Scale dependence is a pervasive feature of socioeconomic networks: even when generated by the same class of economic activities, networks observed at different levels of entity aggregation can exhibit markedly different structural organizations, yet a systematic and testable explanation for such cross-scale divergence remains lacking. This paper presents a U.S. patent dataset together with a unified analytical framework that combines empirical network analysis with mechanism-based modeling to quantify and interpret structural differences across scales. The dataset contains 1,225,373 granted USPTO utility patents filed during 2000–2020 and integrates assignee geography (state/county/city), firm identifiers, CPC classification codes, and patent texts; technologies are defined at the 4-digit CPC level. To measure technology activity when patents involve multiple technologies, we use co-citation information to allocate each patent's technological shares across its associated CPC codes, thereby obtaining technology-share weights beyond naive equal counting. Using these weights, we construct entity-technology bipartite networks at four scales (state, county, city, and firm) and derive two technology space networks, one based on technology co-occurrence across entities and the other based on patent-text similarity. We characterize network structure using bipartite modularity (Q), global nestedness (N), and in-block nestedness (I), and evaluate statistical significance against degree-constrained null models based on the bipartite configuration model (BiCM). Empirically, modularity increases as the entity scale becomes finer; state- and county-level networks are closer to a globally nested organization, whereas city- and firm-level networks exhibit a pronounced shift toward in-block nestedness. Temporal analysis further shows that the formation of new entity-technology links reflects a scale-dependent balance between preference for globally central technologies and reliance on relatedness density to the existing technological portfolio, with smaller-scale entities exhibiting a stronger dependence on relatedness density. Finally, we propose an evolutionary model that incorporates both relatedness-density preference and technology-centrality preference under empirical degree constraints. Simulations demonstrate that tuning these two preferences reproduces the observed transition from global nestedness to in-block nestedness, providing a mechanism-based explanation for scale-dependent structural patterns in technological innovation networks. The dataset presented in this paper is openly available at https://doi.org/10.57760/sciencedb.j00213.00265.