Systems and user interfaces for data analysis including artificial intelligence algorithms for generating optimized packages of data items

• US 10,452,673 B1
• Filed: 04/07/2017
• Issued: 10/22/2019
• Est. Priority Date: 12/29/2015
• Status: Active Grant

• Current Assignee: Palantir Technologies Incorporated
• Original Assignee: Palantir Technologies Incorporated
• Inventors: Huang, Yifei, Garde, Grace, Singh, Nikhita, Gershkon, Sarah, Winchester, James, Pliuskys, Laurynas
• Primary Examiner(s): Alam, Shahid A
• Application Number:
  US15/482,443
• Time in Patent Office: 928 Days
• Field of Search: 707723
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

Systems and user interfaces enable integration of data items from disparate sources to generate optimized packages of data items. For example, the systems described herein can obtain data items from various sources, score the data items, and present, via an interactive user interface, options for packaging the data items based on the scores. The systems may include artificial intelligence algorithms for selecting optimal combinations of data items for packaging. Further, the interactive user interfaces may enable a user to efficiently add data items to, and remove data items from, the data packages. The system may interactively re-calculate and update scores associated with the package of data items as the user interacts with the data package via the user interface. The systems and user interfaces may thus, according to various embodiments, enable the user to optimize the packages of data items based on multiple factors quickly and efficiently.

Claims

1. A system comprising:

one or more processors configured to execute software instructions to cause the system to;

analyze a subset of data items from a plurality of data items to determine a score associated with the subset of data items;

access historical information associated with one or more of the plurality of data items;

analyze the subset of data items to determine an item score for each data item of one or more of the plurality of data items, wherein determining the item score for a first data item comprises at least one of;

evaluating a cost associated with the first item, determining an availability of other data items similar to the first data item, determining an expected demand for the first data item, determining a prioritization of the first data item, or determining a historic demand for the first data item;

determine, for each data item of at least some of the plurality of data items, and based at least in part on the historical information, respective effects on the score if the respective data items are added to or removed from the subset of data items;

determine, based at least in part on the respective effects, a ranking of the at least some of the plurality of data items; and

generate user interface data usable for rendering a user interface, the user interface including;

an indication of the score, and

a selectable list of the at least some of the plurality of data items ordered based on the ranking.

2. The system of claim 1, wherein determining the item score for a first data item further comprises combining a plurality of item scores.

3. The system of claim 2, wherein the plurality of item scores comprises:

a first item score that is determined based on demographic information, and

a second item score that is determined based on a history of a data item to which the second item score corresponds.

4. The system of claim 1, wherein the one or more processors are configured to execute the software instructions to cause the system to further:

receive a selection, by a user, of one or more selected data items from the selectable list; and

add the one or more selected data items to the subset of data items to generate an updated subset of data items.

5. The system of claim 4, wherein the one or more processors are configured to execute the software instructions to cause the system to further:

in response to the selection of the one or more selected data items;

analyze the updated subset of data items to determine an updated score associated with the updated subset of data items; and

update the user interface to include an indication of the updated score.

6. A computer-implemented method comprising:

by one or more processors executing program instructions;

analyzing a subset of data items from a plurality of data items to determine a score associated with the subset of data items;

accessing historical information associated with one or more of the plurality of data items;

determining, for each data item of at least some of the plurality of data items, and based at least in part on the historical information, respective effects on the score if the respective data items are added to or removed from the subset of data items;

determining, based at least in part on the respective effects, an item ranking of the at least some of the plurality of data items, wherein;

determining the item ranking comprises determining a first ranking and a second ranking and combining the first and second rankings, and

determining the first ranking comprises at least one of;

determining an expected demand for a first data item, determining a prioritization of a first data item, determining a demand for a first data item, comparing a transaction record of a user of the system with the plurality of data items, evaluating a cost associated with a first data item, or determining an availability of other data items similar to a first data item; and

generating user interface data usable for rendering a user interface, the user interface including;

an indication of the score, and

a selectable list of the at least some of the plurality of data items ordered based on the item ranking.

7. The method of claim 6 further comprising:

by the one or more processors executing program instructions;

detecting a change in the plurality of data items; and

re-determining the item ranking based on the plurality of data item, taking into account the change.

8. The method of claim 6, wherein the item ranking is at least partially determined based on at least one of:

a collaborative filtering algorithm, a monte carlo-based algorithm, a genetic algorithm, or a simulated annealing algorithm.

9. The method of claim 6 further comprising:

by the one or more processors executing program instructions;

receiving a selection, by a user, of one or more selected data items from the selectable list; and

adding the one or more selected data items to the subset of data items to generate an updated subset of data items.

10. The method of claim 6 further comprising:

by the one or more processors executing program instructions;

in response to the selection of the one or more selected data items;

analyzing the updated subset of data items to determine an updated score associated with the updated subset of data items; and

updating the user interface to include an indication of the updated score.