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公开(公告)号：US20230401284A1
公开(公告)日：2023-12-14
申请号：US17746574
申请日：2022-05-17
Applicant: BANK OF AMERICA CORPORATION
Inventor： Maharaj Mukherjee , Utkarsh Raj
CPC classification number: G06K9/6256 , G06N10/40 , G06N10/60
Abstract: A system for performing optimization of hyper parameters in machine learning typically includes a classical computer apparatus and a quantum optimizer in communication with the classical computer apparatus. The classical computer apparatus is configured for gathering data sets associated with an application, identifying parameters associated with the application, constructing a machine learning model using the data sets and the parameters, determining conditions associated with optimizing the machine learning model, transmitting the machine learning model, the data sets, and the conditions to the quantum optimizer. The quantum optimizer computing a set of optimal hyperparameters for the machine learning model based on the data sets and the conditions and transmitting the set of optimal hyperparameters to the classical computer apparatus.
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公开(公告)号：US20230130267A1
公开(公告)日：2023-04-27
申请号：US17557683
申请日：2021-12-21
Applicant: Bank of America Corporation
Inventor： Maharaj Mukherjee , Utkarsh Raj , Carl M. Benda , Elvis Nyamwange , Suman Roy Choudhury
IPC: G06F16/2452 , G06F16/242 , G06F16/25 , G06F16/21 , G06F40/205
Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a query formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database by: 1) extracting non-essential portions of the query from the query, and replacing the non-essential portions of the query with pointers to create a query key; 2) storing, along with their corresponding pointers, the non-essential portions of the query as query parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key and including the corresponding pointers; and 4) updating the translated query to include the query parameters based on the corresponding pointers to create an output query. The computing platform may execute the output query on the second database.
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公开(公告)号：US20230128406A1
公开(公告)日：2023-04-27
申请号：US17557300
申请日：2021-12-21
Applicant: Bank of America Corporation
Inventor： Utkarsh Raj , Carl M. Benda , Maharaj Mukherjee , Suman Roy Choudhury , Elvis Nyamwange
IPC: G06F16/2452 , G06F16/242
Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform receive a source query formatted in a first format for execution on a source database. The computing platform may execute the source query on the source database to produce a first data result. The computing platform may input the first data result into a reversal logic engine to produce a target query formatted in a second format corresponding to a target database. The computing platform may execute the target query on the target database to produce a second data result. Based on identifying that the second data result matches the first data result, the computing platform may validate the target query. Based on identifying that the second data result does not match the first data result, the computing platform may adjust the reversal logic engine based on the discrepancy.
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Patent Agency Ranking
公开(公告)号：US20220366337A1
公开(公告)日：2022-11-17
申请号：US17875708
申请日：2022-07-28
Applicant: Bank of America Corporation
Inventor： Veerandra S. Srivastava , Maharaj Mukherjee , Mehul Jayant Pandya , Utkarsh Raj
Abstract: Aspects of the disclosure relate to using machine learning for resource planning. A computing platform may detect an occupancy modification event for a physical space. Based on detecting the occupancy modification event, the computing platform may send commands directing display of a data collection prompt to end user devices, which may prompt for work to be performed by users of the end user devices in the physical space during a first day. Using natural language processing, the computing platform may analyze user input information and other occupancy data to determine whether or not the users of the end user devices have permission to occupy the physical space during the first day. The computing platform may cause the end user devices to display a resource management interface indicating whether or not the users of the end user devices have valid permission to physically occupy the physical space during the first day.
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公开(公告)号：US11461713B1
公开(公告)日：2022-10-04
申请号：US16988876
申请日：2020-08-10
Applicant: Bank of America Corporation
Inventor： Utkarsh Raj , Mehul Jayant Pandya , Maharaj Mukherjee , Veerandra S. Srivastava
Abstract: Aspects of the disclosure relate to using machine learning for resource planning. A computing platform may detect an occupancy modification event for a physical space. Based on detecting the occupancy modification event, the computing platform may send commands directing display of a data collection prompt to end user devices, which may prompt for work to be performed by users of the end user devices in the physical space during a first day. Using natural language processing, the computing platform may analyze user input information and other occupancy data to determine whether or not the users of the end user devices have permission to occupy the physical space during the first day. The computing platform may cause the end user devices to display a resource management interface indicating whether or not the users of the end user devices have valid permission to physically occupy the physical space during the first day.
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46.发明申请公开(公告)号：US20220222568A1
公开(公告)日：2022-07-14
申请号：US17144366
申请日：2021-01-08
Applicant: Bank of America Corporation
Inventor： Maharaj Mukherjee , Utkarsh Raj
Abstract: Aspects of the disclosure relate to improving training data used for model generation. The computing platform may receive, from one or more data sources, a labelled data set. The computing platform may apply, to the labelled data set, an unsupervised learning algorithm, which may result in a clustered data set corresponding to the labelled data set. The computing platform may compare, for each data point in the labelled data set, corresponding clustering information and labelling information to identify discrepancies. The computing platform may flag, for data points with identified discrepancies between the corresponding clustering information and labelling information, a data labelling error. Using data points without identified discrepancies between the corresponding clustering information and labelling information, the computing platform may train a supervised learning model. The computing platform then may store the trained supervised learning model.
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47.发明申请公开(公告)号：US20220222546A1
公开(公告)日：2022-07-14
申请号：US17144299
申请日：2021-01-08
Applicant: Bank of America Corporation
Inventor： Maharaj Mukherjee , Utkarsh Raj
Abstract: Aspects of the disclosure relate to evaluating supervised learning models. A computing platform may receive initial training data, train supervised learning models using the initial training data, and form a composite model based on the supervised learning models. The computing platform may receive additional training data and corresponding prediction parameters, indicating actual outcomes. The computing platform may input the additional training data into the composite model to generate model-predicted outcome data, and may compare the model-predicted outcome data to the actual outcomes. Based on results of the comparison of the model-predicted outcome data to the actual outcomes, the computing platform may score each of the supervised learning models to reflect corresponding reliability levels. The computing platform may store a matrix relating the scores to their corresponding supervised learning models, which may cause the computing platform to weight results obtained from each supervised learning model when applying the composite model.
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公开(公告)号：US20220222486A1
公开(公告)日：2022-07-14
申请号：US17144345
申请日：2021-01-08
Applicant: Bank of America Corporation
Inventor： Maharaj Mukherjee , Utkarsh Raj
Abstract: Aspects of the disclosure relate to evaluating sources of training data for model generation. A computing platform may receive, from one or more data sources, a labelled data set. The computing platform may apply, to the labelled data set, an unsupervised learning algorithm, resulting in a clustered data set. The computing platform may compare, for each data point in the labelled data set, corresponding clustering information and labelling information to identify discrepancies. The computing platform may flag, for data points with identified discrepancies between the clustering information and labelling information, a labelling error. The computing platform may grade, based on the flagged labelling errors, each of the one or more data sources. Using remaining data of the labelled data set, not flagged with labelling errors, the computing platform may train a supervised learning model by weighting the remaining data based on: a corresponding data source and its grade.
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