公开(公告)号：US20230351182A1

公开(公告)日：2023-11-02

申请号：US18348875

申请日：2023-07-07

申请人： Cangrade, Inc.

发明人： Steven Lehr ,  Gershon Goren ,  Liana Epstein

IPC分类号： G06N3/08

CPC分类号： G06N3/08 ,  G06Q10/06398

摘要： A hardware processor can receive a set of input data individually describing a particular asset associated with an entity. The hardware processor can receive a set of inputs individually responsive to a respective subset of a plurality of queries for a particular user. The hardware processor can generate a predictive model based on the set of input data. The hardware processor can calculate a predictive outcome for the particular user by applying the predictive model to the set of inputs. The hardware processor can identify a target score impacting the predictive outcome for the particular user. The hardware processor can assign a training program to the particular user corresponding to the target score.

公开(公告)号：US12124955B2

公开(公告)日：2024-10-22

申请号：US18345712

申请日：2023-06-30

申请人： Cangrade, Inc.

发明人： Steven Lehr ,  Gershon Goren ,  Liana Epstein

IPC分类号： G06N3/08 ,  G06Q10/06 ,  G06Q10/0639

CPC分类号： G06N3/08 ,  G05B2219/33044 ,  G06Q10/06398

摘要： A hardware processor can receive a set of input data individually describing a particular asset associated with an entity. The hardware processor can receive sets of inputs individually responsive to a respective subset of queries. The hardware processor can generate a predictive model using the set of input data. The hardware processor can calculate predictive outcomes individually associated with a respective user by applying the predictive model to each respective set of inputs of the sets of inputs. The hardware processor can generate a list ranked according to the predictive outcomes for the particular asset.

公开(公告)号：US20230342608A1

公开(公告)日：2023-10-26

申请号：US18345712

申请日：2023-06-30

申请人： Cangrade, Inc.

发明人： Steven Lehr ,  Gershon Goren ,  Liana Epstein

IPC分类号： G06N3/08

CPC分类号： G06N3/08 ,  G06Q10/06398

摘要： A hardware processor can receive a set of input data individually describing a particular asset associated with an entity. The hardware processor can receive sets of inputs individually responsive to a respective subset of queries. The hardware processor can generate a predictive model using the set of input data. The hardware processor can calculate predictive outcomes individually associated with a respective user by applying the predictive model to each respective set of inputs of the sets of inputs. The hardware processor can generate a list ranked according to the predictive outcomes for the particular asset.

公开(公告)号：US11734566B2

公开(公告)日：2023-08-22

申请号：US17875900

申请日：2022-07-28

申请人： Cangrade, Inc.

发明人： Steven Lehr ,  Gershon Goren ,  Liana Epstein

IPC分类号： G06N3/08 ,  G06Q10/06 ,  G06Q10/0639

CPC分类号： G06N3/08 ,  G05B2219/33044 ,  G06Q10/06398

摘要： A hardware processor can receive sets of input data describing assets associated with an entity. The hardware processor can receive inputs responsive to queries of a user. The hardware processor can individually generate predictive models based on a respective set of input data. The hardware processor can calculate predicted outcomes for the user by applying each of models to the inputs. The hardware processor can generate a user interface comprising the predictive outcomes for the user for each of the predictive models.

公开(公告)号：US20220366256A1

公开(公告)日：2022-11-17

申请号：US17875900

申请日：2022-07-28

申请人： Cangrade, Inc.

发明人： Steven Lehr ,  Gershon Goren ,  Liana Epstein

IPC分类号： G06N3/08

摘要： A hardware processor can receive sets of input data describing assets associated with an entity. The hardware processor can receive inputs responsive to queries of a user. The hardware processor can individually generate predictive models based on a respective set of input data. The hardware processor can calculate predicted outcomes for the user by applying each of models to the inputs. The hardware processor can generate a user interface comprising the predictive outcomes for the user for each of the predictive models.

公开(公告)号：US11429859B2

公开(公告)日：2022-08-30

申请号：US16749954

申请日：2020-01-22

申请人： Cangrade, Inc.

发明人： Steven Lehr ,  Gershon Goren ,  Liana Epstein

IPC分类号： G06N3/08 ,  G06Q10/06

摘要： A hardware processor can generate an artificial intelligence neural network that is predictive of performance. The hardware processor can process the artificial intelligence neural network to determining whether a validity value for the artificial intelligence neural network meets a validity threshold. A predictive bias can be computed for the artificial neural network based on non-factored inputs. Nodes of the artificial neural network can be scored to compute an effect on the predictive bias. Another artificial intelligence neural network predictive of performance can be generated excluding a combination of parameters associated with a highest scored node of the artificial intelligence neural network.

公开(公告)号：US12124956B2

公开(公告)日：2024-10-22

申请号：US18348875

申请日：2023-07-07

申请人： Cangrade, Inc.

发明人： Steven Lehr ,  Gershon Goren ,  Liana Epstein

IPC分类号： G06N3/08 ,  G06Q10/06 ,  G06Q10/0639

CPC分类号： G06N3/08 ,  G05B2219/33044 ,  G06Q10/06398

摘要： A hardware processor can receive a set of input data individually describing a particular asset associated with an entity. The hardware processor can receive a set of inputs individually responsive to a respective subset of a plurality of queries for a particular user. The hardware processor can generate a predictive model based on the set of input data. The hardware processor can calculate a predictive outcome for the particular user by applying the predictive model to the set of inputs. The hardware processor can identify a target score impacting the predictive outcome for the particular user. The hardware processor can assign a training program to the particular user corresponding to the target score.

公开(公告)号：US20200160180A1

公开(公告)日：2020-05-21

申请号：US16749954

申请日：2020-01-22

申请人： Cangrade, Inc.

发明人： Steven Lehr ,  Gershon Goren ,  Liana Epstein

IPC分类号： G06N3/08

摘要： Disclosed are various embodiments for generating an artificial intelligence neural network predictive of performance. A hardware processor can process the artificial intelligence neural network to determining whether a validity value for the artificial intelligence neural network meets a validity threshold. A predictive bias can be computed for the artificial neural network based on non-factored inputs. Nodes of the artificial neural network can be scored to compute an effect on the predictive bias. Another artificial intelligence neural network predictive of performance can be generated excluding a combination of parameters associated with a highest scored node of the artificial intelligence neural network.

公开(公告)号：US20180046987A1

公开(公告)日：2018-02-15

申请号：US15236568

申请日：2016-08-15

申请人： Cangrade Inc.

发明人： Gershon Goren ,  Steven Lehr

IPC分类号： G06Q10/10

CPC分类号： G06Q10/1053

摘要： Disclosed are various embodiments for predicting a fit of a candidate for a job position based on past success of employees. Data can be received for employees at the company. The employees can answer survey questions to determine scales for the employees. A predictive model can be generated using the scales and the employee data. A candidate can be scored using the predictive model based on answers to survey questions provided by the candidate.