Respiratory gating can be used to separate a PET acquisition into a series of near motion-free bins. This is typically done using additional gating hardware; however, software-based methods can derive the respiratory signal from the acquired data itself. The aim of this work was to extend a data-driven respiratory gating method to acquire gated, 3D, whole body PET images of clinical patients. The existing method, previously demonstrated with 2D, single bed-position data, uses a spectral analysis to find regions in raw PET data which are subject to respiratory motion. The change in counts over time within these regions is then used to estimate the respiratory signal of the patient. In this work, the gating method was adapted to only accept lines of response from a reduced set of axial angles, and the respiratory frequency derived from the lung bed position was used to help identify the respiratory frequency in all other bed positions. As the respiratory signal does not identify the direction of motion, a registration-based technique was developed to align the direction for all bed positions. Data from 11 clinical FDG PET patients were acquired, and an optical respiratory monitor was used to provide a hardware-based signal for comparison. All data were gated using both the data-driven and hardware methods, and reconstructed. The centre of mass of manually defined regions on gated images was calculated, and the overall displacement was defined as the change in the centre of mass between the first and last gates. The mean displacement was 10.3 mm for the data-driven gated images and 9.1 mm for the hardware gated images. No significant difference was found between the two gating methods when comparing the displacement values. The adapted data-driven gating method was demonstrated to successfully produce respiratory gated, 3D, whole body, clinical PET acquisitions.