Information Management System

• US 20150356107A1
• Filed: 08/18/2015
• Published: 12/10/2015
• Est. Priority Date: 08/27/2008
• Status: Active Grant

• Current Assignee: Revvity Signals Software Incorporated
• Original Assignee: PerkinElmer Informatics, Inc. (Revvity, Inc.)
• Inventors: Smith, Robin Young, Ballard, William Brian
• Granted Patent: US 9,575,980 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/152 using file content signatures, e.g. hash values
• CPC Class Codes: G06F 16/20 of structured data, e.g. relational data
• CPC Class Codes: G06F 16/25 Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 16/256 in federated or virtual databases
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 40/157 using dictionaries or tables
• CPC Class Codes: G06F 40/205 Parsing

Abstract

An information management system creates data structures based entirely on the content of source files, then compares these data structures to discover synergies and commonalities. In one embodiment, the system accepts a first collection of source files, and extracts text from each source file. The text is compared to tags in one or more dictionaries, which comprise hierarchical listing of tags. Tags matching the text are associated with each source file. The system then generates a virtual relational network in which each source file having matching tags is a node. Tags associated with two or more source files are links between the nodes. This virtual relational network may be compared with another virtual relational network to discover common nodes or links. Source files later added to a collection are massively linked by associating all tags from all source files with the newly added source file, and vice versa.

Claims

1-32. -32. (canceled)

33. A method of operating an information management system, comprising:

obtaining a first collection of source files;

for each source file in the first collection;

parsing, by a processor of a computing device, the respective source file to extract one or more tags,

comparing, by the processor, the one or more tags to tags in at least one dictionary to determine one or more matching tags, wherein the at least one dictionary comprises a hierarchical listing of tags, and

associating, by the processor, with the respective source file, the one or more matching tags;

generating, by the processor, a first virtual relational network comprising the source files in the first collection, wherein the first virtual relational network comprises;

one or more nodes, wherein each node of the one or more nodes represents a particular matching tag of the one or more matching tags associated with a particular source file of the source files in the first collection, and

one or more links, wherein each link of the one or more links represents a connection between a pair of nodes, wherein each node of the pair of nodes is associated with a same tag; and

comparing, by the processor, the first virtual relational network to a second virtual relational network to identify at least one of (a) nodes common to the first and second virtual relational networks, and (b) links common to the first and second virtual relational networks, wherein

the second virtual relational network was created from a second collection of source files different from the first collection of source files, and

the second virtual relational network was created using one or more dictionaries of the at least one dictionary.

34. The method of claim 33, wherein tags comprise text terms.

35. The method of claim 33, wherein tags comprise representations of embedded objects.

36. The method of claim 33, wherein parsing each source file in the first collection to extract the one or more tags comprises, for each source file in the first collection:

identifying a source file type; and

inputting the respective source file to a software module that recognizes the source file type.

37. The method of claim 33, wherein associating the one or more matching tags comprises, for each dictionary of the at least one dictionary, associating the one or more matching tags from the respective dictionary with the respective source file as a separate set of tags.

38. The method of claim 33, further comprising, for each source file in the collection, transforming the respective source file to a respective archive file of a common file type, wherein the respective archive file includes at least all content extracted from the respective source file as tag content.

39. The method of claim 38, wherein the common file type is a text-readable Portable Document Format (PDF).

40. The method of claim 33, wherein associating the one or more matching tags with the respective source file further comprises associating a respective hierarchical level of each tag of the one or more matching tags, wherein the respective hierarchical level relates to a hierarchical level within the dictionary.

41. The method of claim 40, wherein generating the first virtual relational network comprises generating a hierarchical network corresponding to a hierarchy of matching dictionary tags.

42. The method of claim 33, further comprising causing, by the processor, a graphic representation of at least part of the first and second virtual relational networks to be displayed on a computer display.

43. The method of claim 42, wherein each link of the one or more links is configured, upon display, to visually indicate an associated dictionary of the at least one dictionary, wherein a respective matching tag associated with the link resides within the associated dictionary.

44. The method of claim 42, wherein the graphic representation comprises a Venn diagram.

45. The method of claim 33, wherein the second collection of source files is associated with a different entity than an entity associated with the first collection of source files.

46. The method of claim 33, wherein the first collection of source files is associated with an entity at a first time, and the second collection of source files is associated with the same entity at a later time.

47. The method of claim 33, further comprising comparing other virtual relational networks in addition to the first and second virtual relational networks.

48. A non-transitory computer readable medium having instructions stored thereon that, when executed, cause a processor to:

obtain a first collection of source files;

for each source file in the first collection;

parse the respective source file to extract one or more tags,

compare the one or more tags to tags in at least one dictionary to determine one or more matching tags, wherein the at least one dictionary comprises a hierarchical listing of tags, and

associate, with the respective source file, the one or more matching tags;

generate a first virtual relational network comprising the source files in the first collection, wherein the first virtual relational network comprises;

one or more nodes, wherein each node of the one or more nodes represents a particular matching tag of the one or more matching tags associated with a particular source file of the source files in the first collection, and

one or more links, wherein each link of the one or more links represents a connection between a pair of nodes, wherein each node of the pair of nodes is associated with a same tag; and

compare the first virtual relational network to a second virtual relational network to identify at least one of (a) nodes common to the first and second virtual relational networks, and (b) links common to the first and second virtual relational networks, wherein

the second virtual relational network was created from a second collection of source files different from the first collection of source files, and

the second virtual relational network was created using one or more dictionaries of the at least one dictionary.

49. The non-transitory computer readable medium of claim 48, wherein tags comprise text terms.

50. The non-transitory computer readable medium of claim 48 wherein tags comprise representations of embedded objects.

51. The non-transitory computer readable medium of claim 48, wherein the instructions comprise at least one tag extraction and file transformation module operative, when executed, to parse each source file in the first collection and to extract tags therefrom.

52. The non-transitory computer readable medium of claim 51, wherein the tag extraction and file transformation module is further operative, when executed, to transform each source file to an archive file of a common file type.

53. The non-transitory computer readable medium of claim 48, wherein the instructions, when executed, cause the processor to display a graphic representation of at least part of the first and second virtual relational networks.

54. The non-transitory computer readable medium of claim 48, wherein the instructions, when executed, cause the processor to compare other virtual relational networks in addition to the first and second virtual relational networks.

55. The non-transitory computer readable medium of claim 48, wherein the second collection of source files is associated with a different entity than an entity associated with the first collection of source files.

56. The non-transitory computer readable medium of claim 48, wherein the first collection of source files is associated with an entity at a first time, and the second collection of source files is associated with the same entity at a later time.

57. A system comprising:

a processor; and

a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to;

obtain a first collection of source files;

for each source file in the first collection;

parse the respective source file to extract one or more tags,

compare the one or more tags to tags in at least one dictionary to determine one or more matching tags, wherein the at least one dictionary comprises a hierarchical listing of tags, and

associate, with the respective source file, the one or more matching tags;

generate a first virtual relational network comprising the source files in the first collection, wherein the first virtual relational network comprises;

one or more nodes, wherein each node of the one or more nodes represents a particular matching tag of the one or more matching tags associated with a particular source file of the source files in the first collection, and

one or more links, wherein each link of the one or more links represents a connection between a pair of nodes, wherein each node of the pair of nodes is associated with a same tag; and

obtain a second collection of source files different than the first collection of source files;

for each source file in the second collection;

parse the respective source file to extract one or more second tags,

compare the one or more second tags to tags in one or more dictionaries to determine one or more matching second tags, wherein

the one or more dictionaries comprise at least a first dictionary of the at least one dictionary, and

each dictionary of the one or more dictionaries comprises a hierarchical listing of tags, and

associate, with the respective source file, the one or more second matching tags;

generate a first virtual relational network comprising the source files in the first collection, wherein the first virtual relational network comprises;

one or more nodes, wherein each node of the one or more nodes represents a particular matching tag of the one or more matching tags associated with a particular source file of the source files in the first collection, and

one or more links, wherein each link of the one or more links represents a connection between a pair of nodes, wherein each node of the pair of nodes is associated with a same tag;

generate a second virtual relational network comprising the source files in the second collection, wherein the second virtual relational network comprises;

one or more nodes, wherein each node of the one or more nodes represents a particular second matching tag of the one or more second matching tags associated with a particular source file of the source files in the second collection, and

one or more links, wherein each link of the one or more links represents a connection between a pair of nodes, wherein each node of the pair of nodes is associated with a same tag; and

compare the first virtual relational network to the second virtual relational network to identify at least one of (a) nodes common to the first and second virtual relational networks, and (b) links common to the first and second virtual relational networks.

58. The system of claim 57, wherein the first collection of source files is obtained at a first time, and the second collection of source files is obtained at a second time, wherein the second time is a predetermined period after the first time.

59. The system of claim 57, wherein the at least one dictionary comprises a first dictionary and a second dictionary, wherein the second dictionary comprises a customized dictionary created by a user of the system.

60. The system of claim 59, wherein the second dictionary comprises entity-specific tags.