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1. 发明申请

US20170337578A1 DYNAMIC MEDIA BUY OPTIMIZATION USING ATTRIBUTION-INFORMED MEDIA BUY EXECUTION FEEDS 审中-公开
公开(公告)号：US20170337578A1
公开(公告)日：2017-11-23
申请号：US15491908
申请日：2017-04-19
申请人： Anto Chittilappilly , Parameshvyas Laxminarayan , Payman Sadegh , Philip Gross
发明人： Anto Chittilappilly , Parameshvyas Laxminarayan , Payman Sadegh , Philip Gross
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0249 , G06Q30/0246
摘要： Touchpoint encounters, which represent exposure to messages transmitted through a network to users, include attributes that define universal unique identifiers (UUIDs) for user devices and at least one cross-device user engagement stack. The cross-device user engagement stack consolidates the touchpoint encounters from the user devices with different UUIDs but associated with a single user. A stimulus attribution predictive model outputs attribution parameters to estimate an effectiveness of the messages to elicit positive responses from the users. Media buy execution feed parameters are generated to quantify a set of spending amounts, based on the attribution parameters, so as to specify a cost-effective amount to deliver one of the messages to the users. The media buy execution feed parameters are delivered to programmatic media buying execution platforms that attempt to deliver the messages in accordance with the spending amounts.

2. 发明申请

US20170337588A1 CROSS-DEVICE MESSAGE TOUCHPOINT ATTRIBUTION 审中-公开
公开(公告)号：US20170337588A1
公开(公告)日：2017-11-23
申请号：US15490757
申请日：2017-04-18
申请人： Anto Chittilappilly , Parameshvyas Laxminarayan , Payman Sadegh , Philip Gross
发明人： Anto Chittilappilly , Parameshvyas Laxminarayan , Payman Sadegh , Philip Gross
IPC分类号： G06Q30/02 , G06N7/00 , G06N99/00 , G06N5/02
CPC分类号： G06Q30/0277 , G06N5/022 , G06N7/005 , G06N20/00 , G06Q30/0244
摘要： Fragmented user engagement stacks are generated from users that use multiple devices to view messages. The fragmented user engagement stacks include a universal unique identifier (UUID). A computer platform stores cross-device mapping information, derived from a shared characteristic between two or more devices, that associates the UUIDs of multiple devices to a single user. The computer platform processes the cross-device mapping data to identify the UUIDs from different devices associated with a single user and to join touchpoint encounters from the single user to generate at least one cross-device user engagement stack. The computer platform uses the cross-device user engagement stack and the response data to determine attribution as a measure of influence attributed to touchpoint encounters from a single user.

3. 发明申请

US20170046734A1 ENHANCING TOUCHPOINT ATTRIBUTION ACCURACY USING OFFLINE DATA ONBOARDING 审中-公开
标题翻译：使用离线数据登录提高触发点的属性
公开(公告)号：US20170046734A1
公开(公告)日：2017-02-16
申请号：US14823670
申请日：2015-08-11
申请人： Anto Chittilappilly , Parameshvyas Laxminarayan , Payman Sadegh
发明人： Anto Chittilappilly , Parameshvyas Laxminarayan , Payman Sadegh
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0246
摘要： A method, system, and computer program product for forming correlations and measurements between online data items and offline data items. An online and offline touchpoint attribution model is constructed by collating user records that correspond to an audience of online users taken from an audience of users that have interacted with both online and offline touchpoints. Individual user interactions with particular touchpoints are codified as touchpoint records. Online user interactions are captured from online observations taken at the time of the interaction. Offline user interactions are collected by an agent and are imported into the attribution model. A set of transitions through both online and offline touchpoints can be aggregated to form commonly-traversed progression paths through touchpoints that reach a conversion event. A contribution value that quantifies influence attributable to each of the respective ones of the touchpoints is calculated and used to manage makeup and spending in media plans.
摘要翻译：一种在线数据项和离线数据项之间形成相关性和测量的方法，系统和计算机程序产品。在线和离线接触点归因模型是通过将与从线上和离线接触点进行互动的用户受众取得的在线用户对象相对应的用户记录进行整理而构建的。与特定接触点的个人用户交互编纂为接触点记录。在线用户互动是从互动时的在线观察中获取的。离线用户交互由代理收集并导入归因模型。通过在线和离线接触点的一组转换可以聚合，以形成通过接触点达到转换事件的常用进度路径。计算用于量化每个相应接触点的影响的贡献值，并用于管理媒体计划中的构成和支出。

4. 发明申请

US20170091810A1 BRAND ENGAGEMENT TOUCHPOINT ATTRIBUTION USING BRAND ENGAGEMENT EVENT WEIGHTING 审中-公开
公开(公告)号：US20170091810A1
公开(公告)日：2017-03-30
申请号：US15137628
申请日：2016-04-25
申请人： Michael McGovern , Philip Gross , Payman Sadegh , Anto Chittilappilly
发明人： Michael McGovern , Philip Gross , Payman Sadegh , Anto Chittilappilly
IPC分类号： G06Q30/02 , G06N99/00
CPC分类号： G06Q30/0246 , G06N20/00 , G06Q30/0247
摘要： A method, system, and computer program product for classifying, weighting, and quantifying audience responses to stimulation. A method commences by forming a predictive model comprising parameters derived from response data records taken from the Internet and stimulus data records takers item the performance or execution of a media plan. A database of user configurations is consulted to access brand engagement event weighting parameters. The brand engagement event weighting parameters are combined with simulation data to generate weighted touchpoint contribution values that can in turn be used to predict future responses front the audience or a similar future audience. The weighted touchpoint contribution values are used to calculate audience engagement scores. A selected set of audience engagement scores are used to determine spending in a media plan.

5. 发明申请

US20150186928A1 REAL-TIME MARKETING PORTFOLIO OPTIMIZATION AND REAPPORTIONING 审中-公开
标题翻译：实时营销组合优化和重新启动
公开(公告)号：US20150186928A1
公开(公告)日：2015-07-02
申请号：US14584588
申请日：2014-12-29
申请人： Anto Chittilappilly , Payman Sadegh , Rakesh Pillai
发明人： Anto Chittilappilly , Payman Sadegh , Rakesh Pillai
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0244 , G06Q30/0243
摘要： The present disclosure provides a detailed description of techniques used in systems, methods, and in computer program products for real-time marketing portfolio optimization and reapportioning. The claimed embodiments enable calculating and saving a maximum efficiency response curve of a media portfolio, the maximum efficiency response curve comprising a range of response values resulting from a set of media portfolio input characteristics. A user interacts with an interactive graphical user interface which accepts an input characteristic change to alter a spending apportionment in the media portfolio, relative to the calculated and saved a maximum efficiency response curve. Using the stored maximum efficiency response curve as a baseline, results of the reapportionment(s) are generated and displayed. To provide interactive response times, the change to the spending apportionment is modeled as an incremental change record that is used in conjunction with the retrieved stored representation of the maximum efficiency response curve.
摘要翻译：本公开提供了用于实时营销组合优化和重新分配的系统，方法和计算机程序产品中使用的技术的详细描述。所要求保护的实施例使得能够计算和保存媒体组合的最大效率响应曲线，最大效率响应曲线包括由一组媒体组合输入特征产生的响应值的范围。用户与交互式图形用户界面交互，其接受输入特征变化以改变媒体投资组合中的支出分摊，相对于计算并保存的最大效率响应曲线。使用存储的最大效率响应曲线作为基准，生成并显示重新分配的结果。为了提供交互式响应时间，支出分摊的变化被建模为与最大效率响应曲线的检索的存储表示结合使用的增量变化记录。

6. 发明申请

US20150186924A1 MEDIA SPEND OPTIMIZATION USING A CROSS-CHANNEL PREDICTIVE MODEL 审中-公开
标题翻译：使用交叉通道预测模型进行媒体优化
公开(公告)号：US20150186924A1
公开(公告)日：2015-07-02
申请号：US14145625
申请日：2013-12-31
申请人： Anto Chittilappilly , Payman Sadegh , Darius Jose
发明人： Anto Chittilappilly , Payman Sadegh , Darius Jose
IPC分类号： G06Q30/02
CPC分类号： G06N20/00 , G06N5/04 , G06Q30/0242 , G06Q30/0243
摘要： A method, system, and computer program product for advertising portfolio management. The method form processes steps for determining effectiveness of marketing stimulations in a plurality of marketing channels included in a marketing campaign. The method commences upon receiving data comprising a plurality of marketing stimulations and respective measured responses, then determining from the marketing stimulations and the respective measured responses, a set of cross-channel weights to apply to the respective measured responses, where the cross-channel weights are indicative of the influence that a particular stimulation applied to a first channel has on the measure responses of other channels. The cross-channel weights are used in calculating the effectiveness of a particular marketing stimulation over an entire marketing campaign. The marketing campaign can comprise stimulations quantified as a number of direct mail pieces, a number or frequency of TV spots, a number of web impressions, a number of coupons printed, etc.
摘要翻译：用于广告投资组合管理的方法，系统和计算机程序产品。该方法形成处理在营销活动中包括的多个营销渠道中确定营销刺激有效性的步骤。该方法在接收到包括多个营销刺激和相应的测量响应的数据之后开始，然后根据营销刺激和相应的测量响应确定一组跨渠道权重以应用于相应的测量响应，其中跨渠道权重指示施加到第一信道的特定刺激对其他信道的测量响应的影响。跨渠道权重用于计算特定营销刺激在整个营销活动中的有效性。营销活动可以包括量化为直接邮件数量的刺激，电视节目的数量或频率，网页印象的数量，打印的优惠券数等。

7. 发明申请

US20180005261A9 A METHOD , COMPUTER READABLE MEDIUM AND SYSTEM FOR DETERMINING TOUCHPOINT ATTRIBUTION 审中-公开
公开(公告)号：US20180005261A9
公开(公告)日：2018-01-04
申请号：US13789453
申请日：2013-03-07
申请人： Anto Chittilappilly , Madan Bharadwaj , Payman Sadegh , Darius Jose
发明人： Anto Chittilappilly , Madan Bharadwaj , Payman Sadegh , Darius Jose
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0241
摘要： A system and method for allocating credit for an advertising conversion among various advertising touchpoints encounter by the consumer is provided. The system and method comprise receiving data pertaining to touchpoints and conversions of an advertising campaign across multiple channels. Users are correlated across the channels and the various conversions, touchpoints, and touchpoint attributes are identified. Each touchpoint attribute and touchpoint attribute value is assigned a weight. An attribution algorithm is selected, and coefficients are calculated using the assigned weights. The algorithm is executed and true scores corresponding to the touchpoints encountered by each converting user are computed.

8. 发明申请

US20180005140A1 MACHINE LEARNING TECHNIQUES THAT IDENTIFY ATTRIBUTION OF SMALL SIGNAL STIMULUS IN NOISY RESPONSE CHANNELS 审中-公开
公开(公告)号：US20180005140A1
公开(公告)日：2018-01-04
申请号：US15630806
申请日：2017-06-22
申请人： Anto Chittilappilly , Payman Sadegh
发明人： Anto Chittilappilly , Payman Sadegh
IPC分类号： G06N99/00 , G06Q30/02 , G06N7/00
CPC分类号： G06N99/005 , G06N7/00 , G06Q30/0246
摘要： A method, system, and computer program product identifies attribution of small signal stimulus in noisy response channels. Using machine-learning techniques in a computer, a small signal correlation engine correlates time series stimuli data vectors to time series response data vectors, and generates correlation coefficients that identify contributions of event notifications, including small signal attributes, to aggregated response data. Also using machine-learning techniques in a computer, a learning model simulates variations of stimuli data to predict user responses using the correlation coefficients, including computing a contribution of the small signal attributes of an event notification.

9. 发明申请

US20170337505A1 MEDIA SPEND MANAGEMENT USING REAL-TIME PREDICTIVE MODELING OF MEDIA SPEND EFFECTS ON INVENTORY PRICING 审中-公开
公开(公告)号：US20170337505A1
公开(公告)日：2017-11-23
申请号：US15491903
申请日：2017-04-19
申请人： Anto Chittilappilly , Payman Sadegh
发明人： Anto Chittilappilly , Payman Sadegh
IPC分类号： G06Q10/08 , G06N5/04 , G06N99/00 , G06Q30/02
CPC分类号： G06Q10/087 , G06N5/04 , G06N20/00 , G06Q30/0277
摘要： A method, system, and computer program product for media spend management. An Internet media planning and purchasing application executes on a management interface device. Servers execute operations to predict various inventory and pricing effects that result from a particular Internet media planning and purchasing plan. Machine learning techniques are used to form a stimulus attribution predictive model based on stimulus data records and respective response data records received over a network path. Additional predictive models are formed, including (1) an ad inventory predictive model derived from ad inventory data records and (2) an ad pricing predictive model derived from ad pricing data records. A set of media spend allocation parameters are received from the management interface, and those parameters are used to produce predicted inventory changes that in turn affect parameters in the ad pricing predictive model. Media spend allocation performance parameters are predicted based on the affected media prices.

10. 发明申请

US20160055519A1 APPORTIONING A MEDIA CAMPAIGN CONTRIBUTION TO A MEDIA CHANNEL IN THE PRESENCE OF AUDIENCE SATURATION 审中-公开
标题翻译：在媒体播放过程中对媒体频道的媒体播放贡献
公开(公告)号：US20160055519A1
公开(公告)日：2016-02-25
申请号：US14465838
申请日：2014-08-22
申请人： Anto Chittilappilly , Payman Sadegh , Darius Jose
发明人： Anto Chittilappilly , Payman Sadegh , Darius Jose
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0244
摘要： A method, system, and computer program product for managing Internet advertising campaigns. Embodiments commence upon receiving (e.g., over a network) advertisement touchpoint data pertaining to a plurality of touchpoints. The advertisement touchpoint data comprises measured stimulation data (e.g., impressions) and measured response data (e.g., conversions). The stimulation data and response data is formatted into an initial succession of candidate touchpoint contribution values where each of the individual touchpoints contributes its respective portion of the total contribution from the total set of measured responses. A non-linear model is applied over the succession of candidate touchpoint contribution values to form a non-linear succession of candidate touchpoint contributions. Individual touchpoints receive an apportionment based on the non-linear succession of candidate touchpoint contributions. Non-linear successions of candidate touchpoint contributions can follow a non-linear diminishing returns curve such that later contributions by touchpoints are not over weighted. Compensation is calculated based on the fair apportionments.
摘要翻译：一种用于管理互联网广告活动的方法，系统和计算机程序产品。在多个接触点接收（例如，通过网络）广告接触点数据时，实施例开始。广告接触点数据包括测量的刺激数据（例如，印象）和测量的响应数据（例如，转换）。刺激数据和响应数据被格式化为候选接触点贡献值的初始序列，其中每个单个接触点从总的测量响应集合贡献其对总贡献的相应部分。在连续的候选接触点贡献值上应用非线性模型以形成候选接触点贡献的非线性连续。个人接触点基于候选接触点贡献的非线性连续性获得分配。候选接触点贡献的非线性序列可以遵循非线性递减曲线，使得接触点的后续贡献不会过度加权。补偿是根据公平分配计算的。

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