On November 13, 2025, China’s National Intellectual Property Administration (CNIPA) released the Decision of the CNIPA on Revising the Guidelines for Patent Examination (国家知识产权局关于修改《专利审查指南》的决定). The Decision, effective January 1, 2026, revises the Guidelines, which are roughly analogous to the U.S. Patent & Trademark Office’s (USPTO) Manual for Patent Examining Procedure (MPEP). The revisions clarify the relationship between patent protection and the new plant variety protection; provides examples of rejections based on social, public interest in illegality rationales; provides examples for examination of bitstream and artificial intelligence applications; requires abandonment of a utility model patent before granting of a corresponding invention patent application; ends straw man filings of invalidation requests; clarifies priority claims in divisional applications; and more.
A translation follows. The original text can be found here (Chinese only).
I. Regarding the revisions to Section 4.1.2 of Chapter 1, Part 1
(i) The first paragraph of Section 4.1.2 of Chapter 1, Part I of the Patent Examination Guidelines is amended as follows:
Article 14 of the Implementing Regulations of the Patent Law stipulates that an inventor refers to a person who has made a creative contribution to the substantive features of an invention. False inventors must not be listed. In the patent examination process, examiners generally do not examine whether the inventor listed in the request meets this requirement, unless there is evidence to suggest that the inventor listed in the request does not meet this requirement.
(ii) The second paragraph of Section 4.1.2 of Chapter 1 of Part I of the Patent Examination Guidelines, which reads “The inventor shall be an individual, and the request form shall not include an entity or collective, or the name of the artificial intelligence, such as ‘×× Research Group’ or ‘Artificial Intelligence ××’, shall not be written,” shall be amended to read “The inventor shall be an individual (i.e., a natural person), and the request form shall include the identity information of all inventors, ensuring the information is true. The request form shall not include an entity or collective, or the name of the artificial intelligence, such as ‘×× Research Group’ or ‘Artificial Intelligence ××’, etc.”
The rest of this section remains unchanged.
II. Regarding the revisions to Section 4.1.6 of Chapter 1, Part 1.
(i) Add a new sentence to the second paragraph of Section 4.1.6 of Chapter 1, Part I of the Patent Examination Guidelines, as follows:
Patent agencies shall verify the applicant’s identity information and contact information filled in the request form.
(ii) A new last paragraph is added to Section 4.1.6 of Chapter 1, Part 1 of the “Guidelines for Patent Examination,” as follows:
If a patent agency or patent attorney applies for a patent or requests the invalidation of a patent right in its own name, it shall be handled in accordance with the Patent Agency Regulations.
The rest of this section remains unchanged.
III. Regarding the revisions to Section 6.2 of Chapter 1 of Part 1
A new last paragraph has been added to Sections 6.2.1.2 and 6.2.2.2 of Chapter 1, Part 1 of the “Guidelines for Patent Examination,” with the following content:
If the original application of a divisional application claims priority, but the applicant did not declare that priority was claimed in the request when filing the divisional application, the divisional application shall be deemed not to have claimed priority, and the examiner shall issue a notice of deemed non-claim of priority.
The rest of this section remains unchanged.
IV. Regarding the revisions to Section 4.4 of Chapter 1 in Part Two.
The first paragraph of Section 4.4 of Chapter 1, Part II of the “Guidelines for Patent Examination” is revised as follows:
Animals and plants are living organisms. According to Article 25, Paragraph 1, Item (4) of the Patent Law, animal and plant varieties cannot be granted patent rights. The term “animal” in the Patent Law does not include humans; it refers to organisms that cannot synthesize their own nutrients and can only sustain life by consuming natural carbohydrates and proteins. The term “plant variety” in the Patent Law refers to a group of plants that have been artificially selected or discovered and improved, exhibiting consistent morphological and biological characteristics and relatively stable genetic traits. Animal and plant varieties can be protected by laws and regulations other than the Patent Law; for example, new plant varieties can be protected under the “Regulations on the Protection of New Plant Varieties.”
The rest of this section remains unchanged.
V. Regarding the amendments to Section 6.2.2 of Chapter 3 in Part II.
The last paragraph of Section 6.2.2 of Chapter 3, Part II of the “Guidelines for Patent Examination” is revised as follows:
If the same applicant applies for both a utility model patent and an invention patent for the same invention on the same day (referring only to the application date), according to Article 47 of the Implementing Regulations of the Patent Law, the applicant should state in each application that another patent has already been applied for for the same invention. If no statement is made, it shall be handled in accordance with Article 9, Paragraph 1 of the Patent Law, which stipulates that only one patent right can be granted for the same invention. If a statement is made, and no grounds for rejection are found after examination of the invention patent application, the applicant should be notified to declare the abandonment of the utility model patent right within a prescribed period. If the applicant declares the abandonment, a decision to grant the invention patent right shall be made, and the applicant’s declaration of abandonment of the utility model patent right shall be announced together with the publication of the invention patent right. If the applicant does not agree to the abandonment, the invention patent application shall be rejected; if the applicant fails to respond within the time limit, the invention patent application shall be deemed withdrawn.
If an applicant abandons a granted utility model patent right, they shall attach a written statement of abandonment of the utility model patent right when responding to the examination opinion. At this time, a notice of grant shall be issued for the invention patent application that meets the grant conditions but has not yet been granted, and the written statement of abandonment of the aforementioned utility model patent right shall be forwarded to the relevant examination department. The Patent Office shall register and publish the statement, noting in the publication that the aforementioned utility model patent right terminates from the date of publication of the grant of the invention patent right.
The rest of this section remains unchanged.
VI. Regarding the revisions to Section 6.4 of Chapter 4 in Part II.
Section 6.4 of Chapter 4 in Part II of the “Guidelines for Patent Examination” is amended as follows:
Whether an invention possesses inventiveness is determined in relation to the claimed invention; therefore, the evaluation of inventiveness should be conducted on the technical solution defined by the claims. When assessing inventiveness, the entire technical solution defined by the claims should be evaluated, i.e., whether the entire technical solution possesses inventiveness, rather than evaluating whether a single technical feature possesses inventiveness.
Technical features that contribute to the prior art, such as those that produce unexpected technical effects or those that overcome technical biases, should be included in the claims; otherwise, even if described in the specification, they will not be considered when evaluating the inventiveness of the invention. Features that do not contribute to solving the technical problem, even if included in the claims, generally will not affect the inventiveness of the technical solution.
【For example】
An invention relating to a camera addresses the technical problem of achieving more flexible shutter control, which is accomplished by improving the internal mechanical and circuitry of the camera. After the examiner pointed out that the claims lacked inventive step, the applicant added features to the claims, including the shape of the camera housing, the size of the display screen, and the location of the battery compartment. The specification does not explain any connection between these newly added features and the solution to the claimed technical problem. These added features are either conventional components implied in the subject matter of the claims themselves, or they could be obtained by a person skilled in the art based on their ordinary technical knowledge and conventional experimental methods. The applicant has also failed to provide evidence or sufficient reason to demonstrate that these technical features bring any further technical effects to the claimed solution. Therefore, the aforementioned technical features do not contribute to the solution of the claimed technical problem and do not bring inventive step to the claimed technical solution.
VII. Regarding the revisions to Section 6 of Chapter 9 in Part Two
(i) The title of Section 6 of Chapter 9 of Part II of the Patent Examination Guidelines is amended to: Provisions on the Examination of Invention Patent Applications Involving Artificial Intelligence, Big Data, etc., that Contain Algorithmic Features or Business Rules and Methods.
(ii) Section 6.1 of Chapter 9 of Part II of the Patent Examination Guidelines is amended as follows:
The examination should be conducted on the claimed solution, that is, the solution defined in the claims, and, where necessary, on the content of the specification. During the examination, technical features should not be simply separated from algorithmic features or business rules and methodological features; rather, all the contents recorded in the claims should be considered as a whole, and the technical means involved, the technical problem solved, and the technical effects achieved should be analyzed.
(iii) A new section 6.1.1 is added to Chapter 9, Section 6.1 of Part II of the Patent Examination Guidelines, with the following content:
6.1.1 Examination in accordance with Article 5, Paragraph 1 of the Patent Law
For invention patent applications that contain algorithmic features or business rules and methods, if the data collection, tag management, rule setting, recommendation decision-making, etc., contain content that violates the law, social morality, or harms the public interest, then according to Article 5, Paragraph 1 of the Patent Law, they cannot be granted patent rights.
(iv) In Section 6.1 of Chapter 9 of Part II of the Patent Examination Guidelines, “6.1.1 Examination in accordance with Article 25, Paragraph 1, Item (ii) of the Patent Law” is amended to “6.1.2 Examination in accordance with Article 25, Paragraph 1, Item (ii) of the Patent Law”, “6.1.2 Examination in accordance with Article 2, Paragraph 2 of the Patent Law” is amended to “6.1.3 Examination in accordance with Article 2, Paragraph 2 of the Patent Law”, and “6.1.3 Examination of novelty and inventiveness” is amended to “6.1.4 Examination of novelty and inventiveness”.
(v) A new item (1) is added to Section 6.2 of Chapter 9 of Part II of the Patent Examination Guidelines, as follows:
(1) Invention patent applications containing algorithmic features or business rules and methods cannot be granted patent rights if they violate laws, social ethics or harm public interests.
【Example 1】
A Big Data-Based In-Shopping Mall Mattress Sales Assistance System
Application Content Overview
The invention patent application presents a solution for an in-store mattress sales assistance system based on big data. It collects customer facial feature information and obtains customer identification information through a camera module and a facial recognition module. The collected information is then analyzed to assess customers’ true preferences for mattresses, helping businesses to conduct precise marketing.
Claims of the application
A big data-based in-shopping mall mattress sales support system, comprising mattress display equipment and a management center, characterized in that:
The mattress display device includes a control module and an information acquisition module, used to display and assist in the sale of mattress products and collect customer data; the control module is used to interact with the management center; the information acquisition module includes a camera module and a face recognition module, used to collect facial feature information of customers, adjust facial posture using a key point detection algorithm to obtain a normalized face image, locate the face region to be identified using a face detection algorithm, and extract facial features within the face region using principal component analysis, thereby obtaining the customer’s identity information;
The management center includes a management server and an analysis support system; the management server manages multiple mattress display devices; the analysis support system analyzes the data collected by the mattress display devices based on the customer’s identification information to obtain the customer’s true preferences and feeds back the analysis results to the management center.
Analysis and Conclusion
Relevant provisions of the Personal Information Protection Law of the People’s Republic of China stipulate that the installation of image collection and personal identification equipment in public places must be necessary for maintaining public safety, comply with relevant national regulations, and be accompanied by prominent warning signs. The collected personal images and identification information may only be used for the purpose of maintaining public safety and may not be used for other purposes, except with the individual’s separate consent.
The proposed solution reveals that using image capture and facial recognition for targeted mattress marketing in shopping malls and other business establishments is not essential for maintaining public safety. Furthermore, the collection of customers’ facial information and identification details to obtain and analyze their true mattress preferences is clearly conducted without their knowledge, and the application fails to demonstrate the legality or compliance of the data acquisition or information gathering. Therefore, this invention violates the law and, according to Article 5, Paragraph 1 of the Patent Law, cannot be granted a patent.
【Example 2】
A Method for Establishing an Emergency Decision-Making Model for Autonomous Vehicles
Application Content Overview
The solution proposed in the invention patent application is a method for establishing an emergency decision-making model for autonomous vehicles. It uses the pedestrian’s gender and age as obstacle data, and uses the trained decision-making model to determine the protected object and the object to be collided with when it is impossible to avoid the obstacle.
Claims of the application
A method for establishing an emergency decision-making model for autonomous vehicles, characterized by comprising:
The system acquires historical environmental data and historical obstacle data for autonomous vehicles. The historical environmental data includes the vehicle’s speed, distance to obstacles in its lane, distance to obstacles in adjacent lanes, speed and direction of movement of obstacles in its lane, and speed and direction of movement of obstacles in adjacent lanes. The historical obstacle data includes the gender and age of pedestrians.
Feature extraction is performed on the historical environmental data and historical obstacle data, which are used as input data for the decision model. The historical driving trajectory of the vehicle when it is unable to avoid the obstacle is used as the output data of the decision model. The decision model is trained based on the historical data. The decision model is a deep learning model.
By acquiring real-time environmental and obstacle data, and when the autonomous vehicle encounters an obstacle that it cannot avoid, the trained decision model is used to determine the autonomous vehicle’s trajectory.
Analysis and Conclusion
This invention relates to a method for establishing an emergency decision-making model for autonomous vehicles. Human life has equal value and dignity, regardless of age or gender. If an autonomous vehicle’s emergency decision-making model, in unavoidable accidents, selects between protected and struck individuals based on the pedestrian’s gender and age, this contradicts the public’s ethical and moral concept that all lives are equal. Furthermore, this decision-making method reinforces existing gender and age biases in society, raises public concerns about public transportation safety, and undermines public trust in technology and social order. Therefore, this invention contains content that violates social morality and, according to Article 5, Paragraph 1 of the Patent Law, cannot be granted a patent.
(vi) The following changes are made to the provisions of Section 6.2 of Chapter 9 of Part II of the Patent Examination Guidelines: “(1) Invention patent applications containing algorithmic features or business rules and methods that fall within the scope of Article 25, Paragraph 1, Item (ii) of the Patent Law are not subject to patent protection.” “(2) Invention patent applications containing algorithmic features or business rules and methods that fall within the scope of Article 25, Paragraph 1, Item (ii) of the Patent Law are not subject to patent protection.” “[Example 1]” is changed to “[Example 3]”. “(2) Invention patent applications containing algorithmic features or business rules and methods that utilize technical means to solve technical problems and obtain technical effects fall within the scope of Article 25, Paragraph 1, Item (ii) of the Patent Law are not subject to patent protection.” The technical solutions stipulated in Article 2, Paragraph 2, are therefore subject to patent protection. This is amended to: “(3) Invention patent applications that utilize technical means to solve technical problems and obtain technical effects, including algorithmic features or business rules and methodological features, are technical solutions stipulated in Article 2, Paragraph 2 of the Patent Law, and therefore are subject to patent protection.” “[Example 2]” is amended to “[Example 4]”, “[Example 3]” to “[Example 5]”, “[Example 4]” to “[Example 6]”, “[Example 5]” to “[Example 7]”, “[Example 6]” to “[Example 8]”, “[Example 7]” to “[Example 9]”, and “(3)” does not solve the technical problem, or does not…” “Invention patent applications that utilize technical means or do not achieve technical effects but contain algorithmic features or business rules and methods do not fall under the technical solutions stipulated in Article 2, Paragraph 2 of the Patent Law, and therefore are not subject to patent protection.” is amended to “(4) Invention patent applications that do not solve technical problems, do not utilize technical means, or do not achieve technical effects but contain algorithmic features or business rules and methods do not fall under the technical solutions stipulated in Article 2, Paragraph 2 of the Patent Law, and therefore are not subject to patent protection.” “[Example 8]” is amended to “[Example 10]”, “[Example 9]” is amended to “[Example 11]”, “[Example 10]” is amended to “[Example 12]”, and “(4) In the process of…” is amended to “(4) In the process of…” When conducting an inventive step examination, the contribution of algorithmic features or business rules and methods that are functionally mutually supportive and interactive with the technical features to the technical solution should be considered. This is amended to “(5) When conducting an inventive step examination, the contribution of algorithmic features or business rules and methods that are functionally mutually supportive and interactive with the technical features to the technical solution should be considered.” “[Example 11]” is amended to “[Example 13]”, “[Example 12]” is amended to “[Example 14]”, “[Example 13]” is amended to “[Example 15]”, “[Example 14]” is amended to “[Example 16]”, and “[Example 15]” is amended to “[Example 17]”.
(vii) Add Example 18 and Example 19 to Section 6.2 of Chapter 9 of Part II of the Patent Examination Guidelines, as follows:
【Example 18】
A method for identifying the number of ships
Application Content Overview
The invention patent application proposes a method for identifying the number of vessels, which acquires vessel image data and trains a detection data model through deep learning to solve the technical problem of accurately identifying the number of vessels in the current sea area.
Claims of the application
A method for identifying the number of ships, characterized by comprising:
Obtain a dataset of ship images, preprocess the image information in the dataset, mark the position and boundary information of the ships in the images, and divide the dataset into a training dataset and a test dataset.
Deep learning is performed using the training dataset to build a training model;
The test data is input into the training model to obtain ship test result data.
The actual number of ships is determined by multiplying the ship test results data with a preset error parameter.
Analysis and Conclusion
Comparative document 1 discloses a method for identifying the number of fruits on a tree, and specifically discloses the steps of acquiring image information, marking the position and boundaries of fruits in the image, dividing the dataset, training the model, and determining the actual number of fruits.
The only difference between the solution in the invention patent application and prior art document 1 is the difference in the objects to be identified. Although the ships and fruits themselves differ in appearance, size, and environment, for those skilled in the art, the steps required to identify the actual quantity—such as information labeling, dataset partitioning, and model training—all target the positional relationships of the objects to be identified in the image. The claims do not demonstrate any changes to the training methods or model levels during deep learning or model training due to the different objects being identified. Labeling ship data in the image versus labeling fruit data in the image to obtain a training dataset and then training the model does not involve any adjustments or improvements to the deep learning, model construction, or training process. Therefore, the claimed invention lacks inventiveness.
【Example 19】
A method for establishing a neural network model for classifying scrap steel grades
Application Content Overview
Scrap steel needs to be graded according to its average size during storage. However, the storage is often disorganized and piled up, making manual size measurement and grading inefficient and inaccurate. This invention patent application proposes a method for establishing a neural network model for scrap steel grading. By using a convolutional neural network to learn and form a grading neural network model with grading outputs, the efficiency and accuracy of scrap steel grading can be improved.
Claims of the application
A method for establishing a neural network model for classifying scrap steel grades, the model being used to classify stored scrap steel into grades, comprising:
Multiple images are acquired, different scrap steel grades are determined from the multiple images, the images are preprocessed, image data features of different grades are extracted, and convolutional neural network learning is performed on the extracted image data features of different grades to form a grade classification neural network model with grade classification output.
The extraction of image data features involves extracting the set of convolutional neural network calculations performed on the pixel matrix data of the image. This includes: extracting the color, edge features, and texture features of objects in the image from the output set of multiple lines composed of convolutional layers or convolutional layers plus pooling layers, as well as extracting the correlation features between the edges and textures of objects in the image.
The extraction of object color and edge features in the image is achieved by the output set of three lines consisting of convolutional layers and pooling layers, including a first line with a pooling layer, a second line with a convolutional layer, and a third line with a convolutional layer from left to right; the extraction of texture features in the image is achieved by aggregating the extraction results of object color and edge features in the image and then using the output set of three lines consisting of convolutional layers, including a first line with a convolutional layer of 0, a second line with a convolutional layer of 2, and a third line with a convolutional layer of 3 from left to right.
The number of circuits calculated by the convolutional layer for extracting the correlation features between edges and textures is greater than the number of circuits calculated by the convolutional layer for extracting the color, edge, and texture features of objects in the image.
Analysis and Conclusion
To address the challenges of identifying scrap steel as a type of raw material, stamping waste, bread iron, or other materials due to the complexity, variety, and material differences of recycled resources, and to improve the recycling rate of recycled resources, Comparative Document 1 provides a method for identifying scrap steel types based on a convolutional neural network model. Specifically, it discloses the steps of acquiring image data of multiple identified scrap steel types, preprocessing the image data for feature extraction, and training the convolutional neural network to obtain a product model.
The difference between the solution in the invention patent application and prior art document 1 lies in the different training data and extracted features, as well as the number and level settings of convolutional and pooling layers. Compared to prior art document 1, the key technical problem addressed by the invention is how to improve the accuracy of scrap steel grading. Prior art document 1 uses image data of pre-defined scrap steel types for feature extraction and model training. The invention patent application, in order to grade scrap steel based on its average size, needs to identify the shape and thickness of the scrap steel from chaotic and overlapping images. To extract features such as color, edges, and texture from the images, the number and level settings of convolutional and pooling layers were adjusted during model training. These algorithmic and technical features are mutually supportive and interactive, improving the accuracy of scrap steel grading. The contribution of these algorithmic features to the technical solution should be considered. The aforementioned adjustments to the number and level settings of convolutional and pooling layer circuits have not been disclosed in other prior art documents, nor are they common knowledge in the field. The prior art as a whole does not provide any inspiration for improving the aforementioned prior art document 1 to obtain an invention patent application, and the claimed invention technology solution possesses inventiveness.
(viii) The first paragraph of Section 6.3.1 of Chapter 9 in Part II of the Patent Examination Guidelines is amended as follows:
The specification of an invention patent application that includes algorithmic features or business rules and methodological features should clearly and completely describe the solution adopted by the invention to solve its technical problem. In addition to the technical features, the solution may further include algorithmic features or business rules and methodological features that functionally support and interact with the technical features. If the invention involves the construction or training of an artificial intelligence model, the specification should generally clearly describe the necessary modules, levels, or connections of the model, as well as the specific steps and parameters required for training. If the invention involves the application of an artificial intelligence model or algorithm in a specific field or scenario, the specification should generally clearly describe how the model or algorithm is combined with the specific field or scenario, and how the input and output data of the algorithm or model are set to indicate their inherent relationships, so that a person skilled in the art can implement the invention’s solution according to the description.
(ix) A new section 6.3.3 is added to Chapter 9, Section 6.3 of Part II of the Patent Examination Guidelines, with the following content:
6.3.3 Review Example
【Example 20】
A method for generating facial features
Application Content Overview
The invention patent application achieves information sharing among the second convolutional neural networks by sharing the feature region image set generated by the first convolutional neural network with a spatial transformation network. This reduces memory resource consumption and improves the accuracy of face image generation results.
Claims of the application
A method for generating facial features includes:
Acquire the image of the face to be identified;
The face image to be identified is input into a first convolutional neural network to generate a set of feature region images of the face image to be identified, wherein the first convolutional neural network is used to extract feature region images from the face image;
Each feature region image in the feature region image set is input into the corresponding second convolutional neural network to generate the regional face features of the feature region image, wherein the second convolutional neural network is used to extract the regional face features of the corresponding feature region image;
The facial feature set of the face image to be identified is generated based on the regional facial features of each feature region image in the feature region image set;
The first convolutional neural network also includes a spatial transformation network for determining the feature regions of the face image; and
The process of inputting the face image to be identified into a first convolutional neural network to generate a set of feature region images of the face image to be identified includes: inputting the face image to be identified into the spatial transformation network to determine the feature regions of the face image to be identified; and inputting the face image to be identified into the first convolutional neural network to generate a set of feature region images of the face image to be identified based on the determined feature regions.
Relevant paragraphs of the specification
The method for generating facial features provided in this application firstly generates a set of feature region images of the face image to be recognized by inputting the acquired face image to be recognized into a first convolutional neural network. The first convolutional neural network can be used to extract feature region images from the face image. Then, each feature region image in the feature region image set can be input into a corresponding second convolutional neural network to generate the regional face features of that feature region image. The second convolutional neural network can be used to extract the regional face features of the corresponding feature region image. Subsequently, based on the regional face features of each feature region image in the feature region image set, a set of facial features of the face image to be recognized can be generated. In other words, the set of feature region images generated by the first convolutional neural network can share information among the various second convolutional neural networks. This reduces the amount of data, thereby reducing memory resource consumption and improving generation efficiency.
To improve the accuracy of the generated results, a spatial transformation network can also be included in the first convolutional neural network to determine the feature regions of the face image. In this case, the electronic device can input the face image to be recognized into the spatial transformation network to determine its feature regions. Then, the first convolutional neural network can extract images matching the feature regions on the feature layer based on the feature regions determined by the spatial transformation network, thereby generating a set of feature region images of the face image to be recognized. The specific location of the spatial transformation network within the first convolutional neural network is not limited in this application. The spatial transformation network can continuously learn to determine the feature regions of different features in different face images.
Analysis and Conclusion
The invention patent application requests protection for a method for generating facial features. In order to improve the accuracy of the facial image generation results, a spatial transformation network can be set in the first convolutional neural network to determine the feature regions of the facial image. However, the specification does not describe the specific setting position of the spatial transformation network in the first convolutional neural network.
Those skilled in the art will understand that the spatial transformation network, as a whole, can be inserted into any position within a first convolutional neural network (CNN) to form a nested CNN structure. For example, the spatial transformation network can serve as the first layer or an intermediate layer of the first CNN, without affecting its ability to identify feature regions of an image. Through training, the spatial transformation network can determine the feature regions containing different features of different face images. Therefore, the spatial transformation network can not only guide the first CNN to segment feature regions but also perform simple spatial transformations on the input data to improve the processing performance of the first CNN. Accordingly, the hierarchical structure of the model used in the invention patent application is clear, and the input/output relationships between each layer are also clear. Both the CNN and the spatial transformation network are well-known algorithms, and those skilled in the art can construct the corresponding model architecture based on the above description. Therefore, the solution claimed in the invention patent application has been fully disclosed in the specification and complies with Article 26, Paragraph 3 of the Patent Law.
【Example 21】
A method for predicting cancer based on bioinformatics
Application Content Overview
The invention patent application provides a method for predicting cancer based on bioinformatics. By using a trained malignant tumor enhancement screening model, blood routine, blood biochemical test indicators and facial image features as inputs to the screening model, a malignant tumor prediction value is obtained, thereby solving the technical problem of improving the accuracy of malignant tumor prediction.
Claims of the application
A method for predicting cancer based on bioinformatics, characterized by comprising:
Obtain the complete blood count and blood biochemistry test reports of the individuals to be screened, and identify the test indicators, age, and gender in the complete blood count and blood biochemistry test reports;
Obtain a frontal, unedited facial image of the person to be screened and extract facial image features;
Based on the enhanced screening model for malignant tumors, the predicted value of malignant tumor incidence in the corresponding individuals to be screened is calculated.
The training process of the enhanced screening model for malignant tumors is as follows: a large-scale population sample set is constructed, which includes the blood routine, blood biochemistry and facial images of the same person; learning samples are established using the features of the blood routine, blood biochemistry and facial images; and machine learning algorithm model is trained using the learning samples to obtain the enhanced screening model for malignant tumors.
Relevant paragraphs of the specification
Currently, when using tumor markers to identify malignant tumors, a tumor marker level exceeding a threshold cannot definitively diagnose malignancy, while a level below the threshold does not rule out malignancy. Therefore, the accuracy of predicting cancer based on tumor markers is low. This application utilizes complete blood count (CBC), blood biochemistry indicators, and facial images to improve the accuracy of identifying various malignant tumors. This application, while utilizing blood test data, also considers the health status of the person being screened as reflected in facial images, enabling a more accurate prediction of the probability of developing malignant tumors. The selection of features for the enhanced malignant tumor screening model can utilize some or all of the indicators from CBC and blood biochemistry.
Analysis and Conclusion
The technical problem this invention patent application aims to solve is how to improve the accuracy of malignant tumor prediction. To address this problem, the solution utilizes a pre-trained enhanced malignant tumor screening model, taking complete blood count, blood biochemistry indicators, and facial image features as inputs to obtain a predicted value for malignant tumor incidence. However, both complete blood count and blood biochemistry tests contain dozens of indicators. The specification does not specify which indicators are key indicators related to tumor prediction accuracy, or whether it considers all indicators and assigns different weights to each for prediction. Those skilled in the art cannot determine which indicators can be used to diagnose malignant tumors. Furthermore, based on current scientific research, the correlation between facial features and malignant tumor incidence is uncertain, except for a few types of tumors such as facial skin cancer. The specification also does not describe or prove a causal relationship between the “basis factors for judgment” and the “judgment result.” In addition, the specification does not provide any validation data to prove that the accuracy of this solution in identifying various malignant tumors is higher than that of using tumor markers, or significantly higher than the accuracy of randomly judging the probability of malignant tumor incidence. Based solely on the disclosure in the specification, a person skilled in the art would be unable to determine that the solution in this application can solve the technical problem it seeks to address. Therefore, the technical solution for which protection is sought in the invention patent application is not fully disclosed in the specification, and the specification does not comply with Article 26, Paragraph 3 of the Patent Law.
The rest of this section remains unchanged.
VIII. Regarding the addition of Section 7 of Chapter 9 in Part Two
A new Section 7 has been added to Chapter 9 of Part II of the “Guidelines for Patent Examination,” with the following content:
7. Regulations concerning the examination of invention patent applications containing bitstreams
In applications 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 to provide specific regulations for the examination of the subject matter of invention patent applications containing bitstreams, as well as for the drafting of the specification and claims, in accordance with the provisions of the Patent Law and its Implementing Regulations.
7.1 Examination of the object of protection
7.1.1 Examination in accordance with Article 25, Paragraph 1, Item (ii) of the Patent Law
If the subject matter of a claim relates only to a simple bitstream, then the claim falls under the category of rules and methods for intellectual activities as defined in Article 25, Paragraph 1, Item (2) of the Patent Law, and is not subject to patent protection. For example, “A bitstream, characterized in that it includes syntax element A, syntax element B, …”.
If a claim, apart from its subject matter title, refers solely to a simple bitstream, then the claim falls under the category of rules and methods for intellectual activities as defined in Article 25, Paragraph 1, Item (2) of the Patent Law, and is not subject to patent protection. For example, “A method for generating a bitstream, characterized in that the bitstream includes syntax element A, syntax element B, …”.
7.1.2 Examination in accordance with Article 2, Paragraph 2 of the Patent Law
In the field of digital video encoding and decoding, video data is typically encoded into bitstreams using video encoding methods, and these bitstreams are then decoded into video data using video decoding methods. If a specific video encoding method for generating a bitstream falls under the technical solution described in Article 2, Paragraph 2 of the Patent Law, then the method for storing or transmitting the bitstream, as defined by that specific video encoding method, and the computer-readable storage medium for storing the bitstream, can achieve optimized allocation of storage or transmission resources. Therefore, the storage or transmission method and the computer-readable storage medium defined by that specific video encoding method fall under the technical solution described in Article 2, Paragraph 2 of the Patent Law and are subject to patent protection.
7.2 Drafting the Specification and Claims
7.2.1 Writing the Specification
The specification of an invention patent application containing a bitstream generated by a specific video coding method should clearly and completely describe that specific video coding method so that a person skilled in the art can implement it. If the subject matter relates to a method for storing or transmitting the bitstream and a computer-readable storage medium for storing the bitstream, the specification should also include a corresponding description to support the claims.
7.2.2 Drafting the Claims
Patent applications that include bitstreams generated by a specific video coding method can be drafted as claims for storage methods, transmission methods, and computer-readable storage media. Such claims should generally be based on claims related to the specific video coding method used to generate the bitstream, and should be drafted by referencing those claims or by including all features of those specific video coding methods.
【Example 1】
An invention patent application relating to video encoding and decoding technology can have its claims drafted as follows.
1. A video encoding method, characterized by comprising the following steps:
Frame partitioning steps, …
…
Entropy coding steps, …
2. A video encoding apparatus, characterized in that it comprises the following units:
Frame division unit, …
…
Entropy coding unit, …
3. A video decoding method, characterized by comprising the following steps:
Entropy decoding steps, …
…
Frame output steps, …
4. A video decoding device, characterized in that it comprises the following units:
Entropy decoding unit, …
…
Frame output unit, …
5. A method for storing a bitstream, characterized in that the video encoding method of claim 1 is performed to generate the bitstream; and the bitstream is stored.
6. A method for transmitting a bit stream, characterized in that the video encoding method of claim 1 is performed to generate the bit stream; and the bit stream is transmitted.
7. A computer-readable storage medium storing a computer program/instructions and a bit stream thereon, characterized in that the computer program/instructions, when executed by a processor, implement the video encoding method of claim 1 to generate the bit stream.
IX. Regarding the revisions to Section 9 of Chapter 10 in Part Two
(i) The second paragraph of Section 9, Chapter 10, Part II of the Patent Examination Guidelines is amended as follows:
The definition of the term “animal” is governed by the provisions of Section 4.4 of Chapter 1 of this Part. The term “plant” refers to an organism that sustains itself by synthesizing carbohydrates and proteins from inorganic substances such as water, carbon dioxide, and inorganic salts through photosynthesis, and which is generally non-motile. The terms “animal” and “plant” can refer to various levels of taxonomic units, such as kingdom, phylum, class, order, family, genus, and species.
(ii) The last two paragraphs of Section 9.1.2.3 of Chapter 10, Part II of the Patent Examination Guidelines are amended as follows:
Wild plants found in nature that have not undergone technical treatment and exist naturally fall under the category of scientific discoveries as defined in Article 25, Paragraph 1, Item (1) of the Patent Law and cannot be granted patent rights. However, when wild plants are artificially selected or improved and have industrial value, the plant itself does not fall within the scope of scientific discoveries.
According to the definition of “plant variety” in Section 4.4 of Chapter 1 of this Part, plants and their propagation materials obtained through artificial breeding or improvement of discovered wild plants cannot be considered “plant varieties” if they do not have consistent morphological characteristics and biological properties or relatively stable genetic traits in their population. Therefore, they do not fall within the scope of Article 25, Paragraph 1, Item (4) of the Patent Law.
(iii) Section 9.1.2.4 of Chapter 10, Part II of the Patent Examination Guidelines is amended as follows:
Genetically modified animals or plants are animals or plants obtained through biological methods such as recombinant DNA technology in genetic engineering. If they still fall within the scope of “animal varieties” or “plant varieties” as defined in Section 4.4 of Chapter 1 of this part, they cannot be granted patent rights according to Article 25, Paragraph 1, Item (4) of the Patent Law.
The rest of this section remains unchanged.
10. Regarding the revisions to Section 5.2.3.2 of Chapter 1, Part 3.
The fifth paragraph of Section 5.2.3.2 of Chapter 1, Part III of the “Guidelines for Patent Examination” is amended as follows:
In case (3), unless the applicant has made a valid declaration of priority during the international phase, the applicant shall submit relevant supporting documents. These supporting documents shall be signed or sealed by all applicants of the prior application. The supporting documents shall be originals or notarized copies.
The rest of this section remains unchanged.
XI. Regarding the revisions to Section 7.3 of Chapter 1 of Part III.
Delete item (3) of section 7.3 of chapter 1 in Part III of the Patent Examination Guidelines.
The rest of this section remains unchanged.
12. Regarding the revisions to Section 6.2 of Chapter 1 of Part IV.
(i) The first paragraph of Section 6.2 of Chapter 1, Part IV of the Patent Examination Guidelines is amended as follows:
The review decision typically includes the following:
(ii) Delete the last paragraph of Section 6.2 (4) of Chapter 1, Part IV of the Patent Examination Guidelines.
The rest of this section remains unchanged.
XIII. Regarding the amendments to Section 2.1 of Chapter 3 of Part IV.
The first paragraph of Section 2.1 of Chapter 3, Part IV of the “Guidelines for Patent Examination” is amended as follows:
If a request for invalidation is filed again for a patent right involved in an invalidation case that has already been examined, based on the same or substantially the same reasons and evidence, it will not be accepted or examined.
The rest of this section remains unchanged.
XIV. Regarding the revisions to Section 3.2 of Chapter 3 in Part IV.
(a) A new item (2) is added to Section 3.2 of Chapter 3, Part IV of the Patent Examination Guidelines, as follows:
(2) The request for invalidation was not made in accordance with the true intention of the requester.
(ii) Modify “(2)” in Section 3.2 of Chapter 3, Part IV of the Patent Examination Guidelines to “(3)”, “(3)” to “(4)”, and “(4)” to “(5)”.
The rest of this section remains unchanged.
XV. Regarding the revisions to Section 3.3 of Chapter 3 in Part IV.
The third section, paragraph 3.3 (3) of Part IV of the Patent Examination Guidelines is amended as follows:
If, after the Reexamination and Invalidation Division has made an invalidation request decision on a patent right, another invalidation request is filed with the same or substantially the same reasons and evidence, it will not be accepted, except in cases where the reasons or evidence were not considered by the decision due to time limits or other reasons.
The rest of this section remains unchanged.
XVI. Regarding the revisions to Section 4.6 of Chapter 3 in Part IV.
A new section 4.6.4 has been added to Chapter 3, Section 4.6 of Part IV of the “Guidelines for Patent Examination,” with the following content:
4.6.4 Submitting Modified Text
If the patentee amends the claims, he/she shall submit the full replacement pages and a comparison table of the amendments.
If multiple amended texts submitted by the patentee in the same invalidation request hearing proceedings all comply with the provisions of Section 4.6.3 of this chapter, the last amended text shall prevail, and the remaining amended texts shall not be used as the basis for examination.
The rest of this section remains unchanged.
XVII. Regarding the revisions to Section 1 of Chapter 2 of Part V.
The third paragraph of Section 1, Item (1) of Chapter 2, Part 5 of the Patent Examination Guidelines is amended as follows:
The application surcharge is a fee payable when the description (including drawings and sequence lists) of the application documents exceeds 30 pages or the claims exceed 10. The amount of this fee is calculated based on the number of pages or claims. For computer-readable sequence lists submitted in the prescribed format, the page count is not included.
The rest of this section remains unchanged.
18. Regarding the amendments to Section 4.2.1 of Chapter 2 of Part 5.
(i) Section 4.2.1.1 of Chapter 2, Part V of the Patent Examination Guidelines is amended as follows:
(1) Overpayment: If the party should pay an annual fee of 600 yuan, but actually pays 650 yuan within the prescribed period, they may request a refund of the overpaid 50 yuan.
(2) Circumstances where fees are paid again: If a request to change a bibliographic item is made, a fee of RMB 200 shall be paid. If the party pays RMB 200 and then pays RMB 200 again, the party may request a refund for the RMB 200 paid again.
(3) Circumstances of incorrect payment of fees: If the party makes a mistake in writing the type of fee or the application number (or patent number) when making the payment; or if the rights are lost due to insufficient payment or overdue payment; or if the patent fees are paid after the rights have been lost, the party may request a refund.
(4) If, before the Patent Office issues a notification that the invention patent application has entered the substantive examination stage, the patent application has been deemed withdrawn, the divisional application has been deemed not filed, or the declaration of withdrawal of the patent application has been approved, the party may request a refund of the substantive examination fee already paid.
(5) If a party voluntarily withdraws an invention patent application that has entered the substantive examination stage before the deadline for responding to the first examination opinion, it may request a refund of 50% of the substantive examination fee for the invention patent application, except for those who have already submitted a response.
(6) A party may request a refund of the annual fee paid after the announcement of the decision to terminate the patent right or to declare the patent right invalid in its entirety.
(7) If the Patent Office decides not to restore the rights after the examination procedure for the request to restore the rights has been initiated, the party concerned may request a refund of the fee for the request to restore the rights and related expenses already paid.
(ii) Section 4.2.1.2 of Chapter 2 of Part 5 of the Patent Examination Guidelines is deleted.
(iii) The section “4.2.1.3 Circumstances under which refunds will not be given” in Chapter 2 of Part 5 of the Patent Examination Guidelines is amended to “4.2.1.2 Circumstances under which refunds will not be given”.
The rest of this section remains unchanged.
19. Regarding the revisions to Section 8 of Chapter 7 of Part Five.
(i) A second paragraph is added to Section 8.1 of Chapter 7 of Part V of the Patent Examination Guidelines, as follows:
At the applicant’s request, patent applications may be examined as needed, including priority examination, expedited examination, or deferred examination.
(ii) A new section 8.3 is added to Chapter 7 of Part V of the Patent Examination Guidelines, as follows:
8.3 Rapid Examination
Patent applications submitted after passing the preliminary examination by a national-level intellectual property protection center or rapid rights protection center, and meeting the relevant regulations for rapid examination, can be examined quickly.
(iii) In the fifth part, chapter seven, section eight of the Patent Examination Guidelines, “8.3 Delayed Examination” is amended to “8.4 Delayed Examination”, and “8.4 Patent Office initiates examination on its own” is amended to “8.5 Patent Office initiates examination on its own”.
The rest of this section remains unchanged.
20. Regarding the revisions to Section 1.3.2.6 of Chapter 8 in Part Five.
Section 1.3.2.6 of Chapter 8, Part V of the Patent Examination Guidelines is amended as follows:
The items published for patent term adjustment include: main classification number, patent number, application date, authorization announcement date, original patent term expiration date, and current patent term expiration date.
The items included in the publication of the patent term extension for pharmaceutical products are: main classification number, patent number, application date, authorization announcement date, drug name and approved indications, original patent expiration date, and current patent expiration date.
21. Regarding the revisions to Section 1.3.2.7 of Chapter 8 in Part 5.
Section 1.3.2.7 of Chapter 8, Part 5 of the “Guidelines for Patent Examination” is amended as follows:
The items included in the filing and publication of a patent licensing agreement include: main classification number, patent number, filing number, licensor, licensee, invention name, application date, invention publication date, authorization announcement date, license type (exclusive, exclusive, ordinary), and filing date.
The items included in the announcement of changes to the filing of patent implementation license contracts are: main classification number, patent number, filing number, change date, changes (license type, licensor, licensee) and the content before and after the change.
The items included in the publication of the cancellation of patent licensing contract filings are: main classification number, patent number, filing number, licensor, licensee, and date of termination of the licensing contract filing.
22. Regarding the revisions to Section 1.2.1 of Chapter 9 in Part Five.
A new last paragraph has been added to Section 1.2.1 of Chapter 9 in Part V of the “Guidelines for Patent Examination,” as follows:
For international applications or divisional applications, the name of the inventor or designer, or the name of the applicant, recorded on the patent certificate at the patent application date refers to the name of the inventor or designer, or the name of the applicant, at the time the international application entered the Chinese national phase or the date the divisional application was filed.
The rest of this section remains unchanged.
23. Regarding the amendments to Section 2.2.1 of Chapter 9 of Part Five.
Section 2.2.1 of Chapter 9, Part V of the “Guidelines for Patent Examination” is amended as follows:
The following delays are considered reasonable delays in the authorization process: re-examination procedures for amending patent application documents in accordance with Article 66 of the Implementing Regulations of the Patent Law; suspension procedures in accordance with Article 103 of the Implementing Regulations of the Patent Law; preservation measures in accordance with Article 104 of the Implementing Regulations of the Patent Law; other reasonable circumstances such as re-examination procedures for revoking the rejection decision based on new reasons stated or new evidence submitted by the re-examination requester; and administrative litigation procedures.
This decision shall take effect on January 1, 2026.
