  The impulses, cutting entropy functional (EF) measure on trajectories Markov
diffusion process, integrate information path functional (IPF) composing
discrete information Bits extracted from observing random process. Each cut
brings memory of the cutting entropy, which provides both reduction of the
process entropy and discrete unit of the cutting entropy a Bit. Consequently,
information is memorized entropy cutting in random observations which process
interactions. The origin of information associates with anatomy creation of
impulse enables both cut entropy and stipulate random process generating
information under the cut. Memory of the impulse cutting time interval freezes
the observing events dynamics in information processes. Diffusion process
additive functional defines EF reducing it to a regular integral functional.
Compared to Shannon entropy measure of random state, cutting process on
separated states decreases quantity information concealed in the states
correlation holding hidden process information. Infinite dimensional process
cutoffs integrate finite information in IPF whose information approaches EF
restricting process maximal information. Within the impulse reversible
microprocess, conjugated entropy increments are entangling up to the cutoff
converting entropy in irreversible information. Extracting maximum of minimal
impulse information and transferring minimal entropy between impulses implement
maxmin-minimax principle of optimal conversion process entropy to information.
Macroprocess extremals integrate entropy of microprocess and cutoff information
of impulses in the IPF information physical process. IPF measures Feller kernel
information. Estimation extracting information confirms nonadditivity of EF
measured process increments.
