Moac Blockchain Tech Inc.

Microsoft Technology Licensing, LLC

Yang Chen

Thomas Moscibroda

Jinyu Li

Mark Eugene Russinovich

James Ernest Johnson

Lidong Zhou

Ajay Mani

Xiaohu Chen

Qing Xu

Shuo Chen

Ankit Gupta

Jurgen Aubrey Willis

Xinle Yang

Zhengpeng Li

Yuechao Ma

Xiannong Fu

Souvik Ghosh

Jing Wang

John P Moore

William Thomas

Xiaojing Chen

Mao Yang

Haifeng Zhao

Chencheng Wu

Qi Liu

Qingfen Lin

Zhongxin Guo

Yifan Wang

Steven Bowles

Haowen Cao

Jiashuo Wang

Lukasz Janusz Karolewski

Xian Zhang

Keyi Yu

Mira Thambireddy

Xiaobing Guo

Qiufeng Yin

Youyou Han Oppenlander

Guillaume Didier Jean-Marc Dufour

Weiqi Wang

Henry Heyburn Pistell

Ying Yan

Jiaxing Zhang

Haoran Wang

According to implementations of the subject matter described herein, a solution is provided for pirated copy tracing based on a third party. In the solution, a report on a pirated copy of a digital content is received from a third party, wherein the report comprises first secret information for characterizing a first identification, time information and tracing information of the pirated copy. Subsequently, a request for verifying the report is received to determine whether the report is valid. When the report is determined as valid, a licensee associated with the report is marked as a first status to indicate that the pirated copy might be leaked by the licensee. Therefore, the pirated copy may be effectively traced based on third parties. The tracing information in the report can be hidden, and other third parties can therefore be prevented from using the tracing information for duplicate reports.

Third party based pirated copy tracing

In an example embodiment, an embedding model is used to generate an embedding of a natural language searching goal specified by a user, the embedding representing user intent of the user. Playbooks in a database of playbooks are also run through the embedding model to generate an embedding for each playbook indicative of a meaning of each playbook. A semantic relationship score can then be computed for each combination of the natural language search goal and a playbook, using the embeddings. These semantic relationship scores can then be passed into a ranking machine learning model, along with measured success rates for the playbooks, to generate a ranking of the playbooks. Based on this ranking, a set of filters and action corresponding to at least one of the playbooks may then be recommended to the user.

Semantic-aware next best action recommendation

The disclosure herein describes systems and methods for object data storage. In some examples, the method includes generating a profile for an object in a directory, the profile including a first feature vector corresponding to the object and a global unique identifier (GUID) corresponding to the first feature vector in the profile; generating a search scope, the search scope including at least the GUID corresponding to the profile; generating a second feature vector from a live image scan; matching the generated second feature vector from the live image scan to the first feature vector using the generated search scope; identifying the GUID corresponding to the first feature vector that matches the second feature vector; and outputting information corresponding to the object of the profile identified by the GUID corresponding to the first feature vector.

Biometric data storage using feature vectors and associated global unique identifier

Techniques for a flexible data security and machine learning system for merging third-party data are provided. In one technique, the system receives a data set from a third-party entity and receives selection data that indicates that the third-party entity selected a set of data security policies that includes an encryption option and a data mixing option from among multiple data mixing options. In response to receiving the selection data, the system stores data that associates the set of data security policies with the data set, encrypts the data set according to the encryption option, and persistently stores the encrypted data set. Later, the system decrypts the encrypted data set in volatile memory, generates, based on the data mixing option, training data based on the decrypted version of the data set, trains a machine-learned model based on the training data, and stores the machine-learned model in association with the data set.

Flexible data security and machine learning system for merging third-party data

Nodes determine a first measure of difficulty for a first branch of a split blockchain and a second measure of difficulty for a second branch of the split blockchain. The first measure of difficulty is based on a measure of block generation frequency of each node that mined for the split blockchain during a window that comprises blocks of the first branch. The second measure of difficulty is based on a measure of block generation frequency of each node that mined for the split blockchain during a window that comprises blocks of the second branch. One of the first branch and the second branch is identified as a legitimate branch based on a comparison of the first measure of difficulty and the second measure of difficulty.

Apparatus and method for distinguishing between legitimate and malicious branches of a split blockchain

A record of authorization including user information is received and appended to a blockchain. The record of authorization authorizes access by a third-party application to the user information for an access duration. The user information is encrypted by a group key and access duration is based on a change to the group key. The group key comprises a public/private key pair, and the access duration is implemented by an authorization group of nodes having the group key. The group key corresponds to either a valid group key at or near the start of the access duration, that enables decryption of a message in the record of authorization that includes the user information, or an incompatible group key at or after the end of the access duration, that does not enable decryption of the message in the record of authorization that includes the user information.

Apparatus and method for terminating an authorization to access a record in a blockchain ledger

A mechanism for managing a decentralized application functioning within an application shard in a decentralized network includes an access control mechanism and a resource control mechanism. The access control mechanism includes access logic that determines if a request to submit a transaction for execution by the decentralized application is valid. The request is received from an originator through the decentralized application. The resource control mechanism is called when a request is determined by the access control mechanism to be a valid request. The resource control mechanism includes resource logic that determines if a criterion is satisfied. The criterion may include one or more of a first criterion associated with the originator and a second criterion associated with the application shard. The resource control mechanism further includes forwarding logic that initiates a forwarding of the validated request to the application shard if the criterion is satisfied.

Apparatus and method for flexible access control and resource control in a decentralized system

A packet including a message intended for a smart contract associated with the second subchain and a node identifier is constructed at each of a plurality of nodes associated with the first subchain. The message constructed by the plurality of nodes are identical and may be a transaction request requesting the smart contract to execute on a blockchain. Each of the packets is sent to one or more nodes associated with the second subchain. The packets and messages received by one or more of a plurality of nodes associated with the second subchain are monitored and processed at the second subchain to determine if a consensus on request has been reached from the first subchain. If a consensus on request has been reached from the first subchain, the smart contract associated with the second subchain executes on the blockchain in accordance with the transaction request.

Apparatus and method for communication between chains in a decentralized system

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Bellevue, WA, United States of America

Yang Chen