Source: https://patents.google.com/patent/US9286385B2/en
Timestamp: 2019-04-25 21:28:49+00:00

Document:
The present invention provides a method and system for providing access to information of potential interest to a user. Closed-caption information is analyzed to find related information on the Internet. User interactions with a TV which receives programming including closed-caption information are monitored to determine user interests or topics.
This application is a continuation-in-part of prior, co-pending U.S. patent application Ser. No. 11/789,609, filed on Apr. 25, 2007, which is incorporated herein by reference in its entirety for all purposes.
The present invention relates to a method and a system for providing users access to information of interest.
The number of networked devices in local area networks such as home networks is on the rise, and so is the amount of data stored on them. Typically, home network users store and access several types of content (such as audio, video, image and other data files) in different formats on/via their home devices. In addition to accessing these, home users also commonly access audio/video broadcast data streams via broadcast television or cable networks.
Further, the amount of information available on sources such as external networks, the Internet (e.g., the World Wide Web), etc. is continually on the rise. For example, it is very likely that a user can find useful information on the Internet related to at least some of the data stored on the devices in the user's home network. It is highly likely that the user can find large quantities of such related information in different formats (structured, semi-structured and unstructured) via multiple sources.
However, there is no system available that would allow a user to access such related information easily and seamlessly. The only way a user can achieve this is by manually performing a search for the desired information using an Internet search engine or by directly accessing a website (through a Web browser) that the user believes may contain such related information. Thus, the user is forced to comprehend and analyze large quantities of information to identify/access the exact information the user is looking for.
There are existing approaches in which a user can obtain information in a network of resources. In one approach, the user requests the information. The user specifies information using keywords and then browses the information to find the piece of information that satisfies the user's needs. However, specifying keywords using devices without keyboards, such as consumer electronics (CEs) devices, can be a tedious task.
Another approach involves a configuration that uses a TV and a PC. The PC analyzes the subtitles of the TV program and categorizes the program as general, news, medical, etc. The hierarchy of categories is fixed and built from questions posed to broadcast TV viewers. Content of a particular program is mapped to a fixed number of categories. The user can view additional information only when the content matches one of the specified categories. Queries are linked to fixed sources, limiting the amount of information that can be retried for the user. Further, the PC is required and the system cannot function when the PC is turned off. There is, therefore, a need for a method and a system for analyzing and obtaining information of interest to the user, without limiting specific sources of information.
In one embodiment of the present invention, a method for extracting a sentence from on incoming stream of text corresponding to a program is provided, the method comprising: retrieving end-of-sentence punctuation marks for a language identified for the incoming stream of text; locating punctuation marks in the incoming stream of text that match one of more of the retrieved end-of-sentence punctuation marks; comparing characters around the located punctuation marks to a list of word-punctuation pairs for the identified language to determine when a located punctuation mark is a valid end-of-sentence punctuation marks as opposed to an invalid one not to be considered an end-of-sentence punctuation mark despite its presence in the retrieved end-of-sentence punctuation marks for the identified language; and for any located valid punctuation marks, identifying a group of words between located valid punctuation marks as sentences.
In another embodiment of the present invention, a method for identifying a language of an incoming stream of text corresponding to a program is provided, the method comprising: comparing the incoming stream of text against one or more character sets, each of the character sets identifying characters used in a different language; identifying stop words in the incoming stream of text and comparing the identified stop words with stop words corresponding to one or more languages; and identifying a particular language for the incoming stream of text based on a marched character set and identified stop words corresponding to the particular language.
In another embodiment of the present invention, a method for validating a topic extracted from a stream of text corresponding to a program is provided, the method comprising: locally validating the topic by comparing the topic against one or more local word lists; and remotely validating the topic by submitting the topic as a query to an Internet search engine and comparing the number of results received from the Internet search engine to a predefined threshold.
In another embodiment of the present invention, an apparatus is provided comprising: a closed-caption decoder configured to decode a raw closed caption stream for a program and produce closed caption text; a language detection module configured to determine a language for the closed caption text; a sentence detection module configured to determine sentences within the closed caption text; a tagger configured to tag keywords based in the closed caption text based on the determined language and based on determined sentences; a topic extractor configured to extract topics based on the tagged keywords; and a validation module configured to validate the extracted topics.
FIG. 1 shows an example of a network of electronic devices that implements an embodiment of the present invention.
FIG. 2 shows a functional block diagram of an example system for analyzing TV closed-caption information to find related information on the Internet, according to an embodiment of the present invention.
FIG. 3A shows a flowchart of example steps of a process for analyzing TV closed-caption information to find related information on the Internet, according to an embodiment of the present invention.
FIG. 3B shows a flowchart of example steps of keyword extraction in the process of FIG. 3A.
FIG. 4 shows a functional block diagram of another example system for analyzing TV closed-caption information to find related information on the Internet, according to another embodiment of the present invention.
FIG. 5 is a flow diagram illustrating how these last few embodiments can be incorporated into a topic determination process in accordance with an embodiment of the present invention.
The present invention provides a method and a system for analyzing and obtaining information of interest to a user, without limiting specific sources of information. Potential information that the user may be interested in is determined by monitoring the user's interactions with a device in a local network of devices, connected to an external network. Such a device can be a CE device in a local area network (e.g., a home network that is connected to the Internet).
In one implementation, this involves receiving close-captioned programming including closed-caption (CC) information, and analyzing the closed-caption information for key information indicating user interests. The key information is then used to find related information from sources of information such as the Internet, which the user may potentially be interested in.
On a typical CE device such as a TV, in the absence of a keyboard, it is difficult for a user to search for information on the Internet by entering keywords. If a user is watching a TV program, that is a good indication that the user is interested in the content of the TV program. Therefore, the content of the TV program is analyzed by gathering and analyzing text received as CC information for the TV program. Further, contextual information is gathered from the information about the channel being watched. The CC information and the contextual information can be combined and used to make recommendations to the user about information the user may potentially be interested in.
The gathered information is used to determine one or more keywords of potential interest to the user. The keywords are then used to search for related information on the Internet. For example, if the user is watching a news coverage involving Baltimore, the word “Baltimore” is extracted as a keyword. That keyword is used to form a query to search the Internet by using a search engine to find information, such as websites that include information about Baltimore city or Baltimore Ravens, etc.
The search results are presented to the user as recommendations, comprising potential search queries which may be selected by the user and executed to find further information on the Internet that may be of interest to the user. For example, while the user is watching a documentary on Antarctica on a TV, the keyword Antarctica is selected as a keyword and a search on the Internet returns “polar bears” as a recommendation of potential interest to the user. The user can then choose that recommendation to find more information about polar bears. If so, a query for “polar bears” is sent to a search engine and the results are displayed for the user.
Searching is not limited to a predetermined or fixed number of categories or queries or information sources. In one example, keywords are identified based on the CC information for searching. The keywords may be suggested to the user, wherein upon user selection, additional information is obtained using search engines that search available sources on the Internet (different websites available to the search engines), rather than a predetermined and/or a fixed number of sources such as one or more particular websites.
FIG. 1 shows a functional architecture of an example network 10, such as a local network (e.g., a home network), according to an embodiment of the present invention. The network 10 embodies a process for analyzing TV CC information to find related information on the Internet 50. The network 10 comprises electronic devices 20 which may include content, and CE devices 30 (e.g., TV, DVD player, cell phone, PDA, etc.) which may include content. The network 10 further includes an interface 40 that connects the network 10 to an external network 50 (e.g., another local network, the Internet, etc.). The external network 50 is connected to one or more servers 51. Though the devices 20 and 30 are shown separately, a single physical device can include one or more logical devices. As described further below, in one example, a process for analyzing TV CC information and suggesting information of interest to the user, according to the present invention can be implemented in a device 30 in FIG. 1.
The devices 20 and 30, respectively, can implement the UPnP protocol for communication there between. Those skilled in the art will recognize that the present invention is useful with other network communication protocols (e.g., Jini, HAVi, IEEE 1394, etc.). Further, the network 10 can be a wired network, a wireless network, or a combination thereof.
A system that implements a process for analyzing TV CC information receives a TV signal as input. The channel being viewed by the user is monitored and corresponding CC information that is a part of the TV signal is analyzed. Then, a set of keywords are determined which capture the gist of what is being viewed by the user.
FIG. 2 shows a functional block diagram of an example system 200 for analyzing TV CC information according to an embodiment of the present invention. The system 200 utilizes a channel monitor 201, a TV Signal to Text Converter 202, a Stop Word Filter 204, a Proper Noun Detector 206, an Indexer 208, a Dictionary 210 and a Keyword Extractor 212.
Step 302: The CC information transformed into text by the converter 202 using known transformation methods.
Step 304: The stop words in the text are removed by the Stop Word Filter 204. Stop words include words such as “of”, “on”, “the” etc., which have no meaning by themselves.
Step 306: Using the Proper Noun Detector 206, when case information is absent, proper nouns are detected (identified) by comparing each word in the remaining text against the Dictionary 210. Typically, proper nouns start with uppercase letters. On other occasions, the text obtained is case insensitive. The Dictionary 210 includes words that are not proper nouns. If the word under consideration is not present in the Dictionary 210, then it is assumed to be a proper noun. Proper nouns convey useful contextual information as they refer to specific names of people, places or things. Words identified as proper nouns are therefore tagged by the Proper Noun Detector 206 for ranking later.
Step 308: Using the Proper Noun Detector 206, when case information is present, proper nouns are detected based on the case of the first character of each word. Proper nouns are tagged by the Proper Noun Detector 206 for ranking later.
Step 310: The remaining words, and the number of their occurrences (frequency), is recorded by the Indexer 208. More frequent words are important words, from which keywords will be selected. The record of words are reset as soon as the channel is changed or a topic change is discovered using special characters in the text obtained from the CC information.
Step 312: The Indexer 208 maintains words identified within an n second window (i.e., words received within an n second period of time, which may be the last n seconds) and their frequencies and proper noun related information. For example, an n=10 second window includes the words gathered in the past 10 seconds. The keywords indexed in step 310 cover the entire current TV program/story, etc., being watched, while the keywords discussed in this step, cover those gathered in the last ‘n’ seconds.
Step 314: The frequency of all words is used by the Keyword Extractor 212 to extract words (i.e., keywords) of interest from all words gathered from the past n seconds.
Step 316: The keywords are used for forming queries in a Query Formation module 205 for performing searches on available resources such as search engines on the Internet 50 by a Searching module 207. A User Interface (UI) module 203 presents the search results to the user as recommendations for optional further selection. Step 318: The user selects among the recommendations, wherein the selected recommendation are used by the Searching module 207 for searching the available resources for additional information of interest to the user. Such additional information is presented to the user by the UI module 203.
Steps 316 and 318 allow the user to find more information about a program that the user recently viewed on the TV, and can be repeated as the user desires to provide the user with additional and/or further refined information of interest to the user.
Step 314A: When the user activates a specific button on the TV control (e.g., a TV remote control), this provides a User Request that includes the user action. Upon a User Request, the words in the last n seconds are set aside as the words of most importance by the Keyword Extractor 212.
Step 314B: Further, each word is ranked based on its frequency or another ranking mechanism.
Step 314C: Each word is also checked for proper noun status, using the tag generated in steps 308 or 310.
Step 314D: A ranked list is produced wherein, in one example, high frequency words that are also proper nouns occupy the top of the list. Then follow the high frequency words and then words that are proper nouns. The top i words from the ranked list along with the high frequency words and the proper nouns identified since the start of the TV program, capture the context of what the user is watching. The information from the ranked list is used as keywords. Words captured in the n second window represent the context at a finer level of detail, whereas the proper nouns and high frequency words captured since the start of the program represent the context at a higher level.
Using Electronic Program Guide (EPG) information, which includes information about TV programs on cable TV, satellite TV, etc., the name of the channel being viewed, is used to frame the queries in steps 316, 318, along with the channel and program information. For example, when the user is viewing the “Panorama” program on BBC America, the words “Panorama” and “BBC America” are appended to the extracted keywords to provide related information in the context of the channel and program for searching.
Further, the extracted keywords can be converted into different languages and used for searching to find additional information on the Internet 50. Further, converting keywords, as opposed to sentences, from one language to the other is simple and can be done using a language-to-language dictionary. This is beneficial to users who may understand only a minor portion of the language in the TV program being watched.
In this embodiment, the Keyword Extractor 212 not only relies on information from the Proper Noun Detector 206 and the Indexer 208, but also uses information from the Phrase Extractor 214 to obtain keywords. The Phrase Extractor 214 includes a phrase identifier function that identifies important phrases using frequency and co-occurrence information recorded by the Indexer 208, along with a set of rules. This is important in identifying multi-word phrases such as “United Nations”, “Al Qaeda,” etc.
1. A word ending with punctuation cannot be in the middle of a phrase.
2. For a phrase length of two words or more, the first word cannot be a stop word other than the two articles: ‘the’ (definite), and ‘a/an’ (indefinite), and the rest of the words cannot be stop words other than conjunctive stop words like ‘the’, ‘on’, ‘at’, ‘of’, ‘in’, ‘by’, ‘for’, ‘and,’ etc. This is because the above mentioned stop words are often used to combine two or more words: e.g., “war on terror,” “wizard of oz,” “the beauty and the beast,” etc.
3. Proper nouns and words not present in the dictionary are treated as meaningful phrases.
The Phrase Extractor 214 includes a term extractor function which extracts the highest score terms and phrases from the index. The terms and phrases are presented to the user and can be used for further searching to provide additional information of interest to the user.
Alternatively, the Phrase Extractor 214 includes a natural language processing (NLP) tagger and a set of extraction rules to extract important phrases. In operation, the NLP tagger tags each word in the closed caption text with its part-of-speech (i.e. whether the word is a ‘noun,’ ‘adjective,’ ‘proper noun,’ etc.) The extraction rules define the kinds of sequences of such tags that are important. For example, a rule can be to extract phrases which are “a sequence of more than one ‘proper nouns’” and another rule can be to extract “a sequence of one or more ‘adjectives’ followed by one or more ‘nouns’.” The Phrase Extractor applies these rules to the text tagged by the part-of-speech tagger and extracts phrases that follow these sequences. It can also be used to extract single word keywords by using appropriate rules.
In one example, in FIGS. 2 and/or 4, all the elements except the Internet/Search Engine 50, reside on a CE device (e.g. CE device 30). In another example, in FIGS. 2 and/or 4, the Internet/Search Engine 50 resides on the Internet, the Dictionary 210 resides on the network (local or Internet), while the other elements reside on the CE device.
Although, in the examples provided herein, a TV is used to receive closed-caption information, the present invention can be applied to other devices (e.g., music player, etc.) that receive information that can be used for analysis to determine and search for information of interest to the user, according to the present invention.
Further, although in FIG. 1 a CE device 30 which is used to receive programming in including CC information, is shown connected to a home network, such a home network is not required and the CE device can be a stand-alone device that receives programming from cable, satellite, DSL, or other sources of programming, without connection to a home network. Such a stand-alone device need only be connected to sources of information such as connected to the Internet 50, without the need for a home/local network. Further, the present invention can be implemented in non-CE devices (e.g., a device 20 in FIG. 1) that may be a stand-alone device or connected to a home/local network. Further, although in the examples herein closed caption information is used for analysis and determination of user interests, in another implementation the present invention is useful with other types of information that represent the type of programming or content being accessed/viewed by a user.
Some natural language processing techniques, such as “part of speech tagging”, require a complete sentence to be analyzed in order to correctly tag it. In such instances, it becomes important for the system to be able to parse an incoming stream of text into complete sentences, as opposed to merely looking for keywords or phrases. In an embodiment of the present invention, a sentence detection module is provided to perform this process.
’. Then, a language specific list of word-punctuation pairs that do not constitute the end of a sentence are used to check for “false positives”, in other words situations where the typical end of sentence punctuation marks actually do not constitute an end of a sentence, such as ‘Mr.’, ‘Mrs.’, ‘Ms.’, ‘P.O.’, etc. If no such false positives are found, then the process can assume that an end of a sentence has been correctly identified.
In another embodiment of the present invention, the system can be configured to detect the language of an incoming stream of text. This is a useful adjunct to the sentence detection module embodiment described above, but also is independently useful because a number of natural language processing techniques, such as “part of speech tagging” and “stop words” are language-dependent. Therefore, in order to collect high-quality topics and to avoid selecting bad topics, it can be important to determine the language of the text.
A number of factors can be use to determine the language in the language detection module. The first such factor is character sets. There are certain characters that are unique to certain languages. In the easy case, some languages, such as Arabic and Hebrew have character sets made up entirely or nearly entirely of unique characters. In the harder cases, some languages, such as Spanish, French, and English, can have many common characters but at least a few unique characters to each language (such as é or ñ for Spanish). Identification of these unique characters in the stream of text can be used as an indicator of the language. Of course, such a factor may not be completely determinative. There may be some instances where characters from another language are used in a stream of text that is in English. A prime example is the present specification, which is in English but contains the Spanish characters reproduced above. Because of this, other factors must also be considered.
Another factor to be considered is a closed caption language descriptor. If the stream of text is from a television program closed caption stream, such closed caption streams typically include a descriptor element that explicitly states the language of the stream. If such a descriptor is present in the stream of text, it can be used as an indicator of the language of the stream.
Another factor to be considered are stop words. If the stream of text contains, for example, English stop words such as ‘the’ and ‘of’, then it may be English, whereas if it contains Spanish stop words such as ‘el’ and ‘la’ then it might be Spanish. Similar stop words can be identified for additional languages.
Another factor to be considered are tags returned by the Tagger for a text segment. If the ratio of words in a sentence marked as a proper noun to the total number of words in the sentence is greater than a threshold, then the language used in the tagger may be incorrect, in that it is more common to have a lower ration of proper nouns to other words in the sentence (common nouns, verbs, etc.). In other words, if the Tagger has identified a large number of proper nouns in the sentence, it may be that the tagger is using the wrong language and has misidentified common nouns and verbs as proper nouns.
Internally, the language detection module may maintain a confidence table that keeps track of the likelihood of each language being the current language of the program. The values are influenced by the factors mentioned above. The final determination of the current language is made by choosing the language having the highest confidence value.
In an embodiment of the present invention, there is an interdependent relationship between the sentence detection module and the language detection module. Detecting a sentence can be helpful in determining the language of the words in the sentence, but detecting the language can be helpful in determining when the sentence ends. As such, there may be much interplay between the sentence detection module and the language detection module in order to fulfill both goals. A feedback loop can be used in such instances. The feedback loop may first use the language detection module based on information already available. For example, the closed caption language descriptor or, if the program has not changed, use the language of the last sentence, this information may be used to derive a rough estimate of the language. This estimated language can then be used to detect a sentence. The extracted sentence may then be fed back to the language detection module to get a better estimate of the language. If the confidence increases and is more than a threshold, then the system can confidently use the topics extracted from the sentence. Otherwise, the confidence in the quality of the topics from these sentences is low.
In another embodiment of the present invention, TV programs can be categorized into genres. For example, a TV program can be factual or non-factual. Factual programs are those which are about real events, informational, educational, news, documentaries, etc. Non-factual programs are those that contain humor, emotions, such as sitcoms and dramas. Factual programs usually contain a lot of content that the user may be interested in finding more information about, such as names, locations, organization names, objects, etc. Non-factual programs have much less content in them, usually just names. Using the extraction rules that are designed for factual programs on non-factual programs can result in a lot of junk topics.
To overcome this problem, a table can be used that provides different extraction rules depending on the type of the program. This table may be called a genre-rules map. The mapping doesn't just discriminate between factual and non-factual, but instead has different extraction rules for many different genres. Indeed, the mapping is only limited based on the number of different genres that can be detected or determined. In one embodiment, the mapping is hierarchical, in that general genres can include sub-genres, each of which may have their own extraction rules.
The topic extractor looks at the genre of the program and asks the genre-rules map to provide it the rules for the genre. The result of using this map is improved quality of topics extracted, especially for non-factual programs such as sitcoms.
In another embodiment of the present invention, validation is performed, even once topics have been extracted, to ensure that the topics are of high quality. This may take place in two steps. In the first step, the topic can be checked against one or more local word lists, such as names and locations, to determine if it is a valid topic. This not only helps validate the topic, but also provides semantics for the topic. For example, if the topic is found in a local locations list, then the topic is known to be a location and this information can be used to determine what additional information to provide to the user (e.g., open a weather application or widget and search the identified location). This step is known as local validation or semantic tagging.
In the second step, the topic is checked against online sources to determine its validity. It may be sent as a query to a search engine to check the number of results returned. If there are very few results, such as only 10-20, then the topic may be invalid. This may be caused, for example, if there are junk characters in the closed caption stream. Using this second step, many low quality topics can be removed. This also can aid the system in obtaining topics for which useful services can actually be provided. For example, even if the topic is a valid location like Paris, the weather widget may be only be able to show weather for U.S. addresses, and thus the topic should be ignored.
FIG. 5 is a flow diagram illustrating how these last few embodiments can be incorporated into a topic determination process in accordance with an embodiment of the present invention. At 500, a closed caption decoder receives a raw closed captioning stream and decodes it into closed caption text. At 502, a language detection module then detects the language of the closed caption text. This module works in a feedback look with a sentence detection module, which at 504 detects the sentences in the closed caption text. Stop words 506 and character sets 508, both of which are language specific, can be used by the language detection module. Punctuation 510 can also be used by the language detection module as well as the sentence detection module. The result of this is a cleaned up sentence. A POS tagger 512 then tags the resulting sentence using the detected language. The tagged sentence is then fed to a topic extractor 514, which extracts the corresponding topics. The rules that the topic extractor applies are stored in a rule library 516, which is mapped by a genre-rule map 518 based on the genre of the underlying program.
At 520, local validation is performed using a semantic database 522, such as a list of people and locations. If the topic is found to be semantically tagged at 524, then the topic is deemed valid at 526. If not, then remote validation is performed at 528, using an external data source 530 such as an Internet search engine. If the topic is deemed to be remotely validated at 532, then the topic is valid (526), otherwise the topic is invalid at 534.
a validation module configured to validate the extracted topics by checking each extracted topic against at least one local source and at least one remote source.
2. The apparatus of claim 1, wherein the tagger includes one or more natural language processing functions.
the one or more extraction rules relate to the genre identified, such that the extracted topics are relevant to the genre identified.
the memory storage device includes a genre-rule map providing a mapping between the different extraction rules and different genres.
5. The apparatus of claim 1, wherein the one or more extraction rules are stored in a rule library.
a remote validation module for checking each extracted topic against at least one remote source.
the remote validation module is configured to submit each extracted topic as a query to online source that includes an Internet search engine.
8. The apparatus of claim 1, wherein the language detection module receives one or more character sets for one or more languages and one or more stop words corresponding to one or more languages as input.
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