# US 2020/0401930 A1 · Design of customizable machine learning services > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401930 A1 - Document ID: US20200401930A1 - Status: Published application - Kind: A1 - Application number: 16/445,807 - Filed: 2019-06-19 - Published: 2020-12-24 - Earliest claimed: 2019-06-19 - Assignees: Sap SE - Applicants: Sap SE - Inventors: Sergey SMIRNOV; Francesco ALDA; Evgeny ARNAUTOV; Michael HAAS; Amrit RAJ; Ekaterina SUTTER - Web page: https://patentgrep.com/patent/US20200401930A1 ## CPC - G06N 20/00: Machine learning - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Disclosed herein are system, method, and computer program product embodiments for classifying a new record. An embodiment operates by receiving a dataset unique to a user, wherein the dataset includes a plurality of records separate from the new record, and receiving a dataset schema. Thereafter, the dataset is validated based on the dataset schema. Subsequently, a request for creating a machine learning model based on a selected model template and dataset is received. After creating the custom machine learning model, a request for classifying the new record based on the created machine learning model is received. Upon determining the classification of the new record based on the custom machine learning model, the classification for the new record is outputted to the user. ## Claims ### Claim 1 A computer-implemented method for classifying a new record, comprising: receiving, by at least one processor, a dataset schema; receiving, by the at least one processor, a dataset unique to a user, wherein the dataset includes a plurality of records separate from the new record; validating, by the at least one processor, the dataset based on the dataset schema; receiving, by the at least one processor, a selection of a model template; receiving, by the at least one processor, a request for creating a custom machine learning model based on the model template, the dataset, and the dataset schema; receiving, by the at least one processor, a request for a classification of the new record; determining, by the at least one processor, the classification of the new record based on the custom machine learning model; and outputting, by the at least one processor, the classification of the new record to the user. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the dataset schema is unique to the user. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the dataset and the dataset schema are uploaded by the user. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, further comprising: learning, by the at least one processor, one or more relationships between the plurality of records of the dataset, wherein the custom machine learning model is created based on the learning of the one or more relationships. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein the model template is selected from a plurality of model templates. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, wherein the plurality of model templates includes a first model template and a second model template different from the first model template, and wherein the selected model template is the first model template. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, wherein the learning of the one or more relationships between the plurality of records for the first model template is based on a first generic machine learning algorithm, and wherein the learning of the one or more relationships between the records of the dataset for the second model template is based on a second generic machine learning algorithm. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the dataset and the dataset schema are received by a data manager module, wherein the request for creating the custom machine learning model is received by a model manager module, and wherein the request for classifying the new record is received by an inference module. ### Claim 9 (depends on 7) The computer-implemented method of claim 7, further comprising: triggering, by the at least one processor, a training data module to create the custom machine learning model, wherein the triggering is performed by the model manager module. ### Claim 10 (depends on 8) The computer-implemented module of claim 8, wherein the custom machine learning model is stored in a model container. ### Claim 11 (depends on 9) The computer-implemented method of claim 9, wherein the inference module sends the request for classifying the new record to the model container to determine the classification based on the custom machine learning model. ### Claim 12 A system, comprising: a memory; and at least one processor coupled to the memory and configured to: receive a dataset schema, receive a dataset unique to a user, wherein the dataset includes a plurality of records, validate the dataset based on the dataset schema, receive a selection of a model template, receive a request for creating a custom machine learning model based on the model template, the dataset, and the dataset schema, receive a request for a classification of a new record separate from the plurality of records, determine the classification of the new record based on the custom machine learning model, and output the classification of the new record to the user. ### Claim 13 (depends on 11) The system of claim 11, wherein the dataset schema is unique to the user. ### Claim 14 (depends on 12) The system of claim 12, wherein the dataset and the dataset schema are uploaded by the user. ### Claim 15 (depends on 11) The system of claim 11, wherein the custom machine learning model is created based on a generic machine learning algorithm configured to learn relationships between the plurality of records of the dataset. ### Claim 16 (depends on 11) The system of claim 11, wherein the dataset and the dataset schema are received by a data manager module, wherein the request for creating the custom machine learning model is received by a model manager module, and wherein the request for classifying the new record is received by an inference module. ### Claim 17 (depends on 16) The system of claim 16, wherein the model manager module is configured to trigger a training data module to create the custom machine learning model. ### Claim 18 (depends on 17) The system of claim 17, wherein the custom machine learning model is stored in a model container. ### Claim 19 (depends on 18) The system of claim 18, wherein the inference module sends the request for classifying the new record to the model container to determine the classification based on the custom machine learning model. ### Claim 20 A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: receiving a dataset schema; receiving a dataset unique to a user, wherein the dataset includes a plurality of records; validating the dataset based on the dataset schema; receiving a selection of a model template; receiving a request for creating a custom machine learning model based on the model template, the dataset, and the dataset schema; receiving a request for a classification of a new record separate from the plurality of records based on the custom machine learning model; determining the classification of the new record based on the custom machine learning model; and outputting the classification of the new record to the user.