Systems and methods for representing, diagnosing, and recommending interaction sequences

• US 9,710,787 B2
• Filed: 07/31/2013
• Issued: 07/18/2017
• Est. Priority Date: 07/31/2013
• Status: Active Grant

• Current Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Original Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Inventors: Leifer, Larry J., Mabogunje, Adegboyega, Sonalkar, Neeraj, Khandelwal, Shashikant
• Primary Examiner(s): Spooner, Lamont
• Assistant Examiner(s): OGUNBIYI, OLUWADAMILOL M
• Application Number:
  US13/955,425
• Publication Number:
  US 20150039289A1
• Time in Patent Office: 1,448 Days
• Field of Search: 705 10, 704 9
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

Systems and methods for representing and diagnosing interaction sequences in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a group interaction diagnosis and recommendation server system includes a processor and a memory configured to store a set of reference interaction data, where the reference interaction data includes a set of reference interaction sequences, wherein a group interaction diagnosis application configures the processor to obtain a set of group interaction data, generate an interaction model based on the group interaction data and an interaction dynamics language, determine at least one interaction sequence within the set of group interaction data based on the generated interaction model, identify at least one matching interaction sequence within the determined at least one interaction sequence, and recommend at least one improved interaction sequence based on the identified at least one matching interaction sequence and the set of reference interaction data.

Claims

1. A group interaction diagnostic and recommendation server system for processing combined verbal, gestural, and body language communications data, comprising:

a processor; and

a memory configured to store;

a group interaction diagnosis application; and

a set of reference interaction data, where the reference interaction data comprises a set of reference interaction sequences;

wherein the group interaction diagnosis application configures the processor to;

obtain a set of group interaction data, the group interaction data comprising video data and audio data recording verbal, gestural, and body language communications within a working team;

generate an interaction model based on the group interaction data and an interaction dynamics language, where the interaction dynamics language comprises a set of labels describing actions that can occur in response to an action by a speaker in an interaction sequence including forward labels, blocking labels, and supporting labels;

determine at least one interaction sequence within the set of group interaction data based on the generated interaction model;

identify at least one matching interaction sequence by comparing the determined at least one interaction sequence with the set of reference interaction sequences;

determine a recommendation including at least one improved interaction sequence within the set of reference interaction sequences, based on the identified at least one matching interaction sequence and the set of reference interaction data, wherein the at least one improved interaction sequence is identified as being more conducive to group interaction than a corresponding interaction sequence within the determined at least one interaction sequence; and

presenting the recommendation on an output device during communications within the working team.

2. The group interaction diagnosis and recommendation server system of claim 1, wherein the set of labels in the interaction dynamics language comprises a subset of forward labels.

3. The group interaction diagnosis and recommendation server system of claim 2, wherein the subset of forward labels is selected from the set of labels consisting of a “

move”

label, a “

question”

label, an “

overcoming”

label, a “

deflection”

label, and a “

yes and”

label.

4. The group interaction diagnosis and recommendation server system of claim 1, wherein the set of labels in the interaction dynamics language comprises a subset of blocking labels.

5. The group interaction diagnosis and recommendation server system of claim 4, wherein the subset of blocking labels is selected from the set of labels consisting of a “

hesitation”

label, a “

block”

label, and an “

interruption”

label.

6. The group interaction diagnosis and recommendation server system of claim 1, wherein the set of labels in the interaction dynamics language comprises a subset of supporting labels.

7. The group interaction diagnosis and recommendation server system of claim 6, wherein the subset of supporting labels is selected from the set of labels consisting of a “

humor”

label, a “

support for move”

label, and “

a support for block”

label.

8. The group interaction diagnosis and recommendation server system of claim 1, wherein the set of labels in the interaction dynamics language comprises a label selected from the group consisting of a “

beginning”

label and an “

ending”

label.

9. The group interaction diagnosis and recommendation server system of claim 1, wherein a label in the set of labels in the interaction dynamics language includes timing data describing the time associated with the action corresponding to the label.

10. The group interaction diagnosis and recommendation server system of claim 1, wherein a label in the set of labels in the interaction dynamics language includes member metadata describing the group member performing the action corresponding to the label.

11. The group interaction diagnosis and recommendation server system of claim 1, wherein the group interaction diagnosis application further configures the processor to identify at least one matching interaction sequence within the determined at least one interaction sequence by:

determining at least one subsequence of labels within the at least one interaction sequence;

locating a relevant portion of the set of reference interaction data, where the relevant portion of the set of reference interaction data comprises at least one reference subsequence of labels; and

aligning the at least one subsequence of labels to the relevant portion of the set of reference interaction data so that the at least one subsequence of labels corresponds to the at least one reference subsequence of labels.

12. The group interaction diagnosis and recommendation server system of claim 11, wherein the group interaction diagnosis application further configures the processor to align the at least one subsequence of labels utilizing genomic sequence alignments selected from the group consisting of global alignments and local alignments.

13. The group interaction diagnosis and recommendation server system of claim 11, wherein the group interaction diagnosis application further configures the processor to score the aligned subsequences based on the number of corresponding labels between the at least one subsequence of labels and the at least one reference subsequence of labels.

14. The group interaction diagnosis and recommendation server system of claim 13, wherein the group interaction diagnosis application further configures the processor to recommend at least one improved interaction sequence based on the scored aligned subsequences.

15. The group interaction diagnosis and recommendation server system of claim 1, wherein the group interaction diagnosis application further configures the processor to refine the reference interaction data based on the identified interaction sequences.

16. The group interaction diagnosis and recommendation server system of claim 15, wherein the group interaction diagnosis application further configures the processor to refine the reference interaction data by:

locating at least one new interaction sequence within the at least one interaction sequence not present within the reference interaction data; and

including the at least one new interaction sequence in the reference interaction data.

17. The group interaction diagnosis and recommendation server system of claim 1, wherein a label in the set of labels in the interaction dynamics language comprises space metadata describing the space associated with the action corresponding to the label.

18. The group interaction diagnosis and recommendation server system of claim 17, wherein the space metadata is selected from the group consisting of an idea space and a reality space.

19. The group interaction diagnosis and recommendation server system of claim 1, wherein the group interaction data further comprises video data and audio data recording facial expression communications within a working team.

20. The group interaction diagnosis and recommendation server system of claim 1, wherein the set of labels further includes elaborating labels.

21. The group interaction diagnosis and recommendation server system of claim 1, wherein:

the memory is further configured to store an intervention database, where the intervention database comprises a set of intervention data and a set of interaction/intervention relationships; and

the group interaction diagnosis application further configures the processor to identify at least one piece of intervention data based on the recommended at least one interaction sequence, where a piece of intervention data in the set of intervention data comprises data utilized by a coach to guide the members of the group through an interaction process.

22. A method for representing, diagnosing, and recommending group interactions based on combined verbal, gestural, and body language communications data, comprising:

obtaining a set of group interaction data using a group interaction diagnosis and recommendation server system, the group interaction data comprising video data and audio data recording verbal, gestural, and body language communications within a working team;

generating an interaction model based on the group interaction data and an interaction dynamics language using the group interaction diagnosis and recommendation server system, where the interaction dynamics language comprises a set of labels describing actions that can occur in response to an action by a speaker in an interaction sequence including forward labels, blocking labels, and supporting labels;

determining at least one interaction sequence within the set of group interaction data based on the generated interaction model using the group interaction diagnosis and recommendation server system;

identifying at least one matching, interaction sequence by comparing the determined at least one interaction sequence with a set of reference interaction sequences using the group interaction diagnosis and recommendation server system, where a set of reference interaction data comprises the set of reference interaction sequences; and

determining a recommendation including at least one improved interaction sequence within the set of reference interaction sequences, based on the identified at least one matching interaction sequence and the set of reference interaction data using the group interaction diagnosis and recommendation server system, wherein the at least one improved interaction sequence is identified as being more conducive to group interaction than a corresponding interaction sequence within the determined at least one interaction sequence; and

presenting the recommendation on an output device during communications within the working team.

23. The method of claim 22, wherein the set of labels in the interaction dynamics language comprises a subset of forward labels.

24. The method of claim 23, wherein the subset of forward labels is selected from the set of labels consisting of a “

move”

label, a “

question”

label, an “

overcoming”

label, a “

deflection”

label, and a “

yes and”

label.

25. The method of claim 22, wherein the set of labels in the interaction dynamics language comprises a subset of blocking labels.

26. The method of claim 25, wherein the subset of blocking labels is selected from the set of labels consisting of a “

hesitation”

label, a “

block”

label, and an “

interruption”

label.

27. The method of claim 22, wherein the set of labels in the interaction dynamics language comprises a subset of supporting labels.

28. The method of claim 27, wherein the subset of supporting labels is selected from the set of labels consisting of a “

humor”

label, a “

support for move”

label, and “

a support for block”

label.

29. The method of claim 22, wherein the set of labels in the interaction dynamics language comprises a label selected from the group consisting of a “

beginning”

label and an “

ending”

label.

30. The method of claim 22, wherein a label in the set of labels in the interaction dynamics language includes timing data describing the time associated with the action corresponding to the label.

31. The method of claim 22, wherein a label in the set of labels in the interaction dynamics language includes member metadata describing the group member performing the action corresponding to the label.

32. The method of claim 22, wherein identifying at least one matching interaction sequence within the determined at least one interaction sequence comprises:

determining at least one subsequence of labels within the at least one interaction sequence using the group interaction diagnosis and recommendation server system;

locating a relevant portion of the set of reference interaction data using the group interaction diagnosis and recommendation server system, where the relevant portion of the set of reference interaction data comprises at least one reference subsequence of labels; and

aligning the at least one subsequence of labels to the relevant portion of the set of reference interaction data so that the at least one subsequence of labels corresponds to the at least one reference subsequence of labels using the group interaction diagnosis and recommendation server system.

33. The method of claim 32, wherein aligning the at least one subsequence of labels utilizes genomic sequence alignments selected from the group consisting of global alignments and local alignments.

34. The method of claim 32, further comprising scoring the aligned subsequences based on the number of corresponding labels between the at least one subsequence of labels and the at least one reference subsequence of labels using the group interaction diagnosis and recommendation server system.

35. The method of claim 34, further comprising recommending at least one improved interaction sequence based on the scored aligned subsequences using the group interaction diagnosis and recommendation server system.

36. The method of claim 22, further comprising refining the reference interaction data based on the identified interaction sequences using the group interaction diagnosis and recommendation server system.

37. The method of claim 36, wherein refining the reference interaction data comprises:

locating at least one new interaction sequence within the at least one interaction sequence not present within the reference interaction data using the group interaction diagnosis and recommendation server system; and

including the at least one new interaction sequence in the reference interaction data using the group interaction diagnosis and recommendation server system.

38. The method of claim 22, wherein a label in the set of labels in the interaction dynamics language comprises space metadata describing the space associated with the action corresponding to the label.

39. The method of claim 38, wherein the space metadata is selected from the group consisting of an idea space and a reality space.

40. The method of claim 22, further comprising identifying at least one piece of intervention data based on the recommended at least one interaction sequence using the group interaction diagnosis and recommendation server system, where a piece of intervention data in the set of intervention data comprises data utilized by a coach to guide the members of the group through a more effective interaction process.

41. The method of claim 22, wherein the group interaction data further comprises video data and audio data recording facial expression communications within a working team.

42. The method of claim 22, wherein the set of labels further includes elaborating labels.