a. Field of the Invention
This invention relates generally to telephony, and more particularly to a method and system for automatically monitoring and retrieving the content of telephone calls.
b. Background Art
There is a general need for systems that automatically monitor the contents of telephone conversations. There is also a general need for methods that analyze the contents of monitored telephone conversations for a plurality of characteristics such as, for example, topics, phrases (e.g., the use of slang suggesting inappropriate behavior or criminal activity), or evidence of the addition of third parties so that the calls may later be retrieved on the basis of these characteristics.
Although most correctional facilities record all telephone calls made by inmates, it is believed that only a very small proportion of the calls is monitored by correctional officers. Many correctional facilities record 2,000 inmate telephone calls a day, such that monitoring all telephone calls, or even a large fraction of all calls, on a regular basis would require too much personnel and would be cost prohibitive. Inmates are aware of this and know that there is little chance that any one particular conversation will be monitored. Thus, it is thought that inmates may use telephone calls to engage in inappropriate conduct or criminal behavior while incarcerated.
Research has shown that inmate telephone recordings often contain evidence of inappropriate or illegal activity, such as terrorist activity, the intelligence value of which is not fully exploited by correctional facilities. This may result in the loss of potentially valuable information that may help solve or prevent additional crimes. For example, it has been shown that inmates may use telephones to inappropriately contact witnesses, make drug deals, discuss gang related issues, and in general continue to exert criminal influence on the very communities in which they were convicted. Inmates may also discuss issues related to non-criminal activities that are of interest to correctional officers, including suicidal thoughts or tendencies, medical or psychological problems, and political or religious radicalization.
Patterns of inappropriate or criminal activity may not always be readily apparent from a single telephone conversation. Rather, patterns may only appear when information from multiple conversations is combined, for example from a single inmate or multiple inmates, from a single correctional facility or multiple correctional facilities, or a combination thereof. Thus, even if all inmate telephone conversations at all correctional facilities were to be monitored, some patterns might still not be recognized unless the same individual monitors all conversations. Doing so would be physically impossible at most correctional facilities.
From an investigative viewpoint, as well as from strategic and tactical intelligence viewpoints, it is also desirable, whenever possible, to combine data from multiple sources, such as inmate telephone systems, inmate management systems, and investigative case management systems. Each source of information may provide additional insight into patterns of inmate activity, and may also assist in establishing evidence of inappropriate or criminal activity.
In certain cases it is imperative that correctional officials are notified as soon as possible when an inmate discusses a specific topic. This is especially true for instances where threats of death, violence, destruction, or suicide are uttered during conversations. The sooner law enforcement or intelligence officials become aware of these threats, the greater the chances that they can prevent individuals from coming to harm.
It is also well established that it is of advantage to view data in multiple formats, or modes. This is especially true in instances where a query results in large amounts of data. For example, in addition to tabular representations of data, data might also be viewed on a map, a link chart, or a timeline chart. Each mode of visualization can provide additional insight into any patterns evident in the data that other modes of visualization may not provide.
It is therefore desirable to be able to automate the monitoring of inmate telephone calls and provide correctional facilities with the ability to search for and retrieve telephone calls that contain potential evidence of inappropriate conduct or illegal activity.
It is also desirable to be able to go directly to the point in the conversation where the inappropriate conduct or illegal activity was discussed without having to listen to the entire conversation.
In addition, it is desirable to provide correctional officers with the ability to search for and retrieve telephone calls in multiple modes, i.e. query from and display on multiple modes of visualization and analytical techniques. Multi-modal queries and analytical techniques assist in identifying patterns across calls and correctional facilities.
It is also desirable to combine data from multiple sources and/or multiple correctional facilities to provide further insight into patterns of inappropriate conduct or criminal activity and assist in establishing evidence of inappropriate conduct or criminal activity that a single source of information may not reveal.
Also, it is desirable to provide a means with which correctional officials can instruct the monitoring system to immediately notify them when certain preset or user defined characteristics are detected in a telephone conversation.
The present invention provides a method and a system for the automated monitoring of inmate telephone conversations as well as multi-modal search, retrieval and playback capabilities for the monitored telephone conversations. A general term for such capabilities is multi-modal audio mining. The invention is designed to provide an efficient means for organizations such as correctional facilities to identify and monitor the contents of telephone conversations and to provide evidence of possible inappropriate conduct or criminal activity of inmates in instances where such activity indeed occurs.
Disclosed herein is a system for data-mining a monitored telephone conversation. The system generally includes a speech recognition processor configured to transcribe the monitored telephone conversation and associate a plurality of characteristics of the monitored telephone conversation with a transcript of the monitored telephone conversation; an event detection processor configured to analyze the monitored telephone conversation to detect one or more events within the monitored telephone conversation; a tagging processor configured to associate metadata information indicative of the detected one or more events with the transcript of the monitored telephone conversation; and a multimedia data warehouse configured to store at least the transcript of the monitored telephone conversation and the metadata information and characteristics associated therewith as a data record for the monitored telephone conversation. The system may also include a data normalization processor configured to atomize the data record for the monitored telephone conversation with respect to a plurality of data records stored within the multimedia data warehouse and a link creation processor configured to create one or more logical links between the data record for the monitored telephone conversation and one or more of the plurality of data records stored within the multimedia data warehouse. In addition, an optional synchronization processor may associate timing information for the detected one or more events with a sound recording of at least a portion of the conversation or position information (e.g., word or character counts) for the detected one or more events with the transcript of the monitored telephone conversation).
In some embodiments of the invention, the data record may include a sound recording of at least a portion of the monitored telephone conversation. Alternatively, a sound recording of at least a portion of the monitored telephone conversation may be stored in a sound recording database, and a reference thereto may be stored in the multimedia data warehouse as part of the data record.
The speech recognition processor will typically utilize at least one domain-specific language model to transcribe the monitored telephone conversation, for example a language model that is domain-specific to inmate speech. It is also contemplated that the language model or models may be domain specific to a plurality of regional dialects, a plurality of ethnic groups, or a plurality of languages.
The event detection processor may include a three-way call detection processor configured to analyze the plurality of characteristics for an indication of a third party being added to the monitored telephone conversation.
The event detection processor typically also includes an entity identification processor configured to analyze at least the transcript of the monitored telephone conversation to identify one or more entities mentioned during the monitored telephone conversation. At least one entity database may be coupled to the event detection processor. The at least one entity database may include at least one domain-specific Hidden Markov Model, such as a Hidden Markov Model domain-specific to inmate speech. The Hidden Markov Model or Models may also be domain-specific to a plurality of regional dialects, a plurality of ethnic groups, or a plurality of language. Further, it is contemplated that the Hidden Markov Models may be configured to utilize trigrammatic word transitional probabilities, bigrammatic word transitional probabilities, or both.
The event detection processor may utilize any suitable algorithm to identify one or more entities mentioned during the monitored telephone conversation. For example, the entity identification processor may utilize one or more Viterbi search or string matching algorithms to search the at least one entity database. Alternatively, the entity identification processor may use one or more heuristic algorithms to heuristically identify one or more entities mentioned during the monitored telephone conversation.
The event detection processor may also include a topic detection processor configured to analyze at least the transcript of the monitored telephone conversation to identify one or more topics discussed during the monitored telephone conversation. At least one topic database may be coupled to the topic detection processor. The topic detection processor may utilize one or more text clustering or phrase-matching algorithms to search the at least one topic database in order to identify one or more topics discussed during the monitored telephone conversation. Optionally, the topic detection processor may be configured to perform automatic topic annotation of the conversation using at least one domain-specific Hidden Markov Model and at least one domain-specific topic language model stored in the at least one topic database. As with other domain-specific aspects of the invention, the Hidden Markov Model and the topic language model used for topic detection may be domain-specific to inmate speech or to one or more ethnic groups, regional dialects, languages, or other linguistic differences, and the Hidden Markov Model may utilize either or both of trigrammatic and bigrammatic transitional word probabilities.
In some embodiments of the invention, at least one of a spatial information database and a telephone subscriber information database is coupled to the event detection processor. The event detection processor may be configured to associate information retrieved from at least one of the spatial information database and the telephone subscriber information database with at least the transcript of the monitored telephone conversation based upon the one or more events detected within the monitored telephone conversation.
A multi-modal query interface configured to accept user-specified search criteria may also be included. A search processor may be configured to search the multimedia data warehouse and retrieve one or more data records meeting the user-specified search criteria, and a multi-modal output interface may be configured to display the retrieved one or more data records. In other embodiments of the invention, a monitoring processor is configured to monitor the multimedia data warehouse for addition of a data record matching the user-specified search criteria. A notification processor may then notify a user upon addition of the data record meeting the user-specified search criteria.
Also disclosed is a system for data-mining a monitored telephone conversation, including: a multimedia data warehouse configured to store a plurality of data records, each of the data records associated with a monitored telephone conversation; a data transformation processor configured to receive data from at least one data source, the received data including at least audio of a monitored telephone conversation and metadata information regarding the monitored telephone conversation; a data augmentation processor configured to augment the received data with data retrieved from one or more of a telephone subscriber database and a spatial information database; a speech recognition processor configured to transcribe the monitored telephone conversation and associate a plurality of characteristics of the monitored telephone conversation with a transcript of the monitored telephone conversation; and a data mapping processor configured to store a data record for the monitored telephone conversation to the multimedia data warehouse, the data record including at least the transcript of the monitored telephone conversation, the plurality of characteristics of the monitored telephone conversation, the metadata information regarding the monitored telephone conversation, and the retrieved data. The system optionally includes a data cleaning processor configured to clean the received data, and, in some embodiments, to verify the suitability of the received data for further processing.
An optional data normalization processor is configured to atomize the data record for the monitored telephone conversation with respect to the multimedia data warehouse, while an optional link creation processor is configured to create one or more logical links between the data record for the monitored telephone conversation and one or more data records within the multimedia data warehouse. The data normalization processor may use a fuzzy logic algorithm, a matching algorithm (e.g., a soundex algorithm), a natural language processing algorithm, or another suitable algorithm to atomize the data record with respect to the multimedia data warehouse.
The at least one data source will typically include an inmate telephone system (e.g., a system for monitoring and recording inmate telephone calls, typically from a single correctional institution). The at least one data source may also include an inmate management system and/or an investigative case management system. Further, the at least one data source may originate from a single correctional facility or a plurality of correctional facilities.
The invention also provides a method of data-mining a monitored telephone conversation including the following steps: transcribing a monitored telephone conversation; extracting a plurality of characteristics of the monitored telephone conversation; associating the extracted plurality of characteristics of the monitored telephone conversation with a transcript of the monitored telephone conversation; analyzing the monitored telephone conversation to detect one or more events within the monitored telephone conversation; associating metadata information indicative of the detected one or more events with the transcript of the monitored telephone conversation; and storing at least the transcript of the monitored telephone conversation and the characteristics and metadata information associated therewith in a multimedia data warehouse as a data record for the monitored telephone conversation. The method optionally further includes: atomizing the data record for the monitored telephone conversation with respect to a plurality of data records stored within the multimedia data warehouse; and creating one or more logical links between the data record for the monitored telephone conversation and one or more of the plurality of data records stored within the multimedia data warehouse. Atomicity may be ensured by using any of a number of suitable algorithms, including, without limitation, fuzzy logic algorithms, matching algorithms (e.g., soundex algorithms), and natural language processing algorithms. The detection of events may include detecting the addition of one or more third parties to the conversation, the discussion of one or more topics during the conversation, and/or the mention of one or more entities during the conversation.
In another aspect of the invention, a method of data-mining a monitored telephone conversation includes: receiving data from at least one data source, the received data including at least audio of a monitored telephone conversation and metadata information regarding the monitored telephone conversation; retrieving data from one or more of a telephone subscriber database and a spatial information database based upon the metadata information regarding the monitored telephone conversation; associating the retrieved data with the received data; transcribing the monitored telephone conversation; extracting a plurality of characteristics of the monitored telephone conversation; associating the extracted plurality of characteristics of the monitored telephone conversation with a transcript of the monitored telephone conversation; generating a data record for the monitored telephone conversation, the data record including at least the transcript of the monitored telephone conversation, the plurality of characteristics of the monitored telephone conversation, the metadata information regarding the monitored telephone conversation, and the retrieved data; and storing the data record for the monitored telephone conversation to a multimedia data warehouse. The method may also include cleaning the received data. Cleaning the data may include one or more of the following steps: stripping leading blanks from the audio of the monitored telephone conversation, stripping trailing blanks from the audio of the monitored telephone conversation, standardizing telephone numbers within the metadata information regarding the monitored telephone conversation, spell-checking names within the metadata information regarding the monitored telephone conversation, verifying suitability of the received data for further processing, and any combinations thereof.
An advantage of the present invention is that it operates independently of any particular type of telephone system, such as VOIP or POTS.
Another advantage of the present invention is that it works with any external data source that utilizes an open database architecture standard.
Still another advantage of the present invention is that it allows correctional officers, intelligence analysts, and other officials to concentrate their efforts on analysis rather than data collection and transformation. The data is always available in the format that it is needed and when it is needed.
A further advantage of the present invention is that it allows correctional officers to perform multiple modes of analysis and visualization within a single system.
Another advantage of the present invention is that it permits correctional officers to search for and retrieve telephone conversations on the basis of topics discussed without having to know the exact words that were uttered.
Yet another advantage of the present system is that it permits one to “jump” directly to the point in the telephone conversation where the search criteria were found without having to listen to the entire telephone conversation.
Another advantage of the present invention is that the monitoring of telephone conversations is fully automated, thus resulting in considerable manpower savings.
Still another advantage of the present invention is that it allows correctional officers to be automatically notified as soon as a telephone conversation meeting preset or user-defined criteria is monitored, which may prevent a crime from occurring or a person from coming in harms way.
Yet a further advantage of the present invention is that it allows for the analysis of patterns of telephone conversations across multiple correctional facilities.
The foregoing and other aspects, features, details, utilities and advantages of the present invention will be apparent from reading the following description and claims, and from reviewing the accompanying drawings.
The following description illustrates the invention with reference to a real world domain, and in particular with respect to correctional facilities. However, it should be understood that the practice of the invention is not limited to any particular domain or application. Rather, it is intended to be more generally applicable to any domain where there is a need for the automated monitoring and multi-modal query and retrieval of recorded or live telephone conversations.
The present invention allows users, such as law enforcement personnel, correctional officers, intelligence analysts, and investigators, to automatically monitor telephone conversations and search for evidence of inappropriate conduct, criminal activity, or other topics of discussion that should be brought to their attention. While prior art has typically focused on simple key word searches of transcripts of recorded telephone conversations, the present invention advantageously enables users to query, retrieve, visualize and analyze the contents of monitored telephone conversations (also referred to herein as “telephone conversations, “conversations,” or simply “calls”) in a plurality of modalities, as well as across a plurality of correctional facilities. Further, the present invention provides the ability to integrate other data known about persons, such as the inmates making the calls and called parties. Examples of such data include, without limitation, telephone subscriber identity, addresses, and type of service, records from inmate management systems, and data from investigative case management systems.
In
In some embodiments of the invention, the external databases include an inmate management system 102, an inmate telephone system 104, which preferably includes recorded, real-time, or near real-time audio of monitored inmate telephone conversations and information regarding the monitored telephone conversations, as well as a generic database containing other data sources 106 that might include, for example, an investigative case management system. It is contemplated, however, that the invention may be practiced with only a single data source that includes at least audio of a monitored telephone conversation and information regarding the monitored telephone conversation, such as the identity of the caller, the phone number called, the date and time of the call, and the duration of the call. For purposes of this application, and as one of ordinary skill will understand, an inmate telephone system is a telephone system that provides telephone services to the inmates for a corrections facility; typically, it contains hardware and software that is housed in the facility, but it may also be located at a remote site. Similarly, a inmate management system is a combination of hardware and software that permits a corrections facility to process and monitor its inmates. An inmate management system is typically separate from an inmate telephone system, but they may be electronically coupled to share information about inmates. As one of ordinary skill will appreciate, an investigative case management system is a combination of hardware and software that permits a user (typically a person who works in the corrections industry) to monitor the process and status of one or more individuals who have been processed at some time in a corrections facility.
The external databases 102, 104, 106 may reside on a single computer or multiple computers. Further, it is contemplated that a single database may be stored across multiple computers or storage devices. In addition, the external databases 102, 104, 106 may be provided by and processed from a plurality of correctional facilities. The data transfer process 108 may include stored procedures that run on the external databases 102, 104, 106, software programs that are executed on one or more computers and that access the external databases 102, 104, 106 remotely, or a combination of both.
In the multimedia data warehouse holding area 112, sound recordings (in whole or in part) of monitored telephone conversations received from the inmate telephone system 104 may be stored internally as binary large objects (BLOBs). Alternatively, sound recordings of monitored telephone conversations may be stored in an external file system, in which case the multimedia data warehouse holding area 112 preferably includes a reference to the external file system.
A data retrieval process 202 retrieves a record (e.g., the data received from databases 102, 104, 106) from the multimedia data warehouse holding area 112 and passes it to a data cleaning process 204. The data cleaning process 204 cleans the record (e.g., each element of the record) by performing a plurality of functions including, but not limited to, stripping leading and trailing blanks or non alphanumeric characters from strings; standardizing telephone numbers (e.g., the phone number called, which may be included in the information regarding the monitored telephone conversation); ensuring that a minimum number of elements are present in address records so that these records may later be processed for mapping; checking the spelling of first and last names (e.g., the first and last names of the inmate making the call, or the first and last names of the called party, which may be included in the information regarding the monitored telephone conversation); and flagging those records where the quality of individual data elements is so inadequate as to render the entire record unusable and unsuitable for further processing, in which case the record may be discarded in some embodiments of the invention.
After successful cleaning, the data record may be passed on to the data augmentation process 208. Wherever possible, the data augmentation process 208 adds further information to the data record in order to make it more useful for analytical purposes and to allow additional cross-referencing with other records. Typically, the data augmentation process 208 will utilize the received data, such as the information regarding the monitored telephone conversation, to retrieve data from one or more databases. For example, in
By augmenting the received data with the retrieved data, the ability of the present invention to query and display in multiple modalities (e.g., query and display on the basis of inmate name, dialed phone number, name of called party, address of called party, ZIP code of called party, police precinct of called party, and the like) is enhanced. For example, augmenting a land based telephone number of a called party with subscriber information (e.g., name of subscriber and type of service) and address information allows the location of the telephone number to be displayed on a map and queried based on location. The data augmentation process is illustrated in more detail in
Next the record may be passed to the data integration process 210. The data integration process performs a final check to ensure that each element of a record (e.g., the received data and the retrieved data) can readily be integrated with a plurality of application specific data warehouse schemas. It should be understood that the term “application specific data warehouse” refers to data warehouses that are specialized in certain applications, such as a spatial data warehouse that stores data used by geographic information systems. By using a plurality of application specific data warehouses, the present invention is better able to provide multiple modalities for query and visualization of results.
A data normalization and schema mapping process 212 maps each data element within the data record to the plurality of application specific data warehouses and ensures that each entity (e.g., address, person, organization, telephone number) is only represented once within the plurality of application specific data warehouses (referred to herein as atomization and/or atomicity). Each record or element of a record may then be inserted into a corresponding application specific data warehouse, including links, references, and/or pointers to other application specific data warehouses as necessary. Typical application specific data warehouses include a multimedia data warehouse 216 and a spatial data warehouse 214. The collective schema of data warehouses is referred to herein as a “multimedia data warehouse.”
As mentioned above, one of the functions of the data normalization and schema mapping process 212 is to prevent duplication of data. That is, the data normalization and schema mapping process 212 ensures atomicity of the data record for a monitored telephone conversation relative to the multimedia data warehouse. From an analytical perspective, data duplication can result in missed linkages or patterns. Thus, it is desirable for the data normalization and schema mapping process 212 to maintain master tables within the plurality of application specific data warehouses (e.g., within the multimedia data warehouse). In doing so, the data normalization and schema mapping process 212 may utilize a number of methods including, without limitation, fuzzy logic, soundex and other matches, and natural language processing techniques. For example, the data normalization and schema mapping process 212 may maintain a master table of called parties. If the called party in a particular monitored telephone conversation is unique, an entry is added to the master table of called parties. If, however, the called party is already within the master table, no entry is added to the master table, though a link may be created as further described below.
As described above, where an element of a data record consists of binary data, such as a sound recording (in whole or in part) of the monitored telephone conversation, the data element may be inserted directly as a binary large object (BLOB) within the appropriate application specific data warehouse within the multimedia data warehouse. Alternatively, the data element may be stored on an external file system, with a reference to the data element within the external file system stored in the multimedia data warehouse.
One aspect of the data augmentation process 208 involves augmentation of telephone numbers within the received data, such as the telephone number of the called party. To this end, a subscriber lookup process 306 may be coupled to an external subscriber database 206 that includes information such as the subscriber's name, the subscriber's address, the type of service (e.g., cell, POTS, VOIP), the type of subscriber (e.g., private, business, organization) and the name of the telephone company providing the service. The subscriber lookup process may access the subscriber database 206 and retrieve any or all of this information and associate it with the data record for the monitored telephone conversation.
Another aspect of the data augmentation process 208 involves augmentation of addresses within the data record. Thus, an address matching and standardization process 304, which may be coupled to the spatial data warehouse 214, may perform a number of functions to augment addresses with geographic features. This advantageously facilitates visualization and query of data from a geographic information system (GIS) interface. The processor preferably references at least one address layer stored within the spatial data warehouse 214. The address layer may be line based, where the geographic coordinates of an address are derived via interpolation along a street segment, point based, where the geographic coordinates of an address are derived directly based upon the polygon centroids of land parcels or buildings, or a combination of both line based and point based. Typical functions performed by the address matching and standardization process 304 include, but are not limited to, address matching, spatial overlays, and address standardization. Spatial overlays add information that is not present in the original address record such as, but not limited to, census tract numbers, census block numbers, county information, and police zones or patrol sectors. This additional information can then be used for strategic analyses of calling patterns. The spatial data warehouse preferably includes at least one spatial index of the spatial features extracted in the address matching and standardization process 304.
Also shown in
In
Speech recognition process 404 performs a number of functions, of which one is converting the spoken audio to text (transcription). Preferably, the entire conversation is transcribed, but it is contemplated that only portions of the conversation may be transcribed as well. In doing so, the speech recognition process utilizes at least one language model 408. When transcribing speech to text, it is desirable to ensure that the language model used is domain specific. A “domain specific” language model is a language model that accurately reflects the linguistic nuances of the participants of a telephone conversation, for example a language model that is domain specific to inmate speech or telephony. Preferably, therefore, at least one domain specific language model is used.
In some embodiments of the invention, it is contemplated that multiple domain specific language models may be used, which may be trained for a plurality of ethnic groups, a plurality of regional dialects, or other language differences. Using multiple domain specific language models has been shown to significantly improve speech recognition and transcription accuracy. It is also contemplated that, in instances where foreign languages are spoken, multiple domain specific language models trained for a plurality of foreign languages may be used. Further, a translation processor may be utilized to translate the transcript of the monitored telephone conversation from a first language to a second language (e.g., to translate a conversation from Spanish into English).
In addition to converting spoken audio into text, the speech recognition process extracts a number of verbal and non-verbal characteristics from the telephone conversation. These include, but are not limited to, speaker turns (e.g., as determined by voice recognition of the speakers in the telephone conversation); gaps in audio; dial, pulse and ring tones; verbal cues (e.g., mentions of telephone numbers, mentions of people or organizations to call, or telephone salutations such as “hello,” “hi,” and the like); speech and phraseology patterns; and timing information that includes the beginning and end times (e.g., as measured in either seconds or milliseconds from the beginning of the telephone conversation) of utterances, audio gaps, dial, pulse and ring tones, and speaker turns. The characteristics are preferably associated with the transcript of the monitored telephone conversation.
Once the speech recognition process has completed processing the telephone conversation, it outputs the results to a file. One suitable format for the output file is an XML file 412. the output file is then processed by an output processor 410 that extracts each component from the XML file and inserts it as a call record into the multimedia data warehouse 216, for example as a binary large object (BLOB). That is, as illustrated in
As shown in
One aspect of the event detection process detects three-way calls-that is, the addition of one or more third parties to the monitored telephone conversation. Thus, a three way call detection process 504 may analyze the monitored telephone conversation (e.g., the transcript and the plurality of characteristics of the monitored telephone associated therewith) for evidence of one or more additions of third parties within the telephone conversation transcript and associated call characteristics. Upon successful detection of a three way call, information regarding the addition of a third party may be generated and added to the plurality of call characteristics extracted in
Another aspect of the event detection process is the detection of one or more entities mentioned during the monitored telephone conversation, as well as, in some embodiments of the invention, the point during the monitored telephone conversation at which the entity was mentioned. Accordingly, upon completion of three way call detection, an entity extraction process 506 may search the transcript and extract entities therefrom. The term “entities” includes, but is not limited to, person names, names and abbreviations of organizations, addresses, place names, telephone numbers, dates, times and monetary amounts.
In some embodiments of the invention, the entity extraction process utilizes at least one domain specific Hidden Markov Model (HMM) that is stored in an external entity database 514 and one or more suitable search algorithms including, but not limited to, Viterbi searches, to maximize the likelihood that each utterance in the transcript does or does not represent all or part of an entity, and, if it does represent all or part of an entity, what the nature of the entity is (e.g., distinguishing a phone number from a monetary amount). A variety of other techniques including, but not limited to, string matches of known entities and heuristic methods to determine whether a sequence of numbers uttered during a telephone conversation represent a telephone number, may also be used as part of the entity extraction process.
If the entity extraction process determines that a telephone number has been uttered in the telephone conversation, the telephone number may be looked up in the subscriber database 206 to verify whether it is indeed a registered telephone number. If it is a registered telephone number, subscriber information may be added as in the subscriber lookup process 306 depicted in
It is desirable to ensure that the entity extraction process utilizes at least one domain specific Hidden Markov Model that accurately reflects the linguistic nuances of the participants of a telephone conversation, for example, a Hidden Markov Model that is domain specific to inmate speech or telephony. Preferably, therefore, at least one domain specific Hidden Markov Model is used. In some embodiments of the invention, it is contemplated that multiple Hidden Markov Models may be used, which may be trained for a plurality of ethnic groups, a plurality of regional dialects, or other language differences. It is also contemplated that, in instances where foreign languages are spoken, multiple domain specific Hidden Markov Models trained for a plurality of foreign languages may be used. Using multiple domain specific Hidden Markov Models has been shown to significantly improve recognition accuracy within entity extraction processes and to minimize false positive identification of entities. It is also contemplated that in some embodiments of the present invention the plurality of Hidden Markov Models may incorporate trigrammatic transitional word probabilities in addition to bigrammatic transitional word probabilities.
Upon completion of the entity extraction process 506, a topic detection process 508 may determine one or more topics discussed during the telephone conversation and the point or points during the telephone conversation the topics were discussed. In the preferred embodiment of the invention, the topic detection process 508 matches phrases contained in an external topic database, such as slang database 516. One advantage of using a slang database is that it provides a repository of slang utterances that may be made during a telephone conversation including, but not limited to, prison slang, gang slang, radical religious slang, terrorism related slang, slang for weapons, violence, and threats of violence, and drug related slang. Preferably, each slang term within the slang database is associated with both a major topic and a minor topic, though it is within the spirit and scope of the invention to associate any number of topics with a particular slang term. For instance, for the slang term “blow” a major topic might be drugs and a minor topic might be drug dealing. By detecting topics discussed during a monitored telephone conversation by analyzing at least the transcript of the monitored telephone conversation, a user, such as a correctional officer, may query the multimedia data warehouse for telephone conversations involving discussions of, for example, drugs in general and drug dealing in particular without having to know the exact phrase or phrases used during the conversation. Of course, additional slang categories may also be incorporated within the topic database.
Other topic detection techniques are also regarded as within the spirit and scope of the present invention. Such techniques include, but are not limited to: text clustering techniques; and at least one domain specific Hidden Markov Model combined with at least one domain specific topic language model that perform automated topic annotation of inmate telephone conversations. In some embodiments of the invention, it is contemplated that multiple domain specific Hidden Markov and topic language models may be used, which may be trained for a plurality of ethnic groups, a plurality of regional dialects, or other language differences. It is also contemplated that, in instances where foreign languages are spoken, multiple domain specific Hidden Markov and topic language models trained for a plurality of foreign languages may be used. Using multiple domain specific Hidden Markov and topic language models has been shown to significantly improve recognition accuracy within topic segmentation and annotation processes and to minimize false positive annotation of topics. It is also contemplated that in some embodiments of the present invention the plurality of Hidden Markov and topic language models may incorporate trigrammatic transitional word probabilities in addition to bigrammatic transitional word probabilities.
Once the topic detection process has been completed, a tagging process 510 may tag the monitored telephone conversation with information indicative of each event (e.g., each three way call, entity, slang phrase, and topic) of the telephone conversation being processed that was detected by the event detection process. The tags may then be added to the multimedia data warehouse 216 as part of the call record, for example as call record metadata (which is distinct from the underlying actual data such as the sound recording of the telephone conversation and the transcript thereof).
The tagging process 510 then passes control to the synchronization process 512. The synchronization process identifies timing information for the detected events (e.g., the likely beginning and, where applicable, end of each detected three way call, entity mention, phrase and topic discussion). For example, the synchronization process 512 may add one or, where applicable, two time stamps or other timing information to a sound recording of the monitored telephone conversation, where the timing information identifies the number of seconds or, in some embodiments, milliseconds from the beginning of the call at which a three way call, utterance of an entity, or discussion of a topic began and/or ended. The synchronization process 512 may also add position information about how many words or characters into the transcript a three way call, utterance of an entity, or discussion of a topic began and/or ended. Adding timing and/or position information advantageously may permit a user reviewing the monitored telephone conversation to “jump” directly to the point in the conversation where the three-way call occurred, where a particular entity was mentioned, or where a particular topic was discussed, thereby avoiding the need to review the entire conversation.
The multimedia data warehouse 216 may also be updated to include at least one index of three way calls, topics and entities and the characteristics associated with them.
As shown in
An agent monitor 608, which is typically a software program, but may be hardware-implemented without departing from the spirit and scope of the present invention, picks up the newly created database agent and continuously monitors the multimedia data warehouse (e.g., the spatial data warehouse 214 and the multimedia data warehouse 216) for any new records or patterns that match the search criteria defined in the database agent. Some embodiments of the present invention may deploy multiple agent monitors simultaneously on one or more computers. It is also contemplated that a plurality of specialized agent monitors may be executed simultaneously including, but not limited to: application specific agent monitors, agent monitors that specialize in pattern detection, and agent monitors that specialize in less complex queries. It should also be understood that, in addition to executing within the multimedia data warehouse as a stored procedure, the agent monitor may also be stored and executed externally as a software process.
Upon detecting the addition of a call record, pattern, or other criteria that match the search criteria of a database agent operating on or within the multimedia data warehouse, the agent monitor 608 forwards the call record, pattern or other criteria to an agent report generator 606. The agent report generator constructs a report that includes a plurality of aspects of the call record, pattern, or other criteria. A report may include, but is not limited to, call transcripts, subscriber information, inmate details, a detailed description of the identified pattern, and a listing of phrases that matched the database agent criteria.
The report is then forwarded to a user notification manager 610. The user notification manager notifies the user that created the database agent via the at least one method by which the user prefers to be notified and, optionally, may also forward a copy of the report generated by the agent report generator 606. Notification methods include, but are not limited to: email, pager, voice mail or database alerts that are triggered the next time the user logs in to the query and retrieval systems, such as illustrated in
Upon completion of the selection of query criteria, the criteria are forwarded to a query generator 710. The query generator translates the multi-modal query into, for example, SQL code that can then be applied to the multimedia data warehouse 216, the spatial data warehouse 214, and any other application specific data warehouses within the multimedia data warehouse. After the SQL code has been constructed, the query generator 710 executes the query.
It should be contemplated that other embodiments of the invention may include other types or combinations of analytical display and modes of visualization without limitation. Other embodiments of the invention may also utilize additional external application servers in instances where web-based services are used. These include, but without limitation, a geographic information system application server and a link charting application server. For modes of visualization that permit the playback of retrieved recorded telephone conversation any characteristics identified by any of the previous processes may be visually identified within the transcript. For example, the point in a transcript where a three way call was detected may be highlighted and, optionally, where it was terminated. In some embodiments of the invention time stamps generated by the synchronization process 512 illustrated in
Although only a few illustrative embodiments of this invention have been described above with a certain degree of particularity, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of this invention. For example, one of ordinary skill in the art will appreciate that any of the systems disclosed herein (e.g., data transfer system 100, speech recognition system 400) may be software-implemented (e.g., a software program executed by one or more computer systems or processors), hardware-implemented (e.g.,. a series of instructions stored in one or more solid-state devices), or a combination of both, and that multiple instances of such systems may be simultaneously executed on a single computer or on multiple computers. In addition, it is contemplated that the various processors described herein may be integrated into one or more processors incorporated into one or more computers without departing from the spirit and scope of the present invention. Further, it is contemplated that the present invention may be practiced in real-time, near real-time, or on pre-recorded telephone conversations.
It is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative only and not limiting. Changes in detail or structure may be made without departing from the spirit of the invention as defined in the appended claims.
This application claims the benefit of U.S. provisional application No. 60/901,342, filed 15 Feb. 2007. This application is also related to U.S. application Ser. No. 11/706,431, filed 15 Feb. 2007. The foregoing are hereby incorporated by reference as though fully set forth herein.
Number | Name | Date | Kind |
---|---|---|---|
4670628 | Boratgis et al. | Jun 1987 | A |
4691347 | Stanley et al. | Sep 1987 | A |
4737982 | Boratgis et al. | Apr 1988 | A |
4813070 | Humphreys et al. | Mar 1989 | A |
4907221 | Pariani et al. | Mar 1990 | A |
4918719 | Daudelin | Apr 1990 | A |
4943973 | Werner | Jul 1990 | A |
4995030 | Helf | Feb 1991 | A |
5185781 | Dowden et al. | Feb 1993 | A |
5210789 | Jeffus et al. | May 1993 | A |
5291548 | Tsumura et al. | Mar 1994 | A |
5319702 | Kitchin et al. | Jun 1994 | A |
5319735 | Preuss et al. | Jun 1994 | A |
5345501 | Shelton | Sep 1994 | A |
5345595 | Johnson et al. | Sep 1994 | A |
5379345 | Greenberg | Jan 1995 | A |
5425091 | Josephs | Jun 1995 | A |
5438616 | Peoples | Aug 1995 | A |
5485507 | Brown et al. | Jan 1996 | A |
5502762 | Andrew et al. | Mar 1996 | A |
5517555 | Amadon et al. | May 1996 | A |
5535194 | Ashley et al. | Jul 1996 | A |
5535261 | Brown et al. | Jul 1996 | A |
5539731 | Haneda et al. | Jul 1996 | A |
5539812 | Kitchin et al. | Jul 1996 | A |
5555551 | Rudokas et al. | Sep 1996 | A |
5583925 | Bernstein | Dec 1996 | A |
5590171 | Howe | Dec 1996 | A |
5592548 | Sih | Jan 1997 | A |
5613004 | Cooperman et al. | Mar 1997 | A |
5619561 | Reese | Apr 1997 | A |
5627887 | Freedman | May 1997 | A |
5634086 | Rtischev et al. | May 1997 | A |
5636292 | Rhoads | Jun 1997 | A |
5640490 | Hansen et al. | Jun 1997 | A |
5646940 | Hotto | Jul 1997 | A |
5649060 | Ellozy et al. | Jul 1997 | A |
5655013 | Gainsboro | Aug 1997 | A |
5687236 | Moskowitz et al. | Nov 1997 | A |
5710834 | Rhoads | Jan 1998 | A |
5719937 | Warren et al. | Feb 1998 | A |
5745558 | Richardson, Jr. et al. | Apr 1998 | A |
5745569 | Moskowitz | Apr 1998 | A |
5745604 | Rhoads | Apr 1998 | A |
5748726 | Unno | May 1998 | A |
5748763 | Rhoads | May 1998 | A |
5748783 | Rhoads | May 1998 | A |
5757889 | Ohtake | May 1998 | A |
5768355 | Salibrici | Jun 1998 | A |
5768426 | Rhoads | Jun 1998 | A |
5774452 | Wolosewicz | Jun 1998 | A |
5796811 | McFarlen | Aug 1998 | A |
5805685 | McFarlen | Sep 1998 | A |
5822432 | Moskowitz | Oct 1998 | A |
5822436 | Rhoads | Oct 1998 | A |
5832068 | Smith | Nov 1998 | A |
5832119 | Rhoads | Nov 1998 | A |
5835486 | Davis et al. | Nov 1998 | A |
5841886 | Rhoads | Nov 1998 | A |
5841978 | Rhoads | Nov 1998 | A |
5850481 | Rhoads | Dec 1998 | A |
5861810 | Nguyen | Jan 1999 | A |
5862260 | Rhoads | Jan 1999 | A |
5867562 | Scherer | Feb 1999 | A |
5883945 | Richardson et al. | Mar 1999 | A |
5889868 | Moskowitz et al. | Mar 1999 | A |
5899972 | Miyazawa et al. | May 1999 | A |
5907602 | Peel et al. | May 1999 | A |
5915001 | Uppaluru | Jun 1999 | A |
5920834 | Sih et al. | Jul 1999 | A |
5926533 | Gainsboro | Jul 1999 | A |
5930369 | Cox et al. | Jul 1999 | A |
5930377 | Powell et al. | Jul 1999 | A |
5937035 | Andruska et al. | Aug 1999 | A |
5953049 | Horn et al. | Sep 1999 | A |
5960080 | Fahlman et al. | Sep 1999 | A |
5963909 | Warren et al. | Oct 1999 | A |
5982891 | Ginter et al. | Nov 1999 | A |
5991373 | Pattison et al. | Nov 1999 | A |
5999828 | Sih et al. | Dec 1999 | A |
6011849 | Orrin | Jan 2000 | A |
6026193 | Rhoads | Feb 2000 | A |
6035034 | Trump | Mar 2000 | A |
6038315 | Strait et al. | Mar 2000 | A |
6052462 | Lu | Apr 2000 | A |
6058163 | Pattison et al. | May 2000 | A |
6064963 | Gainsboro | May 2000 | A |
6072860 | Kek et al. | Jun 2000 | A |
6078567 | Traill et al. | Jun 2000 | A |
6078645 | Cai et al. | Jun 2000 | A |
6111954 | Rhoads | Aug 2000 | A |
6118860 | Hillson et al. | Sep 2000 | A |
6122392 | Rhoads | Sep 2000 | A |
6122403 | Rhoads | Sep 2000 | A |
6138119 | Hall et al. | Oct 2000 | A |
6141406 | Johnson | Oct 2000 | A |
6173284 | Brown | Jan 2001 | B1 |
6175831 | Weinreich et al. | Jan 2001 | B1 |
6185416 | Rudokas et al. | Feb 2001 | B1 |
6185683 | Ginter et al. | Feb 2001 | B1 |
6205249 | Moskowitz | Mar 2001 | B1 |
6233347 | Chen et al. | May 2001 | B1 |
6237786 | Ginter et al. | May 2001 | B1 |
6243480 | Zhao et al. | Jun 2001 | B1 |
6243676 | Witteman | Jun 2001 | B1 |
6253193 | Ginter et al. | Jun 2001 | B1 |
6263507 | Ahmad et al. | Jul 2001 | B1 |
6266430 | Rhoads | Jul 2001 | B1 |
6278772 | Bowater et al. | Aug 2001 | B1 |
6278781 | Rhoads | Aug 2001 | B1 |
6289108 | Rhoads | Sep 2001 | B1 |
6301360 | Bocionek et al. | Oct 2001 | B1 |
6308171 | De La Huerga | Oct 2001 | B1 |
6312911 | Bancroft | Nov 2001 | B1 |
6314192 | Chen et al. | Nov 2001 | B1 |
6324573 | Rhoads | Nov 2001 | B1 |
6324650 | Ogilvie | Nov 2001 | B1 |
6330335 | Rhoads | Dec 2001 | B1 |
6343138 | Rhoads | Jan 2002 | B1 |
6343738 | Ogilvie | Feb 2002 | B1 |
6389293 | Clore et al. | May 2002 | B1 |
6526380 | Thelen et al. | Feb 2003 | B1 |
6542602 | Elazar | Apr 2003 | B1 |
6625261 | Holtzberg | Sep 2003 | B2 |
6633846 | Bennett et al. | Oct 2003 | B1 |
6636591 | Swope et al. | Oct 2003 | B1 |
6639977 | Swope et al. | Oct 2003 | B1 |
6639978 | Draizin et al. | Oct 2003 | B2 |
6647096 | Milliorn et al. | Nov 2003 | B1 |
6665376 | Brown | Dec 2003 | B1 |
6665644 | Kanevsky et al. | Dec 2003 | B1 |
6668045 | Mow | Dec 2003 | B1 |
6671292 | Haartsen | Dec 2003 | B1 |
6688518 | Valencia et al. | Feb 2004 | B1 |
6728682 | Fasciano | Apr 2004 | B2 |
6760697 | Neumeyer et al. | Jul 2004 | B1 |
6763099 | Blink | Jul 2004 | B1 |
6782370 | Stack | Aug 2004 | B1 |
6880171 | Ahmad et al. | Apr 2005 | B1 |
6895086 | Martin | May 2005 | B2 |
6907387 | Reardon | Jun 2005 | B1 |
6920209 | Gainsboro | Jul 2005 | B1 |
6947525 | Benco | Sep 2005 | B2 |
7032007 | Fellenstein et al. | Apr 2006 | B2 |
7039171 | Gickler | May 2006 | B2 |
7039585 | Wilmot et al. | May 2006 | B2 |
7050918 | Pupalaikis et al. | May 2006 | B2 |
7062286 | Grivas et al. | Jun 2006 | B2 |
7075919 | Wendt et al. | Jul 2006 | B1 |
7079636 | McNitt et al. | Jul 2006 | B1 |
7079637 | McNitt et al. | Jul 2006 | B1 |
7103549 | Bennett et al. | Sep 2006 | B2 |
7106843 | Gainsboro et al. | Sep 2006 | B1 |
7123704 | Martin | Oct 2006 | B2 |
7133511 | Buntin et al. | Nov 2006 | B2 |
7133828 | Scarano et al. | Nov 2006 | B2 |
7133845 | Ginter et al. | Nov 2006 | B1 |
7149788 | Gundla et al. | Dec 2006 | B1 |
7248685 | Martin | Jul 2007 | B2 |
7256816 | Profanchik et al. | Aug 2007 | B2 |
7277468 | Tian et al. | Oct 2007 | B2 |
7417983 | He et al. | Aug 2008 | B2 |
7424715 | Dutton | Sep 2008 | B1 |
7466816 | Blair | Dec 2008 | B2 |
7519169 | Hingoranee et al. | Apr 2009 | B1 |
7529357 | Rae et al. | May 2009 | B1 |
7596498 | Basu et al. | Sep 2009 | B2 |
7639791 | Hodge | Dec 2009 | B2 |
7664243 | Martin | Feb 2010 | B2 |
7672845 | Beranek et al. | Mar 2010 | B2 |
7698182 | Falcone et al. | Apr 2010 | B2 |
7826604 | Martin | Nov 2010 | B2 |
7860222 | Sidler et al. | Dec 2010 | B1 |
7961860 | Mcfarlen | Jun 2011 | B1 |
8295446 | Apple et al. | Oct 2012 | B1 |
20010036821 | Gainsboro et al. | Nov 2001 | A1 |
20010043697 | Cox et al. | Nov 2001 | A1 |
20010056461 | Kampe et al. | Dec 2001 | A1 |
20020002464 | Petrushin | Jan 2002 | A1 |
20020010587 | Pertrushin | Jan 2002 | A1 |
20020032566 | Tzirkel-Hancock et al. | Mar 2002 | A1 |
20020046057 | Ross | Apr 2002 | A1 |
20020067272 | Lemelson et al. | Jun 2002 | A1 |
20020069084 | Donovan | Jun 2002 | A1 |
20020076014 | Holtzberg | Jun 2002 | A1 |
20020107871 | Wyzga et al. | Aug 2002 | A1 |
20020147707 | Kraay et al. | Oct 2002 | A1 |
20020174183 | Saeidi | Nov 2002 | A1 |
20030002639 | Huie | Jan 2003 | A1 |
20030023444 | St. John | Jan 2003 | A1 |
20030023874 | Prokupets et al. | Jan 2003 | A1 |
20030040326 | Levy et al. | Feb 2003 | A1 |
20030070076 | Michael | Apr 2003 | A1 |
20030093533 | Ezerzer et al. | May 2003 | A1 |
20030099337 | Lord | May 2003 | A1 |
20030126470 | Crites et al. | Jul 2003 | A1 |
20030174826 | Hesse | Sep 2003 | A1 |
20030190045 | Huberman et al. | Oct 2003 | A1 |
20040008828 | Coles et al. | Jan 2004 | A1 |
20040161086 | Buntin et al. | Aug 2004 | A1 |
20040169683 | Chiu et al. | Sep 2004 | A1 |
20040252184 | Hesse et al. | Dec 2004 | A1 |
20050010411 | Rigazio et al. | Jan 2005 | A1 |
20050027723 | Jones et al. | Feb 2005 | A1 |
20050080625 | Bennett et al. | Apr 2005 | A1 |
20050114192 | Tor et al. | May 2005 | A1 |
20050128283 | Bulriss et al. | Jun 2005 | A1 |
20050144004 | Bennett et al. | Jun 2005 | A1 |
20050170818 | Netanel et al. | Aug 2005 | A1 |
20050182628 | Choi | Aug 2005 | A1 |
20060064037 | Shalon et al. | Mar 2006 | A1 |
20060087554 | Boyd et al. | Apr 2006 | A1 |
20060087555 | Boyd et al. | Apr 2006 | A1 |
20060200353 | Bennett | Sep 2006 | A1 |
20060285650 | Hodge | Dec 2006 | A1 |
20070041545 | Gainsboro | Feb 2007 | A1 |
20070071206 | Gainsboro | Mar 2007 | A1 |
20070185717 | Bennett | Aug 2007 | A1 |
20070237099 | He et al. | Oct 2007 | A1 |
20070244690 | Peters | Oct 2007 | A1 |
20080000966 | Keiser | Jan 2008 | A1 |
20080021708 | Bennett et al. | Jan 2008 | A1 |
20080106370 | Perez et al. | May 2008 | A1 |
20080118045 | Polozola et al. | May 2008 | A1 |
20080198978 | Olligschlaeger | Aug 2008 | A1 |
20080201143 | Olligschlaeger et al. | Aug 2008 | A1 |
20080201158 | Johnson et al. | Aug 2008 | A1 |
20080260133 | Hodge et al. | Oct 2008 | A1 |
20080300878 | Bennett | Dec 2008 | A1 |
20100177881 | Hodge | Jul 2010 | A1 |
20100202595 | Hodge et al. | Aug 2010 | A1 |
Number | Date | Country |
---|---|---|
2579676 | Apr 2013 | EP |
2 075313 | Nov 1981 | GB |
59225626 | Dec 1984 | JP |
60010821 | Jan 1985 | JP |
61135239 | Jun 1986 | JP |
3065826 | Mar 1991 | JP |
PCTUS9514230 | Nov 1995 | WO |
WO 0174042 | Oct 2001 | WO |
Entry |
---|
National Alliance of Gang Investigators Associations, 2005 National Gang Threat Assessment, 2005, pp. vi and 5-7, Bureau of Justice Assistance, Office of Justice Programs, U.S. Department of Justice. |
Statement for the Record of John S. Pistole, Assistant Director, Counterterrorism Division, Federal Bureau of Investigation, Before the Senate Judiciary Committee, Subcommittee on Terrorism, Technology, and Homeland Security, Oct. 14, 2003. |
Michael G. Christel, Andreas M. Olligschlaeger, and Chang Huang, Interactive Maps for a Digital Video Library, IEEE Special Edition on Multimedia Computing, Jan.-Mar. 2000, pp. 60-67, IEEE, United States. |
Ian R. Lane, Tatsuya Kawahara, and Tomoko Matsui, Language Model Switching Based on Topic Detection for Dialog Speech Recognition, Proceedings of the IEEE-ICASSP, vol. 1, pp. 616-619, 2003, IEEE, United States. |
Andreas M. Olligschlaeger, Criminal Intelligence Databases and Applications, in Marilyn B. Peterson, Bob Morehouse, and Richard Wright, Intelligence 2000: Revising the Basic Elements—A Guide for Intelligence Professionals, 2000, a joint publication of IALEIA and LEIU, United States. |
Office of the Inspector General, Department of Justice, Criminal Calls: A Review of the Bureau of Prisons' Management of Inmate Telephone Privileges, Chapter 4, 1999, United States. |
National Major Gang Task Force, A Study of Gangs and Security Threat Groups in America's Adult Prisons and Jails, 2002, United States. |
Coherent Announces Industry's First Remote Management System for Echo Canceller, Business Wire, Mar. 3, 1997. |
Audioconferencing options. (Teleconference Units, Conference Bridges and Service Bureaus) includes related articles on speech processing and new conferencing technology), Frankel, Elena, Teleconnect. v. 14. n5. p. 131(3). May 1996. |
Digital on Call, Silberg, Lurie, HFN The Weekly Newspaper for the Home Furnishing Network, p. 97, Mar. 17, 1997. |
Inmate Telephone Services: Large Business: Voice, Oct. 2, 2001. |
Office action mailed Mar. 30, 2011 in U.S. Appl. No. 11/706,431. |
Amendment and Response Under 37 C.F.R. 1.111, dated Sep. 30, 2011, in U.S. Appl. No. 11/706,431. |
Final Office Action, mailed Jun. 1, 2011, in U.S. Appl. No. 11/819,834, filed Jun. 29, 2007, 26 pages. |
Final Office Action, mailed Oct. 17, 2011, in U.S. Appl. No. 11/706,431, filed Feb. 15, 2007, 13 pages. |
Non-Final Office Action, mailed Sep. 22, 2010, in U.S. Appl. No. 11/819,834, filed Jun. 29, 2007, 23 pages. |
Bender, W., et al., “Techniques for Data Hiding,” IBM Systems Journal, vol. 35, Nos. 3&4, 1996. |
Boney, L., et al., “Digital Watermarks for Audio Signals” Proceedings of the International Conference on Multimedia Computing Systems, p. 473-480, IEEE Computer Society Press, United States (1996). |
Boney, L., et al., “Digital Watermarks for Audio Signals” Proceedings of EUSIPC0-96, Eight European Signal processing Conference, Trieste, Italy, 10-13 (1996). |
Olligschlaeger, A. M., “Criminal Intelligence Databases and Applications”, in Marilyn B. Peterson, Bob Morehouse, and Richard Wright, Intelligence 2000: Revising the Basic Elements—A Guide for Intelligence Professionals, 2000, a joint publication of IALEIA and LEIU, United States. |
Newton's Telecom Dictionary, 18th Edition, Feb. 2002, pp. 168, section “coding theory”. |
Supplementary European Search Report for EP Application No. EP 04 80 9530, Munich, Germany, completed on Mar. 25, 2009. |
International Search Report for International Application No. PCT/US04/025029, European Patent Office, Netherlands, mailed on Mar. 14, 2006. |
Office Action mailed Dec. 1, 2011, in Canadian Patent Application No. 2,534,767, DSI-ITI, LLC, filed Aug. 4, 2004. |
Dye, Charles, “Oracle Distributed Systems,” O'Reilly Media, Inc., Apr. 1, 1999. |
McCollum, “Federal Prisoner Health Care Copayment Act of 2000,” House of Representatives Report 106-851, 106th Congress 2d Session, Sep. 14, 2000. |
Fischer, Alan D., “COPLINK nabs criminals faster,” Arizona Daily Star, Jan. 7, 2001. |
Wilkinson, Reginald A., “Visiting in Prison,” Prison and Jail Administration's Practices and Theory, 1999. |
i2 Investigative Analysis Software; “i2 TextChart—Text Visualized”, URL: http://www.i2.co.uk/Products/i2TextChart/. Jun. 13, 2005. |
i2 Investigative Analysis Software; “iBase—lnformation Captured”, URL: http://www.i2.co.uk/Products/iBase/. Jun. 13, 2005. |
i2 Investigative Analysis Software; “iBridge”, URL: http://www.i2.eo.uk/Products/iBridge/. Jun. 13, 2005. |
i2 Investigative Analysis Software; “Chart Reader”, URL: http://www.i2.eo.uk/Products/Chart Reader. Jun. 13, 2005. |
i2 Investigative Analysis Software; “Pattern Tracer”, URL: http://www.i2.co.uk/Products/Pattern Tracer/. Jun. 13, 2005. |
i2 Investigative Analysis Software; “Prisons”, URL: http://www.i2.co.uk/Solutions/Prisons/default.aso. Jun. 13, 2005. |
i2 Investigative Analysis Software; “Setting International Standards for Investigative Analysis”, URL: htto://www.i2.co.uk/Products/index.htm. Jun. 13, 2005. |
Microsoft White Paper: “Integrated Justice Information Systems”, retrieved from Microsoft Justice & Public Safety Solutions (Nov. 5, 2002) [http://jps.directtaps.net—vti bin/owssvr.dll?Using=Default%2ehtm] 22 pages. |
IMAGIS Technologies, Inc. “Computer Arrest and Booking System”, [retrieved from http://www.imagistechnologies.com/Product/CABS.htm] (Nov. 5, 2002) 5 pages. |
I MAGIS Technologies, Inc. “Integrated Justice System—Web-based Image and Data Sharing” [retrieved from http://www.imagistechnologies.com/Product/IJISFramework.htm>] (Nov. 5, 2002) 4 pages. |
O'Harrow, Robert Jr.. “U.S. Backs Florida's New Counterterrorism Database; ‘Matrix’ Offers Law Agencies Faster Access to Americans' Personal Records”; The Washington Post. Washington, D.C.: Aug. 6, 2003. p. A. 01. |
O'Harrow, Robert Jr.. “Database will make tracking suspected terrorists easier”, The Dallas Morning News. Dallas, TX: Aug. 6, 2003. p. 7A. |
Article entitled “Co Plink: A Case of Intelligent Analysis and Knowledge Management,” by Hauck et al., published in 1999. |
Article entitled “Building an Infrastructure for Law Enforcement Information Sharing and Collaboration: Design Issues and Challenges,” by Chau et al., published in 2001. |
Coden, A. R. and Brown, E. W., “Speech Transcript Analysis for Automatic Search,” Proceedings of the 34th Hawaii International Conference on System Sciences, IEEE, United States (2001). |
Number | Date | Country | |
---|---|---|---|
20080201143 A1 | Aug 2008 | US |
Number | Date | Country | |
---|---|---|---|
60901342 | Feb 2007 | US |