Granted Patent B2

US 12,608,536 · App. 18/523,631 · Granted Apr 21, 2026

Inventors: Dylan Myers (Coral Gables, FL); Li Pang (White Rock, CA)

Assignee: Oracle International Corporation

G06F40/174G06F3/04842G06N20/00

Abstract

Claims (81)

1 . One or more non-transitory computer-readable media comprising instructions which, when executed by one or more hardware processors, cause performance of operations comprising:

obtaining a plurality of forms, each form (a) being associated with a form type, (b) comprising a respective set of fields, and (c) comprising a respective set of values for the respective set of fields;

predicting a first association between a first form type and a second form type based at least on historical usage of the first form type and the second form type together;

predicting a second association between a first field in-a the first form type with a second field in the second form type based at least on (a) values for the first field in a first subset of the plurality of forms that are of the first form type and (b) values for the second field in a second subset of the plurality of forms that are of the second form type;

based on predicting the first association between the first form type and the second form type and predicting the second association between the first field in the first form type and the second field in the second form type: storing a mapping between the first field in the first form type and the second field in the second form type;

receiving a first user input comprising a particular value corresponding to the first field in a first target form of the first form type;

storing the particular value in relation to the first field in the first target form; and

based at least on the mapping between the first field in the first form type and the second field in the second form type: storing the particular value in relation to the second field in a second target form of the second form type.

2 . The one or more non-transitory computer-readable media of claim 1 , wherein the operations further comprise:

training a machine learning model using sets of training data, each set of training data defining associations between different fields in different form types;

wherein the predicting the second association between the first field in the first form type and the second field in the second form type comprises applying the machine learning model;

receiving feedback for the predicted second association between the first field in the first form type and the second field in the second form type; and

updating the machine learning model based on the feedback.

3 . The one or more non-transitory computer-readable media of claim 1 , wherein predicting the first association between the first form type and the second form type is based on a frequency with which forms of the first form type are submitted concurrently with forms of the second form type.

4 . The one or more non-transitory computer-readable media of claim 1 , wherein storing the particular value in relation to the second field in the second target form is based further on user characteristics that are used to determine an association between the first field in the first target form and the second field in the second target form.

5 . The one or more non-transitory computer-readable media of claim 1 , wherein the operations further comprise: generating an explanation for the predicted second association between the first field of the first form type and the second field of the second form type.

6 . The one or more non-transitory computer-readable media of claim 1 , wherein the operations further comprise: displaying a visual indication in relation to the second field in the second target form that indicates the second field was auto-completed, without receiving user input in relation to the second field in the second target form.

7 . The one or more non-transitory computer-readable media of claim 1 , wherein at least one of the first target form or the second target form was selected, by a system, for completion by a user based at least on: historical data, a pattern of form completion, and/or an upcoming form completion requirement.

8 . The one or more non-transitory computer-readable media of claim 1 , wherein the operations further comprise:

prior to storing the mapping between the first field in the first form type and the second field in the second form type:

presenting, in a graphical user interface (GUI), the second association between the first field in the first form type and the second field in the second form type as a first candidate association for user approval;

receiving, via the GUI, a second user input approving the first candidate association between the first field in the first form type and the second field in the second form type,

wherein the mapping between the first field in the first form type and the second field in the second form type is stored in response to receiving the second user input;

predicting a third association between a third field in the first form type and a fourth field in the second form type based at least on (a) values for the third field in the first subset of the plurality of forms that are of the first form type and (b) values for the fourth field in the second subset of the plurality of forms that are of the second form type;

presenting, in the GUI, the third association between the third field in the first form type and the fourth field in the second form type as a second candidate association for user approval;

receiving, via the GUI, a third user input disapproving the second candidate association between the third field in the first form type and the fourth field in the second form type; and

responsive to receiving the third user input, refraining from mapping the third field in the first form type and to the fourth field in the second form type.

9 . The one or more non-transitory computer-readable media of claim 1 , wherein storing the second association between the first field in the first form type and the second field in the second form type comprises:

generating a data form comprising a data field, the data field being mapped to (a) the first field in the first form type and (b) the second field in the second form type; and

wherein receiving the first user input comprises:

presenting the data form; and

receiving, in relation to the data field of the data form, the first user input comprising the particular value.

10 . A method comprising:

obtaining a plurality of forms, each form (a) being associated with a form type, (b) comprising a respective set of fields, and (c) comprising a respective set of values for the respective set of fields;

predicting a first association between a first form type and a second form type based at least on historical usage of the first form type and the second form type together;

predicting a second association between a first field in the first form type with a second field in the second form type based at least on (a) values for the first field in a first subset of the plurality of forms that are of the first form type and (b) values for the second field in a second subset of the plurality of forms that are of the second form type;

based on predicting the first association between the first form type and the second form type and predicting the second association between the first field in the first form type and the second field in the second form type: storing a mapping between the first field in the first form type and the second field in the second form type;

receiving a first user input comprising a particular value corresponding to the first field in a first target form of the first form type;

storing the particular value in relation to the first field in the first target form; and

based at least on the mapping between the first field in the first form type and the second field in the second form type: storing the particular value in relation to the second field in a second target form of the second form type,

wherein the method is performed by at least one device including a hardware processor.

11 . The method of claim 10 , further comprising:

training a machine learning model using sets of training data, each set of training data defining associations between different fields in different form types;

wherein the predicting the second first association between the first field in the first form type and the second field in the second form type comprises applying the machine learning model;

receiving feedback for the predicted second first association between the first field in the first form type and the second field in the second form type; and

updating the machine learning model based on the feedback.

12 . The method of claim 10 , wherein predicting the first association between the first form type and the second form type is based on a frequency with which forms of the first form type are submitted concurrently with forms of the second form type.

13 . The method of claim 10 , wherein storing the particular value in relation to the second field in the second target form is based further on user characteristics that are used to determine an association between the first field in the first target form and the second field in the second target form.

14 . The method of claim 10 , wherein the operations further comprise: generate generating an explanation for the predicted second association between the first field of the first form type and the second field of the second form type.

15 . The method of claim 10 , wherein the operations further comprise: displaying a visual indication in relation to the second field in the second target form that indicates the second field was auto-completed, without receiving user input in relation to the second field in the second target form.

16 . The method of claim 10 , wherein at least one of the first target form or the second target form was selected, by a system, for completion by a user based at least on:

historical data, a pattern of form completion, and/or an upcoming form completion requirement.

17 . The method of claim 10 , further comprising:

prior to storing the mapping between the first field in the first form type and the second field in the second form type:

presenting, in a graphical user interface (GUI), the second association between the first field in the first form type and the second field in the second form type as a first candidate association for user approval;

receiving, via the GUI, a second user input approving the first candidate association between the first field in the first form type and the second field in the second form type;

wherein the mapping between the first field in the first form type and the second field in the second form type is stored in response to receiving the second user input;

predicting a third association between a third field in the first form type and a fourth field in the second form type based at least on (a) values for the third field in the first subset of the plurality of forms that are of the first form type and (b) values for the fourth field in the second subset of the plurality of forms that are of the second form type;

presenting, in the GUI, the third association between the third field in the first form type and the fourth field in the second form type as a second candidate association for user approval;

receiving, via the GUI, a third user input disapproving the second candidate association between the third field in the first form type and the fourth field in the second form type; and

responsive to receiving the third user input, refraining from mapping the third field in the first form type to the fourth field in the second form type.

18 . The method of claim 10 , wherein the mapping between the first field in the first form type and the second field in the second form type comprises:

generating a data form comprising a data field, the data field being mapped to (a) the first field in the first form type and (b) the second field in the second form type; and

wherein receiving the first user input comprises:

presenting the data form; and

receiving, in relation to the data field of the data form, the first user input comprising the particular value.

19 . A system comprising:

at least one device including a hardware processor;

the system being configured to perform operations comprising:

obtaining a plurality of forms, each form (a) being associated with a form type, (b) comprising a respective set of fields, and (c) comprising a respective set of values for the respective set of fields;

predicting a first association between a first form type and a second form type based at least on historical usage of the first form type and the second form type together;

predicting a second association between a first field in the first form type with a second field in the second form type based at least on (a) values for the first field in a first subset of the plurality of forms that are of the first form type and (b) values for the second field in a second subset of the plurality of forms that are of the second form type;

based on predicting the first association between the first form type and the second form type and predicting the second association between the first field in the first form type and the second field in the second form type: storing a mapping between the first field in the first form type and the second field in the second form type;

receiving a first user input comprising a particular value corresponding to the first field in a first target form of the first form type;

storing the particular value in relation to the first field in the first target form; and

based at least on the mapping between the first field in the first form type and the second field in the second form type: storing the particular value in relation to the second field in a second target form of the second form type.

20 . The system of claim 19 , wherein the operations further comprise:

training a machine learning model using sets of training data, each set of training data defining associations between different fields in different form types;

wherein the predicting the second association between the first field in the first form type and the second field in the second form type comprises applying the machine learning model;

receiving feedback for the predicted second association between the first field in the first form type and the second field in the second form type; and

updating the machine learning model based on the feedback.

Assignments (1)

Continuity (1)

References Cited (27)