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Both factors are challenged by a fivefold increase in the average number of proton\u2013proton collisions per bunch crossing, corresponding to a change in the detector operation conditions for the LHCb Upgrade I phase, recently started. A further tenfold increase is expected in the Upgrade II phase, planned for the next decade. The limits in the storage capacity of the trigger will bring an inverse relationship between the number of particles selected to be stored per event and the number of events that can be recorded. In addition the background levels will rise due to the enlarged combinatorics. To tackle both challenges, we propose a novel approach, never attempted before in a hadronic collider: a Deep-learning based Full Event Interpretation (DFEI), to perform the simultaneous identification, isolation and hierarchical reconstruction of all the heavy-hadron decay chains per event. This strategy radically contrasts with the standard selection procedure used in LHCb to identify heavy-hadron decays, that looks individually at subsets of particles compatible with being products of specific decay types, disregarding the contextual information from the rest of the event. Following the DFEI approach, once the relevant particles in each event are identified, the rest can be safely removed to optimise the storage space and maximise the trigger efficiency. We present the first prototype for the DFEI algorithm, that leverages the power of Graph Neural Networks (GNN). 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