Scoring concept terms for online advertising using deep learning: non-technical

The application underlying the present decision relates to generating scores for concept terms using a deep neural network. The scores may then be used to select the optimal keywords for online advertising auctions . However, the European Patent Office refused to grant a patent since the the distinguishing features would only refer to the mere automation by means of one or more computers. Here are the practical takeaways of the decision T 0872/19 (Concept terms scoring/GOOGLE) dated October 14, 2021 of Technical Board of Appeal 3.5.07:

Key takeaways

The invention

The Board in charge summarized the invention underlying the present decision as follows:

1. The application relates to online advertisement auctions. When an online advertisement auction is to be conducted to select one or more advertisements to be included in a particular presentation of a resource (for example, a web page), resource features are extracted from the particular resource.
2. A concept term scoring system 100 uses the received resource features to predict a vector of scores that includes a score for each of the set of concept terms. A score for the concept term represents the predicted “relevance” of the concept described by the term to the resource. The concept term scoring system 100 can generate a score for each of a set of concept terms that may be used as advertising keywords for selecting advertisements for participation in the auction.
3. The system can select a specified number of one or more highest-scoring concept terms or each concept term having a score that
   satisfies a threshold value as advertising keywords to be used in selecting candidate advertisements for participation in an online advertising auction.

 

Fig. 1 of WO 2014/160344 A1

Here is how the invention is defined in claim 1 of the main request:

Is it technical?

According to the Board, in accordance with the Appellant’s view, D1 forms the closest prior art for the subject-matter of claim 1 of. The Board identified the following distinguishing features over D1:

In the Board’s view, D1 does not disclose a classifier configured to generate a respective relevance score for each concept term in a pre-determined set of concept terms, wherein each of the respective relevance scores measures a predicted relevance of the corresponding concept term to the resource.

Based on the above-identified distinguishing features, the Board in charge discussed the existence of a technical effect concerning these features as follows:

The Board pointed to the fact that concept terms might be “advertising keywords” which are “relevant” to a resource (e.g., a web page determined by its URL). As a result, the keywords most relevant for the resource in terms of advertising can be selected.

Accordingly, the Board stated that increasing the relevance of a resource for advertising is a non-technical effect. Instead, it relates to a method of doing business, wherein the relevance to the resource of the advertisement could be measured in clicks by users on the advertisement embedded in the resource. Thus, the “relevance” of the advertisement to the resource appears to be linked to the semantic content or visual attractiveness for the user accessing the resource (e.g., web page), which does not consitute a technical feature.

As a result, the Board stated that claim 1 of the main request does not provide any technical effect which goes beyond the mere automation of a business method.

Therefore, the Board dismissed the appeal due to lack of inventive step.

More information

You can read the whole decision here: T 0872/19 (Concept terms scoring/GOOGLE)

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