Technological Improvement Rates and Evolution of Energy-Based Therapeutics.

This paper examines the field of energy-based medical therapies based on the analysis of patents. We define the field as the use of external stimuli to achieve biomedical modifications to treat disease and to increase health. Based upon distinct sets of patents, the field is subdivided into sub-domains for each energy category used to achieve the stimulation: electrical, magnetic, microwave, ultrasound, and optical. Previously developed techniques are used to retrieve the relevant patents for each of the stimulation modes and to determine main paths along the trajectory followed by each sub-domain. The patent sets are analyzed to determine key assignees, number of patents, and dates of emergence of the sub-domains. The sub-domains are found to be largely independent as to patent assignees. Electrical and magnetic stimulation patents emerged earliest in the 1970s and microwave most recently around 1990. The annual rate of improvement of all sub-domains (12-85%) is found to be significantly higher than one we find for an aggregate pharmaceutical domain (5%). Overall, the results suggest an increasingly important role for energy-based therapies in the future of medicine.

Functional performance improvement data and patent sets for 30 technology domains with measurements of patent centrality and estimations of the improvement rate.

This article accompanies the study presented in Triulzi et al. (2020) [1]. It briefly describes and makes available the data on functional performance for 30 technology domains, their patent sets, the measurement of patent centrality and the method to estimate the yearly technology performance improvement rate (TIR) that underly that study. Some of this data (performance time series and the lists of patents for 28 domains) has been collected by other authors for previous studies but were previously unavailable to the public. Measurements of patent centrality and other patent-based indicators for the 30 domains, and for 5.259.906 utility patents granted by the United States Patent and Trademark Office between 1976 and 2015 are novel data contributed by Triulzi et al. (2020) [1]. Here we organize, describe and make available the collection of data in its entirety. This allows anyone interested to replicate the study or use the method to estimate the improvement rate of a given technology for which patents can be identified. For a detailed description of the data and methods see Triulzi et al. (2020) [1].

Pre-existing technological core and roots for the CRISPR breakthrough.

This paper applies objective methods to explore the technological origins of the widely acclaimed CRISPR breakthrough in the technological domain of genome engineering. Previously developed patent search techniques are first used to recover a set of patents that well-represent the genome editing domain before CRISPR. Main paths are then determined from the citation network associated with this patent set allowing identification of the three major knowledge trajectories. The most significant of these trajectories for CRISPR involves the core of genome editing with less significant trajectories involving cloning and endonuclease specific developments. The major patents on the core trajectory are consistent with qualitative expert knowledge of the topical area. A second set of patents that we call the CRISPR roots are obtained by finding the patents directly cited by the recent CRISPR patents along with patents cited by that set of patents. We find that the CRISPR roots contain 8 key patents from the genome engineering main path associated with restriction endonucleases and the expected strong connection of CRISPR to prior genome editing technology such as Zn finger nucleases. Nonetheless, analysis of the full CRISPR roots shows that a very wide array of technological knowledge beyond genome engineering has contributed to achieving the CRISPR breakthrough. Such breadth in origins is not surprising since "spillover" is generally perceived as important and previous qualitative studies of CRISPR have shown not only technological breadth in origins but scientific breadth as well. In addition, we find that the estimated rate of functional performance improvement of the CRISPR roots set is about 9% per year compared to the genome engineering set (~4% per year). These estimates indicate below average rates of improvement and may indicate that CRISPR (and perhaps yet undiscovered) genome engineering developments could evolve in effectiveness over an upcoming long rather than short time period.

The Emergence and Collapse of Knowledge Boundaries.

The dynamics of knowledge transfer is an important topic for engineering managers. In this paper, we study knowledge boundaries - barriers to knowledge transfer - in groups of experts, using topic modeling, a natural language processing technique, applied to transcript data from the U.S. Food and Drug Administration's Circulatory Systems Advisory Panel. As predicted by prior theory, we find that knowledge boundaries emerge as the group faces increasingly challenging problems. Beyond this theory, we find that knowledge boundaries cease to structure communications between communities of practice when the group's expert ability is insufficient to solve its task, such as in the presence of high novelty. We conjecture that the amount of expert knowledge that the group can collectively bring to bear is a determining factor in boundary formation. This implies that some of the factors underlying knowledge boundary formation may aid - rather than hinder - knowledge aggregation. We briefly explore this conjecture using qualitative exploration of several relevant meetings. Finally, we discuss implications of these results for organizations attempting to leverage their expertise given the state of their collective knowledge.

Artifact interactions retard technological improvement: An empirical study.

Empirical research has shown performance improvement of many different technological domains occurs exponentially but with widely varying improvement rates. What causes some technologies to improve faster than others do? Previous quantitative modeling research has identified artifact interactions, where a design change in one component influences others, as an important determinant of improvement rates. The models predict that improvement rate for a domain is proportional to the inverse of the domain's interaction parameter. However, no empirical research has previously studied and tested the dependence of improvement rates on artifact interactions. A challenge to testing the dependence is that any method for measuring interactions has to be applicable to a wide variety of technologies. Here we propose a novel patent-based method that is both technology domain-agnostic and less costly than alternative methods. We use textual content from patent sets in 27 domains to find the influence of interactions on improvement rates. Qualitative analysis identified six specific keywords that signal artifact interactions. Patent sets from each domain were then examined to determine the total count of these 6 keywords in each domain, giving an estimate of artifact interactions in each domain. It is found that improvement rates are positively correlated with the inverse of the total count of keywords with Pearson correlation coefficient of +0.56 with a p-value of 0.002. The results agree with model predictions, and provide, for the first time, empirical evidence that artifact interactions have a retarding effect on improvement rates of technological domains.

Physical Texturing for Superhydrophobic Polymeric Surfaces: A Design Perspective.

Surface wetting on the textured surface is classically explained by the theories of Cassie-Baxter or Wenzel. However, in recent years, an increasing number of complex surface topographies with superhydrophobic properties have been achieved without prediction or simulation using these theories. One example is biomimetic surfaces. In many instances, theories were used to explain surface properties found in nature but have not led to or predicted the complex topographies. Although new wetting theories continue to emerge, there is not yet a set of design rules to guide the selection of surface topographies to achieve superhydrophobicity. By grouping known surface topographies into common geometrical descriptions and length scale, this paper suggests a set of surface topography classifications to provide selection guidelines for engineering superhydrophobic surfaces. Two key outcomes emerged from the design analysis: first, categorization of frequently reported surface patterns shows that there exists a set of commonly used descriptions among diverse designs; second, the degree of hydrophobicity improvement within a class of topography design can be used to predict the limit of improvement in superhydrophobicity for a given material. The presentation of topography descriptors by categories of design and performance may serve as a prologue to an eventually complete set of design guidelines for superhydrophobic performance.

Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach.

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents.

Quantitative determination of technological improvement from patent data.

The results in this paper establish that information contained in patents in a technological domain is strongly correlated with the rate of technological progress in that domain. The importance of patents in a domain, the recency of patents in a domain and the immediacy of patents in a domain are all strongly correlated with increases in the rate of performance improvement in the domain of interest. A patent metric that combines both importance and immediacy is not only highly correlated (r = 0.76, p = 2.6*10(-6)) with the performance improvement rate but the correlation is also very robust to domain selection and appears to have good predictive power for more than ten years into the future. Linear regressions with all three causal concepts indicate realistic value in practical use to estimate the important performance improvement rate of a technological domain.

Does seating location impact voting behavior on Food and Drug Administration advisory committees?

Food and Drug Administration (FDA) advisory committees have a significant influence on patterns of clinical practice worldwide. Recent guidance to the committees by the FDA has focused on attempting to eliminate sources of bias due to committee voting procedures. Nevertheless, major sources of social influence have not been addressed. We analyzed transcripts of Circulatory Systems Devices Panel meetings from 1997 to 2005 in which the panel cast votes on premarket approval and for which a voting minority existed. Committee members who are assigned to speak later are significantly more likely to be in the voting minority (P < 0.001). This effect holds for meetings with sequential voting (P = 0.0058) and for meetings with simultaneous voting (P = 0.045). A weaker effect shows that, for meetings with sequential voting, committee members who vote later are significantly more likely to be in the voting minority (P = 0.018). Speaking order and voting order are both determined by seating location. We therefore conclude that voting behavior on FDA expert advisory committees is strongly associated with seating location. This suggests the presence of a possible social dynamic that is not addressed by existing FDA committee procedures.