The first entry is here.
 The second entry is here.
 The third entry is here.

 In the development related to the neural network, it is likely to have many inventions at two stages below.
  1. Selection of input data and output data.
  2. Structure determination of neural network.
As for 1. mentioned above the decision of input (or output) to achieve the purpose, is an important invention itself. In this development, we confirmed that authors can be estimated with high accuracy using “habit of document expression” and “habit of flow of logic “. And we can say that this way of thinking is the invention. When we use a deep neural network, useful results can be obtained by inputting a large number of data and advancing learning by the deep neural network without considering what input data is important for achieving the purpose.  But even so, I think that it is important to identify which of the input data among many input data contributes greatly to the achievement of the purpose. Because the claim should write essential elements only.
 Various strategies are conceivable as a patenting strategy when the essential input data A can be identified. I would like to pick up some of them here and write.

 First of all, I would like to mention some of the strategies for expressing devices that estimate with learned data as the following claims.
“An apparatus C comprising:
an acquiring unit for acquiring input data A,
a converting unit for converting the input data A into output data B based on machine-learned information, and
an estimating unit for estimating “something” based on output data B.”
· Input data A (possibly also output data B) is a feature of the invention, and the process of machine learning is not characterized. As experienced in the 1st to 3rd entries, determining the input data is an invention and it can be said to be an important factor to achieve the objective.
· In addition, it is considered that there are many cases that a neural network can advance machine learning with well-known technologies (examples shown by the 1st to 3rd entries seem to correspond to this case).  For devices that do not learn after learning with learned data, it is important to make a claim that does not include a learning unit that performs machine learning as an element. If the learning unit is included in the element of claims, injunction by direct infringement against an apparatus that is learned before shipment and not learned after shipment will be impossible.
· However, it will be necessary to take countermeasures in the claims or specifications so that they are not obscured by the phrase “based on machine learned information”. In the case where the technique for advancing machine learning is generic, we should explain the technique is generic and we should give some examples. And if it is not the feature of the invention, its reason should be described in the specification and satisfy the enabling requirement.
· Since parameters after machine learning, such as weighting factors, bias, etc., are created by machine learning, we think that it is preferable to write a specification so that it meets the criteria for product by process examination.
· Since the feature of the invention lies in the input data A, we think that it is preferable to develop the features of input data from a superordinate concept to a subordinate concept. In the case of the 1st to 3rd entries, there are many perspectives such as statistics of punctuation marks, statistics of conjunctions, statistics of end-of-sentence expression.

 Next, I would like to mention some strategies when we make following claims of an apparatus that can learn.
“An apparatus D comprising:
a learning unit for learning a parameter for converting the sample input data a into the correct data b based on a set of the sample input data a and the correct data b,
an acquiring unit for acquiring the input data A, and
a conversion unit for converting input data A to output data B.”
· This claim will be effective if products that users can conduct learning and conversion (conversion of input data A to output data B) are sold.
· It is worth considering to make a claim that a learning apparatus comprising a learning unit (a device not equipped with acquiring unit and conversion unit).
 · Only essential pairs of sample input data a and correct answer data b should be written in independent claims, and other pairs examined in the development process (for example, groups that contributed to improve the accuracy rate but are not the most important) should be written in the dependent claims.
 · If the user lets the device learn, the method claim is effective.

 Finally, as for the structure of the neural network, I think that the structure of the neural network is patentable if the structure of the neural network has novelty and has the inventive step. However, in this case it is thought that it is necessary to carefully prepare claims. Even the claims featured by the structure of the neural network are patented, it is often difficult to prove that the suspected product of a third party is using the claims. Since it is expected that the structure of the neural network is often a black box when viewed from the outside, there are many cases where it is not effective even if the structure of the neural network itself is patented. If it is virtually impossible to prove that a suspected product of a third party is using that claim, it is meaningless to file for infringing goods countermeasures. If there is no other circumstance (for example, sooner or later it will be announced at the academic conference), I think that we should think carefully whether we should apply for the application or not.
· If it is necessary to patent the structure of the neural network, we think that it is necessary to find features that can be observed when analyzing a device that uses the structure of the neural network. In the meeting before the patent application, it is necessary to find the observable features. If we cannot find the observable features, I think we have to ask the inventors to analyze for finding features.

 Although it is not a structure of a neural network, it is considered effective to apply parameters optimized by machine learning. For example, it is conceivable to specify the optimal numerical ranges such as the composition of the functional material and the temperature, pressure, etc. when preparing the material by machine learning, and the numerical range of that is claimed. It is normal practice to describe actual measurement results within the numerical range and actual measurement results at the boundary of the numerical ranges in the specification. But by describing the process of deriving the numerical range by machine learning in the specification, is it possible to satisfy the description requirements (enforceability requirement etc.) of the specification? If the numerical range is obtained by machine learning and the effect in that range is logical, the specification is described so that the invention in a specific numerical range can be implemented by showing the process of machine learning, I feel that the cases where the description requirements are satisfied may come out. It is unknown at this moment how examiners and judges think, but it seems like an interesting topic.