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公开(公告)号：US20180096267A1
公开(公告)日：2018-04-05
申请号：US15712911
申请日：2017-09-22
申请人： salesforce.com, inc.
发明人： Chalenge Masekera , Vitaly Gordon , Leah McGuire , Shubha Nabar
摘要： In accordance with embodiments, there are provided mechanisms and methods for facilitating single model-based behavior predictions in an on-demand services environment in an on-demand services environment according to one embodiment. In one embodiment and by way of example, a method comprises collecting, by a model selection and application server device (“model device”), information associated with customers of a tenant, and extracting, from the information, behavior traits of the customers as they relate to products or services offered by the tenant. The method further includes dynamically selecting, by the model device, a single model from a set of models to convert the behavior traits into predictions indicating anticipated conduct of each customer in relation to one or more products or one or more of the services of the tenant, where the single model performs multiple processes to convert the behavior traits into predictions, and where the multiple processes include at least two of the following: evaluating data, cleansing the data, transforming the data. The method may further include writing the data, and transmitting, over a communication medium, the predictions to the tenant.
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公开(公告)号：US20190138946A1
公开(公告)日：2019-05-09
申请号：US15884878
申请日：2018-01-31
申请人： salesforce.com, inc.
发明人： Sara Beth Asher , John Emery Ball , Vitaly Gordon , Till Christian Bergmann , Kin Fai Kan , Chalenge Masekera , Shubha Nabar , Nihar Dandekar , James Reber Lewis
摘要： A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.
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3.发明申请公开(公告)号：US20180096028A1
公开(公告)日：2018-04-05
申请号：US15713069
申请日：2017-09-22
申请人： salesforce.com, inc.
发明人： Chalenge Masekera , Simon Chan , Kit Pang Szeto
CPC分类号： G06F16/2453 , G06F16/2462 , G06F21/6227
摘要： In accordance with embodiments, there are provided mechanisms and methods for facilitating a framework for management of machine learning models for tenants in an on-demand services environment according to one embodiment. In one embodiment and by way of example, a method comprises determining, by a model management server computing device (“management device”), business criteria for a tenant in a multi-tenant environment, where the business criteria are based on business preferences of the tenant. The method may further include building, by the management device, multiple models dedicated to the tenant based on the business criteria such that each model is trained and fitted to perform one or more combinations of processes based on one or more integrations of the business criteria. The method may further include dynamically selecting, by the management device, a model from the multiple models to perform a combination of processes involving an integration of two or more criterion of the business criteria as requested by the tenant.
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