DISCOVERY ENGINE

• US 20170109449A1
• Filed: 10/26/2016
• Published: 04/20/2017
• Est. Priority Date: 04/06/2012
• Status: Abandoned Application

• Current Assignee: Enlyton, Inc.
• Original Assignee: Enlyton, Inc.
• Inventors: Johns, Mark Ellingham, McKinzie, Chris
• Application Number:
  US15/334,910
• Publication Number:
  US 20170109449A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/953 Querying, e.g. by the use of web search engines
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06F 16/9566 URL specific, e.g. using aliases, detecting broken or misspelled links

Abstract

A method that is relatively inexpensive to implement and that permits a user to conduct searches of electronically stored documents using an entire document, multiple documents or portions of a document as the search criteria and to collect, store and to share the relevant documents from the search.

Claims

1-26. -26. (canceled)

27. :

A method of semantically searching documents in a way that improves the efficiency of computer resources, comprising;

indexing by a processor a data set of documents having words by counting the words in the entire data set and determining a first frequency score and a first uniqueness score for each word of the data set of documents;

receiving a user input by the processor of a document of interest;

determining by the processor a second frequency score and a second uniqueness score for each word in the document of interest;

generating by the processor a respective similarity score for the document of interest compared to each of the documents in the data set of documents in a flat manner by comparing the second frequency score and the second uniqueness score for each word in the document of interest to the first frequency score and the first uniqueness score for each word of the data set of documents; and

presenting by the processor the most similar documents from the data set to the document of interest using the respective similarity score for the document of interest compared to each of the documents in the data set of documents.

28. :

The method of claim 27, wherein the presenting includes sorting by the processor the respective similarity score to obtain at least one most similar document of the data set of documents to the document of interest and displaying a ranked listing of the at least one most similar document.

29. :

The method of claim 28, further comprising;

receiving a next user input to the processor of the at least one most similar document of the data set of documents;

determining by the processor a third frequency score and a third uniqueness score for each word in the at least one most similar document;

generating by the processor a respective second similarity score for the at least one most similar document compared to each of the documents in the data set of documents in a flat manner by comparing the third frequency score and the third uniqueness score for each work in the at least one most similar document to the first frequency score and the first uniqueness core for each word of the data set of documents; and

presenting by the processor the most similar documents from the data set to the at least one most similar document using the respective second similarity score for the at least one most similar document compared to each of the documents in the data set of documents.

30. :

The method of claim 27 wherein;

the user input includes a uniform resource locator (URL); and

receiving the user input includes accessing by the processor information residing at a location designated by the URL.

31. :

The method of claim 27, further comprising;

normalizing by the processor the respective similarity score for the document of interest compared to each of the documents of the data set of documents.

32. :

A system for semantically searching documents to improve efficiency of computer resources, comprising;

a memory containing a set of instructions; and

a processor for processing the set of instructions, wherein the instructions cause the processor to perform a method comprising;

indexing a data set of documents having words by counting the words in the entire data set and determining a first frequency score and a first uniqueness score for each word of the data set of documents;

receiving a user input of a document of interest;

determining a second frequency score and a second uniqueness score for each word in the document of interest;

generating a respective similarity score for the document of interest compared to each of the documents in the data set of documents in a flat manner by comparing the second frequency score and the second uniqueness score for each word in the document of interest to the first frequency score and the first uniqueness score for each word of the data set of documents; and

presenting the most similar documents from the data set to the document of interest using the respective similarity score for the document of interest compared to each of the documents in the data set of documents.

33. :

The system of claim 32, wherein presenting includes sorting the respective similarity score to obtain at least one most similar document of the data set of documents to the document of interest and displaying a ranked listing of the at least one most similar document.

34. :

The system of claim 33, wherein the instructions cause the processor to perform a method further comprising;

receiving a next user input of the at least one most similar document of the data set of documents;

determining a third frequency score and a third uniqueness score for each word in the at least one most similar document;

generating a respective second similarity score for the at least one most similar document compared to each of the documents in the data set of documents in a flat manner by comparing the third frequency score and the third uniqueness score for each work in the at least one most similar document to the first frequency score and the first uniqueness core for each word of the data set of documents; and

presenting the most similar documents from the data set to the at least one most similar document using the respective second similarity score for the at least one most similar document compared to each of the documents in the data set of documents.

35. :

The system of claim 32, wherein;

the user input includes a uniform resource locator (URL); and

receiving the user input includes accessing information residing at a location designated by the URL.

36. :

The system of claim 32, wherein the instructions cause the processor to perform a method further comprising;

normalizing the respective similarity score for the document of interest compared to each of the documents of the data set of documents.

37. :

A non-transitory computer-readable medium having tangibly embodied thereon and accessible therefrom processor-executable instructions that, when executed by at least one data processing device of at least one computer, causes said at least one data processing device to perform a method comprising;

indexing a data set of documents having words by counting the words in the entire data set and determining a first frequency score and a first uniqueness score for each word of the data set of documents;

receiving a user input of a document of interest;

determining a second frequency score and a second uniqueness score for each word in the document of interest;

generating a respective similarity score for the document of interest compared to each of the documents in the data set of documents in a flat manner by comparing the second frequency score and the second uniqueness score for each word in the document of interest to the first frequency score and the first uniqueness score for each word of the data set of documents; and

presenting the most similar documents from the data set to the document of interest using the respective similarity score for the document of interest compared to each of the documents in the data set of documents.

38. :

The non-transitory computer readable medium of claim 37, wherein the presenting includes sorting the respective similarity score to obtain at least one most similar document of the data set of documents to the document of interest and displaying a ranked listing of the at least one most similar document.

39. :

The non-transitory computer readable medium of claim 38, wherein the method further comprises;

receiving a next user input to the processor of the at least one most similar document of the data set of documents;

determining by the processor a third frequency score and a third uniqueness score for each word in the at least one most similar document;

generating by the processor a respective second similarity score for the at least one most similar document compared to each of the documents in the data set of documents in a flat manner by comparing the third frequency score and the third uniqueness score for each work in the at least one most similar document to the first frequency score and the first uniqueness core for each word of the data set of documents; and

presenting by the processor the most similar documents from the data set to the at least one most similar document using the respective second similarity score for the at least one most similar document compared to each of the documents in the data set of documents.

40. :

The non-transitory computer readable medium of claim 37, wherein;

the user input includes a uniform resource locator (URL); and

receiving the user input includes accessing by the processor information residing at a location designated by the URL.

41. :

The non-transitory computer readable medium of claim 37, wherein the method further comprises;

normalizing by the processor the respective similarity score for the document of interest compared to each of the documents of the data set of documents.