公开(公告)号：US10915827B2

公开(公告)日：2021-02-09

申请号：US16198449

申请日：2018-11-21

Applicant: salesforce.com, inc.

Inventor： Kristen Noriko Muramoto ,  Son Thanh Chang ,  Clement Jacques Antoine Tussoit ,  Melissa Hoang ,  Chaitanya Malla ,  Orjan N. Kjellberg ,  Carlos Enrique Mogollan Jimenez ,  George Hu

IPC: G06N5/04 ,  G06F9/451 ,  G06N99/00 ,  G06F3/0484 ,  G06F3/0482

Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.

公开(公告)号：US11301766B2

公开(公告)日：2022-04-12

申请号：US17247764

申请日：2020-12-22

Applicant: salesforce.com, inc.

Inventor： Kristen Noriko Muramoto ,  Son Thanh Chang ,  Clement Jacques Antoine Tussoit ,  Melissa Hoang ,  Chaitanya Malla ,  Orjan N. Kjellberg ,  Carlos Enrique Mogollan Jimenez ,  George Hu

IPC: G06N5/04 ,  G06F9/451 ,  G06N99/00 ,  G06F3/04847 ,  G06F3/0482

Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.

公开(公告)号：US11079909B2

公开(公告)日：2021-08-03

申请号：US15785068

申请日：2017-10-16

Applicant: salesforce.com, inc.

Inventor： Kristen Noriko Muramoto ,  Clément Jacques Antoine Tussiot ,  Orjan Nils Kjellberg

IPC: G06F3/0484 ,  G06F3/0481 ,  G06F9/451 ,  G06F8/34 ,  G06F8/20 ,  G06F9/455

Abstract: Disclosed herein are system, method, and computer-readable storage medium embodiments for ordered macro building. An embodiment may include operations of displaying, via a user interface, a first user interface object and a second user interface object, populating a macro building pane comprising a first macro building object and a second macro building object, in response to the first user interface object receiving a first interaction and in response to the second user interface object receiving a second interaction, and compiling a macro with at least the first interaction and the second interaction, the first interaction being with a first application and the second interaction being with a second application.

公开(公告)号：US20210182716A1

公开(公告)日：2021-06-17

申请号：US17247764

申请日：2020-12-22

Applicant: salesforce.com, inc.

Inventor： Kristen Noriko Muramoto ,  Son Thanh Chang ,  Clement Jacques Antoine Tussoit ,  Melissa Hoang ,  Chaitanya Malla ,  Orjan N. Kjellberg ,  Carlos Enrique Mogollan Jimenez ,  George Hu

IPC: G06N5/04 ,  G06F9/451 ,  G06N99/00 ,  G06F3/0484 ,  G06F3/0482

Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.