Patent Application
20140359034
Consumers of information have always received, in various formats, news reports and opinions relating to current events. In the past, most of this information came in the form of person-to-person interactions or through printed media. And in response to receipt of news reports and opinions, consumers of information may have chosen to engage in some form of advocacy, for example, by writing a letter to the editor, by forming or joining a group of other consumers, or by making a financial contribution in support of or in opposition to the news report or opinion.
In today's electronic age, “person-to-person” transmission of information may take the form of a network news show where the news anchor presents the events of the day, and “printed” media may take the form of the daily news presented on the newspaper's online web page. However, a consumer of information may also receive news reports and opinions, not just at a preschedule time of day, but soon as then have been written. Moreover, these electronically-delivered streams of information can come from virtually any source, not just from the traditional news outlets.
And as was the case with non-electronically-delivered information, the consumer of information may choose to respond with some form of advocacy. However, a number of factors may hamstring the recipient's efforts:
First, the sheer volume of information can overwhelm even the most devoted consumer. Not only are there thousands of online newspapers delivering news reports and opinions, there are hundreds of millions of “tweets” posted to Twitter each day, hundreds of millions of publicly-accessible bogs, over a billion active Facebook users posting regularly, and scores of other streams of electronic information using not only the written word but also photographs, video, and audio.
Second, whereas a traditional newspaper might have a printed edition delivered every morning, and television network news might have an evening edition and a weekly summary on Sunday mornings, electronically-delivered streams of information know no such cycle. Indeed, as demonstrated by recent events, social media sources such as Twitter provide streams of information in near real time as the events unfold. By the time a consumer of information would have received the same news reports from a regularly scheduled news source, the news may be hours or days old.
Third, history shows that a consumer of information who is inclined to respond to a current event is most likely to act immediately after learning the event. For example, a day after a tornado devastates a town, donations of money and supplies may pour into charitable and relief organizations. But a month later, even though the need is just a great, those charitable and relief organizations may be struggling to find sufficient donors to meet the needs of the devastated town.
Fourth, with the deluge of information available via the Internet, savvy consumers of information recognize that not every source is reputable. Some news and opinions come from those who simply lack sufficient facts to reach the conclusions reflected in their tweets or bogs, while other news and opinions come from those having an agenda to distort the facts for political or monetary gain. Prior to making some act of advocacy—for example, donation money or posting a counter-position—a consumer of information may want to investigate the legitimacy of the source of the news report. However, by the time the consumer is satisfied as to the veracity of the report, the news may have become stale and the consumer may have lost the sense of urgency to make a response.
Fifth, different consumers of information will be motivated to respond to different events with different forms of advocacy. For example, a consumer with an interest in gun control may want to monitor specific blogs, Twitter feeds, and Facebook pages related to the National Rifle Association. In response to specific content, for example, the use of the phrase “assault rifle,” one consumer may choose to make a donation to the NRA, another may choose to make a donation to the Brady Campaign to Prevent Gun Violence, and another may simply want to share a link to the information on Facebook.
There are known solutions for certain aspects of these problems. For example, sophisticated traders may electronically monitor stock market tickers and then buy or sell based on predefined prices, email filters may electronically scan incoming mail and automatically handle the messages according to predefined keywords or sender names, and social media platforms may present specific advertisements based on user behavior.
However, what has heretofore been lacking is an integrated system that would monitor a predefined stream of electronic information for occurrences of predefined trigger, and in response, would make a predefined act of advocacy in support of or opposition to the trigger.
The present invention addresses the foregoing problems by providing an inventive computer-based platform that allows a consumer of information to preconfigure (a) a public electronic information stream to be monitored, (b) a keyword, phrase, or other recognizable trigger condition, and (c) an advocacy response to be taken automatically upon occurrence of the trigger in the information stream. Once configured, the inventive platform electronically monitors the designated electronic information stream. When the inventive platform recognizes an occurrence of the predefined trigger in the stream, it will automatically make the advocacy response that was preconfigured by the consumer.
By way of example and not limitation, the electronic information stream could be an automated feed such as Twitter or RSS, it could come from automatically periodically polling a web site for new postings to a Facebook page, blog, or bulletin board such as Reddit, or it could be a live news feed coming from a traditional or Internet news broadcast. The electronic information stream could be in the form of a simple text message, but it could also be the close-captioning text accompanying the audio or video portion of a broadcast. Further, the inventive platform could include voice-recognition software that converts voice found in audio or video feeds into a text stream. Further, the inventive platform could include pattern-recognition software that looks for specific objects or faces in a motion video and/or still photograph stream, or specific songs or artists in an audio or video stream.
By way of example and not limitation, the inventive platform could be counting the number of occurrences of a trigger in a single message, the number of occurrences of the trigger coming from a specific source, the number of postings by a specific source, or any combination of these. Further, the inventive platform could take action based on every occurrence of the trigger or it could take action after the count reached some threshold value. Further, the inventive platform could be aggregating occurrences, for example by making a fixed donation for every 100 likes of some organization's Facebook page.
By way of example and not limitation, in response to the occurrence of a trigger in a monitored electronic information stream, the inventive platform could generate an electronic payment from a consumer to a charity of the consumer's choosing, using an electronic check, credit card, PayPal, or any other electronic payment mechanism. Alternatively, or in addition, the inventive platform could incorporate a reference to the occurrence of the trigger into a predefined message suitable for tweeting or posting to a social media platform.
In addition to these components, the inventive platform would also include a suitable user interface that allows the consumer of information to create accounts, configure electronic information streams to be monitored, configure triggers, and configure actions to be taken upon occurrences of the triggers in the electronic information streams. Further, the inventive platform would also include suitable electronic interfaces for communicating with electronic information streams as well as with electronic payments systems. These user and electronic interfaces would be built using application programming interfaces and other software tools known to those of ordinary skill in the art.
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(Note that prior to going “live,” Control Computer 2 would likely have performed basic verification on the configuration, for example, to confirm that the system had set up a subscription to follow @NRA, that user Jane Smith had an active PayPal account, and that the system had the necessary information for making a PayPal payment to the Brady Campaign to Prevent Gun Violence.)
In Step 201 of
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Further, in this example, Step 207 used a simple text matching comparison—testing to determine if “second amendment” was found in the string “Support the second amendment.” However, in other embodiments, Step 207 could involve more complex text matching, such as wildcard matching, case independent matching, synonym matching, homophone matching, proximity matching, regular expression pattern matching, and so on.
Further, in embodiments supporting audio in Electronic Information Stream Interface 5, Step 207 could use the presence of a coherent signal—that is, any content that is not noise—as a trigger. Such a trigger might be used when waiting for a one-time broadcast such a speech. The audio content can be parsed to identify triggers based on keywords using speech recognition software. Further, the audio signal itself can be analyzed and matched to known works, such as music performances identified by the application Shazam. For example, a user could select both a known work, for example “It Was a Very Good Year” performed by Frank Sinatra as a first trigger and the key word “girl” as a second trigger. Both the first and second triggers would be satisfied by the same song appearing within the stream.
Further, in embodiments supporting still images in Electronic Information Stream Interface 5, Step 207 could use the presence of an image within an Instagram or Flickr stream as a trigger. Images can be parsed for triggers based on keywords through the use of optical character recognition processing. Optical character recognition analyzes image data to identify numbers and letters present within the image and these individual characters can be combined into words that can be compared to the trigger keywords. Images can also be parsed for other content recognition, such as shapes, faces, symbols, or other visual elements submitted or selected by the user as a trigger.
Further, in embodiments supporting video in Electronic Information Stream Interface 5, Step 207 could use the presence of video content within the stream as a trigger, such as the beginning of a streaming broadcast of an event. Video media streams can also be parsed based on the length of the video such as if a speech lasts longer than a single threshold limit, for example, over 8 minutes, or for a reoccurring threshold, for example, every 1 minute of length is an additional trigger. Video media streams can also be parsed for keyword triggers by analyzing any accompanying audio as detailed above or by comparing keywords to a transcript of the video. Individual frames of a video stream can also be analyzed like still images as described in the previous paragraph.
Further, some Electronic Information Stream Interface 5s may include geographic location information. For example, photographs may include latitude and longitude information about where the photograph was taken, and social media platforms may allow the user to add “location” or “checking in” information to a posting. In some embodiments, Step 207 could compare the geographic location information in a stream—for example a geographic longitude/latitude or a named location—with a location at or near a user-specified location. “Near” in this example could be a default proximity value defined by the system or a user-defined proximity value. A match could, for example, invoke an advocacy counter-statement or donation whenever there was a message indicating that the poster had “checked in” to a political event held at an identifiable location.
Further, in some embodiments, Step 207 could involve threshold comparisons, for example comparing the number of Twitter “followers” or Facebook “likes” or “shares” to a threshold set by the user, and Steps 208-209 could make the size of the donation be a multiple of the user's threshold, for example, $1 for every 10 followers. Further, in some embodiments, rather than examining the stream for specific content, Step 207 could involve counting the number of tweets, postings, messages, comments, and so forth, and invoking Steps 209 and/or 211 when that count exceeded the user's threshold.
Further, in some embodiments, Step 207 could include multiple related or unrelated comparisons combined using Boolean operators. By way of example, a compound test might be testing for the presence of “gun control” AND (“senator smith” OR “senator brown”) AND NOT “H.R. 1234.”
Further, in some embodiments, Steps 208-211 could further involve recording the number of donations, the recipients of tweets, and other statistics associated with advocacy response taken on behalf of users and/or for the benefit of advocacy groups. In some embodiments, the system could place periodic caps on the number of advocacy messages sent and/or the aggregate amount of money donated. By way of example and not limitation, a user may choose to limit weekly donations to a specific beneficiary to $30/month.
Further, in some embodiments where the advocacy response was a donation, Step 209 may aggregate payments such that they are made on a periodic basis—once a day, once a week, and so on. Alternatively, Step 209 may aggregate payments such that they are made only after reaching a certain amount. These embodiments would avoid, for example, making a dozen $0.50 electronic payment system transactions over the course of a day, and instead making a single $6.00 electronic payment system transaction at the end of the day.
Additionally, a second simplified embodiment could involve a customized Step 207 which understands how to parse and interpret an electronic information stream for more information than just an instance of a keyword. This could, by way of example and not limitation, include receipt and interpretation of a live sport play-by-play feed or periodically scraping a sports website for box scores, player statistics, specific game events, and so forth. Upon recognition of the occurrence of a preconfigured event, say a missed field goal, the system would automatically make an electronic donation on behalf of the user to the United Way. (This embodiment assumes that the system can accurately parse and interpret the content of the electronic information stream, and it may also require some pre-screening of the donation recipients in order to comply with governmental gaming industry regulations.)
Additionally, a third simplified embodiment could involve monitoring a one-time scheduled electronic information stream rather than a repeated stream. By way of example and not limitation, the operator of a system practicing an embodiment of the present invention could configure an audio or text feed from the president's annual state of the union address, and for a limited time prior to the speech, allow users to set up donations based on the presence of words in the speech. So for example, every time the president used the word “economy” in the speech, the system would automatically make a donation to a charity of the user's choice.
While specific embodiments have been illustrated and described, numerous modifications come to mind without significantly departing from the spirit of the invention and the scope of protection is only limited by the scope of the accompanying Claims.
This application claims the benefit of U.S. Provisional Application Ser. No. 61/829,322, filed May 31, 2013, and incorporates it by reference in its entirety for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 61829322 | May 2013 | US |
