TV move backwards use case

Global use case developed by Aura Platform Team that allows users to move the content backwards on the TV by using Aura

Introduction

The TV move backwards use case is a global experience designed and developed by Aura Global Team that allows Telefónica customers to move the content backwards on the TV using a vocal interface.

Find additional information in following the documents:

Specifications

Request-response model

TV move backwards use case is adapted to the new request-response normalized model v3.

Available channels

Once TV move backwards v3 use case is fully normalized, it will be available for any channel that implements normalized v3 request-response model including TV related data.

Currently, STB channel in ES is available for channelData V1. And channelData V3 is suitable for STB in Brazil with time entity recognition: ent.time_length_hour.

Display features

Currently, the normalized TV move backwards use case includes basic move backwards features for some entities:

  • Move the TV content backwards
  • Move backwards by specifying the desired time

This UC also includes specific verbs without entities. For AP repository the key verbs are: “retroceder”, “retornar” and “rebobinar”. For Brazil, they are “recuar” and “retroceder”.

Therefore, the answer could be of this type: “Okay, I’ll rewind the TV content.”

Use case development

The TV move backwards use case development includes these components:

Understanding features

  • TV move backwards use case intent: intent.tv.move_backwards

  • TV move backwards use case entities:

Entity Example
ent.audiovisual_genre “Move the movie backwards”
ent.time_length_hour “Move the movie backwards 2 hours”
ent.time_length_min “Move the movie backwards 2 minutes”
ent.time_length_sec “Move the movie backwards 2 seconds”
ent.time_instant “Move it backwards now”

⚠️ Other entities in the user’s request are not taken into account in the move backwards process.

In order to understand users’ requests (utterances), Aura is trained with:

  • NLP expression to recognize the user’s utterance and detect the user’s intention.

Use case logic

Once Aura has recognized the user’s utterance based on NLP components, the use case should be resolved based on: