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on Intellectual Property Rights |
| By: | Lauren Cohen; Umit Gurun; Katie Moon; Paula Suh |
| Abstract: | Analyzing millions of patents granted by the USPTO between 1976 and 2020, we find a pattern where specific patents only rise to prominence after considerable time has passed. Amongst these late-blooming influential patents, we show that there are key players (patent hunters) who consistently identify and develop them. Although initially overlooked, these late-blooming patents have significantly more influence on average than early-recognized patents and are associated with significantly more new product launches. Patent hunters, as early detectors and adopters of these late-blooming patents, are also associated with significant positive rents. Their adoption of these overlooked patents is associated with a 6.4% rise in sales growth (t = 3.02), a 2.2% increase in Tobin’s Q (t = 3.91), and a 2.2% increase in new product offerings (t = 2.97). We instrument for patent hunting, and find strong evidence that these benefits are causally due to patent hunting. The rents associated with patent hunting on average exceed those of the original patent creators themselves. Patents hunted are closer to the core technology of patent hunters, more peripheral to writers, and in less competitive spaces. Lastly, patent hunting appears to be a persistent firm characteristic and to have an inventor-level component. |
| JEL: | L1 O31 O33 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32965 |
| By: | Ina Ganguli; Jeffrey Lin; Vitaly Meursault; Nicholas F. Reynolds |
| Abstract: | As distorted maps may mislead, Natural Language Processing (NLP) models may misrepresent. How do we know which NLP model to trust? We provide comprehensive guidance for selecting and applying NLP representations of patent text. We develop novel validation tasks to evaluate several leading NLP models. These tasks assess how well candidate models align with both expert and non-expert judgments of patent similarity. State-of-the-art language models significantly outperform traditional approaches such as TF-IDF. Using our validated representations, we measure a secular decline in contemporaneous patent similarity: inventors are “spreading out” over an expanding knowledge frontier. This finding is corroborated by declining rates of multiple invention from newly-digitized historical patent interference records. In contrast, selecting another single representation without validating alternatives yields an ambiguous or even opposing trend. Thus, our framework addresses a fundamental challenge of selecting among different black-box NLP models that produce varying economic measurements. To facilitate future research, we plan to provide our validation task data and embeddings for all US patents from 1836–2023. |
| JEL: | C81 L19 O31 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32934 |
| By: | Jeremy Pearce; Liangjie Wu |
| Abstract: | We study the interaction of customer capital and productivity through brand reallocation across firms. We develop a firm dynamics model with brands as transferable customer capital, heterogeneous firm productivity, and variable markups. We study the matching process between transferable brand capital and core productivity, which can be inefficient with significant welfare implications. We link USPTO trademark data with Nielsen sales data to study the prevalence of brand reallocation and the response of sales and prices to reallocation. Quantitatively, brand reallocation reduces welfare. Optimal policies deviate substantially from the literature due to the complementarity between brand capital and productivity. |
| Keywords: | firm dynamics; productivity; market concentration; product innovation; reallocation; Mergers & acquisitions; brands; Trademarks; intangible assets |
| JEL: | O31 O32 O34 O41 D22 D43 L11 L13 L22 |
| Date: | 2024–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:98772 |