation of trademark recommendation, and accelerate trademark design efficiency.
FIG. 2 is a schematic diagram of an embodiment of an artificial intelligence based trademark intelligence recommendation system. As shown in fig. 2, the system includes: a name generation module 210, a graphic generation module 220, and a target trademark generation module 230.
The name generation module 210 is configured to obtain massive trademark data of a trademark library; wherein the trademark data at least comprises trademark categories and trademark names under each trademark category; inputting trademark data into an initial artificial intelligent model for training to obtain a first recommended model; acquiring user design requirements, inputting the user design requirements into a first recommendation model, and outputting to obtain a target trademark name;
in an alternative manner, the name generation module 210 is further configured to: aiming at each trademark category, arranging trademark names under the trademark category, inputting an initial artificial intelligent model, applying a deduplication algorithm, and obtaining a plurality of trademark categories, wherein each trademark category corresponds to a plurality of trademark names; determining a plurality of trademark categories and a plurality of trademark names as comparison databases, and constructing a first recommendation model according to the comparison databases;
wherein, the user design requirement includes at least: the user's nominated intention word, the user's required trademark font, trademark category, trademark color.
In an alternative manner, the name generation module 210 is further configured to: if the similarity between the nominated meaning word and any trademark name is equal to a preset threshold value, outputting a non-passing word sample by the first recommendation model to prompt a user to re-input the nominated meaning word until the first recommendation model outputs the passing word sample; determining the passed nominated meaning word as a target trademark name; wherein the target brand name is one or more.
In an alternative manner, the name generation module 210 is further configured to: inputting the nominated intention words into a first recommendation model to generate feature vectors which are expressed as%) The method comprises the steps of carrying out a first treatment on the surface of the Selecting trademark names with the same number of feature vectors as the famous intention words in the comparison database as a target comparison database; generating a feature vector representation for each brand name in the target alignment library as (++>) The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Splitting words in the nominated intention words; />Splitting words in trade name; sequentially calculating +.>And->Is a similarity of (3).
In an alternative manner, the name generation module 210 is further configured to: if it is=/>Then the similarity is processed by adding 1; when the preset threshold is taken as n, namely the similarity is equal to n, the first recommendation model outputs a word which does not pass through; the preset threshold value is equal to the number of feature vectors of the nominated meaning words.
The graphic generation module 220 is configured to input the user design requirement and the target trademark name into a second recommendation model created in advance, and output the second recommendation model to obtain a target trademark graphic; wherein, at least, the second recommendation model comprises: a mass design template database;
in an alternative manner, the graphics-generating module 220 is further configured to: inputting the trademark fonts, trademark categories, trademark colors and target trademark names required by the users into a second pre-created recommended model, and generating target trademark graphs according to corresponding templates in the trademark fonts, trademark categories, trademark colors and target trademark names application design template database required by the users; wherein the target trademark pattern is one or more.
A target trademark generation module 230, configured to generate a target trademark according to a target trademark name and a target trademark pattern combination;
in an alternative manner, the target trademark generation module 230 is further configured to: the user selects one target trademark name and one target trademark graph from the target trademark names and the target trademark graphs as a final target trademark name and a final target trademark graph; or, the target brand name and the target brand pattern are determined as the final target brand name and the final target brand pattern.
By adopting the system of the embodiment, massive trademark data of a trademark library are obtained; the trademark data at least comprises trademark categories and trademark names under each trademark category; inputting trademark data into an initial artificial intelligent model for training to obtain a first recommended model; acquiring user design requirements, inputting the user design requirements into a first recommendation model, and outputting to obtain a target trademark name; inputting the design requirement of the user and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph; generating a target trademark according to the target trademark name and the target trademark graph; the system can intelligently help users to screen trademark names, avoid repeated names and influence auditing deadlines, automatically generate trademark graphs (logo) according to user demands and generated target trademark names, conveniently and rapidly generate trademarks for the users, and promote user experience.
The embodiment of the invention provides a nonvolatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the trademark intelligent recommendation method based on artificial intelligence in any method embodiment.
FIG. 3 illustrates a schematic diagram of an embodiment of a computing device of the present invention, and the embodiments of the present invention are not limited to a particular implementation of the computing device.
As shown in fig. 3, the computing device may include:
a processor (processor), a communication interface (Communications Interface), a memory (memory), and a communication bus;
wherein: the processor, communication interface, and memory communicate with each other via a communication bus. A communication interface for communicating with network elements of other devices, such as clients or other servers, etc. And the processor is used for executing a program, and can specifically execute relevant steps in the embodiment of the trademark intelligent recommendation method based on the artificial intelligence.
In particular, the program may include program code including computer-operating instructions.
The processor may be a central processing unit, CPU, or specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included by the server may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
And the memory is used for storing programs. The memory may comprise high-speed RAM memory or may further comprise non-volatile memory, such as at least one disk memory.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.
Claims (5)
1. An artificial intelligence based trademark intelligent recommendation method is characterized by comprising the following steps:
acquiring mass trademark data of a trademark library; wherein the trademark data at least comprises trademark categories and trademark names under each trademark category;
inputting the trademark data into an initial artificial intelligent model for training to obtain a first recommended model;
acquiring user design requirements, inputting the user design requirements into the first recommendation model, and outputting to obtain a target trademark name;
inputting the user design requirement and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph; wherein, at least, the second recommendation model comprises: a mass design template database;
generating a target trademark according to the target trademark name and the target trademark graph combination;
wherein the user design requirements include at least: the method comprises the steps of enabling a user to take up intention words, and enabling a trademark to be required by the user to be in fonts, trademark categories and trademark colors;
aiming at each trademark category, arranging trademark names under the trademark category, inputting an initial artificial intelligent model, applying a deduplication algorithm, and obtaining a plurality of trademark categories, wherein each trademark category corresponds to a plurality of trademark names;
determining the trademark categories and the trademark names as comparison databases, and constructing a first recommendation model according to the comparison databases;
inputting the user design requirement and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph further comprises:
inputting the trademark fonts, trademark categories, trademark colors and the target trademark names required by the users into a second pre-created recommendation model, and generating target trademark graphs by applying corresponding templates in the design template database according to the trademark fonts, trademark categories, trademark colors and the target trademark names required by the users;
wherein the target trademark graph is one or more;
the user design requirements include at least: the user's nominated intent word;
inputting the user design requirement into the first recommendation model, and outputting the target trademark name further comprises:
inputting the nominated meaning word into the first recommendation model, and sequentially performing similarity calculation on brand names in a database compared with the first recommendation model;
if the similarity between the nominated meaning word and any trademark name is equal to a preset threshold, outputting a non-passing word sample by the first recommendation model to prompt a user to re-input the nominated meaning word until the first recommendation model outputs the passing word sample;
determining the passed nominated meaning word as a target trademark name;
wherein the target brand name is one or more;
the step of inputting the nominated meaning word into the first recommendation model, and the step of sequentially performing similarity calculation on brand names in a database compared with the first recommendation model further comprises the following steps:
inputting the nominated meaning word into the first recommendation model to generate a feature vector expressed as @）；
Selecting trademark names with the same number of feature vectors as the famous intention words in the comparison database as a target comparison database;
generating a feature vector representation for each brand name in the target alignment library as%) The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Splitting words in the nominated intention words; />Splitting words in trade name;
sequentially calculating according to the sequence of the feature vectorsAnd->Similarity of (2);
if the similarity between the nominated meaning word and any trademark name is equal to a preset threshold, the outputting of the first recommendation model without the word pattern further comprises:
if it is=/>Then the similarity is processed by adding 1; when the preset threshold is taken as n, that is, the similarity is equal to n, the first recommendation model outputs a word which does not pass through; the preset threshold value is equal to the number of feature vectors of the nominated meaning word.
2. The method according to claim 1, wherein the method further comprises:
the user selects one target trademark name and one target trademark graph from the target trademark names and the target trademark graphs as a final target trademark name and a final target trademark graph;
or, the target trademark name and the target trademark pattern are determined as a final target trademark name and a final target trademark pattern.
3. An artificial intelligence based trademark intelligent recommendation system, comprising:
the name generation module is used for acquiring mass trademark data of the trademark library; wherein the trademark data at least comprises trademark categories and trademark names under each trademark category; inputting the trademark data into an initial artificial intelligent model for training to obtain a first recommended model; acquiring user design requirements, inputting the user design requirements into the first recommendation model, and outputting to obtain a target trademark name;
the graph generating module is used for inputting the user design requirement and the target trademark name into a second recommendation model which is created in advance, and outputting to obtain a target trademark graph; wherein, at least, the second recommendation model comprises: a mass design template database;
the target trademark generation module is used for generating a target trademark according to the target trademark name and the target trademark graph;
wherein the user design requirements include at least: the method comprises the steps of enabling a user to take up intention words, and enabling a trademark to be required by the user to be in fonts, trademark categories and trademark colors;
wherein the name generation module is further configured to: aiming at each trademark category, arranging trademark names under the trademark category, inputting an initial artificial intelligent model, applying a deduplication algorithm, and obtaining a plurality of trademark categories, wherein each trademark category corresponds to a plurality of trademark names;
determining the trademark categories and the trademark names as comparison databases, and constructing a first recommendation model according to the comparison databases;
the user design requirements include at least: the user's nominated intent word;
inputting the user design requirement into the first recommendation model, and outputting the target trademark name further comprises:
inputting the nominated meaning word into the first recommendation model, and sequentially performing similarity calculation on brand names in a database compared with the first recommendation model;
if the similarity between the nominated meaning word and any trademark name is equal to a preset threshold, outputting a non-passing word sample by the first recommendation model to prompt a user to re-input the nominated meaning word until the first recommendation model outputs the passing word sample;
determining the passed nominated meaning word as a target trademark name;
wherein the target brand name is one or more;
the step of inputting the nominated meaning word into the first recommendation model, and the step of sequentially performing similarity calculation on brand names in a database compared with the first recommendation model further comprises the following steps:
inputting the nominated meaning word into the first recommendation model to generate a feature vector expressed as @）；
Selecting trademark names with the same number of feature vectors as the famous intention words in the comparison database as a target comparison database;
generating a feature vector representation for each brand name in the target alignment library as%) The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Splitting words in the nominated intention words; />Splitting words in trade name;
sequentially calculating according to the sequence of the feature vectorsAnd->Similarity of (2);
if the similarity between the nominated meaning word and any trademark name is equal to a preset threshold, the outputting of the first recommendation model without the word pattern further comprises:
if it is=/>Then the similarity is processed by adding 1; when the preset threshold is taken as n, that is, the similarity is equal to n, the first recommendation model outputs a word which does not pass through; the preset threshold value is equal to the number of the feature vectors of the nominated meaning words;
the graphics generation module is further to: inputting the trademark fonts, trademark categories, trademark colors and the target trademark names required by the users into a second pre-created recommendation model, and generating target trademark graphs by applying corresponding templates in the design template database according to the trademark fonts, trademark categories, trademark colors and the target trademark names required by the users;
wherein the target trademark graph is one or more.
4. A computing device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to an artificial intelligence based brand intelligence recommendation method according to any one of claims 1-2.
5. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to an artificial intelligence based brand intelligence recommendation method of any one of claims 1-2.
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