Patents by Inventor Christian Posse

Christian Posse has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 9576274
    Abstract: A first user of a presentation machine may be a recruiter that initiates an action in reference to a first user profile. The first user profile may describe a first candidate for a job. The presentation machine may identify the first user profile and determine a similarity score that indicates a degree of similarity between the first user profile and a second user profile that describes a second candidate for the job. The presentation machine may also access a volatility score that indicates a likelihood that the second candidate is receptive to a proposal that the second candidate be employed by an employer. Based on the similarity score and on the volatility score, the presentation machine may determine a rank of the second user profile. Based on the determined rank, the presentation machine may present the second user profile to the first user.
    Type: Grant
    Filed: December 3, 2012
    Date of Patent: February 21, 2017
    Assignee: LinkedIn Corporation
    Inventors: Elizabeth Ethel Burstein, Christian Posse, Abhishek Gupta, Anmol Bhasin, Dmytro Andriyovich Ivchenko, Parker R. Barrile
  • Publication number: 20170046442
    Abstract: Disclosed in some examples are methods, systems and machine readable medium for recommending an out-of-network communication by determining a set of potential recommended members of a social networking service based upon one or more recommendation criteria. In some examples the recommendation criteria may include: a profile similarity to a previous target of an out-of-network communication, a degree of correspondence between an interest and intent of the sending member, and a likelihood of response.
    Type: Application
    Filed: October 27, 2016
    Publication date: February 16, 2017
    Inventors: Junghoon (Andrew) Ahn, Abhishek Gupta, Christian Posse, Anmol Bhasin, Yurong Shi, Jian Li, Jacob Kessler
  • Patent number: 9544392
    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: Grant
    Filed: January 22, 2015
    Date of Patent: January 10, 2017
    Assignee: LinkedIn Corporation
    Inventors: Christian Posse, Abhishek Gupta, Anmol Bhasin, Monica Rogati
  • Publication number: 20160343088
    Abstract: A statistically overrepresented token in the descriptions of users associated with a target entity may be descriptive of the target entity. This may be true regardless of whether a primary description of the entity includes the overrepresented token. Accordingly, the entity description machine may access multiple descriptions of multiple users associated with the target entity. A portion of the multiple descriptions may each include a token descriptive of the target entity and of a subset of the multiple users. The entity description machine may determine that the token is overrepresented among the tokens within the multiple descriptions and generate a supplemental description of the target entity, where the supplemental description includes the overrepresented token. Once the supplemental description is generated, the entity description machine may use the supplemental description in referencing the target entity.
    Type: Application
    Filed: May 19, 2016
    Publication date: November 24, 2016
    Inventors: Anmol Bhasin, Jiong Wang, Abhishek Gupta, Alexis Pribula, Ramesh Dommeti, Christian Posse
  • Patent number: 9497156
    Abstract: Disclosed in some examples are methods, systems and machine readable medium for recommending an out-of-network communication by determining a set of potential recommended members of a social networking service based upon one or more recommendation criteria. In some examples the recommendation criteria may include: a profile similarity to a previous target of an out-of-network communication, a degree of correspondence between an interest and intent of the sending member, and a likelihood of response.
    Type: Grant
    Filed: October 26, 2015
    Date of Patent: November 15, 2016
    Assignee: LinkedIn Corporation
    Inventors: Junghoon (Andrew) Ahn, Abhishek Gupta, Christian Posse, Anmol Bhasin, Yurong Shi, Jian Li, Jacob Kessler
  • Patent number: 9372930
    Abstract: A statistically overrepresented token in the descriptions of users associated with a target entity may be descriptive of the target entity. This may be true regardless of whether a primary description of the entity includes the overrepresented token. Accordingly, the entity description machine may access multiple descriptions of multiple users associated with the target entity. A portion of the multiple descriptions may each include a token descriptive of the target entity and of a subset of the multiple users. The entity description machine may determine that the token is overrepresented among the tokens within the multiple descriptions and generate a supplemental description of the target entity, where the supplemental description includes the overrepresented token. Once the supplemental description is generated, the entity description machine may use the supplemental description in referencing the target entity.
    Type: Grant
    Filed: September 25, 2014
    Date of Patent: June 21, 2016
    Assignee: LinkedIn Corporation
    Inventors: Anmol Bhasin, Jiong Wang, Abhishek Gupta, Alexis Pribula, Ramesh Dommeti, Christian Posse
  • Patent number: 9361584
    Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.
    Type: Grant
    Filed: September 16, 2015
    Date of Patent: June 7, 2016
    Assignee: LinkedIn Corporation
    Inventors: Christian Posse, Anmol Bhasin, Wing Li
  • Publication number: 20160043987
    Abstract: Disclosed in some examples are methods, systems and machine readable medium for recommending an out-of-network communication by determining a set of potential recommended members of a social networking service based upon one or more recommendation criteria. In some examples the recommendation criteria may include: a profile similarity to a previous target of an out-of-network communication, a degree of correspondence between an interest and intent of the sending member, and a likelihood of response.
    Type: Application
    Filed: October 26, 2015
    Publication date: February 11, 2016
    Inventors: Junghoon (Andrew) Ahn, Abhishek Gupta, Christian Posse, Anmol Bhasin, Yurong Shi, Jian Li, Jacob Kessler
  • Publication number: 20160019461
    Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.
    Type: Application
    Filed: September 16, 2015
    Publication date: January 21, 2016
    Inventors: Christian Posse, Anmol Bhasin, Wing Li
  • Patent number: 9191356
    Abstract: Disclosed in some examples are methods, systems and machine readable medium for recommending an out-of-network communication by determining a set of potential recommended members of a social networking service based upon one or more recommendation criteria. In some examples the recommendation criteria may include: a profile similarity to a previous target of an out-of-network communication, a degree of correspondence between an interest and intent of the sending member, and a likelihood of response.
    Type: Grant
    Filed: February 13, 2015
    Date of Patent: November 17, 2015
    Assignee: LinkedIn Corporation
    Inventors: Junghoon (Andrew) Ahn, Abhishek Gupta, Christian Posse, Anmol Bhasin, Yurong Shi, Jian Li, Jacob Kessler
  • Patent number: 9171257
    Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.
    Type: Grant
    Filed: January 14, 2015
    Date of Patent: October 27, 2015
    Assignee: LinkedIn Corporation
    Inventors: Christian Posse, Anmol Bhasin, Wing Li
  • Patent number: 9105069
    Abstract: A system may include a network interface, a user interface, and a recommendation engine. The user interface may be configured to receive a company characteristic of a company profile of a company posted to the social network and a company bid from an entity related to company to the social network. The recommendation engine may be configured to determine an aggregate company score for the user based on a relevance of the company characteristic to a user characteristic and the company bid. The network interface may be configured to transmit a message related to the company to the user based, at least in part, on the aggregate company score.
    Type: Grant
    Filed: November 16, 2012
    Date of Patent: August 11, 2015
    Assignee: LinkedIn Corporation
    Inventors: Christian Posse, Michael Grishaver
  • Publication number: 20150206193
    Abstract: A system may include a network interface, a user interface, and a recommendation engine. The user interface may be configured to receive a company characteristic of a company profile of a company posted to the social network and a company bid from an entity related to company to the social network. The recommendation engine may be configured to determine an aggregate company score for the user based on a relevance of the company characteristic to a user characteristic and the company bid. The network interface may be configured to transmit a message related to the company to the user based, at least in part, on the aggregate company score.
    Type: Application
    Filed: April 1, 2015
    Publication date: July 23, 2015
    Inventors: Christian Posse, Michael Grishaver
  • Publication number: 20150163190
    Abstract: Disclosed in some examples are methods, systems and machine readable medium for recommending an out-of-network communication by determining a set of potential recommended members of a social networking service based upon one or more recommendation criteria. In some examples the recommendation criteria may include: a profile similarity to a previous target of an out-of-network communication, a degree of correspondence between an interest and intent of the sending member, and a likelihood of response.
    Type: Application
    Filed: February 13, 2015
    Publication date: June 11, 2015
    Inventors: Junghoon (Andrew) Ahn, Abhishek Gupta, Christian Posse, Anmol Bhasin, Yurong Shi, Jian Li, Jacob Kessler
  • Publication number: 20150142584
    Abstract: A system, apparatus, method and computer-program product are provided for determining affinities between members of an on-line service and/or one member's likely propensity for content published by or on behalf of another member. Members of the service include individuals and organizations. A content item may be an announcement by or for a member, an advertisement, a job listing or something else. Content items available for service to an individual member are ranked based on the member's propensity for consuming them, as reflected in scores computed for each item. An item's propensity score may be calculated based on the relevance and/or proximity between the member and an organization featured in or associated with the item. Relevance may measure the similarity between profiles of the individual and the organization. Proximity may be affected by whether the individual and/or associates of the individual follow the organization, visit a page of the organization, etc.
    Type: Application
    Filed: November 18, 2013
    Publication date: May 21, 2015
    Applicant: LinkedIn Corporation
    Inventors: Haishan Liu, Baoshi Yan, Anmol Bhasin, Christian Posse
  • Publication number: 20150134745
    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: January 22, 2015
    Publication date: May 14, 2015
    Inventors: Christian Posse, Abhishek Gupta, Anmol Bhasin, Monica Rogati
  • Publication number: 20150127584
    Abstract: A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.
    Type: Application
    Filed: January 14, 2015
    Publication date: May 7, 2015
    Inventors: Christian Posse, Anmol Bhasin, Wing Li
  • Publication number: 20150081576
    Abstract: A statistically overrepresented token in the descriptions of users associated with a target entity may be descriptive of the target entity. This may be true regardless of whether a primary description of the entity includes the overrepresented token. Accordingly, the entity description machine may access multiple descriptions of multiple users associated with the target entity. A portion of the multiple descriptions may each include a token descriptive of the target entity and of a subset of the multiple users. The entity description machine may determine that the token is overrepresented among the tokens within the multiple descriptions and generate a supplemental description of the target entity, where the supplemental description includes the overrepresented token. Once the supplemental description is generated, the entity description machine may use the supplemental description in referencing the target entity.
    Type: Application
    Filed: September 25, 2014
    Publication date: March 19, 2015
    Inventors: Anmol Bhasin, Jiong Wang, Abhishek Gupta, Alexis Pribula, Ramesh Dommeti, Christian Posse
  • Patent number: 8972414
    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: Grant
    Filed: July 29, 2011
    Date of Patent: March 3, 2015
    Assignee: LinkedIn Corporation
    Inventors: Christian Posse, Abhishek Gupta, Anmol Bhasin, Monica Rogati
  • Patent number: 8972417
    Abstract: Disclosed in some examples are methods, systems and machine readable medium for recommending an out-of-network communication by determining a set of potential recommended members of a social networking service based upon one or more recommendation criteria. In some examples the recommendation criteria may include: a profile similarity to a previous target of an out-of-network communication, a degree of correspondence between an interest and intent of the sending member, and a likelihood of response.
    Type: Grant
    Filed: February 21, 2013
    Date of Patent: March 3, 2015
    Assignee: LinkedIn Corporation
    Inventors: Junghoon Ahn, Abhishek Gupta, Christian Posse, Anmol Bhasin, Yurong Shi, Jian Li, Jacob Kessler