Techniques to response to respond to user requests using natural-language machine learning based on branching example conversations are described. In one embodiment, an apparatus may comprise a bot application interface component operative to receive an example-interaction repository, the example-interaction repository comprising a plurality of example user-to-bot interactions, including one or more branching example user-to-bot interactions; and an interaction processing component operative to generate a linearized example-interaction repository by replacing the one or more branching example user-to-bot interactions with the plurality of linearized example user-to-bot interactions; submit the example-interaction repository to a natural-language machine learning component; and receive a sequence model from the natural-language machine learning component in response to submitting the example-interaction repository; and a client communication component operative to perform a user-to-bot conversation based on the sequence model. Other embodiments are described and claimed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: receiving an example-interaction repository, the example-interaction repository comprising a plurality of example user-to-bot interactions, the plurality of example user-to-bot interactions comprising one or more branching example user-to-bot interactions; generating a plurality of linearized example user-to-bot interactions, wherein each of the one or more branching example user-to-bot interactions is traversed multiple times to produce multiple linearized example user-to-bot interactions of the plurality of linearized example user-to-bot interactions; generating a linearized example-interaction repository by replacing the one or more branching example user-to-bot interactions with the plurality of linearized example user-to-bot interactions; submitting the linearized example-interaction repository to a natural-language machine learning component; receiving a sequence model from the natural-language machine learning component in response to submitting the linearized example-interaction repository; and performing a user-to-bot conversation based on the sequence model. 2. The method of claim 1, further comprising: traversing a branching example user-to-bot interaction by randomly selecting an outcome of each of a plurality of branches for the branching example user-to-bot interaction to generate a linearized example user-to-bot interaction based on the branching example user-to-bot interaction; detecting that the linearized example user-to-bot interaction has exceeded a predefined stopping length; stopping the traversing of the branching example user-to-bot interaction; and adding the linearized example user-to-bot interaction to the plurality of linearized example user-to-bot interactions. 3. The method of claim 1, wherein a branching example user-to-bot interaction comprises a branching jump instruction from a later part of the branching example user-to-bot interaction to an earlier part of the branching example user-to-bot interaction, the branching jump instruction empowering conditional repetition of a portion of the branching example user-to-bot interaction. 4. The method of claim 1, wherein a branching example user-to-bot interaction comprises a multiway branching instruction between two or more paths forward through the branching example user-to-bot interaction, the multiway branching instruction empowering selection of a which portion of the branching example user-to-bot interaction is performed. 5. The method of claim 1, wherein one or more of the plurality of example user-to-bot interactions includes one or more quick-reply prompts to a bot message, wherein each of the one or more quick-reply prompts is a suggested user response to the bot message. 6. The method of claim 5, wherein one or more of the one or more quick-reply prompts is generated from production-performed user-to-bot conversations. 7. The method of claim 5, wherein one or more of the one or more quick-reply prompts is associated with one or more branches for a branching example user-to-bot interaction. 8. An apparatus, comprising: a processor circuit; an interaction processing component operative on the processor circuit to generate a plurality of linearized example user-to-bot interactions, wherein each of one or more branching example user-to-bot interactions is traversed multiple times to produce multiple linearized example user-to-bot interactions of the plurality of linearized example user-to-bot interactions; generate a linearized example-interaction repository by replacing the one or more branching example user-to-bot interactions with the plurality of linearized example user-to-bot interactions; and submit the linearized example-interaction repository to a natural-language machine learning component, and receive a sequence model from the natural-language machine learning component in response to submitting the linearized example-interaction repository; a client communication component operative to perform a user message exchange with a user client to perform a user-to-bot conversation based on the sequence model; and a bot application interface component operative to receive the example-interaction repository from a bot application, the example-interaction repository comprising a plurality of example user-to-bot interactions, the plurality of example user-to-bot interactions comprising the one or more branching example user-to-bot interactions, and perform a bot information exchange with the bot application to perform the user-to-bot conversation based on the sequence model, wherein performing the user-to-bot conversation based on the sequence model comprises applying the sequence model to a user-request context, a user-to-bot conversation history, semantic analysis information for one or more user messages, and extracted unsupervised information for the one or more user messages. 9. The apparatus of claim 8, further comprising: the interaction processing component operative to traverse a branching example user-to-bot interaction by randomly selecting an outcome of each of a plurality of branches for the branching example user-to-bot interaction to generate a linearized example user-to-bot interaction based on the branching example user-to-bot interaction; detect that the linearized example user-to-bot interaction has exceeded a predefined stopping length; stop the traversing of the branching example user-to-bot interaction; and add the linearized example user-to-bot interaction to the plurality of linearized example user-to-bot interactions. 10. The apparatus of claim 8, wherein a branching example user-to-bot interaction comprises a branching jump instruction from a later part of the branching example user-to-bot interaction to an earlier part of the branching example user-to-bot interaction, the branching jump instruction empowering conditional repetition of a portion of the branching example user-to-bot interaction. 11. The apparatus of claim 8, wherein a branching example user-to-bot interaction comprises a multiway branching instruction between two or more paths forward through the branching example user-to-bot interaction, the multiway branching instruction empowering selection of a which portion of the branching example user-to-bot interaction is performed. 12. The apparatus of claim 8, wherein one or more of the plurality of example user-to-bot interactions includes one or more quick-reply prompts to a bot message, wherein each of the one or more quick-reply prompts is a suggested user response to the bot message. 13. The apparatus of claim 12, wherein one or more of the one or more quick-reply prompts is generated from production-performed user-to-bot conversations. 14. The apparatus of claim 12, wherein one or more of the one or more quick-reply prompts is associated with one or more branches for a branching example user-to-bot interaction. 15. At least one computer-readable storage medium comprising instructions that, when executed, cause a system to: receive an example-interaction repository, the example-interaction repository comprising a plurality of example user-to-bot interactions, the plurality of example user-to-bot interactions comprising one or more branching example user-to-bot interactions; generate a plurality of linearized example user-to-bot interactions, wherein each of the one or more branching example user-to-bot interactions is traversed multiple times to produce multiple linearized example user-to-bot interactions of the plurality of linearized example user-to-bot interactions; generate a linearized example-interaction repository by replacing the one or more branching example user-to-bot interactions with the plurality of linearized example user-to-bot interactions; submit the linearized example-interaction repository to a natural-language machine learning component; receive a sequence model from the natural-language machine learning component in response to submitting the linearized example-interaction repository; and perform a user-to-bot conversation based on the sequence model. 16. The computer-readable storage medium of claim 15, wherein a branching example user-to-bot interaction comprises a branching jump instruction from a later part of the branching example user-to-bot interaction to an earlier part of the branching example user-to-bot interaction, the branching jump instruction empowering conditional repetition of a portion of the branching example user-to-bot interaction. 17. The computer-readable storage medium of claim 15, wherein a branching example user-to-bot interaction comprises a multiway branching instruction between two or more paths forward through the branching example user-to-bot interaction, the multiway branching instruction empowering selection of a which portion of the branching example user-to-bot interaction is performed. 18. The computer-readable storage medium of claim 15, wherein one or more of the plurality of example user-to-bot interactions includes one or more quick-reply prompts to a bot message, wherein each of the one or more quick-reply prompts is a suggested user response to the bot message. 19. The computer-readable storage medium of claim 18, wherein one or more of the one or more quick-reply prompts is generated from production-performed user-to-bot conversations. 20. The computer-readable storage medium of claim 18, wherein one or more of the one or more quick-reply prompts is associated with one or more branches for a branching example user-to-bot interaction.