Key points
- The application relates to a system for recommending content in a video-on-demand platform. The system is trained and retrained.
- The application states that "providing data from a huge number of users to retrain a recommender system presents challenges in that it takes up system resources." Moreover, "training activities have a high computational cost for the recommender system. Thus, the need to retrain must be balanced against the quality of recommendations being provided."
- The claim specifies, essentially, measuring the performance, comparing it to a desired performance level, and using the comparison result to set the amount of training data provided to the recommender system. Moreover, the recommender system is trained for each user separately.
- "[t]he recommender input controller 16 seeks to adapt the type and amount of usage data provided as training data to the recommender system 18 for each individual client device 22 to provide the minimum amount of data to drive the recommender system 18 towards the predetermined level of recommendation performance yref for each client device 22"
- " there is a positive correlation between the amount of training data specified by the control parameter and the measured performance metric received in the subsequent iteration"
- "In the subsequent iteration, training data are derived from "usage data" from the client device associated with a user to which the recommendations are provided. The amount of training data derived from the usage data is based on the generated value or values of the control parameter"
- " The technical effect of the distinguishing features listed under point 8.1 is that the use of network bandwidth required to provide the training data to the recommender system is minimised, as is the amount of storage necessary for storing said training data in the communications system including the client device and the recommender system"
- "The board has come to the conclusion that this technical effect is achieved, on average, over substantially the whole scope of the claim (see, for example, G 0001/19, point 82)."
- " Since achieving a maximum performance metric of the recommender system is of paramount importance in the method of document D1, the skilled person would not use a "reference performance metric" which might be different from [i.e. lower than] a "maximum achievable performance metric", and would have no motivation to consider using a closed-loop control algorithm as claimed."
- Starting from the disclosure of document D1, if the reference performance metric is exceeded, the skilled person would stop changing the amount of training data but would not decrease it, so that the measured performance metric oscillates towards the reference performance metric" [i.e. the possibility of decreasing the amount of training data results in oscillation of the performance].
- The board therefore considers the subject-matter of claim 10 and that of the corresponding claims 1 and 14 of the main request to be inventive (Article 56 EPC). It follows that the decision of the examining division is to be set aside.
EPO
The link to the decision is provided after the jump, as well as (an extract of) the decision text.
source http://justpatentlaw.blogspot.com/2024/01/t-018321-inventive-training-method-for.html
