Key Points Of The Latest Amendments To Guidelines For Patent Examination

The China National Intellectual Property Administration (CNIPA) recently revised the Guidelines for Patent Examination (hereinafter referred to as "the Guidelines"). The revised Guidelines will take effect on January 1, 2026. The main contents of this revision are summarized as follows:

1. Dual-filing Strategy

Regarding invention and utility model applications filed under dual-filing [or both-filing] strategy, the original provision in the Guidelines stated: Where an applicant files both a utility model patent application and an invention patent application for the same invention-creation on the same day (referring only to the filing date), if the utility model patent obtained earlier has not yet terminated and the applicant has made separate statements at the time of filing, double-patenting issue may be avoided not only by amending the invention patent application but also by abandoning the utility model patent. Therefore, during examination of the aforementioned invention patent application, if the application meets other conditions for granting a patent, the applicant shall be notified to make a selection or amendment.

This provision has now been amended to: Where an applicant files both a utility model patent application and an invention patent application for the same invention-creation on the same day (referring only to the filing date), in accordance with Rule 47 of the Implementing Regulations of the Patent Law, separate statements shall be made at the time of filing, indicating that another patent application has been filed for the same invention-creation; failure to make such statements shall be handled in accordance with Paragraph 1 of Article 9 of the Patent Law, which provides that only one patent may be granted for the same invention-creation; where statements have been made, if the invention patent application is examined and no grounds for rejection are found, the applicant shall be notified to declare abandonment of the utility model patent within a prescribed time limit. Where the applicant declares abandonment, a decision to grant the invention patent shall be made, and the applicant's declaration of abandonment shall be published together with the announcement of the grant of the invention patent. Where the applicant refuses to abandon, the invention patent application shall be rejected; where the applicant fails to respond within the time limit, the invention patent application shall be deemed withdrawn.

Through the above amendment, an invention patent can only be obtained by abandoning the utility model patent; applicants will no longer be able to obtain an invention patent by amending the invention patent application.

2. Examination of Invention Patent Applications Involving Algorithm Features or Business Rule and Method Features Such as Artificial Intelligence and Big Data
2.1 "Examination Criteria"

The original provision stated that "examination shall be directed to the solution as claimed, i.e., the solution defined by the claims." This has been amended to add "when necessary, examination shall also be directed to the content of the description."

Additionally, a new subsection "Examination under Paragraph 1 of Article 5 of the Patent Law" has been added, stating: " For invention patent applications that contain algorithmic features or features related to business rules and methods, if among others, data collection, label management, rule setting, or recommendation and decision-making therein includes content that violates laws, social morality, or impairs public interests, then such invention patent applications cannot be granted pursuant to the provisions of Paragraph 1 of Article 5 of the Patent Law."

2.2 "Examination Examples"
2.2.1 Addition of Provisions and Examples on Exclusion from Patentability for Inventions Contrary to Law, Social Morality, or Public Interest

New content is as follows:

(1) An invention patent application that contains algorithmic features or features related to business rules and methods shall not be granted if it violates laws, social morality, or impairs public interests.

[Example 1]
A Big-Data-Based In-Mall Mattress Sales Assistance System

Summary of the Application Content
The solution proposed in this invention patent application is a big-data-based in-mall mattress sales assistance system, which utilizes a camera module and a facial recognition module to collect customers' facial feature information and obtain their identity recognition information. Through data analysis of the collected information, it assesses customers' true preferences for mattresses, thereby assisting merchants in precise marketing.

Claim of the Application
A big-data-based in-mall mattress sales assistance system, comprising a mattress display device and a management center, characterized in that:
the mattress display device includes a control module and an information collection module, which are used to display and assist in the sale of mattress products and collect customer data; the control module is used for data interaction with the management center; the information collection module includes a camera module and a facial recognition module, which are used to collect customers' facial feature information, adjust facial postures using keypoint detection algorithms to obtain normalized facial images, locate facial regions to be recognized in the normalized facial images through facial detection algorithms, and extract facial features within the facial regions using principal component analysis, thereby obtaining customers' identity recognition information;
the management center includes a management server and an analytical assistance system; the management server manages a plurality of mattress display devices; the analytical assistance system analyzes data collected by the mattress display devices based on customers' identity recognition information to determine their true preferences and provides feedback on analysis results to the management center.

Analysis and Conclusion
According to relevant provisions of the Personal Information Protection Law of the People's Republic of China, the installation of image collection and personal identity recognition equipment in public places should be necessary for maintaining public safety, comply with relevant national regulations, and be accompanied by conspicuous signs. The collected personal images and identity recognition information can only be used for the purpose of maintaining public safety and shall not be used for other purposes, unless individual consent is obtained separately.
From the solution claimed in this invention-creation, it can be seen that the use of image collection and facial recognition methods for precise mattress marketing in commercial venues such as shopping malls is not necessary for maintaining public safety. Furthermore, to obtain and analyze customers' true preferences for mattresses, the collection of their facial information and acquisition of their identity recognition information are apparently conducted without the customers' awareness. The application also does not indicate that data acquisition or information collection is legal and compliant. Therefore, this invention-creation contradicts the law and, pursuant to the provisions of Paragraph 1 of Article 5 of the Patent Law, cannot be granted a patent.

[Example 2]
A Method for Establishing an Emergency Decision-Making Model for Autonomous Vehicles

Summary of the Application Content
The solution proposed in this invention patent application is a method for establishing an emergency decision-making model for autonomous vehicles. This method utilizes pedestrian gender and age as obstacle data and employs a trained decision-making model to determine the protected entity and the entity to be struck in situations where obstacles cannot be avoided.

Claim of the application
A method for establishing an emergency decision-making model for autonomous vehicles, characterized by comprising the following steps:
acquiring historical environmental data and historical obstacle data of an autonomous vehicle, wherein the historical environmental data includes the vehicle's driving speed, distance to obstacles in the same lane, distance to obstacles in adjacent lanes, motion speeds and motion directions of obstacles in the same lane, motion speeds and motion directions of obstacles in adjacent lanes; the historical obstacle data includes pedestrian gender and age;
extracting features from the historical environmental data and historical obstacle data to serve as input data for the decision-making model, and using historical driving trajectories of the vehicle when obstacles cannot be avoided as output data for the decision-making model, and training the decision-making model based on historical data, wherein the decision-making model is a deep learning model;
acquiring real-time environmental data and real-time obstacle data, and when an autonomous vehicle encounters a situation where obstacles cannot be avoided, utilizing the trained decision-making model to determine the driving trajectory of the autonomous vehicle.

Analysis and Conclusion
This invention-creation relates to a method for establishing an emergency decision-making model for autonomous vehicles. Human lives possess equal value and dignity, regardless of age or gender. In accidents where obstacles cannot be avoided, if an emergency decision-making model for autonomous vehicles selects the protected entity and the entity to be struck based on pedestrian gender and age, this contradicts the public's ethical and moral perception of equality for all in the face of life. Furthermore, such a decision-making approach would reinforce existing gender and age biases in society, raise public concerns about the safety of public transportation, and undermine public trust in technology and social order. Therefore, this invention-creation contains content that violates social morality and, pursuant to the provisions of Paragraph 1 of Article 5 of the Patent Law, cannot be granted a patent.

2.2.2 Addition of Examples on Inventiveness Examination

New examples are as follows:

[Example 18]
A Method for Identifying the Number of Ships

Summary of the Application Content
The invention patent application proposes a method for identifying the number of ships, which acquires image data of ships and trains a detection data model through deep learning to address the technical problem of accurately identifying the number of ships in a given sea area.

Claim of the Application
A method for identifying the number of ships, characterized by comprising the following steps:
acquiring a dataset of ship images and preprocessing image information within the dataset, marking the positions and boundary information of ships in the images, and dividing the dataset into a training dataset and a testing dataset;
conducting deep learning using the training dataset to construct a training model;
inputting the testing data into the training model for training to obtain ship testing result data;
multiplying the ship testing result data by a preset error parameter to determine the actual number of ships.

Analysis and Conclusion
Reference 1 discloses a method for identifying the number of fruits on trees and specifically discloses steps such as acquiring image information, marking the positions and boundaries of fruits in the images, dividing datasets, model training, and determining the actual number of fruits.
The difference between the solution proposed in the invention patent application and Reference 1 lies only in the different identification targets. Although ships and fruits differ in terms of appearance, size, and the environments in which they exist, for those skilled in the relevant technical field, the steps required for identifying the actual number, such as marking information, dividing datasets, and model training, all pertain to the positional relationships of the objects to be identified in the images. The claims do not reflect any modifications made to the training methods, model hierarchy, etc., in the deep learning or model training process due to the different identification targets. Marking ship data in images and marking fruit data in images to obtain datasets for training and conducting model training do not involve any adjustments or improvements to the deep learning, model construction, or training process. Therefore, the claimed technical solution of the invention does not possess inventiveness.

[Example 19]
A method for establishing a neural network model for grading of scrap steel grades

Summary of the Application Content
During the collection and storage, scrap steel requires grading based on the average size of the steel pieces. However, due to the random stacking and overlapping of scrap steel, manual measurement and grading are inefficient and often inaccurate. This invention patent application proposes a method for establishing a neural network model for scrap steel grading. The grade classification neural network model with grade classification output is formed through convolutional neural network learning, which can significantly improve both the efficiency and accuracy of scrap steel grading.

Claim of the Application
A method for establishing a neural network model for scrap steel grade classification, wherein the model is used for grading collected and stored scrap steel, comprising:
acquiring a plurality of images, determining different scrap steel grades for the plurality of images, preprocessing the images, extracting image data features of different grades, performing convolutional neural network learning on the extracted image data features of different grades to form a grade classification neural network model with grade classification output;
extracting the image data features is extracting the set of convolutional calculations performed by a convolutional neural network on the pixel matrix data of the images, which includes: extracting the color, edge features and texture features of objects in the images, as well as the correlation features between edges and textures of objects in the images, from the output set of multiple lines composed of convolutional layers or convolutional layers plus pooling layers;
wherein, the extraction of color and edge features of objects in the images is implemented by an output set of three lines composed of convolutional layers plus pooling layers, including from left to right a first line with one pooling layer, a second line with two convolutional layers and a third line with four convolutional layers; the extraction of texture features in the image is implemented by collecting the extraction results of color and edge features of objects in the aforementioned images, and then by the output set of three lines composed of convolutional layers, including from left to right the first line with zero convolutional layers, the second line with two convolutional layers and the third line with three convolutional layers;
the number of lines for convolutional layer calculation used for extracting correlation features between edges and textures is greater than the number of lines for convolutional layer calculation used for extracting color, edge and texture features of objects in the images.

Analysis and Conclusion
In order to solve the problem that recycled resources come from complex sources, include many types, and exhibit large material differences, and to accurately identify whether scrap steel belongs to material beans, stamping leftovers, bread iron, or some other category so as to improve the recycling rate of recycled resources, Reference 1 provides a method for classifying scrap steel types based on a convolutional neural network model. Reference 1 specifically discloses the relevant steps of: acquiring a plurality of image data of determined scrap steel types, preprocessing the image data to extract features, and training with a convolutional neural network to obtain the product model.
The difference between the solution of the invention patent application and Reference 1 lies in the different training data and extracted features, and the different number of lines and hierarchical settings of the convolution layer and the pooling layer. Compared with Reference 1, it is determined that the technical problem actually solved by the invention is how to improve the accuracy of scrap steel grading. Reference 1 performs feature extraction and model training using the image data of scrap steel with determined types, while the invention patent application, in order to grade scrap steel according to its average size, needs to identify the shape and thickness of scrap steel according to the chaotic and overlapping scrap steel images. In order to extract features such as color, edge and texture of scrap steel in the images, the number of lines and hierarchical settings of convolutional layers and pooling layers are adjusted during the model training. The above algorithm features and technical features support each other functionally and have an interactive relationship, which can improve the accuracy of scrap steel grading, and the contribution of the algorithm features to the technical solution should be considered. The above-mentioned contents such as adjusting the number of lines and hierarchical setting of convolution layers and pooling layers have not been disclosed by other References, nor do they belong to the common knowledge in the field. In the prior art as a whole, there is no inspiration to improve the above-mentioned Reference 1 to obtain the technical solution of the invention patent application, and the claimed invention technical solution is inventive.

2.3 "Drafting of the Description"

The following content has been added:

If an invention involves the construction or training of an artificial intelligence model, it is generally necessary to clearly record in the description the necessary modules, layers, or connection relationships of the model, as well as specific steps and parameters, etc. required for training; if an invention involves applying an artificial intelligence model or algorithm in a specific field or scenario, it is generally necessary to clearly record in the description how the model or algorithm is combined with the specific field or scenario, how the input and output data of the algorithm or model are set to demonstrate their inherent correlation, thereby enabling a person skilled in the art to implement the solution of the invention based on the content recorded in the description.

2.4 Addition of Examination Examples in "Drafting of the Description and Claims"

New examples are as follows:

[Example 20]
A Method for Generating Facial Features

Summary of the Application Content
The invention patent application enables information sharing among second convolutional neural networks by using a set of feature region images generated by a first convolutional neural network provided with a spatial transformation network, which reduces memory resource usage while improving the accuracy of facial image generation results.

Claim of the Application
A method for generating facial features, comprising:
acquiring a facial image to be recognized;
inputting the facial image to be recognized into a first convolutional neural network to generate a set of feature region images of the facial image to be recognized, wherein the first convolutional neural network is used to extract feature region images from the facial image;
inputting each feature region image in the set of feature region images into a corresponding second convolutional neural network to generate regional facial features for that feature region image, wherein the second convolutional neural network is used to extract regional facial features from the corresponding feature region image;
generating a set of facial features for the facial image to be recognized based on the regional facial features of each feature region image in the set of feature region images;
wherein, the first convolutional neural network is further provided with a spatial transformation network for determining the feature regions of the facial image; and
inputting the facial image to be recognized into the first convolutional neural network to generate the set of feature region images of the facial image to be recognized comprises: inputting the facial image to be recognized into the spatial transformation network, determining the feature regions of the facial image to be recognized; inputting the facial image to be recognized into the first convolutional neural network and, based on the determined feature regions, generating the set of feature region images of the facial image to be recognized.

Relevant Paragraphs from the Description
The method for generating facial features provided in the embodiments of the present application first inputs the acquired facial image to be recognized into the first convolutional neural network to generate a set of feature region images of the facial image to be recognized. The first convolutional neural network is used to extract feature region images from the facial image. Then, each feature region image in the set of feature region images is input into a corresponding second convolutional neural network to generate regional facial features for that feature region image. The second convolutional neural network is used to extract regional facial features from the corresponding feature region image. Subsequently, based on the regional facial features of each feature region image in the set of feature region images, a set of facial features for the facial image to be recognized is generated. In other words, the set of feature region images generated by the first convolutional neural network enables information sharing among the second convolutional neural networks. This reduces data volume, thereby reducing memory resource usage and improving generation efficiency.
To enhance the accuracy of the generation results, the first convolutional neural network may also include a spatial transformation network for determining the feature regions of the facial image. In this case, an electronic device can input the facial image to be recognized into the spatial transformation network to determine the feature regions of the facial image to be recognized. Thus, for the input face image to be recognized, the first convolutional neural network can then extract images matching the feature regions from the feature layer based on the feature regions determined by the spatial transformation network, thereby generating the set of feature region images of the facial image to be recognized. The specific placement position of the spatial transformation network within the first convolutional neural network is not limited in the present application. The spatial transformation network can continuously learn to determine the feature regions of different features in different facial images.

Analysis and Conclusion
This invention patent application seeks to protect a method for generating facial features. To improve the accuracy of facial image generation results, the first convolutional neural network may include a spatial transformation network for determining the feature regions of the facial image. However, the description does not specify the exact placement position of the spatial transformation network within the first convolutional neural network.
Those skilled in the art understand that the spatial transformation network, as a whole, can be inserted at any position within the first convolutional neural network to form a nested convolutional neural network structure. For example, the spatial transformation network may serve as the first layer of the first convolutional neural network or as an intermediate layer. Its placement position does not affect its ability to identify feature regions of the image. Through training, the spatial transformation network can determine the feature regions of different features in different facial images. Thus, the spatial transformation network not only guides the first convolutional neural network in feature region segmentation but also performs simple spatial transformations on the input data to enhance the processing effectiveness of the first convolutional neural network. Therefore, the model according to the patent application has a clear hierarchical structure, with well-defined inputs/outputs and relationships between the layers. Wherein, both convolutional neural networks and spatial transformation networks are well-known algorithms, and those skilled in the art can construct the corresponding model architecture based on the above description. Accordingly, the solution for which protection is sought in the invention patent application has been sufficiently disclosed in the description and complies with the provisions of Article 26(3) of the Patent Law.

[Example 21]
A Method for Predicting Cancer Based on Biological Information

Summary of the Application Content
The invention patent application provides a method for predicting cancer based on biological information. By using a trained enhanced malignancy screening model, routine blood test indicators, blood biochemical test indicators, and facial image features are jointly used as inputs to the screening model to obtain a malignancy risk prediction value. This solves the technical problem of improving the accuracy of malignancy prediction.

Claim of the Application
A method for predicting cancer based on biological information, characterized by comprising:
acquiring the routine blood test report and blood biochemical test report of the subject to be screened, and identifying the test indicators, age, and gender from the routine blood and blood biochemical test reports;
acquiring a bare-faced frontal facial image of the subject to be screened and extracting facial image features;
predicting the malignancy risk value for the corresponding subject to be screened based on an enhanced malignancy screening model;
wherein, the training process of the enhanced malignancy screening model is: constructing a large-scale population sample set, wherein the samples include routine blood test data, blood biochemical test data, and facial images of the same individual; using the routine blood test data, blood biochemical test data, and facial image features to create learning samples; training a machine learning algorithm model using the learning samples to obtain the enhanced malignancy screening model.

Relevant Paragraphs from the Description
Currently, when tumor markers are used to identify malignancies, a tumor marker value above the threshold cannot definitively confirm malignancy, nor can a value below the threshold rule out malignancy. Predicting cancer based on tumor markers alone lacks high accuracy. The present application utilizes routine blood test indicators, blood biochemical test indicators, and facial image features to improve the identification accuracy of various malignancies. The present application, while utilizing blood test data, also references the health status of the subject as reflected in facial images, enabling more accurate prediction of the probability of malignancy. Wherein, the selection of computational features for the enhanced malignancy screening model may include some or all indicators from routine blood test data and blood biochemical test data.

Analysis and Conclusion
The technical problem to be solved by this invention patent application is how to improve the accuracy of malignancy prediction. To solve this problem, the solution adopts a trained enhanced malignancy screening model, with routine blood test indicators, blood biochemical test indicators, and facial image features jointly serving as inputs to the screening model, to obtain a malignancy risk prediction value. However, routine blood tests and blood biochemical tests each include dozens of test indicators. The description does not specify which specific indicators are key to tumor prediction accuracy, nor whether all indicators are referenced or different weights are assigned to each indicator for prediction. Those skilled in the art cannot determine which indicators can be used to identify malignancies. Furthermore, based on current scientific research, aside from a few types of tumors such as facial skin cancer, it remains uncertain whether there is any association between facial features and the development of malignancies. The description does not record or demonstrate a causal relationship between the "factors used for judgment" and the "results of the judgment." Additionally, the description provides no validation data to prove that the accuracy of identifying various malignancies using this solution is higher than that achieved using tumor markers, or significantly higher than the accuracy level of random malignancy probability judgment. Based solely on the content disclosed in the description, those skilled in the art cannot confirm that the solution of this application can solve the intended technical problem. Therefore, the technical solution for which protection is sought in this patent application has not been sufficiently disclosed in the description, and the description does not comply with the provisions of Article 26(3) of the Patent Law.

3. Addition of Provisions on Examination of Invention Patent Applications Involving Bitstreams

New content is as follows:

In application fields such as streaming media, communication systems, and computer systems, various types of data are generally generated, stored, and transmitted in the form of bitstreams. This section aims, in accordance with the Patent Law and its implementing rules, to set out specific provisions for examining the patentable subject matter of invention applications involving bitstreams, and for drafting the description and the claims.

7.1 Examination of Patentable Subject Matters
7.1.1 Examination under Item (2), Paragraph 1, Article 25 of the Patent Law

If the subject matter of a claim relates solely to a pure bitstream, the claim falls under rules and methods of mental activities as stipulated in Item (2), Paragraph 1, Article 25 of the Patent Law and does not belong to patentable subject matter. For example, "A bitstream, characterized in that it comprises syntax element A, syntax element B, ...".

If, apart from its title, every limitation of a claim relates only to a pure bitstream, the claim falls under rules and methods of mental activities as stipulated in Item (2), Paragraph 1, Article 25 and does not belong to patentable subject matter. For example: "A method of generating a bitstream, characterized in that the bitstream comprises syntax element A, syntax element B, ..."

7.1.2 Examination under Paragraph 2 of Article 2 of the Patent Law

In the technical field of digital video coding/decoding, video data is usually encoded into a bitstream by a video encoding method, and the bitstream is decoded back into video data by a video decoding method. If a particular video encoding method that generates the bitstream constitutes a technical solution under Paragraph 2 of Article 2 of the Patent Law, then a method of storing or transmitting the bitstream as well as a computer-readable storage medium storing the bitstream as limited by that particular encoding method, are capable of achieving optimized allocation of storage or transmission resources, etc. Consequently, such storage or transmission methods and computer-readable storage medium as limited by that particular video encoding method constitute a technical solution under Paragraph 2 of Article 2 and belong to patentable subject matter.

7.2 Drafting of the Description and the Claims
7.2.1 Drafting of the Description

For an invention application involving a bitstream generated by a particular video encoding method, the description shall set forth the particular encoding method in a clear and complete manner to such an extent that a person skilled in the art can carry it out. Where the claimed subject matter relates to a method of storing or transmitting the bitstream, or a computer-readable storage medium storing the bitstream, the description shall also contain corresponding disclosures to support the claims.

7.2.2 Drafting of the Claims

For an invention application involving a bitstream generated by a particular video encoding method, claims can be drafted as a storage method, a transmission method, or a computer-readable storage medium. Such claims should generally be based on a claim directed to the particular video encoding method that generates the bitstream, be drafted either by referring to that method claim or by incorporating all features of that method claim.

[Example 1]
An invention application relating to video coding/decoding technology may have its claims drafted as follows:
1. A video encoding method, characterized by comprising the following steps:
a frame partitioning step, ...
...
an entropy encoding step, ....
2. A video encoding apparatus, characterized by comprising the following units:
a frame partitioning unit, ...
...
an entropy encoding unit, ....
3. A video decoding method, characterized by comprising the following steps:
an entropy decoding step, ...
...
a frame outputting step, ....
4. A video decoding apparatus, characterized by comprising the following units:
an entropy-decoding unit, ...
...
a frame-outputting unit, ....
5. A method of storing a bitstream, characterized by: performing the video encoding method of claim 1 to generate the bitstream; and storing the bitstream.
6. A method of transmitting a bitstream, characteriszd by: performing the video encoding method of claim 1 to generate the bitstream; and transmitting the bitstream.
7. A computer-readable storage medium having stored thereon a computer program/instruction and a bitstream, characterized in that, when the computer program/instruction is executed by a processor, the video encoding method of claim 1 is carried out to generate the bitstream.

4. Provisions on fees for sequence listings

A new provision is added: "For computer-readable sequence listings submitted in the prescribed format, the page count shall not be included." Provisions regarding fee calculation for sequence listings exceeding 400 pages—"If the nucleotide and/or amino acid sequence listing, as a separate part of the description, exceeds 400 pages, the sequence listing shall be calculated as 400 pages"—have been deleted.

5. Patent Term Compensation

A new circumstance of reasonable delay during the granting process is added: "Reexamination proceedings where a rejection decision is revoked based on new reasons stated or new evidence submitted by the reexamination petitioner." Delays caused by this circumstance shall not be eligible for patent term compensation.

6. Non-Contributory Features Do Not Establish Inventiveness

A new provision is added: "Features that do not contribute to solving a technical problem, even if written in the claims, typically will not have an impact on the inventiveness of the technical solution."

A corresponding example is added:

[Example]
An invention related to a camera, aiming to address the technical problem of achieving more flexible shutter control, which is realized by improving the relevant mechanical and circuit structures inside the camera. After the Examiner pointed out that the claims lacked inventiveness, the applicant added features such as the shape of the camera housing, the size of the display screen, and the location of the battery compartment to the claims. The description did not indicate any association between the newly added features in the claims and the solution to the stated technical problem. These newly added features were either conventional components implicitly contained in the claimed subject matter itself or obtainable by those skilled in the art based on their common technical knowledge and routine experimental means. The applicant also did not provide evidence or sufficient reasons to demonstrate that these technical features could bring about any further technical effects to the claimed technical solution. Therefore, the aforementioned technical features do not contribute to the solution to the stated technical problem and thus do not impart inventiveness to the claimed technical solution.

7. Amended Texts in Invalidation Proceedings

A new provision is added: Where multiple amended texts submitted by a patentee in the same invalidation proceedings all satisfy relevant amendment requirements, the last submitted amended text shall prevail, and other amended texts shall not serve as the examination basis.

8. Plant Varieties and Patentable Subject matters

Item (1), Paragraph 1, Article 25, of the Patent Law provides that scientific discoveries are not patentable, and Item (4) provides that animal and plant varieties are not patentable.

This revision to the Guidelines adds a definition of plant variety: "Plant varieties as referred to in the Patent Law mean plant groups that have been artificially bred or discovered and improved, have consistent morphological characteristics and biological properties, and have relatively stable genetic traits."

Additionally, it adds: "Wild plants found in nature that have not undergone technical treatment and exist naturally fall under scientific discoveries as stipulated in Item (1), Paragraph 1, Article 25 of the Patent Law and cannot be granted patents. However, when wild plants have been artificially bred or improved and have industrial utilization value, the plants themselves do not fall under the category of scientific discoveries." It also adds: "Plants and their reproductive materials obtained through artificial breeding or improvement of discovered wild plants, if they do not have consistent morphological characteristics and biological properties or relatively stable genetic traits in their populations, cannot be considered 'plant varieties' and therefore do not fall under the category stipulated in Item (1), Paragraph 1, Article 25 of the Patent Law."

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.