Aura –

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

TV LLM Conversational None UC

Global Use Case developed by Aura Platform Team that informs users of the platform’s available actions, ensuring correct invocation for proper system understanding.

Introduction

The video LLM Conversational None use case is a global experience designed and developed by Aura Global Team that provides Telefónica customers guidance on gaining knowledge of available platform actions and how to perform them correctly.

Find additional information in the following documents:

Discover the Conversational None specifications and the particularities of the use case development.
Learn how to configure the conversational-none-dialog for Conversational None use case.
Access the TV Retrieve plugin for LLM None UC.
Discover the resources used by the Conversational None use case.
Find the AI configuration for each OB for the TV LLM Conversational None UC.

Specifications

Request-response model

The TV LLM Conversational None UC is adapted to both request-response V1 and to its normalized model V3.

Available channels

The TV LLM Conversational None UC can be enabled in any channel in both v1 and v3 and TV-related payloads.

Use case development

The TV LLM Conversational None UC includes the following components:

Understanding features

Intent: intent.conversational.none

Use case logic

Once Aura recognizes the user’s utterance, the UC is resolved via:

Aura Bot dialog

TV LLM Conversational None UC is built over conversational-none-dialog.
See the Dialog configuration for the required settings and bypass configuration.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

TV LLM Conversational Search UC

Global use case developed by Aura Platform Team that allows users searching for recommendations by holding a conversation with Aura

Introduction

The video LLM Conversational Search use case is a global experience designed and developed by Aura Global Team that allows Telefónica customers to hold a conversation with Aura for having TV content recommendations using a voice interface.

Find additional information in the following documents:

Discover the Conversational search specifications and the particularities of the use case development.
Learn how to configure the conversational-search-dialog for Conversational Search use case.
Access the TV Retrieve plugin for LLM Search UC.
Discover the resources used by the Conversational search use case.
Find the AI configuration for each OB for the TV LLM Conversational Search UC.

Specifications

Kernel API

To resolve the user’s request, Aura uses the normalized Video Contents Kernel API.

Request-response model

The Conversational search use case is adapted to both request-response normalized model v3 and v1.

Available channels

The Conversational search use case can be enabled in any channel in both v1 and v3 and TV-related payloads.

Use case development

The Conversational search use case includes the following components:

Understanding features

Intent: intent.conversational.search

Use case logic

Once Aura recognizes the user’s utterance, the UC is resolved via:

Aura Bot dialog

The Conversational search use case is built over conversational-search-dialog.
See the Dialog configuration for the required settings and bypass configuration.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

Global use case developed by Aura Platform Team that allows users to get personalized TV/video recommendations in Aura

Introduction

The video LLM Recommend use case is a global experience designed and developed by Aura Global Team that allows Telefónica customers to ask Aura for content recommendations (live channels and catalog items) using a voice interface.

Find additional information in the following documents:

Discover the TV LLM Recommend specifications and the particularities of the use case development.
Learn how to configure the generic-dialog for TV LLM Recommend use case.
Access the TV LLM Recommend plugin.
Discover the resources used by the TV LLM Recommend use case.
Find the AI configuration for each OB for the TV Recommend use case.

Specifications

Kernel API

To resolve the user’s request, Aura uses the normalized Video Contents Kernel API.

Request-response model

The TV LLM Recommend UC is available only when using the normalized request-response model v3.

Available channels

The TV LLM Recommend UC can be enabled in any channel implementing the normalized v3 request-response model and TV-related payloads.

The normalized TV LLM Recommend UC includes personalized recommendations:

Suggest a single live channel (with a zap action).
Suggest a single catalog item (e.g., VOD).
Suggest a list of items when multiple results are available.
Return no content when the API returns no results.

Current limitations

Currently, the TV LLM Recommend UC does not include:

Fallback/contingency search (as in TV search).
Filters customized (as persons, genres, participants, …).
Third-party catalogs outside Aura/Kernel scope.

Use case development

The TV LLM Recommend UC includes these components:

Understanding features

Intent: intent.tv.recommend

Use case logic

Once Aura recognizes the user’s utterance, the UC is resolved via:

Aura Bot dialog

TV LLM Recommend UC is built over generic-dialog-v3.
See the Dialog configuration for the required settings.

Response

The response follows the standard video plugin response schema.

Where:

intent: input intent.
entities: input entities as received.
result_intent: usually MEDIA.SEARCH; CHANNEL.PLAY is used when returning a single live channel.
resources: List of response resources that includes three main parameters:
- type: Type of the response resource.
- name: Name of the response resource. Example: tv:video.recommend.multiple-results, tv:video.recommend.by-default.simple-result, tv:video.recommend.display.single-result, tv:video.recommend.no-results.
- params: This field returns any required parameter in the resource. Example: channelId or deviceId for tv:video.recommend.display.single-result.
payload: Information provided by the Kernel API, when we make the content get info request. If we receive a response, this field includes the following parameters:
- type: The value of this field depends on the type of data included in the field data (info returned by the API):
  - If it is a value: details
  - If it is a list: content_list
- data: It returns the information provided by the Kernel API.
status: Final status request. This field includes the following parameters:
- code: Status code.
- message: Status message, which describes the status code.
- params: Parameter that sends details of status. This field does not appear if it is empty.
actions: Actions to follow with the result of request. This plugin could include the following actions:
- REMOTEON.ZAPTO: Used to tune a live channel on STB.
conditions: conditions for actions to be applied.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

TV LLM Search UC

Global use case developed by Aura Platform Team that allows users to search specific TV content in Aura

Introduction

The video LLM Search use case is a global experience designed and developed by Aura Global Team that allows Telefónica customers to ask Aura for TV content (live content and catalog items) using a voice interface.

Find additional information in the following documents:

Discover the TV LLM Search specifications and the particularities of the use case development.
Learn how to configure the generic-dialog for TV LLM Search use case.
Access the TV Retrieve plugin for LLM Search UC.
Discover the resources used by the TV LLM Search use case.
Find the AI configuration for each OB for the TV LLM Search use case.

Specifications

Kernel API

To resolve the user’s request, Aura uses the normalized Video Contents Kernel API.

Request-response model

The TV LLM Search UC is adapted to the normalized request-response model v3.

Available channels

The TV LLM Search UC can be enabled in any channel implementing the normalized v3 request-response model and TV-related payloads.

TV LLM Search features

The normalized TV LLM Search UC includes basic retrieval features for entertainment content:

Search by channel (including live channels and third-party streaming platforms).
Search by person (e.g., presenters/actors).
Search by free-form query strings. (e.g., TV content titles, genres, subgenres, …).
Support for multiple values per entity.

When a single live content is unambiguously found, the plugin can trigger a channel tune action (REMOTEON.ZAPTO) to start playback on the STB.

Current limitations

Currently, the TV LLM Search UC does not include:

Contingency/fallback search mode (no secondary search if zero results).
Temporal filters (time intervals).
Third-party catalogs outside Aura/Kernel scope.

Use case development

The TV LLM Search UC includes the following components:

Understanding features

Intent: intent.tv.retrieve
Entities consumed by the plugin:

Entity	Example (utterance)	Notes
`ent.audiovisual_channel`	“Pon #0” / “Abrir Movistar LaLiga”	Multiple channels allowed via `canon` with `
`ent.audiovisual_person`	“Lo de Broncano”	Multiple persons allowed via `canon` with `
`ent.audiovisual_query`	“noticias Oviedo”	Multiple tokens allowed via `canon` with `

⚠️ NLP should provide the canon field concatenated with || (double pipe) when multiple values are recognized (see NLP configuration) ⚠️ Other entities (e.g., time, devices, etc.) are not used by this plugin and should be omitted for this UC.

Use case logic

Once Aura recognizes the user’s utterance, the UC is resolved via:

Aura Bot dialog

TV LLM Search UC is built over generic-dialog-v3.
See the Dialog configuration for the required settings.

Response

The response follows the standard video plugin response schema.

Where:

intent: input intent.
entities: input entities as received.
result_intent: usually MEDIA.SEARCH; CHANNEL.PLAY is used when returning a single live channel.
resources: List of response resources that includes three main parameters:
- type: Type of the response resource.
- name: Name of the response resource. Example: tv:video.retrieve.multiple-results, tv:video.retrieve.by-default.simple-result, tv:video.retrieve.display.single-result, tv:video.retrieve.no-results.
- params: This field returns any required parameter in the resource. Example: queryDisplayed.
payload: Information provided by the Kernel API, when we make the content get info request. If we receive a response, this field includes the following parameters:
- type: The value of this field depends on the type of data included in the field data (info returned by the API):
  - If it is a value: details
  - If it is a list: content_list
- data: It returns the information provided by the Kernel API.
status: Final status request. This field includes the following parameters:
- code: Status code.
- message: Status message, which describes the status code.
- params: Parameter that sends details of status. This field does not appear if it is empty.
actions: Actions to follow with the result of request. This plugin could include the following actions:
- REMOTEON.ZAPTO: Used to tune a live channel on STB.
conditions: conditions for actions to be applied.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

TV LLM Triage UC

Global use case developed by Aura Platform Team that routes unanswered or unclassified requests to different LLMs

Introduction

The video LLM Triage use case is a global experience designed and developed by Aura Global Team that routes unanswered or unclassified requests to different LLMs to improve the chances of returning relevant results to the user.

Find additional information in the following documents:

Find the AI configuration for each OB for the TV LLM Triage use case.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

TV Custom Recommendation UC

Global use case developed by Aura Platform Team that allows users to launch a custom recommendation based on Large Language Models (LLMs), through the use of ATRIA

Introduction

The TV Custom Recommendation use case is a global experience designed and developed by Aura Global Team that allows Telefónica customers to ask Aura for a TV recommendation based on their mood and likes, using a vocal interface.

Find additional information in the following documents:

Discover the TV Custom Recommendation specifications and the particularities of the use case development.
Discover which are the resources used by the TV Custom Recommendation use case.
Find out the NLP configuration for each OB for the TV Custom Recommendation use case.

Specifications

Kernel API

In order to resolve the user’s request, Aura uses Video Contents normalized Kernel API.

Request-response model

TV custom recommendation UC is available for use in both the deprecated request-response model v1 and the current request-response model v3.

Available channels

The TV Custom Recommendation UC is available for STB channel both in Spain and in Brazil.

Custom recommendation features

Currently, the TV custom recommendation use case includes:

A conversation flow to identify the user’s likes and mood
Once all the information is captured and treated by the LLM, a search by topic is launched to the TV APIs

Use case development

The TV custom recommendation use case development includes these components:

Understanding features

TV custom recommendation use case intent: intent.tv.custom-recommendation

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:

Aura Bot dialog:

TV Custom Recommendation is built over the tv-custom-recommendation-v1.

Use case configuration

Check the section Configuration of the TV Custom Recommendation use case in order to know the required configuration for the TV Custom Recommendation experience for each OB.

Aura –

Docs:

TV LLM Conversational None UC

Introduction

Specifications

Request-response model

Available channels

Use case development

Understanding features

Use case logic

Docs:

TV LLM Conversational Search UC

Introduction

Specifications

Kernel API

Request-response model

Available channels

Use case development

Understanding features

Use case logic

Docs:

TV LLM Recommend UC

Introduction

Specifications

Kernel API

Request-response model

Available channels

TV LLM Recommend features

Current limitations

Use case development

Understanding features

Use case logic

Response

Docs:

TV LLM Search UC

Introduction

Specifications

Kernel API

Request-response model

Available channels

TV LLM Search features

Current limitations

Use case development

Understanding features

Use case logic

Response

Docs:

TV LLM Triage UC

Introduction

Docs:

TV Custom Recommendation UC

Introduction

Specifications

Kernel API

Request-response model

Available channels

Custom recommendation features

Use case development

Understanding features

Use case logic

Use case configuration