Abstract: A system includes a computer storage device to store a first and second set of information about individuals, and user interface design components. A computer server is coupled to the computer storage and is programmed to receive a request for a webpage from an individual's device and analyze the first and second set of information to predict differences in at least one key performance indicator for that individual. The server is further programmed to automatically select a user interface design component for presentation to the individual, automatically generate and transmit a first customized webpage with the selected user interface design component to the device, and in response to an interaction with the selected user interface design component on the first customized webpage, automatically generate and transmit a second customized webpage to the device to create a customized page flow.

Abstract: Example search quality improvement systems and methods are described. In one implementation, a search implementation engine receives a request for an item that includes a first color. The search implementation engine identifies a color representation associated with the first color and determines a second color perceptually similar to the first color based on the color representation. The search implementation engine then identifies an instance of the item having a second color and communicates data associated with the instance of the item to a source of the request.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Email messages may include a URL and a server may select content for the URL when it is accessed based on activity of the user after the email is sent. Triggers may be generated based on events and a user history. Access of a delivery mode for content invokes the trigger to provide content to the user. Email content may be provided by third parties based on the user history, such as contents of an abandoned electronic shopping cart.

Abstract: Advertising impressions are collected and a block in a block chain is created for each impression or a set of impressions. Each block includes a hash of a preceding block in the block chain. The block chain may be traversed to analyze advertising impressions and determine their effectiveness. Advertising content may include embedded code that invokes transmission of a notification to a server. The server creates a block in response to the notification that includes data such as the advertising content (or a reference to it), attributes of a target consumer, information regarding an advertising campaign, and/or other data describing the advertising impression.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. This may include comparing hashes of one or more values to identify similarities. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser, device, or of a more global nature, e.g. time zone, user attributes, etc. Records associated with different devices may be associated with one another by evaluating hashes of data values submitted by a user on the different devices. Where the hash values of two records intersect, they may be merged with one another.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser. Profile values are generated by clustering users using first parameters and scoring the clusters using second parameters, the first and second parameters being data describing user behavior. Profile values may be generated by processing cluster scores according to a mapping function.

Abstract: Data from browser requests is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. This may include comparing hashes of one or more values to identify similarities. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser, device, or of a more global nature, e.g. time zone, user attributes, etc. Records associated with different devices may be associated with one another by evaluating hashes of data values submitted by a user on the different devices. Where the hash values of two records intersect, they may be merged with one another.

Abstract: Example search quality improvement systems and methods are described. In one implementation, a search implementation engine receives a request for an item that includes a first color. The search implementation engine identifies a color representation associated with the first color and determines a second color perceptually similar to the first color based on the color representation. The search implementation engine then identifies an instance of the item having a second color and communicates data associated with the instance of the item to a source of the request.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. A probability assigned to each candidate vector may be adjusted in response to inconsistency in other data. Email messages may include a URL and a server may select content for the URL when it is accessed based on activity of the user after the email is sent. Triggers may be generated based on events and a user history. Access of a delivery mode for content invokes the trigger to provide content to the user.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. This may include comparing hashes of one or more values to identify similarities. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser, device, or of a more global nature, e.g. time zone, user attributes, etc. Records associated with different devices may be associated with one another by evaluating hashes of data values submitted by a user on the different devices. Where the hash values of two records intersect, they may be merged with one another.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. This may include comparing hashes of one or more values to identify similarities. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser, device, or of a more global nature, e.g. time zone, user attributes, etc. Records associated with different devices may be associated with one another by evaluating hashes of data values submitted by a user on the different devices. Where the hash values of two records intersect, they may be merged with one another.

Abstract: A system includes a computer storage device to store a first and second set of information about individuals, and user interface design components. A computer server is coupled to the computer storage and is programmed to receive a request for a webpage from an individual's device and analyze the first and second set of information to predict differences in at least one key performance indicator for that individual. The server is further programmed to automatically select a user interface design component for presentation to the individual, automatically generate and transmit a first customized webpage with the selected user interface design component to the device, and in response to an interaction with the selected user interface design component on the first customized webpage, automatically generate and transmit a second customized webpage to the device to create a customized page flow.

Abstract: A system includes a computer storage device to store a first and second set of information about individuals, and user interface design components. A computer server is coupled to the computer storage and is programmed to receive a request for a webpage from an individual's device and analyze the first and second set of information to predict differences in at least one key performance indicator for that individual. The server is further programmed to automatically select a user interface design component for presentation to the individual, automatically generate and transmit a first customized webpage with the selected user interface design component to the device, and in response to an interaction with the selected user interface design component on the first customized webpage, automatically generate and transmit a second customized webpage to the device to create a customized page flow.

Abstract: A content specification containing a data structure describing a digital volume to be incorporated into a digital library is received. A digital volume is a digital representation of human-readable content, such as a digitized book. Volume content of the digital volume is obtained based on references in the content specification to the locations of the volume content. The volume content is indexed based at least in part on information in the content specification, including volume metadata such as the title or author of the volume. The content specification includes markup language elements, each element specifying information about the digital volume. A plurality of tags are associated with the markup language elements. A structural tag specifies structural information associated with the digital volume, including the location of a portion of volume content of the digital volume. A metadata tag specifies metadata describing or identifying the digital volume.

Abstract: A content specification containing a data structure describing a digital volume to be incorporated into a digital library is received. A digital volume is a digital representation of human-readable content, such as a digitized book. Volume content of the digital volume is obtained based on references in the content specification to the locations of the volume content. The volume content is indexed based at least in part on information in the content specification, including volume metadata such as the title or author of the volume. The content specification includes markup language elements, each element specifying information about the digital volume. A plurality of tags are associated with the markup language elements. A structural tag specifies structural information associated with the digital volume, including the location of a portion of volume content of the digital volume. A metadata tag specifies metadata describing or identifying the digital volume.