Using design database structures to characterize freedom-to-operate in a design space: A legal case study

Novelty, and specifically freedom-to-operate (FTO), assessment is crucial step in launching and patenting a new product. We compare a traditional FTO analysis performed by a patent lawyer and expert in chemistry and pharmaceutical technology with the outcomes of a computational method. The computational method discovers the structural form of a set of patents using a text-derived similarity data, creating a descriptive space of the potentially relevant prior art. A test formulation of a fabricated new pharmaceutical drug was developed for the FTO and computational analysis, and strengths and areas for improvement of the computational method were identified. FTO analysis is time consuming and labor intensive, and results indicated that with further development, the computational method could aid patent lawyers in getting a faster and fresh snapshot of the space of prior art, and even point to patents most relevant to a proposed new product. Areas for improvement are intrinsic knowledge of the computational method in the field of application, and finding which sections of patents lead to most accurate representations of the space, and further automation and efficiency.

[1]  Susan P. Besemer,et al.  The development, reliability, and validity of the revised creative product semantic scale , 1989 .

[2]  Steven M. Smith,et al.  Metrics for measuring ideation effectiveness , 2003 .

[3]  Jonathan Cagan,et al.  Discovering Structure in Design Databases Through Functional and Surface Based Mapping , 2013 .

[4]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[5]  Jef R. Peeters,et al.  Refined Metrics for Measuring Novelty in Ideation , 2010 .

[6]  Prabhakar Raghavan,et al.  Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies , 1998, The VLDB Journal.

[7]  Peter W. Foltz,et al.  The Measurement of Textual Coherence with Latent Semantic Analysis. , 1998 .

[8]  Robert P. W. Duin,et al.  Outlier Detection Using Classifier Instability , 1998, SSPR/SPR.

[9]  Peter W. Foltz,et al.  An introduction to latent semantic analysis , 1998 .

[10]  Kenneth D. Forbus,et al.  MAC/FAC: A Model of Similarity-Based Retrieval , 1995, Cogn. Sci..

[11]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[12]  David W. Rosen,et al.  Refined metrics for measuring ideation effectiveness , 2009 .

[13]  Michael Brady,et al.  Novelty detection for the identification of masses in mammograms , 1995 .

[14]  Amaresh Chakrabarti,et al.  Comparison of the degree of creativity in the design outcomes using different design methods , 2012 .

[15]  Amaresh Chakrabarti,et al.  The effect of representation of triggers on design outcomes , 2008, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[16]  Charles Kemp,et al.  The discovery of structural form , 2008, Proceedings of the National Academy of Sciences.

[17]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..

[18]  Thomas Ertl,et al.  Iterative integration of visual insights during patent search and analysis , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[19]  Sougata Mukherjea,et al.  Information retrieval and knowledge discovery utilizing a biomedical patent semantic Web , 2005, IEEE Transactions on Knowledge and Data Engineering.

[20]  Irem Y. Tumer,et al.  A comparison of creativity and innovation metrics and sample validation through in-class design projects , 2013 .

[21]  T. M. Amabile Social psychology of creativity: A consensual assessment technique. , 1982 .