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1. 发明申请

WO2019052869A1 A MACHINE LEARNING METHOD AND SYSTEM FOR PREDICTING ONLINE USER INTERACTIONS 审中-公开
公开(公告)号：WO2019052869A1
公开(公告)日：2019-03-21
申请号：PCT/EP2018/073841
申请日：2018-09-05
申请人： AMADEUS S.A.S.
发明人： ACUNA AGOST, Rodrigo , MOTTINI D'OLIVEIRA, Alejandro Ricardo , RENAUDIE, David
IPC分类号： G06Q30/02 , G06N5/00
摘要： A computer-implemented method comprises accessing an online data store to retrieve records relating to content placement events, and records relating to user interaction events. A set of enriched training feature vectors is computed from raw feature values, and used with interaction event tags to train a machine learning model. A processor is configured to execute the machine learning model, and receives information relating to an online content placement slot and information relating to a user. The processor computes an enriched estimation feature vector based upon a content item selected for placement within the online content placement slot, the information relating to the user, and the information relating to the online content placement slot. The processor executes the machine learning model to determine an estimate of likelihood of the user interacting with the selected content item, based upon the enriched estimation feature vector.

2. 发明申请

WO2015165562A1 VISUAL BOOKING SYSTEM 审中-公开
标题翻译：视觉预订系统
公开(公告)号：WO2015165562A1
公开(公告)日：2015-11-05
申请号：PCT/EP2015/000654
申请日：2015-03-26
申请人： AMADEUS S.A.S.
发明人： RENAUDIE, David , HAUVILLER, Nicolas , MONTEGUT, Francois
IPC分类号： G06Q10/02 , G06K9/00 , G06Q50/14
CPC分类号： G06Q10/02 , G06K9/00597 , G06Q50/14
摘要： A method, apparatus, and program product implement visual booking operations to search for travel products and/or present travel recommendations associated with travel products to users based upon visual elements in one or more digital images captured by a wearable or mobile device. Visual elements may be extracted and inferred to identify one or more travel destination locations that are geographically remote from a current location of a user, and the identified travel destination locations may be used to search a travel database to identify at least one travel product for travel from a travel origination location to a travel destination location.
摘要翻译：一种方法，装置和程序产品根据由可穿戴或移动设备捕获的一个或多个数字图像中的视觉元素来实现视觉预订操作，以搜索旅行产品和/或将旅行产品相关联的旅行建议呈现给用户。可以提取和推断视觉元素以识别在地理上远离用户的当前位置的一个或多个旅行目的地位置，并且所识别的旅行目的地位置可以用于搜索旅行数据库以识别用于旅行的至少一个旅行产品从出发地点到旅游目的地。

3. 发明申请

WO2014048572A1 SYSTEM AND METHOD FOR LOAD DISTRIBUTION IN A NETWORK 审中-公开
标题翻译：网络中负载分配的系统与方法
公开(公告)号：WO2014048572A1
公开(公告)日：2014-04-03
申请号：PCT/EP2013/002897
申请日：2013-09-26
申请人： AMADEUS S.A.S.
发明人： LATAILLE, Norbert , SBRAGIA, Alexandre , ARNOUX-PROUST, Renaud , BOUSQUET, Eric , RENAUDIE, David
IPC分类号： H04L29/08 , G06Q10/02
CPC分类号： G06Q10/087 , G06Q10/02 , H04L67/1002 , H04L67/2842
摘要： A computer-implemented reservation method and a corresponding system are utilized for controlling execution of a decision process to maintain data access efficiency upon receipt of a computation inquiry. The method comprises associating to a computer backend machine a configuration file containing at least a decision rule that drives the decision process and that is computed at least from a current value of a statistical indicator and a target value of the statistical indicator; periodically obtaining an updated value of the statistical indicator; upon detection that the updated value is differing from the target value, dynamically updating the configuration file and storing in real-time a recomputed decision rule in the configuration file.
摘要翻译：利用计算机实现的预约方法和对应的系统来控制执行决策处理以在接收到计算查询时保持数据访问效率。所述方法包括：向计算机后端机器配置至少包含决定规则的配置文件，所述决定规则驱动所述决策过程，并至少根据所述统计指标的当前值和所述统计指标的目标值计算出; 定期获取统计指标的更新值; 在检测到更新的值与目标值不同的情况下，动态地更新配置文件并实时地存储配置文件中的重新计算的决策规则。

4. 发明申请

WO2019052870A1 A METHOD AND SYSTEM FOR INTELLIGENT ADAPTIVE BIDDING IN AN AUTOMATED ONLINE EXCHANGE NETWORK 审中-公开
公开(公告)号：WO2019052870A1
公开(公告)日：2019-03-21
申请号：PCT/EP2018/073845
申请日：2018-09-05
申请人： AMADEUS S.A.S.
发明人： ACUNA AGOST, Rodrigo , MOTTINI D'OLIVEIRA, Alejandro Ricardo , RENAUDIE, David
IPC分类号： G06Q30/02
摘要： A computer-implemented method comprises receiving, from an ad exchange server via a data communications network, a message comprising a bid request which includes site information and user information relating to an available ad slot. For each offer in ranked list generated, an offer-level estimate of probability of user interaction with the offer is computed. For a combination of offers included in the ranked list, an ad-level bid price is computed, based on at least the computed offer-level estimates of probability of user interaction, corresponding offer-level interaction revenues, and an aggressiveness parameter. A bid response includes a bid-priced ad which comprises the combination of offers and the ad-level bid price. Machine learning models for predicting behaviour of online users are able to automatically determine estimates of probability of user interaction with online content elements based upon aggregated behaviour of prior users in similar contexts.

5. 发明申请

WO2019052868A1 A METHOD AND SYSTEM FOR REAL-TIME ONLINE TRAVELLER SEGMENTATION USING MACHINE LEARNING 审中-公开
公开(公告)号：WO2019052868A1
公开(公告)日：2019-03-21
申请号：PCT/EP2018/073838
申请日：2018-09-05
申请人： AMADEUS S.A.S.
发明人： ACUNA AGOST, Rodrigo , LHERITIER, Alix , MOTTINI D'OLIVEIRA, Alejandro Ricardo , RENAUDIE, David
IPC分类号： G06Q30/02 , G06Q30/06 , G06Q50/14
摘要： A computer-implemented method for real-time online traveller segmentation comprises accessing at least one offline data store that contains records of prior travel bookings of a plurality of individually-distinguishable travellers. For each record of a training set of records selected from the offline data store, a feature vector is computed comprising corresponding values for the plurality of features. A machine learning classifier is trained using the computed feature vectors and associated tags corresponding with records in the training set. A processor is configured to execute the machine learning classifier, which receives a feature vector comprising values of the plurality of features corresponding with an unidentified user in the online context. The processor executes the machine learning classifier to determine an estimate of whether the unidentified user is a member or non-member of a predetermined traveller category.

6. 发明公开

EP3682403A1 A METHOD AND SYSTEM FOR INTELLIGENT ADAPTIVE BIDDING IN AN AUTOMATED ONLINE EXCHANGE NETWORK 审中-公开
公开(公告)号：EP3682403A1
公开(公告)日：2020-07-22
申请号：EP18769115.9
申请日：2018-09-05
申请人： Amadeus S.A.S.
发明人： ACUNA AGOST, Rodrigo , MOTTINI D'OLIVEIRA, Alejandro Ricardo , RENAUDIE, David
IPC分类号： G06Q30/02

7. 发明公开

EP3682401A1 A METHOD AND SYSTEM FOR REAL-TIME ONLINE TRAVELLER SEGMENTATION USING MACHINE LEARNING 无效
公开(公告)号：EP3682401A1
公开(公告)日：2020-07-22
申请号：EP18765117.9
申请日：2018-09-05
申请人： Amadeus S.A.S.
发明人： ACUNA AGOST, Rodrigo , LHERITIER, Alix , MOTTINI D'OLIVEIRA, Alejandro Ricardo , RENAUDIE, David
IPC分类号： G06Q30/02 , G06Q30/06 , G06Q50/14

8. 发明公开

EP3682402A1 A MACHINE LEARNING METHOD AND SYSTEM FOR PREDICTING ONLINE USER INTERACTIONS 审中-实审
公开(公告)号：EP3682402A1
公开(公告)日：2020-07-22
申请号：EP18769114.2
申请日：2018-09-05
申请人： Amadeus S.A.S.
发明人： ACUNA AGOST, Rodrigo , MOTTINI D'OLIVEIRA, Alejandro Ricardo , RENAUDIE, David
IPC分类号： G06Q30/02 , G06N5/00

9. 发明公开

EP3648041A1 RECOMMENDER SYSTEMS AND METHODS USING CASCADED MACHINE LEARNING MODELS 审中-公开
公开(公告)号：EP3648041A1
公开(公告)日：2020-05-06
申请号：EP19204224.0
申请日：2019-10-21
申请人： Amadeus S.A.S.
发明人： LARDEUX, Benoit , RENAUDIE, David , ACUNA AGOST, Rodrigo Alejandro , THOMAS, Eoin , BOUDIA, Mourad , SANE, Papa Birame
IPC分类号： G06Q30/02
摘要： A computer-implemented method of providing personalized recommendations to a user of items available in an online system comprises receiving, via a communications channel, context data (602) comprising user information and computing a plurality of first-level features comprising context features based upon the context data. A first-level machine learning model (608) is then evaluated using the first-level features to generate predictions (610) of user behaviour in relation to a plurality of individual items available via the online system. A list of proposed item recommendations is constructed based upon the predictions generated by the first-level machine learning model and a plurality of second-level features is computed, comprising context features based upon the context data and list features based upon the list of proposed item recommendations and the corresponding predictions generated by the first-level machine learning model. A second-level machine learning model (612) is evaluated using the second-level features to generate a prediction (614) of user behaviour in relation to the list of proposed item recommendations. A personalized list of item recommendations is provided, via the communications channel, based upon the prediction generated by the second-level machine learning model.

10. 发明公开

EP3588327A1 SYSTEM AND METHOD FOR EVALUATING AND DEPLOYING UNSUPERVISED OR SEMI-SUPERVISED MACHINE LEARNING MODELS 有权
公开(公告)号：EP3588327A1
公开(公告)日：2020-01-01
申请号：EP19178728.2
申请日：2019-06-06
申请人： Amadeus S.A.S.
发明人： RENAUDIE, David , ZULUAGA, Maria , ACUNA AGOST, Rodrigo
IPC分类号： G06F17/18
摘要： A method of evaluating and deploying machine learning models for anomaly detection of a monitored system includes providing a plurality of candidate machine learning algorithms configured for anomaly detection of the monitored system. For each type of anomalous activity, a benchmarking dataset is generated, which comprises samples drawn from a pool of negative samples, and a smaller number of samples drawn from a relevant pool of positive samples. For each combination of candidate machine learning algorithm with type of anomalous activity, the method includes drawing a plurality of training and cross-validation sets from the benchmarking dataset. Using each of the training and cross-validation sets, a machine-learning model based on the candidate algorithm is trained and validated using the cross-validation set, with average precision as a performance metric. A mean average precision value is then computed across these average precision performance metrics. A ranking value is computed for each candidate machine learning algorithm, and a machine learning algorithm is selected from the candidate machine learning algorithms based upon the computed ranking values. A machine learning model based on the selected algorithm is deployed a to a monitoring system, whereby the monitoring system executes the deployed machine learning model to detect anomalies of the monitored system.

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