Engineers and technology firms must continually explore new design opportunities and directions to sustain or thrive in technology competition. However, the related decisions are normally based on personal gut feeling or experiences. Although the analysis of user preferences and market trends may shed light on some design opportunities from a demand perspective, design opportunities are always conditioned or enabled by the technological capabilities of designers. Herein, we present a data-driven methodology for designers to analyze and identify what technologies they can design for the next, based on the principle—what a designer can currently design condition or enable what it can design next. The methodology is centered on an empirically built network map of all known technologies, whose distances are quantified using more than 5 million patent records, and various network analytics to position a designer according to the technologies that they can design, navigate technologies in the neighborhood, and identify feasible paths to far fields for novel opportunities. Furthermore, we have integrated the technology space map, and various map-based functions for designer positioning, neighborhood search, path finding, and knowledge discovery and learning, into a data-driven visual analytic system named InnoGPS. InnoGPS is a global position system (GPS) for finding innovation positions and directions in the technology space, and conceived by analogy from the GPS that we use for positioning, neighborhood search, and direction finding in the physical space.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    InnoGPS for Data-Driven Exploration of Design Opportunities and Directions: The Case of Google Driverless Car Project



    Erschienen in:

    Erscheinungsdatum :

    2017




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    50.15 Konstruktionslehre / 52.20 Antriebstechnik, Getriebelehre / 52.15 Maschinenelemente, Maschinenbaugruppen
    Lokalklassifikation TIB:    770/5315/5330



    Big data driverless system

    THE INVENTOR HAS WAIVED THE RIGHT TO BE CITED | Europäisches Patentamt | 2020

    Freier Zugriff


    Automatic obstacle avoidance driverless vehicle driverless system

    PANG QUNYING | Europäisches Patentamt | 2021

    Freier Zugriff

    CONTROLLER FOR DRIVERLESS VEHICLE, AND DRIVERLESS VEHICLE

    LI YUANFENG | Europäisches Patentamt | 2020

    Freier Zugriff

    DRIVERLESS TRANSPORTER

    MATTERN THOMAS | Europäisches Patentamt | 2020

    Freier Zugriff