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21.发明授权公开(公告)号：US11921815B2
公开(公告)日：2024-03-05
申请号：US17019258
申请日：2020-09-13
发明人： Alberto Polleri , Sergio Aldea Lopez , Marc Michiel Bron , Dan David Golding , Alexander Ioannides , Maria del Rosario Mestre , Hugo Alexandre Pereira Monteiro , Oleg Gennadievich Shevelev , Larissa Cristina Dos Santos Romualdo Suzuki , Xiaoxue Zhao , Matthew Charles Rowe
IPC分类号： G06F18/213 , G06F8/75 , G06F8/77 , G06F11/30 , G06F11/34 , G06F16/21 , G06F16/23 , G06F16/2457 , G06F16/28 , G06F16/36 , G06F16/901 , G06F16/9035 , G06F16/907 , G06F18/10 , G06F18/2115 , G06F18/214 , G06N5/01 , G06N5/025 , G06N20/00 , G06N20/20 , H04L9/08 , H04L9/32
CPC分类号： G06F18/213 , G06F8/75 , G06F8/77 , G06F11/3003 , G06F11/3409 , G06F11/3433 , G06F11/3452 , G06F11/3466 , G06F16/211 , G06F16/2365 , G06F16/24573 , G06F16/24578 , G06F16/285 , G06F16/367 , G06F16/9024 , G06F16/9035 , G06F16/907 , G06F18/10 , G06F18/2115 , G06F18/2155 , G06N5/01 , G06N5/025 , G06N20/00 , G06N20/20 , H04L9/088 , H04L9/3236
摘要： A server system can receive an input identifying a problem to generate a solution using a machine-learning application. The method selects a machine-learning model template from a plurality of templates based at least in part on the input. The method analyzes one or more formats of the customer data to generate a customer data schema based at least in part a data ontology that applies to the identified problem. The method determines whether the customer data schema is misaligned with one or more key features of the selected machine-learning model template. Based on this determination, the method analyzes the metadata for the selected machine-learning model template to determine what additional information is required to re-align the customer data with the data expectations. The method can include gathering the addition information required to re-align the customer data with the data expectations of the selected machine-learning model template.
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公开(公告)号：US11847578B2
公开(公告)日：2023-12-19
申请号：US18100458
申请日：2023-01-23
发明人： Alberto Polleri , Sergio Aldea Lopez , Marc Michiel Bron , Dan David Golding , Alexander Ioannides , Maria del Rosario Mestre , Hugo Alexandre Pereira Monteiro , Oleg Gennadievich Shevelev , Larissa Cristina Dos Santos Romualdo Suzuki , Xiaoxue Zhao , Matthew Charles Rowe
摘要： The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.
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公开(公告)号：US20230222043A1
公开(公告)日：2023-07-13
申请号：US17719705
申请日：2022-04-13
CPC分类号： G06F11/3068 , G06F16/2228
摘要： Techniques for creating a custom metric type to be added to a set of metrics generated by a data monitoring platform at run-time are disclosed. A system receives values defining properties of a custom metric type based on a custom metric template and a custom schema template. The system generates an instruction set, based on the values associated with the custom metric template, for generating the custom metric type on an executing data monitoring system. The system validates the instruction set and the custom schema to verify that the definitions for the custom metric type and the custom schema may be executed by the data monitoring system. The system adds the custom metric type, at run-time, to a set of metrics generated by the data monitoring system.
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公开(公告)号：US11562267B2
公开(公告)日：2023-01-24
申请号：US16893193
申请日：2020-06-04
发明人： Alberto Polleri , Sergio Aldea Lopez , Marc Michiel Bron , Dan David Golding , Alexander Ioannides , Maria del Rosario Mestre , Hugo Alexandre Pereira Monteiro , Oleg Gennadievich Shevelev , Larissa Cristina Dos Santos Romualdo Suzuki , Xiaoxue Zhao , Matthew Charles Rowe
摘要： The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.
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25.发明授权公开(公告)号：US11475374B2
公开(公告)日：2022-10-18
申请号：US16893073
申请日：2020-06-04
发明人： Alberto Polleri , Larissa Cristina Dos Santos Romualdo Suzuki , Sergio Aldea Lopez , Marc Michiel Bron , Dan David Golding , Alexander Ioannides , Maria del Rosario Mestre , Hugo Alexandre Pereira Monteiro , Oleg Gennadievich Shevelev , Xiaoxue Zhao , Matthew Charles Rowe
IPC分类号： G06F16/28 , G06N20/20 , G06N5/00 , G06F16/36 , G06N20/00 , G06F16/901 , G06F11/34 , G06F16/907 , G06F16/9035 , G06F8/75 , G06F8/77 , G06N5/02 , G06F16/21 , G06F16/2457 , H04L9/08 , H04L9/32 , G06K9/62 , G06F16/23
摘要： The present disclosure relates to systems and methods for a self-adjusting corporation-wide discovery and integration feature of a machine learning system that can review a client's data store, review the labels for the various data schema, and effectively map the client's data schema to classifications used by the machine learning model. The various techniques can automatically select the features that are predictive for each individual use case (i.e., one client), effectively making a machine learning solution client-agnostic for the application developer. A weighted list of common representations of each feature for a particular machine learning solution can be generated and stored. When new data is added to the data store, a matching service can automatically detect which features should be fed into the machine-learning solution based at least in part on the weighted list. The weighted list can be updated as new data is made available to the model.
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公开(公告)号：US11475214B1
公开(公告)日：2022-10-18
申请号：US17341886
申请日：2021-06-08
发明人： Ranjit Joseph Chacko , Hugo Alexandre Pereira Monteiro , Beat Nuolf , Alberto Polleri , Oleg Gennadievich Shevelev
IPC分类号： G06F40/174 , G06N20/00 , G06F3/0482 , G06F3/04847
摘要： Systems and methods described herein relate to determining whether to provide auto-completed values for fields in a digital form. More specifically, for a given field in the digital form, a machine-learning model can be trained to transform an input data set into a predicted field value and can further generate a corresponding confidence metric. A relative-loss parameter can be determined for the field, where the relative-loss parameter represents a loss of responding to an inaccurate predicted field value for the field relative to a loss corresponding to a human user providing a field value for the field. A confidence-metric threshold can be determined for the field based on the relative-loss parameter. For a given usage of the digital form, it can then be determined whether to auto-complete the field with a predicted field value generated by the model by determining whether the corresponding confidence metric exceeds the confidence-metric threshold.
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公开(公告)号：US20210081848A1
公开(公告)日：2021-03-18
申请号：US16892935
申请日：2020-06-04
发明人： Alberto Polleri , Larissa Cristina Dos Santos Romualdo Suzuki , Sergio Aldea Lopez , Marc Michiel Bron , Dan David Golding , Alexander Ioannides , Maria del Rosario Mestre , Hugo Alexandre Pereira Monteiro , Oleg Gennadievich Shevelev , Xiaoxue Zhao , Matthew Charles Rowe
摘要： The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.
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