SEARCH RESULTS BASED ON MODELS DERIVED FROM DOCUMENTS

• US 20200134096A1
• Filed: 10/30/2018
• Published: 04/30/2020
• Est. Priority Date: 10/30/2018
• Status: Active Application

• Current Assignee: Longsand Limited (Open Text Corporation)
• Original Assignee: Longsand Limited (Open Text Corporation)
• Inventors: Blanchflower, Sean, Cowe, Brian
• Application Number:
  US16/174,916
• Publication Number:
  US 20200134096A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

In some examples, a system accesses, in response to a search received from a first entity, a model derived from documents produced by the first entity or a group of entities comprising the first entity during operation of the first entity or the group of entities, the model comprising indications of importance of terms extracted from the documents. The system returns a search result that is based on the query and on the model.

Claims

1. A non-transitory machine-readable storage medium comprising instructions that upon execution cause a system to:

in response to a search received from a first entity, access a model derived from documents produced by the first entity or a group of entities comprising the first entity during operation of the first entity or the group of entities, the model comprising indications of importance of terms extracted from the documents; and

return a search result that is based on the query and on the model.

2. The non-transitory machine-readable storage medium of claim 1, wherein the model is derived from documents produced by the group of entities that collaborate with one another.

3. The non-transitory machine-readable storage medium of claim 2, wherein the group of entities are part of an enterprise that provides a product or a service.

4. The non-transitory machine-readable storage medium of claim 1, wherein the terms comprise jargon words or phrases used by the first entity or the group of entities.

5. The non-transitory machine-readable storage medium of claim 1, wherein the terms comprise terms specific to a domain of the group of entities.

6. The non-transitory machine-readable storage medium of claim 1, wherein the indications of importance of terms in the model comprises weights, and wherein the instructions upon execution cause the system to:

identify search results that are relevant for the query; and

select the returned search result from the identified search results based on the weights.

7. The non-transitory machine-readable storage medium of claim 6, wherein selecting the returned search result from the identified search results comprises determining presence of given terms of the model in the identified search results, and the weights assigned the given terms in the model.

8. The non-transitory machine-readable storage medium of claim 1, wherein the instructions upon execution cause the system to:

extract terms from the documents produced by the first entity or the group of entities during operation of the first entity or the group of entities;

count respective numbers of occurrences of the extracted terms; and

compute the indications of importance for the extracted terms based on the respective numbers of occurrences of the extracted terms.

9. The non-transitory machine-readable storage medium of claim 8, wherein the instructions upon execution cause the system to:

derive the model that comprises the extracted terms and the computed indications of importance for the extracted terms.

10. The non-transitory machine-readable storage medium of claim 8, wherein the instructions upon execution cause the system to:

identify terms that occur with a frequency in the documents exceeding a frequency threshold; and

exclude the identified terms from the extracted terms.

11. A system comprising:

a processor; and

a non-transitory storage medium storing instructions executable on the processor to;

extract terms from documents produced by a group of entities during operation of the group of entities;

determine indications of importance of the extracted terms;

derive a model comprising the indications of importance of the extracted terms;

in response to a search received from a first entity that is part of the group of entities, access the model; and

return a search result that is based on the query and on the model.

12. The system of claim 11, wherein the instructions are executable on the processor to:

include the extracted terms and the indications of importance of the extracted terms in the model.

13. The system of claim 11, wherein the instructions are executable on the processor to:

identify search results that are relevant for the query; and

select the returned search result from the identified search results based on the indications of importance.

14. The system of claim 13, wherein the instructions are executable on the processor to:

select the returned search result from the identified search results by determining presence of given terms of the model in the identified search results, and the indications of importance assigned the given terms in the model.

15. The system of claim 11, wherein the instructions are executable on the processor to:

count respective numbers of occurrences of the extracted terms; and

compute the indications of importance for the extracted terms based on the respective numbers of occurrences of the extracted terms.

16. The system of claim 11, wherein the documents are produced by the group of entities based on collaboration among entities of the group of entities.

17. The system of claim 11, wherein the instructions are executable on the processor to:

derive the model that further comprises information associated with individual entities of the group of entities and information associated with the group of entities.

18. The system of claim 17, wherein the information associated with the individual entities of the group of entities comprises identifiers and location information of the individual entities, and the information associated with the group of entities comprises a group identifier and location information of the group of entities.

19. A method performed by a system comprising a hardware processor, comprising:

in response to a search received from a first entity, accessing a model derived from documents produced by a group of entities comprising the first entity during operation of the group of entities, the model comprising indications of importance of terms extracted from the documents; and

returning a search result that is based on the query and on the model.

20. The method of claim 19, further comprising:

extracting terms from the documents produced by the group of entities during operation of the group of entities;

counting respective numbers of occurrences of the extracted terms; and

computing the indications of importance for the extracted terms based on the respective numbers of occurrences of the extracted terms.