# US 2017/0193398 A1 · System and method for auto-query generation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0193398 A1 - Document ID: US20170193398A1 - Status: Published application - Kind: A1 - Application number: 15/351,002 - Filed: 2016-11-14 - Published: 2017-07-06 - Earliest claimed: 2012-08-31 - Assignees: Nutonian, Inc. - Applicants: Nutonian, Inc. - Inventors: Michael Schmidt - Web page: https://patentgrep.com/patent/US20170193398A1 ## CPC - G06N 20/00: Machine learning - G06F 16/2428: {Query predicate definition using graphical user interfaces, including menus and forms (G06F16/2423 takes precedence)} - G06F 16/2453: Query optimisation - G06F 40/18: of spreadsheets (form-filling G06F40/174) - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Various systems and methods provide an intuitive user interface that enables automatic specification of queries and constraints for analysis by ML component. Various implementations provide methodologies for automatically formulating machine learning (“ML”) and optimization queries. The automatic generation of ML and/or optimization queries can be configured to use examples to facilitate formulation of ML and optimization queries. One example method includes accepting input data specifying variables and data values associated with the variables. Within the input data any unspecified data records are identified, and a relationship between the variables specified in the input data and a variable associated with the at least one unspecified data record is automatically determined. The relationship can be automatically determined based on training data contained within the input data. Once a relationship is established a ML problem can be automatically generated. ## Claims ### Claim at position 1 1-46. (canceled) ### Claim 47 A method, comprising: accessing, by a computer system, input data comprising: training data comprising a first plurality of data records, each of the first plurality of data records having numeric data values at least for first and second variables in a plurality of variables; and a second plurality of data records comprising at least a first not fully specified data record that does not include a first numeric data value for the first variable in the plurality of variables; generating, by the computer system and using the training data, a representation of a relationship between the first variable and at least the second variable in the plurality of variables; and obtaining, by the computer system and using the generated representation, at least the first numeric data value for the first variable in the first not fully specified data record. ### Claim 48 (depends on 47) The method of claim 47, wherein generating the representation comprises using a machine learning technique to generate a mathematical model representing the relationship between the first variable and at least the second variable. ### Claim 49 (depends on 47) The method of claim 47, wherein generating the mathematical model representing the relationship between the first variable and at least the second variable is performed by using symbolic regression. ### Claim 50 (depends on 47) The method of claim 47, wherein accessing the input data is performed using a spreadsheet user interface, and wherein generating the representation of the relationship comprises: generating one or more candidate query patterns that relate a blank cell in the spreadsheet user interface to its surrounding non-blank cells in the spreadsheet user interface; for each pattern in the one or more candidate query patterns: collecting data from the spreadsheet user interface that matches the pattern, generating, using a machine learning technique and the collected data, a respective candidate mathematical model representing a relationship between a variable associated with the blank cell and one or more variables associated with the surrounding non-blank cells; determining a confidence associated with the generated candidate mathematical model; and selecting the generated candidate mathematical model having a highest confidence level as the representation of the relationship. ### Claim 51 (depends on 47) The method of claim 47, further comprising: identifying, by the computer system, the first not fully specified data record in the second plurality of data records. ### Claim 52 (depends on 47) The method of claim 47, wherein accessing the input data comprises: receiving, within a spreadsheet user interface, a selection of the first plurality of data records and the second plurality of data records. ### Claim 53 (depends on 52) The method of claim 52, wherein the generating and obtaining occur in response to receiving the selection. ### Claim 54 (depends on 47) The method of claim 47, further comprising: determining a confidence value for the first numeric data value; and displaying the first numeric data value with an intensity that depends on the confidence value. ### Claim 55 (depends on 47) The method of claim 47, wherein the first not fully specified data record does include a second numeric data value for the second variable in the plurality of variables and does not include a third numeric data value for a third variable in the plurality of variables, and wherein the obtaining comprises obtaining, using the generated representation and an optimization technique, the first numeric data value and the third numeric data value based on the second numeric data value. ### Claim 56 (depends on 55) The method of claim 55, wherein the optimization technique comprises a gradient search technique. ### Claim 57 A system, comprising: using at least one hardware processor to perform: accessing input data comprising: training data comprising a first plurality of data records, each of the first plurality of data records having numeric data values at least for first and second variables in a plurality of variables; and a second plurality of data records comprising at least a first not fully specified data record that does not include a first numeric data value for the first variable in the plurality of variables; generating, using the training data, a representation of a relationship between the first variable and at least the second variable in the plurality of variables; and obtaining, by the computer system and using the generated representation, at least the first numeric data value for the first variable in the first not fully specified data record. ### Claim 58 (depends on 57) The system of claim 57, wherein generating the representation comprises using a machine learning technique to generate a mathematical model representing the relationship between the first variable and at least the second variable. ### Claim 59 (depends on 57) The system of claim 57, wherein generating the mathematical model representing the relationship between the first variable and at least the second variable is performed by using symbolic regression. ### Claim 60 (depends on 57) The system of claim 57, wherein accessing the input data is performed using a spreadsheet user interface, and wherein generating the representation of the relationship comprises: generating one or more candidate query patterns that relate a blank cell in the spreadsheet user interface to its surrounding non-blank cells in the spreadsheet user interface; for each pattern in the one or more candidate query patterns: collecting data from the spreadsheet user interface that matches the pattern, generating, using a machine learning technique and the collected data, a respective candidate mathematical model representing a relationship between a variable associated with the blank cell and one or more variables associated with the surrounding non-blank cells; determining a confidence associated with the generated candidate mathematical model; and selecting the generated candidate mathematical model having a highest confidence level as the representation of the relationship. ### Claim 61 (depends on 57) The system of claim 57, further comprising: identifying, by the computer system, the first not fully specified data record in the second plurality of data records. ### Claim 62 (depends on 57) The system of claim 57, wherein accessing the input data comprises: receiving, within a spreadsheet user interface, a selection of the first plurality of data records and the second plurality of data records. ### Claim 63 (depends on 62) The system of claim 62, wherein the generating and obtaining occur in response to receiving the selection. ### Claim 64 (depends on 57) The system of claim 57, further comprising: determining a confidence value for the first numeric data value; and displaying the first numeric data value with an intensity that depends on the confidence value. ### Claim 65 (depends on 57) The system of claim 57, wherein the first not fully specified data record does include a second numeric data value for the second variable in the plurality of variables and does not include a third numeric data value for a third variable in the plurality of variables, and wherein the obtaining comprises obtaining, using the generated representation and an optimization technique, the first numeric data value and the third numeric data value based on the second numeric data value. ### Claim 66 (depends on 65) The system of claim 65, wherein the optimization technique comprises a gradient search technique.