# US 11,030,250 B2 · Cognitive search query validation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,030,250 B2 - Document ID: US11030250B2 - Status: Granted - Kind: B2 - Application number: 16/681,549 - Filed: 2019-11-12 - Published: 2020-03-12 - Granted: 2021-06-08 - Earliest claimed: 2017-05-12 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Lisa Seacat DeLuca; Stefan A. Gert van der stockt - Web page: https://patentgrep.com/patent/US11030250B2 ## CPC - G06F 16/9032: Query formulation - G06F 16/951: Indexing; Web crawling techniques - G06F 16/953: Querying, e.g. by the use of web search engines - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Methods, computer program products, and systems are presented. The methods include, for instance: generating domain language detection models respective to a plurality of domains. A query is mapped to a domain language detection model and intent of the query is determined by use of classification labels of the domain language detection model. Based on the classification labels that may be identified as the intent of the query, alternative queries are formed to be meaningful valid and are produced to a user. ## Claims ### Claim 1 A computer implemented method comprising: generating a plurality of domain language detection models respectively corresponding to respective domains from a plurality of domains; obtaining a query as submitted by a user; selecting a domain language detection model applicable to the query from the plurality of domain language detection models; determining intent of the query, wherein the determining intent includes using the domain language detection model from the selecting; formulating alternative queries based on one or more classification label corresponding to the intent of the query from the determining; and producing the alternative queries. ### Claim 2 (depends on 1) The computer implemented method of claim 1, wherein the plurality of domain language detection models include respective hierarchies of classification labels applicable for respective content of the respective domains. ### Claim 3 (depends on 1) The computer implemented method of claim 1, wherein each of the alternative queries is semantically valid. ### Claim 4 (depends on 1) The computer implemented method of claim 1, wherein the generating a plurality of domain language detection models includes generating a plurality of domain language detection models respectively corresponding to each domain from a plurality of domains, wherein different ones of the plurality of domains are differentiated by subject matter. ### Claim 5 (depends on 1) The computer implemented method of claim 1, wherein the method includes receiving feedback from a user as to accuracy of the alternative queries, and adjusting a domain language detection model for a certain domain in dependence on the feedback. ### Claim 6 (depends on 1) The computer implemented method of claim 1, wherein the method includes receiving input from a user in response to alternative queries, and submitting a query of the alternative queries based on the input. ### Claim 7 (depends on 1) The computer implemented method of claim 1, wherein the determining the intent of the query includes selecting hierarchical classification labels of the domain language detection model from the selecting based on terms of the query. ### Claim 8 (depends on 1) The computer implemented method of claim 1, wherein a hierarchy of classification labels of the domain language detection model from the selecting includes (a) a top level having a classification label specifying a general product category, (b) a second level below the top level having classification labels that specify product types within the general product category, and (c) a third level below the second level having classification labels, for a certain product type referenced in the second level, different brands of the certain product type. ### Claim 9 (depends on 1) The computer implemented method of claim 1, wherein a hierarchy of classification labels of the domain language detection model from the selecting includes (a) a top level having a classification label specifying a general product category, (b) a second level below the top level having classification labels that specify product types within the general product category, and (c) a third level below the second level having classification labels, for a certain product type referenced in the second level, different brands of the certain product type, wherein the determining the intent of the query includes providing hierarchical classification labels of the domain language detection model from the selecting based on terms of the query, wherein the method includes receiving feedback from a user as to an accuracy of the alternative queries, and adjusting a domain language detection model for a certain domain in dependence on the feedback. ### Claim 10 (depends on 1) The computer implemented method of claim 1, wherein the formatting includes generating a first alternative query from a preconfigured number of alternative queries by use of a first classification label corresponding to a greatest confidence value, and one or more other classification label relevant to the first classification label such that the first alternative query is semantically valid and most likely to mean what the user might have meant by the query, wherein the determining comprises generating a plurality of tuples, wherein each tuple includes a classification label from the domain language detection model and a confidence value, wherein the classification label indicates one of intents that may be meant by the query, and wherein the confidence value indicates how probable the query meant the classification label corresponding to the confidence value. ### Claim 11 (depends on 1) The computer implemented method of claim 1, wherein the determining comprises generating a plurality of tuples, wherein each tuple includes a classification label from the domain language detection model and a confidence value, wherein the classification label indicates one of intents that may be meant by the query, and wherein the confidence value indicates how probable the query meant the classification label corresponding to the confidence value. ### Claim 12 (depends on 1) The computer implemented method of claim 1, wherein a hierarchy of classification labels of the domain language detection model from the selecting includes (a) a top level having a classification label specifying a general product category, (b) a second level below the top level having classification labels that specify product types within the general product category, and (c) a third level below the second level having classification labels, for a certain product type referenced in the second level, different brands of the certain product type, wherein the determining the intent of the query includes providing hierarchical classification labels of the domain language detection model from the selecting based on terms of the query, wherein the method includes receiving feedback from a user as to an accuracy of the alternative queries, and adjusting a domain language detection model for a certain domain in dependence on the feedback, wherein the method includes receiving input from the user in response to the alternative queries, and submitting a query of the alternative queries based on the input, wherein the formatting includes generating a first alternative query from a preconfigured number of alternative queries by use of a first classification label corresponding to a greatest confidence value, and one or more other classification label relevant to the first classification label such that the first alternative query is semantically valid and most likely to mean what the user might have meant by the query, wherein the determining comprises generating a plurality of tuples, wherein each tuple includes a classification label from the domain language detection model and a confidence value, wherein the classification label indicates one of intents that may be meant by the query, and wherein the confidence value indicates how probable the query meant the classification label corresponding to the confidence value. ### Claim 13 (depends on 1) The computer implemented method of claim 1, the generating comprising: associating, respectively, words from content of a domain into classification labels, by use of natural language classification tools; and organizing the classifications labels in a hierarchy suitable for the domain. ### Claim 14 (depends on 1) The computer implemented method of claim 1, the obtaining comprising: receiving the query from a search engine, responsive to sending the query by the search engine as being unsuccessful to generate a search result corresponding to the query. ### Claim 15 (depends on 1) The computer implemented method of claim 1, the selecting comprising: applying preconfigured domain selection rules selected from: a category of a present page from which the query was submitted, a department of the present page, one or more user-selected tags for narrowing a search result responding to the query, a topic of the query as returned by a high level topic detector, and combinations thereof; and determining one of the plurality of domain language detection model amongst respective domain language detection models corresponding to domains that fit best to the preconfigured domain selection rules as the domain language detection models applicable to the query. ### Claim 16 (depends on 1) The computer implemented method of claim 1, the determining comprising: generating a plurality of tuples, wherein each tuple includes a classification label from the domain language detection model and a confidence value, wherein the classification label indicates one of intents that may be meant by the query, and wherein the confidence value indicates how probable the query meant the classification label corresponding to the confidence value. ### Claim 17 (depends on 1) The computer implemented method of claim 1, the formulating comprising: generating a first alternative query from the alternative queries by use of a first classification label corresponding to the greatest confidence value, and one or more other classification label relevant to the first classification label such that the first alternative query is semantically valid and most likely to mean what a user might have meant by the query. ### Claim 18 (depends on 1) The computer implemented method of claim 1, the producing further comprising: providing a search result corresponding to one of a preconfigured number of alternative queries in association with the alternative queries. ### Claim 19 A computer program product comprising: a computer readable storage medium readable by one or more processing circuit and storing instructions for execution by one or more processor for performing a method comprising: generating a plurality of domain language detection models respectively corresponding to respective domains from a plurality of domains; obtaining a query as submitted by a user; selecting a domain language detection model applicable to the query from the plurality of domain language detection models; determining intent of the query, wherein the determining intent includes using the domain language detection model from the selecting; formulating alternative queries based on one or more classification label corresponding to the intent of the query from the determining; and producing the alternative queries. ### Claim 20 A system comprising: a memory; one or more processor in communication with the memory; and program instructions executable by the one or more processor via the memory to perform a method comprising: generating a plurality of domain language detection models respectively corresponding to respective domains from a plurality of domains; obtaining a query as submitted by a user; selecting a domain language detection model applicable to the query from the plurality of domain language detection models; determining intent of the query, wherein the determining intent includes using the domain language detection model from the selecting; formulating alternative queries based on one or more classification label corresponding to the intent of the query from the determining; and producing the alternative queries.