Knowledge Partners Patent Professional Corporation


Addition of patent examination handbook on AI related technology.

Blog > 実務 > Addition of patent examination handbook on AI related technology. ブログ村

 It seems that a case has been added to the patent examination handbook on IOT related technology and AI related technology. It is summarized as reference material at the bottom of this page of the JPO website. As for AI related technology, following examples are added.
 Case 3-2: A case where machine learning was applied to forecasting sugar content data of apple.
 Case 2-13: Data structure of the dialogue scenario of the spoken dialogue system.
 Case 2-14: Learned model for analyzing the reputation of accommodation facilities.
 Case 31: a learning system having an in-vehicle device and a server.
 Case 32: Quality control program for the production line.

 Case 3-2.
 This is the example to describe that a feature of ”predicting and outputting sugar content data of apple at the time of shipment in the future using the sugar content data of apple for a predetermined period and past / future weather condition data as input” can be statutory subject matter. In this example, the artificial intelligence related technology is not explicitly stated in the claims, which is no different from the style of the conventional claim. It can be said that it obviously falls under the invention from the software related examination procedure. The significance of the addition of this case seems to be that the forecast is machine learning. Machine learning does not appear in the claims, but ”analysis for prediction is performed by machine learning” and ”realization method of machine learning” are supposed to be disclosed in the specification. When creating a claim on prediction using machine learning, in many cases, I think that the style of this claim will be adopted. Because, as we wrote in the previous blog, determining the input data in many artificial intelligence-related technologies is an important factor for achieving the objective and invention, and the technical features do not appear in machine learning itself. Since case 3-2 introduced here is a case to discuss statutory subject matter of invention, it is necessary to note that the patent office does not say anything about the validity of the description in the description. When applying for a patent application, it is obvious that we have to disclose more information than example specification. For example, as a disclosure example of the specification, how to proceed machine learning is shown, but in case of actually applying for a patent, it is considered that machine learning should be disclosed in more detail.
 Also, if the disclosure for prediction is machine learning alone, there is a fear that it may correspond to excessive generalization. We have to think that whether prediction method other than machine learning is available or not, whether prediction methods other than machine learning may be useful for the applicants or not, etc. If necessary, the prediction method other than machine learning should be written in the specification.

 Case 2 – 13, Case 31, Case 32.
 Case 2 – 13 is an example where the claim as a data structure of the dialog scenario used in the spoken dialogue system corresponds to ”invention”.
 Case 31 is an example of denying the inventive step of claim which improves the parameter of image recognition performed by the in-vehicle device by machine learning within the server.
 Case 32 is an example of denying the inventive step of claiming machine learning based on inspection results and manufacturing conditions in the server in order to perform quality control of the production line. In these examples, artificial intelligence and machine learning are used as embodiments, but it seems that an artificial intelligence related technology does not affect the statutory subject matter or inventive step. Is it simply the addition of cases of artificial intelligence related technology? In a few days, the patent office examiner will have a workshop to explain additional cases, so I would like to confirm the position of this case.

 Case 2-14.
 This is an example showing that the claimed ”learned model” corresponds to ”invention”. I think that it is a very useful case. At the moment, the Patent Office seems to believe that the claim of the category of learned model can be statutory subject matter. I do not know how the court thinks, but as there is no worry that the claims called learned models will be rejected due to inadequacies in the category for a while, the choice of application strategy will increase. Without this case you may have created a claim as a program rather than a learned model. Furthermore, in this case, the claim of the learned model is created with the structural feature of the neural network. Weighting coefficients that can change by learning also appear in the claims, but the features of the weighting coefficients are not stipulated in the claims in detail so as to suggest a relationship of input and output. Weighting coefficients are specified in the claims as much as necessary to describe the structure features of the neural network. From these facts, at least the JPO seems to think that the structure of the neural network can be statutory subject matter. From now on, applicants who apply artificial intelligence related technology have to be constantly conscious of whether neural network structure is novelty or inventive step. Of course, it is always necessary to consider whether applying the structure of the neural network is significant for the applicant’s patenting strategy.

 Cases mentioned above were added ones. In the added cases, neural network and support vector machine will appear, but I think that it was good if the examples of other technologies, such as reinforcement learning, are added too (I believe that reinforcement learning is important for artificial intelligence related technology). Let’s expect for future additions.