Automatically creating lists from existing lists

• US 8,126,893 B1
• Filed: 12/26/2007
• Issued: 02/28/2012
• Est. Priority Date: 04/30/2003
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Tong, Simon, Dean, Jeff
• Primary Examiner(s): Trujillo, James
• Assistant Examiner(s): Davanlou, Soheila
• Application Number:
  US11/964,341
• Time in Patent Office: 1,525 Days
• Field of Search: 707/100, 707/200, 707/736, 707/737
• US Class Current: 707/737
• CPC Class Codes: G06F 40/247 Thesauruses; Synonyms
• CPC Class Codes: G06F 40/274 Converting codes to words; Guess-ahead of partial word inputs

Abstract

A system automatically creates a list from items in existing lists. The system receives one or more example items corresponding to the list and assigns weights to the items in the existing lists based on the one or more example items. The system then forms the list based on the items and the weights assigned to the items.

Claims

1. A method comprising:

identifying, by one or more processors, preexisting lists of items within one or more documents available on a network, where each of the identified lists includes a plurality of items and at least one of the items is searched;

receiving, by the one or more processors, a plurality of example items from a client;

determining, by the one or more processors, a probability that each of a plurality of the identified lists is associated with the example items;

assigning, by the one or more processors, a weight to each item of the plurality of identified lists based on the probability that each of a plurality of the identified lists is associated with the example items;

forming, by the one or more processors, a list from items in the plurality of the identified lists based on the determined probabilities and the assigned weights; and

providing, by the one or more processors, the list to the client.

2. The method of claim 1, where identifying the lists within the one or more documents includes:

identifying a plurality of documents on the network, and

determining whether each of the documents includes a list of items.

3. The method of claim 2, where determining whether each of the documents includes a list of items includes:

identifying the list of items as items associated with a tag indicative of a list, as a group of items stored in a table, or as a group of items separated by tabs, commas, or semicolons.

4. The method of claim 1, further comprising:

creating a hit list index from the identified lists, the hit list index providing a mapping of items to one or more of the identified lists in which the items appear.

6. The method of claim 1, where determining the probability that each of the plurality of the identified lists is associated with the example items includes:

determining a probability that one of the plurality of the identified lists is associated with the example items based on a probability that each of the items in the one of the plurality of the identified lists is associated with the example items.

7. The method of claim 1, further comprising:

adding the weight for each of the items to generate total weights for the items.

8. The method of claim 7, further comprising:

determining whether the total weight for one or more of the items is below a threshold; and

discarding one of the one or more of the items from consideration for the list when the total weight for the one of the one or more items is below the threshold.

9. The method of claim 7, where forming the list includes:

forming the list based on the items from the plurality of the identified lists and the total weights for the items.

10. The method of claim 7, further comprising:

determining new probabilities for the plurality of the identified lists based on the items and the total weights for the items.

11. The method of claim 10, where forming the list includes:

forming the list based on the plurality of the identified lists and the new probabilities.

12. The method of claim 1, where the probability that one of the plurality of the identified lists is associated with the example items is related to a number of items in the one of the plurality of the identified lists.

14. The method of claim 1, further comprising:

receiving a plurality of other example items unrelated to a topic associated with the example items; and

determining a probability that each of the plurality of the identified lists is associated with the other example items.

15. The method of claim 14, where forming the list from items in the plurality of the identified lists is based on the determined probabilities that the plurality of the identified lists are associated with the example items and the determined probabilities that the plurality of the identified lists are associated with the one or more other example items.

16. The method of claim 1, where the method is performed by a plurality of processors operating in concert.

32. The computer-readable memory device of claim 16, where the first probability that one of the plurality of the identified lists is associated with the example items is related to a number of items in the one of the plurality of the identified lists.

33. The computer-readable memory medium of claim 16, where the list includes a plurality of selectable items, where selection of one of the selectable items causes a search to be performed and information associated with documents relating to the one selectable item to be provided.

5. A system, comprising:

a memory to store instructions; and

one or more processors to execute the instructions to;

identify preexisting lists of items stored within a database, where the identification is performed independent of receiving any example items from a client and at least one of the items is searched;

receive a plurality of example items from a client;

determine a probability that each of a plurality of the identified lists is associated with the example items;

assign a weight to each item of the plurality of identified lists based on the probability that each of a plurality of the identified lists is associated with the example items;

form a list from items in the plurality of the identified lists based on the determined probabilities and the assigned weights; and

provide the list to the client.

13. The system of claim 5, where the list includes a plurality of selectable items, and where the one or more processors are further to:

receive selection of one of the selectable items;

perform a search to identify documents relating to the one selectable item; and

provide information regarding the identified documents.

17. The system of claim 5, where the one or more processors are further to:

identify additional lists of items stored in one or more databases.

18. One or more computer devices, comprising:

a list identifier to identify lists of items located within one or more documents available on a network or stored within one or more databases, where at least one of the items is searched;

a list classifier to;

receive a plurality of example items,

determine a probability that each of a plurality of the identified lists is associated with the example items, and

assign a weight to each item of the plurality of identified lists based on the probability that each of a plurality of the identified lists is associated with the example items; and

a list processor to;

form a list from items in the plurality of the identified lists based on the determined probabilities and the assigned weights, and

output the list.

19. The one or more computer devices of claim 18, where the list processor is further to identify a plurality of the items from the plurality of the identified lists, that have the determined probabilities above a threshold, to include on the list.

20. The one or more computer devices of claim 18, where the list processor is further to identify a particular number of the items from the plurality of the identified lists, that have highest probabilities of the determined probabilities, to include on the list.

21. The one or more computer devices of claim 18, where the list processor is further to:

identify one of the items from the plurality of the identified lists that includes a quantity of words that is greater than a threshold quantity of words, and

remove the identified one of the items from consideration for the list.

22. The one or more computer devices of claim 18, where the list processor is further to identify a plurality of the items from the plurality of the identified lists, based on a distribution of the determined probabilities, to include on the list.

23. The one or more computer devices of claim 18, where when identifying the lists within the one or more documents, the list identifier is further to:

identify a plurality of documents on the network, and

determine whether each of the documents includes a list of items.

24. The one or more computer devices of claim 23, where when determining whether each of the documents includes a list of items, the list identifier is further to:

identify the list of items as items associated with a tag indicative of a list, as a group of items stored in a table, or as a group of items separated by tabs, commas, or semicolons.

25. The one or more computer devices of claim 18, where the list identifier is further to:

create a hit list index from the identified lists, the hit list index providing a mapping of items to one or more of the identified lists in which the items appear.

26. The one or more computer devices of claim 18, where when determining the probability that each of the plurality of the identified lists is associated with the example items, the list classifier is further to:

determine a probability that one of the plurality of the identified lists is associated with the example items based on a probability that each of the items in the one of the plurality of the identified lists is associated with the example items.

27. The one or more computer devices of claim 18, where the list classifier is to:

add the weight for each of the items to generate total weights for the items.

28. The one or more computer devices of claim 27, where the list processor is further to:

determine whether the total weight for one or more of the items is below a threshold, and

discard one of the one or more of the items from consideration for the list when the total weight for the one of the one or more items is below the threshold.

29. The one or more computer devices of claim 27, where when forming the list, the list processor is further to:

form the list based on the items from the plurality of the identified lists and the total weights for the items.

30. The one or more computer devices of claim 27, where the list classifier is further to:

determine new probabilities for the plurality of the identified lists based on the items and the total weights for the items.

31. The one or more computer devices of claim 30, where when forming the list, the list processor is further to:

form the list based on the plurality of the identified lists and the new probabilities.

34. The one or more computer devices of claim 18, where the list classifier is further to:

receive a plurality of other example items that are unrelated to a topic associated with the example items, and

determine a probability that each of the plurality of the identified lists is associated with the other example items.

35. The one or more computer devices of claim 34, where the list processor is further to form the list from items in the plurality of the identified lists based on the determined probabilities that the plurality of the identified lists are associated with the example items and the determined probabilities that the plurality of the identified lists are associated with the other example items.

36. A non-transitory computer-readable medium comprising:

one or more instructions which, when executed by at least one processor, cause the at least one processor to identify preexisting lists of items within one or more documents available on a network, where the identification is performed independent of receiving any example items from a client and at least one of the items is searched;

one or more instructions which, when executed by the at least one processor, cause the at least one processor to receive, from a client, a plurality of example items relating to a particular topic;

one or more instructions which, when executed by the at least one processor, cause the at least one processor to receive, from the client, a plurality of other example items unrelated to the particular topic;

one or more instructions which, when executed by the at least one processor, cause the at least one processor to determine a first probability that each of a plurality of the identified lists is associated with the example items;

one or more instructions which, when executed by the at least one processor, cause the at least one processor to assign a first weight to each item of the plurality of identified lists based on the first probability that each of a plurality of the identified lists is associated with the example items;

one or more instructions which, when executed by the at least one processor, cause the at least one processor to determine a second probability that each of the plurality of the identified lists is associated with the other example items;

one or more instructions which, when executed by the at least one processor, cause the at least one processor to assign a second weight to each item of the plurality of identified lists based on the second probability that each of a plurality of the identified lists is associated with the other example items;

one or more instructions which, when executed by the at least one processor, cause the at least one processor to form a list from items in the plurality of the identified lists based on the first probability, the second probability, the assigned first weight, and the assigned second weight; and

one or more instructions which, when executed by the at least one processor, cause the at least one processor to provide the list to the client.