In March 2018, revision of patent / utility model examination handbook was announced. It seems that the Manual of Patent Examining Procedure of the computer software related invention and the examination handbook accompanied therewith were revised. Points of revision are summarized on the JPO website.

 I would like to make a note of the points I noted in this revision on Annex B of the Examination Handbook.
 ● Appendix B Chapter 1 2.1.1.2
 In the items to be noted (iii) are as follows “Even if only “the computer (Information processing device)” is described in the claims as a hardware resources, as long as the claim states that the calculation or processing of specific information according to the purpose of use, this claim is interpreted as describing the concrete means in which the hardware resources and the software cooperate with”. In short, as long as the process is embodied, it seems that you can think that the claims are accepted as a computer software related invention by making a computer appear as a hardware resource in the claims. This is natural for a practitioner, though making CPU and memory etc. appear in the claims(example written in Japanese MPEP) is unnatural. In any case, according to the notice (iii), it is clearly indicated that “cooperation between hardware resources and software” can be represented by “computer + concrete processing”, if this format is set we can expect that we will not receive unnecessary refusal.

● Appendix B Chapter 1 2.1.2
 In the explanation of Example 1, it is obvious that the claim is described as “data structure”, even if name of the invention is written as “character” at the end of the claim. It seems that Japanese Patent Office thinks that it is possible to create a “data structure” claim in which the end of the data structure claim is “character”. (Character is person in games or something like that.)

● Annex B Chapter 1 2.2.3.3
 Here, examples are described in which the inventive step is affirmed or denied. Examples 1, 2, 4, 5 were technologies related to artificial intelligence.
 · Example 1
 Claim: A method for predicting the welding characteristics of a steel sheet to be manufactured by rolling and cooling after reheating the cast steel piece, wherein the composition of the steel and the production conditions are input values, and welding characteristics of a steel sheet are predicted by neural network.
 Main cited invention: A method of predicting the welding characteristics of a steel sheet using a mathematical model in which actual values of steel composition and production conditions are input values.
 Secondary cited invention: A method of estimating the material of glass by a neural network model using predetermined input values.
 · Example 2
 Claim: An image obtained by dividing a myocardial wall of a myocardial section into small regions is input, and it is judged whether or not a necrotic myocardial tissue is included by a neural network.
 Main cited invention: A system for dividing a myocardial wall of a myocardial cross section into small regions and judging whether or not necrotic myocardial tissue is included in each small region from the image of the small region by the average density of the image of the small region.
 Secondary citation invention: A neural network learned to determine presence or absence of a predetermined characteristic with respect to the image of the small region divided by larger image.
 · Example 4
 Claim: A method of detecting internal pressure based on a cylinder internal pressure estimation signal outputted from a neural network with a vibration detection signal detected by a vibration sensor of an internal combustion engine as an input value, the method comprising the steps of: changing sampling rates of input values of training and inferring based on the the rotational speed of an internal combustion engine.
 Cited invention: Although it is specified that the sampling rates at the time of training and inferring are matched, it is not specified to change the sampling rates according to the rotational speed of the internal combustion engine.
 · Example 5
 Claim: A method of estimating NOx concentration in soot by using pressure data in heating furnace, data on temperature of soot and data on CO2 concentration and O2 concentration in soot as input data of neural network.
 Cited invention: Although it is specified that the data on the soot temperature and the data on the CO2 concentration and the O2 concentration in the soot are used as the input data, it is not specified that the pressure data of the heating furnace is used as the input data.

 In conclusion, there is no inventive step in Examples 1 and 2, and in Examples 4 and 5 it has inventive step.
 In Examples 1 and 2, it is said that the subjects, functions and actions are common between the main cited invention and the secondary cited invention, and there are no advantageous effects or impeding factors. Under this premise, the main cited invention can be combined with the secandary cited invention, and those skilled in the art will easily invent claimed invention. In Examples 1 and 2, combining the main cited invention and the secandary cited invention will have the same structure as the claims, so it is natural that the inventive step is denied. If the combination of the main cited invention and the secondary cited invention coincides so much with the claims, it is natural that there is no inventive step and there is no problem specific to artificial intelligence. However, in Examples 1 and 2, the characteristic of claim is the input values inputted to a neural network to estimate specific characteristics, and as I mentioned earlier, I believe that such claims are effective in patenting artificial intelligence related technology. Because the characteristic is not the part of the black box, but the characteristic is easy to identify infringement. In Examples 1 and 2, the combination of the main cited invention and the secandary cited invention was the claim itself, but in many cases, even if there was only a small difference between the claimed invention and the combination of the main cited invention and the secandary cited invention, it is thought that there is a difference. If there is a difference, I believe that by creating a claim so that the effect caused by this difference can be said to be unpredictable from the cited documents, we can increase the possibility to have inventive step.
 Example 5 is exactly this example. In the claim, pressure data is entered into the neural network, but in the cited invention it is not specified to use pressure data as input data. Thus, if the content of input data is different from the prior art, there is the possibility of acquiring the inventive step in that respect. Therefore, in the practice of artificial intelligence related technology, it is thought that it is important to clarify whether input data has characteristics or not, and if it has characteristics, we have to claim that.
 Moreover, in the case of Example 5, did the inventor invent only technology related to machine learning? I think that in this case we should consider securing broader rights. For example, in the case of Example 5, we may be possible to complete the invention such as a mathematical model for estimating NOx concentration by inputting pressure in a heating furnace etc. to the mathematical model. In other words, in the invention of machine learning, input-output relations were clarified using machine learning, but after the input-output relationships became clear, this relationship can be applied to the something to achieve original goal without using machine learning. I believe that the right to such inventions should also be kept in mind at all times. Because a patent of machine learning can be too narrow, and I think that it is not easy to identify infringement.

 In example 4, the claim expresses scheme in sampling the input value to the neural network, and the cited invention does not express the same scheme. When input values to the neural network and output values from the neural network are known, it is obviously difficult to acquire the inventive step in the claims focusing only on inputs and outputs. In this case, as described in example 4, it is considered to be one good practice to focus on factors different from the input value itself, such as scheme to prepare input data. In particular, in CNN (Convolutional Neural Network) etc., in many cases, it is considered that the input data format is merely RGB data and image data without characteristic. In such a case, it seems that there are many cases that you have to search for characteristics in input data processing and interpretation of output data. When patenting artificial intelligence related technology, we should keep in mind that.

 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.