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Apps: Wie entsteht ein digitaler Assistent bei Porsche?

Interessanter Artikel über die Entwicklung eines digitalen Assistenten bei Porsche.

The system features various unique aspects which we want to highlight. The first one is the speech interface as there are several reasons for using this approach. Natural speech is already used in the process of calling the Service Desk and we do not want the users to learn a new tool. Instead, we simply integrate the digital assistant in already existing processes, allowing the users to continue using their telephone. Moreover, spoken language in combination with body language is the most natural way for humans to communicate, it is in our nature to convey messages with our voices and bodies. Voice interaction not only allows a pleasant contact to the digital assistant, it is also more correct for analysis of input than for example chats or filling out forms. Having this interface allows for data collection too, since all calls and their transcription could be recorded for enhancing the language understanding model.What is the major key to creating a successful digital assistant?The spoken language has always spurred a lot of interest in art, philosophy and research: from creating poems, to analyzing grammar, we humans deal with the spoken word in different ways. Its fascination lies amongst other things in the fact that there is still no definite way to create a natural conversation. Over our lifetime, we simply learn to converse with others and follow general rules, but to this day there is still no program written on what these rules exactly are or how to learn these rules. This makes the design of a natural conversation between user and digital assistant so interesting.Photo by Pablo Gentile on UnsplashNow the next questions are: how do we create a chatbot to support a natural conversation structure and also solve the user’s issue reliably? In contrast to other digital assistants, we do not want the user to utter commands to the chatbot but rather we want to extract information from a conversation, from the dialog between caller and digital assistant. This is the same way humans interact: we do not utter commands to each other, but rather converse and extract necessary information from a conversation. For realizing this within the digital assistant, we do not have a fixed conversation, instead we guide the user through different conversational paths, depending on the case at hand and the user’s input. To realize this, we implement a mixture of finite state machines with decision trees. Depending on the user’s utterance, we not only recognize the context which it was said in, but we also extract necessary information to later on trigger certain actions. To make the conversation more natural, our digital assistant is aware of its boundaries: if an utterance was not understood, we kindly ask the caller to repeat him- or herself.Yet, there are more reasons leading to the decision to build up the chatbot ourselves: since we have a clearly defined problem domain and a specific goal in mind, we need to create the conversation to support Porsche-specific phrases. Moreover, the context and terms used in this company do differ from the same terms used in other environments. Take the word “bank” as an example: it can be used as the financial institution, but it can also be the building where this financial institution is offering its services. It can be used as a verb or combined with the word “river” it has a completely different meaning. Just from these few examples of a single word, it is clear that the support of a custom company language is essential. This led us to the decision to create our own context identification algorithm, to not only allow us to gain the necessary knowledge in the process, but to also find alternative ways to solve our issues. In addition, we were able to find and fix errors and bugs since we could always identify and spot what is going on instead of relying on a black box solution.Since the digital assistant is supposed to help humans in their everyday tasks, human factors for the utilization of this technology must not be neglected: trust in our prototype must be established for new users to adopt the technology as current experiences with other assistant systems may not have been pleasant for the users so far. Think for example of Clippy or your last experience with an automated chatbot service. This issue was tackled by providing different paths to take in the conversation but also transparency: the user was able to perceive the technology’s boundaries but we also allow the user to reset every action with a keyword.

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