The Experts below are selected from a list of 6648 Experts worldwide ranked by ideXlab platform

Sundar Krishnamurty - One of the best experts on this subject based on the ideXlab platform.

  • The Obscure Features Hypothesis in Design Innovation
    International Journal of Design Creativity and Innovation, 2014
    Co-Authors: Tony Mccaffrey, Sundar Krishnamurty
    Abstract:

    A new cognitive theory of Innovation, the Obscure Features Hypothesis (OFH), is applied to the area of engineering Design Innovation. The OFH states that all innovative solutions are built upon at least one overlooked (i.e., obscure) feature of the problem at hand. In this paper, we first highlight the types of features that exist and the cognitive obstacles that hinder people's ability to notice the obscure members of various feature types. We then detail five Innovation techniques we have developed to help Designers search the obscure realms of the space of features. Each of these techniques counteracts a specific cognitive obstacle to Innovation: Design fixation, functional fixedness, narrow verb associations, assumption blindness, and analogy blindness. We compare our approach with other approaches to Innovation in psychology (the representation change view and the distant association view) and engineering (theory of inventive problem solving and C–K theory). Finally, we show how the Innovation techni...

  • The Obscure Features Hypothesis in Design Innovation
    International Journal of Design Creativity and Innovation, 2014
    Co-Authors: Tony Mccaffreya, Tony Mccaffrey, Sundar Krishnamurty
    Abstract:

    A new cognitive theory of Innovation, the Obscure Features Hypothesis (OFH), is applied to the area of engineering Design Innovation. The OFH states that all innovative solutions are built upon at least one overlooked (i.e., obscure) feature of the problem at hand. In this paper, we first highlight the types of features that exist and the cognitive obstacles that hinder people's ability to notice the obscure members of various feature types. We then detail five Innovation techniques we have developed to help Designers search the obscure realms of the space of features. Each of these techniques counteracts a specific cognitive obstacle to Innovation: Design fixation, functional fixedness, narrow verb associations, assumption blindness, and analogy blindness. We compare our approach with other approaches to Innovation in psychology (the representation change view and the distant association view) and engineering (theory of inventive problem solving and C–K theory). Finally, we show how the Innovation techniques can be implemented in software to assist users in the Design process.

  • Semantic methods supporting engineering Design Innovation
    Advanced Engineering Informatics, 2011
    Co-Authors: Rui Fernandes, Sundar Krishnamurty, Ian R. Grosse, Paul Witherell, Jack C. Wileden
    Abstract:

    In this paper, we present a metric based on semantic relatedness which operates on semantic knowledge representations of engineering Design and show how it can support Design Innovation. Our semantic knowledge representation is composed of an ontology representing Design concepts using the National Institute of Standards and Technology (NIST) functional basis formalism. We assert that the uniqueness of a Design concept is directly proportional to the mean semantic distance between itself and the set of competing Design concepts represented as instances within our functional basis ontology. This leads to our Semantic Relatedness Uniqueness Metric called SeRUM. SeRUM draws upon semantic functional model representations of Design concepts and computer science semantic relatedness techniques. SeRUM provides Design teams a measure of their effectiveness in terms of generating unique Design concepts. To highlight SeRUM’s application in engineering Design Innovation, a Design Innovation case study is detailed and the results are discussed.

John Zimmerman - One of the best experts on this subject based on the ideXlab platform.

  • CHI - UX Design Innovation: Challenges for Working with Machine Learning as a Design Material
    Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017
    Co-Authors: Graham Dove, Jodi Forlizzi, Kim Halskov, John Zimmerman
    Abstract:

    Machine learning (ML) is now a fairly established technology, and user experience (UX) Designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of Design Innovation that other technologies have, and this might be because it is a new and difficult Design material. To better understand why we have witnessed little Design Innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX Design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction Design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of Design thinking to re-imagine what ML might be and might do.

  • UX Design Innovation: Challenges for Working with Machine Learning as a Design Material
    CHI '17 Proceedings of the 2017 annual conference on Human factors in computing systems, 2017
    Co-Authors: Graham Dove, Jodi Forlizzi, Kim Halskov, John Zimmerman
    Abstract:

    Machine learning (ML) is now a fairly established technology, and user experience (UX) Designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of Design Innovation that other technologies have, and this might be because it is a new and difficult Design material. To better understand why we have witnessed little Design Innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX Design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction Design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of Design thinking to re-imagine what ML might be and might do.

Tony Mccaffreya - One of the best experts on this subject based on the ideXlab platform.

  • The Obscure Features Hypothesis in Design Innovation
    International Journal of Design Creativity and Innovation, 2014
    Co-Authors: Tony Mccaffreya, Tony Mccaffrey, Sundar Krishnamurty
    Abstract:

    A new cognitive theory of Innovation, the Obscure Features Hypothesis (OFH), is applied to the area of engineering Design Innovation. The OFH states that all innovative solutions are built upon at least one overlooked (i.e., obscure) feature of the problem at hand. In this paper, we first highlight the types of features that exist and the cognitive obstacles that hinder people's ability to notice the obscure members of various feature types. We then detail five Innovation techniques we have developed to help Designers search the obscure realms of the space of features. Each of these techniques counteracts a specific cognitive obstacle to Innovation: Design fixation, functional fixedness, narrow verb associations, assumption blindness, and analogy blindness. We compare our approach with other approaches to Innovation in psychology (the representation change view and the distant association view) and engineering (theory of inventive problem solving and C–K theory). Finally, we show how the Innovation techniques can be implemented in software to assist users in the Design process.

Tony Mccaffrey - One of the best experts on this subject based on the ideXlab platform.

  • The Obscure Features Hypothesis in Design Innovation
    International Journal of Design Creativity and Innovation, 2014
    Co-Authors: Tony Mccaffrey, Sundar Krishnamurty
    Abstract:

    A new cognitive theory of Innovation, the Obscure Features Hypothesis (OFH), is applied to the area of engineering Design Innovation. The OFH states that all innovative solutions are built upon at least one overlooked (i.e., obscure) feature of the problem at hand. In this paper, we first highlight the types of features that exist and the cognitive obstacles that hinder people's ability to notice the obscure members of various feature types. We then detail five Innovation techniques we have developed to help Designers search the obscure realms of the space of features. Each of these techniques counteracts a specific cognitive obstacle to Innovation: Design fixation, functional fixedness, narrow verb associations, assumption blindness, and analogy blindness. We compare our approach with other approaches to Innovation in psychology (the representation change view and the distant association view) and engineering (theory of inventive problem solving and C–K theory). Finally, we show how the Innovation techni...

  • The Obscure Features Hypothesis in Design Innovation
    International Journal of Design Creativity and Innovation, 2014
    Co-Authors: Tony Mccaffreya, Tony Mccaffrey, Sundar Krishnamurty
    Abstract:

    A new cognitive theory of Innovation, the Obscure Features Hypothesis (OFH), is applied to the area of engineering Design Innovation. The OFH states that all innovative solutions are built upon at least one overlooked (i.e., obscure) feature of the problem at hand. In this paper, we first highlight the types of features that exist and the cognitive obstacles that hinder people's ability to notice the obscure members of various feature types. We then detail five Innovation techniques we have developed to help Designers search the obscure realms of the space of features. Each of these techniques counteracts a specific cognitive obstacle to Innovation: Design fixation, functional fixedness, narrow verb associations, assumption blindness, and analogy blindness. We compare our approach with other approaches to Innovation in psychology (the representation change view and the distant association view) and engineering (theory of inventive problem solving and C–K theory). Finally, we show how the Innovation techniques can be implemented in software to assist users in the Design process.

Graham Dove - One of the best experts on this subject based on the ideXlab platform.

  • CHI - UX Design Innovation: Challenges for Working with Machine Learning as a Design Material
    Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017
    Co-Authors: Graham Dove, Jodi Forlizzi, Kim Halskov, John Zimmerman
    Abstract:

    Machine learning (ML) is now a fairly established technology, and user experience (UX) Designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of Design Innovation that other technologies have, and this might be because it is a new and difficult Design material. To better understand why we have witnessed little Design Innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX Design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction Design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of Design thinking to re-imagine what ML might be and might do.

  • UX Design Innovation: Challenges for Working with Machine Learning as a Design Material
    CHI '17 Proceedings of the 2017 annual conference on Human factors in computing systems, 2017
    Co-Authors: Graham Dove, Jodi Forlizzi, Kim Halskov, John Zimmerman
    Abstract:

    Machine learning (ML) is now a fairly established technology, and user experience (UX) Designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of Design Innovation that other technologies have, and this might be because it is a new and difficult Design material. To better understand why we have witnessed little Design Innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX Design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction Design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of Design thinking to re-imagine what ML might be and might do.