1. A computer implemented method performed by a processor, comprising:
observing usage patterns by one or more users in an on-line community in connection with an on-line asset;
identifying usefulness of the on-line asset by observing user implicit behaviors in connection with the usage patterns of the on-line asset and by extracting behavioral patterns from the user implicit behaviors;
refining the identified on-line asset usefulness by context, wherein a context of the on-line asset is automatically detected based on one or more observed terms obtained by observing user implicit behaviors with respect to the identified on-line asset, wherein the identified on-line asset has a plurality of term vectors;
assigning a term vector entry of a term vector of the plurality of term vectors that describes a degree to which the identified on-line asset has an affinity with the observed terms, wherein each term vector of the plurality of term vectors is associated with a different user of the one or more users;
identifying for each user of the one or more users an expertise vector by
identifying on-line assets with respect to which the each user has engaged in one or more of the user implicit behaviors; and
generating the expertise vector by summing the plurality of term vectors for the identified on-line assets;
receive query including query terms;
obtaining search result documents;
determining that at least one search result document is an identified on-line asset for an expert user of the one or more users; and
ranking the at least one search result based on a relationship of the query terms to the expertise vector of the expert user.
2. The computer implemented method of claim 1, further comprising:
based upon the usage patterns, automatically determining the topic of the on-line asset.
3. The computer implemented method of claim 1, further comprising:
performing an analysis of the on-line community based on the usage patterns;
wherein users of the on-line community are grouped into peer groups based on similarities in the usage patterns; and
providing a visualization for understanding the groups and for understanding relationships between the groups.
4. The computer implemented method of claim 1, further comprising:
building an automatic marketing list through sponsored advertising by identifying firms that are purchasing keywords and banner ad space on public websites for advertising purposes, using information found in an online ad, and combining the information with other public information sources;
back-tracing to buyers of the ads; and
automatically generating a list of firms who are prospects for improved lead generation through their websites.
5. The computer implemented method of claim 1, further comprising:
activating an application when a second user arrives on a Website due to referral from an external search engine;
detecting a query from the external search engine; and
displaying recommendations to the second user that are targeted to the query.
6. The computer implemented method of claim 1, further comprising:
performing a usage-driven link analysis, wherein if a link is clicked by a second user of a Website, text within that link is captured to augment future searches and navigation within a site associated with the link;
determining that the navigation trail leads to a successful discovery; and
wherein the text is noted in response to the determining that a navigation trail leads to a successful document discovery.
7. The computer implemented method of claim 1, further comprising:
implicitly building context terms that describe what an asset concerns; and
learning the context terms by watching how users implicitly use various terms to describe content through link usage and searches associated with successful usage of content.
8. The computer implemented method of claim 1, wherein observing usage patterns further comprises observing user dwell time, the user dwell time having a range that is selected as a matter of choice, depending on the complexity of an asset that is being inspected.
9. The computer implemented method of claim 1, further comprising:
considering a percentage of people who revisit a piece of content and frequency of those revisits in determining usefulness of the on-line asset.
10. The computer implemented method of claim 1, further comprising:
observing a think usage pattern comprising a combination of actions that indicate that the user is thinking about the on-line asset, wherein the think usage pattern comprises a period of inactivity followed by action.
11. The computer implemented method of claim 1, further comprising:
generating an activeness vector by accumulating usage information every time the user uses the on-line asset;
wherein the activeness vector also indicates how much the on-line asset is used.
12. The computer implemented method of claim 1, further comprising:
providing a visualization for understanding groups of related experts and relationships between groups using the expert vectors of the one or more users.
13. The computer implemented method of claim 1, further comprising:
detecting peers and experts based on the usage patterns; and
further cross-examining the usage patterns of the peer and expert groups to validate observed individual behaviors and reduce noise from the observed individual behaviors.
14. The computer implemented method of claim 1, further comprising:
determining a current context interest of the user based on the on-line asset used and searches performed;
identifying relationships between the one or more users based on similarity between identified contextsinterests; and
detecting which of the on-line assets of a plurality of on-line assets are most used and useful to the on-line community.
15. The computer implemented method of claim 1, wherein the observed usage patterns comprise one or more of:
a user on-line search, navigation, and interaction behavior, the behavior including position in user navigation trail;
assets viewed and position in user navigation trail.
16. The computer implemented method of claim 1, further comprising, assigning a user a factor to specify expertise of the user based on the user implicit behaviors.
17. The computer implemented method of claim 1, further comprising, transparently executing within an existing enterprise application and repository environment based upon conventional engine provided by a third party;
wherein no user training or adoption of new interfaces is required.
18. The computer implemented method of claim 1, further comprising, developing implicit observations that predict user intentions with strong confidence, the observations comprising any of think time, virtual bookmarks, virtual print, and virtual email;
wherein users have not performed a bookmark, print, or email against an asset.
19. The computer implemented method of claim 1, further comprising, providing contextual information derived from the observed usage patterns that a user is not directly asking for via a search query or a keyword.
20. The computer implemented method of claim 1, further comprising,
implementing the method as a wrapper for an existing search mechanism;
wherein when a user issues a search query, the query is handled initially by a dedicated system;
wherein the dedicated system, in turn, forwards the query to the existing search mechanism;
wherein the dedicated system optionally performs one or more searches or related operations against its own internal indexes and databases; and
wherein once results from the searches have been obtained, they are merged together into a single set of results.
21. The computer implemented method of claim 1, further comprising, inferring that results are irrelevant to the user if a user does not perform any action at all against results from a query; and
retaining the inference and using it to influence results of future queries by the user.
22. The computer implemented method of claim 1, further comprising, detecting experts by examining a community and individuals who have the ability to discover and collect the most useful assets having the most impact.
23. The computer implemented method of claim 1, further comprising, providing an online user interface (UI) rendered using tags comprising a result set derived from a selected view on the observed usage patterns and distilled wisdom from the patterns, including a next step.
24. The computer implemented method of claim 1, further comprising, providing an ordered sequence of search processors in which a first search processor comprises an independent search processor and the second and subsequent search processors act as filters for search processors preceding them.
25. The computer implemented method of claim 1, further comprising, providing an explicit bias search processor for recognizing certain queries or query keywords and injecting a fixed set of documents into results for those queries, each with a fixed score.
26. The computer implemented method of claim 1, further comprising, providing a popularity search processor comprising an ancillary filter for detecting popular queries and increasing a ranking of assets in results that have historically been selected and used by previous users making the same query.
27. The computer implemented method of claim 1, further comprising, dynamically producing a report comparing a ratio of good-to-poor search results for queries that were enhanced using the method to a same ratio for queries that were not enhanced by using the method.
28. The computer implemented method of claim 1, wherein the method is implemented in an independent and content agnostic system that looks at a location of an asset and how users interact with that asset, rather than content of the asset itself.
29. The computer implemented method of claim 1, further comprising, creating a federation across multiple applications, Websites, and repositories based upon a user’s pattern of usage of information from and across the multiple applications, Websites, and repositories; and
automatically performing a federated search across the multiple applications, Websites, and repositories, wherein when a query is searched again, information from the multiple applications, Websites, and repositories is recommended.
30. The computer implemented method of claim 1, further comprising, performing an augmented search by blending traditional full-text search with preferences and activeness information from global, peer, and expert populations.
31. The computer implemented method of claim 1, further comprising, returning the n most popular links that have to do with a query term of a user in response to a user query;
wherein if there is no term, then the top n most popular links are returned.
32. The computer implemented method of claim 31, further comprising, limiting a set of most popular links and associated assets to those meeting constraints of one or more configured filters.
33. The computer implemented method of claim 1, further comprising, preloading content; and
showing a user a preview of what content is associated with a link.
34. The computer implemented method of claim 33, further comprising,
temporarily replacing a current asset being displayed with a previewed asset for as long as the preview is active; and
seamlessly returning to an original asset view when the preview is deactivated.
35. The computer implemented method of claim 1, further comprising, identifying a spectrum of values ranging from short-lived information, to mid-range knowledge, to long-lived wisdom.
36. The computer implemented method of claim 1, further comprising, observing usage patterns in a content-agnostic way;
wherein usage patterns can be detected for any asset type, including non-HTML content.
37. The computer implemented method of claim 1, further comprising, performing a gap analysis of use of assets, data, and applications by ascertaining what users are actually looking for and what is missing when the user exhibits search or navigation behavior; and
providing a gap analysis report for detected gaps in the assets.
38. The computer implemented method of claim 1, further comprising, performing an analysis of use of assets over time including what pieces of the assets are most useful to a community of users at a given time and how that usefulness trends over time; and
providing an asset a report for visualizing the usage patterns.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1-30. (canceled)
31. A system comprising:
an actuatable prosthetic device being moveable about a joint;
at least one sensor;
a control system comprising a processor and memory, the control system communicatively coupled to the at least one sensor and configured to:
receive a time series of motion signals indicative of a movement of the prosthetic device,
filter at least one time series of motion signal of the time series of motion signals,
process the filtered at least one time series of motion signal to identify at least one limb-motion category, and
cause an actuator to control movement of the actuatable prosthetic device based at least in part on the identified at least one limb-motion category.
32. The system of claim 31, wherein the at least one sensor comprises a myoelectric sensor.
33. The system of claim 31, wherein the at least one sensor comprises at least one of an accelerometer and a gyroscope.
34. The system of claim 31, wherein the limb-motion category comprises at least one of level ground walking, walking up or down stairs, walking up or down ramps, running, inclination, declination, step, and non-step.
35. The system of claim 31, wherein the memory is configured to store at least one of data associated with the received time series signal, experimental data, or calibration data.
36. The system of claim 31, further comprising an interface for communication with a user or an external device.
37. The system of claim 31, wherein, to process the filtered at least one time series of motion signal to identify at least one limb-motion category, the control system is configured to compare the filtered at least one time series of motion signal with a set of sample time series of motion signals.
38. The system of claim 31, wherein the control system is further configured to identify at least one of heel strike, toe down, heel off, toe off, midswing or end of swing.
39. A method of controlling operation of an actuatable prosthetic device being moveable about a joint, the method comprising:
producing, using at least one sensor, a time series of motion signals indicative of a movement of the actuatable prosthetic device;
receiving at a processor the time series of motion signals indicative of the movement of the prosthetic device;
filtering, using said processor, selected at least one time series of motion signal of the time series of motion signals;
processing, using said processor, the filtered at least one time series of motion signal to identify at least one limb-motion category; and
causing an actuator to control movement of the actuatable prosthetic device based at least in part on the identified at least one limb-motion category.
40. The method of claim 39, wherein the at least one sensor comprises a myoelectric sensor.
41. The method of claim 39, wherein the at least one sensor comprises at least one of an accelerometer and a gyroscope.
42. The method of claim 39, wherein the limb-motion category comprises at least one of level ground walking, walking up or down stairs, walking up or down ramps, running, inclination, declination, step, and non-step.
43. The method of claim 39, further comprising storing data associated with at least one of the time series of motion signals, experimental data, or calibration data.
44. The method of claim 39, further comprising communicating with a user or an external device through an interface.
45. The method of claim 39, wherein said processing the filtered at least one time series of motion signal comprises comparing the filtered at least one time series of motion signal with a set of sample series of motion signals to identify the at least one limb-motion category.
46. The method of claim 39, further comprising identifying at least one of heel strike, toe down, heel off, toe off, midswing and end of swing.
47. A system comprising:
a means for producing a time series of motion signals indicative of a movement of an actuatable prosthetic device;
a means for receiving the time series of motion signals indicative of the movement of the actuatable prosthetic device;
a means for filtering selected at least one time series of motion signal of the time series of motion signals;
a means for processing the filtered at least one time series of motion signal to identify at least one limb-motion category; and
a means for controlling movement of the actuatable prosthetic device based at least in part on the identified at least one limb-motion category.
48. The system of claim 47, wherein the means for producing a time series of motion signals indicative of a movement of an actuatable prosthetic device comprises a myoelectric sensor.
49. The system of claim 47, further comprising a means for identifying at least one limb-motion event comprising at least one of heel strike, toe down, heel off, toe off, midswing and end of swing.
50. The system of claim 47, wherein the at least one limb-motion category comprises at least one of level ground walking, walking up or down stairs, walking up or down ramps, running, inclination, declination, step, and non-step.