DE102024003438A1 - Interactive communication interface and interactive communication method in a vehicle - Google Patents

Abstract

The invention relates to an interactive communication interface (1) and an interactive communication method for a mutual exchange of signals and data, comprising an input unit (1.1) and an output unit (1.2) in a vehicle (2), wherein predetermined questions (F1 to Fn) can be or are output to a user in the vehicle (2) by means of the output unit (1.2) and answers (A1 to An) of the user recorded by means of the input unit (1.1) can be or are fed to a computing unit (1.3), and wherein the computing unit (1.3) is set up to analyze and categorize the supplied answers (A1 to An) in relation to the predetermined questions (F1 to Fn) and optionally store them and/or feed them to an external unit (1.5).

Description

The invention relates to an interactive communication interface and an interactive communication method in a vehicle.

Modern car manufacturers are faced with the fact that their customers do not make high use of the wide variety of digital services and vehicle features being developed, which has a huge impact on the development and maintenance costs associated with them.

The main online customer feedback currently considered is based on usage statistics for each of these services, which in several cases prove to be low. However, little or nothing is done to explore the reasons why customers do this, as customers are usually not asked for their opinions or are asked insufficiently.

Car manufacturers are known to use metrics that show the usage rate of their customer fleet for the various digital extras and vehicle features. In other words, it digitally records whether a user/vehicle uses a certain service or not, but not why that service is used or not used.

In addition, a separate agreement specifically for an in-vehicle voice assistant (for example, “Hey Mercedes”) currently allows a customer to give their consent for the automaker to use their data to improve the quality of their vehicle functionality.

From the US 20200335183A1 A method for intelligent generation of custom questions by a processor is known. The method comprises the following steps: generating one or more customized questions or questionnaires according to a user profile, similar profiles of alternative users, one or more historical interactions with the alternative users, one or more goals defined by a subject matter expert, expertise, historical questions, or a combination thereof using a machine learning process.

The invention is based on the object of providing an interactive communication interface and an interactive communication method in a vehicle, which supports and improves the use of the vehicle.

The first-mentioned object is achieved according to the invention by an interactive communication interface in a vehicle having the features of claim 1. The second-mentioned object is achieved according to the invention by an interactive communication method in a vehicle having the features of claim 6.

Advantageous embodiments of the invention are the subject of the subclaims.

The interactive communication interface according to the invention for a mutual exchange of signals and data comprises an input unit, in particular a microphone, a touch screen, a mobile terminal or the like, and an output unit, in particular a loudspeaker, a screen, a mobile terminal or the like, in a vehicle, wherein predetermined questions can be or are output to a user in the vehicle by means of the output unit and answers from the user recorded by means of the input unit can be or are fed to a computing unit, and wherein the computing unit is set up to analyze and categorize the answers supplied in relation to the predetermined questions and optionally to store them and/or to feed them to an external unit.

The interactive communication method according to the invention for a mutual exchange of signals and data by means of an input unit and an output unit in a vehicle is characterized in that predetermined questions are output to a user in the vehicle by means of the output unit and answers from the user recorded by means of the input unit are fed to a computing unit, wherein the answers fed are analyzed, categorized and optionally stored in relation to the predetermined questions and/or fed to an external unit.

In other words: The invention enables a new interactive communication interface with a broadcast transmission of questions to occupants/users in the vehicle and thus to record answers from the occupants/users directly on site in the vehicle and to carry out an analysis so that the users/occupants can give their feedback and opinions seamlessly and in a friendly manner from the vehicle, whereby the information contained in the answers can be collected and processed, for example by means of an algorithm based on artificial intelligence. The information and thus the answers can be categorized using a Large Language Model (LLM), in particular for a better understanding and a solution in given categories. categories. Questions or questionnaires tailored to the vehicle, vehicle functions, vehicle services, vehicle applications, external services, external functions, the user or the like can be automatically generated and issued. Alternatively or additionally, voice/audio feedback can be implemented.

The advantages achieved by the invention for a user of the vehicle and/or a vehicle owner are in particular that they can communicate their problems and/or their positive experiences with the vehicle in question to the vehicle manufacturer in a simple and direct manner, in the hope that the feedback sent to the vehicle manufacturer in the form of replies will be heard and that the vehicle manufacturer will take appropriate action if necessary. In addition, trust between the customer and the vehicle manufacturer is improved.

The advantages achieved with the invention are, in particular, for the vehicle manufacturer that a direct and easy-to-collect communication channel with customers is enabled. This helps to stop the development/operating costs of products that are not in demand, to focus on those products, functions, services or the like that are in demand, and to help establish personalized countermeasures, in particular to provide better documentation support for those who do not know how to use certain vehicle services, vehicle functions or the like, to optimize pricing policy, to reduce or improve quality problems in the technology or the like.

Embodiments of the invention are explained in more detail below with reference to drawings.

• 1 schematisch eine interaktive Kommunikationsschnittstelle in einem Fahrzeug, und
• 2 schematisch ein Ablaufdiagramm für ein interaktives Kommunikationsverfahren im Fahrzeug.

The following show:

• 1 schematically an interactive communication interface in a vehicle, and
• 2 schematically shows a flow chart for an interactive communication process in the vehicle.

1 shows a schematic exploded view of an interactive communication interface 1 in a vehicle 2.

The communication interface 1 is set up for a mutual exchange of signals and data in the vehicle 2. The communication interface 1 comprises an input unit 1.1 in the vehicle 2, for example a microphone, a touch screen, a joystick or the like, and an output unit 1.2, in particular a screen. The input unit 1.1 is integrated at a suitable location in the vehicle 1, for example in the vehicle roof, in a dashboard, in a center console or the like. The output unit 1.2 is integrated at a suitable location in the vehicle 1, for example in the vehicle roof, in a dashboard, in a center console or the like.

By means of the output unit 1.2, predetermined questions F1 to Fn, in particular one of several question catalogs FK1 to FKm, can be issued to a user or passenger in the vehicle 2 according to arrow 100. The questions F1 to Fn can be generated automatically according to arrow 102 by means of a computing unit 1.3, in particular a computing unit 1.3 based on artificial intelligence AI (hereinafter also referred to as AI-based computing unit 1.3). and

The user's answers A1 to An recorded by means of the input unit 1.1 can be fed to the AI-based computing unit 1.3 according to arrow 104. The computing unit 1.3 is set up to analyze and categorize the supplied answers in relation to the given questions F1 to Fn and optionally to store them in a storage unit 1.4 and/or to feed them to an external unit 1.5. The computing unit 1.3 can, for example, be a cloud and AI-based computing unit 1.3 with a storage unit 1.4 designed as a cloud storage, in which both the generated questions F1 to Fn and the recorded answers A1 to An can be stored.

The questions F1 to Fn generated by means of the computing unit 1.3 and/or read from the memory 1.4 and/or the recorded answers A1 to An can be sent to the external unit 1.5, in particular a central server, according to arrows 106 for further evaluation and analysis, if necessary an analysis based on artificial intelligence Al, in particular via a wireless interface, for example a radio interface, an Internet connection, a Bluetooth connection or the like.

The computing unit 1.3 can also be set up to generate the questions F1 to Fn in a predetermined or desired language and to output them via the output unit 1.2. In addition, the computing unit 1.3 can be set up to convert the recorded answers A1 to An into a predetermined language or a desired language and, if necessary, to store them in the storage unit 1.4.

The storage unit 1.4 can, for example, store the predetermined, in particular generated, question catalogs FK1 to FKm with a plurality of questions F1 to Fn relating to a predetermined vehicle topic, a vehicle component, a vehicle system, a vehicle service, a vehicle function, a vehicle application, external services, external functions or the like and a use or non-use of these.

Furthermore, the computing unit 1.3 can be configured to automatically generate a number of question catalogs FK1 to FKm with a plurality of questions F1 to Fn relating to a given vehicle topic, a vehicle component, a vehicle system, a vehicle service, a vehicle function, a vehicle application and a use or non-use by means of a question generator 1.3.1 based on artificial intelligence AI and/or to adapt and/or update them depending on the recorded answers A1 to An.

For example, the computing unit 1.3 can be coupled to a sensor unit 1.7 which detects the use or non-use of a vehicle component, a vehicle system, a vehicle application or the like, wherein, depending on the detected use or non-use, a corresponding questionnaire FK1 to FKm can be automatically generated by means of the question generator 1.3.1 implemented in the computing unit 1.3 or can be read from the storage unit 1.4 and can be output in an acoustically and/or optically readable manner by means of the output unit 1.2.

In addition, the computing unit 1.3 can comprise at least one analysis algorithm 1.3.2, which is set up to analyze and categorize the user's answers A1 to An recorded in response to the issued questionnaire FK1 to FKm by means of the input unit 1.1.

The computing unit 1.3, the input unit 1.1 and the output unit 1.2 can also be set up and interact with each other in such a way that different languages can be set and the respective question F1 to Fn is simultaneously generated and output in the desired language and the answer A1 to An is simultaneously stored in the desired language and, if necessary, transmitted to the external unit 1.5 and/or to a mobile unit 1.6, in particular a mobile radio device.

The Al-based computing unit 1.3 and/or the Al-based external unit 1.5 can also be continuously trained using currently generated questions F1 to Fn and/or currently recorded answers A1 to An. Different questions F1 to Fn can be specified for different customer types, for example for customers who use a certain service and for customers who do not. The processing of the recorded answers A1 to An can also be automated. For example, the answers A1 to An can be searched for keywords using software implemented in the computing unit 1.3 and the various answers A1 to An can be divided into categories without human interaction. In particular, the implemented algorithm can be designed and trained as a so-called Large Language Model (LLM).

2 shows a schematic flow chart for an interactive communication process in the vehicle 2.

• In einem ersten Schritt 201 teil das Fahrzeug 2 der cloud-basierten Recheneinheit 1.3 verschiedene Fahrzeugsignale, wie zum Beispiel Nutzungsmetriken eines bestimmten digitalen Dienstes, eines Fahrzeugdienstes oder einer Fahrzeugfunktion, aktuelle Spracheinstellungen des Fahrzeugs 2, Fahrzeugdiagnosesignale, Fahrzeugstandort oder dergleichen, mit.

The interactive communication process includes, for example, the following steps:

• In a first step 201, the vehicle 2 communicates various vehicle signals to the cloud-based computing unit 1.3, such as usage metrics of a specific digital service, a vehicle service or a vehicle function, current language settings of the vehicle 2, vehicle diagnostic signals, vehicle location or the like.

In a second step 202, the cloud and AI-based computing unit 1.3 processes these vehicle signals and/or usage data, in particular the usage metrics of each service, each vehicle service or each vehicle function, current language settings of the vehicle 2, vehicle diagnostic signals, vehicle location or the like, for example by means of an implemented first AI agent, and classifies the various users into categories that can be stored in a correspondingly assigned manner, for example in table form. The question generator 1.3.1 implemented in the computing unit 1.3 then generates a list of questions F1 to Fn for the next step, for example as a question catalog FK1 to FKm, optionally depending on the user profile (also called customer profile).

In a third step 203, in which the classification of the user(s) into categories is completed, one or more interactive communications can be activated/triggered as service user (customer) feedback using the cloud-based computing unit 1.3 based on the generated questionnaires FK1 to FKm and a notification can be sent to the vehicle 2 in which the user is asked to give his opinion as answers A1 to An on the questions F1 to Fm issued in the vehicle 2, for example as a voice message or audio message. This is received by the input unit 1.1 in the vehicle 2 and fed to the computing unit 1.3.

In a fourth step 204, as soon as the answers A1 to An of the interactive communication have been received in the computing unit 1.3 and/or the external unit 1.5, in particular a fleet control center, at least some of these answers A1 to An can be output via the output unit 1.2 and/or to the external unit 1.5 and/or to the mobile unit 1.6 and/or stored in the storage unit 1.4, in particular in tabular form and in relation to the questions F1 to Fn asked.

In a fifth step 205, the computing unit 1.3 can finally analyze the recorded answers A1 to An of the user using large language metrics by means of an implemented second Al agent, save them and categorize them, in particular classify them into categories, which can then be output, in particular visualized, via the output unit 1.2, for example a customer feedback dashboard for each triggered/activated questionnaire Ak1 to Akm, in particular each triggered campaign, each triggered service.

• Die Erfindung schlägt grundsätzlich die Verwendung von künstlicher Intelligenz AI für folgende Verfahrensschritte vor:
  1. a) für die Erstellung von personalisierten Fragekatalogen AK1 bis AKm, zum Beispiel in Form von Fragebögen, für verschiedene Themen für Besitzer, die ein bestimmtes Merkmal, wie eine bestimmte Fahrzeugfunktion, einen Fahrzeugdienst, ein Fahrzeugsystem oder dergleichen, nutzen oder nicht nutzen,
  2. b) für die Nachbearbeitung der erfasstem Antworten A1 bis An durch die Suche nach Schlüsselwörtern und die Kategorisierung der Hauptgründe für die Rückmeldungen eines bestimmten Kunden/Nutzers.

Various implementation examples are described below:

• The invention basically proposes the use of artificial intelligence AI for the following process steps:
  1. (a) for the creation of personalised questionnaires AK1 to AKm, for example in the form of questionnaires, on various topics for owners who use or do not use a particular feature, such as a particular vehicle function, vehicle service, vehicle system or the like,
  2. b) for post-processing the collected responses A1 to An by searching for keywords and categorizing the main reasons for the feedback of a particular customer/user.

• Der Fragekatalog FKm hat beispielsweise zum Thema „Autonomes Fahren“ und bezieht sich auf ein negatives Feedback von Kunden, die diese Funktion nicht nutzen.
• Frage F1: "Nennen Sie mir die Hauptgründe, warum ein Kunde nicht bereit sein könnte, sein Auto im autonomen Modus zu fahren

An example of step a) is given below as a first question-answer example. As an example, questions F1 to Fm were generated using the question generator 1.3.1:

• For example, the FKm questionnaire is on the topic of “autonomous driving” and refers to negative feedback from customers who do not use this function.
• Question F1: "Tell me the main reasons why a customer might not be willing to drive his car in autonomous mode

1. 1. **Sicherheitsbedenken**: Angst vor Unfällen aufgrund möglicher Fehlfunktionen oder der Unfähigkeit des Systems, komplexe Fahrszenarien zu bewältigen.
2. 2. **Vertrauensmangel**: Misstrauen gegenüber der Technologie und ihrer Fähigkeit, sichere Fahrentscheidungen zu treffen.
3. 3. **Rechts- und Haftungsfragen**: Ungewissheit darüber, wer im Falle eines Unfalls verantwortlich ist.
4. 4. **Technische Beschränkungen**: Bedenken hinsichtlich der Grenzen der Technologie bei ungünstigen Wetterbedingungen, in komplexen städtischen Umgebungen oder in ländlichen Gebieten mit schlechter Straßeninfrastruktur.
5. 5. **Kosten**: Hohe Kosten der autonomen Fahrzeugtechnologie und potenzielle Ausgaben für ihre Wartung und Reparatur.
6. 6. **Privatsphärenproblematik**: Bedenken hinsichtlich des Datenschutzes und der Verwendung der von autonomen Fahrzeugen gesammelten Daten.
7. 7. **Verlust der Kontrolle**: Unbehagen, die Kontrolle über das Fahrzeug abzugeben und sich ganz auf das autonome System zu verlassen.
8. 8. **Unvertrautheit**: Mangelnde Vertrautheit mit der Technologie und ihren Merkmalen, was zu einer Abneigung gegen ihre Nutzung führt.
9. 9. **Regulierungs- und Infrastrukturherausforderungen**: Ungewissheit über die Vorschriften für autonome Fahrzeuge und die Bereitschaft der Straßeninfrastruktur, diese zu unterstützen.
10. 10. **Ethische Bedenken**: Ethische Dilemmata im Zusammenhang mit der Entscheidungsfindung in unvermeidlichen Unfallszenarien.

Here are some examples of possible user answers A1 to question F1 as to why a customer may not be willing to drive their car in autonomous mode:

1. 1. **Safety concerns**: Fear of accidents due to possible malfunctions or the inability of the system to handle complex driving scenarios.
2. 2. **Lack of trust**: Distrust of technology and its ability to make safe driving decisions.
3. 3. **Legal and liability issues**: Uncertainty about who is responsible in the event of an accident.
4. 4. **Technical limitations**: Concerns about the limitations of the technology in adverse weather conditions, in complex urban environments or in rural areas with poor road infrastructure.
5. 5. **Cost**: High cost of autonomous vehicle technology and potential expenses for its maintenance and repair.
6. 6. **Privacy Issues**: Concerns about data protection and the use of data collected by autonomous vehicles.
7. 7. **Loss of control**: Discomfort at giving up control of the vehicle and relying entirely on the autonomous system.
8. 8. **Unfamiliarity**: Lack of familiarity with the technology and its features, leading to an aversion to using it.
9. 9. **Regulatory and infrastructure challenges**: Uncertainty about autonomous vehicle regulations and the readiness of road infrastructure to support them.
10. 10. **Ethical concerns**: Ethical dilemmas related to decision making in unavoidable accident scenarios.

These reasons highlight the various psychological, technical and societal barriers to widespread acceptance of autonomous driving technology.

Another questionnaire AKm also has the topic “Autonomous Driving” and refers to positive feedback from customers/users who use this function.

Question F2: Tell me the main reasons why a customer should be willing to drive his car in autonomous mode

1. 1. **Sicherheitsverbesserungen**: Autonome Fahrzeuge haben das Potenzial, menschliche Fehler zu reduzieren, die eine der Hauptunfallursachen sind.
2. 2. **Komfort und Bequemlichkeit**: Weniger Stress und Ermüdung beim Fahren, so dass sich die Fahrgäste entspannen oder auf andere Aktivitäten konzentrieren können.
3. 3. **Erhöhte Zugänglichkeit**: Größere Mobilität für Personen, die nicht selbst fahren können, wie ältere oder behinderte Menschen.
4. 4. **Effizienz**: Potenzial für einen effizienteren Verkehrsfluss und weniger Staus, was zu kürzeren Reisezeiten führt.
5. 5. **Kraftstoffeinsparung**: Optimierte Fahrmuster, die die Kraftstoffeffizienz verbessern und die Emissionen reduzieren können.
6. 6. **Produktivität**: Die Möglichkeit, während des Pendelns zu arbeiten, zu lesen oder anderen produktiven Tätigkeiten nachzugehen.
7. 7. **Verbesserte Sicherheitsmerkmale**: Fortschrittliche Sensoren und Systeme, die ein besseres Situationsbewusstsein und schnellere Reaktionszeiten als menschliche Fahrer ermöglichen.
8. 8. **Umweltnutzen**: Mögliche Verringerung der Treibhausgasemissionen durch effizienteres Fahren.
9. 9. **Versicherungsvorteile**: Möglicherweise niedrigere Versicherungsprämien aufgrund des geringeren Unfallrisikos.
10. 10. **Technologischer Fortschritt**: Mit den neuesten technologischen Innovationen Schritt halten und deren Vorteile genießen.

Here are some examples of possible user answers A2 to question F2 as to why a customer might be willing to drive their car in autonomous mode:

1. 1. **Safety improvements**: Autonomous vehicles have the potential to reduce human error, which is a leading cause of accidents.
2. 2. **Comfort and Convenience**: Reduces stress and fatigue while driving, allowing passengers to relax or concentrate on other activities.
3. 3. **Increased accessibility**: Greater mobility for people who cannot drive themselves, such as the elderly or disabled.
4. 4. **Efficiency**: Potential for more efficient traffic flow and less congestion, resulting in shorter travel times.
5. 5. **Fuel Savings**: Optimized driving patterns that can improve fuel efficiency and reduce emissions.
6. 6. **Productivity**: The ability to work, read, or engage in other productive activities while commuting.
7. 7. **Enhanced safety features**: Advanced sensors and systems that enable better situational awareness and faster reaction times than human drivers.
8. 8. **Environmental benefits**: Potential reduction in greenhouse gas emissions through more efficient driving.
9. 9. **Insurance benefits**: Possibly lower insurance premiums due to lower accident risk.
10. 10. **Technological advancement**: Keeping pace with the latest technological innovations and enjoying their benefits.

These reasons highlight the potential benefits of autonomous driving in terms of safety, comfort, efficiency and overall quality of life.

Another example shows a possible audio/text response from two different customers: The customer does not use autonomous driving.

Question F1 to the customer: Tell me the main reasons why this person is not willing to use autonomous driving

Customer's audio/text response A1: "I am 50 years old and I am not sure if I can trust my car to drive home from work, and I don't know what will happen if I have an accident. Who will cover the costs then, the car manufacturer? Even limiting the maximum speed of autonomous driving to 50 km/h will not help me get to work on time, so I had to get up earlier.

1. 1. **Vertrauen und Sicherheitsbedenken**: Die Person ist unsicher, ob sie dem Auto das autonome Fahren zutraut, was auf ein mangelndes Vertrauen in die Sicherheit und Zuverlässigkeit der Technologie hindeutet.
2. 2. **Ungewissheit über die Haftung**: Die Person ist besorgt über die Folgen eines Unfalls und darüber, wer für die Kosten aufkommen würde, was auf Bedenken hinsichtlich der rechtlichen und finanziellen Haftung hinweist.
3. 3. **Geschwindigkeitsbeschränkungen**: Die begrenzte Höchstgeschwindigkeit von 50 km/h für autonomes Fahren wird als Nachteil angesehen, da die Person dadurch früher aufstehen müsste, um rechtzeitig zur Arbeit zu kommen, was ihren Tagesablauf und ihre Bequemlichkeit beeinträchtigen würde.

Based on this answer A1, here are the main reasons why this person is not willing to use autonomous driving. These main reasons can be determined using the analysis algorithm 1.3.2 using keywords such as "not safe", "trust", "cost" or the like and categorized accordingly as follows:

1. 1. **Trust and safety concerns**: The person is unsure whether they trust the car to drive autonomously, which indicates a lack of trust in the safety and reliability of the technology.
2. 2. **Uncertainty about liability**: The person is concerned about the consequences of an accident and who would pay for the costs, indicating concerns about legal and financial liability.
3. 3. **Speed limits**: The limited maximum speed of 50 km/h for autonomous driving is considered a disadvantage as it would require the person to get up earlier to get to work on time, which would affect their daily routine and convenience.

These reasons include doubts about the reliability and safety of autonomous technology, concerns about liability and accident costs, and practical issues related to speed and time management.

Another example shows a possible audio/text response from two different customers: The customer uses autonomous driving.

Question F2 to the other customer: Tell me the main reasons why this person uses autonomous driving in their car.

Audio/Text Response A2 from the other customer: “I consider myself a techie and have always been willing to try new things. The first time I got to experience the autonomous driving feature of my car live, I was really freaked out. I learned that instead of wasting my time driving, I can save 30 minutes every time I drive to the office - and that's five days a week, time that I use to check my emails and prepare for the first meeting in the morning as soon as I And why shouldn't I say it, I'll make my neighbors aware of it too!"

1. 1. **Interesse an Technologie**: Als selbsternannter „Technikfreak“ hat er ein starkes Interesse daran, mit neuen Technologien und Innovationen zu experimentieren.
2. 2. **Aufregung und Vergnügen**: Die ersten Erfahrungen mit dem autonomen Fahren waren aufregend und unterhaltsam, was ihre Begeisterung für diese Funktion noch verstärkt hat.
3. 3. **Zeitersparnis**: Das autonome Fahren spart ihnen 30 Minuten pro Fahrt, die sie produktiv nutzen können, indem sie E-Mails durchsehen und sich auf Meetings vorbereiten.
4. 4. **Bequemlichkeit**: Die Möglichkeit, die Pendelzeit für andere Aktivitäten zu nutzen, erhöht den Komfort und die Effizienz des Tagesablaufs.
5. 5. **Status und sozialer Einfluss**: Die Person genießt es, ihre Nachbarn neidisch zu machen, was darauf hindeutet, dass sie den sozialen Status und das Prestige schätzt, die mit dem Besitz eines autonomen Hightech-Fahrzeugs verbunden sind.

Based on this statement, here are the main reasons why this person uses the autonomous driving feature in their car. These main reasons can be determined using the analysis algorithm 1.3.2 based on keywords such as "freaked out", "techie", "save time", "save time" or the like and categorized accordingly as follows:

1. 1. **Interest in technology**: As a self-proclaimed “techie,” he has a strong interest in experimenting with new technologies and innovations.
2. 2. **Excitement and enjoyment**: The first experiences with autonomous driving were exciting and fun, which increased their enthusiasm for this feature.
3. 3. **Time savings**: Autonomous driving saves them 30 minutes per trip, which they can use productively by checking emails and preparing for meetings.
4. 4. **Convenience**: The ability to use commuting time for other activities increases the comfort and efficiency of the daily routine.
5. 5. **Status and social influence**: The person enjoys making his neighbors jealous, suggesting that he values the social status and prestige associated with owning a high-tech autonomous vehicle.

These reasons reflect a combination of personal interest in technology, practical benefits such as time savings and convenience, and the social prestige associated with using cutting-edge features.

The above examples of interactive communication of a user/customer simulated in the vehicle 2 were generated by means of the AI-based computing unit 1.3, for example by means of a suitably implemented application such as ChatGPT, but can also be created and programmed using a so-called Large Language Model (LLM) in order to obtain the desired type of syntax recognition.

• Dabei kann die Recheneinheit 1.3 alle gegebenen Antworten A1 bis An, zum Beispiel mehrere Hunderttaussende, mittels der AI-Agenten analysiert, zum Beispiel auf fünf vorgegebene Kategorien beschränkt analysiert werden. Dabei kann die Al-basierte Recheneinheit 1.3 innerhalb weniger Stunden die gesamten Antworten A1 bis An verarbeiten und die Hauptkategorien bestimmen. Hierdurch erhält der Fahrzeughersteller ein besseres Verständnis und eine Gegenmessung. Beispielsweise können die erfassten Antworten A1 bis An in folgende vorgegebene Kategorien klassifiziert werden:
  1. 1 Kunden, die den Dienst zu teuer finden A%
  2. 2 Kunden, die nicht wissen, wie sie den Dienst aktivieren/nutzen können B%
  3. 3 Kunden, die den Dienst genutzt haben und auf Probleme gestoßen sind C%
  4. 4 Kunden, die kein Interesse an diesem Dienst haben D%
  5. 5 Kunden, die den Nutzen des Dienstes nicht kennen E%

The following examples show the different content that could be displayed to three different user types: Low Performer, Average Performer and Top Performer, where the questions asked F1 to Fn can be different and are generated by the question generator 1.3.1 using artificial intelligence AI and evaluated using the analysis algorithm 1.3.2:

• The computing unit 1.3 can analyze all given answers A1 to An, for example several hundred thousand, using the AI agents, for example limited to five predefined categories. The AI-based computing unit 1.3 can process all of the answers A1 to An within a few hours and determine the main categories. This gives the vehicle manufacturer a better understanding and a counter-measurement. For example, the recorded answers A1 to An can be classified into the following predefined categories:
  1. 1 Customers who find the service too expensive A%
  2. 2 Customers who do not know how to activate/use the service B%
  3. 3 customers who used the service and encountered problems C%
  4. 4 customers who are not interested in this service D%
  5. 5 customers who do not know the benefits of the service E%

• - Wenn der Prozentsatz A zu hoch war, kann ein Verkaufsteam die Antworten besprechen und neue Preise festlegen. Zusätzlich kann in einer zweiten interaktiven Kommunikation oder Kampagne bei diesen Kunden nachfragt werden, welchen Preis sie bereit wären, dafür zu zahlen.
• - Falls B% zu hoch war, ist der Aktivierungs-/Nutzungsprozess wahrscheinlich zu komplex und muss neu gestaltet werden. Wenn B% niedrig war, kann ein Erklärungsvideo oder eine Dokumentation an die Kunden gesendet werden, die sich darüber beschwert haben
• - Falls C% mittel oder hoch ist, bedeutet dies, dass ein Qualitätsproblem mit diesem Dienst möglicherweise vorliegt, das von Technikern behoben werden muss. Zusätzlich kann eine entsprechende Nachricht an das zuständige Team übermittelt werden.
• - Falls D% zu hoch ist, können dieser Dienst und die damit verbundenen Betriebskosten wahrscheinlich aus dem Portfolio des Fahrzeugherstellers gestrichen werden. Ein niedriger Prozentsatz würde bedeuten, dass die Kunden an der Dienstleistung interessiert sind und das Hauptproblem wahrscheinlich in einem der anderen Punkte liegt.
• - Falls E% zu hoch ist, oder zumindest für die Kunden, die dies behaupten, kann die Liste der Gründe überprüft werden, worin die Kunden, die die Funktion nutzen, ihren Mehrwert sehen und ihre Erfahrungen mit denen teilen, die es noch nicht wissen

Here are some examples of what you can learn from this:

• - If percentage A was too high, a sales team can discuss the responses and set new prices. In addition, a second interactive communication or campaign can ask these customers what price they would be willing to pay.
• - If B% was too high, the activation/usage process is probably too complex and needs to be redesigned. If B% was low, an explanatory video or documentation can be sent to the customers who complained about it
• - If C% is medium or high, it means that there may be a quality issue with this service that needs to be addressed by technicians. Additionally, a message can be sent to the relevant team.
• - If D% is too high, this service and the associated operating costs can probably be eliminated from the vehicle manufacturer's portfolio. A low percentage would mean that customers are interested in the service and the main problem probably lies in one of the other points.
• - If E% is too high, or at least for those customers who claim it is, the list of reasons why customers who use the feature see its added value can be reviewed and share their experiences with those who don't yet know

The invention further proposes to facilitate a current so-called MBUX language setting to help the artificial intelligence AI and the so-called Speech2Text algorithms to identify the original text to be analyzed (English, Spanish, French, etc.) by reading the current language setting of the vehicle 2 and the interactive communication interface 1 of an infotainment system in the vehicle 2.

• Eine erste Möglichkeit wäre die Verwendung von Fahrzeugbenachrichtigungen durch die zuvor beschriebenen Beispiele einer interaktiven Kommunikation eines Nutzers im Fahrzeug 2 über die interaktive Kommunikationsschnittstelle 1, wie sie in 1 dargestellt ist.

A user interface “Tell me” or “Tell Mercedes” function can be implemented in different ways:

• A first possibility would be the use of vehicle notifications through the previously described examples of interactive communication of a user in the vehicle 2 via the interactive communication interface 1, as described in 1 is shown.

For example, notifications could display the query types described previously and/or directly open an application or service, such as an app, that contains the queries and/or a microphone audio recorder that prompts the customer to leave their feedback as an audio message.

Another future possibility of the user interface is that an audio car avatar application (= implemented application, called app for short) starts a conversation with the user in the vehicle 2 and asks him for his opinion or feedback on a certain topic, instead of asking the user/customer for feedback via vehicle notifications and/or pop-ups where the user would have to interact with the input unit 1.1 designed as a touchscreen (for example by pressing the microphone button to leave a message). For example, the audio car avatar application narrates a content which is the same as that which is later output with a different content for customers who use or do not use a certain product, for example shown on the output unit 1.2, so that basically the concepts explained here remain. The advantage is that the output unit 1.2 does not have to be designed as a touchscreen and interactive communication with the user, in particular a direct conversation with the user, can take place in the vehicle 2.

For example, the user sends a “Tell Me” call to the computing unit 1.3 via the input unit 1.1, which then automatically uses the implemented applications to ask the user for his or her opinion on a function currently being used, a service currently being used, a service currently being selected, such as a DrivePilot function for autonomous driving, using the generated questions F1 to Fn from one of the question catalogs FK1 to FKm.

• Beispielsweise kann die AI-basierte Recheneinheit 1.3 anhand der implementierten Anwendungen und Algorithmen die Hauptgründe identifizieren, warum ein Fahrer das Produkt nutzen möchte oder nicht nutzen möchte, wie es anhand der Beispiele zuvor aufgezeigt wurde.

On the basis of vehicle data already available in cloud services, in particular in external units 1.6, of the vehicle manufacturer and their processing by means of the computing unit 1.3, it is automatically determined and is thus available for reading or retrieval how often a specific driver/user uses this function and/or this service in a specific period of time. By comparing the use of the vehicles 2 of an entire vehicle fleet of a vehicle manufacturer, the drivers of the vehicles 2 of the vehicle fleet can then be divided into categories of different user types, such as: low performers, average performers and top performers, whereby the questions F1 to Fn asked can be different in relation to the different user types and are generated by the question generator 1.3.1 individually for each user using artificial intelligence AI and optionally evaluated using the analysis algorithm 1.3.2 and/or output using the output unit 1.2:

• For example, the AI-based computing unit 1.3 can use the implemented applications and algorithms to identify the main reasons why a driver wants or does not want to use the product, as shown in the examples above.

The feedback contents of responses A1 to An requested from and/or displayed to the user, such as a driver, passenger, customer, or the like, are different, since a user who generally uses a product very frequently will give positive feedback and a user who does not use the product will be more likely to give negative feedback.

Some suggestions for improving the function, service or the like may also be included in the audio message to the user and/or vice versa to the computing unit 1.3. However, these suggestions should generally be those points of the function, service or the like that the user would choose and that are positive, since otherwise the user would not use this function/service.

On the other hand, a user who does not use a product frequently is likely to only receive negative/improving advice. For this reason, the information provided in the vehicle 2, in the email or in the application, for example a “MeApp” displayed content may vary, as in the following examples.

• Frage F1: Wie war Ihre Erfahrung beim letzten Kundendienst oder der letzten Reparatur in einer Werkstatt. In diesem Fall kann die Auslösung der TellMe-Aufforderung an den Nutzer/Kunden, eine Audionachricht zu hinterlassen, auf der Zeit basieren (wenn das Datum der geplanten Reparatur erkennbar ist) und/oder geolokalisiert sein, wenn das Fahrzeug in einen Werkstattbereich einfährt oder ausfährt.
  • - Fragen Sie den Kunden, warum er eine Reparatur in einer nicht offiziellen Fahrzeughersteller-Werkstatt durchführt, wie dies beispielhaft in DE 10 2020 000 259 A1 beschrieben ist. DE 10 2020 000 259 A1 offenbart eine grundsätzliche Meldung, wenn bestimmte externe Diagnosetätigkeiten im Fahrzeug 2 in einer Werkstatt durchgeführt werden und/oder die Motorhaube geöffnet wird.
  • - Auch kann eine Frage F1 auf die Meinung des Nutzers über sein neues Auto sechs Monate oder 24 Monate nach dem Kaufdatum gerichtet sein. Auch über die Autoversicherung, das Leasing oder jede andere Dienstleistung kann die Recheneinheit 1.3 Fragen F1 bis Fn erzeugen, die an den Nutzer interaktiv, wie zuvor beschrieben ausgegeben werden, um seine Antworten A1 bis An hierzu zu erfassen, zu analysieren und optional zu speichern.

The use of the Tell Mercedes function does not have to be limited to feedback on the use or non-use of digital extras or vehicle services, but can also be extended to any other customer product feedback relating to the vehicle 2, as shown below by way of example:

• Question F1: What was your experience during the last service or repair at a workshop? In this case, the triggering of the TellMe request to the user/customer to leave an audio message can be time-based (if the date of the scheduled repair is identifiable) and/or geolocated when the vehicle enters or exits a workshop area.
  • - Ask the customer why he is having a repair carried out in a non-official vehicle manufacturer workshop, as is the case in DE 10 2020 000 259 A1 described. DE 10 2020 000 259 A1 reveals a general message when certain external diagnostic activities are carried out in vehicle 2 in a workshop and/or the bonnet is opened.
  • - A question F1 can also be directed at the user's opinion about his new car six months or 24 months after the purchase date. The computing unit 1.3 can also generate questions F1 to Fn about car insurance, leasing or any other service, which are issued to the user interactively, as described above, in order to record, analyze and optionally save his answers A1 to An.

list of reference symbols

1

interactive communication interface

1.1

input unit

1.2

output unit

1.3

arithmetic unit

1.3.1

question generator

1.3.2

analysis algorithm

1.4

storage unit

1.5

External Unit

1.6

Mobile Unit

1.7

sensor unit

2

vehicle

100

Arrow

102

Arrow

104

Arrow

106

Arrow

201

first step

202

second step

203

third step

204

fourth step

205

fifth step

A1, An

Answer

F1, Fn

Ask

FK1, FKm

questionnaire

QUOTES INCLUDED IN THE DESCRIPTION

This list of documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions.

Cited patent literature

• US 20200335183A1 [0006]
• DE 10 2020 000 259 A1 [0065]

Claims (6)

Interactive communication interface (1) for a mutual exchange of signals and data, comprising an input unit (1.1) and an output unit (1.2), in a vehicle (2), characterized in that predetermined questions (F1 to Fn) can be or are output to a user in the vehicle (2) by means of the output unit (1.2) and answers (A1 to An) of the user recorded by means of the input unit (1.1) can be or are fed to a computing unit (1.3), wherein the computing unit (1.3) is set up to analyze and categorize the supplied answers (A1 to An) in relation to the predetermined questions (F1 to Fn) and optionally to store them and/or to feed them to an external unit (1.5). Interactive communication interface (1) according to claim 1 , characterized in that a storage unit (1.4) is provided on which a number of questionnaires (FK1 to FKm) with a plurality of questions (F1 to Fn) relating to a predetermined vehicle topic, a vehicle component, a vehicle system, a vehicle service, a vehicle function, a vehicle application, an external service and/or an external function and a use or non-use thereof are stored. Interactive communication interface (1) according to claim 1 or 2 , characterized in that the computing unit (1.3) is set up to automatically generate by means of artificial intelligence (AI) a number of questionnaires (FK1 to FKm) with a plurality of questions (F1 to Fn) relating to a predetermined vehicle topic, a vehicle component, a vehicle system, a vehicle service, a vehicle function, a vehicle application, an external service and/or an external function and a use or non-use and/or to adapt and/or update them depending on the recorded answers (A1 to An). Interactive communication interface (1) according to claim 3 or 4 , characterized in that the computing unit (1.3) is coupled to a sensor unit (1.7) which detects use or non-use of a vehicle component, a vehicle system, a vehicle application, a service, a function or the like, wherein, depending on a detected use or a detected non-use, a corresponding questionnaire (FK1 to FKm) can be automatically generated by means of a question generator (1.3.1) and/or can be read from the storage unit (1.4) and can be output. Interactive communication interface (1) according to one of the preceding claims, characterized in that the computing unit (1.3) comprises at least one analysis algorithm (1.3.2) which is set up to analyze and categorize the user's answers (A1 to An) recorded in response to the issued questionnaire (FK1 to FKm) by means of the input unit (1.1). Interactive communication method for a mutual exchange of signals and data by means of an input unit (1.1) and an output unit (1.2) in a vehicle (2), characterized in that predetermined questions (F1 to Fn) are output to a user in the vehicle (2) by means of the output unit (1.2) and answers (A1 to An) of the user recorded by means of the input unit (1.1) are fed to a computing unit (1.3), wherein the supplied answers (A1 to An) are analyzed, categorized and optionally stored in relation to the predetermined questions (F1 to Fn) and/or fed to an external unit (1.5).

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