Conversational flow apparatus and technique

1 . An adaptive conversational flow engine comprising:

a machine-learning model comprising at least one sequence of states of a conversational flow;

an anomaly detector operable to monitor said at least one sequence of states in operation;

data capture logic operable in response to said anomaly detector to capture data linked to a detected anomaly at an anomaly-detected state of said at least one sequence of states in operation;

annotator logic operable in response to said data capture logic to link a tag with at least said data to said anomaly-detected state to create a tagged state; and

refinement logic to refine said machine-learning model according to inputs obtained using said tagged state.

2 . The adaptive conversational flow engine of claim 1 , operable to support natural-language interaction.

3 . The adaptive conversational flow engine of claim 1 , said anomaly detector operable to detect at least one of an ambiguity in an interaction, a failure to extract meaning from a response, a divergence in conversational flow topic, a missing response, an indicator of noise interference, an indicator of emotive response and an indicator of a misunderstood question-response interaction.

4 . The adaptive conversational flow engine of claim 1 , said anomaly detector further operable to localise an effect of said anomaly-detected state to a phase in the operation of the conversational flow engine.

5 . The adaptive conversational flow engine of claim 1 , said data capture logic further operable to capture data linked to at least one predecessor state of said anomaly-detected state.

6 . The adaptive conversational flow engine of claim 1 , said data capture logic further operable to capture data linked to at least one potential successor state of said anomaly-detected state.

7 . The adaptive conversational flow engine of claim 1 , said refinement logic operable to retrain said machine-learning model.

8 . The adaptive conversational flow engine according to claim 1 , said inputs obtained using said tagged state comprising a noise adjustment algorithm output.

9 . The adaptive conversational flow engine according to claim 1 , said inputs obtained using said tagged state comprising previously-stored data associated with a user of said adaptive conversational flow engine.

10 . The adaptive conversational flow engine according to claim 1 , said inputs obtained using said tagged state comprising outputs of machine reasoning over said data linked to said detected anomaly.

11 . The adaptive conversational flow engine according to claim 1 , further comprising a knowledge base provisioned with data derived from at least one prior instance of handling an anomaly.

12 . The adaptive conversational flow engine according to claim 1 , further comprising explainer logic to store and make available reasoning data for at least one instance of handling an anomaly.

13 . A method of operating a conversational flow engine comprising:

accessing a machine-learning model comprising at least one sequence of states of a conversational flow;

monitoring said at least one sequence of states in operation to detect at least one anomaly;

responsive to detection of said at least one anomaly, capturing data linked to a detected anomaly at an anomaly-detected state of said at least one sequence of states in operation;

responsive to said capturing data, linking a tag with at least said data to said anomaly-detected state to create a tagged state; and

refining said machine-learning model according to inputs obtained using said tagged state.

14 . The method of claim 13 , further comprising operating said anomaly detector to detect at least one of an ambiguity in an interaction, a failure to extract meaning from a response, a divergence in conversational flow topic, a missing response, an indicator of noise interference, an indicator of emotive response and an indicator of a misunderstood question-response interaction.

15 . The method of claim 13 , further comprising operating said anomaly detector to localise an effect of said anomaly-detected state to a phase in the operation of the conversational flow engine.

16 . The method of claim 13 , further comprising operating said data capture logic to capture data linked to at least one of a predecessor state of said anomaly-detected state and a potential successor state of said anomaly-detected state.

17 . The method of claim 13 , further comprising operating said refinement logic to retrain said machine-learning model.

18 . The method of claim 13 , said inputs obtained using said tagged state comprising at least one of a noise adjustment algorithm output, previously-stored data associated with a user of said adaptive conversational flow engine and outputs of machine reasoning over said data linked to said detected anomaly.

19 . The method of claim 13 , further comprising operating explainer logic to store and make available reasoning data for at least one instance of handling an anomaly.

20 . A computer program product stored on a non-transitory computer-readable storage medium and comprising computer program code to, when loaded into a computer system and executed thereon, cause said computer to:

access a machine-learning model comprising at least one sequence of states of a conversational flow;

monitor said at least one sequence of states in operation to detect at least one anomaly;

responsive to detection of said at least one anomaly, capture data linked to a detected anomaly at an anomaly-detected state of said at least one sequence of states in operation;

responsive to said capturing data, link a tag with at least said data to said anomaly-detected state to create a tagged state; and

refine said machine-learning model according to inputs obtained using said tagged state.