EI patents offer significant advantages to patent holders
Kazuyoshi Nishizawa, Fumio Takahashi, Satoshi Furuichi and Yoshikuni Kajii of Shiga International Patent Office examine the benefits of EI patents for patent holders, including the fact that they can be enforced against both AI and non-AI systems
Artificial intelligence (AI) patents in Japan reflect a pro-patent policy as a result of the fourth industrial revolution. In the machine learning field, examiners at the Japanese Patent Office (JPO) reported that the number of patent applications has increased by 78% per year and the patent grant rate has reached 90%. In addition, the JPO made it clear that claims of trained classifiers (trained models) could be patented.
In this article, we propose adding extended intelligence patents (EI patents) to your patent portfolios. EI patents relate to the human creation of technical ideas extended by AI. EI patents are typically related to the behaviour of products. The behaviour is based on outputs of trained classifiers. The behaviour directly solves business and technical problems in various situations (see Figure 1). The behaviour could be implemented using AI or without AI.
We will discuss an outline of EI patents, trained classifiers and IF patents and an example of an EI patent. Those who are not familiar with AI technology are encouraged to read the section on the simplest example of an EI patent.
EI patents can often be better conceived by product experts than AI specialists.
Figure 1: Extended intelligence patents
Figure 2: Third step and EI patents
AI system process
1) Training step
AI is training datasets.
2) Runtime step
AI output to inputs.
3) Solution step
AI controls products which resolve problems.
AI patent at each step
Patent related to behaviour of products with outputs at “2) runtime step”.
AI runtime patent
Patents related to input/output device with classifier trained at “1) Training step”.
AI training patents
Patents related to training devices.
Figure 3.1: AI training patent at the (1) training step
Figure 3.2: AI runtime patent at the (2) runtime step
Figure 3.3: EI patents at the (3) solution step
Outline of EI patents
AI systems seem to have only a training step and a runtime step when it comes to patents. However, we suggest a solution step as a third step (see Figure 2).
EI patents are AI patents at the solution step. They are related to the behaviour of products, structures of products, and materials that are based on the outputs of trained classifiers. The behaviour is movement, control, prediction, or graphical user interface (GUI) output. For example, AI performs a control which causes products to perform a behaviour.
Examples of behaviour
In the cases in the grid entitled "Examples of behaviour", the subject of each claim of the EI patent is the behaviour of the car, the robot arm, the production machinery, or the trading device, respectively.
In another example, when AI predicts more suitable structures and materials for laser radiators or control chips, EI patents are patents for the structures and the materials predicted by AI. In the final section of this article, we will introduce the simplest example of EI patents.
Effectiveness of EI patents
We will compare patents at each step in light of patent enforcement.
EI patents have two advantages for the patent holder: it is difficult to avoid EI patent infringement and it is easy to discover EI patent infringement.
AI training patent infringement
AI training patents are patents at the training step.
AI training patents are related to the gathering, selecting, and converting of training datasets or reward signals. Alternatively, they are related to selecting and setting classifiers, or new classifiers. It is possible to enforce AI training patents against AI systems at the training step.
Figure 4: Training step and runtime step
However, it is impossible to enforce AI training patents against non-AI systems because non-AI systems are developed by programming, but are not trained. It is impossible to enforce the patent of one type of model against other kinds of models (see Figure 3.1). Furthermore, it is difficult to discover AI training patent infringement in some cases because, apart from at the runtime step, AI training can be conducted confidentially.
AI runtime patent infringement
AI runtime patents are patents at the runtime step.
AI runtime patents are related to input/output devices with trained classifiers. It is possible to enforce AI runtime patents against AI systems at the runtime step.
However, it is impossible to enforce AI runtime patents against non-AI systems because non-AI systems run with application programming interfaces (APIs), databases, and functions, but not with trained classifiers (see Figure 3.2).
EI patent infringement
EI patents are patents at the solution step.
It is possible to enforce EI patents against both AI systems and non-AI systems because EI patents are related to the behaviour of products. It is also difficult to avoid EI patent infringement because the behaviour which can resolve problems is the same regardless of whether it is with trained classifiers or with API/database/functions. Furthermore, it is easy to discover patent infringement since the behaviour of products is directly observed (see Figure 3.3).
EI patents are very important patents. Prior to explaining EI patents in detail, we will introduce Japanese AI patents.
Trained classifiers and IF patents
A long time ago, in March 2017, the JPO presented claims of trained classifiers in the Examination Handbook. Patents for trained classifiers were a hot topic. They are patents at the training step and runtime step. In these steps, we will introduce claims of a process for producing trained classifiers and interface patents (IF patents) for AI (see Figure 4).
Figure 5.1: Example of IF patent at the training step
Figure 5.2: Example of IF patent at runtime step
Figure 5.3: Example of production methods of trained classifiers
Patents related to trained classifiers
Claims for trained classifiers
The Handbook contains the case example, "Case 2-14 Trained Classifier to Analyse Reputations of Accommodations". Through this example, the JPO made it clear that the claims of trained classifiers could be patented. The JPO interpreted trained classifiers to be a "computer program, etc." (Japanese Patent Law, Article 2, Paragraph 3, Number 1). Thus, the inventions of trained classifiers can fall under an invention and be patented.
Claims of a process for producing a trained classifier
Along with proposing claims of trained classifiers, we additionally suggest claims of a process for producing trained classifiers. Firstly, the patents of the suggested claims can protect both the process and the product (trained classifiers) in Japan. Secondly, we noticed that the claims for trained classifiers can be written as product-by-process (PBP) claims. We may need to consider avoiding the issue of PBP claims. Thirdly, we found a Japanese patent (patent number 6216024) which has claims of a process for producing trained classifiers.
IF patents with an example
IF patents are input/output patents without inner structures that are for example, the topologies of neural networks setting each layer of deep learning. In other words, there is no inner structure of a classifier in their claims. IF patents at the training step are related to training datasets.
IF patents at the runtime step are related to input/output devices with a trained classifier.
IF patents are important patents because they can be enforced against not only the same kind of classifiers but also other kinds of classifiers. It is a common function of training to minimise the errors between training datasets and input/output with training classifiers.
The first example of a claim (Japanese Patent Number 6063016) as an IF patent at the training step is shown in Figure 5.1.
Training datasets are status variables and the results of judging in the example.
There are training datasets but no classifiers in the claim. Thus, the patents of the claims can enforce the right against any kind of classifier and any inner structures of a classifier.
If you would like to include distillation models in the scope of the patent right, it is best to change "a status observing part observing … as status variables" to "a status acquiring part acquiring … as status variables".
The second example of a possible Japanese claim as an IF patent at the runtime step is shown in Figure 5.2.
Inputs are status variables, and outputs are correction of the operation command in the example.
There are inputs/outputs and the classifier, but no inner structures of the classifier in the claim. Thus, the patents of the claims can enforce the right against any kinds of classifiers and any inner structures of a classifier too.
In the case of supervised learning, according to the above claim of IF patent at the training step (claim 1 of Japanese patent number 6063016), the outputs from the classifier are considered to be the probabilities of whether or not an abnormality will occur in the electric motor. In this case, it is possible to change "the classifier which…according to claim 1" to "the classifier which outputs information indicating whether or not an abnormality will occur in the electric motor for the status variables inputted" in the above claim of IF patent at the runtime step.
Production method of trained classifier of IF patents
We would suggest adding the invention category production methods of trained classifiers. There are no inner structures of classifiers in the claims of IF patents (see Figure 5.3).
We also propose claims of trained classifiers, but it is necessary to pay attention to the fact that there are inner structures of a classifier (two neural networks) in the JPO's Case 2-14.
Whether the internal structure should exist in the claim or not depends on future practices and application contents.
Simplest example of EI patent
Behaviour of a robot arm
There is an interesting video showing a robot arm controlled by AI. The robot arm picks up cylinders similar to bins. The video shows the success rate of picking up the cylinders. After AI trained 5,000 samples, the success rate dramatically increased from 60% to 90%. We must pay attention to the result of the analysis. The result is that "round-shaped facets are actually easy targets" (see Figure 6 or video) after training.
As the simplest example of EI patents, we propose the claim in Figure 6.2 based on the result of the analysis. The claim is directed to a robot arm, and there is no description about AI at all in this claim.
Figure. 6.1: Behaviour of robot arm
Figure 6.2: Simplest example of an EI patent
Advantages of EI patents
The claim in Figure 6.2 of an EI patent shows the following advantages.
Firstly, in the claim, the structure of AI systems at the training step and runtime step was not described at all. In other words, it is possible to enforce this EI patent against a robot arm performing the above behaviour due to programming in non-AI systems. Secondly, it is also possible to keep training methods and inner structures of a classifier confidential in AI systems because it is not necessary to describe them in the patent application. Thirdly, the patent practices for EI patents are as usual since the claims of EI patents are the same as claims which used to be described in non-AI systems. See Figure 7 for advantages of AI patents.
Advantages of EI patents
Figure 7: Advantages of EI patents
Methods for conceiving EI patents
The methods for conceiving EI patents are shown in Figure 8.1.
Figure 8.1: Methods for conceiving EI patents
Opportunities to conceive EI patents
Most applicants have applied for patents for AI inventions before training and it ends there. However, AI will begin to resolve problems after training.
After training, it is necessary to provide opportunities to conceive EI patents. They include opportunities after learning more data than the previous training since AI causes products to behave in a more effective manner after that.
We propose setting up these opportunities after training AI.
Comparison before and after training
At these opportunities, what you should do is observe the products.
We propose comparing the behaviour of products before and after training (see Figure 8.2). Hints for EI patents are hidden in the differences in behaviour before and after. That is why EI patents can often be better conceived by product experts than AI specialists.
Figure 8.2: Comparison before and after learning
The methods in Figure 8.1 are effective in the case of reinforcement learning. The methods for conceiving EI patents depend on the type of machine learning algorithms. The types are "unsupervised learning", "supervised learning", and "reinforcement learning" (see Figure 9). In the cases of "unsupervised learning" and "supervised learning", it is necessary to analyse details of the relationship between AI inputs and outputs.
Figure 9: Types of algorithms and methods for conceiving EI patents
Summary of suggestions
At the training step and runtime step in AI systems, we introduced IF patents and claims of a production method of trained classifiers. Furthermore, we added a solution step to clarify EI patents. EI patents are typically related to the behaviour of products. EI patents are very important because the behaviour directly solves problems. Even non-AI systems find it hard to avoid EI patent infringement. It is much easier to discover patent infringement via behaviour than from the configuration of AI systems.
Artificial intelligence extends human intelligence, while human intelligence extends artificial intelligence. That is true for intellectual property. Since the patents we proposed relate to the human creation of technical ideas extended by AI, we named them extended intellectual patents.
We believe that you should add our EI patents to your patent portfolios.
Kazuyoshi Nishizawa specialises in information and communication technology and physics. He has expertise in standard essential patents. Nishizawa has experience in the design and development of computer systems for solution business.
Fumio Takahashi specialises in electronics and software and was registered in 2015. Takahashi has experience in SoC design and verification for Renesas Electronics Corporation.
Satoshi Furuichi specialises in electronic and electrical engineering, software, telecommunications, mechanical control, plant control and business models. Furuichi was registered in 2013.
Yoshikuni Kajii specialises in mechanical engineering and was registered in 2004. Kajii has experience in domestic and international patent applications and prosecution mainly in relation to structures and controlling methods of electronic devices.