Patent Application
20130007009
Within any number of business, social, or academic enterprises, various members of a given enterprise may have varying levels of expertise associated with different topics or concepts. For example, one member of a given enterprise may have specialized expertise associated with object oriented programming. Ideally, a member of the enterprise desiring the assistance of the member with the specialized expertise could use a keyword driven search to find contact information for the enterprise member having the specialized expertise, however, such a keyword driven search would require that people within the enterprise are tagged with keywords associated with their particular expertise. Typically, members of various enterprises are reluctant to tag themselves with such identifying information which makes performing searches on the members a problem.
In addition, when a member of a given enterprise is new to the enterprise, one of the first tasks that the member often performs is signing up for appropriate mailing lists, work groups, and projects. This task is complicated by various factors. For example, the user may not know what resources and projects are initially available, and therefore, the user does not know those resources and projects to which the user should request access. Moreover, even if the user knows what resources and/or projects are available, the user still must determine which resources and projects are relevant to the user's daily tasks and work.
It is with respect to these and other considerations that the present invention has been made.
Embodiments of the present invention solve the above and other problems by automatically tagging individual users for identifying expertise or other relevant skills associated with the individual users based on various sources of information used or interacted with by the users. According to embodiments, keyword tags are automatically associated with individual users by monitoring electronic mails they send, documents they create, edit, or otherwise interact with, social networks they utilize, others they interact with, and other sources of information. A keyword ranking application ranks keywords generated for individual users, and highly ranked tags are suggested to the individual users as expertise tags. For example, a suggested expertise tag of “software developer” may be suggested for a given user to allow other users to identify him or her for a given software development project. According to one embodiment, the expertise tagging may take the form of a set of keywords or a summary of one or more keywords that is indicative of expertise or skill sets associated with the given user that may be searchable for finding expertise information for the user.
After expertise tags are established for an individual user, the expertise tagging and other information, for example, the user's position on organization charts, the user's activities, and information about project groups or workspaces to which the user belongs, may be used for automatically suggesting a user for membership in one or more other project groups or workspaces that may be a good fit for the user's expertise or other relevant skills.
The details of one or more embodiments are set forth in the accompanying drawings and description below. Other features and advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. It is to be understood that the following detailed description is explanatory only and is not restrictive of the invention as claimed.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended as an aid in determining the scope of the claimed subject matter.
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present invention. In the drawings:
As briefly described above, embodiments of the present invention are directed to automatically tagging individual users for identifying expertise or other relevant skills associated with the individual users based on various sources of information used or interacted with by the users. Expertise tagging may include one or more keywords, a set of keywords or a summary of keywords (all searchable) for associating a given expertise or skill set to various users. After expertise tags are established for an individual user, the expertise tagging and other information about the user's profile and computing activities may be used for automatically suggesting a user for membership in one or more other project groups or workspaces that may be a good fit for the user's expertise or other relevant skills. Other users may find a tagged user when searching for a particular area of expertise that relates to the tags associated with various users.
The following description refers to the accompanying drawings. Whenever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While embodiments of the invention may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the invention. Instead, the proper scope of the invention is defined by the appended claims.
Referring now to the drawings, in which like numerals represent like elements through the several figures, aspects of the present invention and the exemplary operating environment will be described. While the invention will be described in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a personal computer, those skilled in the art will recognize that the invention may also be implemented in combination with other program modules.
Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
A second category of information that may be used in defining the expertise of an individual user includes information that describes the user through one or more profiles associated with the user and/or associated with a project workspace environment. Other such information includes user-entered expertise tags, user approval expertise tags applied to them by other users, expertise information for the user's contacts or colleagues, descriptions of mailing lists and forums subscribed to by the user, or project workspaces to which the user belongs or to which the user has subscribed. Keywords extracted from these types of information may then be ranked by a keyword ranker based on various factors including the type of source and a confidence score associated with each type of source. Highly ranked keywords may then be outputted to the user in a user interface to allow the user to provide feedback on expertise tagging applied to the user. For example, if documents generated by the user contain keywords of “software designer” or “software engineer” and if information from profile information associated with the user likewise includes the terms “software designer” or “software engineer,” then an expertise tag of “software designer” or “software engineer” may be suggested to the user as expertise tagging that will be applied to the user to allow others in the user's organization or outside the user's organization to interact with the user based on the user's applied expertise tagging.
Referring still to
User documents 106 are illustrative of any documents, for example, letters, memoranda, specifications, spreadsheet documents, slide presentation documents, and the like authored by an individual user that may contain keywords that will be helpful in applying expertise tagging to the user. For example, if a number of documents authored by the user contain keywords, as described above, for example “software,” “designer,” and the like, such keywords may be used subsequently for applying expertise tagging to the subject user. The question and answers repository 108 is illustrative of stored question and answer pairings associated with a user, for example, where a stored question and answer pairing was asked by or answered by the subject user. Such question and answers may often be directed to work projects associated with the user, technologies in which the user has expertise or involvement, and the like. Thus, such question and answer pairings may provide many possible keywords that may be reviewed for application of expertise tagging to the subject user.
Blogs 110 are illustrative of any other forum in which the user may author text-based communications or documentation that may include keywords that may be used for defining and tagging the expertise or other relevant skills of the user. For example, the blogs repository 110 may be illustrative of Internet-based chat forums, organization discussion boards, and the like through which the user may author various text-based documents or communications and from which keywords may be extracted for use in applying expertise tagging to the user.
According to embodiments, text and associated metadata 122 from each of the aforementioned text content sources may be extracted and may be passed to a keyword extractor 124 for extracting various keywords from the text content sources for ultimate use in developing and applying expertise tagging to the subject user. According to one embodiment, the keyword extractor includes a text processing operation for breaking received text content into individual text components (e.g. words, terms, numeric strings, etc.) that may be used for developing expertise tags. Received text content and metadata are analyzed and formatted as necessary for text processing described below. According to embodiments, the text content and metadata analysis may be performed by a text parser operative to parse text content and metadata for processing the text into one or more text components (e.g., sentences and terms comprising the one or more sentences). For example, if the text content and metadata are formatted according to a structured data language, for example, Extensible Markup Language (XML), the text content and metadata analysis may include parsing the retrieved text content and metadata according to the associated structured data language for processing the text as described herein. For another example, the text content and metadata may be retrieved from an online source such as an Internet-based chat forum where the retrieved text may be formatted according to a formatting such as Hypertext Markup Language (HTML). According to embodiments, the text content and metadata analysis may be include formatting the retrieved text content and metadata from such a source so that it may be processed for conversation topics as described herein.
A text processing application may be employed whereby the text is broken into one or more text components for determining whether the received/retrieved text may contain terms that may be formed expertise tags or that may be used for searching for stored expertise tags. Breaking the text into the one or more text components may include breaking the text into individual sentences followed by breaking the individual sentences into individual tokens, for example, words, numeric strings, etc.
Such text processing is well known to those skilled in the art and may include breaking text portions into individual sentences and individual tokens according to known parameters. For example, punctuation marks and capitalization contained in a text portion may be utilized for determining the beginning and ending of a sentence. Spaces contained between portions of text may be utilized for determining breaks between individual tokens, for example, individual words, contained in individual sentences. Alphanumeric strings following known patterns, for example, five digit numbers associated with zip codes, may be utilized for identifying portions of text. In addition, initially identified sentences or sentence tokens may be passed to one or more recognizer programs for comparing initially identified sentences or tokens against databases of known sentences or tokens for further determining individual sentences or tokens. For example, a word contained in a given sentence may be passed to a database to determine whether the word is a person's name, the name of a city, the name of a company, or whether a particular token is a recognized acronym, trade name, or the like. As should be appreciated, a variety of means may be employed for comparing sentences or tokens of sentences against known, words, or other alphanumeric strings for further identifying those text items.
After the text content sources and associated metadata are broken into individual text components, as described above, the keyword extractor may collect keywords for use in developing and applying expertise tagging to the subject user. As part of the collection of keywords, certain terms, for example, basic articles such as “a,” “and,” “the,” and the like may be discarded along with other words that are not useful in developing expertise tagging. According to one embodiment, a keyword store may be utilized by the keyword extractor for comparing keywords extracted from retrieved text content sources for determining which keywords extracted from the text content sources should be kept for use in developing expertise tagging and for determining which keywords should be discarded. For example, the keyword store utilized by the keyword extractor may include a list of keywords commonly utilized for expertise tagging as well as a list of words and terms that are seldom or never used in developing expertise tagging. Extracted keywords are passed to the keywords, metadata and weights component operation 128 for combination with metadata information, as described below.
As briefly described above, in addition to keywords extracted from various text content sources, metadata associated with the user and associated with other users and sources of information related to the user may also be used for developing and applying expertise tagging to an individual user. The meta-sources repository 112 is illustrative of a variety of metadata sources that may provide information useful in developing and applying expertise tagging to an individual user. For example, colleague tags 114 is illustrative of expertise tagging applied to various colleagues of the user, as defined by other users and common project workspaces with the subject user, other users to which the user regularly communicates, other users in the same product development team as the user, and the like. Metadata indicating expertise tagging associated with such other users may be very helpful in developing expertise tagging for the subject user including ranking keywords extracted from text content sources as to their weight relative to each other and developing expertise tagging for the subject user.
Expertise tags or personal tags 116 applied to friends of the user may provide helpful information regarding expertise tagging that may be developed for the subject user. For example, if friends to which the user associates through social networking sites, friends lists on electronic mail systems, and the like are tagged with various expertise tagging, metadata associated with such expertise tagging may be useful and further defining expertise tagging that may be applied to the subject user. Manual tag entries 118 are illustrative of any manual metadata entered by the user that may be valuable in determining expertise tagging for the user. For example, manual entries may include entries entered by the user on company forms, information entered by the user on user profiles for company databases, social networks, and the like. Such information is valuable because such information is entered directly by the user and provides a good source of information for defining expertise tagging for the user. For example, if a company form is prepared by the user wherein the user lists various types of expertise, skills, or experience, such information may be useful in developing expertise tagging for application to the subject user. Mailing lists/distribution list keywords 120 is illustrative of metadata associated with various communications sent to, received by, responded by, or otherwise interacted with by the user that similarly may provide useful information for developing and applying expertise tagging to the subject user.
At the meta-sources aggregator component/operation 126, metadata sources are processed into individual text components in a similar manner as described above for the text content sources. That is, metadata information is broken into individual terms that may be used for developing and applying expertise tags to the subject user. Once the keywords or terms are aggregated from the various meta-sources they may be passed to the keywords, metadata component/operation 128. In addition to passing the individual keywords and terms extracted from the text content sources and metadata sources, any weighting to those keywords and terms that is available from the sources from which the terms are extracted is also aggregated. For example, manual entries 118 may be weighted higher than information from the friends tags 116 because the manual entries are entered directly by the subject user and may be associated with a higher confidence of having accurate information that may be used for developing and applying expertise tagging to the user. Likewise, documents 106 authored by the subject user may receive a higher weighting and/or confidence score than information obtained from blog 110 that may contain text entered by a variety of different users.
Once keywords and terms are extracted from text content sources and meta-sources, as described above, the extracted keywords and terms are passed to the user/keyword ranker component/operation 130 for developing a ranked list of extracted keywords and terms. As described above, weights and confidence scores may be applied to various sources of keywords and terms, and weights and confidence scores may likewise be applied to various keywords and terms. For example, as described above, information manually entered by a user may receive a high weighting and/or confidence score owing to the source of the entered information. Information received from documents authored directly by the user likewise may receive a high weighting and/or confidence score. On the other hand, information from sources containing text or data entered by a variety of users, for example, a blog site (for example, blogs not written by the subject user) or Internet-based chat forum may receive a lower ranking and/or confidence score because information contained from such sources may not be easily associated with the subject user as opposed to other users contributing text to such sources.
In addition, weighting and/or confidence scores may be applied to various keywords and terms. As described above, the keyword extractor may utilize lists of previous extracted and weighted keywords and terms stored in a keyword and term store for obtaining weights and/or confidence scores associated with certain keywords and/or terms. For example, a keyword of “engineer” may receive a higher weighting and/or confidence score than a keyword or term of “software”, because the term “engineer” indicates a particular skill or expertise, whereas the term “software” may indicate a product used by a subject. On the other hand, combinations of keywords or terms, for example, “software engineer” may receive a higher weighting and/or confidence score, because the combination of such keywords or terms further defines an expertise or skill that may be associated with a subject user.
After the extracted keywords and terms are ranked, highly ranked keywords and/or terms, for example, the top ten keywords or terms, the top five keywords or terms, or the like may be presented to the user at the user feedback operation/component 132. As will be described below with reference to
As briefly described above, expertise tags associated with a given user may be more than single keywords identifying a particular expertise. According to embodiments, the expertise tags may include a set or collection of keywords, or a summary of a set or collection of keywords that may be automatically stored for a given user or that may be stored after review, modification or replacement by a reviewing user as described below with reference to
For example, the tags “software engineer,” “design tester” and “code writer” are illustrated in the text box 155 as candidate expertise tags that may be applied to the subject user. According to embodiments, the user may accept or reject the candidate expertise tags, or the user may propose replacement expertise tags in the text box or field 160, followed by selection of the “Accept New Tag” button 180. According to one embodiment, a menu, for example, a drop down menu, associated with the proposed tag text box or field 160 may be provided to allow the user to select from other expertise tags that have been developed and applied to other users. In addition, the user may make inline revisions/corrections to the candidate expertise tags displayed in the text box/field 155, followed by selecting the button 180 for submitting the revised/corrected tags. If the user accepts candidate expertise tags, or if the user submits replacement expertise tags, the accepted or replacement expertise tags may be stored in the expertise tags store 134, as described above. If the user rejects the candidate expertise tags, but provides no replacement expertise tags, then no expertise tags will be applied to the user until additional expertise tags are subsequently developed and presented to the user.
According to embodiments, once expertise tagging is developed and applied to a user, as described above, expertise tagging for the user along with other information about the user, for example, the user's membership or association with others having membership in one or more organization charts, the user's activities, for example, the documents they read, or the other users with which they communicate, and information about existing project or workspace memberships may be used for developing a recommendation that the user should be included in a particular project membership. For example, if expertise tagging, organizational chart information, activities, information about existing memberships, and other useful information indicates that a user should be recommended for membership in a particular project associated with the development of a new software product for processing sales data, then a recommendation to the user for membership in such a group may be offered to the user via a user interface component that may be accepted, rejected, or modified by the user.
At operation 215, a search for project memberships that may be suggested to the user begins. At operation 220, content frequently used by the subject user may be parsed for keywords and terms that may be compared to keywords and terms associated with various project teams or workspaces that may be suggested to the user. At operation 225, electronic mail items for frequent contacts, and keywords associated with such electronic mail items and frequent contacts may be parsed for use in project membership searches. At operation 230, organization charts that may provide information about co-workers nearest to the subject user in the organization chart, as well as, other users associated with the organization chart may be parsed for obtaining keywords and terms that may be used for searching similar keywords and terms associated with various projects and associated with other users who are currently members of such projects. As should be appreciated, the keywords and terms for information described with respect to operations 220, 225, 230 may include the same keywords and terms extracted from the text content sources and meta-sources utilized for developing and applying expertise tagging to the subject user, as described above with reference to
At operation 235, keywords and terms associated with text content activity, for example, documents generated or edited by the subject user, electronic mail items sent or received by the subject user and the like may be used for searching a keyword or term database associated with various projects that may be suggested to the subject user for similar keywords and terms. For example, if keywords or terms such as “project AB” are found in various documents of the subject user and are likewise found in various documents associated with a particular project workspace, that project workspace may ultimately be suggested to the subject user for membership.
At operation 240, keywords and terms extracted from communications such as electronic mail items, text messages, and the like associated with various other users, for example, frequently used contacts may be used for similarly searching a database of keywords and terms associated with various project workspaces. For example, if one or more user identifications are extracted from various electronic mail items and such user identifications match one or more users who are currently associated with or who are members of a particular project workspace, that project workspace might be suggested to the subject user for membership.
At operation 245, information from organization charts, information about the subject user's membership in existing project workspaces, and the like may be used for searching databases of keywords and terms associated with various project workspaces for matching the subject user to one or more particular project workspaces. For example, if one or more users are identified in close proximity to the subject user on an organizational chart, and if those one or more users are identified as members of a particular project workspace, then that project workspace may be suggested to the subject user for membership.
At operation 250, information from each of the above-described database searches, including information from combinations thereof, may be used for developing project membership recommendations. For example, if keywords or terms from documents prepared by the subject user are found in documents stored in association with a particular project workspace, and if members of the subject user's frequently used contacts are identified with the particular project workspace, and if users on the subject user's organization chart are also indicated as members of the particular project workspace, then that project workspace may have a high ranking or high confidence as a suggested project workspace for the subject user. On the other hand, if a member of the subject user's organization chart is identified in association with a particular project workspace, but no other information associated with the subject user, for example, text content, communications content, or the like, is matched between the user and information associated with that project workspace, then recommendation of the subject user to that project workspace may be ranked lower and/or receive a lower confidence score.
Thus, at operation 250, recommendations for membership in one or more project workspaces for the subject user may be ranked, and at operation 255, highly ranked project membership recommendations may be presented to the user via a user interface component, as described below with reference to
If the user rejects membership in the suggested project group memberships, the user may receive subsequent project membership recommendations based on additional activities, for example, additional documents, electronic communications, and the like, developed or conducted by the user. Alternatively, the user may propose a different project workspace or group for membership in the text box or field 286. As should be appreciated, the text box or field 286 may be associated with a menu, for example, a drop down menu that may provide a listing of various project groups or workspaces to which the user may be associated or that the user may join. If the user enters or selects a proposed different project group or workspace, the user may select the “Accept Proposed Group” button 294 for automatically associating with the selected or proposed project group or workspace, and such proposed project group or workspace will be stored for the user, as described above. According to an embodiment, the user's feedback in response to the proposed project memberships, including entry or selection of a proposed replacement project group or workspace or membership may be used by the system 200 for enhancing its analysis of keywords or terms associated with information obtained about the user for subsequent recommendations of project group or workspace memberships.
As should be appreciated, the user interface components illustrated and described herein are for purposes of example and illustration only. The placement and orientation of textboxes, titles, buttons, functionality controls and the like in the example user interface components are not limiting of the vast number of placements and orientations that may be selected for generation and display of suitable user interface components for use as described herein.
As described above, embodiments of the invention may be implemented via local and remote computing and data storage systems, including the systems illustrated and described with reference to
With reference to
Computing device 400 may have additional features or functionality. For example, computing device 400 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in
As stated above, a number of program modules and data files may be stored in system memory 404, including operating system 405. While executing on processing unit 402, programming modules 406 and may include the expertise tag generation system 100 and the project membership suggestion/recommendation system 200 each of which may include program modules containing sufficient computer-executable instructions, which when executed, performs functionalities as described herein. The aforementioned process is an example, and processing unit 402 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present invention may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, etc.
Generally, consistent with embodiments of the invention, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Embodiments of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
Furthermore, embodiments of the invention may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the invention may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the invention may be practiced within a general purpose computer or in any other circuits or systems.
Embodiments of the invention, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. Accordingly, the present invention may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present invention may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory 404, removable storage 409, and non-removable storage 410 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 400. Any such computer storage media may be part of device 400. Computing device 400 may also have input device(s) 412 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc. Output device(s) 414 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.
The term computer readable media as used herein may also include communication media. Communication media may be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.
Embodiments of the present invention, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the invention. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
While certain embodiments of the invention have been described, other embodiments may exist. Furthermore, although embodiments of the present invention have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, floppy disks, or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods' stages may be modified in any manner, including by reordering stages and/or inserting or deleting stages, without departing from the invention.
All rights including copyrights in the code included herein are vested in and the property of the Applicant. The Applicant retains and reserves all rights in the code included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.
While the specification includes examples, the invention's scope is indicated by the following claims. Furthermore, while the specification has been described in language specific to structural features and/or methodological acts, the claims are not limited to the features or acts described above. Rather, the specific features and acts described above are disclosed as example for embodiments of the invention.
