Adobe, Inc.

John Bevil Bates

William Brandon George

Kevin Gary Smith

Xuejun Xu

James Meyer

Gavin Stuart Peter Miller

Ritwik Sinha

Shriram Venkatesh Shet Revankar

Max Gray Edell

Matthew Keith Albright

Benjamin R Gaines

Ryan Elliott Cobourn

Brooke Suzanne Wyckoff

Fan Du

Shiv Kumar Saini

Iftikhar Ahamath Burhanuddin

Sunav Choudhary

Sanket Vaibhav Mehta

Nathan A Purser

Ivan Andrus

Said Kobeissi

Kristopher Paries

Chris Challis

Prithvi Bhutani

Joseph Ward

Harvineet Singh

The present disclosure is directed toward systems and methods for identifying contributing factors associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify sub-factors associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors that caused the particular anomaly and how those factors correlate with each other.

Identifying contributing factors to a metric anomaly

Embodiments of the present invention relate to providing intelligent alerting and automation for marketing analytics software. In implementation, intelligent alerting is initiated by a user, which enables deep learning models to analyze various data patterns. Intelligent alerting learns about preferences and data consumption patterns of the user with marketing analytics software. Intelligent alerting also accounts for and learns from any manually created alerts set up by the user and/or alerts created automatically by anomaly detection. Intelligent alerting analyzes all other users within the organization of the user to find similar users based on their consumption patterns. An on-demand game may be provided to the user to determine the criticality of one metric change over another. This enables intelligent alerting to automatically provide alerts which pass a critical threshold of importance to the user and context to help the user understand why a metric changes in a significant way.

Reinforcement machine learning for personalized intelligent alerting

Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Intelligent customer journey mining and mapping

The present disclosure is directed toward systems and methods for identifying contributing audience segments associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify audience segments associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors and segments that caused the particular anomaly and how those factors correlate with each other.

Determining audience segments of users that contributed to a metric anomaly

Embodiments provide traceability of edits to a document, i.e., a verifiable and immutable provenance chain for the document. Systems and methods enable traceability of edits, by encoding, for states of the document, a fingerprint (e.g., a cryptographic hash of the document's contents) and an edit history within a block written to a distributed ledger (e.g., a blockchain). The ledger is maintained via a self-organizing peer-to-peer distributed ledger network. Once added to the ledger, the contents of a block (e.g., the document's fingerprint and edit history) are immutable and the integrity of the edit history encoded in the ledger is secure. The algorithm that generates the fingerprint is sensitive to edits of the document. The non-corruptible fingerprint encoded in the ledger is employable to detect any edits that are not included in the encoded edit history and/or inconsistent with a currently available version of the document.

Traceability of edits to digital documents via distributed ledgers

Embodiments of the present invention provide systems, methods, and computer storage media directed to a proof and attestation service that can confirm the veracity of a claim or a statement of truth based on data dynamically-retrieved from various data repositories. A server device receives, from a client device, a request to determine the veracity of a claim or a statement of truth. The server device is generally a trusted computing device, having privileged-access to a variety of data repositories that the client device may or may not access. The server device can select one or more data repositories based on the claim, obtain results data from the selected one or more data repositories, and evaluate each result to determine whether it corresponds to or contradicts the claim. A veracity score can be calculated for the claim or for a result that corresponds to or contradicts the claim. The server device can generate an attestation notification that includes the claim or the result for communication to the requesting client device. In this way, the client device can utilize the proof and attestation service, facilitated via the server device, to determine a claim's veracity or its calculated likelihood of being true.

System for providing proof and attestation services for claim verification

In various embodiments, a request may be received identifying items of information and an entity requesting the items. A set of entities may be determined It may be determined, for a response to the request, that collectively has access to a set of values that captures each of the items requested by the entity. A transaction of a transaction block may be created on a blockchain maintained at least in part by one or more nodes of a distributed ledger system. The transaction may be digitally signed and identify the set of entities, and contribution scores of the values to the set of values. Each contribution score for a given value of an item being based on a level of scarcity of the item and a level of usage of the item. The blockchain may identify a scoring algorithm used to determine the contribution scores.

Contribution of multiparty data aggregation using distributed ledger technology

In various embodiments, a request is received for a claim that is responsive to an interrogatory, where the interrogatory uses values of items of interest regarding a subject user to produce the claim. Based on the request, it is determined that one or more permissions, included in one or more digitally signed transactions of one or more transaction blocks on one or more copies of a blockchain maintained at least in part by nodes of a distributed ledger system, represents the subject user granting a receiving entity with a right to receive claims that are responsive to interrogatories that use the values to produce the claims. Based on the determining, the receiving entity is granted access to the claim.

Entity-sovereign data wallets using distributed ledger technology

Identifying audiences that contribute to metric anomalies

Identifying factors that contribute to a metric anomaly

In some embodiments, a real-time and interactive preview of alerts is provided in a user interface. A computer system parses a set of rules that specifies an alert definition. Each rule identifies a set of observations and an alert trigger criterion based on user input in the user interface. For a rule, the computer system accesses historical data corresponding to the set of observations identified by the rule and determines, based on an analysis of the historical data, time points that trigger alerts over a time period according to the rule. The analysis is based on the alert trigger criterion identified by the rule. The computer system aggregates, based on the alert definition, the time points determined for the rule with time points determined for another rule from the set of rules. Further, the computer system generates an alert preview over the time period for presentation at the user interface.

Communication notification trigger modeling preview

Techniques for automatic identification of sources of web metric changes are described. In one or more implementations, changes in a web metric that indicate a measurable attribute associated with a website are determined, and the web metric is analyzed to identify sources that contributed to the changes in the web metric. In implementations, data is queried to obtain actual values for dimension elements along one or more dimensions of the web metric. In addition, expected values for the dimension elements are estimated along the dimensions of the web metric based on historical data. Then, deviations between the actual values and the expected values are calculated by using comparable statistics. Subsequently, the comparable statistics can be analyzed to identify corresponding dimension elements as the sources that contributed to the changes in the web metric.

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Highland, UT, United States of America

John Bevil Bates