WO2017144097A1 - Procédé et dispositif de création d'un contenu multimédia sur la base de données de consommation - Google Patents

Procédé et dispositif de création d'un contenu multimédia sur la base de données de consommation Download PDF

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Publication number
WO2017144097A1
WO2017144097A1 PCT/EP2016/053937 EP2016053937W WO2017144097A1 WO 2017144097 A1 WO2017144097 A1 WO 2017144097A1 EP 2016053937 W EP2016053937 W EP 2016053937W WO 2017144097 A1 WO2017144097 A1 WO 2017144097A1
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WO
WIPO (PCT)
Prior art keywords
customer
media content
dataset
media
consumption
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PCT/EP2016/053937
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English (en)
Inventor
Els DESCHEEMAEKER
Original Assignee
Rwe Ag
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Priority to PCT/EP2016/053937 priority Critical patent/WO2017144097A1/fr
Publication of WO2017144097A1 publication Critical patent/WO2017144097A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • G06Q30/0271—Personalized advertisement
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
    • H04N21/23424—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving splicing one content stream with another content stream, e.g. for inserting or substituting an advertisement
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
    • H04N21/81—Monomedia components thereof
    • H04N21/812—Monomedia components thereof involving advertisement data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
    • H04N21/81—Monomedia components thereof
    • H04N21/8126—Monomedia components thereof involving additional data, e.g. news, sports, stocks, weather forecasts

Definitions

  • the subject matter relates to a method and a device arranged for creating media content depending on consumption data.
  • Media content for instance video content, audio content or the like has been used as means for communicating between companies and customers for a long time.
  • companies have created media content which is sent to users, explaining these services and or devices.
  • Such media content is static and always the same for each customer, independent of any information about the customer.
  • a consumption dataset may comprise information about consumption of resources by a user.
  • Consumption may be, for instance, consumption of electricity, consumption of gas, consumption of water, consumption of prepaid pay TV or the like. Any type of consumption can be user specific, resulting in different consumption datasets for different
  • At least one parameter may be extracted.
  • Parameters within consumption datasets may be, for instance, an amount of service used.
  • an amount of service may be a fixed price for each electricity meter and/or a price per unit for each kWh of electricity used.
  • an individual first media sequence is created.
  • This first media sequence is individual depending on the extracted consumption parameter. This means that the media sequence is tailored to the exact information of the first parameter.
  • the output of the first media sequence is for instance a video, which is individual for each parameter, e.g. individual for each customer.
  • the first media sequence is combined with a generic media content to obtain a consumption data enriched output media content.
  • the generic media content is capable of being combined with the media sequence in order to generate one single output media content.
  • the generic media content may comprise all information which is common for all or a certain number of users, and the added first media sequence is user specific, i.e. relates to the consumption parameter(s) obtained for one specific user.
  • each media sequence can be individualized to a certain consumption behavior of a certain user.
  • media content can be overlaid to create one output media content.
  • video overlaying technologies are well known in the art. These technologies allow merging two individual videos into one output video. Seamless overlaying results in that in the output video it is not possible to detect, which source a certain video frame stems from. Also, in one video frame, two sources may provide different content, which content is then merged into one video frame. Video overlaying technologies are for instance used in on-screen display or subtitling.
  • the overlaid object(s) may, according to the subject matter, be media sequence(s), which are generated depending on the first parameter.
  • the generic media content may have a duration which is longer than the duration of the media sequence.
  • the media sequence is merged into the generic media content by adding one or more video frames to the already existing video frames of the generic media content. It should be noted, that not only video frames can be merged, but also audio content or other content and the description relating to video frames may also apply to audio content or any other media content.
  • a second parameter is also extracted from the consumption dataset.
  • the second parameter may relate, for instance, to different consumption data than the first parameter.
  • a second, different media sequence is generated depending on the second parameter.
  • the second media sequence is combined with the generic media content.
  • the combination technique can be the same as the combination between the first media sequence and the media content. It is preferred that both media sequences are combined with the generic media content at different time instances. That is, the generic media content can be enriched during different time intervals with the first and second video sequences.
  • the consumption dataset comprises at least one parameter, preferably more than one parameter of the set of the following parameters name and/or address of a customer, date and/or type of a contract, amount of resources used, in particular kWh electricity or m 3 gas, and/or a term of a contract, and/or a price per unit of resource.
  • Using the name and/or the address of a customer can, for instance, be used for generating a personalized first or second media sequence.
  • the customer may be addressed personally, for instance, by verbalizing his name and/or by printing his name or the like.
  • the same may apply for the date and/or the type of a contract.
  • the day of a contract may be, for instance, the date when the contract was signed.
  • a type of a contract may contain information about certain contract options. The information may be used for making the video sequence more individual.
  • the amount of resources used can also be a parameter.
  • a parameter may, for instance, state the amount of kWh a consumer has used during the last billing period. The same may apply for the volume of gas, for instance measured in m 3 .
  • Such an amount may be printed and/or verbalized in the media sequence.
  • a video sequence may have in spoken words information about the amount of resources used. For instance, within the video there may be an off comment, stating, for instance, "you have used 3625 kWh of electricity during the last 12 months".
  • Another information may be an end of term of a contract. Such an information may be also verbalized in the media sequence by stating, when the contract ends.
  • the price per unit may be used as a parameter.
  • the price per unit can also be verbalized during the media sequence.
  • the generic media content may be, however, not generic for all customers, but may be generic only for a certain type of customer.
  • the generic media content first is created based on generic sequences put together along as so called "story line". Which sequences are put together to create the generic media content may be dependent on at least one parameter of the dataset, e.g. at least one parameter defining a type of a customer.
  • different media sequences each being depended on a value of a parameter, can be appended to each other.
  • a first sequence "A” can be used and for customers having contract type "B”
  • a second sequence "B” can be used.
  • a sequence "C” can be appended and if a customer uses less than 2.000 kWh during a billing period, a sequence "D" can be appended. This may be done until the whole generic media content is generated based on the different media sequences each being selected depending on the value of a parameter.
  • video or audio data may be used within the generic media content.
  • a video content and/or an audio content may be used within the generic media content.
  • At least one of the media sequences can also be at least video content or audio content. It may be possible to merge a generic media content being a video content with a media sequence only being audio content. Moreover, it may be advantageous if the generic media content and the media sequences are of the same content type, i.e. video content.
  • a content of at least one of the media sequences contains at least information relating to the parameter and/or the value of the parameter. In particular, information relating to the parameter and/or the value of the parameter is reflected in the content using clear text.
  • audio can be used to verbalize the parameter or the value of the parameter.
  • a combination of textual, graphical or audio content can be used for expressing the parameter or the value of the parameter.
  • the consumption dataset is linked to the customer dataset. It has been found that not all customers need to be addressed with output media content. Moreover, it has been found that certain types of customers are more inclined to inquire the service provider than others. Based on statistical data, it can be calculated which customer is likely to inquire the service provider, e.g. by phone. A selection of which customer is about to inquire the service provider can be done using the customer dataset. At least two variables can be extracted from the customer dataset for each customer. These two variables can be compared with statistical data of these variables. From this, similar customers may be detected. For these similar customers, the likelihood of an inquiry to the service provider can be provided based on statistical data. If a certain percentage of similar customers have inquired the service provider, then it can be decided that for the current customer output media content is generated. Thus, by comparing a certain customer with statistical data of customers it is decided, whether or not output media content is generated.
  • variables can for instance be billing amount, positive or negative bill shock, a period of being a customer, demographics, language of a customer or the like.
  • a certain customer is compared with similar customers from a historical customer database. Similar customers are found by comparing customer variables with similar variables in the historical database.
  • the variables, which are compared there are for instance the name and address of a customer, the age of a customer, a type of a household of a customer, a type of a contract of a customer, a martial status of a customer and/or a creditworthiness of a customer.
  • the customer dataset is used for extracting at least one of these variables, preferably more than one of these variables. If the variables are extracted, the values of the variables are used to compare the variables with historical data.
  • Historical data can be updated with new information about user behavior. As will be described below, customer reactions can be tracked and used to update historical user data. Thus, there is a closed loop, allowing improving the historical database constantly.
  • customer contact information can be obtained from the customer dataset.
  • the customer is contacted using the output media content by transmitting the output media content as such, i.e. as a file, or as a link, e.g. as a hyperlink, e.g. an URL to the customer.
  • the customer may then consume the media content.
  • usage information of the output media content by the customer can be monitored. It can be checked, whether the customer consumes the media content at all, whether the customer only consumes part of the media content and the time and/or the number of times the customer is consuming the media content. All this information may be fed back to the historical database.
  • the usage data obtained is fed back into the customer dataset. Costumer dataset can then be added to the historical database for a further comparison with new customer datasets.
  • the statistical data is generated, according to embodiments based on the historical database.
  • the statistical data reflect information about a number of customer inquires and/or a type of a customer inquiry and/or a time of a customer inquiry and/or a type of an event causing the inquiry.
  • the statistical data could also have information about other types of services sold to the customer - other then gas and electricity, e.g. complaints or inquiries about solar panels, meters or any other asset, so that the datasets will become grow over time.
  • a type of a customer inquiry may be, for instance, a complaint about a service, a question about an invoice or the like.
  • a time of a customer inquiry may be, for instance, a time span, which has lapsed between when the customer has received its bill and when he inquired.
  • a time of a customer inquiry can be the duration of his inquiry.
  • an event can be the reception of a bill, a new contract, a
  • a customer is classified by comparing the customer dataset with the historical data. For the customer dataset, at least two variables may be extracted. The values of these variables may be used to look for customers, which had similar values. If these customers are found, it is checked, what is the likelihood of these customers to inquire the service provider, for instance by phone. Depending on the classification of a current customer, statistical information about customer inquiries of customers having a same classification is extracted. Thus, customers are classified depending on the value of their variables and historic inquiry information. Based on this information about customer inquiries, the output media content is generated or not.
  • consumption data is received from a smart meter metering the consumption data.
  • the consumption data may be fed automatically into the consumption dataset.
  • the consumption data may be fed manually with meter readings.
  • Fig. 1 an evaluation of historic user data
  • Fig. 2 response probabilities for different user groups
  • Fig. 3 a comparison of a user dataset with historic user data; generation of generic media content based on user data;
  • Fig. 5a a consumption dataset
  • Fig. 5c an enriched output media content
  • Fig. 6 a feedback loop for distributing enriched output media content.
  • Fig. 1 there is depicted a plurality of user datasets 2.
  • the user datasets 2 there are stored numerous variables describing consumers. Among these variables, there are, for instance, information about at least one of the name and address of a consumer, a type of a consumer contract, an age of a customer, a type of a household of the customer, creditworthiness of the customer and others.
  • Within a user dataset 2 there may also be provided information about an inquiry of the user.
  • this information about an inquiry by the user there may be stored a reason (event) for this inquiry.
  • All user datasets 2 thus contain historic data about users and their behavior, in particular inquiries of the users to the service provider, in particular in response to certain events.
  • the user datasets 2 are used to create user categories 4a-4c and to use these to categorize users.
  • Each user category 4a-c is defined by certain characteristics of the users. All user datasets, which fall within this characteristic, are grouped in the respective user category 4a-c.
  • Fig. 2 shows the likelihood of an inquiry, for instance, in percent for different user categories 4a-c. For the sake of completeness, Fig. 2 shows more user categories 4d-f.
  • a user dataset 6 is checked for each user as depicted in Fig. 3.
  • a user dataset 6 is first compared with the user categories 4a-c and the user of the user dataset 6 is categorized into one of the user categories 4a-c. This may be done by checking, which user category is most similar to the actual user dataset 6.
  • For each user category 4a-c for instance a median value of the respective variables may be calculated. It may also be possible to use a logistic regression model which makes correlations to identify the main parameters of the model and to calculate the variables.
  • each variable may be compared with the variable in the user dataset and a difference in values may be calculated. By summing the differences and for instance weighting the differences in the sum, it may be found out which user category 4a-c suits best to the current user dataset 6.
  • the user category 4c fits best to the user dataset 6.
  • user dataset 6 is put into user category 4c for further use.
  • the event is checked and the likelihood of an inquiry for this event for this user category 4c is checked, for instance within a graph according to Fig. 2.
  • the likelihood of a response for user in category 4c is above threshold 5.
  • the service provider receives a signal to generate the output content.
  • the output content is a video.
  • Fig. 4 depicts a flowchart for generating generic media content.
  • Generic media content may, for instance, already be to some extent tailored to a certain customer type based on customer information. For this reason, certain variables from the actual user dataset 6 are extracted. These variables may, for instance a martial status and/or age of the customer. With this information, the video sequence may be generated as described below.
  • the video sequence has five intervals 8a-d.
  • video interval 8a there is a video sequence 8a', which is a generic for all customers.
  • any one of video sequences 8b'-b"' can be appended, which selection is based on customer data. For instance, the age of a customer is analyzed from the user dataset 6 and three different age categories may exist. Depending on the category of the age, in the current case the video sequence 8b' is selected and appended to video sequence 8a'.
  • the martial status of the user is extracted from user dataset 6 and depending on the martial status, one of the video sequences 8c'-c"' is appended to video sequence 8b'. In the current case, this is video sequence 8c'.
  • the generic media content may already reflect user information.
  • a consumption dataset 10 may be obtained for the current user by comparing user data in user dataset 6 with user data in consumption dataset 10.
  • Each user can be identified by a user identification, which can be stored on consumption dataset 10 and in user dataset 6.
  • consumption data can be linked to user data.
  • the user identification is stored, but for instance also a type of a contract "123" and an amount of resources used "ABC”. More information may be stored in a consumption dataset 10, however, for the sake of brevity, only two consumption data information are used here. Based on a extracted type of contract "123", a first video sequence 12 is generated.
  • the contract type "123” may be illustrated as graphics, text, symbols or using audio or the like. Then, the amount of resources consumed “ABC” is obtained from consumption dataset 10 and a second video sequence 14 is generated. Within this second video sequence 14, the amount of resources use "ABC” can be played back either as text, graphic, symbols, audio or any other type. Both video sequences 12 and 14, which contain consumption data "123" and "ABC” are then used to be overlaid over the video sequence 8a'-d'. This is illustrated in Fig. 5b.
  • video sequence 8a'-d' video sequence 12 and video sequence 14 may be respectively combined.
  • Fig. 5b it is illustrated that video sequence 12 is overlaid, combined, input, overlaid or in any other way merged to video sequence 8b'.
  • Video sequence 14 is combined, input, overlaid or in any other way merged with video sequence 8c'.
  • a resulting output media content 20 is generated as illustrated in Fig. 5c.
  • video sequences 8a' and 8d' is enriched with consumption data resulting in video sequence 16 being a combination of video sequence 8b' and 12 and video sequence 18 being a combination of video sequence 8c" and 14.
  • This output content 20 may then be forwarded to the customer, which is identified from the user dataset 6, as illustrated in Fig. 6.
  • the user datasets 2 storing historic data and being categorized in user categories 4a-c are stored in database 24.
  • the consumption data and the actual user data 6, 10, are stored in a service provider database 26.
  • service provider database 26 From service provider database 26, user dataset 6 and consumption dataset 10 are obtained and output content 20 is created as has been described above.
  • the user categories 4a-c need to be known from the user datasets 2, which are obtained from a data base 24.
  • an address for instance an electronic address is known for the respective customer.
  • Output content 20 is sent to this address, either in the content of an electronic communication or as a link, for instance a hyperlink.
  • a user receives at his device 22, e.g. a smartphone, tablet, computer, smart glass or the like, the communication. Once the customer consumes output content 20, he activates the respective link or opens the file storing the output content 20. This interaction of the user on his computer 22 with the output content 20 may be fed back to database 24. Therein, it can be stored that a user actually has consumed the output content 20. This consumption is an indication that the user was about to inquire the service provider and may be used in the statistical data for creating a graph as illustrated in Fig. 2.

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  • Finance (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Signal Processing (AREA)
  • Multimedia (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
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Abstract

La présente invention concerne un procédé de création d'un contenu multimédia en fonction de données de consommation. Ledit procédé comprend les étapes consistant à : obtenir au moins un ensemble de données de consommation (10) ; extraire au moins un premier paramètre de l'ensemble de données de consommation (10) ; créer au moins une première séquence multimédia (12, 14) en fonction du premier paramètre ; et combiner la première séquence multimédia (12, 14) à un contenu multimédia générique (8) de façon à obtenir un contenu multimédia de sortie enrichi en données de consommation (20).
PCT/EP2016/053937 2016-02-25 2016-02-25 Procédé et dispositif de création d'un contenu multimédia sur la base de données de consommation WO2017144097A1 (fr)

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PCT/EP2016/053937 WO2017144097A1 (fr) 2016-02-25 2016-02-25 Procédé et dispositif de création d'un contenu multimédia sur la base de données de consommation

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090222870A1 (en) * 2005-11-10 2009-09-03 Qdc Technologies Pty. Ltd. Personalized video generation
US20150358653A1 (en) * 2014-06-06 2015-12-10 Baidu Online Network Technology (Beijing) Co., Ltd Method and apparatus for processing audio/video file
WO2016003285A1 (fr) * 2014-07-03 2016-01-07 Storymail B.V. Système permettant de générer une vidéo

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090222870A1 (en) * 2005-11-10 2009-09-03 Qdc Technologies Pty. Ltd. Personalized video generation
US20150358653A1 (en) * 2014-06-06 2015-12-10 Baidu Online Network Technology (Beijing) Co., Ltd Method and apparatus for processing audio/video file
WO2016003285A1 (fr) * 2014-07-03 2016-01-07 Storymail B.V. Système permettant de générer une vidéo

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