CN114491134B - Trademark registration success rate analysis method and system - Google Patents
Trademark registration success rate analysis method and system Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/5846—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using extracted text
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/5866—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, manually generated location and time information
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Abstract
The invention relates to the technical field of data analysis, and particularly discloses a trademark registration success rate analysis method and a system, wherein the method comprises the steps of receiving a trademark registration request sent by an applicant, acquiring applicant information, and determining the grade of the applicant according to the applicant information; receiving a trademark image which is uploaded by an applicant and contains trademark information, and determining a trademark grade according to the trademark information; inputting the applicant grade and the trademark grade into a trained range determination model to obtain a retrieval range; and searching the trademark image according to the searching range, and determining the registration success rate according to the searching result. The invention determines the applicant grade by acquiring the applicant information; the trademark information is obtained, the trademark level is determined, the retrieval range is determined through the applicant level and the trademark level, then the trademark retrieval is carried out based on the retrieval range, the success rate is determined, the independent success rate analysis process is realized, the result is accurate, and the analysis efficiency is high.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a trademark registration success rate analysis method and a trademark registration success rate analysis system.
Background
The dream of each enterprise is to make the enterprise work strong, and the trademark registration is an important factor for the success of the enterprise, so that social public can know and like the trademark by propagandizing the trademark and then recognize the trademark to purchase the trademark, thereby further expanding the enterprise market. Moreover, the trademark is also an intangible asset of enterprises, and the value of the trademark is more difficult to measure as long as the trademark has a certain degree of awareness.
With the change of the concept of trademark registration, people who want to register trademarks are more and more, and people often want to know a rough success rate before the trademark registration, but in the existing technical scheme, the success rate judgment and the trademark retrieval process are not separated, and a separate success rate analysis technology is almost not available.
Disclosure of Invention
The invention aims to provide a trademark registration success rate analysis method and a trademark registration success rate analysis system, which are used for solving the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a trademark registration success rate analysis method, the method comprising:
receiving a trademark registration request sent by an applicant, acquiring applicant information based on a preset information template, and determining the grade of the applicant according to the acquired applicant information;
receiving a trademark image containing trademark information uploaded by an applicant, acquiring heat data of the trademark information, and determining a trademark grade based on the heat data;
inputting the applicant grade and the trademark grade into a trained range determination model to obtain a retrieval range;
and searching the trademark image according to the searching range, and determining the registration success rate according to the searching result.
As a further scheme of the invention: the steps of receiving a trademark registration request sent by an applicant, acquiring applicant information based on a preset information template, and determining the grade of the applicant according to the acquired applicant information comprise:
receiving a trademark registration request sent by an applicant, and determining enterprise information related to the applicant and corresponding relevancy;
acquiring a qualification file of an enterprise based on a preset information template, carrying out content identification on the qualification file, and calculating the safety degree of the enterprise according to a content identification result;
when the safety degree reaches a preset safety threshold value, product information of an enterprise is obtained, and the safety degree of the enterprise is corrected based on the product information;
determining the applicant level according to the safety degree and the relevance degree of the enterprise; wherein the applicant level is set to a preset reference value when the applicant does not have the related business information.
As a further scheme of the invention: the steps of acquiring a qualification file of an enterprise based on a preset information template, identifying the content of the qualification file, and calculating the security of the enterprise according to the content identification result include:
acquiring a qualification file of an enterprise based on a preset information template, extracting the content of the qualification file, and generating a text file, an audio file, an image file and a video file containing the name of the qualification file;
generating a text file library, an audio file library, an image file library and a video file library according to the text file, the audio file, the image file and the video file;
and traversing and analyzing the text file library, the audio file library, the image file library and the video file library, marking the problem files, and calculating the enterprise safety degree according to the number of the problem files.
As a further scheme of the invention: the steps of traversing and analyzing a text file library, an audio file library, an image file library and a video file library, marking problem files and calculating the security of the enterprise according to the number of the problem files comprise:
traversing an image file library, and identifying color values of the image files;
extracting an information area according to the color value identification result; the information area comprises signature information of a first party and a second party;
comparing the information area with a reference area in a preset reference area library to sequentially generate similarity;
and when the similarity between the image file and all the reference areas in the reference area library is smaller than a preset similarity threshold, marking the image file.
As a further scheme of the invention: the method comprises the following steps of receiving a trademark image containing trademark information uploaded by an applicant, acquiring heat data of the trademark information, and determining the grade of a trademark based on the heat data, wherein the step comprises the following steps:
receiving a trademark image which is uploaded by an applicant and contains trademark information, and extending the trademark information to obtain a characteristic information group;
sequentially reading each data item in the characteristic information group, inputting the data item into a search box of App with the access amount reaching a preset number threshold, and determining the heat value of each data item; wherein, the corresponding heat values of different operation instructions are different;
and comparing the heat value with a preset heat threshold value, and determining the grade of the trademark according to the comparison result.
As a further scheme of the invention: the step of searching the trademark image according to the searching range and determining the registration success rate according to the searching result comprises the following steps:
receiving a region mark input by a user, and determining a detection region containing a text region in the trademark image based on the region mark;
identifying text information in the text area, and determining a reference database according to the text information;
acquiring a storage rule of the reference database, and performing geometric correction processing on the detection area based on the storage rule;
traversing the reference database according to the detection area after geometric correction processing, and calculating the similarity rate in real time to obtain a similarity rate array;
and inputting the similarity rate array into a trained statistical analysis model to obtain the registration success rate.
As a further scheme of the invention: the step of identifying text information within the text region comprises:
identifying textual information in the text region using a neural network model, the neural network model comprising: a convolutional layer and a pooling layer; the convolutional layer comprises a standard convolutional kernel and an expanded convolutional kernel which are alternately connected, and the width of the receptive field of the expanded convolutional kernel is larger than that of the standard convolutional kernel; and the block window of the pooling layer is rectangular, standard maximum pooling and average pooling weighting mixed pooling are adopted, and the pooling weight coefficient is determined according to the global maximum and the average of the block pictures.
The technical scheme of the invention also provides a trademark registration success rate analysis system, which comprises:
the information analysis module is used for receiving a trademark registration request sent by an applicant, acquiring applicant information based on a preset information template, and determining the grade of the applicant according to the acquired applicant information;
the heat analysis module is used for receiving the trademark image containing the trademark information uploaded by the applicant, acquiring heat data of the trademark information and determining the trademark grade based on the heat data;
the range determining module is used for inputting the applicant grade and the trademark grade into a trained range determining model to obtain a retrieval range;
and the retrieval module is used for retrieving the trademark image according to the retrieval range and determining the registration success rate according to the retrieval result.
As a further scheme of the invention: the heat analysis module includes:
the information continuation unit is used for receiving the trademark image containing the trademark information uploaded by the applicant, and extending the trademark information to obtain a characteristic information group;
the system comprises a heat value calculation unit, a characteristic information group calculation unit and a data item input unit, wherein the heat value calculation unit is used for sequentially reading each data item in the characteristic information group, inputting the data item into a search box of the App with the visit amount reaching a preset number threshold value, and determining the heat value of each data item; wherein, the corresponding heat values of different operation instructions are different;
and the comparison unit is used for comparing the heat value with a preset heat threshold value and determining the trademark grade according to a comparison result.
As a further scheme of the invention: the retrieval module comprises:
the area determining unit is used for receiving an area mark input by a user, and determining a detection area containing a text area based on the trademark image;
the text recognition unit is used for recognizing the text information in the text area and determining a reference database according to the text information;
the region correction unit is used for acquiring a storage rule of the reference database and carrying out geometric correction processing on the detection region based on the storage rule;
the array generating unit is used for traversing the reference database according to the detection area after geometric correction processing, calculating the similarity rate in real time and obtaining a similarity rate array;
and the processing execution unit is used for inputting the similarity rate array into a trained statistical analysis model to obtain the registration success rate.
Compared with the prior art, the invention has the beneficial effects that: the invention determines the applicant grade by acquiring the applicant information; the trademark information is obtained, the trademark level is determined, the retrieval range is determined through the applicant level and the trademark level, then the trademark retrieval is carried out based on the retrieval range, the success rate is determined, the independent success rate analysis process is realized, the result is accurate, and the analysis efficiency is high.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 is a flow chart of a trademark registration success rate analysis method.
Fig. 2 is a first sub-flow block diagram of a trademark registration success rate analysis method.
Fig. 3 is a second sub-flow chart of the trademark registration success rate analysis method.
Fig. 4 is a third sub-flow chart of the trademark registration success rate analysis method.
Fig. 5 is a block diagram showing a configuration of a trademark registration success rate analysis system.
Fig. 6 is a block diagram showing a structure of a heat analysis module in the trademark registration success rate analysis system.
Fig. 7 is a block diagram showing a configuration of a search module in the trademark registration success rate analysis system.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
Fig. 1 is a flowchart of a trademark registration success rate analysis method, and in an embodiment of the present invention, the method includes steps S100 to S400:
step S100: receiving a trademark registration request sent by an applicant, acquiring applicant information based on a preset information template, and determining the grade of the applicant according to the acquired applicant information;
the applicant can be a natural person or a legal person, the trademark registration process does not pay much attention to the applicant information in a normal state, but in the subsequent data statistics process, the applicant information and the registration success rate are in certain correlation; in general, if a certain enterprise applies for malicious applications all the time, the registration success rate of the enterprise is not high; the above process is a process of obtaining the applicant level;
step S200: receiving a trademark image containing trademark information uploaded by an applicant, acquiring heat data of the trademark information, and determining trademark grade based on the heat data;
each trademark has trademark information related to itself, the trademark information represents the trademark type or purpose, and the like, wherein important consideration is to consider creative sources in the trademark information, and how a certain creative source is a current hotspot, then similar trademark applications are possibly more, which obviously reduces the registration success rate.
Step S300: inputting the applicant grade and the trademark grade into a trained range determination model to obtain a retrieval range;
the input of the range determination model is the applicant level and the trademark level, the output is the retrieval range, and the applicant level and the trademark level only influence the detection range in the technical scheme of the invention; the detection range is a reference database in the detection process.
Step S400: searching the trademark image according to the searching range, and determining the registration success rate according to the searching result;
the determination process of the registration success rate does not leave the search process, and step S400 is the search process performed on the basis of the detection range.
Fig. 2 is a first sub-flow block diagram of a trademark registration success rate analysis method, where the steps of receiving a trademark registration request sent by an applicant, obtaining applicant information based on a preset information template, and determining an applicant rank according to the obtained applicant information include steps S101 to S104:
step S101: receiving a trademark registration request sent by an applicant, and determining enterprise information related to the applicant and corresponding relevancy;
step S102: acquiring a qualification file of an enterprise based on a preset information template, carrying out content identification on the qualification file, and calculating the security of the enterprise according to a content identification result;
step S103: when the safety degree reaches a preset safety threshold value, product information of an enterprise is obtained, and the safety degree of the enterprise is corrected based on the product information;
step S104: determining the applicant level according to the safety degree and the relevance degree of the enterprise; wherein the applicant level is set to a preset reference value when the applicant does not have the related business information.
If the applicant is a natural person, enterprise information of employment of the applicant is inquired, if the applicant is an enterprise, the enterprise information can be directly inquired, the safety degree of the enterprise can be determined according to qualification files and product information of the enterprise, and then the grade of the applicant is determined according to the correlation degree of the applicant and the enterprise and the safety degree of the enterprise; it is worth mentioning that if the applicant is an enterprise, the degree of correlation is an extreme value; if the applicant is not employment, then the applicant rank is the default.
Further, the steps of obtaining a qualification file of an enterprise based on a preset information template, performing content identification on the qualification file, and calculating the security of the enterprise according to a content identification result include:
acquiring a qualification file of an enterprise based on a preset information template, extracting the content of the qualification file, and generating a text file, an audio file, an image file and a video file containing the name of the qualification file;
generating a text file library, an audio file library, an image file library and a video file library according to the text file, the audio file, the image file and the video file;
and traversing and analyzing the text file library, the audio file library, the image file library and the video file library, marking the problem files, and calculating the enterprise safety degree according to the number of the problem files.
The qualification files of the enterprises are separated and converted into a text file library, an audio file library, an image file library and a video file library, and then the libraries are subjected to content identification according to a preset mode; it should be noted that these libraries are not necessarily all data-bearing, for example, the data in the video file library is very small, and the video file library is provided for the purpose of expanding the types of qualification files, for example, videos at a show party, and the like.
Specifically, the step of traversing and analyzing a text file library, an audio file library, an image file library and a video file library, marking problem files, and calculating the enterprise security according to the number of the problem files comprises the following steps:
traversing an image file library, and identifying color values of the image files;
extracting an information area according to a color value identification result; the information area comprises signature information of a first party and a second party;
comparing the information area with a reference area in a preset reference area library to sequentially generate similarity;
and when the similarity between the image file and all the reference areas in the reference area library is smaller than a preset similarity threshold, marking the image file.
The purpose of the above is very clear, namely, the content identification is performed on some stamped PDF files or some stamped image files, which are also the most important and most important files, and therefore, special description is required.
However, the image recognition process is not limited to the above, for example, some photos and related people can be face-recognized, and if the leader of a business and the benchmarking people in the industry are combined, the business can be obviously scored.
Fig. 3 is a second sub-flow diagram of a trademark registration success rate analysis method, where the trademark image containing trademark information uploaded by an applicant is received, heat data of the trademark information is obtained, and the step of determining the trademark level based on the heat data includes steps S201 to S203:
step S201: receiving a trademark image which is uploaded by an applicant and contains trademark information, and extending the trademark information to obtain a characteristic information group;
step S202: sequentially reading each data item in the characteristic information group, inputting the data item into a search box of App with the access amount reaching a preset number threshold, and determining the heat value of each data item; wherein, the corresponding heat values of different operation instructions are different;
step S203: and comparing the heat value with a preset heat threshold value, and determining the grade of the trademark according to the comparison result.
The query scheme of the heat data is specifically limited in steps S201 to S203, and first, continuation is performed on the trademark information, where the continuation means that related information of the trademark information is obtained, many existing software has a push function, and these push links are a way of continuation, and a feature information group can be generated according to the related information; then, the degree of heat of the characteristic information group is inquired, and the trademark grade can be determined.
Fig. 4 is a third sub-flow diagram of the trademark registration success rate analysis method, where the steps of retrieving the trademark image according to the retrieval range and determining the registration success rate according to the retrieval result include steps S401 to S405:
step S401: receiving a region mark input by a user, and determining a detection region containing a text region in the trademark image based on the region mark;
step S402: identifying text information in the text area, and determining a reference database according to the text information;
step S403: acquiring a storage rule of the reference database, and performing geometric correction processing on the detection area based on the storage rule;
step S404: traversing the reference database according to the detection area after geometric correction processing, and calculating the similarity rate in real time to obtain a similarity rate array;
step S405: and inputting the similarity rate array into a trained statistical analysis model to obtain the registration success rate.
In the content, according to input information of a user, a detection area is determined, a text area is determined in the detection area, then information extraction is carried out on the text area, the detection range is further narrowed, then geometric correction processing is carried out on the whole detection area, feature recognition is carried out on the detection area after geometric correction processing, and the similarity rate is calculated in real time; and determining the registration success rate according to the similarity rate and the number of the reference data with the similarity rate reaching a certain degree.
As a preferred embodiment of the technical solution of the present invention, the step of identifying the text information in the text area includes:
identifying textual information in the text region using a neural network model, the neural network model comprising: a convolutional layer and a pooling layer; the convolutional layer comprises a standard convolutional kernel and an expanded convolutional kernel which are alternately connected, and the width of the receptive field of the expanded convolutional kernel is larger than that of the standard convolutional kernel; and the block window of the pooling layer is rectangular, standard maximum pooling and average pooling weighting mixed pooling are adopted, and the pooling weight coefficient is determined according to the global maximum and the average of the block pictures.
Example 2
Fig. 5 is a block diagram of a structure of a trademark registration success rate analysis system, and in an embodiment of the present invention, the trademark registration success rate analysis system includes:
the information analysis module 11 is configured to receive a trademark registration request sent by an applicant, acquire applicant information based on a preset information template, and determine an applicant level according to the acquired applicant information;
the heat analysis module 12 is used for receiving the trademark image which is uploaded by the applicant and contains the trademark information, acquiring heat data of the trademark information, and determining the trademark level based on the heat data;
a range determining module 13, configured to input the applicant level and the trademark level into a trained range determining model to obtain a retrieval range;
and the retrieval module 14 is used for retrieving the trademark image according to the retrieval range and determining the registration success rate according to the retrieval result.
Fig. 6 is a block diagram illustrating a structure of a heat analysis module 12 in the trademark registration success rate analysis system, where the heat analysis module 12 includes:
the information continuation unit 121 is used for receiving the trademark image which is uploaded by the applicant and contains the trademark information, and extending the trademark information to obtain a characteristic information group;
the heat value calculating unit 122 is configured to read each data item in the characteristic information group in sequence, input the data item into a search box of the App with an access amount reaching a preset number threshold, and determine a heat value of each data item; wherein, the corresponding heat values of different operation instructions are different;
and the comparison unit 123 is configured to compare the heat value with a preset heat threshold, and determine a trademark grade according to a comparison result.
Fig. 7 is a block diagram illustrating a structure of a search module 14 in a trademark registration success rate analysis system, where the search module 14 includes:
an area determination unit 141, configured to receive an area mark input by a user, and determine a detection area containing a text area based on the area mark;
a text recognition unit 142, configured to recognize text information in the text region, and determine a reference database according to the text information;
an area correction unit 143 configured to acquire a storage rule of the reference database, and perform geometric correction processing on the detection area based on the storage rule;
the array generating unit 144 is configured to traverse the reference database according to the detection area after the geometric correction processing, and calculate the similarity rate in real time to obtain a similarity rate array;
and the processing execution unit is used for inputting the similarity rate array into a trained statistical analysis model to obtain the registration success rate.
The functions which can be realized by the trademark registration success rate analysis method are all completed by computer equipment, the computer equipment comprises one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to realize the functions of the trademark registration success rate analysis method.
The processor fetches instructions and analyzes the instructions one by one from the memory, then completes corresponding operations according to the instruction requirements, generates a series of control commands, enables all parts of the computer to automatically, continuously and coordinately act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
Illustratively, a computer program can be partitioned into one or more modules, which are stored in memory and executed by a processor to implement the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the service device is merely exemplary and not limiting of the terminal device, and may include more or less components than those described, or combine certain components, or different components, such as may include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are also included in the scope of the present invention.
Claims (3)
1. A trademark registration success rate analysis method is characterized by comprising the following steps:
receiving a trademark registration request sent by an applicant, acquiring applicant information based on a preset information template, and determining the grade of the applicant according to the acquired applicant information;
receiving a trademark image containing trademark information uploaded by an applicant, acquiring heat data of the trademark information, and determining a trademark grade based on the heat data;
inputting the applicant grade and the trademark grade into a trained range determination model to obtain a retrieval range;
searching the trademark image according to the searching range, and determining the registration success rate according to the searching result;
the method comprises the following steps of receiving a trademark image containing trademark information uploaded by an applicant, acquiring heat data of the trademark information, and determining the grade of a trademark based on the heat data, wherein the step comprises the following steps:
receiving a trademark image which is uploaded by an applicant and contains trademark information, and extending the trademark information to obtain a characteristic information group;
sequentially reading each data item in the characteristic information group, inputting the data item into a search box of App with the access amount reaching a preset number threshold, and determining the heat value of each data item; wherein, the corresponding heat values of different operation instructions are different;
comparing the heat value with a preset heat threshold value, and determining the grade of the trademark according to the comparison result;
the step of searching the trademark image according to the searching range and determining the registration success rate according to the searching result comprises the following steps:
receiving a region mark input by a user, and determining a detection region containing a text region in the trademark image based on the region mark;
identifying text information in the text area, and determining a reference database according to the text information;
acquiring a storage rule of the reference database, and performing geometric correction processing on the detection area based on the storage rule;
traversing the reference database according to the detection area after geometric correction processing, and calculating the similarity rate in real time to obtain a similarity rate array;
inputting the similarity rate array into a trained statistical analysis model to obtain the registration success rate;
the steps of receiving a trademark registration request sent by an applicant, acquiring applicant information based on a preset information template, and determining the grade of the applicant according to the acquired applicant information comprise:
receiving a trademark registration request sent by an applicant, and determining enterprise information related to the applicant and corresponding relevancy;
acquiring a qualification file of an enterprise based on a preset information template, carrying out content identification on the qualification file, and calculating the security of the enterprise according to a content identification result;
when the safety degree reaches a preset safety threshold value, product information of an enterprise is obtained, and the safety degree of the enterprise is corrected based on the product information;
determining the applicant level according to the safety degree and the relevance degree of the enterprise; when the applicant does not have the related enterprise information, the applicant level is set as a preset reference value;
the steps of acquiring a qualification file of an enterprise based on a preset information template, identifying the content of the qualification file, and calculating the security of the enterprise according to the content identification result include:
acquiring a qualification file of an enterprise based on a preset information template, extracting the content of the qualification file, and generating a text file, an audio file, an image file and a video file containing the name of the qualification file;
generating a text file library, an audio file library, an image file library and a video file library according to the text file, the audio file, the image file and the video file;
traversing and analyzing a text file library, an audio file library, an image file library and a video file library, marking problem files, and calculating the security of the enterprise according to the number of the problem files;
the steps of traversing and analyzing a text file library, an audio file library, an image file library and a video file library, marking problem files and calculating the security of the enterprise according to the number of the problem files comprise:
traversing an image file library, and identifying color values of the image files;
extracting an information area according to the color value identification result; the information area comprises signature information of a first party and a second party;
comparing the information area with a reference area in a preset reference area library to sequentially generate similarity;
and when the similarity between the image file and all the reference areas in the reference area library is smaller than a preset similarity threshold, marking the image file.
2. The trademark registration success rate analysis method according to claim 1, wherein the step of recognizing the text information in the text area includes:
identifying textual information in the text region using a neural network model, the neural network model comprising: a convolutional layer and a pooling layer; the convolutional layer comprises a standard convolutional kernel and an expanded convolutional kernel which are alternately connected, and the width of the receptive field of the expanded convolutional kernel is larger than that of the standard convolutional kernel; and the block window of the pooling layer is rectangular, standard maximum pooling and average pooling weighting mixed pooling are adopted, and the pooling weight coefficient is determined according to the global maximum and the average of the block pictures.
3. A trademark registration success rate analysis system, characterized in that the system comprises:
the information analysis module is used for receiving a trademark registration request sent by an applicant, acquiring applicant information based on a preset information template, and determining the grade of the applicant according to the acquired applicant information;
the heat analysis module is used for receiving the trademark image containing the trademark information uploaded by the applicant, acquiring heat data of the trademark information and determining the trademark grade based on the heat data;
the range determining module is used for inputting the applicant grade and the trademark grade into a trained range determining model to obtain a retrieval range;
the retrieval module is used for retrieving the trademark image according to the retrieval range and determining the registration success rate according to the retrieval result;
the heat analysis module includes:
the information continuation unit is used for receiving the trademark image containing the trademark information uploaded by the applicant, and extending the trademark information to obtain a characteristic information group;
the system comprises a heat value calculation unit, a characteristic information group calculation unit and a data item input unit, wherein the heat value calculation unit is used for sequentially reading each data item in the characteristic information group, inputting the data item into a search box of the App with the visit amount reaching a preset number threshold value, and determining the heat value of each data item; wherein, the corresponding heat values of different operation instructions are different;
the comparison unit is used for comparing the heat value with a preset heat threshold value and determining the trademark grade according to the comparison result;
the retrieval module comprises:
the region determining unit is used for receiving a region mark input by a user, and determining a detection region containing a text region in the trademark image based on the region mark;
the text recognition unit is used for recognizing the text information in the text area and determining a reference database according to the text information;
the region correction unit is used for acquiring a storage rule of the reference database and carrying out geometric correction processing on the detection region based on the storage rule;
the array generating unit is used for traversing the reference database according to the detection area after geometric correction processing, calculating the similarity rate in real time and obtaining a similarity rate array;
the processing execution unit is used for inputting the similarity rate array into a trained statistical analysis model to obtain the registration success rate;
the receiving of the trademark registration request sent by the applicant, obtaining the applicant information based on a preset information template, and determining the content of the applicant level according to the obtained applicant information includes:
receiving a trademark registration request sent by an applicant, and determining enterprise information related to the applicant and corresponding relevancy;
acquiring a qualification file of an enterprise based on a preset information template, carrying out content identification on the qualification file, and calculating the security of the enterprise according to a content identification result;
when the safety degree reaches a preset safety threshold value, product information of an enterprise is obtained, and the safety degree of the enterprise is corrected based on the product information;
determining the applicant level according to the safety degree and the relevance degree of the enterprise; when the applicant does not have the related enterprise information, the applicant level is set as a preset reference value;
the method comprises the following steps of obtaining a qualification file of an enterprise based on a preset information template, carrying out content identification on the qualification file, and calculating the safety degree of the enterprise according to a content identification result, wherein the content comprises the following steps:
acquiring a qualification file of an enterprise based on a preset information template, extracting the content of the qualification file, and generating a text file, an audio file, an image file and a video file containing the name of the qualification file;
generating a text file library, an audio file library, an image file library and a video file library according to the text file, the audio file, the image file and the video file;
traversing and analyzing a text file library, an audio file library, an image file library and a video file library, marking problem files, and calculating the security of the enterprise according to the number of the problem files;
the traversing analysis of the text file library, the audio file library, the image file library and the video file library, the marking of the problem files, and the calculation of the content of the enterprise security according to the number of the problem files comprise:
traversing an image file library, and identifying color values of the image files;
extracting an information area according to the color value identification result; the information area comprises signature information of a first party and a second party;
comparing the information area with a reference area in a preset reference area library to sequentially generate similarity;
and when the similarity between the image file and all the reference areas in the reference area library is smaller than a preset similarity threshold, marking the image file.
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| CN106844551A (en) * | 2016-12-30 | 2017-06-13 | 全民互联科技（天津）有限公司 | Trademark application success rate automatic analysis method and system based on artificial intelligence |
| CN114202087A (en) * | 2020-09-18 | 2022-03-18 | 阿里巴巴集团控股有限公司 | Information processing method and computing device |
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| CN106844551A (en) * | 2016-12-30 | 2017-06-13 | 全民互联科技（天津）有限公司 | Trademark application success rate automatic analysis method and system based on artificial intelligence |
| CN114202087A (en) * | 2020-09-18 | 2022-03-18 | 阿里巴巴集团控股有限公司 | Information processing method and computing device |
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