CN110969188A - Exosome electron microscope picture judgment system and method based on deep learning - Google Patents
Exosome electron microscope picture judgment system and method based on deep learning Download PDFInfo
- Publication number
- CN110969188A CN110969188A CN201911060639.8A CN201911060639A CN110969188A CN 110969188 A CN110969188 A CN 110969188A CN 201911060639 A CN201911060639 A CN 201911060639A CN 110969188 A CN110969188 A CN 110969188A
- Authority
- CN
- China
- Prior art keywords
- module
- picture
- judging
- exosome
- layer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 210000001808 exosome Anatomy 0.000 title claims abstract description 56
- 238000013135 deep learning Methods 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 title claims abstract description 11
- 238000012549 training Methods 0.000 claims abstract description 49
- 239000011159 matrix material Substances 0.000 claims description 36
- 238000013527 convolutional neural network Methods 0.000 claims description 12
- 238000001000 micrograph Methods 0.000 claims description 6
- 210000000225 synapse Anatomy 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 210000002569 neuron Anatomy 0.000 claims description 5
- 238000012850 discrimination method Methods 0.000 claims description 3
- 239000002245 particle Substances 0.000 description 4
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 210000003618 cortical neuron Anatomy 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000001900 immune effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011528 liquid biopsy Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000008506 pathogenesis Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 230000008929 regeneration Effects 0.000 description 1
- 238000011069 regeneration method Methods 0.000 description 1
- 230000000638 stimulation Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 230000017423 tissue regeneration Effects 0.000 description 1
- 210000000857 visual cortex Anatomy 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Linguistics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The exosome electron microscope picture judging system and the exosome electron microscope picture judging method based on deep learning can simply and reliably identify exosome TEM pictures, and the training set of a training unit is continuously perfected by receiving feedback information of users, so that most users can easily and intuitively judge the obtained TEM pictures without professional training, and professional difficulty and input labor cost are greatly reduced.
Description
Technical Field
The invention relates to the technical field of exosome judgment, in particular to an exosome electron microscope picture judgment system and method based on deep learning.
Background
Exosomes (Exosomes) are a cell-derived Extracellular vesicle (extracelluar vesicles) of 30-150nm diameter. In recent years, exosomes have made a great deal of breakthrough progress in the fields of disease pathogenesis, liquid biopsy and tissue regeneration, and are expected to become a new tool for understanding the disease occurrence and development rule, early disease diagnosis and regeneration medicine field.
The existing methods for identifying exosomes comprise particle size distribution determination, electron microscope observation and immunological marker detection. Among them, electron microscope observation, especially Transmission Electron Microscope (TEM) observation is the most intuitive method among all methods. However, exosomes can be identified in photographs taken by TEM and identified with other particle phases of similar particle size, requiring experts in the field. To promote exosome technology in some small laboratories or clinics, a large number of workers need to be trained systematically and comprehensively, and corresponding qualification assessment is carried out to ensure the accuracy of judgment of exosomes in TEM photographs. In fact, the training and examination of such a scale of organization requires enormous manpower and material resources, which will certainly cause great resistance to the clinical push of exosome technology. Therefore, it is very important to find a technology which is easy to popularize and can simply and quickly identify whether the particles/vesicles in the TEM picture are exosomes.
In deep learning, a convolutional neural network (ConvNet) is a type of deep neural network, which is most commonly used for analyzing visual images. "convolutional" neural networks mean that the network employs a mathematical operation called convolution. Convolution is a special linear operation. And the convolution network refers to a neural network, in which at least one layer is implemented by convolution instead of general matrix multiplication. ConvNet was biologically inspired and its neurons were connected in a manner similar to the tissue of the visual cortex of animals. A single cortical neuron responds to stimulation only in a limited area of the visual field called the receptive field. The receptive fields of the different neurons partially overlap so they cover the entire visual field. ConvNet uses relatively less pre-processing than other image classification algorithms.
Disclosure of Invention
The invention provides a judgment system and a judgment method which can simply and reliably identify a TEM (transmission electron microscope) picture of a exosome by using a convolutional neural network and can realize continuous improvement of the system.
In order to achieve the purpose, the invention provides an exosome electron microscope picture judgment system based on deep learning, which comprises a client module, a judgment module, a feedback module, an expert module and a training module, wherein the client module is used for receiving an exosome electron microscope picture;
the client module is used for transmitting the picture to the judging module by the user and receiving and feeding back the picture judging result output by the judging module;
the judging module is used for judging whether the picture is an exosome picture or not and feeding back a judging result to the client module;
the feedback module is used for receiving the image data fed back by the client module;
the expert module is used for calling picture data fed back by a user from the feedback module by an expert and providing an expert judgment result and picture data for the training module according to whether the picture is an exosome picture;
the training module is used for training the judging module aiming at the picture provided by the expert module and the judging result of the picture, so that the weight matrix of the neural network of the judging module is perfected.
Preferably, the client module is client APP software, and the user can download the APP through the network mobile device to achieve the importing of the picture.
Preferably, the judging module is a convolutional neural network; the convolutional neural network comprises an output layer, an input layer and a plurality of hidden layers;
the input layer converts a picture input by a user into an image matrix and transmits the matrix to the hidden layer;
the hidden layers carry out convolution operation on the image matrix, and the hidden layer at the last layer transmits an operation data result to the output layer;
the output layer receives the operation data result of the hidden layer of the last layer and converts the data result into a decimal between 0 and 1; 1 represents a hundred percent determined as exosome, 0 represents not exosome, a decimal between 0-1, the closer to 1, the higher the probability that the picture content is exosome.
Preferably, the input layer converts an n × n gray scale map into a matrix, n numbers are formed in the abscissa from 0 to (n-1) and n numbers are formed in the ordinate from 0 to (n-1), and the matrix has a value of 0 to 255.
Preferably, the hidden layer is mainly a convolutional layer, a connection node between neurons of the upper and lower hidden layers comprises a synapse, and each synapse is a weight matrix; and the hidden layer of each layer performs convolution operation on the matrix output by the previous layer and the weight matrix of the layer, and outputs a matrix as the input of the hidden layer of the next layer until the last hidden layer outputs the matrix result to the output layer.
Preferably, the feedback module is a cloud database for storing feedback picture data.
Preferably, the expert module is expert APP software, and an expert calls the picture information fed back by the user through the feedback module of the software access server, and inputs the judgment result and the picture information to the training module.
The invention also provides an exosome electron microscope picture discrimination method based on deep learning, which comprises the following steps:
step 1: collecting a large number of TEM pictures;
step 2: judging the TEM picture by an expert;
and step 3: inputting the TEM picture and a corresponding expert judgment result into a training module through an expert module to form a training set;
and 4, step 4: training a judgment module through the training module;
and 5: a user inputs pictures to the judging module through the client module;
step 6: the judging module judges the picture and outputs a judging result;
and 7: the user selects whether to submit the feedback information through the client module;
and 8: if not, judging to end; if yes, entering step 9;
and step 9: the user feeds back the feedback picture to the feedback module;
step 10: the expert calls a feedback picture of the feedback module through the expert module to judge, and uploads the feedback picture and a corresponding expert judgment result to a training set of the training module;
step 11: and when the training set reaches a certain scale, training the judgment module through the training module.
Preferably, the TEM pictures include exosome pictures and non-exosome pictures.
Compared with the prior art, the invention has the advantages that: the judging system and the judging method can realize simple and reliable identification of the TEM picture of the exosome, and continuously perfect the training set of the training unit by receiving the feedback information of the user, so that most users can easily and intuitively judge the obtained TEM picture without professional training, and the professional difficulty and the input labor cost are greatly reduced.
Drawings
FIG. 1 is a structural diagram of an exosome electron microscope image judgment system based on deep learning in the embodiment of the present invention;
fig. 2 is a flowchart of the exosome electron microscope image discrimination method based on deep learning in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be further described below.
As shown in fig. 1, the present invention provides a system for determining an exosome electron microscope image based on deep learning, which includes a client module, a determining module, a feedback module, an expert module, and a training module;
the client module is client APP software, a user can download an APP through network mobile equipment to achieve importing of pictures, and the client module is mainly used for the user to transmit the pictures to the judging module and receive and feed back picture judging results output by the judging module;
the judgment module is a convolutional neural network and is named as: Exo-ConvNet, which comprises an output layer, an input layer and a plurality of hidden layers;
the input layer converts a picture input by a user into an image matrix, namely an n multiplied by n gray-scale image is converted into a matrix, the abscissa of the matrix is from 0 to (n-1) to n digits, the ordinate of the matrix is from 0 to (n-1) to n digits, the n multiplied by n matrix is formed, the value of the matrix is 0 to 255 digits, and the matrix is transmitted to the hidden layer;
the hidden layer is mainly a convolutional layer, a connection node between neurons of the upper hidden layer and the lower hidden layer comprises a synapse, and each synapse is a weight matrix; and the hidden layer of each layer performs convolution operation on the matrix output by the previous layer and the weight matrix of the layer, and outputs a matrix as the input of the hidden layer of the next layer until the last hidden layer outputs the matrix result to the output layer.
The output layer receives the operation data result of the hidden layer of the last layer and converts the data result into a decimal between 0 and 1; 1 represents a hundred percent determined as exosome, 0 represents not exosome, a decimal between 0-1, the closer to 1, the higher the probability that the picture content is exosome.
Therefore, the Exo-ConvNet is used for judging whether the picture is an exosome picture or not and feeding back a judgment result to the client module;
the feedback module is a cloud database used for storing feedback picture data and used for receiving the picture data fed back by the client module.
The expert module is used for the expert to call the picture data of user's feedback from feedback module to whether be the exosome picture to the picture, provide expert's judgement result and picture data to training module, the expert module is expert end APP software, and with judgement result and picture information input to training module.
The training module is used for training the judgment module aiming at the pictures provided by the expert module and the judgment result of the pictures so as to perfect the database of the judgment module.
In order to facilitate understanding of those skilled in the art, as shown in fig. 2, the invention further provides a method for discriminating an exosome electron microscope image based on deep learning, which comprises the following steps:
step 1: collecting a large number of TEM pictures;
step 2: judging the TEM picture by an expert;
and step 3: inputting the TEM picture and a corresponding expert judgment result into a training module through an expert module to form a training set;
and 4, step 4: training the judging module through a training module;
and 5: a user inputs pictures to the judging module through the client module;
step 6: the judging module judges the picture and outputs a judging result;
and 7: the user selects whether to submit the feedback information through the client module;
and 8: if not, judging to end; if yes, entering step 9;
and step 9: the user feeds the feedback picture back to the feedback module;
step 10: the expert calls the feedback picture of the feedback module through the expert module to judge, and uploads the feedback picture and the corresponding expert judgment result to a training set of the training module;
step 11: and when the training set reaches a certain scale, training the judgment module through the training module.
In the present embodiment, the TEM pictures include exosome pictures and non-exosome pictures.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An exosome electron microscope picture judgment system based on deep learning is characterized by comprising a client module, a judgment module, a feedback module, an expert module and a training module;
the client module is used for transmitting the picture to the judging module by the user and receiving and feeding back the picture judging result output by the judging module;
the judging module is used for judging whether the picture is an exosome picture or not and feeding back a judging result to the client module;
the feedback module is used for receiving the image data fed back by the client module;
the expert module is used for calling picture data fed back by a user from the feedback module by an expert and providing an expert judgment result and the picture data for the training module according to whether the picture is an exosome picture;
the training module is used for training the judging module aiming at the picture provided by the expert module and the judging result of the picture, so that the weight matrix of the neural network of the judging module is perfected.
2. The system for judging exosome electron microscope pictures based on deep learning of claim 1, wherein the client module is client APP software, and a user can download APP realization pictures to the judging module through a network mobile device.
3. The system for judging exosome electron microscope pictures based on deep learning according to claim 1, wherein the judging module is a convolutional neural network; the convolutional neural network comprises an output layer, an input layer and a plurality of hidden layers;
the input layer converts a picture input by a user into an image matrix and transmits the matrix to the hidden layer;
the hidden layers carry out convolution operation on the image matrix, and the hidden layer at the last layer transmits an operation data result to the output layer;
the output layer receives the operation data result of the hidden layer of the last layer and converts the data result into a decimal between 0 and 1; 1 represents a hundred percent determined as exosome, 0 represents not exosome, a decimal between 0-1, the closer to 1, the higher the probability that the picture content is exosome.
4. The system for judging exosome electron microscope pictures based on deep learning according to claim 3, wherein the input layer converts an n x n gray scale map into a matrix, the abscissa is from 0 to (n-1) for n numbers, the ordinate is from 0 to (n-1) for n numbers, the n x n matrix is formed, and the value of the matrix is from 0 to 255 numbers.
5. The system for judging exosome electron microscope pictures based on deep learning of claim 3, wherein the hidden layer is mainly a convolutional layer, a connection node between neurons of an upper hidden layer and a lower hidden layer comprises a synapse, and each synapse is a weight matrix; and the hidden layer of each layer performs convolution operation on the matrix output by the previous layer and the weight matrix of the layer, and outputs a matrix as the input of the hidden layer of the next layer until the last hidden layer outputs the matrix result to the output layer.
6. The system for judging exosome electron microscope picture based on deep learning of claim 1, wherein the feedback module is a cloud database for storing feedback picture data.
7. The system for judging exosome electron microscope pictures based on deep learning of claim 1, wherein the expert module is expert-side APP software, an expert accesses the feedback module through the software, calls picture information fed back by a user, and inputs a judgment result and the picture information to the training module.
8. An exosome electron microscope image discrimination method based on deep learning, which uses the exosome electron microscope image discrimination system based on deep learning according to any one of claims 1 to 7, and is characterized by comprising the following steps:
step 1: collecting a large number of TEM pictures;
step 2: judging the TEM picture by an expert;
and step 3: inputting the TEM picture and a corresponding expert judgment result into a training module through an expert module to form a training set;
and 4, step 4: training a judgment module through the training module;
and 5: a user inputs pictures to the judging module through the client module;
step 6: the judging module judges the picture and outputs a judging result;
and 7: the user selects whether to submit the feedback information through the client module;
and 8: if not, judging to end; if yes, entering step 9;
and step 9: the user feeds back the feedback picture to the feedback module;
step 10: the expert calls a feedback picture of the feedback module through the expert module to judge, and uploads the feedback picture and a corresponding expert judgment result to a training set of the training module;
step 11: and when the training set reaches a certain scale, training the judgment module through the training module.
9. The method for discriminating the exosome electron microscope picture based on the deep learning of claim 8, wherein the TEM picture comprises an exosome picture and a non-exosome picture.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911060639.8A CN110969188B (en) | 2019-11-01 | 2019-11-01 | Exosome electron microscope picture judging system and method based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911060639.8A CN110969188B (en) | 2019-11-01 | 2019-11-01 | Exosome electron microscope picture judging system and method based on deep learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110969188A true CN110969188A (en) | 2020-04-07 |
| CN110969188B CN110969188B (en) | 2024-05-14 |
Family
ID=70030022
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911060639.8A Active CN110969188B (en) | 2019-11-01 | 2019-11-01 | Exosome electron microscope picture judging system and method based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110969188B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113689456A (en) * | 2021-08-18 | 2021-11-23 | 山东大学 | Exosome particle size analysis device and method based on deep learning |
| CN117034110A (en) * | 2023-08-08 | 2023-11-10 | 海南省细胞医疗科技有限公司 | Stem cell exosome detection method based on deep learning |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170177997A1 (en) * | 2015-12-22 | 2017-06-22 | Applied Materials Israel Ltd. | Method of deep learining-based examination of a semiconductor specimen and system thereof |
| CN109255364A (en) * | 2018-07-12 | 2019-01-22 | 杭州电子科技大学 | A kind of scene recognition method generating confrontation network based on depth convolution |
| US20190042952A1 (en) * | 2017-08-03 | 2019-02-07 | Beijing University Of Technology | Multi-task Semi-Supervised Online Sequential Extreme Learning Method for Emotion Judgment of User |
| CN109800805A (en) * | 2019-01-14 | 2019-05-24 | 上海联影智能医疗科技有限公司 | Image processing system and computer equipment based on artificial intelligence |
-
2019
- 2019-11-01 CN CN201911060639.8A patent/CN110969188B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170177997A1 (en) * | 2015-12-22 | 2017-06-22 | Applied Materials Israel Ltd. | Method of deep learining-based examination of a semiconductor specimen and system thereof |
| US20190042952A1 (en) * | 2017-08-03 | 2019-02-07 | Beijing University Of Technology | Multi-task Semi-Supervised Online Sequential Extreme Learning Method for Emotion Judgment of User |
| CN109255364A (en) * | 2018-07-12 | 2019-01-22 | 杭州电子科技大学 | A kind of scene recognition method generating confrontation network based on depth convolution |
| CN109800805A (en) * | 2019-01-14 | 2019-05-24 | 上海联影智能医疗科技有限公司 | Image processing system and computer equipment based on artificial intelligence |
Non-Patent Citations (3)
| Title |
|---|
| 陈强;吴平;郑丽敏;: "基于BP神经网络的水牛疾病诊断系统", 计算机工程与设计, no. 06, 28 March 2008 (2008-03-28) * |
| 陈强等: "基于BP神经网络的水牛疾病诊断系统", 《计算机工程与设计》 * |
| 陈强等: "基于BP神经网络的水牛疾病诊断系统", 《计算机工程与设计》, no. 06, 28 March 2008 (2008-03-28) * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113689456A (en) * | 2021-08-18 | 2021-11-23 | 山东大学 | Exosome particle size analysis device and method based on deep learning |
| CN113689456B (en) * | 2021-08-18 | 2023-07-25 | 山东大学 | Exosome particle size analysis device and exosome particle size analysis method based on deep learning |
| CN117034110A (en) * | 2023-08-08 | 2023-11-10 | 海南省细胞医疗科技有限公司 | Stem cell exosome detection method based on deep learning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110969188B (en) | 2024-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TWI705414B (en) | Classification system and classification method of autoantibody immunofluorescence image | |
| CN109002766B (en) | Expression recognition method and device | |
| Waghade et al. | A comprehensive study of healthcare fraud detection based on machine learning | |
| US10943157B2 (en) | Pattern recognition method of autoantibody immunofluorescence image | |
| US11562179B2 (en) | Artificial intelligence system for inspecting image reliability | |
| CN111968746A (en) | Cerebral apoplexy risk prediction method and device based on hybrid deep transfer learning | |
| CN113808738B (en) | Disease identification system based on self-identification image | |
| WO2023221697A1 (en) | Method and apparatus for training image recognition model, device and medium | |
| CN110969188A (en) | Exosome electron microscope picture judgment system and method based on deep learning | |
| CN111696648A (en) | Psychological consultation platform based on Internet | |
| CN115862831A (en) | Intelligent online appointment diagnosis and treatment management system and method | |
| CN111680575A (en) | Human epithelial cell staining and classifying device, equipment and storage medium | |
| CN108052918A (en) | A kind of person's handwriting Compare System and method | |
| CN113360611A (en) | AI diagnosis method, device, storage medium and equipment based on inspection result | |
| CN114330482A (en) | Data processing method and device and computer readable storage medium | |
| CN114005530A (en) | Intelligent reminding and monitoring method and system for medical repeated examination and inspection in area | |
| CN112084913B (en) | End-to-end human body detection and attribute identification method | |
| US8918406B2 (en) | Intelligent analysis queue construction | |
| US11094318B1 (en) | Providing an automated summary | |
| CN108139379A (en) | The crowdsourcing automation of legal medical expert's file examines | |
| US20220261998A1 (en) | Adaptive machine learning system for image-based biological sample constituent analysis | |
| CN111833993B (en) | AI-based regional image remote quality control management system | |
| CN113902743A (en) | Method and device for identifying diabetic retinopathy based on cloud computing | |
| Arora | Leaf disease identification using cnn and raspberry PI | |
| KR102499776B1 (en) | Methods, devices and programs for monitoring competitors' intellectual property rights |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |