Patents by Inventor Parijat DESHPANDE

Parijat DESHPANDE has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 12038427
    Abstract: Health of perishable commodities such as eatables deteriorate over time. State of art systems for health monitoring of perishable commodities rely on measurement of limited parameters and also fail to consider effect of environment on the health of the perishable commodities. Disclosed herein is an apparatus and method for multimodal sensing and monitoring of perishable commodities. The apparatus allows to change environment within a closed chamber in which the perishable commodity being monitored is kept, and in turn allows to generate health data in different environment settings. This data is used to generate a health model. Data collected in real-time are processed with the health model to establish a correlation with at least one image, wherein each of such images in the health model represents certain health state. Based on the established correlation, health of the perishable commodity is determined.
    Type: Grant
    Filed: October 25, 2019
    Date of Patent: July 16, 2024
    Assignee: Tata Consultancy Services Limited
    Inventors: Jayita Dutta, Parijat Deshpande, Beena Rai
  • Patent number: 11977608
    Abstract: Traditional food quality monitoring systems fail to monitor the variation of food quality in real-time scenarios. Existing machine learning approaches require dedicated data models for different classes of food items due to differences in characteristics of different food items. Also, to generate such data models, a lot of annotated data is required per food item, which are expensive. The disclosure herein generally relates to monitoring and shelf-life prediction of food items, and, more particularly, to system and method for real-time monitoring and shelf-life prediction of food items. The system generates a data model using a knowledge graph indicative of a hierarchical taxonomy for a plurality of categories of the plurality of food items, which in turn contains metadata representing similarities in physio-chemical degradation pattern of different classes of the food items. This data model serves as a generic data model for real-time shelf-life prediction of different food items.
    Type: Grant
    Filed: November 1, 2021
    Date of Patent: May 7, 2024
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Jayita Dutta, Parijat Deshpande, Manasi Samarth Patwardhan, Shirish Subhash Karande, Shankar Kausley, Priya Kedia, Shrikant Arjunrao Kapse, Beena Rai
  • Publication number: 20240095606
    Abstract: This disclosure relates generally to method and system for predicting shelf life of perishable food items. In supply chain management, current technology provides limited capability in providing relation between visual image of food item and a quality parameter value at different storage conditions. The system includes a quality parameter prediction module and a shelf life prediction module. The method obtains input data from user comprising a visual data and a storage data of each food item. The quality parameter prediction module determines a current quality parameter value of the food item from a look-up table. The shelf life prediction module predicts the shelf life of food item based on the current quality parameter value, a critical quality parameter value and the storage data. The look-up table comprising a plurality of weather zones are generated based on relationship dynamics between the visual image of food item and the quality parameter value.
    Type: Application
    Filed: August 22, 2023
    Publication date: March 21, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: PRIYA KEDIA, SHANKAR KAUSLEY, MANASI SAMARTH PATWARDHAN, SHIRISH SUBHASH KARANDE, BEENA RAI, JAYITA DUTTA, PARIJAT DESHPANDE, ANAND SRIRAMAN, SHRIKANT ARJUNRAO KAPSE
  • Patent number: 11747275
    Abstract: State of the art food quality measurement techniques fail to determine quality of the food item once it is packed and sealed in an enclosed package. The disclosure herein generally relates to food quality prediction, and, more particularly, to a system and method for predicting liquid food quality in a non-invasive manner. A near infra-red (NIR) radiation is transmitted through a semi-transparent opening configured on an enclosed package containing a liquid food item and the resulting NIR reflection spectra is collected. The quality of the liquid food item is estimated by correlating a plurality of features derived from the NIR reflection spectra with the concentration of the biomarker contained in the liquid food item, using a trained machine learning model and the remaining shelf life of the liquid food item is estimated based on the concentration of the biomarker.
    Type: Grant
    Filed: December 27, 2021
    Date of Patent: September 5, 2023
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Beena Rai, Jayita Dutta, Parijat Deshpande
  • Patent number: 11699505
    Abstract: There is a demand for low-cost robust method to detect corrosion for estimating corrosion inhibitor (CI) concentration sensing. This disclosure herein relates to method and system for estimating corrosion inhibitor (CI) concentration using a multi-electrode array sensor. The method initially obtains a plurality of electrochemical signals using the multi-electrode array sensor from the corroding environment. Further, the plurality of electrochemical signals are analyzed to obtain a plurality of parameters. Further, the method analyses a plurality of features from the plurality of parameters for estimating the corrosion inhibitor (CI) concentration using a trained machine learning model. The method is capable of estimating the corrosion inhibitor concentration of any unknown liquid using the regression model and the classification model.
    Type: Grant
    Filed: November 29, 2019
    Date of Patent: July 11, 2023
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Venkata Muralidhar Kanamarlapudi, Naga Neehar Dingari, Parijat Deshpande, Jayita Dutta, Soumyadipta Maiti, Beena Rai
  • Patent number: 11624719
    Abstract: This disclosure relates generally to a system and method for real-time non-invasive estimation of food quality within enclosed package. Existing works utilize invasive methods that require direct contact of the food item with the sensors. In the present disclosure, a potential is applied over a plurality of frequencies through the food item contained the enclosed package which includes a plurality of polyethylene layers and a conducting layer arranged between two adjacent polyethylene layers using electrochemical impedance spectroscopy. Values of electrical voltages and the electrical impedances of the food item are then obtained. A plurality of features is derived from the obtained values of the electrical voltages and the electrical impedances using a trained model. The present disclosure estimates the quality of the food item in real-time by co-relating the plurality of derived features with the quality of the food item contained inside the enclosed package.
    Type: Grant
    Filed: October 19, 2021
    Date of Patent: April 11, 2023
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Parijat Deshpande, Jayita Dutta, Beena Rai
  • Patent number: 11488017
    Abstract: This disclosure relates generally to a system and method for monitoring and quality evaluation of perishable food items in quantitative terms. Current technology provides limited capability for controlling environmental conditions surrounding the food items in real-time or any quantitative measurement for the degree of freshness of the perishable food items. The disclosed systems and methods facilitate in quantitative determination of freshness of food items by utilizing sensor data and visual data obtained by monitoring the food item. In an embodiment, the system utilizes a pre-trained CNN model and a RNN model, where the pertained CNN model is further fine-tined while training the RNN model to provide robust quality monitoring of the food items. In another embodiment, a rate kinetic based model is utilized for determining reaction rate order of the food item at a particular post-harvest stage of the food item so as to determine the remaining shelf life thereof.
    Type: Grant
    Filed: February 6, 2020
    Date of Patent: November 1, 2022
    Assignee: Tata Consultancy Services Limited
    Inventors: Beena Rai, Jayita Dutta, Parijat Deshpande, Shankar Balajirao Kausley, Shirish Subhash Karande, Manasi Samarth Patwardhan, Shashank Madhukar Deshmukh
  • Patent number: 11430576
    Abstract: This disclosure relates generally to a system and method for monitoring and quality evaluation of perishable food items in quantitative terms. Current technology provides limited capability for controlling environmental conditions surrounding the food items in real-time or any quantitative measurement for the degree of freshness of the perishable food items. The disclosed systems and methods facilitate in quantitative determination of freshness of food items by utilizing sensor data and visual data obtained by monitoring the food item. In an embodiment, the system utilizes a pre-trained CNN model and a RNN model, where the pertained CNN model is further fine-tined while training the RNN model to provide robust quality monitoring of the food items. In another embodiment, a rate kinetic based model is utilized for determining reaction rate order of the food item at a particular post-harvest stage of the food item so as to determine the remaining shelf life thereof.
    Type: Grant
    Filed: February 6, 2020
    Date of Patent: August 30, 2022
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Beena Rai, Jayita Dutta, Parijat Deshpande, Shankar Balajirao Kausley, Shirish Subhash Karande, Manasi Samarth Patwardhan, Shashank Madhukar Deshmukh
  • Publication number: 20220205905
    Abstract: State of the art food quality measurement techniques fail to determine quality of the food item once it is packed and sealed in an enclosed package. The disclosure herein generally relates to food quality prediction, and, more particularly, to a system and method for predicting liquid food quality in a non-invasive manner. A near infra-red (NIR) radiation is transmitted through a semi-transparent opening configured on an enclosed package containing a liquid food item and the resulting NIR reflection spectra is collected. The quality of the liquid food item is estimated by correlating a plurality of features derived from the NIR reflection spectra with the concentration of the biomarker contained in the liquid food item, using a trained machine learning model and the remaining shelf life of the liquid food item is estimated based on the concentration of the biomarker.
    Type: Application
    Filed: December 27, 2021
    Publication date: June 30, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Beena RAI, Jayita DUTTA, Parijat DESHPANDE
  • Publication number: 20220147755
    Abstract: Traditional food quality monitoring systems fail to monitor the variation of food quality in real-time scenarios. Existing machine learning approaches require dedicated data models for different classes of food items due to differences in characteristics of different food items. Also, to generate such data models, a lot of annotated data is required per food item, which are expensive. The disclosure herein generally relates to monitoring and shelf-life prediction of food items, and, more particularly, to system and method for real-time monitoring and shelf-life prediction of food items. The system generates a data model using a knowledge graph indicative of a hierarchical taxonomy for a plurality of categories of the plurality of food items, which in turn contains metadata representing similarities in physio-chemical degradation pattern of different classes of the food items. This data model serves as a generic data model for real-time shelf-life prediction of different food items.
    Type: Application
    Filed: November 1, 2021
    Publication date: May 12, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayita Dutta, Parijat Deshpande, Manasi Samarth Patwardhan, Shirish Subhash Karande, Shankar Kausley, Priya Kedia, Shrikant Arjunrao Kapse, Beena Rai
  • Publication number: 20220120693
    Abstract: State of the art food quality measurement techniques fail to determine quality of the food item once it is packed and sealed in a container. The disclosure herein generally relates to food quality prediction, and, more particularly, to a system and method for predicting food quality in a non-invasive manner. A Color Changing Indicator (CCI) in a biosensor strip forming a component of the enclosed package in which the liquid food item is packed, changes color when came in contact with the liquid food item. For different quality of the liquid food item the CCI has different color. Based on the color of the CCI, and ambient temperature and relative humidity at the time the color of the CCI is determined, a machine learning model determines rate of deterioration of the liquid food item, and then predicts remaining shelf life, which in turn provided as output to a user.
    Type: Application
    Filed: October 19, 2021
    Publication date: April 21, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayita DUTTA, Parijat DESHPANDE, Beena RAI
  • Publication number: 20220120703
    Abstract: This disclosure relates generally to a system and method for real-time non-invasive estimation of food quality within enclosed package. Existing works utilize invasive methods that require direct contact of the food item with the sensors. In the present disclosure, a potential is applied over a plurality of frequencies through the food item contained the enclosed package which includes a plurality of polyethylene layers and a conducting layer arranged between two adjacent polyethylene layers using electrochemical impedance spectroscopy. Values of electrical voltages and the electrical impedances of the food item are then obtained. A plurality of features is derived from the obtained values of the electrical voltages and the electrical impedances using a trained model. The present disclosure estimates the quality of the food item in real-time by co-relating the plurality of derived features with the quality of the food item contained inside the enclosed package.
    Type: Application
    Filed: October 19, 2021
    Publication date: April 21, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: PARIJAT DESHPANDE, JAYITA DUTTA, BEENA RAI
  • Publication number: 20210405009
    Abstract: Health of perishable commodities such as eatables deteriorate over time. State of art systems for health monitoring of perishable commodities rely on measurement of limited parameters and also fail to consider effect of environment on the health of the perishable commodities. Disclosed herein is an apparatus and method for multimodal sensing and monitoring of perishable commodities. The apparatus allows to change environment within a closed chamber in which the perishable commodity being monitored is kept, and in turn allows to generate health data in different environment settings. This data is used to generate a health model. Data collected in real-time are processed with the health model to establish a correlation with at least one image, wherein each of such images in the health model represents certain health state. Based on the established correlation, health of the perishable commodity is determined.
    Type: Application
    Filed: October 25, 2019
    Publication date: December 30, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayita DUTTA, Parijat DESHPANDE, Beena RAI
  • Patent number: 10966681
    Abstract: Identification of pulmonary diseases involves accurate auscultation as well as elaborate and expensive pulmonary function tests. Also, there is a dependency on a reference signal from a flowmeter or need for labelled respiratory phases. The present disclosure provides extraction of frequency and time-frequency domain lung sound features such as spectral and spectrogram features respectively that enable classification of healthy and abnormal lung sounds without the dependencies of prior art. Furthermore extraction of wavelet and cepstral features improves accuracy of classification. The lung sound signals are pre-processed prior to feature extraction to eliminate heart sounds and reduce computational requirements while ensuring that information providing adequate discrimination between healthy and abnormal lung sounds is not lost.
    Type: Grant
    Filed: March 5, 2018
    Date of Patent: April 6, 2021
    Assignee: Tata Consultancy Services Limited
    Inventors: Shreyasi Datta, Anirban Dutta Choudhury, Parijat Deshpande, Sakyajit Bhattacharya, Arpan Pal
  • Publication number: 20200281220
    Abstract: This disclosure relates generally to managing ripening conditions of climacteric fruits and more particularly to a system and method for managing ripening conditions of climacteric fruits using Artificial neural network (ANN) model. The method includes obtaining levels of environment condition parameters associated with ripening of the climacteric fruit over time at periodic intervals by using an enclosure enclosing the climacteric fruit. A respiration rate of the climacteric fruit is computed based at least on the levels of the environment condition parameters using Michaelis Menten kinetics model. A level of ethylene is monitored to determine a climacteric peak of Ethylene for the climacteric fruit. The climacteric peak is indicative of complete natural ripening of the climacteric fruit. An ANN model predicts optimal ripening condition of the climacteric fruit based on the respiration rate of the climacteric fruit and the climacteric peak of ethylene.
    Type: Application
    Filed: March 4, 2020
    Publication date: September 10, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Jayita DUTTA, Parijat DESHPANDE, Beena RAI
  • Publication number: 20200250531
    Abstract: This disclosure relates generally to a system and method for monitoring and quality evaluation of perishable food items in quantitative terms. Current technology provides limited capability for controlling environmental conditions surrounding the food items in real-time or any quantitative measurement for the degree of freshness of the perishable food items. The disclosed systems and methods facilitate in quantitative determination of freshness of food items by utilizing sensor data and visual data obtained by monitoring the food item. In an embodiment, the system utilizes a pre-trained CNN model and a RNN model, where the pertained CNN model is further fine-tined while training the RNN model to provide robust quality monitoring of the food items. In another embodiment, a rate kinetic based model is utilized for determining reaction rate order of the food item at a particular post-harvest stage of the food item so as to determine the remaining shelf life thereof.
    Type: Application
    Filed: February 6, 2020
    Publication date: August 6, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Beena RAI, Jayita DUTTA, Parijat DESHPANDE, Shankar Balajirao KAUSLEY, Shirish Subhash KARANDE, Manasi Samarth PATWARDHAN, Shashank Madhukar DESHMUKH
  • Publication number: 20200251229
    Abstract: This disclosure relates generally to a system and method for monitoring and quality evaluation of perishable food items in quantitative terms. Current technology provides limited capability for controlling environmental conditions surrounding the food items in real-time or any quantitative measurement for the degree of freshness of the perishable food items. The disclosed systems and methods facilitate in quantitative determination of freshness of food items by utilizing sensor data and visual data obtained by monitoring the food item. In an embodiment, the system utilizes a pre-trained CNN model and a RNN model, where the pertained CNN model is further fine-tined while training the RNN model to provide robust quality monitoring of the food items. In another embodiment, a rate kinetic based model is utilized for determining reaction rate order of the food item at a particular post-harvest stage of the food item so as to determine the remaining shelf life thereof.
    Type: Application
    Filed: February 6, 2020
    Publication date: August 6, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Beena RAI, Jayita DUTTA, Parijat DESHPANDE, Shankar Balajirao KAUSLEY, Shirish Subhash KARANDE, Manasi Samarth PATWARDHAN, Shashank Madhukar DESHMUKH
  • Publication number: 20200176088
    Abstract: There is a demand for low-cost robust method to detect corrosion for estimating corrosion inhibitor (CI) concentration sensing. This disclosure herein relates to method and system for estimating corrosion inhibitor (CI) concentration using a multi-electrode array sensor. The method initially obtains a plurality of electrochemical signals using the multi-electrode array sensor from the corroding environment. Further, the plurality of electrochemical signals are analyzed to obtain a plurality of parameters. Further, the method analyses a plurality of features from the plurality of parameters for estimating the corrosion inhibitor (CI) concentration using a trained machine learning model. The method is capable of estimating the corrosion inhibitor concentration of any unknown liquid using the regression model and the classification model.
    Type: Application
    Filed: November 29, 2019
    Publication date: June 4, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: VENKATA MURALIDHAR KANAMARLAPUDI, NAGA NEEHAR DINGARI, PARIJAT DESHPANDE, JAYITA DUTTA, Soumyadipta MAITI, Beena RAI
  • Publication number: 20190008475
    Abstract: Identification of pulmonary diseases involves accurate auscultation as well as elaborate and expensive pulmonary function tests. Also, there is a dependency on a reference signal from a flowmeter or need for labelled respiratory phases. The present disclosure provides extraction of frequency and time-frequency domain lung sound features such as spectral and spectrogram features respectively that enable classification of healthy and abnormal lung sounds without the dependencies of prior art. Furthermore extraction of wavelet and cepstral features improves accuracy of classification. The lung sound signals are pre-processed prior to feature extraction to eliminate heart sounds and reduce computational requirements while ensuring that information providing adequate discrimination between healthy and abnormal lung sounds is not lost.
    Type: Application
    Filed: March 5, 2018
    Publication date: January 10, 2019
    Applicant: Tata Consultancy Services Limited
    Inventors: Shreyasi DATTA, Anirban DUTTA CHOUDHURY, Parijat DESHPANDE, Sakyajit BHATTACHARYA, Arpan PAL
  • Patent number: 9984543
    Abstract: An acoustic array system for anomaly detection is provided. The acoustic array system (100) performs a scan (or a progressive scan of frequencies) of a given volume by transmitting one or more signals, and receives one or more reflected signals from objects within the volume. The reflected signals are then amplified and converted to a set of digital signals. Features of the set of digital signals are extracted both in time and frequency domains. The acoustic array system (100) further performs a comparison of these set of digital extracted features with the reflected signals via machine learning techniques. Based on the comparison, the acoustic array system detects one or more anomalies.
    Type: Grant
    Filed: February 16, 2016
    Date of Patent: May 29, 2018
    Assignee: Tata Consultancy Services Limited
    Inventors: Parijat Deshpande, Ramu Vempada, Ranjan Dasgupta, Arpan Pal, Dibyendu Roy