公开(公告)号：US20240233309A9

公开(公告)日：2024-07-11

申请号：US18492532

申请日：2023-10-23

Applicant: Nielsen Consumer LLC

Inventor： Roberto Arroyo ,  David Jiménez-Cabello ,  Javier Martínez Cebrián

IPC: G06V10/25 ,  G06F18/21 ,  G06F18/22 ,  G06F18/24 ,  G06F18/25 ,  G06N3/08 ,  G06T3/40 ,  G06V10/74 ,  G06V10/764 ,  G06V10/771 ,  G06V10/80 ,  G06V10/82 ,  G06V20/70

CPC classification number: G06V10/25 ,  G06F18/2163 ,  G06F18/22 ,  G06F18/24 ,  G06F18/251 ,  G06N3/08 ,  G06T3/40 ,  G06V10/761 ,  G06V10/764 ,  G06V10/771 ,  G06V10/809 ,  G06V10/82 ,  G06V20/70 ,  G06V2201/10

Abstract: Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

公开(公告)号：US12028638B2

公开(公告)日：2024-07-02

申请号：US17940955

申请日：2022-09-08

Applicant: Micron Technology, Inc.

Inventor： Poorna Kale

IPC: H04N25/771 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G11C7/10 ,  G11C11/54

CPC classification number: H04N25/771 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82 ,  G06V10/945 ,  G11C7/1096 ,  G11C11/54 ,  G06V2201/10

Abstract: A method for a digital camera adaptable to monitor a scene to detect a condition of interest to a user. The digital camera can program, in a first mode, first memory cells according to first weight matrices to classify images captured by the digital camera. Second memory cells are programmed in a second mode to store data representative of the images. The digital camera can perform operations of multiplication and accumulation using the first memory cells to compute first classifications of the images. In response to mismatches between the first classifications and second classifications identified by the user for the images, the digital camera can execute instructions to determine second weight matrices and program, in the first mode, third memory cells, according to the second weight matrices for improved capability in detecting the condition represented by image classifications in a predetermined category.

公开(公告)号：US20240211496A1

公开(公告)日：2024-06-27

申请号：US17887411

申请日：2021-04-14

Applicant: Xero Limited

Inventor： Maksymilian Clark Polaczuk ,  Christopher Darius Herrmann ,  Timothy Matthew Leathart ,  Divya Jitesh Patel

IPC: G06F16/28 ,  G06V30/19

CPC classification number: G06F16/288 ,  G06F16/285 ,  G06V30/19093 ,  G06V2201/09 ,  G06V2201/10

Abstract: A computer implemented method for determining entity attributes. The method comprises determining one or more entity identifiers, determining an entity server address of the entity based on the one or more entity identifiers, wherein the entity server address points to an entity server; verifying the entity server address transmitting a message for request for information to the entity server address, receiving entity information from the entity server; and providing, to a machine learning model, the received entity information. The machine learning model is trained to generate a numerical representations of entities based on the entity information.

公开(公告)号：US20240202631A1

公开(公告)日：2024-06-20

申请号：US18542209

申请日：2023-12-15

Applicant: Insurance Services Office, Inc.

Inventor： Samuel Warren ,  Matthew D. Frei ,  Nicholas Sykes ,  David Baryuding ,  Sihui Shao ,  Bhumika Agrawal ,  Jeffrey Beaulieu ,  Ravi Shankar

IPC: G06Q10/0633 ,  G06Q10/0631 ,  G06V10/776 ,  G06V20/40

CPC classification number: G06Q10/0633 ,  G06Q10/063114 ,  G06V10/776 ,  G06V20/41 ,  G06V2201/10

Abstract: Machine learning systems and methods for validating workflows are provided. The system receives one or more digital images of a work action being performed, and processes the image using a hierarchical modeling process that includes a filter model and a cascaded expert model. The filter model processes the image to ascertain whether the image is suitable for use in validating completion of the work action. If the system determines that the image is suitable, the image is then processed by the expert model to classify whether the image depicts a scene occurring before, during, or after performance of the work action. The hierarchical modeling process can be utilized to validate different workflows based upon a specified workflow type. A plurality of hierarchical modeling processes can be applied in parallel to an input image in the event that a type of workflow to be validated is not specified in advance.

公开(公告)号：US20240171783A1

公开(公告)日：2024-05-23

申请号：US18425803

申请日：2024-01-29

Applicant: Roku, Inc.

Inventor： Ronica JETHWA ,  Nam Vo ,  Fei Xiao ,  Abhishek Bambha

IPC: H04N21/234 ,  G06V20/40 ,  H04N21/25 ,  H04N21/81 ,  H04N21/8549

CPC classification number: H04N21/23418 ,  G06V20/41 ,  H04N21/251 ,  H04N21/812 ,  H04N21/8549 ,  G06V2201/10

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating a scene emotion value for a scene based on a sequence of frame emotion values for a sequence of frames within the scene of a content. The content can include multiple scenes, and a scene can include multiple frames, where a frame emotion value can be generated for each frame. A frame emotion value can be generated based on scene metadata related to the scene, content metadata related to the content, and a frame metadata related to the frame.

公开(公告)号：US20240171680A1

公开(公告)日：2024-05-23

申请号：US18423858

申请日：2024-01-26

Applicant: Pindrop Security, Inc.

Inventor： John CORNWELL ,  Terry NELMS, II

IPC: H04M3/51 ,  G06F18/214 ,  H04M3/22 ,  H04M3/42

CPC classification number: H04M3/5175 ,  G06F18/214 ,  H04M3/2218 ,  H04M3/2281 ,  H04M3/42059 ,  G06V2201/10

Abstract: Embodiments described herein provide for passive caller verification and/or passive fraud risk assessments for calls to customer call centers. Systems and methods may be used in real time as a call is coming into a call center. An analytics server of an analytics service looks at the purported Caller ID of the call, as well as the unaltered carrier metadata, which the analytics server then uses to generate or retrieve one or more probability scores using one or more lookup tables and/or a machine-learning model. A probability score indicates the likelihood that information derived using the Caller ID information has occurred or should occur given the carrier metadata received with the inbound call. The one or more probability scores be used to generate a risk score for the current call that indicates the probability of the call being valid (e.g., originated from a verified caller or calling device, non-fraudulent).

公开(公告)号：US11977608B2

公开(公告)日：2024-05-07

申请号：US17515834

申请日：2021-11-01

Applicant: Tata Consultancy Services Limited

Inventor： Jayita Dutta ,  Parijat Deshpande ,  Manasi Samarth Patwardhan ,  Shirish Subhash Karande ,  Shankar Kausley ,  Priya Kedia ,  Shrikant Arjunrao Kapse ,  Beena Rai

IPC: G06N3/045 ,  G01N33/02 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2413 ,  G06N5/02 ,  G06N20/10 ,  G06N20/20 ,  G06T7/00 ,  G06V10/84 ,  G06V20/68

CPC classification number: G06F18/2413 ,  G01N33/02 ,  G06F18/214 ,  G06F18/22 ,  G06T7/001 ,  G06T2207/20081 ,  G06T2207/30128 ,  G06V20/68 ,  G06V2201/10

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.

公开(公告)号：US20240144547A1

公开(公告)日：2024-05-02

申请号：US18359287

申请日：2023-07-26

Applicant: Samsung Electronics Co., Ltd.

Inventor： Woojung HAN ,  Gajin SONG ,  Hoseon SHIN ,  Dongchoon HWANG ,  Kyungtae KIM ,  Kanghee LEE

IPC: G06T11/00 ,  G06V20/50

CPC classification number: G06T11/00 ,  G06V20/50 ,  G06V2201/10

Abstract: A processor is configured to identify a specified event based on data output from one or more sensors. The processor is configured to, in response to identifying occurrence of the specified event, transmit, to an external electronic device connected via a communication circuit, a first signal requesting information associated with both the specified event and a virtual space provided by the external electronic device. The processor is configured to provide, by controlling the display based on receiving a second signal corresponding to the first signal from the external electronic device, information included in the second signal in a state that is executable by a second application different from the first application for displaying the virtual space.

公开(公告)号：US20240135669A1

公开(公告)日：2024-04-25

申请号：US18492532

申请日：2023-10-22

Applicant: Nielsen Consumer LLC

Inventor： Roberto Arroyo ,  David Jiménez-Cabello ,  Javier Martínez Cebrián

IPC: G06V10/25 ,  G06F18/21 ,  G06F18/22 ,  G06F18/24 ,  G06F18/25 ,  G06N3/08 ,  G06T3/40 ,  G06V10/74 ,  G06V10/764 ,  G06V10/771 ,  G06V10/80 ,  G06V10/82 ,  G06V20/70

CPC classification number: G06V10/25 ,  G06F18/2163 ,  G06F18/22 ,  G06F18/24 ,  G06F18/251 ,  G06N3/08 ,  G06T3/40 ,  G06V10/761 ,  G06V10/764 ,  G06V10/771 ,  G06V10/809 ,  G06V10/82 ,  G06V20/70 ,  G06V2201/10

Abstract: Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

公开(公告)号：US11966967B2

公开(公告)日：2024-04-23

申请号：US17702139

申请日：2022-03-23

Applicant: Painted Dog, Inc.

Inventor： Vincent Alexander Crossley ,  Jared Max Browarnik ,  Tyler Harrison Cooper ,  Carl Ducey Jamilkowski

IPC: G06Q30/0601 ,  G06F16/955 ,  G06V20/20 ,  G06V20/40 ,  H04N21/237 ,  H04N21/431 ,  H04N21/4725 ,  H04N21/478 ,  H04N21/858

CPC classification number: G06Q30/0643 ,  G06F16/9558 ,  G06V20/20 ,  G06V20/46 ,  H04N21/237 ,  H04N21/4316 ,  H04N21/4725 ,  H04N21/47815 ,  H04N21/858 ,  G06V2201/10

Abstract: Current interfaces for displaying information about items appearing in videos are obtrusive and counterintuitive. They also rely on annotations, or metadata tags, added by hand to the frames in the video, limiting their ability to display information about items in the videos. In contrast, examples of the systems disclosed here use neural networks to identify items appearing on- and off-screen in response to intuitive user voice queries, touchscreen taps, and/or cursor movements. These systems display information about the on- and off-screen items dynamically and unobtrusively to avoid disrupting the viewing experience.