US 12,217,758 B2
Automated systems and methods that generate affect-annotated timelines
Vladimir Brayman, Mercer Island, WA (US); John Gottman, Deer Harbor, WA (US); Connor Eaton, Seattle, WA (US); Yuriy Gulak, Highland Park, NJ (US); and Rafael Lisitsa, Seattle, WA (US)
Assigned to Affective Software, Inc., Seattle, WA (US)
Filed by Affective Software, Inc., Seattle, WA (US)
Filed on Jan. 23, 2024, as Appl. No. 18/420,383.
Application 18/420,383 is a continuation of application No. 17/410,791, filed on Aug. 24, 2021, granted, now 11,915,702.
Claims priority of provisional application 63/069,838, filed on Aug. 25, 2020.
Prior Publication US 2024/0161751 A1, May 16, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G10L 15/26 (2006.01); G06N 7/01 (2023.01); G10L 15/02 (2006.01); G10L 15/22 (2006.01)
CPC G10L 15/26 (2013.01) [G06N 7/01 (2023.01); G10L 15/02 (2013.01); G10L 15/22 (2013.01); G10L 2015/221 (2013.01)] 16 Claims
OG exemplary drawing
 
1. An affect-annotated-timeline data structure stored in one of a data-storage device, a data-storage appliance, and an electronic memory within a computer system, the affect-annotated-timeline data structure generated from a monitored conversation by an automated affect-annotation system, the affect-annotated-timeline data structure comprising:
multiple affect-annotation records, each affect-annotation record
representing a conversation unit that
corresponds to a subinterval of the monitored conversation,
that is attributed to a particular source, and
that is generated, by the automated affect-annotation system, from one or more units of language for affect coding, referred to as “ULACs,” each ULAC
corresponding to a minimal aggregation of words extracted from a conversation that conveys an intra-contextual meaning, and
identified by the automated affect-annotation system based on lexical dependency graphs, and
containing
a textual representation of the conversation unit,
an indication of a source of the conversation unit,
an indication of a temporal duration of the conversation unit and an indication of a temporal position of the conversation unit within the monitored conversation, and
an affect-code probability distribution.