# US 2018/0322121 A1 · Answer sequence discovery and generation

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- Number: US 2018/0322121 A1
- Document ID: US20180322121A1
- Status: Published application
- Kind: A1
- Application number: 16/035,813
- Filed: 2018-07-16
- Published: 2018-11-08
- Earliest claimed: 2014-11-05
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Kevin S. Barker; Thomas J. Eggebraaten; Mark G. Megerian; John E. Petri
- Web page: https://patentgrep.com/patent/US20180322121A1

## CPC

- G06F 16/24522: {Translation of natural language queries to structured queries}
- G06F 16/24575: {using context}
- G06F 16/24578: {using ranking}
- G06F 16/284: {Relational databases}
- G06F 16/285: {Clustering or classification}
- G06F 16/3329: Natural language query formulation
- G06F 16/3332: Query translation
- G06F 16/3334: {Selection or weighting of terms from queries, including natural language queries}
- G06F 16/3344: {using natural language analysis}
- G06F 16/367: {Ontology}
- G06F 16/90324: {using system suggestions}
- G06F 16/951: Indexing; Web crawling techniques
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06F 16/9538: Presentation of query results
- G06F 16/955: using information identifiers, e.g. uniform resource locators [URL]
- G06F 40/205: Parsing
- G06F 40/211: Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
- G06F 40/284: Lexical analysis, e.g. tokenisation or collocates
- G06F 40/30: Semantic analysis
- G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30)
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06N 5/046: Forward inferencing; Production systems
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- G06Q 30/0203: Market surveys; Market polls
- G09B 7/00: Electrically-operated teaching apparatus or devices working with questions and answers (mechanically operated G09B3/00; computing arrangements G06F)
- G09B 7/02: of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
- G16H 10/20: for electronic clinical trials or questionnaires
- G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR

## Abstract

Aspects of the present disclosure are directed toward discovering and generating answer sequences. Aspects are directed toward parsing, by a natural language processing technique configured to analyze syntactic and semantic content, a corpus of data for a subject matter. Aspects are also directed toward detecting a first set of answers including a first answer corresponding to a first answer category and a second set of answers including a second answer corresponding to a second answer category. Both the first and the second answer categories may relate to the subject matter. Aspects are also directed toward identifying a first set of ordering data for the first set of answers and the second set of answers. Aspects are also directed toward determining a first answer sequence corresponding to an order of the first set of answers and the second set of answers.

## Claims

### Claim 1

A method for treating a human patient having a particular disease, the method comprising:
providing medical history information about the patient a processor of medical question-answering system;
parsing, by the processor using a natural language processing technique configured to analyze syntactic and semantic content, the medical history information;
performing, based on the parsed medical history information, a first search of a medical corpus containing a plurality of sources having information about the particular disease;
detecting, by the processor and based on the first search, a group of medical treatments relevant to the particular disease, wherein the detected medical treatments are identified from different sources of the plurality of sources from each other;
analyzing, by the processor and after the group of medical treatments have been detected, similarities and difference among the detected medical treatments;
sorting, by the processor and based on the analysis, the detected medical treatments into at least three treatment categories, including sorting a first plurality of the detected medical treatments into a first treatment category, a second plurality of the detected medical treatments into a second treatment category, and a third plurality of the detected medical treatments into a third treatment category, wherein each treatment category includes medical treatments that are similar in type to each other as determined based on the analysis;
performing, by the processor and based on the sorting of the detected medical treatments, a second search of the medical corpus;
identifying, by the processor and based on the second search, ordering data indicating a particular arrangement of a first medical treatment of the first treatment category, a second medical treatment of the second treatment category, and a third medical treatment of the third treatment category relative to each other, wherein the ordering data is identified based on a particular at least one source that is different from any of the sources from which the first medical treatment, second medical treatment, and third medical treatment were identified based on the first search;
generating, by the processor and based on the ordering data, a medical treatment plan for the patient that includes a combination of the first medical treatment, the second medical treatment, and the third medical treatment ordered within the medical treatment plan as indicated by the ordering data; and
treating the patient for the particular disease with the first medical treatment, the second medical treatment, and the third medical treatment in an order dictated by the medical treatment plan.

### Claim 2

A computer program product for treating a human patient having a particular disease, the computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a processor, causes the processor to perform a method comprising:
receiving, by the processor which is part of a medical question-answering system, medical history information about the patient;
parsing, by the processor using a natural language processing technique configured to analyze syntactic and semantic content, the medical history information;
performing, based on the parsed medical history information, a first search of a medical corpus containing a plurality of sources having information about the particular disease;
detecting, by the processor and based on the first search, a group of medical treatments relevant to the particular disease, wherein the detected medical treatments are identified from different sources of the plurality of sources from each other;
analyzing, by the processor and after the group of medical treatments have been detected, similarities and difference among the detected medical treatments;
sorting, by the processor and based on the analysis, the detected medical treatments into at least three treatment categories, including sorting a first plurality of the detected medical treatments into a first treatment category, a second plurality of the detected medical treatments into a second treatment category, and a third plurality of the detected medical treatments into a third treatment category, wherein each treatment category includes medical treatments that are similar in type to each other as determined based on the analysis;
performing, by the processor and based on the sorting of the detected medical treatments, a second search of the medical corpus;
identifying, by the processor and based on the second search, ordering data indicating a particular arrangement of a first medical treatment of the first treatment category, a second medical treatment of the second treatment category, and a third medical treatment of the third treatment category relative to each other, wherein the ordering data is identified based on a particular at least one source that is different from any of the sources from which the first medical treatment, second medical treatment, and third medical treatment were identified based on the first search; and
generating, by the processor and based on the ordering data, a medical treatment plan for the patient that includes a combination of the first medical treatment, the second medical treatment, and the third medical treatment ordered within the medical treatment plan as indicated by the ordering data,
wherein the patient is treated for the particular disease with the first medical treatment, the second medical treatment, and the third medical treatment in an order dictated by the medical treatment plan.

### Claim 3

A computer system for treating a human patient having a particular disease, the computer system comprising:
a memory; and
a processor coupled to the memory, the processor configured to obtain instructions from the memory that cause the processor to perform a method comprising:
receiving medical history information about the patient;
parsing, using a natural language processing technique configured to analyze syntactic and semantic content, the medical history information;
performing, based on the parsed medical history information, a first search of a medical corpus containing a plurality of sources having information about the particular disease;
detecting, based on the first search, a group of medical treatments relevant to the particular disease, wherein the detected medical treatments are identified from different sources of the plurality of sources from each other;
analyzing, after the group of medical treatments have been detected, similarities and difference among the detected medical treatments;
sorting, based on the analysis, the detected medical treatments into at least three treatment categories, including sorting a first plurality of the detected medical treatments into a first treatment category, a second plurality of the detected medical treatments into a second treatment category, and a third plurality of the detected medical treatments into a third treatment category, wherein each treatment category includes medical treatments that are similar in type to each other as determined based on the analysis;
performing, based on the sorting of the detected medical treatments, a second search of the medical corpus;
identifying, based on the second search, ordering data indicating a particular arrangement of a first medical treatment of the first treatment category, a second medical treatment of the second treatment category, and a third medical treatment of the third treatment category relative to each other, wherein the ordering data is identified based on a particular at least one source that is different from any of the sources from which the first medical treatment, second medical treatment, and third medical treatment were identified based on the first search; and
generating, based on the ordering data, a medical treatment plan for the patient that includes a combination of the first medical treatment, the second medical treatment, and the third medical treatment ordered within the medical treatment plan as indicated by the ordering data,
wherein the patient is treated for the particular disease with the first medical treatment, the second medical treatment, and the third medical treatment in an order dictated by the medical treatment plan.