METHOD AND SYSTEM FOR AUTO LEARNING, ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS DEVELOPMENT, OPERATIONALIZATION AND EXECUTION

    公开(公告)号:US20190171950A1

    公开(公告)日:2019-06-06

    申请号:US16271873

    申请日:2019-02-10

    Inventor: Kumar Srivastava

    Abstract: Disclosed is a method and a system a method and a system for auto learning, artificial intelligence (AI) applications development, and execution. Various applications or operations may be associated with training environment-agnostic AI models, automated AI app application performance monitoring, fault, quality and performance remediation through prediction of failures or suboptimal performance, privacy and secure AI training and inference mechanism for data and AI model sharing between untrusted parties, and building auto learning applications that can automatically learn and improve.

    Social engineering protection appliance
    87.
    发明授权
    Social engineering protection appliance 有权
    社会工程保护用具

    公开(公告)号:US09123027B2

    公开(公告)日:2015-09-01

    申请号:US12907721

    申请日:2010-10-19

    Abstract: Methods and systems for detecting social engineering attacks comprise: extracting one or more non-semantic data items from an incoming email; determining whether the one or more non-semantic data items match information stored in a data store of previously collected information; performing behavioral analysis on the one or more non-semantic data items; analyzing semantic data associated with the email to determine whether the non-semantic data matches one or more patterns associated with malicious emails; and based on the determining, performing, and analyzing, identifying the email as potentially malicious or non-malicious. The system also includes processes for collecting relevant information for storage within the data store and processes for harvesting information from detected social engineering attacks for entry into the data store and seeding of the collection processes.

    Abstract translation: 用于检测社会工程攻击的方法和系统包括:从传入的电子邮件中提取一个或多个非语义数据项; 确定所述一个或多个非语义数据项是否与先前收集的信息的数据存储中存储的信息相匹配; 对一个或多个非语义数据项执行行为分析; 分析与所述电子邮件相关联的语义数据,以确定所述非语义数据是否匹配与恶意电子邮件相关联的一个或多个模式; 并且基于确定,执行和分析,将该电子邮件识别为潜在的恶意或非恶意的。 该系统还包括用于收集用于在数据存储器内存储的相关信息的过程,以及用于从检测到的社会工程攻击中收集信息的过程,以进入数据存储和收集流程。

    DIFFERENTIAL DEALS IN A THEME GROUP
    88.
    发明申请
    DIFFERENTIAL DEALS IN A THEME GROUP 审中-公开
    主题集团的差价

    公开(公告)号:US20130311258A1

    公开(公告)日:2013-11-21

    申请号:US13473337

    申请日:2012-05-16

    CPC classification number: G06Q30/02 G06Q30/0207

    Abstract: Techniques are provided for offering a deal to a group of users, and for determining differential discounts for the users of the group that participate in the deal. A deal is offered to users of a group of users formed in association with a corresponding topic. Each user of the group is enabled to selectively accept the deal. The deal is confirmed with a plurality of users of the group that accepted the deal. Discounts for the deal are differentially assigned to the plurality of users based on at least one parameter such that at least two users that accepted the deal have discounts that are different from each other.

    Abstract translation: 提供技术用于向一组用户提供交易,并确定参与交易的组的用户的差价折扣。 向与相应主题相关联的一组用户的用户提供交易。 该组的每个用户都能够选择性地接受交易。 该交易被接受交易的组的多个用户确认。 基于至少一个参数,交易的折扣差异地分配给多个用户,使得接受交易的至少两个用户具有彼此不同的折扣。

    Methods and systems for file replication utilizing differences between versions of files
    90.
    发明授权
    Methods and systems for file replication utilizing differences between versions of files 有权
    文件复制的方法和系统利用文件版本之间的差异

    公开(公告)号:US08306954B2

    公开(公告)日:2012-11-06

    申请号:US12951561

    申请日:2010-11-22

    Abstract: Methods and systems for efficient file replication are provided. In some embodiments, one or more coarse signatures for blocks in a base file are compared with those coarse signatures for blocks of a revised file, until a match is found. A fine signature is then generated for the matching block of the revised file and compared to a fine signature of the base file. Thus, fine signatures are not computed unless a coarse signature match has been found, thereby minimizing unneeded time-consuming fine signature calculations. Methods are also provided for determining whether to initiate a delta file generation algorithm, or whether to utilize a more efficient replication method, based upon system and/or file parameters. In accordance with additional embodiments, the lengths of valid data on physical blocks are obtained from physical block mappings for the files, and these lengths and mappings are utilized for delta file generation, to minimize unnecessary signature computations.

    Abstract translation: 提供了有效文件复制的方法和系统。 在一些实施例中,将用于基本文件中的块的一个或多个粗略签名与用于修改文件的块的那些粗略签名进行比较,直到找到匹配。 然后为修改后的文件的匹配块生成精细签名,并与基本文件的精细签名进行比较。 因此,除非已经找到粗略的签名匹配,否则不会计算精细签名,从而最小化不需要的耗时的精细签名计算。 还提供了用于基于系统和/或文件参数来确定是否启动增量文件生成算法或者是否利用更有效的复制方法的方法。 根据另外的实施例,从文件的物理块映射获得物理块上的有效数据的长度,并且这些长度和映射用于增量文件生成,以最小化不必要的签名计算。

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