Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: In one embodiment, the interactive virtual meeting assistant implements a meeting debrief post-processing operation. For a given meeting that the interactive virtual meeting assistant participated in, a meeting post-processing engine enables one or more participants of the meeting to associate a debrief with the meeting. The debrief may be an audio recording, a video recording, text, or any file. The meeting post-processing engine stores the debrief in data stores and provides access to the debrief via the meeting GUI associated with the meeting. The meeting post-processing engine also processes the debrief to generate tasks to be assigned to participants or other entities and/or schedule reminders to be provided to the participants or other entities. The debrief may be private, such that only the participant who provided the debrief may subsequently access the debrief.

Abstract: The present disclosure provides a detailed description of techniques used in methods, systems, and computer program products for associating anonymous information to personally identifiable information without sharing any personally identifiable information. A method receives a specification record comprising one or more specified demographic attributes to be used in user record selection operations, the results of which operations include user records that comprise a user identifier and at least some non-personally-identifiable information. A candidate group is formed by applying a set of rules over the retrieved user records to reject-out one or more user records that comprise mutually-exclusive characteristics with respect to the other user records in the candidate group. An anonymity measure is calculated over the candidate group to satisfy a threshold of anonymity. If needed to satisfy the threshold of anonymity, additional user records are added to the group before any sharing operations.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: The present disclosure provides a detailed description of techniques used in methods, systems, and computer program products for associating anonymous information with personally identifiable information without sharing any personally identifiable information. A method receives a specification record comprising one or more specified demographic attributes to be used in user record selection operations, the results of which operations include user records that comprise a user identifier and at least some non-personally-identifiable information. A candidate group is formed by applying a set of rules over the retrieved user records to exclude one or more user records that comprise mutually-exclusive characteristics with respect to the other user records in the candidate group. An anonymity measure is calculated over the candidate group to satisfy a threshold of anonymity. If needed to satisfy the threshold of anonymity, additional user records are added to the group before any sharing operations.

Abstract: In one embodiment, the interactive virtual meeting assistant implements a meeting debrief post-processing operation. For a given meeting that the interactive virtual meeting assistant participated in, a meeting post-processing engine enables one or more participants of the meeting to associate a debrief with the meeting. The debrief may be an audio recording, a video recording, text, or any file. The meeting post-processing engine stores the debrief in data stores and provides access to the debrief via the meeting GUI associated with the meeting. The meeting post-processing engine also processes the debrief to generate tasks to be assigned to participants or other entities and/or schedule reminders to be provided to the participants or other entities. The debrief may be private, such that only the participant who provided the debrief may subsequently access the debrief.

Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: In one embodiment, the interactive virtual meeting assistant implements a meeting debrief post-processing operation. For a given meeting that the interactive virtual meeting assistant participated in, a meeting post-processing engine enables one or more participants of the meeting to associate a debrief with the meeting. The debrief may be an audio recording, a video recording, text, or any file. The meeting post-processing engine stores the debrief in data stores and provides access to the debrief via the meeting GUI associated with the meeting. The meeting post-processing engine also processes the debrief to generate tasks to be assigned to participants or other entities and/or schedule reminders to be provided to the participants or other entities. The debrief may be private, such that only the participant who provided the debrief may subsequently access the debrief.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: The present disclosure provides a detailed description of techniques used in methods, systems, and computer program products for associating anonymous information to personally identifiable information without sharing any personally identifiable information. A method receives a specification record comprising one or more specified demographic attributes to be used in user record selection operations, the results of which operations include user records that comprise a user identifier and at least some non-personally-identifiable information. A candidate group is formed by applying a set of rules over the retrieved user records to reject-out one or more user records that comprise mutually-exclusive characteristics with respect to the other user records in the candidate group. An anonymity measure is calculated over the candidate group to satisfy a threshold of anonymity. If needed to satisfy the threshold of anonymity, additional user records are added to the group before any sharing operations.

Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: Embodiments are directed towards employing multiple profiles to track identification of users across multiple domains. Multiple application-domain profiles and multiple web-domain profiles may each include a statistical identifier. A profile enrichment store may be generated from the application-domain profiles and web-domain profiles, where each entry in the profile enrichment store may be based on statistical identifiers. The profile enrichment store may be utilized to determine categories associated with a statistical identifier for a request from a client device. A targeted advertisement may be determined based on the determined category and may be provided to the client device. A client caching identifier may be used in a mobile web domain, in combination with the statistical identifier, to track mobile web users. If a request is associated with a profile that includes an affirmative opt-out flag, categories in the profile enrichment store for a corresponding statistical identifier may be cleaned up.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: Embodiments are directed towards an advertising pricing system that uses striped aggressive discounting and shared audience auctions. A plurality of stripes is determined for an audience, where at least one stripe is assigned a different discount. Each user in the audience is associated with one of the stripes. All audience data can then be distributed to and shared between data buyers; thus, each data buyer can use the audience independent of other data buyers. In one embodiment, data buyers can provide content to individual users regardless of the stripe that is associated with the user. Data buyers can then be billed based on the stripe discount associated with each user that the data buyer provided content. In one embodiment, the number, size, and discount associated with each stripe may be modified to dynamically produce an optimum yield based on shared audience auction bidding volume using striped aggressive discounting.

Abstract: A facility for Performing on-line advertising at a subject web site relative to a selected user activity is described. The facility receives a plurality of bids. Each bid is submitted on behalf of an advertiser, and specifies an advertising rate that the advertiser will pay to display an advertising messages to users performing the selected activity with respect to the subject web site. When a request for any web page of the subject web site containing advertising space is received from a user that has previously performed the selected activity, irrespective of the identity of the requested web page, the facility both (1) identifies the received bid specifying the largest advertising rate, and (2) causes an advertising message to be displayed in the advertising space of the requested web page for the advertiser on whose behalf the identified bid was submitted.

Abstract: Embodiments of the invention are directed to managing an exchange of user profile data through an auction that controls distribution to one or more top bidders. Visiting users of a data seller's website are tagged based on the visiting user's interactions with the data seller's website. The data seller or a network provider may tag the visiting users. The data sellers submit user data and user profile identifiers to an exchange service. Data buyers submit corresponding categories of user data for a campaign, and submit bids for profile data or user data of each user. A bid is generally specified for a specific user, where the bid may be per user, per category. The exchange service associates user data provided by a data seller with categories of data requested by a data buyer, and ranks bids. The highest bidder may receive access to user data for a predefined period.