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AIPD_nlp_sentence_dataset

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21. The method of claim 18 wherein the HAL receives a request from the controller based on the response that proposes additional behavior to be included in the abstract device model.

21. The method of claim 18 wherein the HAL receives a request from the controller based on the response that proposes additional behavior to be included in the abstract device model. 22. The method of claim 21 further comprising: analyzing with the HAL the request to determine if the proposed additional behavior is supported by the target device; and notifying the controller as to whether the proposed additional behavior is supported or not supported by the target device.

1. A method for populating forms based on a user interview, comprising: generating, using a computer processor, a plurality of binary questions according to a first rule to form a top level menu; receiving a plurality of binary answers corresponding to the plurality of binary questions from the user; adjusting, using the computer processor, at least one of the plurality of binary questions based on the plurality of binary answers according to a second rule during the user interview; identifying, using the computer processor, a plurality of forms from a forms library based on the plurality of binary answers according to a third rule, wherein the plurality of forms comprises a Federal tax return form and a state tax return form associated with a first State; extracting, based on the plurality of binary answers, a plurality of data entry fields from the plurality of forms by at least: excluding any duplicate data entry field in the plurality of forms, and combining at least two related data entry fields based on a pre-determined criterion; presenting the plurality of data entry fields to the user in a unified format by: grouping related data entry fields of the plurality of data entry fields based on the plurality of binary answers to form a plurality of data entry menus, wherein the plurality of data entry menus are organized as multiple levels in lower level menu structures associated with the top level menu; and selectively presenting the plurality of data entry menus to the user based on a condition of the plurality of binary answers; receiving data from the user for the plurality of data entry fields, wherein the lower level menu structures of the unified format are further generated and added to the user interview based on the data, wherein further generating the lower level menu structures of the unified format comprises: identifying an additional state tax return form associated with a second State based on the data; and grouping additional binary questions associated with all data entry fields for a particular deduction required by the Federal tax return form, the State tax return form, and the additional State tax return form together in a single data entry menu for the particular deduction; populating at least a portion of the plurality of forms to generate a plurality of populated forms; and storing the plurality of populated forms in a repository on behalf of the user.

1. A method for populating forms based on a user interview, comprising: generating, using a computer processor, a plurality of binary questions according to a first rule to form a top level menu; receiving a plurality of binary answers corresponding to the plurality of binary questions from the user; adjusting, using the computer processor, at least one of the plurality of binary questions based on the plurality of binary answers according to a second rule during the user interview; identifying, using the computer processor, a plurality of forms from a forms library based on the plurality of binary answers according to a third rule, wherein the plurality of forms comprises a Federal tax return form and a state tax return form associated with a first State; extracting, based on the plurality of binary answers, a plurality of data entry fields from the plurality of forms by at least: excluding any duplicate data entry field in the plurality of forms, and combining at least two related data entry fields based on a pre-determined criterion; presenting the plurality of data entry fields to the user in a unified format by: grouping related data entry fields of the plurality of data entry fields based on the plurality of binary answers to form a plurality of data entry menus, wherein the plurality of data entry menus are organized as multiple levels in lower level menu structures associated with the top level menu; and selectively presenting the plurality of data entry menus to the user based on a condition of the plurality of binary answers; receiving data from the user for the plurality of data entry fields, wherein the lower level menu structures of the unified format are further generated and added to the user interview based on the data, wherein further generating the lower level menu structures of the unified format comprises: identifying an additional state tax return form associated with a second State based on the data; and grouping additional binary questions associated with all data entry fields for a particular deduction required by the Federal tax return form, the State tax return form, and the additional State tax return form together in a single data entry menu for the particular deduction; populating at least a portion of the plurality of forms to generate a plurality of populated forms; and storing the plurality of populated forms in a repository on behalf of the user. 2. The method of claim 1 , further comprising: analyzing, using the computer processor, a structure of the plurality of forms to generate at least one selected from a group consisting of the first rule, the second rule, and the third rule.

3. The system of claim 2 wherein said step of generating an improved query vector comprises multiplying said query vector by said transformation matrix to obtain a plurality of improved similarity measures.

3. The system of claim 2 wherein said step of generating an improved query vector comprises multiplying said query vector by said transformation matrix to obtain a plurality of improved similarity measures. 4. The system of claim 3 wherein said similarity measure is an image edit distance.

15. An apparatus for managing text, the apparatus comprising: a processor unit, a memory, and a computer readable storage device; first program instructions to identify a log of research and a text in a document that matches the log of research; and second program instructions to determine a mismatch exists between a search result for the text and the log of research, and if so, modify the text in the document based on the log of research, wherein the first program instructions and the second program instructions are stored in the computer readable storage device for execution by the processor unit via the memory.

15. An apparatus for managing text, the apparatus comprising: a processor unit, a memory, and a computer readable storage device; first program instructions to identify a log of research and a text in a document that matches the log of research; and second program instructions to determine a mismatch exists between a search result for the text and the log of research, and if so, modify the text in the document based on the log of research, wherein the first program instructions and the second program instructions are stored in the computer readable storage device for execution by the processor unit via the memory. 19. The apparatus of claim 15 , wherein the log of research comprises information browsed by a user in response to user input.

1. A pattern recognition system comprising: a training subsystem that receives during a training operation a plurality of training input patterns of a data type from a plurality of subject classes, that forms a set of categories of the training input patterns, that assigns each category a category definition according to training input patterns received within the category, that counts the training input patterns received for each class within each category and that generates for each category a training histogram of the training input patterns received within the category, the training histogram including counts of training input patterns received for each class within the category; and a classifier that receives during a testing operation at least one test input pattern of the data type from the subject, that accesses the set of categories and computes a correlation between a category definition and each test input pattern, that forms a category association between each test input pattern and a category based on the correlation and that forms an observation histogram to classify the subject, the observation histogram being formed from each training histogram of each category of each category association and representing counts of training input patterns received by the training subsystem during the training operation, classification of the subject being determined by a peak class of the observation histogram, the peak class representing the highest training input pattern count of the observation histogram.

1. A pattern recognition system comprising: a training subsystem that receives during a training operation a plurality of training input patterns of a data type from a plurality of subject classes, that forms a set of categories of the training input patterns, that assigns each category a category definition according to training input patterns received within the category, that counts the training input patterns received for each class within each category and that generates for each category a training histogram of the training input patterns received within the category, the training histogram including counts of training input patterns received for each class within the category; and a classifier that receives during a testing operation at least one test input pattern of the data type from the subject, that accesses the set of categories and computes a correlation between a category definition and each test input pattern, that forms a category association between each test input pattern and a category based on the correlation and that forms an observation histogram to classify the subject, the observation histogram being formed from each training histogram of each category of each category association and representing counts of training input patterns received by the training subsystem during the training operation, classification of the subject being determined by a peak class of the observation histogram, the peak class representing the highest training input pattern count of the observation histogram. 4. The pattern recognition system of claim 1 wherein during the training operation, the training subsystem computes a correlation between each training input pattern and a category definition vector for each category.

29. The computer-readable memory device of claim 28 , where the coherence of the terms in the sequence is calculated relative to a collection of documents.

29. The computer-readable memory device of claim 28 , where the coherence of the terms in the sequence is calculated relative to a collection of documents. 30. The computer-readable memory device of claim 29 , where the coherence of the terms in the sequence is calculated as a likelihood ratio that defines a probability of the sequence occurring in the collection of documents relative to parts of the sequence occurring.

1. A method for providing automated assistance for a user using a computing device selected from a group consisting of: a telephone; a wireless communicator; a tablet computer; a laptop computer; a personal digital assistant; a desktop computer; a processor with memory; a kiosk; a consumer electronic device; a consumer entertainment device; a music player; a camera; a television; an electronic gaming unit; and a set-top box, the method comprising: receiving a user request for assistance spoken in a first language; translating the user request spoken in the first language to a second language; determining semantics of the user request and identifying at least one domain, at least one task, and at least one parameter for the user request; searching a semantic database on the Internet for the at least one matching domain, task, and parameter; compensating for translation errors based on user history; generating a response in the second language; and translating the response to the first language and rendering the response to the user and providing information from one of: music, audiobooks, news, weather, traffic, sports, and processing the user request by an assistant software on a computing cloud to purchase, reserve, or order products or services, wherein the assistant software automatically accesses semantic data and services having one or more triples including subject, predicate, and object available over the Internet to find one or more of: movies, events, performances, exhibits, shows, attractions, travel destinations, hotels, restaurants, bars, pubs, entertainment sites, landmarks, summer camps, resorts, places.

1. A method for providing automated assistance for a user using a computing device selected from a group consisting of: a telephone; a wireless communicator; a tablet computer; a laptop computer; a personal digital assistant; a desktop computer; a processor with memory; a kiosk; a consumer electronic device; a consumer entertainment device; a music player; a camera; a television; an electronic gaming unit; and a set-top box, the method comprising: receiving a user request for assistance spoken in a first language; translating the user request spoken in the first language to a second language; determining semantics of the user request and identifying at least one domain, at least one task, and at least one parameter for the user request; searching a semantic database on the Internet for the at least one matching domain, task, and parameter; compensating for translation errors based on user history; generating a response in the second language; and translating the response to the first language and rendering the response to the user and providing information from one of: music, audiobooks, news, weather, traffic, sports, and processing the user request by an assistant software on a computing cloud to purchase, reserve, or order products or services, wherein the assistant software automatically accesses semantic data and services having one or more triples including subject, predicate, and object available over the Internet to find one or more of: movies, events, performances, exhibits, shows, attractions, travel destinations, hotels, restaurants, bars, pubs, entertainment sites, landmarks, summer camps, resorts, places. 7. The method of claim 1 , wherein the semantic database comprises user generated reviews or recommendations.

8. The method of claim 1 , wherein the topological framework model is a simulated world.

8. The method of claim 1 , wherein the topological framework model is a simulated world. 12. The method of claim 8 , wherein the simulated world can create a new agent submodel.

13. The machine-readable medium of claim 10 , wherein the database instruction includes an aggregate function, the aggregate function including the command parameter.

13. The machine-readable medium of claim 10 , wherein the database instruction includes an aggregate function, the aggregate function including the command parameter. 14. The machine-readable medium of claim 13 , wherein the first variable descriptor token includes a first attribute defining a first date type and a second variable descriptor token includes a second attribute defining a second date type, the method further comprising: providing a first object representation and a second object representation, the first object representation including a first user-generated date and a first object type equal to the first attribute, and a second object representation including a second user-generated date and a second object type equal to the second attribute, wherein the document object representation is one of the first and second object representations; wherein generating the database instruction from at least the first template instruction includes substituting the first descriptor token with the first user-generated date and substituting the second descriptor token with the second user-generated date.

40. The method of claim 38 , further comprising: converting the translation text in the second language to a voice in the second language using a voice-for-TTS database, and generating the voice in the second language using the voice-for-TTS database.

40. The method of claim 38 , further comprising: converting the translation text in the second language to a voice in the second language using a voice-for-TTS database, and generating the voice in the second language using the voice-for-TTS database. 41. The method of claim 40 , further comprising outputting the translation voice signal in the second language using a speaker.

1. An apparatus comprising: a processor component; a task selector for execution by the processor component to receive an indication of a specified task to be performed, wherein the specified task comprises first and second subtasks; a source selector for execution by the processor component to receive an indication of a specified source device to perform the first and second subtasks, and to retrieve from the specified source device an indication of a source processing environment currently available within at least the specified source device in response to receiving the indication of the specified source device, wherein: the source device stores a source data set to serve as an input to performance of the specified task; and the indication of the source processing environment comprises indications of an identity and version level of a database routine of the specified source device; and an instruction generator for execution by the processor component to determine a first set of one or more languages able to be interpreted by the database routine of the specified source device based on the identity and version level of the database routine of the specified source device, determine whether to perform the first and second subtasks sequentially or at least partly in parallel based on at least one aspect of the source processing environment, select a language of the first set of languages in which to generate instructions to perform at least the first subtask based on the determination of whether to perform the first and second subtasks sequentially or at least partly in parallel, generate the instructions to perform the first subtask in the selected language, and transmit first task instructions comprising at least the instructions generated to perform at least the first subtask to the specified source device.

1. An apparatus comprising: a processor component; a task selector for execution by the processor component to receive an indication of a specified task to be performed, wherein the specified task comprises first and second subtasks; a source selector for execution by the processor component to receive an indication of a specified source device to perform the first and second subtasks, and to retrieve from the specified source device an indication of a source processing environment currently available within at least the specified source device in response to receiving the indication of the specified source device, wherein: the source device stores a source data set to serve as an input to performance of the specified task; and the indication of the source processing environment comprises indications of an identity and version level of a database routine of the specified source device; and an instruction generator for execution by the processor component to determine a first set of one or more languages able to be interpreted by the database routine of the specified source device based on the identity and version level of the database routine of the specified source device, determine whether to perform the first and second subtasks sequentially or at least partly in parallel based on at least one aspect of the source processing environment, select a language of the first set of languages in which to generate instructions to perform at least the first subtask based on the determination of whether to perform the first and second subtasks sequentially or at least partly in parallel, generate the instructions to perform the first subtask in the selected language, and transmit first task instructions comprising at least the instructions generated to perform at least the first subtask to the specified source device. 8. The apparatus of claim 1 , comprising a destination selector for execution by the processor component to receive an indication of a specified destination device to perform at least a third subtask, and to retrieve from the specified destination device an indication of a destination processing environment currently available within at least the specified destination device in response to receiving the indication of the specified destination device, wherein: the specified task comprises the third subtask; the destination device is to store data generated by performance of the specified task; and the indication of the destination processing environment comprises indications of an identity and version level of a database routine of the specified destination device; and the instruction generator is to determine a second set of one or more languages able to be interpreted by the database routine of the specified destination device based on the identity and version level of the database routine of the specified destination device, determine whether to perform the first and second subtasks sequentially or at least partly in parallel based on at least one aspect of the source processing environment and at least one aspect of the destination processing environment, select a language of the second set of languages in which to generate instructions to perform the third subtask based on the destination processing environment, generate the instructions to perform the third subtask, and transmit second task instructions comprising the instructions generated to perform at least the third subtask to the specified destination device.

5. A computer-implemented method executed by one or more processors, the method comprising: receiving a training phrase; normalizing the training phrase according to one or more lexicographic normalization rules; locating the normalized training phrase in a normalized phrase table, the normalized phrase table including a plurality of key-value pairs, each key-value pair having a key that includes a normalized phrase and a value that includes one or more un-normalized phrases associated with the normalized phrase of the key and one or more parameters associated with each un-normalized phrase; associating one or more weights to one or more un-normalized phrases associated with the key-value pair for the identified normalized training phrase in the normalized phrase table based on a relation of each associated un-normalized phrase to the received training phrase; determining a degree of match between the received training phrase and a specific un-normalized phrase associated with the located normalized training phrase, the degree of match being determined according to a similarity measure, wherein associating one or more weights comprises: associating a first weight to the specific un-normalized phrase when the training phrase has a high degree of match with the specific un-normalized phrase, and associating a second weight to the specific un-normalized phrase when the training phrase has a low degree of match with the specific un-normalized phrase; and training a machine learning model using the one or more un-normalized phrases and the associated one or more weights.

5. A computer-implemented method executed by one or more processors, the method comprising: receiving a training phrase; normalizing the training phrase according to one or more lexicographic normalization rules; locating the normalized training phrase in a normalized phrase table, the normalized phrase table including a plurality of key-value pairs, each key-value pair having a key that includes a normalized phrase and a value that includes one or more un-normalized phrases associated with the normalized phrase of the key and one or more parameters associated with each un-normalized phrase; associating one or more weights to one or more un-normalized phrases associated with the key-value pair for the identified normalized training phrase in the normalized phrase table based on a relation of each associated un-normalized phrase to the received training phrase; determining a degree of match between the received training phrase and a specific un-normalized phrase associated with the located normalized training phrase, the degree of match being determined according to a similarity measure, wherein associating one or more weights comprises: associating a first weight to the specific un-normalized phrase when the training phrase has a high degree of match with the specific un-normalized phrase, and associating a second weight to the specific un-normalized phrase when the training phrase has a low degree of match with the specific un-normalized phrase; and training a machine learning model using the one or more un-normalized phrases and the associated one or more weights. 9. The method of claim 5 , where training the machine learning model includes training a language model.

14. A system comprising: a memory to store instructions; and a processor coupled to the memory, the processor executes the instructions to: receive an input file defining a database schema written in a data dictionary language common to a plurality of different database types; create a data model in view of the input file; identify a first database type of the plurality of different database types for which a first database specific schema file is to be generated; identify a first plugin of a plurality of plugins, wherein the first plugin is associated with the first database type; and generate, by the first plugin, the first database specific schema file for the first database type and the data model.

14. A system comprising: a memory to store instructions; and a processor coupled to the memory, the processor executes the instructions to: receive an input file defining a database schema written in a data dictionary language common to a plurality of different database types; create a data model in view of the input file; identify a first database type of the plurality of different database types for which a first database specific schema file is to be generated; identify a first plugin of a plurality of plugins, wherein the first plugin is associated with the first database type; and generate, by the first plugin, the first database specific schema file for the first database type and the data model. 15. The system of claim 14 , wherein the input file comprises a text file created by a user.

15. A computer-readable memory device with instructions stored thereon for generating speech employing compressed concatenation cost data, the instructions comprising: determining, based on a matrix of concatenation costs, feature vectors for speech segments, wherein the matrix of concatenation costs is constructed along a preceding speech segment and a following speech segment for each segment applying distance weighting to one of: the speech segments and at least two consecutive speech segments, wherein the distance weighting is based on feature vectors associated with the speech segments or is based on feature vectors associated with the at least two consecutive speech segments clustering the speech segments into M preceding segment and N following segment groups such that an average distance between speech segments within each group is minimized; selecting a representative speech segment for each group; generating a compressed concatenation cost matrix such that a concatenation cost between the speech segments and the at least two consecutive speech segments is approximated by a concatenation cost between a representative segment associated with the speech segments and another representative speech segment associated with the at least two consecutive speech segments; and pre-saving the compressed concatenation cost matrix for real time computations in synthesizing speech.

15. A computer-readable memory device with instructions stored thereon for generating speech employing compressed concatenation cost data, the instructions comprising: determining, based on a matrix of concatenation costs, feature vectors for speech segments, wherein the matrix of concatenation costs is constructed along a preceding speech segment and a following speech segment for each segment applying distance weighting to one of: the speech segments and at least two consecutive speech segments, wherein the distance weighting is based on feature vectors associated with the speech segments or is based on feature vectors associated with the at least two consecutive speech segments clustering the speech segments into M preceding segment and N following segment groups such that an average distance between speech segments within each group is minimized; selecting a representative speech segment for each group; generating a compressed concatenation cost matrix such that a concatenation cost between the speech segments and the at least two consecutive speech segments is approximated by a concatenation cost between a representative segment associated with the speech segments and another representative speech segment associated with the at least two consecutive speech segments; and pre-saving the compressed concatenation cost matrix for real time computations in synthesizing speech. 16. The computer-readable memory device of claim 15 , wherein the distance weighting is applied employing distance function: ∑ m = 1 n ⁢ { abs ⁡ ( cc i , m - cc j , m ) * [ K o - ( cc i , m + cc j , m ) ] } 2 , where cc i,j are concatenation costs between speech segments i and j, K o is a predefined constant, and n is a total number of the speech segments.

5. The method according to claim 1 , further comprising creating at least one query tree, each query tree within the at least one query tree comprising the plurality of execution nodes configured with parent child relationships similar to the hierarchical structure, and wherein the combining database query commands from at least two of the plurality of execution nodes comprises: estimating, for each non-leaf node of at least one query tree within the at least one query tree, an estimated query result size for each non-leaf node of the at least one query tree; assigning a product of estimated sizes to each leaf node of the at least one query tree, the product of estimated sizes for a particular leaf node of the at least one query tree equaling a product of all estimated query result sizes that were estimated for each ancestor non-leaf node of the particular leaf node of the at least one query tree; selecting a highest value leaf node, the highest value leaf node having a largest product of estimated sizes; and forming the single database query by merging the database queries specified by ancestor non-leaf nodes of the highest value leaf node.

5. The method according to claim 1 , further comprising creating at least one query tree, each query tree within the at least one query tree comprising the plurality of execution nodes configured with parent child relationships similar to the hierarchical structure, and wherein the combining database query commands from at least two of the plurality of execution nodes comprises: estimating, for each non-leaf node of at least one query tree within the at least one query tree, an estimated query result size for each non-leaf node of the at least one query tree; assigning a product of estimated sizes to each leaf node of the at least one query tree, the product of estimated sizes for a particular leaf node of the at least one query tree equaling a product of all estimated query result sizes that were estimated for each ancestor non-leaf node of the particular leaf node of the at least one query tree; selecting a highest value leaf node, the highest value leaf node having a largest product of estimated sizes; and forming the single database query by merging the database queries specified by ancestor non-leaf nodes of the highest value leaf node. 6. The method according to claim 5 , wherein the estimating comprises multiplying a size of a target table by a pre-determined percentage.

1. A method for improved information retrieval from a database via a query suggestion based on a relationship inferred between a first query comprising a plurality of first terms and a second query comprising a plurality of second terms, the method comprising: relating, by one or more hardware processors, the first query to the second query based on identifying at least one common term between the plurality of first terms and the plurality of second terms; identifying, by one or more hardware processors, one or more dissimilar terms between the plurality of first terms and the plurality of second terms; identifying, by one or more hardware processors, a first number of dissimilar terms included in the first query; identifying, by one or more hardware processors, a second number of dissimilar terms included in the second query; assigning, by one or more hardware processors, a weight to the relationship between the first query and the second query, the weight being based on a difference between the first number and the second number; and in response to receiving a search query from a user device, providing, by one or more hardware processors, the query suggestion based on the weight assigned to the relationship between the first query and the second query.

1. A method for improved information retrieval from a database via a query suggestion based on a relationship inferred between a first query comprising a plurality of first terms and a second query comprising a plurality of second terms, the method comprising: relating, by one or more hardware processors, the first query to the second query based on identifying at least one common term between the plurality of first terms and the plurality of second terms; identifying, by one or more hardware processors, one or more dissimilar terms between the plurality of first terms and the plurality of second terms; identifying, by one or more hardware processors, a first number of dissimilar terms included in the first query; identifying, by one or more hardware processors, a second number of dissimilar terms included in the second query; assigning, by one or more hardware processors, a weight to the relationship between the first query and the second query, the weight being based on a difference between the first number and the second number; and in response to receiving a search query from a user device, providing, by one or more hardware processors, the query suggestion based on the weight assigned to the relationship between the first query and the second query. 4. The method of claim 1 , further comprising building an inverted index from the first query and the second query, wherein the one or more dissimilar terms are identified from the inverted index.

7. A non-transitory computer-readable medium containing instructions for controlling a computer system to perform a method for data cleansing using rule based formatting, the method comprising: obtaining a first input data and a second input data, wherein said first input data is tokenized according to a data dictionary, wherein said second input data is tokenized according to the data dictionary; parsing said first input data and said second input data using a predefined parsing rule including an option operator, wherein the option operator indicates that a particular index defined in the predefined parsing rule is optional; obtaining a formatting rule, wherein said formatting rule includes one or more formatting rule components including at least one conditional format operator, wherein the at least one conditional format operator indicates whether to include a particular string literal in an output data based on the existence of a particular token; including a first token in a first output data if a first formatting rule component in the formatting rule is a first valid index to said first tokenized input data, wherein said first token is associated with said first valid index, and including a first string literal in said first output data if said first formatting rule component in the formatting rule is a string literal; including a second token in a second output data if said first formatting rule component in the formatting rule is a second valid index to said second tokenized input data, wherein said second token is associated with said second valid index and including a second string literal in said second output data if said first formatting rule component in the formatting rule is the string literal; and formatting said first output data and said second output data according to the formatting rule.

7. A non-transitory computer-readable medium containing instructions for controlling a computer system to perform a method for data cleansing using rule based formatting, the method comprising: obtaining a first input data and a second input data, wherein said first input data is tokenized according to a data dictionary, wherein said second input data is tokenized according to the data dictionary; parsing said first input data and said second input data using a predefined parsing rule including an option operator, wherein the option operator indicates that a particular index defined in the predefined parsing rule is optional; obtaining a formatting rule, wherein said formatting rule includes one or more formatting rule components including at least one conditional format operator, wherein the at least one conditional format operator indicates whether to include a particular string literal in an output data based on the existence of a particular token; including a first token in a first output data if a first formatting rule component in the formatting rule is a first valid index to said first tokenized input data, wherein said first token is associated with said first valid index, and including a first string literal in said first output data if said first formatting rule component in the formatting rule is a string literal; including a second token in a second output data if said first formatting rule component in the formatting rule is a second valid index to said second tokenized input data, wherein said second token is associated with said second valid index and including a second string literal in said second output data if said first formatting rule component in the formatting rule is the string literal; and formatting said first output data and said second output data according to the formatting rule. 10. The non-transitory computer-readable medium of claim 7 wherein said first string literal is included in said first output data if a token associated with a second formatting rule component to the immediate left of said first formatting rule component exists.

1. A method of analyzing data, comprising: generating, by an entity, a query based at least in part on a topic of interest; executing the query on a plurality of data sources, at least one of the plurality of data sources comprising data for summarization; selecting, by the entity, a data source from the plurality of data sources for monitoring based on a correlation between the data source and the topic of interest, the correlation determined based on results of the executed query; monitoring, based on a set schedule, the data source for matches to the query to extract data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; extracting data from the data source when at least the update to the stored data matches the query, the newly added data matches the query, or the combination thereof; determining an extraction rate for extracting the data, the extraction rate indicating an amount of the data that is extracted over a first time period; determining a first processing rate for processing the extracted data with a number of parallel processors, the first processing rate indicating an amount of extracted data that is processed over a second time period; dynamically adjusting the number of parallel processors for analyzing the extracted data based on the extraction rate to obtain a second processing rate that is greater than the first processing rate; analyzing, with the parallel processors, the extracted data to determine at least one of a sentiment, an index, a pattern, or a combination thereof; establishing a two-way communication channel, between at least the entity that selected the data source for monitoring and a user device of a user that provided the data to the data source, based on the analysis of the extracted data; transmitting, from the entity via the two-way communication channel, a first message directed to the user device based on the analysis of the extracted data; and receiving, from the user device via the two-way communication channel, a second message in response to the first message directed to the user device.

1. A method of analyzing data, comprising: generating, by an entity, a query based at least in part on a topic of interest; executing the query on a plurality of data sources, at least one of the plurality of data sources comprising data for summarization; selecting, by the entity, a data source from the plurality of data sources for monitoring based on a correlation between the data source and the topic of interest, the correlation determined based on results of the executed query; monitoring, based on a set schedule, the data source for matches to the query to extract data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; extracting data from the data source when at least the update to the stored data matches the query, the newly added data matches the query, or the combination thereof; determining an extraction rate for extracting the data, the extraction rate indicating an amount of the data that is extracted over a first time period; determining a first processing rate for processing the extracted data with a number of parallel processors, the first processing rate indicating an amount of extracted data that is processed over a second time period; dynamically adjusting the number of parallel processors for analyzing the extracted data based on the extraction rate to obtain a second processing rate that is greater than the first processing rate; analyzing, with the parallel processors, the extracted data to determine at least one of a sentiment, an index, a pattern, or a combination thereof; establishing a two-way communication channel, between at least the entity that selected the data source for monitoring and a user device of a user that provided the data to the data source, based on the analysis of the extracted data; transmitting, from the entity via the two-way communication channel, a first message directed to the user device based on the analysis of the extracted data; and receiving, from the user device via the two-way communication channel, a second message in response to the first message directed to the user device. 2. The method of claim 1 , further comprising visualizing the extracted data.

6. The method of claim 2 wherein unreliable label data is replaced.

6. The method of claim 2 wherein unreliable label data is replaced. 8. The method of claim 6 , wherein replacing unreliable label data or predicting new multimedia label data comprise updating a node regularization matrix.

9. A computing device comprising one or more processors and one or more memory devices comprising program instructions that when executed by the one or more processors cause the computing device to perform operations comprising: parsing a first collection of statically-typed executable machine-readable code to locate descriptions of one or more application programming interfaces (APIs) included in the first collection of statically-typed executable machine-readable code; transforming the descriptions of the one or more application programming interfaces of the first collection of statically-typed executable machine-readable code into an alternate form; and checking compatibility of a second collection of dynamically-typed executable machine-readable code with the first collection of statically-typed executable machine-readable code using the transformed descriptions of the one or more application programming interfaces of the first collection of statically-typed executable machine-readable code to verify that call sites of the second collection of dynamically-type executable machine-readable code map to the alternate form.

9. A computing device comprising one or more processors and one or more memory devices comprising program instructions that when executed by the one or more processors cause the computing device to perform operations comprising: parsing a first collection of statically-typed executable machine-readable code to locate descriptions of one or more application programming interfaces (APIs) included in the first collection of statically-typed executable machine-readable code; transforming the descriptions of the one or more application programming interfaces of the first collection of statically-typed executable machine-readable code into an alternate form; and checking compatibility of a second collection of dynamically-typed executable machine-readable code with the first collection of statically-typed executable machine-readable code using the transformed descriptions of the one or more application programming interfaces of the first collection of statically-typed executable machine-readable code to verify that call sites of the second collection of dynamically-type executable machine-readable code map to the alternate form. 10. A computing device as described in claim 9 , wherein the checking compatibility of the second collection of dynamically-typed executable machine-readable code comprises consuming the transformed descriptions of the one or more application programming interfaces of the first collection of statically-typed executable machine-readable code by the second collection of dynamically-typed executable machine-readable code.

11. The system of claim 10 , further comprising: means for determining a number of the plurality of the identified clusters in which the quality score of the one of the first documents is lower than the quality score of at least one of the one or more second documents; and means for generating a spam score based on the determined number.

11. The system of claim 10 , further comprising: means for determining a number of the plurality of the identified clusters in which the quality score of the one of the first documents is lower than the quality score of at least one of the one or more second documents; and means for generating a spam score based on the determined number. 12. The system of claim 11 , further comprising: means for computing a proxy pad score that indicates the likelihood that the organization copies content from the one or more different organizations based on the spam score, the proxy pad score corresponding to the determined information.

17. The system as recited in claim 1 , wherein the SAN management server comprises an alarm service configured to monitor the SAN and collect status and performance information from the SAN, wherein the alarm service is configured to provide the status and performance information to a policy service configured to apply one or more policies to the provided information to evaluate the SAN status and performance information and perform one or more actions in accordance with the policies in response to said evaluation.

17. The system as recited in claim 1 , wherein the SAN management server comprises an alarm service configured to monitor the SAN and collect status and performance information from the SAN, wherein the alarm service is configured to provide the status and performance information to a policy service configured to apply one or more policies to the provided information to evaluate the SAN status and performance information and perform one or more actions in accordance with the policies in response to said evaluation. 18. The system as recited in claim 17 , wherein a policy is a set of user-editable rules for use in managing the SAN by automating responses to SAN events and conditions.

13. An apparatus comprising: a storage that stores at least labeled training data that is composed in a source language; a storage that stores at least a particular document that is composed in a target language that differs from the source language; one or more processors that: (a) train a source language text classifier based on the labeled training data, thereby producing a source language model; (b) translate source language features from the source language model into a target language at least in part by selecting, for at least a particular source language feature of the source language features, and based at least in part on an expectation maximum, a particular target language translation of a plurality of possible target language translations of the particular source language feature; and (c) apply a target language classifier, which uses a target language model produced based at least in part on the selecting, to the particular document, thereby determining whether the particular document belongs to a particular class; wherein the one or more processors select the particular target language translation at least in part by: determining a first probability based at least in part on the particular target language translation during a first iteration; determining a second probability based at least in part on the particular target language translation during a second iteration; determining whether a difference between the first probability and the second probability is less than a specified threshold; in response to determining that the difference between the first probability and the second probability is less than the specified threshold, setting a final translation probability for the particular target language translation equal to the second probability; and selecting the particular target language translation based at least in part on the final translation probability.

13. An apparatus comprising: a storage that stores at least labeled training data that is composed in a source language; a storage that stores at least a particular document that is composed in a target language that differs from the source language; one or more processors that: (a) train a source language text classifier based on the labeled training data, thereby producing a source language model; (b) translate source language features from the source language model into a target language at least in part by selecting, for at least a particular source language feature of the source language features, and based at least in part on an expectation maximum, a particular target language translation of a plurality of possible target language translations of the particular source language feature; and (c) apply a target language classifier, which uses a target language model produced based at least in part on the selecting, to the particular document, thereby determining whether the particular document belongs to a particular class; wherein the one or more processors select the particular target language translation at least in part by: determining a first probability based at least in part on the particular target language translation during a first iteration; determining a second probability based at least in part on the particular target language translation during a second iteration; determining whether a difference between the first probability and the second probability is less than a specified threshold; in response to determining that the difference between the first probability and the second probability is less than the specified threshold, setting a final translation probability for the particular target language translation equal to the second probability; and selecting the particular target language translation based at least in part on the final translation probability. 14. The apparatus of claim 13 , wherein the one or more processors further: apply the target language classifier to a set of unlabelled documents that are composed in the target language, thereby producing a set of automatically labeled target language documents; train a new target language text classifier based on the set of automatically labeled target language documents, thereby producing a tuned target language model; and apply a new target language classifier, which uses the tuned target language model, to the particular document that is composed in the target language, thereby determining whether the particular document belongs to the particular class.

10. The computer program product according to claim 7 , wherein said computer readable program code is configured to filter the list of candidate text strings via post-processing, the post-processing being governed by a level of detail returned within optional response metadata generated with the list of candidate text strings.

10. The computer program product according to claim 7 , wherein said computer readable program code is configured to filter the list of candidate text strings via post-processing, the post-processing being governed by a level of detail returned within optional response metadata generated with the list of candidate text strings. 11. The computer program product according to claim 10 , wherein: the post-processing employs runtime post-processing logic; and the runtime post-processing logic is governed by the level of detail returned in the optional response metadata.

1. A method for generating, with a processor, semantic information associated with spoken voice in order to control an electronic device, comprising: acquiring, with a processor, a first text corpus, including first text data of a first sentence including a first word and described in a natural language, and second text data of a second sentence including a second word different in meaning from the first word, with a second word distribution indicating types and frequencies of words appearing within a predetermined range prior to and subsequent to the second word being similar to a first word distribution within the predetermined range prior to and subsequent to the first word in the first sentence; acquiring, with the processor, a second text corpus including third text data of a third sentence, including a third word identical to at least one of the first word and the second word, with a third word distribution within the predetermined range prior to and subsequent to the third word being not similar to the first word distribution; in accordance with an arrangement of a word string in the first text corpus and the second text corpus, performing, with the processor, a learning process by assigning to the first word a first vector representing a meaning of the first word in a vector space of predetermined dimensions and by assigning to the second word a second vector representing a meaning of the second word in the vector space; storing the first vector in association with the first word, and the second vector spaced by a predetermined distance or longer from the first vector in the vector space in association with the second word; and generating, with the processor, a command based on the first vector and the second vector, wherein the electronics device is controlled in accordance with the command.

1. A method for generating, with a processor, semantic information associated with spoken voice in order to control an electronic device, comprising: acquiring, with a processor, a first text corpus, including first text data of a first sentence including a first word and described in a natural language, and second text data of a second sentence including a second word different in meaning from the first word, with a second word distribution indicating types and frequencies of words appearing within a predetermined range prior to and subsequent to the second word being similar to a first word distribution within the predetermined range prior to and subsequent to the first word in the first sentence; acquiring, with the processor, a second text corpus including third text data of a third sentence, including a third word identical to at least one of the first word and the second word, with a third word distribution within the predetermined range prior to and subsequent to the third word being not similar to the first word distribution; in accordance with an arrangement of a word string in the first text corpus and the second text corpus, performing, with the processor, a learning process by assigning to the first word a first vector representing a meaning of the first word in a vector space of predetermined dimensions and by assigning to the second word a second vector representing a meaning of the second word in the vector space; storing the first vector in association with the first word, and the second vector spaced by a predetermined distance or longer from the first vector in the vector space in association with the second word; and generating, with the processor, a command based on the first vector and the second vector, wherein the electronics device is controlled in accordance with the command. 3. The method according to claim 1 , wherein the first text data and the second text data comprise a word in a first language, and wherein in the third text data, the third word is a word in the first language, and a word within the predetermined range prior to and subsequent to the third word is a word in a second language different from the first language.

12. The system of claim 9 , wherein the computer is to detect the terms related to the environment by performing topic modeling on a corpora comprising the external corpus.

12. The system of claim 9 , wherein the computer is to detect the terms related to the environment by performing topic modeling on a corpora comprising the external corpus. 13. The method of claim 12 , wherein the computer is to detect the terms related to the environment by performing validation of key phrases from the corpora.

3. The method of claim 1, further comprising the step of defining a means for stopping continued scrolling of the text.

3. The method of claim 1, further comprising the step of defining a means for stopping continued scrolling of the text. 4. The method of claim 3, wherein the means for stopping continued scrolling of the text comprises defining at least one region of the display as a stop zone, such that when a defined cursor signifies a stop zone, further scrolling of text ceases.

9. A non-transitory computer-readable storage medium, encoded with computer program instructions that, when executed by a machine, cause the machine to perform a method for automatically marking a document to be read by a text-to-speech reader with voice type identifiers, the method comprising: identifying two or more voice types available to the text-to-speech reader, each voice type having a corresponding voice type identifier; identifying text elements within the document, wherein identifying text elements comprises marking gross structural subdivisions of text with a first set of sequenced tags, marking individual paragraphs of the text with a second set of sequenced tags, and marking text elements with a third set of sequenced tags to generate a hierarchical tree identifying the text elements; grouping similar text elements together, wherein grouping comprises generating one or more clusters according to each identifiable topic of the document, syntactically parsing the document and subsequently performing text mining to determine which text elements in the document are similar, wherein similarity is based upon lexical affinities among the text elements; classifying the grouped text elements according to voice types available to the text-to-speech reader; and marking the classified grouped text elements within the document with corresponding voice type identifiers.

9. A non-transitory computer-readable storage medium, encoded with computer program instructions that, when executed by a machine, cause the machine to perform a method for automatically marking a document to be read by a text-to-speech reader with voice type identifiers, the method comprising: identifying two or more voice types available to the text-to-speech reader, each voice type having a corresponding voice type identifier; identifying text elements within the document, wherein identifying text elements comprises marking gross structural subdivisions of text with a first set of sequenced tags, marking individual paragraphs of the text with a second set of sequenced tags, and marking text elements with a third set of sequenced tags to generate a hierarchical tree identifying the text elements; grouping similar text elements together, wherein grouping comprises generating one or more clusters according to each identifiable topic of the document, syntactically parsing the document and subsequently performing text mining to determine which text elements in the document are similar, wherein similarity is based upon lexical affinities among the text elements; classifying the grouped text elements according to voice types available to the text-to-speech reader; and marking the classified grouped text elements within the document with corresponding voice type identifiers. 14. The non-transitory computer-readable storage medium as claimed in claim 9 , wherein classifying the text elements according to the available voice types further comprises finding the best match between the grouped text elements and the characteristics of the voice types.

8. A computer-implemented system comprising: means for extracting first information from a post to a blog; means for extracting second information, associated with the blog, from a source different than the post to the blog; means for creating a hybrid document by combining the first information and the second information; means for storing the hybrid document in a searchable index; and means for using the searchable index to determine a relevance of the post to a search query.

8. A computer-implemented system comprising: means for extracting first information from a post to a blog; means for extracting second information, associated with the blog, from a source different than the post to the blog; means for creating a hybrid document by combining the first information and the second information; means for storing the hybrid document in a searchable index; and means for using the searchable index to determine a relevance of the post to a search query. 27. The computer-implemented system of claim 8 , further comprising: means for extracting third information from a document to which the blog links, and where the means for creating a hybrid document includes: creating the hybrid document by combining the first information, the second information, and the third information.

16. A method for generating new sound sequences based on input sounds, the method comprising: receiving a plurality of input sound sequences containing sound frequencies with corresponding time duration; converting the plurality of input sound sequences to a finite state automaton using a system that allows over-generation; traversing the finite state automaton with a graph exploration procedure that uses exploration rules and a plurality of path markers to determine paths across the finite state automaton; and storing the paths across the finite state automaton to a path marker data structure to define recorded path markers; wherein the recorded path markers that are not found in the plurality of input sound sequences define a new sound sequence.

16. A method for generating new sound sequences based on input sounds, the method comprising: receiving a plurality of input sound sequences containing sound frequencies with corresponding time duration; converting the plurality of input sound sequences to a finite state automaton using a system that allows over-generation; traversing the finite state automaton with a graph exploration procedure that uses exploration rules and a plurality of path markers to determine paths across the finite state automaton; and storing the paths across the finite state automaton to a path marker data structure to define recorded path markers; wherein the recorded path markers that are not found in the plurality of input sound sequences define a new sound sequence. 20. A method for generating new sound sequences based on input sounds as recited in claim 16 , wherein the path marker history and the path marker registry of particular path markers are updated when traversing the finite state automaton.

1. A computer-implemented method for detecting security events, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising: identifying facets of candidate security events detected by a network security system, the candidate security events comprising network messages; assigning each of the facets of the candidate security events to one of multiple groups of facets to create permutations of the facets; comparing, for each group of facets, the candidate security events with each other according to a similarity algorithm that indicates similarity between the candidate security events, the similarity algorithm indicating similarity according to the facets specific to the respective group; generating, for each group of facets, a weak classifier for detecting security events based on a nearest neighbor graph that connects each node of the graph to a nearest neighbor according to the respective similarity algorithm, the nearest neighbor graph indicating the network messages as nodes; and performing, by the network security system, a remedial action in response to classifying a candidate security event as a security threat by applying the weak classifiers for the groups of facets to the candidate security event by giving each of the weak classifiers a vote on a security score for the candidate security event.

1. A computer-implemented method for detecting security events, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising: identifying facets of candidate security events detected by a network security system, the candidate security events comprising network messages; assigning each of the facets of the candidate security events to one of multiple groups of facets to create permutations of the facets; comparing, for each group of facets, the candidate security events with each other according to a similarity algorithm that indicates similarity between the candidate security events, the similarity algorithm indicating similarity according to the facets specific to the respective group; generating, for each group of facets, a weak classifier for detecting security events based on a nearest neighbor graph that connects each node of the graph to a nearest neighbor according to the respective similarity algorithm, the nearest neighbor graph indicating the network messages as nodes; and performing, by the network security system, a remedial action in response to classifying a candidate security event as a security threat by applying the weak classifiers for the groups of facets to the candidate security event by giving each of the weak classifiers a vote on a security score for the candidate security event. 10. The method of claim 1 , wherein assigning each of the facets of the candidate security events to one of the multiple groups of facets to create permutations of the facets is performed on a random basis such that each of the facets is randomly assigned to one of the multiple groups.

11. An SGML type document managing apparatus for allowing users to create, edit, and use an SGML type document collaboratively, comprising: means for storing and managing an SGML type document as document elements with extended content models, the extended content model attached to every document element being a variant of a content model of an element declaration in a document type definition and representing arrangement of child document elements of its relating document element with instance identifiers of the child document elements; means for automatically creating a partial editing document type definition for a document portion to be edited by using the assigned extended content model so as to independently examine whether or not the portion conforms to the document type definition; and means for allowing a document element in the highest hierarchical level of the edited portion to be deleted, the generic identifier of the document element to be changed, and a new document element to be appended before and after the document portion to be edited on the condition that the edited result does not violate the partial editing document type definition and any other portions of the document can be edited by any users.

11. An SGML type document managing apparatus for allowing users to create, edit, and use an SGML type document collaboratively, comprising: means for storing and managing an SGML type document as document elements with extended content models, the extended content model attached to every document element being a variant of a content model of an element declaration in a document type definition and representing arrangement of child document elements of its relating document element with instance identifiers of the child document elements; means for automatically creating a partial editing document type definition for a document portion to be edited by using the assigned extended content model so as to independently examine whether or not the portion conforms to the document type definition; and means for allowing a document element in the highest hierarchical level of the edited portion to be deleted, the generic identifier of the document element to be changed, and a new document element to be appended before and after the document portion to be edited on the condition that the edited result does not violate the partial editing document type definition and any other portions of the document can be edited by any users. 12. The SGML type document managing apparatus as set forth in claim 11, further comprising: means for storing history information of a document element being copied, moved, exchanged, appended or deleted in an SGML document type; and means for outputting the stored history information in a machine-independent format.

15. A system, comprising a plurality of devices coupled via a network, wherein each device is configured to: receive an abstract query from a requesting entity, wherein the abstract query comprises one or more logical fields defined in a first data abstraction model comprising a plurality of first logical field definitions mapping to physical fields of a first database; wherein the one or more logical fields in the abstract query each have a respective concept code relating corresponding logical field definitions of a plurality of data abstraction models including the first data abstraction model, a second data abstraction model and a third data abstraction model, the second data abstraction model being resident on the device at which the abstract query is received and comprising a plurality of second logical field definitions mapping to physical fields of a second database; modify the abstract query to include one or more of the second logical field definitions from the second data abstraction model based on the respective concept codes; issue the modified abstract query against the second database to retrieve a first set of results for the modified abstract query; send the abstract query to at least one other device of the plurality of devices, the at least one other device comprising the third data abstraction model comprising a plurality of third logical field definitions mapping to physical fields of a third database, wherein the first, second and third data abstraction models, and their respective logical field definitions, are distinct from one another, and wherein the at least one other device is configured to modify the abstract query to include one or more of the third logical field definitions from the third data abstraction model based on the respective concept codes; receive a second set of results for the abstract query from the at least one other device; and provide the first and second set of results to the requesting entity.

15. A system, comprising a plurality of devices coupled via a network, wherein each device is configured to: receive an abstract query from a requesting entity, wherein the abstract query comprises one or more logical fields defined in a first data abstraction model comprising a plurality of first logical field definitions mapping to physical fields of a first database; wherein the one or more logical fields in the abstract query each have a respective concept code relating corresponding logical field definitions of a plurality of data abstraction models including the first data abstraction model, a second data abstraction model and a third data abstraction model, the second data abstraction model being resident on the device at which the abstract query is received and comprising a plurality of second logical field definitions mapping to physical fields of a second database; modify the abstract query to include one or more of the second logical field definitions from the second data abstraction model based on the respective concept codes; issue the modified abstract query against the second database to retrieve a first set of results for the modified abstract query; send the abstract query to at least one other device of the plurality of devices, the at least one other device comprising the third data abstraction model comprising a plurality of third logical field definitions mapping to physical fields of a third database, wherein the first, second and third data abstraction models, and their respective logical field definitions, are distinct from one another, and wherein the at least one other device is configured to modify the abstract query to include one or more of the third logical field definitions from the third data abstraction model based on the respective concept codes; receive a second set of results for the abstract query from the at least one other device; and provide the first and second set of results to the requesting entity. 17. The system of claim 15 , wherein the network is a peer-to-peer network.

1. A document processor having a plurality of input units and a plurality of output units, each input unit and each output unit corresponding to a specific function, comprising: an input document unit capable of holding pieces of input document information converted to a predetermined common format regardless of the input units, after input processing is performed by the input units; a user document unit obtaining user images of a predetermined size to be processed, which are created based on the input document unit, and capable of holding the user images as pieces of user document information formed of the obtained user images; and an output document unit that reshapes an output image in a format to be output from at least one of the output units by using the output image created from the user images held in the user document unit to output the reshaped output image and can hold the output images as pieces of output document information to be output.

1. A document processor having a plurality of input units and a plurality of output units, each input unit and each output unit corresponding to a specific function, comprising: an input document unit capable of holding pieces of input document information converted to a predetermined common format regardless of the input units, after input processing is performed by the input units; a user document unit obtaining user images of a predetermined size to be processed, which are created based on the input document unit, and capable of holding the user images as pieces of user document information formed of the obtained user images; and an output document unit that reshapes an output image in a format to be output from at least one of the output units by using the output image created from the user images held in the user document unit to output the reshaped output image and can hold the output images as pieces of output document information to be output. 5. The document processor according to claim 1 , comprising: a document parallel processor that controls the processing to be executed in parallel, in which the input document unit inputs a part of input documents to be input from the input units, the user document unit obtains the user images created based on the input document unit, and the output document unit reshapes the documents in a format to be output from the output unit by using the output image created from the user images constituting the user document unit and outputs the reshaped document.

1. A non-transitory computer-readable medium embodying a program executable in at least one computing device, comprising: code that obtains a plurality of dialogue search queries from a plurality of users; code that, for individual ones of the plurality of dialogue search queries, determines a respective plurality of clips from a plurality of video content features by executing a respective dialogue search based at least in part on the individual ones of the plurality of dialogue search queries; code that sends a corresponding dialogue search result listing of the respective plurality of clips to respective ones of the plurality of users; code that determines that the plurality of users has expressed an interest in at least two of the clips of one of the plurality of video content features via the corresponding dialogue search result listing; and code that generates an abridgement of the one of the plurality of video content features based at least in part on the interest in the at least two of the clips, the abridgement including a first portion of the one of the plurality of video content features and a second portion of the one of the plurality of video content features and excluding a third portion of the one of the plurality of video content features that is between the first portion and the second portion.

1. A non-transitory computer-readable medium embodying a program executable in at least one computing device, comprising: code that obtains a plurality of dialogue search queries from a plurality of users; code that, for individual ones of the plurality of dialogue search queries, determines a respective plurality of clips from a plurality of video content features by executing a respective dialogue search based at least in part on the individual ones of the plurality of dialogue search queries; code that sends a corresponding dialogue search result listing of the respective plurality of clips to respective ones of the plurality of users; code that determines that the plurality of users has expressed an interest in at least two of the clips of one of the plurality of video content features via the corresponding dialogue search result listing; and code that generates an abridgement of the one of the plurality of video content features based at least in part on the interest in the at least two of the clips, the abridgement including a first portion of the one of the plurality of video content features and a second portion of the one of the plurality of video content features and excluding a third portion of the one of the plurality of video content features that is between the first portion and the second portion. 3. The non-transitory computer-readable medium of claim 1 , further comprising code that recommends the at least two of the clips to another user.

1. A toothpaste dispenser having an appearance of a character, the toothpaste dispenser comprising: a base; a support extending from a first end essentially vertically from the base; and a toothpaste tube holder located at a second end of the support opposite the first end, the toothpaste tube holder capable of being removably coupled to a toothpaste tube, wherein the base comprises a plurality of indentations therein each to receive a first end of a toothbrush and the toothpaste holder comprises a plurality of hooks each correspondingly located thereon to receive a second end of the toothbrush, the toothpaste tube comprises at least one of facial and body features, the dispenser comprising a plurality of toothbrushes, each having a shape of the character's leg, the indentations and hooks located on the dispenser for the toothbrushes to appear as legs extending from the toothpaste tube.

1. A toothpaste dispenser having an appearance of a character, the toothpaste dispenser comprising: a base; a support extending from a first end essentially vertically from the base; and a toothpaste tube holder located at a second end of the support opposite the first end, the toothpaste tube holder capable of being removably coupled to a toothpaste tube, wherein the base comprises a plurality of indentations therein each to receive a first end of a toothbrush and the toothpaste holder comprises a plurality of hooks each correspondingly located thereon to receive a second end of the toothbrush, the toothpaste tube comprises at least one of facial and body features, the dispenser comprising a plurality of toothbrushes, each having a shape of the character's leg, the indentations and hooks located on the dispenser for the toothbrushes to appear as legs extending from the toothpaste tube. 8. The toothpaste dispenser of claim 1 , wherein each of the toothbrushes comprises a bendable knee with a plurality of detents that locks in a shape in one of a plurality of configurations.

1. A computer-implemented method comprising: receiving audio data encoding an utterance, and data specifying a time when the utterance was spoken; determining, for each of multiple communications that were initiated by a user of a mobile device, a time when the communication was initiated or received; determining, for each of the multiple communications, a similarity score based on a similarity between the time when the communication was initiated or received, and the time when the utterance was spoken; determining, for each of multiple contacts associated with the user, a probability associated with the contact based at least on the similarity score for the communications that were initiated or received; weighting a contact disambiguation grammar according to the probabilities; and processing the audio data using the contact disambiguation grammar to select a particular contact.

1. A computer-implemented method comprising: receiving audio data encoding an utterance, and data specifying a time when the utterance was spoken; determining, for each of multiple communications that were initiated by a user of a mobile device, a time when the communication was initiated or received; determining, for each of the multiple communications, a similarity score based on a similarity between the time when the communication was initiated or received, and the time when the utterance was spoken; determining, for each of multiple contacts associated with the user, a probability associated with the contact based at least on the similarity score for the communications that were initiated or received; weighting a contact disambiguation grammar according to the probabilities; and processing the audio data using the contact disambiguation grammar to select a particular contact. 7. The method of claim 1 , comprising: receiving data specifying whether the mobile device had access to a WiFi connection when the utterance was spoken; determining, for each of the multiple communications that were initiated by the user of the mobile device, whether the multiple device had access to a WiFi connection when the communication was initiated or received; and wherein the respective similarity score is determined further based on whether the mobile device had access to a WiFi connection when the communication was initiated or received, and whether the mobile device had access to a WiFi connection when the utterance was spoken.

9. A processing device comprising: at least one processor; and a memory connected to the at least one processor, the memory comprising: instructions for submitting a plurality of requests for processing displayed text, ones of the plurality of submitted requests being submitted independently of receiving a corresponding processing response to an immediately preceding submitted processing request; instructions for receiving respective processing responses including changed or annotated text corresponding to respective ones of the submitted plurality of requests; instructions for determining whether text corresponding to the respective received processing responses is currently being displayed; instructions for replacing only portions of the displayed text corresponding to the changed or annotated text in respective ones of the received processing responses as each of the respective ones of the received processing responses is received, when the determining determines that the text corresponding to the respective ones of the received processing responses is currently being displayed; and instructions for discarding the respective ones of the received processing responses as the respective ones of the received processing responses when the determining fails to determine that the text corresponding to the respective ones of the received processing responses is currently being displayed.

9. A processing device comprising: at least one processor; and a memory connected to the at least one processor, the memory comprising: instructions for submitting a plurality of requests for processing displayed text, ones of the plurality of submitted requests being submitted independently of receiving a corresponding processing response to an immediately preceding submitted processing request; instructions for receiving respective processing responses including changed or annotated text corresponding to respective ones of the submitted plurality of requests; instructions for determining whether text corresponding to the respective received processing responses is currently being displayed; instructions for replacing only portions of the displayed text corresponding to the changed or annotated text in respective ones of the received processing responses as each of the respective ones of the received processing responses is received, when the determining determines that the text corresponding to the respective ones of the received processing responses is currently being displayed; and instructions for discarding the respective ones of the received processing responses as the respective ones of the received processing responses when the determining fails to determine that the text corresponding to the respective ones of the received processing responses is currently being displayed. 14. The processing device of claim 9 , wherein the memory further comprises: instructions for permitting no more than a predetermined number of the submitted plurality of requests to be outstanding.

15. A computer program product comprising: a computer readable storage medium; and program instructions residing in said storage medium for syntactically classifying a natural language sentence by receiving the natural language sentence, parsing the natural language sentence to derive a parse tree having a plurality of nodes, identifying a particular one of the nodes that corresponds to an element of interest in the natural language sentence, extracting syntactic information from the parse tree relative to the particular node corresponding to the element of interest, recording the syntactic information as a classification for the natural language sentence, determining that the classification for the natural language sentence is different from classifications of other natural language sentences in a test set according to at least one predetermined similarity criterion related to the syntactic information wherein the predetermined similarity criterion allows two given sentences to be deemed similar even when the two given sentences have different classifications, and responsively adding the natural language sentence to the test set.

15. A computer program product comprising: a computer readable storage medium; and program instructions residing in said storage medium for syntactically classifying a natural language sentence by receiving the natural language sentence, parsing the natural language sentence to derive a parse tree having a plurality of nodes, identifying a particular one of the nodes that corresponds to an element of interest in the natural language sentence, extracting syntactic information from the parse tree relative to the particular node corresponding to the element of interest, recording the syntactic information as a classification for the natural language sentence, determining that the classification for the natural language sentence is different from classifications of other natural language sentences in a test set according to at least one predetermined similarity criterion related to the syntactic information wherein the predetermined similarity criterion allows two given sentences to be deemed similar even when the two given sentences have different classifications, and responsively adding the natural language sentence to the test set. 16. The computer program product of claim 15 wherein: the parse tree nodes include a root node, one or more interior nodes, and a plurality of terminal nodes representing linguistic elements of the natural language sentence, the particular node corresponding to the element of interest being one of the terminal nodes; each of the parse tree nodes has an associated linguistic identifier; said extracting includes traversing the parse tree along a traversal path starting at the particular terminal node corresponding to the element of interest and ending at the root node; and the syntactic information includes a sequence of linguistic identifiers associated with respective nodes of the traversal path in order of traversal.

7. A method for identifying and extracting text from an electronic document, the method comprising: receiving, at a text identifier and extractor, the electronic document; generating a stream of text tokens representing a plurality of lines of text of the electronic document; matching a pattern to a portion of the stream of text tokens, the pattern including an ordered sequence of a plurality of pattern elements representing the plurality of lines of text, where each pattern element of the plurality of pattern elements describes at least one text token; matching a text token in the stream of text tokens to a pattern element and continuing to consume text tokens from the stream of text tokens until a subsequent text token in the stream of text tokens is matched to a subsequent pattern element having a required attribute, wherein the pattern element and the subsequent pattern element belong to the same pattern; and outputting the text in accordance with the matched pattern, wherein the electronic document is a transcript or certificate.

7. A method for identifying and extracting text from an electronic document, the method comprising: receiving, at a text identifier and extractor, the electronic document; generating a stream of text tokens representing a plurality of lines of text of the electronic document; matching a pattern to a portion of the stream of text tokens, the pattern including an ordered sequence of a plurality of pattern elements representing the plurality of lines of text, where each pattern element of the plurality of pattern elements describes at least one text token; matching a text token in the stream of text tokens to a pattern element and continuing to consume text tokens from the stream of text tokens until a subsequent text token in the stream of text tokens is matched to a subsequent pattern element having a required attribute, wherein the pattern element and the subsequent pattern element belong to the same pattern; and outputting the text in accordance with the matched pattern, wherein the electronic document is a transcript or certificate. 10. The method of claim 7 , further comprising at least one pattern element group, wherein the pattern element group comprises a plurality of pattern elements.

1. A computer-implemented method in an attribute-based access control policy (ABAC) transformer of transforming an ABAC policy to facilitate evaluation in a policy decision point (PDP) of an access request against the ABAC policy, wherein the PDP is communicatively coupled over a communication link to at least one remote attribute source (RAS), and wherein the ABAC policy comprises hierarchically ordered functional expressions, one or more of the functional expressions having at least one other functional expression and/or at least one attribute as a subordinate, the value of each attribute being either locally available at the PDP or remotely available in response to a query submitted from the PDP to one of said at least one RAS, the method comprising: a processor in the ABAC policy transformer identifying, in the ABAC policy, a first functional expression comprising at least one mathematical operator; the processor in the ABAC policy transformer forming, on the basis of a sub-hierarchy having said first functional expression as its hierarch, a remote query intended for a first RAS in such manner that output data from execution of the remote query correspond to the outcome of an evaluation of the first functional expression; and the processor in the ABAC policy transformer transforming the ABAC policy by replacing the sub-hierarchy by a second functional expression representing the remote query.

1. A computer-implemented method in an attribute-based access control policy (ABAC) transformer of transforming an ABAC policy to facilitate evaluation in a policy decision point (PDP) of an access request against the ABAC policy, wherein the PDP is communicatively coupled over a communication link to at least one remote attribute source (RAS), and wherein the ABAC policy comprises hierarchically ordered functional expressions, one or more of the functional expressions having at least one other functional expression and/or at least one attribute as a subordinate, the value of each attribute being either locally available at the PDP or remotely available in response to a query submitted from the PDP to one of said at least one RAS, the method comprising: a processor in the ABAC policy transformer identifying, in the ABAC policy, a first functional expression comprising at least one mathematical operator; the processor in the ABAC policy transformer forming, on the basis of a sub-hierarchy having said first functional expression as its hierarch, a remote query intended for a first RAS in such manner that output data from execution of the remote query correspond to the outcome of an evaluation of the first functional expression; and the processor in the ABAC policy transformer transforming the ABAC policy by replacing the sub-hierarchy by a second functional expression representing the remote query. 8. The method of claim 1 , wherein said forming the remote query by the processor in the ABAC policy transformer includes the processor in the ABAC policy transformer determining a type of the RAS and selecting a language in which to form the remote query in accordance herewith.

1. One or more computer-readable memories having stored thereon executable instructions to perform a method of facilitating access to a resource, the method comprising: abducing a first set of one or more assertions from information that comprises an access request for a first principal to access the resource, a system that performs said abducing not having in possession said first set of one or more assertions, said abducing being performed by acts comprising: receiving a first answer set and a second answer set, said first answer set comprising said first set of one or more assertions, said second answer set comprising a second set of one or more assertions, said first set of said one or more assertions and said second set of said one or more assertions each satisfying a condition that either said first set of one or more assertions or said second set of one or more assertions will, when presented to a guard of the resource, cause said guard to find that a query evaluates to true under a policy implemented by said guard; and determining that said first answer set is not subsumed by said second answer set; receiving a template that specifies said first set of one or more assertions; obtaining a first token that satisfies a first one of said first set of one or more assertions; presenting, to said guard, (a) a set of one or more tokens that comprises said first token, and (b) said access request; receiving access to said resource from said guard; and accessing said resource.

1. One or more computer-readable memories having stored thereon executable instructions to perform a method of facilitating access to a resource, the method comprising: abducing a first set of one or more assertions from information that comprises an access request for a first principal to access the resource, a system that performs said abducing not having in possession said first set of one or more assertions, said abducing being performed by acts comprising: receiving a first answer set and a second answer set, said first answer set comprising said first set of one or more assertions, said second answer set comprising a second set of one or more assertions, said first set of said one or more assertions and said second set of said one or more assertions each satisfying a condition that either said first set of one or more assertions or said second set of one or more assertions will, when presented to a guard of the resource, cause said guard to find that a query evaluates to true under a policy implemented by said guard; and determining that said first answer set is not subsumed by said second answer set; receiving a template that specifies said first set of one or more assertions; obtaining a first token that satisfies a first one of said first set of one or more assertions; presenting, to said guard, (a) a set of one or more tokens that comprises said first token, and (b) said access request; receiving access to said resource from said guard; and accessing said resource. 6. The one or more computer-readable memories of claim 1 , wherein said obtaining comprises: determining, under a rule, that generating said first token on behalf of a second principal is acceptable; generating said first token; and signing said first token by, or on behalf of, said second principal.

4. The method of claim 3 , wherein a story attribute is selected from a group consisting of: a story type, story content, a type of connection between the viewing user and a user associated with the story, the user associated with the story, a time associated with the story, and any combination thereof.

4. The method of claim 3 , wherein a story attribute is selected from a group consisting of: a story type, story content, a type of connection between the viewing user and a user associated with the story, the user associated with the story, a time associated with the story, and any combination thereof. 5. The method of claim 4 , wherein the story type is selected from a group consisting of: a link, a photo, a video, an event, a comment, a note, a status updates, and any combination thereof.

1. A method, comprising: detecting, by a computer system, receipt of one or more text-based electronic communications; identifying, by the computer system and in the one or more text-based electronic communications, first data of interest, the first data of interest comprising a predetermined characteristic, the computer system associated with sending the one or more text-based electronic communications; extracting, by the computer system, a plurality of instances of the identified first data of interest from a plurality of the text-based electronic communications sent to a plurality of recipients, wherein each text-based electronic communication includes first data of interest with a common predetermined characteristic, and wherein each text-based electronic communication includes first data or interest differing from first data of interest of other text-based electronic communications; obtaining, by the computer system and for each identified first data of interest, an initial first set of associated data that is associated with the extracted first data of interest, each initial first set of associated data from at least one electronic resource; and displaying, by the computer system and independently of the text-based electronic communications and the at least one electronic resource, a plurality of instances of the extracted first data of interest, each instance of the extracted first data of interest displayed with the corresponding obtained initial first set of associated data.

1. A method, comprising: detecting, by a computer system, receipt of one or more text-based electronic communications; identifying, by the computer system and in the one or more text-based electronic communications, first data of interest, the first data of interest comprising a predetermined characteristic, the computer system associated with sending the one or more text-based electronic communications; extracting, by the computer system, a plurality of instances of the identified first data of interest from a plurality of the text-based electronic communications sent to a plurality of recipients, wherein each text-based electronic communication includes first data of interest with a common predetermined characteristic, and wherein each text-based electronic communication includes first data or interest differing from first data of interest of other text-based electronic communications; obtaining, by the computer system and for each identified first data of interest, an initial first set of associated data that is associated with the extracted first data of interest, each initial first set of associated data from at least one electronic resource; and displaying, by the computer system and independently of the text-based electronic communications and the at least one electronic resource, a plurality of instances of the extracted first data of interest, each instance of the extracted first data of interest displayed with the corresponding obtained initial first set of associated data. 13. The method of claim 1 , further comprising: receiving, by the computer system, a notification of an update to the initial set of associated data and obtaining, in response to receiving the notification, an additional set of associated data related to the first data of interest in response to receiving the update; and displaying the additional set of associated data in place of the initial set of associated data.

1. A system for encoding video content, comprising: at least one memory that stores computer executable components; and at least one processor that executes the following computer executable components stored in the at least one memory: a near-end encoder component configured to encode the video content; and a near-end dictionary management component configured to identify one or more dictionaries that the near-end encoder component has in common with a far-end decoder component to synchronize common dictionaries between the near-end encoder component and the far-end decoder component, based at least in part on respective unique identifiers associated with respective dictionaries, to facilitate the encoding of the video content, wherein the one or more dictionaries are sparse coding dictionaries, wherein the near-end dictionary management component is further configured to generate a unique identifier, of the respective unique identifiers, wherein the unique identifier is configured to contain a first tier comprising respective terminal addresses of a near-end terminal associated with the near-end encoder component and a far-end terminal associated with the far-end decoder component, and a second tier comprising a dictionary sub-identifier assigned to a dictionary associated with the unique identifier, to facilitate distinguishing the dictionary from other dictionaries associated with the near-end terminal and the far-end terminal.

1. A system for encoding video content, comprising: at least one memory that stores computer executable components; and at least one processor that executes the following computer executable components stored in the at least one memory: a near-end encoder component configured to encode the video content; and a near-end dictionary management component configured to identify one or more dictionaries that the near-end encoder component has in common with a far-end decoder component to synchronize common dictionaries between the near-end encoder component and the far-end decoder component, based at least in part on respective unique identifiers associated with respective dictionaries, to facilitate the encoding of the video content, wherein the one or more dictionaries are sparse coding dictionaries, wherein the near-end dictionary management component is further configured to generate a unique identifier, of the respective unique identifiers, wherein the unique identifier is configured to contain a first tier comprising respective terminal addresses of a near-end terminal associated with the near-end encoder component and a far-end terminal associated with the far-end decoder component, and a second tier comprising a dictionary sub-identifier assigned to a dictionary associated with the unique identifier, to facilitate distinguishing the dictionary from other dictionaries associated with the near-end terminal and the far-end terminal. 2. The system of claim 1 , wherein the near-end dictionary management component is further configured to transmit a first subset of unique identifiers associated with a first subset of dictionaries maintained by the near-end encoder component to the far-end decoder component to facilitate the synchronization of the common dictionaries.

19. A system comprising: memory; one or more processors coupled to the memory and configured to perform operations comprising: ranking indexed queries based on a respective query rank of each indexed query, the query rank calculated based on a frequency of occurrence of the indexed query and a user satisfaction score of the indexed query, the indexed queries including highly-ranked queries and nearby queries that are queries that have a statistically significant probability of being revised to one of the highly-ranked queries, wherein the user satisfaction score of a particular indexed query is determined from estimates of lengths of clicks on search results, wherein the estimates of the lengths of clicks on search results are based on a duration of time from a selection of a search result for the particular indexed query to a subsequent selection of another search result; calculating a respective revision score for each indexed query as a function of a revision probability of a first query and the respective query rank for the particular indexed query, the revision probability based on at least one of a semantic similarity or syntactic similarity between the first query and the particular indexed query; selecting one of the indexed queries as an alternative query to the first query based on the respective revision score of the selected indexed query; and providing the selected query as a suggested revision for the first query.

19. A system comprising: memory; one or more processors coupled to the memory and configured to perform operations comprising: ranking indexed queries based on a respective query rank of each indexed query, the query rank calculated based on a frequency of occurrence of the indexed query and a user satisfaction score of the indexed query, the indexed queries including highly-ranked queries and nearby queries that are queries that have a statistically significant probability of being revised to one of the highly-ranked queries, wherein the user satisfaction score of a particular indexed query is determined from estimates of lengths of clicks on search results, wherein the estimates of the lengths of clicks on search results are based on a duration of time from a selection of a search result for the particular indexed query to a subsequent selection of another search result; calculating a respective revision score for each indexed query as a function of a revision probability of a first query and the respective query rank for the particular indexed query, the revision probability based on at least one of a semantic similarity or syntactic similarity between the first query and the particular indexed query; selecting one of the indexed queries as an alternative query to the first query based on the respective revision score of the selected indexed query; and providing the selected query as a suggested revision for the first query. 20. The system of claim 19 in which the first query is a query revision of an earlier query.

20. The apparatus according to claim 18 , where the at least one input image comprises a plurality of input images, and, where in being programmed to extract the feature set associated with hand gesture detection and hand pose inference from the at least one input image received via the camera, the processor is programmed to: track a region of interest (ROI) between subsequent video frames of the plurality of input images as a flock of features; trigger scale invariant feature transforms (SIFT) feature extraction; and calculate an optical flow path of the flock of features.

20. The apparatus according to claim 18 , where the at least one input image comprises a plurality of input images, and, where in being programmed to extract the feature set associated with hand gesture detection and hand pose inference from the at least one input image received via the camera, the processor is programmed to: track a region of interest (ROI) between subsequent video frames of the plurality of input images as a flock of features; trigger scale invariant feature transforms (SIFT) feature extraction; and calculate an optical flow path of the flock of features. 22. The apparatus according to claim 20 , where, in being programmed to calculate the optical flow path of the flock of features, the processor is programmed to apply at least one constraint on each of the flock of features such that the flock of features maintain a minimum distance from each other.

1. A computer system comprising: a memory; a plurality of answers stored in the memory; a correlator that, for each respective single answer, extracts a plurality of related queries corresponding to the respective single answer, at least two of the related queries corresponding to the respective single answer being different from one another; a classifier that assigns a category to each of the related queries such that there are a plurality of categories assigned to the related queries corresponding to the respective single answer, wherein one of the plurality of categories is a spam category and answers in the spam category are not downloaded; and a statistical tool that determines the relevance of the plurality of categories relative to one another and assigns at least one assigned category, of the categories assigned to the related queries corresponding to the respective single answer, to the respective single answer.

1. A computer system comprising: a memory; a plurality of answers stored in the memory; a correlator that, for each respective single answer, extracts a plurality of related queries corresponding to the respective single answer, at least two of the related queries corresponding to the respective single answer being different from one another; a classifier that assigns a category to each of the related queries such that there are a plurality of categories assigned to the related queries corresponding to the respective single answer, wherein one of the plurality of categories is a spam category and answers in the spam category are not downloaded; and a statistical tool that determines the relevance of the plurality of categories relative to one another and assigns at least one assigned category, of the categories assigned to the related queries corresponding to the respective single answer, to the respective single answer. 2. The computer system of claim 1 , wherein at least one data query is extracted from each of the answers, the correlator matching the data query with the related queries.

1. A method implemented by a computing device, the method comprising: obtaining a description of a parametric type represented in a binary standard of a programming model; and generating an interface identifier based at least in part on the obtained description of the parametric type, the generating performed to automatically follow revision rules such that when a revision is made to an interface the description that is used as a basis to generate the interface identifier is revised automatically.

1. A method implemented by a computing device, the method comprising: obtaining a description of a parametric type represented in a binary standard of a programming model; and generating an interface identifier based at least in part on the obtained description of the parametric type, the generating performed to automatically follow revision rules such that when a revision is made to an interface the description that is used as a basis to generate the interface identifier is revised automatically. 11. A method as described in claim 1 , wherein the interface identifier is generated by employing an algorithm that is operable such that: when a parameterized type is instantiated twice with matching arguments, both instantiations are assigned a matching interface and a matching interface identifier; if two different parameterized types are instantiated with matching arguments, it is statistically unlikely that both instantiations are assigned matching interface identifiers; and if a parameterized type is instantiated twice with different arguments, it is statistically unlikely that both instantiations are assigned matching interface identifiers.

1. A method for dynamically binding a user interface to information stored in a data source, comprising: displaying a user interface, wherein the user interface is operable to display information in a web page, wherein the information is stored in a first data source on a business object, collect additional information from a user, and store the additional information in the first data source on the business object; providing a data binding tag that defines a rendering boundary within the web page for rendering the information, and rules to be applied when the information is rendered, wherein the data binding tag includes a plurality of attributes; specifying, by the data binding tag, a first action which includes reading or updating the information stored in the first data source, wherein at least one of the attributes is associated with the first action; specifying the first data source associated with the first action using a script; and rendering each item in the first data source on the web page in the user interface with a markup language according to the boundary and the rules defined by the data binding tag and based on the first action, including evaluation of the script.

1. A method for dynamically binding a user interface to information stored in a data source, comprising: displaying a user interface, wherein the user interface is operable to display information in a web page, wherein the information is stored in a first data source on a business object, collect additional information from a user, and store the additional information in the first data source on the business object; providing a data binding tag that defines a rendering boundary within the web page for rendering the information, and rules to be applied when the information is rendered, wherein the data binding tag includes a plurality of attributes; specifying, by the data binding tag, a first action which includes reading or updating the information stored in the first data source, wherein at least one of the attributes is associated with the first action; specifying the first data source associated with the first action using a script; and rendering each item in the first data source on the web page in the user interface with a markup language according to the boundary and the rules defined by the data binding tag and based on the first action, including evaluation of the script. 13. The method of claim 1 wherein the data binding tag includes a repeater element wherein data associated with the repeater element is rendered multiple times according to the repeater element's lifecycle.

1. A computer-implemented method, comprising: on a first device, receiving information from a plurality of second devices located within a communication range of the first device, the information comprising a plurality of identifiers, each identifier associated with one of the second devices; creating a digital file on the first device using a digital recording device comprised within the first device, wherein the digital file comprises at least one of audio content, image content, or video content; providing, on the first device and after creating the digital file, a plurality of user interface items for display in association with at least a portion of the digital file, each user interface item having a label and corresponding to one of the second devices, each label corresponding to an identifier of the respective second device and including an identifier of a user of the second device; receiving a selection of one of the user interface items; and associating the label of the selected user interface item with the digital file, including storing the label as metadata of the digital file or storing the digital file in a file directory named using the label.

1. A computer-implemented method, comprising: on a first device, receiving information from a plurality of second devices located within a communication range of the first device, the information comprising a plurality of identifiers, each identifier associated with one of the second devices; creating a digital file on the first device using a digital recording device comprised within the first device, wherein the digital file comprises at least one of audio content, image content, or video content; providing, on the first device and after creating the digital file, a plurality of user interface items for display in association with at least a portion of the digital file, each user interface item having a label and corresponding to one of the second devices, each label corresponding to an identifier of the respective second device and including an identifier of a user of the second device; receiving a selection of one of the user interface items; and associating the label of the selected user interface item with the digital file, including storing the label as metadata of the digital file or storing the digital file in a file directory named using the label. 4. The method of claim 1 , wherein providing the user interface items for display comprises displaying a description of the corresponding second device as the label of each user interface item.

25. The non-transitory computer-readable storage medium of claim 23 , wherein detecting an initial image of an object in a particular initial frame comprises: selecting a plurality of bounding boxes from the initial frame; and selecting an image contained in a particular bounding box of the plurality of bounding boxes as an initial image of the object.

25. The non-transitory computer-readable storage medium of claim 23 , wherein detecting an initial image of an object in a particular initial frame comprises: selecting a plurality of bounding boxes from the initial frame; and selecting an image contained in a particular bounding box of the plurality of bounding boxes as an initial image of the object. 26. The non-transitory computer-readable storage medium of claim 25 , wherein selecting the image contained in the particular bounding box of the plurality of bounding boxes as an initial image of the object comprises: applying an object detector to each of the plurality of bounding boxes to generate a respective detection score for each of the bounding boxes; and selecting a highest-scoring bounding box of the plurality of bounding boxes as containing an initial image of the object.

9. The method of claim 8 further comprising: determining if sufficient volatile memory is available to store the join mappings; if sufficient memory exists, storing the join mapping in volatile memory; and if sufficient memory does not exist, deleting existing join mappings and storing the join mapping.

9. The method of claim 8 further comprising: determining if sufficient volatile memory is available to store the join mappings; if sufficient memory exists, storing the join mapping in volatile memory; and if sufficient memory does not exist, deleting existing join mappings and storing the join mapping. 10. The method of claim 9 wherein deleting existing join mappings includes deleting the least recently used join mappings.

9. A system comprising: a presence server, the presence server operable to determine a presence status for two or more individual persons associated with a group and operable to provide the presence status as individual presence information; a rules engine in communication with the presence server, the rules engine operable to receive the individual presence information for each of the two or more individual persons, operable to determine a group presence based on the received individual presence information, and operable to publish the group presence, wherein the rules engine determines the group presence based on a rule created from a business logic; a rules database in communication with the rules engine, the rules database operable to store the rule; an enterprise directory, the enterprise directory operable to provide the business logic; a group definition component in communication with the rules database, the group definition component operable to define the group based on the business logic; and a rules definition component in communication with the rules database and the group definition component, the rules definition component operable to define the rule based on at least one of the group and the business logic.

9. A system comprising: a presence server, the presence server operable to determine a presence status for two or more individual persons associated with a group and operable to provide the presence status as individual presence information; a rules engine in communication with the presence server, the rules engine operable to receive the individual presence information for each of the two or more individual persons, operable to determine a group presence based on the received individual presence information, and operable to publish the group presence, wherein the rules engine determines the group presence based on a rule created from a business logic; a rules database in communication with the rules engine, the rules database operable to store the rule; an enterprise directory, the enterprise directory operable to provide the business logic; a group definition component in communication with the rules database, the group definition component operable to define the group based on the business logic; and a rules definition component in communication with the rules database and the group definition component, the rules definition component operable to define the rule based on at least one of the group and the business logic. 13. The system as defined in claim 9 , further comprising an LDAP database in communication with the rules engine, the LDAP database operable to store information about the two or more individual persons, wherein the information comprises attributes associated with the two or more individual persons, wherein the rules engine retrieves identities for the two or more individual persons based on attributes associated with the membership of the group.

5. A system comprising: a user interface configured to enable the user to select and provide content items and to select privacy settings and/or sharing and publication setting for each content item and/or different types of contents selected and provided via the interface to control interactions of other users of the network related or connected to the user with each content item and/or multiple different types of content, a content repository for receiving a content item and/or one or more types with an associated privacy setting and/or sharing and publication setting identifying one or more connections and/or set of users and/or determined users and/or subscribers allowed to access the content item and/or one or more types of content, a content publishing module for publishing the content item and/or one or more types of content into a communication channel of the network and providing or presenting or publishing the content item to one or more connections and/or set of users and/or determined users and/or subscribers via the communication channel, wherein the content item is presented or provided or published based on the privacy setting and/or sharing and publication setting associated with the content item and/or one or more types of content, and enable receivers including one or more connections and/or set of users and/or determined users and/or subscribers to manage and/or access the content item and/or one or more types of content, where accessibility to the one or more connections and/or set of users and/or determined users and/or subscribers is determined by the privacy settings and/or sharing and publication setting selected by the user; and wherein the content publishing module is configured to, responsive to receiving a selection and sharing and publication setting to be associated with the content item from locking the content item from being published a communication channel accessible to one or more connections, exclude the content item from the communication channel accessible to the one or more connections.

5. A system comprising: a user interface configured to enable the user to select and provide content items and to select privacy settings and/or sharing and publication setting for each content item and/or different types of contents selected and provided via the interface to control interactions of other users of the network related or connected to the user with each content item and/or multiple different types of content, a content repository for receiving a content item and/or one or more types with an associated privacy setting and/or sharing and publication setting identifying one or more connections and/or set of users and/or determined users and/or subscribers allowed to access the content item and/or one or more types of content, a content publishing module for publishing the content item and/or one or more types of content into a communication channel of the network and providing or presenting or publishing the content item to one or more connections and/or set of users and/or determined users and/or subscribers via the communication channel, wherein the content item is presented or provided or published based on the privacy setting and/or sharing and publication setting associated with the content item and/or one or more types of content, and enable receivers including one or more connections and/or set of users and/or determined users and/or subscribers to manage and/or access the content item and/or one or more types of content, where accessibility to the one or more connections and/or set of users and/or determined users and/or subscribers is determined by the privacy settings and/or sharing and publication setting selected by the user; and wherein the content publishing module is configured to, responsive to receiving a selection and sharing and publication setting to be associated with the content item from locking the content item from being published a communication channel accessible to one or more connections, exclude the content item from the communication channel accessible to the one or more connections. 30. The system of claim 5 , wherein the privacy setting and sharing and publication setting identifies all of the user's connections on the network.

1. A computer-implemented method for context-sensitive searching of fields of a data repository using multiple levels of term expansion, comprising configuring one or more computer processors to perform an operation comprising: receiving, from a user, a relational database query containing a plurality of conditions, wherein at least one condition is a condition for searching at least one field of the data repository, wherein the at least one condition includes at least one base search term providing a keyword to search for in an unstructured text field of the data repository; providing a plurality of sets of expanded search terms for the base search term, each set corresponding to a different level of expansion of a plurality of levels of expansions ranging from a lowest level of expansion to a highest level of expansion, and each set comprising all expanded search terms from any lower level of expansion; obtaining one or more parameters associated with the base search term, wherein the one or more parameters associated with the base search term comprise a user-specified level of expansion selected from the defined plurality of levels of expansions and further include at least a credential associated with the user and a role associated with the user; obtaining, based at least in part on the user-specified level of expansion and the credential associated with the user and the role associated with the user, one or more expanded search terms, wherein obtaining the one or more expanded search terms comprises selecting a set of expanded search terms from the plurality of sets of expanded search terms; and prior to executing the relational database query, modifying the relational-database query to contain one or more additional conditions based on the one or more expanded search terms and by operation of the one or more computer processors.

1. A computer-implemented method for context-sensitive searching of fields of a data repository using multiple levels of term expansion, comprising configuring one or more computer processors to perform an operation comprising: receiving, from a user, a relational database query containing a plurality of conditions, wherein at least one condition is a condition for searching at least one field of the data repository, wherein the at least one condition includes at least one base search term providing a keyword to search for in an unstructured text field of the data repository; providing a plurality of sets of expanded search terms for the base search term, each set corresponding to a different level of expansion of a plurality of levels of expansions ranging from a lowest level of expansion to a highest level of expansion, and each set comprising all expanded search terms from any lower level of expansion; obtaining one or more parameters associated with the base search term, wherein the one or more parameters associated with the base search term comprise a user-specified level of expansion selected from the defined plurality of levels of expansions and further include at least a credential associated with the user and a role associated with the user; obtaining, based at least in part on the user-specified level of expansion and the credential associated with the user and the role associated with the user, one or more expanded search terms, wherein obtaining the one or more expanded search terms comprises selecting a set of expanded search terms from the plurality of sets of expanded search terms; and prior to executing the relational database query, modifying the relational-database query to contain one or more additional conditions based on the one or more expanded search terms and by operation of the one or more computer processors. 3. The method of claim 1 , wherein the one or more parameters associated with the base search term comprise a name of a table containing the at least one field.

9. The method of claim 8 , further comprising, in response to the determining the at least one most relevant aspect name for the category, sorting the determined demand scores for the at least one most relevant aspect name to determine at least one most relevant aspect value for the at least one relevant aspect name.

9. The method of claim 8 , further comprising, in response to the determining the at least one most relevant aspect name for the category, sorting the determined demand scores for the at least one most relevant aspect name to determine at least one most relevant aspect value for the at least one relevant aspect name. 11. The method of claim 9 , further comprising determining a listing to promote based on the listing containing the at least one most relevant aspect value for the at least one most relevant aspect name.

7. A computer implemented method to determine semantic information of a plurality of business applications using a computer, the method comprising: receiving a selection of a first user interface element from a first user interface of a first business application; generating a prompt to provide an option to a user to select a second user interface element from a second user interface of a second business application as analogous to the first user interface element, wherein the first user interface and the second user interface are two separate user interfaces on the computer and the first user interface is generated from the first business application and the second user interface is generated from the second business application and wherein the received selection of the first user interface element of the first business application comprises receiving a selection of a predetermined key combination for determining semantic information of the first business application and the second business application; when a selection of the second user interface element is received as analogous to the first user interface element through the generated prompt, perform the operations comprising: identifying data processing paths to a business object field and at least one corresponding database table field associated with the selected first user interface element and the second user interface element; and grouping the identified data processing paths associated with the first user interface element and the second user interface element to determine the semantic information of the first business application and the second business application; and when the selection is not received through the generated prompt, enable tagging the user interface elements of the first business application and the second business application with additional business information.

7. A computer implemented method to determine semantic information of a plurality of business applications using a computer, the method comprising: receiving a selection of a first user interface element from a first user interface of a first business application; generating a prompt to provide an option to a user to select a second user interface element from a second user interface of a second business application as analogous to the first user interface element, wherein the first user interface and the second user interface are two separate user interfaces on the computer and the first user interface is generated from the first business application and the second user interface is generated from the second business application and wherein the received selection of the first user interface element of the first business application comprises receiving a selection of a predetermined key combination for determining semantic information of the first business application and the second business application; when a selection of the second user interface element is received as analogous to the first user interface element through the generated prompt, perform the operations comprising: identifying data processing paths to a business object field and at least one corresponding database table field associated with the selected first user interface element and the second user interface element; and grouping the identified data processing paths associated with the first user interface element and the second user interface element to determine the semantic information of the first business application and the second business application; and when the selection is not received through the generated prompt, enable tagging the user interface elements of the first business application and the second business application with additional business information. 18. The computer implemented method of claim 7 , wherein the first business application and the second business application is executed by its corresponding application server and wherein an application server comprises one of a metadata repository based server and an application logic based server.

2. The method of claim 1 , further comprising: specializing the secondary ASR recognizer by using an adaptation grammar comprising structure and content appropriate to a putative span type, as determined by NLU processing.

2. The method of claim 1 , further comprising: specializing the secondary ASR recognizer by using an adaptation grammar comprising structure and content appropriate to a putative span type, as determined by NLU processing. 3. The method of claim 2 , further comprising: deriving proper name entities defining structure and content of the adaptation grammar used to specialize the secondary ASR recognizer from any of: (a) business names resulting from a search; (b) business names, personal contact names, or both retrieved from any of a personal phone book, a personal calendar, or both; (c) contents of a music library or personal music storage device, including artist names, song names, album names, and genre names; (d) contents of a video library or personal video storage device, including actor names, director names, and genre names; (e) word sequences that the user has identified as personally significant; and (f) any combination of the foregoing.

15. A computer program product in accordance with claim 14 , wherein program instructions to determine a portion of the text to which the comment is most relevant comprise program instructions to assign scores to segments of the text indicating a relevance of the comment to each segment, and program instructions to identify a segment with the highest score.

15. A computer program product in accordance with claim 14 , wherein program instructions to determine a portion of the text to which the comment is most relevant comprise program instructions to assign scores to segments of the text indicating a relevance of the comment to each segment, and program instructions to identify a segment with the highest score. 16. A computer program product in accordance with claim 15 , wherein program instructions to assign scores to segments of the text indicating a relevance of the comment to each segment comprise program instructions to apply natural language processing techniques to identify semantically related words and phrases appearing in the comment and the segment, respectively.

4. A method, comprising: retrieving, using at least one computing device, over a network, a plurality of content items from a plurality of content sources; identifying, using the at least one computing device, a plurality of advertisements embedded in the plurality of content items; identifying, using the at least one computing device, keywords of the plurality of advertisements; determining, sing the at least one computing device, similarity rankings of the plurality of advertisements based at least in part on the keywords; selecting, using the at least one computing device, a keyword based at least in part on the similarity rankings, where a historical price from a keyword seller for the keyword is less, by a defined amount, than a historical price from a click buyer; and purchasing, using the at least one computing device, the keyword from the keyword seller for placement of a first linked advertisement, the first linked advertisement configured to be selected to cause the presentation of a second advertisement of the click buyer.

4. A method, comprising: retrieving, using at least one computing device, over a network, a plurality of content items from a plurality of content sources; identifying, using the at least one computing device, a plurality of advertisements embedded in the plurality of content items; identifying, using the at least one computing device, keywords of the plurality of advertisements; determining, sing the at least one computing device, similarity rankings of the plurality of advertisements based at least in part on the keywords; selecting, using the at least one computing device, a keyword based at least in part on the similarity rankings, where a historical price from a keyword seller for the keyword is less, by a defined amount, than a historical price from a click buyer; and purchasing, using the at least one computing device, the keyword from the keyword seller for placement of a first linked advertisement, the first linked advertisement configured to be selected to cause the presentation of a second advertisement of the click buyer. 11. The method of claim 4 , further comprising: counting a number of advertisements within a specified similarity ranking to select the keyword.

1. A computer implemented system that facilitates clustering of search results, comprising: a memory having stored therein computer-executable instructions configured to implement the clustering system, including: an input component that receives search results, wherein the search results are a ranked list of titles and snippets associated with documents; an analysis component that: utilizes a frequent itemset algorithm to extract keywords from the search results and identifies frequently occurring words as keywords, with keywords occurring in titles being weighted more heavily than keywords occurring in snippets, calculates properties for each keyword, wherein the properties include phrase frequency and inverted document frequency, phrase length, intra-cluster similarity, cluster entropy and phrase independence, applies a regression model learned from training data that is collected in advance to combine the properties into a salience score for each of the keywords, ranks the keywords in descending order according to their associated salience scores and selects the highest ranked keywords as salient keywords; and a clustering component that: generates one or more candidate clusters of the search results according to the saliency score, wherein the salient keywords are selected as names of the candidate clusters, the names of the candidate clusters being phrases when the salient keywords are merged with other salient keywords, merges the candidate clusters into one or more final clusters, merges a first cluster and a second cluster into a third cluster, when overlap of the first and second clusters exceeds a predetermined threshold, adjusts cluster names of the one or more final clusters to generate a new cluster name for the third cluster, and outputs the search results as a ranked list of associated documents.

1. A computer implemented system that facilitates clustering of search results, comprising: a memory having stored therein computer-executable instructions configured to implement the clustering system, including: an input component that receives search results, wherein the search results are a ranked list of titles and snippets associated with documents; an analysis component that: utilizes a frequent itemset algorithm to extract keywords from the search results and identifies frequently occurring words as keywords, with keywords occurring in titles being weighted more heavily than keywords occurring in snippets, calculates properties for each keyword, wherein the properties include phrase frequency and inverted document frequency, phrase length, intra-cluster similarity, cluster entropy and phrase independence, applies a regression model learned from training data that is collected in advance to combine the properties into a salience score for each of the keywords, ranks the keywords in descending order according to their associated salience scores and selects the highest ranked keywords as salient keywords; and a clustering component that: generates one or more candidate clusters of the search results according to the saliency score, wherein the salient keywords are selected as names of the candidate clusters, the names of the candidate clusters being phrases when the salient keywords are merged with other salient keywords, merges the candidate clusters into one or more final clusters, merges a first cluster and a second cluster into a third cluster, when overlap of the first and second clusters exceeds a predetermined threshold, adjusts cluster names of the one or more final clusters to generate a new cluster name for the third cluster, and outputs the search results as a ranked list of associated documents. 3. The system of claim 1 , wherein a document is assigned to the final cluster based upon its similarity to relevant keywords of the final cluster.

1. A method comprising, by one or more processors associated with one or more computing devices: accessing a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the plurality of nodes corresponding to a plurality of users associated with an online social network, respectively; identifying a plurality of non-overlapping clusters in the social graph using graph clustering, each cluster comprising a discrete set of nodes from the plurality of nodes; providing a treatment to at least a first set of users and a second set of users, the first and second sets of users corresponding to a first set of clusters and a second set of clusters of the plurality of clusters, respectively, the first set of clusters being discrete from the second set of clusters; and determining, for each of at least the first and second sets of users, a treatment effect of the treatment on the users of the set of users based on a network exposure to the treatment for each user, wherein, for each respective cluster, the network exposure of the nodes in the cluster is absolute k-neighborhood exposure, absolute k-core exposure, fractional q-neighborhood exposure, or fractional q-core exposure.

1. A method comprising, by one or more processors associated with one or more computing devices: accessing a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the plurality of nodes corresponding to a plurality of users associated with an online social network, respectively; identifying a plurality of non-overlapping clusters in the social graph using graph clustering, each cluster comprising a discrete set of nodes from the plurality of nodes; providing a treatment to at least a first set of users and a second set of users, the first and second sets of users corresponding to a first set of clusters and a second set of clusters of the plurality of clusters, respectively, the first set of clusters being discrete from the second set of clusters; and determining, for each of at least the first and second sets of users, a treatment effect of the treatment on the users of the set of users based on a network exposure to the treatment for each user, wherein, for each respective cluster, the network exposure of the nodes in the cluster is absolute k-neighborhood exposure, absolute k-core exposure, fractional q-neighborhood exposure, or fractional q-core exposure. 14. The method of claim 1 , wherein identifying the plurality of clusters comprises identifying clusters such that a threshold number of nodes in each cluster is network exposed with respect to the other nodes in the cluster.

17. A non-transitory computer-readable medium having contents that, when executed, cause a computing system to provide information about a plurality of merchants that each sell goods and/or services, by: for each of the plurality of merchants, wherein each merchant does not have an online presence via web sites or web pages specific to each merchant, receiving a plurality of keywords that describe goods or services sold by the merchant, wherein the plurality of keywords comprises hundreds of keywords, descriptors, classification, or advertisement words; and associating each of the plurality of keywords with the merchant and/or with at least one of the goods and/or services sold by the merchant; receiving a search request that includes a keyword and an indication of a location; automatically determining one or more of the plurality of merchants that sells a good and/or service that has an associated keyword that matches the keyword included in the search request, wherein the one or more of the determined plurality of merchants is classified in an off-line directory service only under business classifications that do not deal in the good and/or service, such that the merchant would not be identified in the off-line directory as dealing in the good and/or service were the off-line directory to be searched under business classifications associated with the good and/or service, thereby enabling the determining of one or more of the plurality of merchants proximately located to the indicated location that would not be otherwise found by searching the off-line directory service; transmitting information about the determined one or more merchants; and automatically adjusting the accuracy of the received plurality of keywords with the associated good and/or services of one of the plurality of merchants by only associating the received plurality of keywords with the associated good and/or services of the one of the plurality of merchants when received from an authorized user.

17. A non-transitory computer-readable medium having contents that, when executed, cause a computing system to provide information about a plurality of merchants that each sell goods and/or services, by: for each of the plurality of merchants, wherein each merchant does not have an online presence via web sites or web pages specific to each merchant, receiving a plurality of keywords that describe goods or services sold by the merchant, wherein the plurality of keywords comprises hundreds of keywords, descriptors, classification, or advertisement words; and associating each of the plurality of keywords with the merchant and/or with at least one of the goods and/or services sold by the merchant; receiving a search request that includes a keyword and an indication of a location; automatically determining one or more of the plurality of merchants that sells a good and/or service that has an associated keyword that matches the keyword included in the search request, wherein the one or more of the determined plurality of merchants is classified in an off-line directory service only under business classifications that do not deal in the good and/or service, such that the merchant would not be identified in the off-line directory as dealing in the good and/or service were the off-line directory to be searched under business classifications associated with the good and/or service, thereby enabling the determining of one or more of the plurality of merchants proximately located to the indicated location that would not be otherwise found by searching the off-line directory service; transmitting information about the determined one or more merchants; and automatically adjusting the accuracy of the received plurality of keywords with the associated good and/or services of one of the plurality of merchants by only associating the received plurality of keywords with the associated good and/or services of the one of the plurality of merchants when received from an authorized user. 18. The computer-readable medium of claim 17 wherein the computer-readable medium is a memory in the computing system, and wherein the contents are instructions that, when executed, cause the computing system to perform the method.

14. The system of claim 13 , wherein the travel-related contract resources comprise lodging contract resources.

14. The system of claim 13 , wherein the travel-related contract resources comprise lodging contract resources. 15. The system of claim 14 , wherein the multiple search dimensions comprise an amenity dimension.

1. One or more computer-readable memory comprising computer-executable instructions for parsing resource identifiers, the computer-executable instructions directed to steps comprising: intercepting, at a client computing device, a resource identifier being provided to a non-browser application program executing on the client computing device; identifying, at the client computing device, in response to the obtaining, a remote domain of a remote resource identified by the resource identifier; obtaining, from the remote resource, information regarding a customized parser associated with the remote domain; downloading the customized parser; executing, at the client computing device, the customized parser to parse the resource identifier to derive derivative information therefrom and presenting the derivative information to the non-browser application program instead of the resource identifier that was being provided to the non-browser application program.

1. One or more computer-readable memory comprising computer-executable instructions for parsing resource identifiers, the computer-executable instructions directed to steps comprising: intercepting, at a client computing device, a resource identifier being provided to a non-browser application program executing on the client computing device; identifying, at the client computing device, in response to the obtaining, a remote domain of a remote resource identified by the resource identifier; obtaining, from the remote resource, information regarding a customized parser associated with the remote domain; downloading the customized parser; executing, at the client computing device, the customized parser to parse the resource identifier to derive derivative information therefrom and presenting the derivative information to the non-browser application program instead of the resource identifier that was being provided to the non-browser application program. 4. The computer-readable memory of claim 1 , wherein the computer-executable instructions directed to the utilizing the customized parser comprise computer-executable instructions for providing the resource identifier to a network service, the network service being the customized parser.

3. The method of claim 2 , wherein the justifying passage score for each of the candidate passages and the answer score are final scores, and further comprising: producing one or more algebraic formulas indicating how each of the final scores was computed based on relation match features of the matching.

3. The method of claim 2 , wherein the justifying passage score for each of the candidate passages and the answer score are final scores, and further comprising: producing one or more algebraic formulas indicating how each of the final scores was computed based on relation match features of the matching. 4. The method of claim 3 , further comprising: finding assignments of a plurality of relation weights to the relation match features that minimize a loss function on the final scores with respect to the candidate answer.

16. The apparatus of claim 15 , wherein the means for identifying the set of quota cells further comprises means for determining an incomplete match to a quota cell in response to an edge of a quota cell vertex for the quota cell encountering a profile parameter value vertex representing a value of a profile parameter that is not determined for the panelist.

16. The apparatus of claim 15 , wherein the means for identifying the set of quota cells further comprises means for determining an incomplete match to a quota cell in response to an edge of a quota cell vertex for the quota cell encountering a profile parameter value vertex representing a value of a profile parameter that is not determined for the panelist. 17. The apparatus of claim 16 , further comprising means for eliminating all quota cells from the set that belong only to projects having at least one quota group not matched to at least one of the identified quota cells.

3. A computer implemented method of bifurcated document relevance scoring of documents in a document collection, the method comprising: storing an index of documents, the index comprising: for each of a plurality of phrases, a phrase posting list identifying the documents that contain the phrase, and for each identified document, a phrase relevance score for the phrase with respect to the document; receiving a search query of three or more words; determining a set of valid phrases in the search query by: decomposing, by at least one processor of a computer system, the query into a plurality of candidate phrasifications, including different groupings of words of the query, each candidate phrasification comprising a disjoint union of component phrases, and each component phrase including at least one word or related word of the query; scoring, by at least one of the processors of the computer system, at least two candidate phrasifications, wherein the candidate phrasifications include one or more component phrases, wherein the scoring is based on a probability of occurrence of each of the candidate phrasification's component phrases in a corpus of documents and is based on the number of component phrases constituting the candidate phrasification, wherein candidate phrasifications having relatively fewer component phrases are weighted higher than candidate phrasifications having relatively more component phrases; comparing, by at least one of the processors of the computer system, a score for each scored candidate phrasification to a threshold value; and selecting, by at least one of the processors of the computer system, at least one candidate phrasification, wherein the scores of each selected candidate phrasification exceed a threshold value and identifying the component phrase(s) of the selected candidate phrasification(s) as valid phrases for the search query; for each valid phrase for the search query, obtaining from the phrase posting list for the valid phrase the phrase relevance score for documents in which the valid phrase occurs; and for documents in which a valid phrase of the query occurs, scoring the document to produce a final relevance score using the phrase relevance scores for the document and based on the valid phrases of the search query.

3. A computer implemented method of bifurcated document relevance scoring of documents in a document collection, the method comprising: storing an index of documents, the index comprising: for each of a plurality of phrases, a phrase posting list identifying the documents that contain the phrase, and for each identified document, a phrase relevance score for the phrase with respect to the document; receiving a search query of three or more words; determining a set of valid phrases in the search query by: decomposing, by at least one processor of a computer system, the query into a plurality of candidate phrasifications, including different groupings of words of the query, each candidate phrasification comprising a disjoint union of component phrases, and each component phrase including at least one word or related word of the query; scoring, by at least one of the processors of the computer system, at least two candidate phrasifications, wherein the candidate phrasifications include one or more component phrases, wherein the scoring is based on a probability of occurrence of each of the candidate phrasification's component phrases in a corpus of documents and is based on the number of component phrases constituting the candidate phrasification, wherein candidate phrasifications having relatively fewer component phrases are weighted higher than candidate phrasifications having relatively more component phrases; comparing, by at least one of the processors of the computer system, a score for each scored candidate phrasification to a threshold value; and selecting, by at least one of the processors of the computer system, at least one candidate phrasification, wherein the scores of each selected candidate phrasification exceed a threshold value and identifying the component phrase(s) of the selected candidate phrasification(s) as valid phrases for the search query; for each valid phrase for the search query, obtaining from the phrase posting list for the valid phrase the phrase relevance score for documents in which the valid phrase occurs; and for documents in which a valid phrase of the query occurs, scoring the document to produce a final relevance score using the phrase relevance scores for the document and based on the valid phrases of the search query. 6. The method of claim 3 , further comprising updating the posting list for the identified phrases to indicate the documents for which the phrases are identified, and information about the significance of the phrase to the documents.

32. A system for providing a service to a client comprising an integrated system of providing answers in response to a query or request, comprising: one or more data input and/or communication devices for exchanging data signals with at least one computer system and/or a computer server, having at least one processor; at least one computer executable program for processing a composition of ontological subjects comprises: i. instructions for partitioning the composition into one or more pluralities of partitions, making one or more index lists for the partitions, and obtaining ontological subjects of at least one predefined order, ii. instructions for building one or more data arrays corresponding to one or more participation matrices carrying data of participation pattern of one or more ontological subjects of one or more first predefined orders into one or more pluralities of partitions of the composition of ontological subjects, iii. instructions for calculating score of the partitions based on at least one feature of significance in the participation pattern; one or more computer systems and/or servers with access to repositories of data and/or compositions or partitions of said compositions; computer programming instructions for building and/or accessing one or more data arrays corresponding to a first participation matrix indicating participation of a plurality of ontological subjects of a predefined order into a first plurality of partitions of a composition; computer-program instructions that when executed by at least one processor, provides a first set of answer to the query by selecting some of the first partitions for which entries in the first participation matrix is non-zero; computer programming instructions that when executed by at least one processor provides a plurality of second partitions by further partitioning of said selected some of the first partitions; computer-programming instructions that when executed by at least one processor, builds and/or assembles a second participation matrix indicating the participation of the ontological subjects of a predefined order into said second partitions of said selected first partitions; computer programming instructions that when executed by at least one processor, calculates scores of at least some of the second plurality of partitions using data of the second participation matrix; computer programming instructions that when executed by at least one processor, selects one or more of the second partitions and present said selected second partitions in a predefined format, thereby providing at least a second set of answer in response to the query.

32. A system for providing a service to a client comprising an integrated system of providing answers in response to a query or request, comprising: one or more data input and/or communication devices for exchanging data signals with at least one computer system and/or a computer server, having at least one processor; at least one computer executable program for processing a composition of ontological subjects comprises: i. instructions for partitioning the composition into one or more pluralities of partitions, making one or more index lists for the partitions, and obtaining ontological subjects of at least one predefined order, ii. instructions for building one or more data arrays corresponding to one or more participation matrices carrying data of participation pattern of one or more ontological subjects of one or more first predefined orders into one or more pluralities of partitions of the composition of ontological subjects, iii. instructions for calculating score of the partitions based on at least one feature of significance in the participation pattern; one or more computer systems and/or servers with access to repositories of data and/or compositions or partitions of said compositions; computer programming instructions for building and/or accessing one or more data arrays corresponding to a first participation matrix indicating participation of a plurality of ontological subjects of a predefined order into a first plurality of partitions of a composition; computer-program instructions that when executed by at least one processor, provides a first set of answer to the query by selecting some of the first partitions for which entries in the first participation matrix is non-zero; computer programming instructions that when executed by at least one processor provides a plurality of second partitions by further partitioning of said selected some of the first partitions; computer-programming instructions that when executed by at least one processor, builds and/or assembles a second participation matrix indicating the participation of the ontological subjects of a predefined order into said second partitions of said selected first partitions; computer programming instructions that when executed by at least one processor, calculates scores of at least some of the second plurality of partitions using data of the second participation matrix; computer programming instructions that when executed by at least one processor, selects one or more of the second partitions and present said selected second partitions in a predefined format, thereby providing at least a second set of answer in response to the query. 33. The system of claim 32 , wherein further includes at least one non-transitory computer-readable storage medium to store one or more of the followings: the composition; at least some of said partitions of the compositions; at least some of said ontological subjects of a predefined order, said at least one or more data arrays corresponding to one or more participation matrixes carrying the data of participation pattern of ontological subjects of one or more first predefined orders into one or more pluralities of partitions; one or more lists of said partitions and/or ontological subjects of a predefined order.

1. A method for handling hand-written annotations related to one or more physical documents, the method comprising: receiving a physical document including a hand-written annotation, the hand-written annotation is made by a user corresponding to at least a portion of the physical document, wherein the hand-written annotation is one of a plurality of hand-written annotations corresponding to the portion of the physical document; capturing the hand-written annotation and corresponding first position information of the hand-written annotation, wherein capturing further includes capturing a second position information of the at least portion of the physical document; storing the captured hand-written annotation, first position information of the hand-written annotation and second position information of the at least portion of the physical document; associating the hand-written annotation to the at least portion of the physical document; upon cropping a camera view of the physical document, obtaining a third position information of the at least portion of the physical document; determining a relative position of the hand-written annotation based on a relative position of the at least portion of the physical document in the camera view, wherein the relative position of the at least portion of the physical document is based on the third position information being relative to the second position information of the at least portion of the physical document, and wherein the relative position of the hand-written annotation is based on a fourth position information of the hand-written annotation relative to the first position information of the hand-written annotation; determining that a quantity of the plurality of hand-written annotations corresponding to the portion of the physical document exceeds a pre-defined quantity; displaying, within the camera view, a marker indicating a presence of the plurality of hand-written annotations; and sharing the hand-written annotation with other users, wherein the method is performed by a processor.

1. A method for handling hand-written annotations related to one or more physical documents, the method comprising: receiving a physical document including a hand-written annotation, the hand-written annotation is made by a user corresponding to at least a portion of the physical document, wherein the hand-written annotation is one of a plurality of hand-written annotations corresponding to the portion of the physical document; capturing the hand-written annotation and corresponding first position information of the hand-written annotation, wherein capturing further includes capturing a second position information of the at least portion of the physical document; storing the captured hand-written annotation, first position information of the hand-written annotation and second position information of the at least portion of the physical document; associating the hand-written annotation to the at least portion of the physical document; upon cropping a camera view of the physical document, obtaining a third position information of the at least portion of the physical document; determining a relative position of the hand-written annotation based on a relative position of the at least portion of the physical document in the camera view, wherein the relative position of the at least portion of the physical document is based on the third position information being relative to the second position information of the at least portion of the physical document, and wherein the relative position of the hand-written annotation is based on a fourth position information of the hand-written annotation relative to the first position information of the hand-written annotation; determining that a quantity of the plurality of hand-written annotations corresponding to the portion of the physical document exceeds a pre-defined quantity; displaying, within the camera view, a marker indicating a presence of the plurality of hand-written annotations; and sharing the hand-written annotation with other users, wherein the method is performed by a processor. 2. The method of claim 1 , further comprising scanning the physical document.

32. The method of claim 29 wherein said determining step further includes the step of computing three dimensional displacement vectors defining the differences between said vertex positions of said modified model and said vertex positions of said reference model.

32. The method of claim 29 wherein said determining step further includes the step of computing three dimensional displacement vectors defining the differences between said vertex positions of said modified model and said vertex positions of said reference model. 33. The method of claim 32 wherein said determining step further includes the step of storing said displacement vectors in a table as said animation parameters.

1. A method of modifying search results of a search engine comprising: (a) identifying a first target page, wherein said first target page has a first ranking used by the search engine in responding to queries directed to a target term; (b) identifying one or more spam pages; (c) automatically causing said one or more spam pages to create explicit links to said first target page with a computing system; wherein said explicit links are created by the computing system at a frequency rate sufficient to reduce said first ranking and are included as hypertext markup language (HTML) content in said one or more spam pages; (d) repeating any of steps (b) and/or (c) until said first ranking used by the search engine is reduced below a target threshold, without modifying how the search engine processes input queries.

1. A method of modifying search results of a search engine comprising: (a) identifying a first target page, wherein said first target page has a first ranking used by the search engine in responding to queries directed to a target term; (b) identifying one or more spam pages; (c) automatically causing said one or more spam pages to create explicit links to said first target page with a computing system; wherein said explicit links are created by the computing system at a frequency rate sufficient to reduce said first ranking and are included as hypertext markup language (HTML) content in said one or more spam pages; (d) repeating any of steps (b) and/or (c) until said first ranking used by the search engine is reduced below a target threshold, without modifying how the search engine processes input queries. 6. The method of claim 1 further comprising generating a set of spam pages to be used during step (c).

14. A method according to claim 11 , wherein the shape defined in the display comprises at least two concentric circles and the unique spine groups are placed around an innermost circle of the concentric circles.

14. A method according to claim 11 , wherein the shape defined in the display comprises at least two concentric circles and the unique spine groups are placed around an innermost circle of the concentric circles. 15. A method according to claim 14 , further comprising: positioning the selected spine group at a radius of the innermost circle at an angle of the unique spine group that is most similar.

1. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: receive an input from a user; determine, using a first n-gram language model, a first probability of a stem based at least on a first portion of a previously-input word in the received input; determine, using a second n-gram language model, a second probability of a first suffix based at least on a second portion of the previously-input word in the received input; determine, using a third n-gram language model, a third probability of a second suffix different from the first suffix based at least on a third portion of the previously-input word in the received input, wherein the third n-gram language model includes a tense suffix n-gram language model, and the determining of the third probability of the second suffix includes determining the third probability of a tense suffix based at least in part on a second tense suffix of the previously-input word; determine a fourth probability of at least one predicted word based on the first probability, the second probability and the third probability; and provide an output of the at least one predicted word to the user based on the fourth probability, wherein providing the output comprises at least one of displaying the predicted word or providing an audible playback of the predicted word.

1. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: receive an input from a user; determine, using a first n-gram language model, a first probability of a stem based at least on a first portion of a previously-input word in the received input; determine, using a second n-gram language model, a second probability of a first suffix based at least on a second portion of the previously-input word in the received input; determine, using a third n-gram language model, a third probability of a second suffix different from the first suffix based at least on a third portion of the previously-input word in the received input, wherein the third n-gram language model includes a tense suffix n-gram language model, and the determining of the third probability of the second suffix includes determining the third probability of a tense suffix based at least in part on a second tense suffix of the previously-input word; determine a fourth probability of at least one predicted word based on the first probability, the second probability and the third probability; and provide an output of the at least one predicted word to the user based on the fourth probability, wherein providing the output comprises at least one of displaying the predicted word or providing an audible playback of the predicted word. 4. The non-transitory computer-readable storage medium of claim 1 , wherein the one or more program comprise instructions that cause the electronic device to train the third n-gram language model based at least in part on a first dataset including filtered data expurgated from all stems, non-tense suffixes and non-information bearing words.

12. A method in accordance with claim 1 , wherein: the psychological state of the person is at least one of anger, anxiety, depression, emotional withdrawal, lack of flexibility, impulsiveness and emotional instability.

12. A method in accordance with claim 1 , wherein: the psychological state of the person is at least one of anger, anxiety, depression, emotional withdrawal, lack of flexibility, impulsiveness and emotional instability. 13. A method in accordance with claim 12 further comprising: processing with at least one computer the at least one computer communication of the person with the at least one keyword algorithm to assess any risk posed by the person to the organization, and electronically communicating to the organization with at least one computer any risk discovered from the processing with the at least one keyword algorithm the categories of information.

1. A method comprising: maintaining relationship data describing relationships between data objects in a collection of data objects; identifying search results for a set of query terms, each result of the search results being a subset of related data objects within the collection, the subset comprising, for each particular term of the set of query terms, at least one data object that matches the particular term; wherein identifying the search results comprises: identifying groups of initial data objects to investigate for the search results, the groups including at least a first group matching a first query term, a second group matching a second query term, and a third group matching a third query term; based on how often certain initial data objects appear both in the first group and the second group, removing, from the groups, initial data objects that are not found within both the first group and the second group; identifying particular subsets of related data objects within the collection by, based on the relationships, expanding networks of related data objects from remaining initial data objects; determining which of the particular subsets qualify as the search results.

1. A method comprising: maintaining relationship data describing relationships between data objects in a collection of data objects; identifying search results for a set of query terms, each result of the search results being a subset of related data objects within the collection, the subset comprising, for each particular term of the set of query terms, at least one data object that matches the particular term; wherein identifying the search results comprises: identifying groups of initial data objects to investigate for the search results, the groups including at least a first group matching a first query term, a second group matching a second query term, and a third group matching a third query term; based on how often certain initial data objects appear both in the first group and the second group, removing, from the groups, initial data objects that are not found within both the first group and the second group; identifying particular subsets of related data objects within the collection by, based on the relationships, expanding networks of related data objects from remaining initial data objects; determining which of the particular subsets qualify as the search results. 5. The method of claim 1 , further comprising expanding networks of related data objects in an order that is based on one or more scores assigned to each of at least the remaining initial data objects, the one or more scores for a particular data objects comprising one or more of: a relationship score calculated from a link analysis of the collection; a metadata score for a particular metadata item to which the particular data object conforms, the metadata score calculated from a link analysis of different metadata items to which the data items in the collection conforms.

14. The system of claim 9 wherein initially embedding each media object into a multidimensional space comprises performing a multidimensional scaling-based processing of the sparse graph of media object similarities.

14. The system of claim 9 wherein initially embedding each media object into a multidimensional space comprises performing a multidimensional scaling-based processing of the sparse graph of media object similarities. 16. The system of claim 14 wherein the multidimensional scaling-based processing is a Fast Sparse Embedding process.

13. An interactive voice response (IVR) system for self-authenticating a user to perform transactions which require accessing protected resources, the IVR comprising: an IVR device coupled to a telephony server, the IVR device calibrated to a primary device and one or more secondary devices for self-authentication of a user, the IVR device configured to send a voice request to the calibrated primary device for requesting the calibrated primary device to identify at least one secondary device calibrated to the user; the primary device calibrated to respond to the voice request from the IVR device by sending at least one identified secondary device to the IVR device; in response to receiving the at least one identified secondary device from the calibrated primary device, the IVR device is further configured to send authentication data to the at least one identified secondary device, if the at least one identified secondary device matches the one or more calibrated secondary devices; the at least one identified secondary device calibrated to receive the authentication data, in a form of sound, from the IVR device, wherein the at least one identified secondary device is calibrated to present the authentication data in a form to be transferred to the calibrated primary device; and the calibrated primary device further calibrated to receive the authentication data transferred from the at least one identified secondary device and, in response, send a voice confirmation to the IVR device to authenticate the user.

13. An interactive voice response (IVR) system for self-authenticating a user to perform transactions which require accessing protected resources, the IVR comprising: an IVR device coupled to a telephony server, the IVR device calibrated to a primary device and one or more secondary devices for self-authentication of a user, the IVR device configured to send a voice request to the calibrated primary device for requesting the calibrated primary device to identify at least one secondary device calibrated to the user; the primary device calibrated to respond to the voice request from the IVR device by sending at least one identified secondary device to the IVR device; in response to receiving the at least one identified secondary device from the calibrated primary device, the IVR device is further configured to send authentication data to the at least one identified secondary device, if the at least one identified secondary device matches the one or more calibrated secondary devices; the at least one identified secondary device calibrated to receive the authentication data, in a form of sound, from the IVR device, wherein the at least one identified secondary device is calibrated to present the authentication data in a form to be transferred to the calibrated primary device; and the calibrated primary device further calibrated to receive the authentication data transferred from the at least one identified secondary device and, in response, send a voice confirmation to the IVR device to authenticate the user. 14. The IVR system of claim 13 wherein the authentication data received by the at least one identified secondary device is in the form of a question and, instead of transferring the authentication data to the primary device, transferring an answer to the question.

5. The method according to claim 4 wherein the preview of the content package file contains information about the first level and the second level content files in an hierarchical format.

5. The method according to claim 4 wherein the preview of the content package file contains information about the first level and the second level content files in an hierarchical format. 6. The method according to claim 5 wherein the hierarchical format of the preview is expandable to view the information about the first level and the second level content files.

24. The navigation device of claim 23 , wherein the one or more processors are further configured to: recognize one or more command words in the natural language utterance that define a command in the navigation context; recognize one or more location words in the natural language utterance that define a state associated with the command in the navigation context, wherein the dynamic recognition grammar further organizes the grammar information according to the state associated with the command in the navigation context; and recognize one or more additional location words in the natural language utterance that define a city within the state associated with the command in the navigation context, wherein the dynamic recognition grammar further organizes the grammar information according to multiple street addresses in the city within the state associated with the command in the navigation context.

24. The navigation device of claim 23 , wherein the one or more processors are further configured to: recognize one or more command words in the natural language utterance that define a command in the navigation context; recognize one or more location words in the natural language utterance that define a state associated with the command in the navigation context, wherein the dynamic recognition grammar further organizes the grammar information according to the state associated with the command in the navigation context; and recognize one or more additional location words in the natural language utterance that define a city within the state associated with the command in the navigation context, wherein the dynamic recognition grammar further organizes the grammar information according to multiple street addresses in the city within the state associated with the command in the navigation context. 25. The navigation device of claim 24 , wherein to generate the one or more interpretations associated with the natural language utterance using the dynamic recognition grammar, the one or more processors are further configured to: recognize one or more additional command words in the natural language utterance using the grammar information organized according to the multiple street addresses in the city within the state associated with the command in the navigation context, wherein the one or more additional command words define one or more of the multiple street addresses; and combine the one or more command words and the one or more additional command words recognized in the natural language utterance to generate the one or more interpretations associated with the natural language utterance.

1. A computer-implemented method comprising: receiving a request for protected content from a client, the protected content comprising data; determining a challenge phrase comprising a plurality of characters; dividing, using a computer processor, the challenge phrase into at least two character subsets selected from the plurality of characters comprising the challenge phrase, each of the at least two character subsets comprising less than all of the characters comprising the challenge phrase, wherein a first character subset and a second character subset of the at least two character subsets comprise one or more common characters, and the first character subset includes at least one character not included in the second character subset; obscuring a first part of a common character in the first character subset; obscuring a second part of the common character in the second character subset, the second part of the common character being different than the first part of the common character; sending the at least two character subsets to the client in response to the request; and receiving, from the client and in response to the at least two character subsets, an answer to the challenge phrase, wherein access to the protected content is limited based on whether the answer correctly solves the challenge phrase.

1. A computer-implemented method comprising: receiving a request for protected content from a client, the protected content comprising data; determining a challenge phrase comprising a plurality of characters; dividing, using a computer processor, the challenge phrase into at least two character subsets selected from the plurality of characters comprising the challenge phrase, each of the at least two character subsets comprising less than all of the characters comprising the challenge phrase, wherein a first character subset and a second character subset of the at least two character subsets comprise one or more common characters, and the first character subset includes at least one character not included in the second character subset; obscuring a first part of a common character in the first character subset; obscuring a second part of the common character in the second character subset, the second part of the common character being different than the first part of the common character; sending the at least two character subsets to the client in response to the request; and receiving, from the client and in response to the at least two character subsets, an answer to the challenge phrase, wherein access to the protected content is limited based on whether the answer correctly solves the challenge phrase. 10. The computer-implemented method according to claim 1 , wherein none of the at least two subsets comprises all of the characters of the challenge phrase.

1. A book structure comprising: a plurality of bound pages comprising single sheets stacked one upon the other; at least one supplemental page bound to the back of the stack of single sheets, each said supplemental page comprising: (a) an appendix portion aligned with said stack of single sheets, (b) a memo portion extending from at least one unbound edge of said appendix portion, each said memo portion being extendable out of alignment with said stack of single sheets, and (c) scoring lines separating said appendix portion from each said memo portion, whereby each said memo portion may be folded over at a respective one of said scoring lines to be in alignment with said stack of single sheets.

1. A book structure comprising: a plurality of bound pages comprising single sheets stacked one upon the other; at least one supplemental page bound to the back of the stack of single sheets, each said supplemental page comprising: (a) an appendix portion aligned with said stack of single sheets, (b) a memo portion extending from at least one unbound edge of said appendix portion, each said memo portion being extendable out of alignment with said stack of single sheets, and (c) scoring lines separating said appendix portion from each said memo portion, whereby each said memo portion may be folded over at a respective one of said scoring lines to be in alignment with said stack of single sheets. 4. The book structure of claim 1 wherein said scoring lines comprise small perforations, whereby said memo portion may be separated from said appendix portion.

1. A method for globalizing handling of service management items, comprising the steps of: obtaining a service management item in a language convenient to a first of two or more actors; translating the service management item into a language-neutral format to obtain a language-neutral service management item; applying one or more annotators to the service management item, wherein the one or more annotators comprise one or more personalized annotators; translating the language-neutral service management item into a language convenient to a second of two or more actors acting on the service management item; and routing the translated service management item to the second of two or more actors, wherein one or more of said steps are performed by a hardware device.

1. A method for globalizing handling of service management items, comprising the steps of: obtaining a service management item in a language convenient to a first of two or more actors; translating the service management item into a language-neutral format to obtain a language-neutral service management item; applying one or more annotators to the service management item, wherein the one or more annotators comprise one or more personalized annotators; translating the language-neutral service management item into a language convenient to a second of two or more actors acting on the service management item; and routing the translated service management item to the second of two or more actors, wherein one or more of said steps are performed by a hardware device. 4. The method of claim 1 , wherein the language-neutral format comprise a common base language.

5. The method of claim 4 , wherein the representing print resources further comprises representing one or more print formats.

5. The method of claim 4 , wherein the representing print resources further comprises representing one or more print formats. 6. The method of claim 5 , wherein the representing print formats further comprises representing fonts, images and overlays.

12. A non-transitory computer-readable storage medium comprising a plurality of computer-readable instructions tangibly embodied on the computer-readable storage medium, which, when executed by a data processor, provide for analyzing symbols in a computer system, the plurality of instructions comprising: instructions that cause the data processor to to receive a message including symbols; instructions that cause the data processor to perform a lexical analysis of the message, the lexical analysis generating a sequence of tokens based on the symbols included in the message, wherein a token in the sequence of tokens corresponds to a category of symbols, and wherein the category of symbols corresponds to a plurality of symbols having different values; instructions that cause the data processor to determine a message digest for the sequence of tokens, wherein the message digest is updated as each token in the sequence of tokens is identified during the lexical analysis; instructions that cause the data processor to assign the message to a cluster group based on the message digest.

12. A non-transitory computer-readable storage medium comprising a plurality of computer-readable instructions tangibly embodied on the computer-readable storage medium, which, when executed by a data processor, provide for analyzing symbols in a computer system, the plurality of instructions comprising: instructions that cause the data processor to to receive a message including symbols; instructions that cause the data processor to perform a lexical analysis of the message, the lexical analysis generating a sequence of tokens based on the symbols included in the message, wherein a token in the sequence of tokens corresponds to a category of symbols, and wherein the category of symbols corresponds to a plurality of symbols having different values; instructions that cause the data processor to determine a message digest for the sequence of tokens, wherein the message digest is updated as each token in the sequence of tokens is identified during the lexical analysis; instructions that cause the data processor to assign the message to a cluster group based on the message digest. 14. The non-transitory computer-readable storage medium of claim 12 , the instructions further comprising: instructions that cause the data processor to determine that the message digest is not associated with a cluster identifier; instructions that cause the data processor to fully parse the message to determine a cluster identifier to associate with the message; and instructions that cause the data processor to associate the message digest with the cluster identifier.

1. A method for resolving ambiguity between names and entities through generation of an information architecture comprising the steps of: a) providing: i) an electronically accessible network, ii) service software, iii) a plurality of names for a given entity, and iv) a processor configured to perform the steps of: b) identifying an ambiguity within said plurality of names for said given entity; c) assigning at least one persistent, uniquely identified, addressable information object to each of said names; d) storing said at least one information object associated with each name in said electronically accessible network to generate an information architecture; and e) resolving said ambiguity by accessing said information architecture stored in said electronically accessible network via said service software.

1. A method for resolving ambiguity between names and entities through generation of an information architecture comprising the steps of: a) providing: i) an electronically accessible network, ii) service software, iii) a plurality of names for a given entity, and iv) a processor configured to perform the steps of: b) identifying an ambiguity within said plurality of names for said given entity; c) assigning at least one persistent, uniquely identified, addressable information object to each of said names; d) storing said at least one information object associated with each name in said electronically accessible network to generate an information architecture; and e) resolving said ambiguity by accessing said information architecture stored in said electronically accessible network via said service software. 12. The method of claim 1 , wherein the content of said information object comprises at least one of metadata, data, and descriptive text, said content representing at least one of a biological Name, Taxon, Nomos, Practitioner, or Exemplar.

2. The method of claim 1 , wherein each component is instantiated based on a generic component type and has a set of core attributes comprising an id, a name, a description, a type, and a set of properties and the schema comprises: a propertyDefinition table, a componentType table, a component table, a PropertyCategory table, a propertyDefinition table, a propertyValue table, a relationshipType table and a relationship table, wherein the ComponentType table comprises a set of columns related to fields of a component type, wherein each component is linked to its type of component through a property field, whereby all instantiated components of a given type contain the same set of properties and check logic, wherein the RelationshipType table comprises a set of columns related to fields of a relationship type, wherein each relationship is linked to its type of relationship through a property field, whereby all instantiated relationships of a given type contain the same set of properties and check logic, wherein the PropertyCategory table comprises a set of columns related to the definition of a property, wherein each property is linked to its type of relationship through a property field, whereby all instantiated relationships of a given type contain the same set of properties and check logic, wherein the PropertyDefinition table comprises a set of definitions of particular properties, wherein one or more properties contains a link to a field in and a propertyValue table, wherein each property value comprises a link to a property definition in the propertyDefinition table and one of a link to a field in the ComponentType table or the RelationshipType table.

2. The method of claim 1 , wherein each component is instantiated based on a generic component type and has a set of core attributes comprising an id, a name, a description, a type, and a set of properties and the schema comprises: a propertyDefinition table, a componentType table, a component table, a PropertyCategory table, a propertyDefinition table, a propertyValue table, a relationshipType table and a relationship table, wherein the ComponentType table comprises a set of columns related to fields of a component type, wherein each component is linked to its type of component through a property field, whereby all instantiated components of a given type contain the same set of properties and check logic, wherein the RelationshipType table comprises a set of columns related to fields of a relationship type, wherein each relationship is linked to its type of relationship through a property field, whereby all instantiated relationships of a given type contain the same set of properties and check logic, wherein the PropertyCategory table comprises a set of columns related to the definition of a property, wherein each property is linked to its type of relationship through a property field, whereby all instantiated relationships of a given type contain the same set of properties and check logic, wherein the PropertyDefinition table comprises a set of definitions of particular properties, wherein one or more properties contains a link to a field in and a propertyValue table, wherein each property value comprises a link to a property definition in the propertyDefinition table and one of a link to a field in the ComponentType table or the RelationshipType table. 4. The method of claim 2 , wherein a property field comprises a data type of one of a string, a numeric, a Boolean, a link, a date/time and a custom type, wherein each property field in a component is associated with a property of the entity being represented.

1. A method comprising: determining, based on a dynamic characteristic of a mobile device of a user, a current device context value of at least one of a plurality of device context parameters; receiving, at the mobile device and over a communications network, context information from each client device of a plurality of client devices, wherein each client device corresponds to a different social contact of a plurality of social contacts of the user, wherein the context information received from each client device comprises information relating to a social context of the client device of the social contact, wherein the information relating to the social context of the client device of the social contact relates to historical application usage information of a plurality of applications of the client device of the social contact; determining, based on the context information received from each client device of the plurality of social contacts of the user, a current social context value of at least one of a plurality of social context parameters; for each of a plurality of applications previously downloaded and stored on the mobile device, calculating, by the mobile device, an application relevance score as a function of the current device context value and the current social context value; identifying at least one of the plurality of applications downloaded to the mobile device as a pinned application; and dynamically updating a display of a plurality of application representations on a graphical user interface (GUI) of the mobile device, such that the application representations are arranged according at least to the application relevance scores, each application representation corresponding to one of the plurality of applications downloaded to the mobile device, the arranging comprising removing at least one application representation of the plurality of application representations according to a frequency of use of each of the plurality of application representations, reordering one or more application representations of the plurality of application representations according to the frequency of use of each such application representation, and listing an application representation corresponding to a most recently used application of the plurality of applications downloaded to the mobile device in a user-designated location of the GUI for the most recently used application, wherein the arrangement of the application representation of the pinned application is fixed and is not affected by changes in the application relevance scores.

1. A method comprising: determining, based on a dynamic characteristic of a mobile device of a user, a current device context value of at least one of a plurality of device context parameters; receiving, at the mobile device and over a communications network, context information from each client device of a plurality of client devices, wherein each client device corresponds to a different social contact of a plurality of social contacts of the user, wherein the context information received from each client device comprises information relating to a social context of the client device of the social contact, wherein the information relating to the social context of the client device of the social contact relates to historical application usage information of a plurality of applications of the client device of the social contact; determining, based on the context information received from each client device of the plurality of social contacts of the user, a current social context value of at least one of a plurality of social context parameters; for each of a plurality of applications previously downloaded and stored on the mobile device, calculating, by the mobile device, an application relevance score as a function of the current device context value and the current social context value; identifying at least one of the plurality of applications downloaded to the mobile device as a pinned application; and dynamically updating a display of a plurality of application representations on a graphical user interface (GUI) of the mobile device, such that the application representations are arranged according at least to the application relevance scores, each application representation corresponding to one of the plurality of applications downloaded to the mobile device, the arranging comprising removing at least one application representation of the plurality of application representations according to a frequency of use of each of the plurality of application representations, reordering one or more application representations of the plurality of application representations according to the frequency of use of each such application representation, and listing an application representation corresponding to a most recently used application of the plurality of applications downloaded to the mobile device in a user-designated location of the GUI for the most recently used application, wherein the arrangement of the application representation of the pinned application is fixed and is not affected by changes in the application relevance scores. 13. The method of claim 1 , further comprising: determining a predicted future context based on the current device context value and the current social context value; associating at least one of the plurality of applications downloaded to the mobile device with a template associated with the predicted future context; and dynamically updating the display of the plurality of application representations on the GUI according to the template associated with the predicted future context.

15. A computer system for tracking changes in a Javascript object notation structure, the computer system comprising: one or more computer processors; one or more computer readable storage media; program instructions stored on the computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising: program instructions to adjust a Javascript object notation structure to comprise a tag on at least one object and a tag on at least one array; program instructions to receive data indicating a first set of at least one change to the Javascript object notation structure; program instructions to adjust the tags in the Javascript object notation structure to include the first set of the at least one change in the Javascript object notation structure; program instructions to receive data indicating the first set of the at least one change to the Javascript object notation structure is complete; and program instructions to display the first set of the at least one change to the Javascript object notation structure based upon the adjusted tags.

15. A computer system for tracking changes in a Javascript object notation structure, the computer system comprising: one or more computer processors; one or more computer readable storage media; program instructions stored on the computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising: program instructions to adjust a Javascript object notation structure to comprise a tag on at least one object and a tag on at least one array; program instructions to receive data indicating a first set of at least one change to the Javascript object notation structure; program instructions to adjust the tags in the Javascript object notation structure to include the first set of the at least one change in the Javascript object notation structure; program instructions to receive data indicating the first set of the at least one change to the Javascript object notation structure is complete; and program instructions to display the first set of the at least one change to the Javascript object notation structure based upon the adjusted tags. 17. The computer system of claim 15 , wherein program instructions to receive data indicating a first set of at least one change to the Javascript object notation structure comprises program instructions to: receive at least one change to more than one objects; and receive at least one change to more than one arrays.

3. A computer-implemented method for generating an online summary of documents, comprising: using a central processing unit configured to process information for: receiving a search query to obtain a list of URLs of web pages as search results; obtaining the list of URLs; using an article summarizer generating an online summary of the list of the URLs using a plurality of titles of the web pages; using a web page storage operably coupled to the article summarizer for storing the online summary and the list of URLs of web pages as search results; merging overlapping phrases until there may be no more terms that occur in the titles with a frequency that exceeds a predefined threshold; and applying grammar rules for a language in which the information is expressly merging overlapping phrases until there may be no more terms that occur in the titles with a frequency that exceeds a predefined threshold used to remove a term at the beginning or end of a phrase that should not appear in that position of a sentence in the given language.

3. A computer-implemented method for generating an online summary of documents, comprising: using a central processing unit configured to process information for: receiving a search query to obtain a list of URLs of web pages as search results; obtaining the list of URLs; using an article summarizer generating an online summary of the list of the URLs using a plurality of titles of the web pages; using a web page storage operably coupled to the article summarizer for storing the online summary and the list of URLs of web pages as search results; merging overlapping phrases until there may be no more terms that occur in the titles with a frequency that exceeds a predefined threshold; and applying grammar rules for a language in which the information is expressly merging overlapping phrases until there may be no more terms that occur in the titles with a frequency that exceeds a predefined threshold used to remove a term at the beginning or end of a phrase that should not appear in that position of a sentence in the given language. 4. The method of claim 3 further comprising adding the online summary to the list of the URLs as search results.

11. The method of claim 8 , further comprising processing the new ECG waveform and/or the extracted feature with a general interpretation module to provide a general interpretation output, wherein the general interpretation module is trained on a general database of existing ECG datasets.

11. The method of claim 8 , further comprising processing the new ECG waveform and/or the extracted feature with a general interpretation module to provide a general interpretation output, wherein the general interpretation module is trained on a general database of existing ECG datasets. 12. The method of claim 11 , further comprising displaying the general interpretation output on the user interface.

10. A computer implemented method for searching through multiple databases based on a search expression, said computer implemented method comprising: dividing the search expression into multiple search expressions, wherein dividing is performed using a data distribution table, wherein the data distribution table indicates how the records are distributed in each table of a plurality of tables, wherein ones of the plurality of tables correspond to ones of the multiple databases, and wherein the records correspond to a common key; determining respective search ranges for the multiple search expressions based in part on search rates through the respective search ranges; executing the multiple search expressions to form a multiple search expression output; and transmitting the multiple search expression output to a memory.

10. A computer implemented method for searching through multiple databases based on a search expression, said computer implemented method comprising: dividing the search expression into multiple search expressions, wherein dividing is performed using a data distribution table, wherein the data distribution table indicates how the records are distributed in each table of a plurality of tables, wherein ones of the plurality of tables correspond to ones of the multiple databases, and wherein the records correspond to a common key; determining respective search ranges for the multiple search expressions based in part on search rates through the respective search ranges; executing the multiple search expressions to form a multiple search expression output; and transmitting the multiple search expression output to a memory. 11. The computer implemented method as set forth in claim 10 wherein the determining step determines the respective search ranges such that each of the multiple search expressions is executed in a time less than or equal to a predetermined duration.

16. The speech synthesis method according to claim 13 , wherein said selecting the central segment selects a central segment from among a plurality of speech segments that have a high degree of conformity with a language processing result of the language processing.

16. The speech synthesis method according to claim 13 , wherein said selecting the central segment selects a central segment from among a plurality of speech segments that have a high degree of conformity with a language processing result of the language processing. 18. The speech synthesis method according to claim 16 , wherein said selecting the central segment further includes extracting an important expression included in input text based on the language processing result, and selects a central segment based on the important expression.

13. A non-transitory computer-readable storage medium containing one or more markup language documents for being rendered by a web browser application executing on a computer system, the one or more markup language documents comprising: a hierarchical structure of nodes representing elements of a markup language document and edges connecting the nodes, wherein: a subset of nodes mapped to node types and a root node connected with other nodes via paths comprising nodes and edges; and a specification comprising a mapping from sets of node types to sets of handlers, wherein each set of node types specified in the mapping is mapped to a set of handlers; instructions to a web browser application executing on a computer system for executing handlers in response to an event, the instructions for causing the computer system to: receive a user input associated with a selected node; identify a set of node types encountered in a path connecting the root node with the selected node; identify a set of handlers mapped to the identified set of node types based on the mapping; and execute the handlers in the identified set of handlers.

13. A non-transitory computer-readable storage medium containing one or more markup language documents for being rendered by a web browser application executing on a computer system, the one or more markup language documents comprising: a hierarchical structure of nodes representing elements of a markup language document and edges connecting the nodes, wherein: a subset of nodes mapped to node types and a root node connected with other nodes via paths comprising nodes and edges; and a specification comprising a mapping from sets of node types to sets of handlers, wherein each set of node types specified in the mapping is mapped to a set of handlers; instructions to a web browser application executing on a computer system for executing handlers in response to an event, the instructions for causing the computer system to: receive a user input associated with a selected node; identify a set of node types encountered in a path connecting the root node with the selected node; identify a set of handlers mapped to the identified set of node types based on the mapping; and execute the handlers in the identified set of handlers. 20. The non-transitory computer readable storage medium of claim 13 , wherein a handler function is specified in a scripting language.

14. A non-transitory computer readable recording medium storing an ontology processing program which, when loaded into a memory and run by a computer, causes the computer to execute functions including: correcting a structure of ontology in a prescribed form created from a set of instance data containing a combination of a subject, a property, and an object expressed with a character string; judging whether each of the properties contained in the ontology is an essential property or an unessential property for a concept that is defined within the ontology and related to each of the properties according to statistical features of the objects contained in the set of instance data; correcting the structure of the ontology regarding the corresponding properties according to results of the judgments; and changing a property that is judged as unessential to a property having an inverted definition range and an inverted value range.

14. A non-transitory computer readable recording medium storing an ontology processing program which, when loaded into a memory and run by a computer, causes the computer to execute functions including: correcting a structure of ontology in a prescribed form created from a set of instance data containing a combination of a subject, a property, and an object expressed with a character string; judging whether each of the properties contained in the ontology is an essential property or an unessential property for a concept that is defined within the ontology and related to each of the properties according to statistical features of the objects contained in the set of instance data; correcting the structure of the ontology regarding the corresponding properties according to results of the judgments; and changing a property that is judged as unessential to a property having an inverted definition range and an inverted value range. 17. The non-transitory computer readable recording medium storing the ontology processing program as claimed in claim 14 , which enables the computer to execute: a function of judging whether or not, among the input data, a part of data showing a subject shows a specific concept, and determining a converting method of the part of data showing the subject based on a result of the judgment; a function of judging whether or not, among the input data, a part of data showing a property is an already-known property, and determining a converting method of the part of data showing the property based on a result of the judgment; a function of judging whether a part of data among the input data, which shows an object, is a literal or a resource, and determining a converting method of the part of data showing the object based on a result of the judgment; and a function of writing ontology in a prescribed form by using the result obtained by converting the data showing the subject, the property, and the object with the determined converting methods.

9. A system comprising: at least one computing device configured to determine a confidence-estimation-based inference by performing actions including: receiving a query concerning a patient from a user; accessing an electronic health record (EHR) for the patient, the EHR including a first component regarding the patient; querying the user, using a conversational interface, for a second component regarding the patient, the second component being in a natural language information form; receiving the second component regarding the patient in response to the query; calculating a first probability density function using the first component, and a second probability density function using the second component; combining the first and second probability density functions using a Gaussian mixture model; calculating at least one conditional probability table using the Gaussian mixture model; and providing the confidence-estimation-based inference based on the at least one conditional probability table.

9. A system comprising: at least one computing device configured to determine a confidence-estimation-based inference by performing actions including: receiving a query concerning a patient from a user; accessing an electronic health record (EHR) for the patient, the EHR including a first component regarding the patient; querying the user, using a conversational interface, for a second component regarding the patient, the second component being in a natural language information form; receiving the second component regarding the patient in response to the query; calculating a first probability density function using the first component, and a second probability density function using the second component; combining the first and second probability density functions using a Gaussian mixture model; calculating at least one conditional probability table using the Gaussian mixture model; and providing the confidence-estimation-based inference based on the at least one conditional probability table. 10. The system of claim 9 , wherein the inference is one of a medical diagnosis or a medical prognosis.

1. A machine-assisted method comprising: providing a graphical user interface (GUI) including an idea map window for displaying and editing a graphical representation of a hierarchical network of graphical nodes, the nodes graphically representing ideas, and graphical links between ones of the nodes, the links graphically representing relationships between the ideas, a particular node being capable of being associated with corresponding text and an associated image; providing a word processor document window for displaying textual and image information, associated with respective nodes of the hierarchical network, in a word processor document; automatically organizing the displayed textual information for the user into a textual sequence displayed in the word processor document window, wherein an image associated with the particular node is displayed in the word processor window together with text associated with the particular node; and obtaining the textual sequence by automatically hierarchically processing the graphical representation of the hierarchical network of graphical nodes displayed in the idea map window, including, beginning with a graphically displayed root node, automatically hierarchically processing for a particular graphically displayed node any graphically displayed linked children nodes according to an ordering convention that uses only one of a clockwise manner or an anticlockwise manner as the linked children nodes are graphically displayed about their respective graphically displayed parent nodes in the hierarchical network of graphical nodes displayed in the idea map window, then proceeding to similarly automatically hierarchically process any linked children nodes according to the same ordering convention; concurrently displaying the idea map window and the word processor document window; and automatically updating the word processing document window with information from any changes in the idea map window, including automatically updating the text and image in the word processing window live, in real time, as the corresponding graphical nodes in the idea map window are being manipulated by the user.

1. A machine-assisted method comprising: providing a graphical user interface (GUI) including an idea map window for displaying and editing a graphical representation of a hierarchical network of graphical nodes, the nodes graphically representing ideas, and graphical links between ones of the nodes, the links graphically representing relationships between the ideas, a particular node being capable of being associated with corresponding text and an associated image; providing a word processor document window for displaying textual and image information, associated with respective nodes of the hierarchical network, in a word processor document; automatically organizing the displayed textual information for the user into a textual sequence displayed in the word processor document window, wherein an image associated with the particular node is displayed in the word processor window together with text associated with the particular node; and obtaining the textual sequence by automatically hierarchically processing the graphical representation of the hierarchical network of graphical nodes displayed in the idea map window, including, beginning with a graphically displayed root node, automatically hierarchically processing for a particular graphically displayed node any graphically displayed linked children nodes according to an ordering convention that uses only one of a clockwise manner or an anticlockwise manner as the linked children nodes are graphically displayed about their respective graphically displayed parent nodes in the hierarchical network of graphical nodes displayed in the idea map window, then proceeding to similarly automatically hierarchically process any linked children nodes according to the same ordering convention; concurrently displaying the idea map window and the word processor document window; and automatically updating the word processing document window with information from any changes in the idea map window, including automatically updating the text and image in the word processing window live, in real time, as the corresponding graphical nodes in the idea map window are being manipulated by the user. 26. The method of claim 1 , further comprising automatically scrolling to a corresponding portion of the displayed word processor document live, in real time, when a corresponding node in the displayed idea map is selected by the user.

1. A server providing an authentication platform for determining dynamic trust scores evaluating ongoing online relationships, the server comprising: a hardware processor configured to execute the authentication platform stored in a memory to: receive a first request from an online service for a trust score assigned to an online relationship between a first user and a second user; calculate the trust score using a plurality of user data variables derived from a platform database referencing the first user and the second user, the user data variables including certified data and proffered data, wherein a weight given to each of the plurality of user data variables corresponds to a trust importance of each of the plurality of user data variables, and wherein the weight is adjusted over time for calculation of future trust scores based on a charge in the trust importance of each of the plurality of user data variables; save the trust score as a previous trust score in the memory; send the trust score to the online service in response to the first request, wherein the trust score affects a client of the first user; and performing dynamic recalculation of the trust score in response to a change to the plurality of data variables over a period of time, wherein the change includes varying the weight given to each of the plurality of user data variables over a period of time, wherein the dynamic recalculation includes using the previous trust score.

1. A server providing an authentication platform for determining dynamic trust scores evaluating ongoing online relationships, the server comprising: a hardware processor configured to execute the authentication platform stored in a memory to: receive a first request from an online service for a trust score assigned to an online relationship between a first user and a second user; calculate the trust score using a plurality of user data variables derived from a platform database referencing the first user and the second user, the user data variables including certified data and proffered data, wherein a weight given to each of the plurality of user data variables corresponds to a trust importance of each of the plurality of user data variables, and wherein the weight is adjusted over time for calculation of future trust scores based on a charge in the trust importance of each of the plurality of user data variables; save the trust score as a previous trust score in the memory; send the trust score to the online service in response to the first request, wherein the trust score affects a client of the first user; and performing dynamic recalculation of the trust score in response to a change to the plurality of data variables over a period of time, wherein the change includes varying the weight given to each of the plurality of user data variables over a period of time, wherein the dynamic recalculation includes using the previous trust score. 2. The server of claim 1 , wherein the platform database includes data from an internal database and an external database.

1. A computer-implemented method comprising: under control of one or more computer systems configured with executable instructions, receiving, at a server, a request from a first user to store a media item for later access by an electronic book reader device associated with a second user, wherein the request from the first user does not include information identifying the second user or the electronic book reader device associated with the second user as an intended recipient of the media item; in response to receiving the request at the server, associating a pass phrase with the request to store the media item; sending the pass phrase from the server to a device of the first user or to another device associated with the first user; after sending the pass phrase, receiving, at the server, the media item from the device of the first user; after receiving the media item at the server, storing the media item in association with the pass phrase, the pass phrase being usable for the later access of the media item by the electronic book reader device associated with the second user; receiving, at the server, the pass phrase from the electronic book reader device associated with the second user; and sending the media item from the server to the electronic book reader device associated with the second user at least partly in response to the receiving of the pass phrase at the server.

1. A computer-implemented method comprising: under control of one or more computer systems configured with executable instructions, receiving, at a server, a request from a first user to store a media item for later access by an electronic book reader device associated with a second user, wherein the request from the first user does not include information identifying the second user or the electronic book reader device associated with the second user as an intended recipient of the media item; in response to receiving the request at the server, associating a pass phrase with the request to store the media item; sending the pass phrase from the server to a device of the first user or to another device associated with the first user; after sending the pass phrase, receiving, at the server, the media item from the device of the first user; after receiving the media item at the server, storing the media item in association with the pass phrase, the pass phrase being usable for the later access of the media item by the electronic book reader device associated with the second user; receiving, at the server, the pass phrase from the electronic book reader device associated with the second user; and sending the media item from the server to the electronic book reader device associated with the second user at least partly in response to the receiving of the pass phrase at the server. 19. The computer-implemented method of claim 1 , further comprising: sending a piece of information in addition to the pass phrase from the server to the device of the first user or to the other device associated with the first user, the piece of information also for accessing the media item by the electronic reader device associated with the second user; in response to receiving the media item at the server, storing the media item in association with the piece of information; receiving, at the server, in addition to the pass phrase, the piece of information from the electronic book reader device of the second user; and wherein the sending of the media item to the electronic book reader device associated with the second user is also at least partly in response to the receiving of the piece of information at the server.