To create the next innovative product, participants in science need to understand which existing technologies can be combined, what new science must be discovered, and what new technologies must be invented. Knowledge of these often arrives by means of expert consensus or popularity metrics, masking key information on how intellectual efforts accumulate into technological progress. To address this shortcoming, we first present a method to establish a mathematical link between technological evolution and complex networks: a path of events that narrates innovation bottlenecks. Next, we quantify the position and proximity of documents to these innovation paths. The result is an innovation network that more exhaustively captures deterministic knowledge flows with respect to a marketed innovative product. Our dataset, containing over three million biomedical citations, demonstrates the possibility of quantifying the accumulation, speed, and division of labour in innovation over a sixty-year time horizon. The significance of this study includes the (i) use of a purpose-generated dataset showing causal paths from research to development to product; (ii) analysis of the innovation process as a directed acyclic graph; (iii) comparison between calendar time and network time; (iv) ordering of science funders along technology lifecycles; (v) quantification of innovative activities' importance to an innovative outcome; and (vi) integration of publication, patent, clinical trial, regulatory data to study innovation holistically.