  The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform
high-precision measurements of heavy-hadron decays, which requires the
collection of large data samples and a good understanding and suppression of
multiple background sources. Both factors are challenged by a five-fold
increase in the average number of proton-proton collisions per bunch crossing,
corresponding to a change in the detector operation conditions for the LHCb
Upgrade I phase, recently started. A further ten-fold increase is expected in
the Upgrade II phase, planed for the next decade. The limits in the storage
capacity of the trigger will bring an inverse relation between the amount of
particles selected to be stored per event and the number of events that can be
recorded, and the background levels will raise 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 approach radically contrasts with the standard selection procedure used in
LHCb to identify heavy-hadron decays, that looks individually at sub-sets of
particles compatible with being products of specific decay types, disregarding
the contextual information from the rest of the event. We present the first
prototype for the DFEI algorithm, that leverages the power of Graph Neural
Networks (GNN). This paper describes the design and development of the
algorithm, and its performance in Upgrade I simulated conditions.
