Next for LinkedIn: Who to Acquire?

by workforceplanner

It seems every day I’m reading about analytic management and measurement tools that support these initiatives in recruitment solutions: Workforce Planning/Forecasting with applications that parse Big Data, Talent Acquisition, with Assessment Tools for Cultural Matches and Social Media Recruiting with Message Delivery Platforms – think HootSuite, etc. There’s been significant M&A and investment activity with new ventures as well as established companies – just to name a few, STG backing Findly for the purchase of Bernard Hodes, Edison Ventures supporting RealMatch, Oracle buying Taleo, SAP buying Success Factors, IBM’s purchase of Kenexa…

Being a LNKD shareholder doesn’t give me any special insight regarding their next big move, yet I thought about what their plans might be for the patents they aquired from Digg and how they’d apply innovations such as Digg’s “vote up a story” to  improve their current offerings – LinkedIn Endorsements came to mind…

I also thought about a report I read naming Facebook, LinkedIn and others in patent infringement lawsuits by a company claiming to have broad-based patents that underlie basic social networking functions.

Yet with so much backing going to lesser known entities in the over $100 billion careers market/recruitment solutions/communications/advertising/marketing/sales space that the Street may perceive as threats to LinkedIn, I wondered how defensively or offensively motivated LinkedIn would be to the pursue lesser knowns and/or established players – perhaps by building a better Applicant Tracking System (beyond whatever advanced recruiter tools that might be expected of LinkedIn).  I also considered the idea of LinkedIn possibly involved in R&D or acquisitions that would make them competitive with employee performance measurement entities like Kenexa…

Most of all, I imagined companies with workforce planning and predictive analytics capabilities as leading potential LinkedIn takeover targets. In fact, I can’t justify that statement without deep research yet I was in confirmation bias with this superficial investigation I did with a quick USPTO search:

LinkedIn as Term 1: in Field 1: All Fields. Results of Search in US Patent Collection db for: LinkedIn: 290 patents.

Of course, a search with LinkedIn in *all* fields would return any reference to LinkedIn – and there are plenty of patents that mention Facebook, Twitter, LinkedIn, etc.

Nevertheless, I was curious about what I might discover by refining the search this way:

LinkedIn AND predictive: Results of Search in US Patent Collection db for: (LinkedIn AND predictive): 25 patents.

These struck me as intriguing:

2.     8,489,527     Full-Text     Method and apparatus for neuropsychological modeling of human experience and purchasing behavior

9.     8,437,776     Full-Text     Methods to determine the effectiveness of a physical advertisement relating to a physical business location

11.     8,422,994     Full-Text     Intuitive computing methods and systems

12.     8,417,715     Full-Text     Platform independent plug-in methods and systems for data mining and analytics

13.     8,364,171     Full-Text     Systems and methods to determine the current popularity of physical business locations

This is partial information for #8,489,527:

Inventors: van Coppenolle; Bart (Leuven, BE), Vandormael; Philip W. J. (Leuven, BE)
Assignee:     Holybrain BVBA (Leuven, BE)
Family ID:     45973809
Appl. No.:     13/278,789
Filed:     October 21, 2011

This is partial information for #8,437,776:

Inventors:     Busch; James David (Tempe, AZ)
US
Assignee:     Enhanced Geographic LLC (Tempe, AZ)
Family ID:     39827412
Appl. No.:     13/556,195
Filed:     July 23, 2012

This is partial information for #8,422,994:

Inventors:     Rhoads; Geoffrey B. (West Linn, OR), Rodriguez; Tony F. (Portland, OR)
US
Assignee:     Digimarc Corporation (Beaverton, OR)
Family ID:     44507193
Appl. No.:     13/401,332
Filed:     February 21, 2012

This is partial information for #8,417,715:

Inventors:     Bruckhaus; Tilmann (Cupertino, CA), Kamalakannan; Ramann (Sunnyvale, CA)
US
Family ID:     47999339
Appl. No.:     12/339,088
Filed:     December 19, 2008

This is partial information for #8,364,171:

Inventors:     Busch; James David (Tempe, AZ)
US
Assignee:     Enhanced Geographic LLC (Tempe, AZ)
Family ID:     39827412
Appl. No.:     13/555,202
Filed:     July 23, 2012

Then, I further refined the search – as this text indicates:

Results of Search in US Patent Collection db for: ((LinkedIn AND workforce) AND planning): 2 patents.

1.     8,386,639     Full-Text     System and method for optimized and distributed resource management

This is partial information for # 8,386,639:

Inventors:     Galvin; Brian R. (Seabeck, WA)
US Assignee:     New Voice Media Limited (Basingstoke, GB)
Family ID:     47721342
Appl. No.:     13/602,048
Filed:     August 31, 2012

Finally, I searched for LinkedIn as an Assignee Name in Field 1: Results of Search in US Patent Collection db for: AN/LinkedIn: 4 patents.

Hits 1 through 4 out of 4

PAT. NO.        Title
1.     8,473,503     Full-Text     Method and system for semantic search against a document collection

2 .    8,452,777     Full-Text     Dynamic submission and preference indicator

3.     8,402,374     Full-Text     Audience platform

4.     8,010,460     Full-Text     Method and system for reputation evaluation of online users in a social networking scheme

Searching outside of USPTO led me to this information from Justia Patents:

http://patents.justia.com/company/linkedin

LinkedIn Patents

Application number: 20130185629
Abstract: An audience platform is disclosed. In a first example case, a first question is received. A preference event associated with the first question is received. A score is determined for the first question based at least in part on the preference. In a second example case, indications of a first and second potential interviewee are received. Preference events associated with the first and second potential interviewees are received. Scores are determined for the first and second potential interviewees based at least in part on the received preference events. A designated interviewee is selected based on the first and second scores. In a third example case, indications of a first and second potential awardee are received. Preference events associated with the first and second potential awardee are received. Scores are determined for the first and second potential awardees based at least in part on the received preference events.
Type: Application
Filed: March 5, 2013
Issued: July 18, 2013
Assignee: LINKEDIN CORPORATION
Inventor: Linkedin Corporation
Application number: 20130179454
Abstract: An audience platform is disclosed. In a first example case, a first question is received. A preference event associated with the first question is received. A score is determined for the first question based at least in part on the preference. In a second example case, indications of a first and second potential interviewee are received. Preference events associated with the first and second potential interviewees are received. Scores are determined for the first and second potential interviewees based at least in part on the received preference events. A designated interviewee is selected based on the first and second scores. In a third example case, indications of a first and second potential awardee are received. Preference events associated with the first and second potential awardee are received. Scores are determined for the first and second potential awardees based at least in part on the received preference events.
Type: Application
Filed: February 28, 2013
Issued: July 11, 2013
Assignee: LINKEDIN CORPORATION
Inventor: LINKEDIN CORPORATION
Patent number: 8473503
Abstract: Disclosed in one example is a method for searching. In some examples, the method includes receiving an unstructured search query, parsing the unstructured search query into a plurality of structured search attributes using a search term attribute dictionary, performing a search of a structured database based upon the plurality of structured search attributes to identify a plurality of search results, populating a plurality of form fields of a search form with the plurality of structured search attributes, and displaying the search form in association with the search results.
Type: Grant
Filed: July 13, 2011
Issued: June 25, 2013
Assignee: LinkedIn Corporation
Inventors: Heyning Cheng, Daniel Tunkelang
Patent number: 8452777
Abstract: Indicating preference for a content contribution is disclosed. A request for information associated with the content contribution is received from a first entity. The status of the content contribution is determined. A response is provided to the first entity. Determining the status of the content contribution includes determining whether a submission of the content contribution was previously received.
Type: Grant
Filed: February 1, 2008
Issued: May 28, 2013
Assignee: LinkedIn Corporation
Inventor: R. Kevin Rose
Application number: 20130097531
Abstract: Detecting, for a content item, associated preference events is disclosed. For the content item, a plurality of preference events from a plurality of users is received. The received preference events are accumulated. Associated events are detected. The effect of the events is reduced when assigning a status to the item.
Type: Application
Filed: November 29, 2012
Issued: April 18, 2013
Assignee: LINKEDIN CORPORATION
Inventor: LinkedIn Corporation
Application number: 20130091436
Abstract: Displaying a preference by a user of a content contribution is disclosed. A preference event by the user is detected. A plurality of detected events is stored. In response to a query from a client, at least a portion of the stored detected events is stored. At least a portion of the received events is displayed in an interface.
Type: Application
Filed: November 29, 2012
Issued: April 11, 2013
Assignee: LINKEDIN CORPORATION
Inventor: LINKEDIN CORPORATION
Patent number: 8402374
Abstract: An audience platform is disclosed. In a first example case, a first question is received. A preference event associated with the first question is received. A score is determined for the first question based at least in part on the preference. In a second example case, indications of a first and second potential interviewee are received. Preference events associated with the first and second potential interviewees are received. Scores are determined for the first and second potential interviewees based at least in part on the received preference events. A designated interviewee is selected based on the first and second scores. In a third example case, indications of a first and second potential awardee are received. Preference events associated with the first and second potential awardee are received. Scores are determined for the first and second potential awardees based at least in part on the received preference events.
Type: Grant
Filed: August 18, 2009
Issued: March 19, 2013
Assignee: LinkedIn Corporation
Inventor: R. Kevin Rose
Application number: 20130031090
Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.
Type: Application
Filed: July 29, 2011
Issued: January 31, 2013
Assignee: LINKEDIN CORPORATION
Inventors: Christian Posse, Abhishek Gupta, Anmol Bhasin, Monica Rogati
Application number: 20120191776
Abstract: The present disclosure relates to methods and systems for recommending a context to a user of a social network service, based on detecting an interaction with some item of web content. With some embodiments, after detecting an interaction with a web document, a topic to which the web document is related is determined. A context, such as an online or offline forum, known to be associated with the topic is then identified. Finally, the identified context is presented to the user as a recommendation for a content source that is likely to be of interest to the user.
Type: Application
Filed: January 20, 2011
Issued: July 26, 2012
Assignee: LINKEDIN CORPORATION
Inventors: Jennifer Granito Ruffner, Allen Blue, Sarah Jean Culberson Alpern
Application number: 20120191715
Abstract: The present disclosure relates to methods and systems for clustering individual items of web content, and then utilizing activity and profile data to both select clusters of content items for presentation to a user, and determining how the selected clusters of content items are presented to the user of an online social network service. With some embodiments, the activity data are derived by monitoring and detection interactions with the individual items of web content by an individual user, or other users with whom the individual user is related, as established via, and defined by, the social network service.
Type: Application
Filed: January 20, 2011
Issued: July 26, 2012
Assignee: LINKEDIN CORPORATION
Inventors: Jennifer Granito Ruffner, Eishay Smith, Joseph Paul Betz, Ian McCarthy
Application number: 20120072432
Abstract: A network update interface is presented to a user on a network to display network updates from other users of a mutual social-networking site. The network updates shared by the other users are gathered in a stream and supplied to a facet-filtering system including a network update interface. The user controls the display of certain network update items according to facet-filter characteristics enabled in facet-filter selection panels in the network update interface. The facet-filter characteristics are used by a facet filter to select certain network updates for display to the user in the network update interface. Trending links to further articles with content corresponding to the facet-filter characteristics are displayed to the user according to greatest popularity among the other users. Links to the profiles of the users sharing the articles are also provided in the network update interface.
Type: Application
Filed: September 20, 2011
Issued: March 22, 2012
Assignee: Linkedin Corporation
Inventors: Alejandro Crosa, Esteban Kozak, Yasuhiro Matsuda, Xiaoyang Gu, Hao Yan, John Wang, Chanh Nguyen
Patent number: 8010460
Abstract: A method and system for evaluating the reputation of a member of a social networking system is disclosed. Consistent with an embodiment of the invention, one or more attributes associated with a social networking profile of a member of a social network are analyzed. Based on the analysis, a ranking, rating or score is assigned to a particular category of reputation. When requested, the ranking, rating or score is displayed to a user of the social network.
Type: Grant
Filed: September 1, 2005
Issued: August 30, 2011
Assignee: Linkedin Corporation
Inventors: James Duncan Work, Allen Blue, Reid Hoffman

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Consulting USPTO and simply entering LinkedIn as an Assignee name shouldn’t be perceived as if it will provide conclusive information regarding LinkedIn’s IP and/or products in development, but not finding a strong tie-in to the sort of data accessed and manipulated with tools such as Workforce Locator (and WL competitors) has me feeling more convinced that my thought relating to the possible appeal of these sophisticated analytical recruitment solutions as a LinkedIn offering is a solid idea. The next step should be actionable; to figure out who LinkedIn would pursue. -BJ Giglio

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