Conversational agent

• US 10,152,972 B1
• Filed: 08/31/2013
• Issued: 12/11/2018
• Est. Priority Date: 05/15/2013
• Status: Expired due to Fees

• Current Assignee: Allscripts Software LLC (Veradigm, Inc.)
• Original Assignee: Allscripts Software LLC (Veradigm, Inc.)
• Inventors: Thomas, Matthew David Dreselly, Loftus, William, Wepuri, Harry, Ogan, Arif
• Primary Examiner(s): Dorvil, Richemond
• Assistant Examiner(s): Blankenagel, Bryan
• Application Number:
  US14/016,083
• Time in Patent Office: 1,928 Days
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A method includes converting a user'"'"'s utterance to text; encapsulating the converted text in a rheme object; searching, for each of a plurality of topics, for keywords in the converted text; determining a relevancy metric for each of the plurality of topics based on such searching; selecting one or more topics based on determined relevancy metrics; comparing some or all of the converted text to names in one or more patient lists or databases; identifying a unique patient whose name is contained in the converted; attaching an indication of the identified patient to the rheme object; effecting an action based on the selected one or more topics and the attached patient indication; and saving the topic in a conversation history with a reference to the identified patient.

Claims

1. A processor-based method for operating an electronic device configured to function as an agent for a user interface in a medical environment utilizing conversation histories, the method comprising:

(a) receiving, from a user of the electronic device via a microphone of the electronic device, a first utterance corresponding to a first spoken command, the first spoken command identifying a first patient;

(b) converting, utilizing one or more processors, the first utterance to first text;

(c) encapsulating, utilizing one or more processors, the converted first text in a first rheme object;

(d) searching, for each of a plurality of topics utilizing one or more processors, for keywords in the converted first text;

(e) determining, utilizing one or more processors, a relevancy metric for each of the plurality of topics based on the keyword searching of the converted first text;

(f) selecting, utilizing one or more processors, one or more topics as a first set of one or more relevant topics based on the determined relevancy metrics;

(g) comparing, utilizing one or more processors, some or all of the converted first text to names in one or more patient lists or databases;

(h) determining, utilizing one or more processors based on comparing some or all of the converted first text, that the first utterance included an identification of the first patient based on successful matching during the comparing of some or all of the converted first text;

(i) based on the determination that the first utterance included an identification of the first patient, attaching, utilizing one or more processors, an indication of the identified first patient to the first rheme object;

(j) displaying, to the user via a display device associated with the electronic device, first information associated with the first patient based on the selected first set of one or more relevant topics and the identified first patient;

(k) saving, utilizing one or more processors, the selected first set of one or more relevant topics in a conversation history in association with the identified first patient;

(l) receiving, from the user of the electronic device via the microphone of the electronic device, a second utterance corresponding to a second spoken command, the second spoken command not including an identification of a patient;

(m) converting, utilizing one or more processors, the second utterance to second text;

(n) encapsulating, utilizing one or more processors, the converted second text in a second rheme object;

(o) searching, for each of the plurality of topics utilizing one or more processors, for keywords in the converted second text;

(p) determining, utilizing one or more processors, a relevancy metric for each of the plurality of topics based on the keyword searching of the converted second text;

(q) selecting, utilizing one or more processors, one or more topics as a second set of one or more relevant topics based on the determined relevancy metrics;

(r) comparing, utilizing one or more processors, some or all of the converted second text to names in the one or more patient lists or databases;

(s) determining, utilizing one or more processors based on comparing some or all of the converted second text, that the second utterance did not include an identification of a patient, and, based thereon, attaching an indication of the previously identified first patient to the second rheme object;

(t) displaying, to the user via the display device associated with the electronic device, second information associated with the first patient based on the selected second set of one or more relevant topics and the previous identification of the first patient; and

(u) generating, via the one or more processors, an updated conversation history comprising the selected second set of one or more relevant topics associated with the previously identified first patient.

2. The processor-based method of claim 1, wherein the method further includes:

(a) receiving, from the user of the electronic device via the microphone of the electronic device, a third utterance corresponding to a third spoken command, the third spoken command identifying a second patient;

(b) converting, utilizing one or more processors, the third utterance to third text;

(c) encapsulating, utilizing one or more processors, the converted third text in a third rheme object;

(d) searching, for each of a plurality of topics utilizing one or more processors, for keywords in the converted third text;

(e) determining, utilizing one or more processors, a relevancy metric for each of the plurality of topics based on the keyword searching of the converted third text;

(f) selecting, utilizing one or more processors, one or more topics as a third set of one or more relevant topics based on the determined relevancy metrics;

(g) comparing, utilizing one or more processors, some or all of the converted third text to names in one or more patient lists or databases;

(h) determining, utilizing one or more processors based on comparing some or all of the converted third text, that the third utterance included an identification of the second patient;

(i) attaching, utilizing one or more processors, an indication of the identified second patient to the third rheme object;

(j) displaying, to the user via the display device associated with the electronic device, third information associated with the second patient based on the selected third set of one or more relevant topics and the identified second patient;

and

(k) saving, utilizing one or more processors, the selected third set of one or more relevant topics in a conversation history in association with the identified second patient.

3. The processor-based method of claim 2, wherein the method further includes

(a) receiving, from the user of the electronic device via the microphone of the electronic device, a fourth utterance corresponding to a fourth spoken command, the fourth spoken command not including an identification of a patient;

(b) converting, utilizing one or more processors, the fourth utterance to fourth text;

(c) encapsulating, utilizing one or more processors, the converted fourth text in a fourth rheme object;

(d) searching, for each of a plurality of topics utilizing one or more processors, for keywords in the converted fourth text;

(e) determining, utilizing one or more processors, a relevancy metric for each of the plurality of topics based on the keyword searching of the converted fourth text;

(f) selecting, utilizing one or more processors, one or more topics as a fourth set of one or more relevant topics based on the determined relevancy metrics;

(g) comparing, utilizing one or more processors, some or all of the converted fourth text to names in the one or more patient lists or databases;

(h) determining, utilizing one or more processors based on comparing some or all of the converted fourth text, that the fourth utterance did not include an identification of a patient, and, based thereon, attaching an indication of the previously identified second patient to the fourth rheme object;

(i) displaying, to the user via the display device associated with the electronic device, fourth information associated with the second patient based on the selected fourth set of one or more relevant topics and the previous identification of the second patient; and

(j) saving, utilizing one or more processors, the selected fourth set of one or more relevant topics in a conversation history in association with the previously identified second patient.

4. The processor-based method of claim 1, wherein the electronic device comprises a mobile device.

5. The processor-based method of claim 1, wherein the electronic device comprises a tablet.

6. The processor-based method of claim 1, wherein the electronic device comprises a phone.

7. The processor-based method of claim 1, wherein the electronic device comprises a laptop.

8. The processor-based method of claim 1, wherein the electronic device comprises a desktop.

9. The processor-based method of claim 1, wherein the display device associated with the electronic device comprises a touchscreen.

10. The processor-based method of claim 1, wherein the display device associated with the electronic device comprises a monitor.

11. The processor-based method of claim 1, wherein the display device associated with the electronic device comprises a television.

12. The processor-based method of claim 1, wherein converting, utilizing one or more processors, the first utterance to first text is performed at the electronic device.

13. The processor-based method of claim 1, wherein converting, utilizing one or more processors, the first utterance to first text is performed remote from the electronic device.

14. The processor-based method of claim 1, wherein converting, utilizing one or more processors, the first utterance to first text is performed at a first server remote from the electronic device.

15. The processor-based method of claim 14, wherein encapsulating, utilizing one or more processors, the converted first text in a first rheme object is performed at the first server.

16. The processor-based method of claim 14, wherein encapsulating, utilizing one or more processors, the converted first text in a first rheme object is performed at a second server.

17. The processor-based method of claim 1, wherein displaying first information associated with the first patient comprises displaying results associated with the first patient.

18. The processor-based method of claim 1, wherein each of the determined relevancy metrics comprises a numerical score.

19. A processor-based method for operating an electronic device configured to function as an agent for a user interface in a medical environment utilizing conversation histories, the method comprising:

(a) receiving, from a user of the electronic device via a microphone of the electronic device, a first utterance corresponding to a first spoken command, the first spoken command identifying a first patient;

(b) converting, utilizing one or more processors, the first utterance to first text;

(c) encapsulating, utilizing one or more processors, the converted first text in a first rheme object;

(d) searching, for each of a plurality of topics utilizing one or more processors, for keywords in the converted first text;

(e) determining, utilizing one or more processors, a relevancy metric for each of the plurality of topics based on the keyword searching of the converted first text;

(f) selecting, utilizing one or more processors, one or more topics as a first set of one or more relevant topics based on the determined relevancy metrics;

(g) comparing, utilizing one or more processors, some or all of the converted first text to names in one or more patient lists or databases;

(h) determining, utilizing one or more processors based on comparing some or all of the converted first text, that the first utterance included an identification of the first patient based on successful matching during the comparing of some or all of the converted first text;

(i) based on the determination that the first utterance included an identification of the first patient, attaching, utilizing one or more processors, an indication of the identified first patient to the first rheme object;

(j) generating and displaying, to the user via a display device associated with the electronic device, first information associated with the first patient based on the selected first set of one or more relevant topics and the identified first patient;

(k) receiving, from the user of the electronic device via the microphone of the electronic device, a second utterance corresponding to a second spoken command, the second spoken command not including an identification of a patient;

(l) converting, utilizing one or more processors, the second utterance to second text;

(m) encapsulating, utilizing one or more processors, the converted second text in a second rheme object;

(n) searching, for each of the plurality of topics utilizing one or more processors, for keywords in the converted second text;

(o) determining, utilizing one or more processors, a relevancy metric for each of the plurality of topics based on the keyword searching of the converted second text;

(p) selecting, utilizing one or more processors, one or more topics as a second set of one or more relevant topics based on the determined relevancy metrics;

(q) comparing, utilizing one or more processors, some or all of the converted second text to names in the one or more patient lists or databases;

(r) determining, utilizing one or more processors based on comparing some or all of the converted second text, that the second utterance did not include an identification of a patient, and, based thereon, attaching an indication of the previously identified first patient to the second rheme object; and

(s) generating and displaying, to the user via the display device associated with the electronic device, second information associated with the first patient based on the selected second set of one or more relevant topics and the previous identification of the first patient.

20. A processor-based method for operating a mobile device configured to function as an agent for a user interface in a medical environment utilizing conversation histories, the method comprising:

(a) receiving, from a user of the mobile device via a microphone of the mobile device, a first utterance corresponding to a first spoken command, the first spoken command identifying a first patient;

(b) converting, utilizing one or more processors remote from the mobile device, the first utterance to first text;

(c) encapsulating, utilizing one or more processors, the converted first text in a first rheme object;

(d) searching, for each of a plurality of topics utilizing one or more processors, for keywords in the converted first text;

(e) determining, utilizing one or more processors, a relevancy metric for each of the plurality of topics based on the keyword searching of the converted first text;

(f) selecting, utilizing one or more processors, one or more topics as a first set of one or more relevant topics based on the determined relevancy metrics;

(g) comparing, utilizing one or more processors, some or all of the converted first text to names in one or more patient lists or databases;

(h) determining, utilizing one or more processors based on comparing some or all of the converted first text, that the first utterance included an identification of the first patient based on successful matching during the comparing of some or all of the converted first text;

(i) based on the determination that the first utterance included an identification of the first patient, attaching, utilizing one or more processors, an indication of the identified first patient to the first rheme object;

(j) generating and displaying, to the user via a display device associated with the mobile device, first information associated with the first patient based on the selected first set of one or more relevant topics and the identified first patient;

(k) receiving, from the user of the mobile device via the microphone of the mobile device, a second utterance corresponding to a second spoken command, the second spoken command not including an identification of a patient;

(l) converting, utilizing one or more processors remote from the mobile device, the second utterance to second text;

(m) encapsulating, utilizing one or more processors, the converted second text in a second rheme object;

(n) searching, for each of another plurality of topics utilizing one or more processors, for keywords in the converted second text;

(o) determining, utilizing one or more processors, a relevancy metric for each of the another plurality of topics based on the keyword searching of the converted second text;

(p) selecting, utilizing one or more processors, one or more topics as a second set of one or more relevant topics based on the determined relevancy metrics;

(q) comparing, utilizing one or more processors, some or all of the converted second text to names in the one or more patient lists or databases;

(r) determining, utilizing one or more processors based on comparing some or all of the converted second text, that the second utterance did not include an identification of a patient, and, based thereon, attaching an indication of the previously identified first patient to the second rheme object; and

(s) generating and displaying, to the user via the display device associated with the mobile device, second information associated with the first patient based on the selected second set of one or more relevant topics and the previous identification of the first patient.