# US 2021/0241179 A1 · Real-time predictions based on machine learning models > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241179 A1 - Document ID: US20210241179A1 - Status: Published application - Kind: A1 - Application number: 16/777,686 - Filed: 2020-01-30 - Published: 2021-08-05 - Earliest claimed: 2020-01-30 - Assignees: salesforce.com, inc. - Applicants: salesforce.com, inc. - Inventors: Rakesh Ganapathi Karanth; Arun Kumar Jagota; Kaushal Bansal; Amrita Dasgupta - Web page: https://patentgrep.com/patent/US20210241179A1 ## CPC - G06N 20/00: Machine learning - G06N 20/20: Ensemble learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract An online system performs predictions for real-time tasks and near real-time tasks that need to be performed by a deadline. A client device receives a real-time machine learning based model associated with a measure of accuracy. If the client device determines that a task can be performed using predictions having less than the specified measure of accuracy, the client device uses the real-time machine learning based model. If the client device determines that a higher level of accuracy of results is required, the client device sends a request to an online system. The online system provides a prediction along with a string representing a rationale for the prediction. ## Claims ### Claim 1 A computer-implemented method for performing predictions, the method comprising: receiving, by the client device from an online system, a regression based machine learning model and a measure of accuracy of the regression based machine learning model; receiving a request for a first task; responsive to determining that a level of expected accuracy for the first task is below the measure of accuracy of the regression based machine learning model: extracting features of the first task; executing the regression based machine learning model with the extracted features of the first task as input; and performing the first task using the result of execution of the regression based machine learning model; receiving a request for a second task; and responsive to determining that a level of expected accuracy for the second task is above the measure of accuracy of the regression based machine learning model: extracting features of the second task; serializing the extracted features of the second task; transmitting, to the online system, the serialized features for executing a second machine learning based model; receiving, from the online system, the output of the second machine learning based model from the online system; and performing the second task using the received output. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the second machine learning based model is one of a deep learning based neural network model or a random forest based machine learning model. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising: determining, by a client device, work load exceeding a threshold value, the workload representing tasks associated with the client device, wherein the client device executes the regression based machine learning model further in response to the work load exceeding a threshold value. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, further comprising: determining, by a client device, that the first task needs to be finished within a threshold time interval, wherein the client device executes the regression based machine learning model further in response to determining that the first task needs to be finished within a threshold time interval. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the second machine learning based model is a decision tree based machine learning model. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, further comprising: receiving, from the online system, a string representing a description of a rationale for the prediction of the second machine learning based model, the string generated based on an execution of the decision tree based machine learning model. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the online system is a multi-tenant system and the client device is associated with a user of a tenant of the multi-tenant system, wherein each of the regression based machine learning model and the second machine learning based model is trained using training data set of the tenant. ### Claim 8 A non-transitory computer readable storage medium for storing instructions that when executed by a computer processor cause the computer processor to perform steps for performing predictions, the steps comprising: receiving, by the client device from an online system, a regression based machine learning model del; receiving a request for a first task; responsive to determining that a level of expected accuracy for the first task is below the measure of accuracy of the regression based machine learning model: extracting features of the first task; executing the regression based machine learning model with the extracted features of the first task as input; and performing the first task using the result of execution of the regression based machine learning model; receiving a request for a second task; and responsive to determining that a level of expected accuracy for the second task is above the measure of accuracy of the regression based machine learning model: extracting features of the second task; serializing the extracted features of the second task; transmitting, to the online system, the serialized features for executing a second machine learning based model; receiving, from the online system, the output of the second machine learning based model from the online system; and performing the second task using the received output. ### Claim 9 (depends on 8) The non-transitory computer readable storage medium of claim 8, wherein the second machine learning based model is one of a deep learning based neural network model or a random forest based machine learning model. ### Claim 10 (depends on 8) The non-transitory computer readable storage medium of claim 8, wherein the stored instructions further cause the computer processor to perform steps comprising: determining, by a client device, work load exceeding a threshold value, the work load representing tasks associated with the client device, wherein the client device executes the regression based machine learning model further in response to the work load exceeding a threshold value. ### Claim 11 (depends on 8) The non-transitory computer readable storage medium of claim 8, wherein the stored instructions further cause the computer processor to perform steps comprising: determining, by a client device, that the first task needs to be finished within a threshold time interval, wherein the client device executes the regression based machine learning model further in response to determining that the first task needs to be finished within a threshold time interval. ### Claim 12 (depends on 8) The non-transitory computer readable storage medium of claim 8, wherein the second machine learning based model is a decision tree based machine learning model. ### Claim 13 (depends on 12) The non-transitory computer readable storage medium of claim 12, wherein the stored instructions further cause the computer processor to perform steps comprising: receiving, from the online system, a string representing a description of a rationale for the prediction of the second machine learning based model, the string generated based on an execution of the decision tree based machine learning model. ### Claim 14 (depends on 12) The non-transitory computer readable storage medium of claim 12, wherein the online system is a multi-tenant system and the client device is associated with a user of a tenant of the multi-tenant system, wherein each of the regression based machine learning model and the second machine learning based model is trained using training data set of the tenant. ### Claim 15 A computer system comprising: a computer processor; and a non-transitory computer readable storage medium for storing instructions that when executed by a computer processor cause the computer processor to perform steps for performing predictions, the steps comprising: receiving, by the client device from an online system, a regression based machine learning model and a measure of accuracy of the regression based machine learning model; receiving a request for a first task; responsive to determining that a level of expected accuracy for the first task is below the measure of accuracy of the regression based machine learning model: extracting features of the first task; executing the regression based machine learning model with the extracted features of the first task as input; and performing the first task using the result of execution of the regression based machine learning model; receiving a request for a second task; and responsive to determining that a level of expected accuracy for the second task is above the measure of accuracy of the regression based machine learning model: extracting features of the second task; serializing the extracted features of the second task; transmitting, to the online system, the serialized features for executing a second machine learning based model; receiving, from the online system, the output of the second machine learning based model from the online system; and performing the second task using the received output. ### Claim 16 (depends on 15) The computer system of claim 15, wherein the stored instructions further cause the computer processor to perform steps comprising: determining, by a client device, work load exceeding a threshold value, the work load representing tasks associated with the client device, wherein the client device executes the regression based machine learning model further in response to the work load exceeding a threshold value. ### Claim 17 (depends on 15) The computer system of claim 15, wherein the stored instructions further cause the computer processor to perform steps comprising: determining, by a client device, that the first task needs to be finished within a threshold time interval, wherein the client device executes the regression based machine learning model further in response to determining that the first task needs to be finished within a threshold time interval. ### Claim 18 (depends on 15) The computer system of claim 15, wherein the second machine learning based model is a decision tree based machine learning model. ### Claim 19 (depends on 18) The computer system of claim 18, wherein the stored instructions further cause the computer processor to perform steps comprising: receiving, from the online system, a string representing a description of a rationale for the prediction of the second machine learning based model, the string generated based on an execution of the decision tree based machine learning model. ### Claim 20 (depends on 15) The computer system of claim 15, wherein the online system is a multi-tenant system and the client device is associated with a user of a tenant of the multi-tenant system, wherein each of the regression based machine learning model and the second machine learning based model is trained using training data set of the tenant.