Patent Application
20150269627
The present invention generally relates to the field of mobile computing devices, and more particularly to a user-profiling method for providing advertisement data and/or online content to a user.
The invention also refers to a computer program that uses an algorithm for processing a plurality of keywords in order to implement said method.
The increasing usage of web and mobile devices and location-based services has increased the amount of data online, advertising or other content provided to users.
Many of this advertisements and content may be irrelevant and useless to the user. Moreover, the click-through ratio on the ads is constantly decreasing over the years, and sometimes it may be difficult to identify relevant or useful advertisements because of the high number of advertisements being received.
Some prior patent applications describe methods similar to the present invention. For instance US 20100169091, disclosing a device, system and method for providing targeted advertisements and content. This patent application, contrary to the present invention, focuses on playing voice ads and is based on speech to text conversion, which is too broad and does not include details on how they propose to do it. In general text to speech is a low accuracy technology that likely does not work well.
Another method for profiling users is the one used by Google® which profiles users based on the text of email messages.
However, no mechanism is known which uses voice conversations of users to obtain a user profile and further provide advertisements and online content on the basis of said user profile.
The object of the present invention is to provide a new mechanism to mine voice conversations of users, and create user profiles, which then can be used for advertising and other content targeting mechanisms.
To that end, the present invention relates, in a first aspect, to a user-profiling method for providing advertisement data and/or online content to a user, wherein a user provided with a computing device, i. e. a desktop computer, a tablet or a mobile phone, maintains a voice and/or a video communication with a second user through a communications provider.
The user-profiling method in a characteristic manner comprises:
The processing includes sending a selected list of keywords of said plurality of keywords and the identity of said user by said profiling engine to a voice analysis engine.
Then, a voice analysis engine checks which keywords of said selected list of keywords are present in a further voice or video communication of said user and further returns said matching keywords to the profiling engine.
The identity of the user is extracted from a voice signaling of a signaling server.
In an embodiment, the profiling engine retrieves from a media server the voice media of said user recorded before said voice or said video communication. The recorded voice media can comprise any of a call recording, a plurality of voice mails and/or a plurality of voice messages, among any other voice data.
In another embodiment, the profiling engine stores every configurable period of time in a profile database the matching keywords in order to create said user profile.
The plurality of profiling parameters can be independent. Each of said profiling parameters has associated a number of potential values. In an embodiment, the values assigned to each potential values are exclusive and they usually consist of said user's gender, age, culture, family status and socio-economic level, among others.
The algorithm processed for obtaining said user profile can use a combination of one or more techniques. A first technique comprises processing the keywords of said plurality of keywords more frequently used by said user. A second technique comprises processing a plurality of phrases identifying said plurality of keywords. Yet another third technique comprises determining the accent and pitch or intonation of said user while he or she maintains said voice and/or said video communication.
Finally, the user-profiling method also takes into account using a plurality of keywords related to a second user receiving said voice and/or a video communication for obtaining said user profile.
A second aspect of the present invention, relates to a computer program product having computer-executable instructions for processing by means of an algorithm a plurality of keywords related to data identifying said user obtained from a voice or video communication, in order to provide advertisement data and/or online content to a user.
The previous and other advantages and features will be more fully understood from the following detailed description of embodiments, with reference to the attached, which must be considered in an illustrative and non-limiting manner, in which:
The basic data needed for profiling people, that can be used for targeting ads, deals, coupons, consists of set of parameters, as can be seen in table 1, with a number of potential values for each.
Typically, parameters will be independent of each other, whereas values for each parameter will be exclusive.
Embodiments of the present invention to profile users use a combination of one or more techniques. These techniques are mainly:
Frequency of Typical Keyword:
Keyword based profiling: Several strategies can be used to derive profiling parameters from keywords:
Also, strategies may also take into account not only the keywords uttered by the speaker him/herself, but also the keywords uttered by the other party (eventually, parties) in the communication (voice conversation and/or voice messaging), for instance by allocating different weights or probabilities to the keywords uttered by the other party. A simple such strategy would multiply each weight by a factor F (0<F<1) when the keyword is uttered by the other party instead of the user.
Online Profiling:
When user (1) performs voice or video conversation with a recipient (7), telephony servers (2, 3) send a copy of media (F) and signaling (G) to Profiling Engine (5). Alternatively Profiling Engine (5) can retrieve voice media that was previously recorded (such as call recording, voice mails, or voice messages). Profiling Engine retrieves a list of keywords (8) and sends them to Voice Analysis Engine (9), together with identity of the speakers, extracted from signaling (G). Voice Analysis Engine (9) observes whether those keywords are present in the media and returns every match to profiling engine. Profiling Engine stores each indication of each keyword match in profile DB (6). Every configurable interval (e.g. 24 h), Profiling Engine goes over the list of profiling parameters and calculates a matching score for each value, according to a strategy such as those described above, and to the confidence of matches of the keywords associated with that value, over a configurable period (e.g. 1 month) of time. A possible algorithm to include the confidence of the keyword detection in the score is to multiply the confidence times the weight associated to that keyword, so that low-confidence keywords have limited influence over the computed score. Another possible, slightly more elaborate algorithm to include the confidence of the keyword detection in the score is to dismiss the keyword if the detection confidence is under a certain threshold, and multiply the keyword weight by a factor X (0<X<1) whose value grows with the confidence value, if it is above the threshold. After the scores for each value have been computed, the value with the highest matching score (if above a minimum threshold) is selected and is stored in the profile DB for that parameter and can be used for targeting the user for advertising or deals offering.
Automated learning the profiling parameters: One option is to manually curate a table of profiling values and corresponding keywords. Another option is to derive the representative keywords automatically. A possible method for this is to rely on a representative control set of users, for which manual profiling was done and values were determined (e.g. male, Spanish, 25-30 years old).
The learning system, would then:
Phraseology Based:
Additional set of parameters can be determined based on phraseology which uniquely identifies a given parameter:
Phraseology based profiling works in the same way as keyword based, but a table with idioms needs to be preconfigured, and they are generally available in the dictionaries for each language, and cannot be determined with automated machine learning.
Accent and Pitch Based:
One of existing algorithms that determine speaker accent and pitch can be used to determine gender and culture of a speaker and populate them to the profile table. In any of the strategies described above, a keyword can be substituted by any other speech-related event computed by the same or other ad hoc ASR system, such as speaker accent, intonation or pitch, and assigned a weight or included in a tuple.
A person skilled in the art could introduce changes and modifications in the embodiments described without departing from the scope of the invention as it is defined in the attached claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 12382406.2 | Oct 2012 | ES | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2013/071812 | 10/18/2013 | WO | 00 |
