# US 2017/0286201 A1 · Method and system for facilitating operation of an electronic device > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0286201 A1 - Document ID: US20170286201A1 - Status: Published application - Kind: A1 - Application number: 15/153,781 - Filed: 2016-05-13 - Published: 2017-10-05 - Earliest claimed: 2016-03-30 - Assignees: Wipro Limited - Applicants: Wipro Limited - Inventors: Raghavendra HOSABETTU; Anil Kumar LENKA - Web page: https://patentgrep.com/patent/US20170286201A1 ## CPC - G06F 11/079: {Root cause analysis, i.e. error or fault diagnosis (in a hardware test environment G06F11/22; in a software test environment G06F11/36)} - G06F 16/22: Indexing; Data structures therefor; Storage structures - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method and a system are provided for facilitating operation of an electronic device. The method comprises receiving a semi-structured dataset comprising one or more entities, wherein the semi-structured dataset corresponds to at least an indication of an operation problem associated with an electronic device. The method comprises extracting one or more unique classes associated with one or more entities from the semi-structured dataset. The method comprises creating one or more n-grams representative of a relationship between the one or more entities and the one or more unique classes. The method comprises generating a hypothesis associated with the one or more entities based on a first set of entities from the one or more entities using one or more n-grams, wherein the generated hypothesis corresponds to an operation solution to solve the operation problem associated with the electronic device. ## Claims ### Claim 1 A method for facilitating operation of an electronic device, the method comprising: receiving, by an application server, a semi-structured dataset comprising one or more entities, wherein the semi structured dataset corresponds to at least an indication of an operation problem associated with the electronic device; extracting, by the application server, one or more unique classes associated with one or more entities from the semi-structured dataset; creating, by the application server, one or more n-grams representative of a relationship between the one or more entities and the one or more unique classes; determining, by the application server, a frequency of each of the one or more entities in the one or more n-grams, wherein a weight is assigned to each of the one or more unique classes; and generating, by the application server, a hypothesis associated with the one or more entities based on a first set of entities from the one or more entities using the one or more n-grams, wherein the frequency associated with the first set of entities is greater than a pre-defined threshold, wherein the generated hypothesis corresponds to an operation solution to solve the operation problem associated with the electronic device. ### Claim 2 (depends on 1) The method of, claim 1; wherein the semi-structured dataset corresponds to a SQL database. ### Claim 3 (depends on 1) The method of claim 1, further comprising performing, by the application server, one or more preprocessing operations on the semi-structured dataset, wherein the preprocessing operation comprises at least one of stop word removal, stemming, data curation, Part of Speech (PoS) tagging, synonym/hyponym/hypernym based data enhancement. ### Claim 4 (depends on 1) The method of claim 1, further comprising annotating, by the application server, an unstructured part of the semi-structured dataset based on at least one of the one or more unique classes. ### Claim 5 (depends on 1) The method of claim 1, further comprising ranking, by the application server, the one or more entities based on the determined frequency. ### Claim 6 (depends on 1) The method of claim 1, further comprising validating, by the application server, the generated hypothesis by using one or more natural language models on a test dataset to generate a description of the generated hypothesis. ### Claim 7 (depends on 1) The method of claim 1, further comprising determining, by the application server, one or more dependent variables and one or more independent variables from the semi structured dataset. ### Claim 8 An application server to facilitate operation of an electronic device, the application server comprising: one or more processors configured to; receive a semi-structured dataset comprising one or more entities, wherein the semi-structured dataset corresponds to at least an indication of an operation problem associated with the electronic device; extract one or more unique classes associated with one or more entities from the semi-structured dataset; create one or more n-grams representative of a relationship between the one or more entities and the one or more unique classes; determine a frequency of each of the one or more entities in the one or more n-grams, wherein a weight is assigned to each of the one or ore unique classes; and generate a hypothesis associated with the one or more entities based on a first set of entities from the one or more entities using the one or more n-grams, wherein the frequency associated with the first set of entities is greater than a pre-defined threshold, wherein the generated hypothesis corresponds to an operation solution to solve the operation problem associated with the electronic device. ### Claim 9 (depends on 8) The application server of claim 8, wherein the semi-structured dataset corresponds to a SQL database. ### Claim 10 (depends on 8) The application server of claim 8, wherein the one or more processors are further configured to perform one or more preprocessing operations on the semi-structured dataset, wherein the preprocessing operation comprises at least one of stop word removal, stemming, data curation, Part of Speech (PoS) tagging, synonym/hyponym/hypernym based data enhancement. ### Claim 11 (depends on 8) The application server of claim 8, wherein the one or more processors are further configured to annotate an unstructured part of the semi-structured dataset based on at least one of the one or more unique classes. ### Claim 12 (depends on 8) The application server of claim 8, wherein the one or more processors are further configured to rank the one or more entities based on the determined frequency. ### Claim 13 (depends on 8) The application server of claim 8, wherein the one or more processors are further configured to validate the generated hypothesis by using one or more natural language models on a test dataset to generate a description of the generated hypothesis. ### Claim 14 (depends on 8) The application server of claim 8, wherein the one or more processors are further configured to determine one or more dependent variables and one or more independent variables from the semi structured dataset. ### Claim 15 A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions for causing a computer comprising one or more processors to perform steps comprising: receiving a semi-structured dataset comprising one or more entities, wherein the semi-structured dataset corresponds to at least an indication of an operation problem associated with an electronic device; extracting one or more unique classes associated with one or more entities from the semi-structured dataset; creating one or more n-grams representative of a relationship between the one or more entities and the one or more unique classes; determining a frequency of each of the one or more entities in the one or more n-grams, wherein a weight is assigned to each of the one or more unique classes; and generating a hypothesis associated with the one or more entities based on a first set of entities from the one or more entities using the one or more n-grams, wherein the frequency associated with the first set of entities is greater than a pre-defined threshold, wherein the generated hypothesis corresponds to an operation solution to solve the operation problem associated with the electronic device.