KR20220132174A - Method and apparatus for trademark similarity determination using deep learnig engine trained based on trademark similarity result data - Google Patents

Abstract

Disclosed are a method, device, and computer software stored in a computer-readable recording medium for determining a trademark similarity using a deep learning engine learned based on the trademark similarity determination result data according to one embodiment of the present disclosure. The method comprises: a step of generating the rejection reason data for a trademark application, from the document data including a determination result for a trademark application; a step of generating, from the rejection reason data, the labeled training data including two or more trademarks and a similar determination result for the two or more trademarks; and a step of training a deep neural network using the labeled training data.

Description

A trademark similarity determination method and apparatus using a deep learning engine learned based on the trademark similarity determination result data

The present invention relates to a trademark similarity determination method, and more particularly, to a trademark similarity determination method using a deep learning engine learned based on a trademark similarity determination result.

Deep learning, which has been attracting global attention for several years, has now become the most important methodology in the fields of artificial intelligence and machine learning. Deep learning is an algorithm for extracting and utilizing features from training data using deep neural networks (DNN). Each layer of the neural network receives information extracted from the lower layer, then uses it to generate more abstract information and delivers it to the upper layer. A neural network composed of multiple layers can extract very high-level features through multiple abstractions. Since high-level features contain more information than low-level features and are strong against mutations, high and stable performance can be obtained when recognition is performed using them.

In the past, there were difficulties in using deep neural networks due to various problems, such as theoretical problems such as the diminishing gradient problem, difficulties in collecting learning data, and limitations in computational performance. However, as new learning algorithms are developed and technologies for processing large amounts of data are developed, theoretical and practical problems begin to be overcome, and deep learning is showing excellent performance in various fields.

In particular, convolutional neural networks (CNN) have become the most important method in the field of visual recognition, showing performance that surpasses other methodologies in various image recognition contests.

The origin of convolutional neural networks is the Neocognitron, created by Fukushima in the 1980s to mimic the visual processing of animals. Then, in the 1990s, LeCun's successful application of a gradient-based learning algorithm began to be widely used in practical problems. The excellent performance of the convolutional neural network has attracted explosive attention from many researchers, and thus, research to further improve the convolutional neural network or apply it to new problems is being actively conducted. Also, based on these research results, attempts are being made to apply them to actual products and services.

An object of the present invention is to provide a trademark similarity determination method and apparatus using a deep learning engine learned based on the trademark similarity determination result, and computer software stored in a computer-readable recording medium.

According to an exemplary embodiment of the present disclosure, the method comprising: generating data on reasons for rejection of a trademark application from document data including a judgment result on the trademark application; generating, from the rejection reason data, labeled training data including two or more trademarks and similar determination results for the two or more trademarks; And by using the labeled training data, it is possible to provide a trademark similarity determination method using the learned deep learning engine, comprising the step of training a deep neural network.

According to another exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium comprising a plurality of instructions executed by one or more processors of a computing device, the computer program comprising: , generating, from document data including a judgment result on the trademark application, data on reasons for rejection of the trademark application; generating, from the rejection reason data, labeled training data including two or more trademarks and similar determination results for the two or more trademarks; And by using the labeled training data, it is possible to provide a computer program stored in a computer-readable storage medium comprising the step of training a deep neural network.

According to another exemplary embodiment of the present disclosure, there is provided a computing device for implementing a trademark similarity determination method using a learned deep learning engine, comprising: one or more processors; and a memory storing instructions executable by the one or more processors; The one or more processors are configured to: generate data on reasons for rejection for a trademark application from document data including a judgment result on the trademark application, and, from the reason data for rejection, two or more trademarks and the two or more trademarks Generates labeled training data including a similarity determination result, and uses the labeled training data to train a deep neural network, a computing device for implementing a trademark similarity determination method using a learned deep learning engine can do.

The present disclosure may provide a trademark similarity determination method using a deep learning engine learned based on the trademark similarity determination result, an apparatus, and computer software stored in a computer-readable recording medium.

BRIEF DESCRIPTION OF THE DRAWINGS So that the above-mentioned features of the present disclosure may be understood in detail, with a more specific description, with reference to the following embodiments, some of the embodiments are shown in the accompanying drawings. Also, like reference numbers with drawings are intended to refer to the same or similar functions throughout the various aspects. However, it should be noted that the accompanying drawings show only certain typical embodiments of the present disclosure and are not to be considered as limiting the scope of the present disclosure, and other embodiments having the same effect may be fully appreciated. Take note.
1 is a view for explaining a trademark similarity determination method using a deep learning engine learned based on the trademark similarity determination result according to an embodiment of the present invention.
2 is a block diagram of a computing device for implementing a trademark similarity determination method using a deep learning engine learned based on a trademark similarity determination result according to an embodiment of the present invention.

Various embodiments and/or aspects are now disclosed with reference to the drawings. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of one or more aspects. However, it will also be recognized by one of ordinary skill in the art that such aspect(s) may be practiced without these specific details. The following description and accompanying drawings set forth in detail certain illustrative aspects of one or more aspects. These aspects are illustrative, however, and some of the various methods in principles of various aspects may be employed, and the descriptions set forth are intended to include all such aspects and their equivalents.

Further, various aspects and features will be presented by a system that may include a number of devices, components and/or modules, and the like. It is also noted that various systems may include additional apparatuses, components, and/or modules, etc. and/or may not include all of the apparatuses, components, modules, etc. discussed with respect to the drawings. must be understood and recognized.

As used herein, “embodiment”, “example”, “aspect”, “exemplary”, etc. may not be construed as an advantage or an advantage in any aspect or design described above over other aspects or designs. . The terms 'component', 'module', 'system', 'interface', etc. used below generally mean a computer-related entity, for example, hardware, a combination of hardware and software, and software.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean one of the natural implicit substitutions. That is, X employs A; X employs B; or when X employs both A and B, "X employs A or B" may apply to either of these cases. It should also be understood that the term “and/or” as used herein refers to and includes all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" mean that the feature and/or element is present, but excludes the presence or addition of one or more other features, elements and/or groups thereof. should be understood as not Also, unless otherwise specified or unless it is clear from context to refer to a singular form, the singular in the specification and claims should generally be construed to mean “one or more”.

Prior to describing the specific contents for carrying out the present invention, it should be noted that components not directly related to the technical gist of the present invention are omitted within the scope of not disturbing the technical gist of the present invention. In addition, the terms or words used in the present specification and claims have meanings consistent with the technical spirit of the present invention based on the principle that the inventor can define the concept of appropriate terms in order to best describe his invention. should be interpreted as a concept.

1 is a view for explaining a trademark similarity determination method using a deep learning engine learned based on the trademark similarity determination result according to an embodiment of the present invention.

In an embodiment of the present invention, a trademark similarity determination method using a learned deep learning engine includes: generating data on reasons for rejection of a trademark application from document data including a determination result for a trademark application; generating, from the rejection reason data, labeled training data including two or more trademarks and similar determination results for the two or more trademarks; and training the deep neural network by using the labeled training data.

In an embodiment of the present invention, the trademark similarity determination method using the learned deep learning engine includes: inputting previously filed trademark data into the deep neural network after the training step; and generating, from the input previously applied trademark data, feature data of each trademark data.

In an embodiment of the present invention, a trademark similarity determination method using a learned deep learning engine includes: receiving trademark data that is a similarity determination target; inputting the received trademark data into the deep neural network to generate corresponding feature data; The method may further include selecting similar trademarks from the previously applied trademark data based on the generated corresponding feature data.

In an embodiment of the present invention, in a trademark similarity determination method using a learned deep learning engine, the deep neural network may include a convolutional neural network structure.

2 is a block diagram of a computing device for implementing a trademark similarity determination method using a deep learning engine learned based on a trademark similarity determination result according to an embodiment of the present invention.

2 depicts a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the invention has been described above generally in terms of features that may be executed on a computer or processor in one or more servers, those skilled in the art will recognize that the invention may be implemented in combination with other program modules and/or as a combination of hardware and software. you will know

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the methods of the present invention can be used in single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. (each of which is It will be appreciated that other computer system configurations may be implemented, including those that may operate in connection with one or more associated devices.

The described embodiments of the invention may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Any medium accessible by a computer can be a computer readable medium, and such computer readable media includes volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. including removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media.

Computer-readable storage media includes volatile and non-volatile media, temporary and non-transitory media, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media. A computer-readable storage medium may be RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device, or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store the desired information.

Computer readable transmission media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism, and Includes any information delivery medium. The term modulated data signal means a signal in which one or more of the characteristics of the signal is set or changed so as to encode information in the signal. By way of example, and not limitation, transmit/receive (communication) media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

2 shows an exemplary environment for implementing various aspects of the present invention, including a computing device 602 for implementing a trademark similarity determination method using a deep learning engine learned based on the trademark similarity determination result of the present invention. The computing device 602 includes a processing unit 604 , a system memory 606 , and a system bus 608 . The system bus 608 connects system components, including but not limited to system memory 606 , to the processing unit 604 . The processing unit 604 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as processing unit 604 .

The system bus 608 may be any of several types of bus structures that may further be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 606 includes read only memory (ROM) 610 and random access memory (RAM) 612 . A basic input/output system (BIOS) is stored in non-volatile memory 610, such as ROM, EPROM, EEPROM, etc., which assists in transferring information between components within computing device 602, such as during startup. Includes basic routines. RAM 612 may also include high-speed RAM, such as static RAM, for caching data.

Computing device 602 may also be configured for external use within an internal hard disk drive (HDD) 614 (eg, EIDE, SATA). present), a magnetic floppy disk drive (FDD) 616 (eg, for reading from or writing to removable diskette 618), and an optical disk drive 620 (eg, a CD-ROM). for reading from, or writing to, disk 622 or other high capacity optical media such as DVDs. Hard disk drive 614 , magnetic disk drive 616 , and optical disk drive 620 are connected to a system bus 608 by a hard disk drive interface 624 , a magnetic disk drive interface 626 , and an optical drive interface 628 , respectively. ) can be connected to The interface 624 for external drive implementation includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computing device 602 , drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable storage media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will use zip drives, magnetic cassettes, flash memory cards, cartridges, It will be appreciated that other tangible, computer-readable media and the like may also be used in the exemplary operating environment and any such media may contain computer-executable instructions for performing the methods of the present invention.

A number of program modules may be stored in drives and RAM 612 , including operating system 630 , one or more application programs 632 , other program modules 634 , and program data 636 . Here, the program module 634 may include the hub module 2000 . Also, the program module 634 may include a DBMS module 3000 . All or portions of the operating system, applications, modules, and/or data may also be cached in RAM 612 . It will be appreciated that the present invention may be implemented in a variety of commercially available operating systems or combinations of operating systems.

A user may enter commands and information into computing device 602 via one or more wired/wireless input devices, eg, pointing devices such as keyboard 638 and mouse 640 . Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 604 through an input device interface 642 which is connected to the system bus 608, parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, It may be connected by other interfaces, etc.

A monitor 644 or other type of display device is also coupled to the system bus 608 via an interface, such as a video adapter 646 . In addition to the monitor 644, the computer typically includes other peripheral output devices (not shown), such as speakers, printers, and the like.

Computing device 602 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 648 via wired and/or wireless communications. The remote computer(s) 648 may be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other common network node, and is generally connected to the computing device 602 . Although it includes many or all of the components described for it, only memory storage device 650 is shown for simplicity. The logical connections shown include wired/wireless connections to a local area network (LAN) 652 and/or a larger network, eg, a wide area network (WAN) 654 . Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, for example, the Internet.

When used in a LAN networking environment, the computing device 602 is coupled to the local network 652 through a wired and/or wireless communication network interface or adapter 656 . Adapter 656 may facilitate wired or wireless communication to LAN 652 , which also includes a wireless access point installed therein for communicating with wireless adapter 656 . When used in a WAN networking environment, the computing device 602 may include a modem 658 , connected to a communication server on the WAN 654 , or to establish communications over the WAN 654 , such as over the Internet. have other means. A modem 658 , which may be internal or external and a wired or wireless device, is coupled to the system bus 608 via a serial port interface 642 . In a networked environment, program modules described for computing device 602 , or portions thereof, may be stored in remote memory/storage device 650 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between the computers may be used.

Computing device 602 can communicate with any wireless device or object that is deployed and operates in wireless communication, such as printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communications satellites, wireless detectable tags, and so forth. It operates to communicate with any associated equipment or place, and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network or may simply be an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet, etc. without a wire. Wi-Fi is a wireless technology such as cell phones that allows these devices, eg, computers, to transmit and receive data indoors and outdoors, ie anywhere within range of a base station. Wi-Fi networks use a radio technology called IEEE 802.6 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks may operate in the unlicensed 2.4 and 5 GHz radio bands, for example, at 6 Mbps (802.6a) or 54 Mbps (802.6b) data rates, or products that include both bands (dual band). have.

Those of ordinary skill in the art will recognize that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein are implemented in electronic hardware, (convenience For this purpose, it will be understood that it may be implemented by various forms of program or design code (referred to herein as "software") or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. A person skilled in the art may implement the described functionality in various ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The various embodiments presented herein may be implemented as methods, apparatus, or articles of manufacture using standard programming and/or engineering techniques. The term “article of manufacture” includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash memory. devices (eg, EEPROMs, cards, sticks, key drives, etc.). The term “machine-readable medium” includes, but is not limited to, various other media that can store and hold instruction(s) and/or data.

It is understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. Based on design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present invention. The appended method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

Claims (5)

generating rejection invalid data for the trademark application from document data including the judgment result on the trademark application;
generating, from the rejection/rejection data, labeled training data including two or more trademarks and similarity determination results for the two or more trademarks; and
Using the labeled training data, comprising the step of training a deep neural network,
A trademark similarity judgment method using the learned deep learning engine. The method of claim 1,
After the training step,
inputting previously applied trademark data into the deep neural network; and
Further comprising the step of generating, from the input previously applied trademark data, feature data of each trademark data,
A trademark similarity judgment method using the learned deep learning engine. 3. The method of claim 2,
receiving trademark data to be similarly determined;
inputting the received trademark data into the deep neural network to generate corresponding feature data; and
Based on the generated corresponding feature data, further comprising the step of selecting similar trademarks from the previously applied trademark data,
A trademark similarity judgment method using the learned deep learning engine. 3. The method of claim 2,
The step of generating the feature data includes:
further comprising extracting a text object and an image object from the trademark data;
A trademark similarity judgment method using the learned deep learning engine. A computing device for implementing a trademark similarity determination method using a learned deep learning engine, comprising:
one or more processors; and
a memory storing instructions executable by the one or more processors;
includes,
The one or more processors include:
Generate rejection invalid data for a trademark application from document data including a judgment result on the trademark application,
Generate labeled training data including two or more trademarks and similar determination results for the two or more trademarks, from the rejection and rejection data, and
Training a deep neural network using the labeled training data,
A computing device for implementing a trademark similarity judgment method using the learned deep learning engine.

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