Trax Technology Solutions Pte Ltd.

Bria Artificial Intelligence Ltd.

Other

Trax Technologies Solutions Pte Ltd.

Trax Technololgy Solutions Pte Ltd.

Yair Adato

Ziv Mhabary

Dolev Pomeranz

Dvir Yerushalmi

Daniel Shimon Cohen

Eyal Gutflaish

Itai Lishner

Efrat Taig

Gal Jacobi

Michael Feinstein

Youval Bronicki

Yotam Michael

Aviv Eisenschtat

Alon Grubshtein

Mark Cook

Ori Liberman

Vered Horesh-Yaniv

David M Gottlieb

Ran Achituv

Nir Hemed

Ron Mokady

Nimrod Sarid

Ran Taig

Osnat Yanushevsky

Shlomi Dayan

Yonatan Adar

Maria Kushnir

Dror Yashpe

Galit Peled

Bar Fingerman

Ori Feldstein

Paul Yudkin

Yohai Devir

Systems, methods and non-transitory computer readable media for inference based on different portions of a training set using a single inference model are provided. Textual inputs may be received, each of which may include a source-identifying-keyword. An inference model may be a result of training a machine learning model using a plurality of training examples. Each training example may include a respective textual content and a respective media content. The training examples may be grouped based on source-identifying-keywords included in the textual contents. Different parameters of the inference model may be based on different groups, and thereby be associated with different source-identifying-keywords. When generating new media content using the inference model and a textual input, parameters associated with the source-identifying-keyword included in the textual input may be used.

Inference based on different portions of a training set using a single inference model

Systems, methods and non-transitory computer readable media for identifying visual contents used for training of inference models are provided. A specific visual content may be received. Data based on at least one parameter of an inference model may be received. The inference model may be a result of training a machine learning algorithm using a plurality of training examples. Each training example of the plurality of training examples may include a visual content. The data and the specific visual content may be analyzed to determine a likelihood that the specific visual content is included in at least one training example of the plurality of training examples. A digital signal indicative of the likelihood that the specific visual content is included in at least one training example of the plurality of training examples may be generated.

Identifying visual contents used for training of inference models

A method for identifying perishable products in a retail store and for automatically generating offers relating to the identified products is provided. The method may include receiving a set of images depicting perishable products of at least one product type displayed on at least one shelving unit in a retail store. The method may also include analyzing the set of images to determine information about a displayed inventory of the perishable products. The method may further include accessing a database storing information about the at least one product type. The method may also include using the information about the displayed inventory and the stored information about the at least one product type to determine at least one offer associated with the at least one product type. The method may further include providing the at least one offer to at least one customer of the retail store.

Crowdsourcing incentive based on shelf location

Systems, methods and non-transitory computer readable media for identifying prompts used for training of inference models are provided. In some examples, a specific textual prompt in a natural language may be received. Further, data based on at least one parameter of an inference model may be accessed. The inference model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may include a respective textual content and a respective media content. The data and the specific textual prompt may be analyzed to determine a likelihood that the specific textual prompt is included in at least one training example of the plurality of training examples. A digital signal indicative of the likelihood that the specific textual prompt is included in at least one training example of the plurality of training examples may be generated.

Prompt-based attribution of generated media contents

A system for processing images captured in a retail store is provided. The system may include a processor configured to: access a database storing a group of product models; receive an image depicting at least part of a store shelf having a plurality of products of a same type displayed thereon; analyze the image and determine a first candidate type of the products based on the group of product models and the image analysis; determine a first confidence level associated with the first candidate type; when the first confidence level is below a confidence threshold, determine a second candidate type of the products using contextual information; determine a second confidence level associated with the determined second candidate type of the plurality of products; and when the second confidence level is above the confidence threshold, initiate an action to update the group of product models stored in the database.

Using context to update product models

Methods, systems, and computer-readable media are provided for selecting items for presentation on electronic visual displays in retail stores. In one implementation, a method may comprise: obtaining a plurality of images of products in a retail store; analyzing a first image to determine whether products of a particular product type are available at a first point in time; analyzing a second image to determine whether products of the particular product type are available at a second point in time; and based on the determination of whether products of the particular product type are available at the first point in time and the determination of whether products of the particular product type are available at the second point in time, selecting whether to display a particular item on an electronic visual display in the retail store.

Selecting items for presentation on electronic visual displays in retail stores based on availability of products

Systems, methods and non-transitory computer readable media for generating content using a generative model without relying on selected training examples are provided. An input indicative of a desire to generate a new content using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may be associated with a respective content. Further, an indication of a particular subgroup of at least one but not all of the plurality of training examples may be obtained. Based on the indication, the input and the generative model may be used to generate the new content, abstaining from basing the generation of the new content on any training example included in the particular subgroup. The new content may be provided.

Generating content using a generative model without relying on selected training examples

Systems, methods and non-transitory computer readable media for generating and orchestrating motion of visual contents are provided. A plurality of visual contents may be accessed. Data indicative of a layout of the plurality of visual contents in a user interface may be accessed. A sequence for the plurality of visual contents may be determined based on the layout. For each visual content of the plurality of visual contents, the visual content may be analyzed to generate a video clip including a motion of at least one object depicted in the visual content. A presentation of the plurality of visual contents in the user interface may be caused. The determined sequence for the plurality of visual contents may be used to orchestrate a series of playbacks of the generated video clips.

Generating and orchestrating motion of visual contents in an interactive interface to guide user attention

Systems, methods and non-transitory computer readable media for propagating changes from one visual content to other visual contents are provided. A plurality of visual contents may be accessed. A first visual content and a modified version of the first visual content may be accessed. The first visual content and the modified version of the first visual content may be analyzed to determine a manipulation for the plurality of visual contents. The determined manipulation may be used to generate a manipulated visual content for each visual content of the plurality of visual contents. The generated manipulated visual contents may be provided.

Propagating changes from one visual content to a portfolio of visual contents

Methods, systems, and computer-readable media are provided for customized presentation of items on electronic visual displays in retail stores. In one implementation, a method may comprise: obtaining a plurality of images of products in a retail store captured; analyzing a first image to determine whether products of a particular product type are available at a first point in time; analyzing a second image to determine whether products of the particular product type are available at a second point in time; based on the determination of whether products of the particular product type are available at the first point in time and the determination of whether products of the particular product type are available at the second point in time, selecting at least one display parameter for a particular item; and using the selected at least one display parameter to display the particular item on an electronic visual display.

Customized presentation of items on electronic visual displays in retail stores based on availability of products

Systems, methods and non-transitory computer readable media for attributing generated visual content to training examples are provided. A first visual content generated using a generative model may be received. The generative model may be associated with a plurality of training examples. Each training example may be associated with a visual content. Properties of the first visual content may be determined. Each visual content associated with a training example may be analyzed to determine properties of the visual content. The properties of the first visual content and the properties of the visual contents associated with the plurality of training examples may be used to attribute the first visual content to a subgroup of the plurality of training examples. The visual contents associated with the training examples of the subgroup may be associated with a source. A data-record associated with the source may be updated based on the attribution.

Attributing generated visual content to training examples

Systems, methods and non-transitory computer readable media for attributing generated audio contents to training examples are provided. A first audio content generated using a generative model may be received. The generative model may be a result of training a machine learning model using training examples. Each training example may be associated with a respective audio content. Properties of the first audio content may be determined. For each training example of the training examples, the respective audio content may be analyzed to determine properties of the respective audio content. The properties of the first audio content and the properties of the audio contents associated with the training examples may be used to attribute the first audio content to a subgroup of the training examples. A respective data-record associated with a source associated with the training examples of the subgroup may be updated based on the attribution.

Attributing generated audio contents to training examples

A system for processing images captured in a retail store and automatically identifying misplaced products is provided. The system may comprise at least one processor configured to receive one or more images captured by one or more image sensors from an environment of a retail store, detect in the one or more images a first product, determine that the first product is not located in the first correct display location, cause an issuance of a user-notification associated with the first product, detect in the one or more images a second product, determine that the second product is not located in the second correct display location, and after determining that the second product is not located in the second correct display location and when the second urgency level is lower than the first urgency level, withhold issuance of a user-notification associated with the second product.

Withholding notifications due to temporary misplaced products

Systems, methods and non-transitory computer readable media for attributing aspects of generated visual contents to training examples are provided. A first visual content generated using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Properties of an aspect of the first visual content and properties of visual contents associated with the plurality of training examples may be used to attribute the aspect of the first visual content to a subgroup of the plurality of training examples. For each source of the sources associated with the visual contents associated with the training examples of the subgroup, a data-record associated with the source may be updated based on the attribution of the aspect of the first visual content.

Attributing aspects of generated visual contents to training examples

A system for automatically identifying situations to withhold planogram incompliance notifications in a retail store is disclosed. The system has a processor that receives image data depicting products displayed on a store shelf from image sensors mounted in the retail store. The processor identifies a product type associated with the displayed products and determines a placement of products of that product type on the shelf. The processor accesses a planogram describing a desired placement of products and identifies, based on the planogram, a discrepancy between the determined placement of products and the desired placement of product. The processor determines whether a notice-override condition exists. If a notice-override condition exists, the processor, withholds issuance of a user-notification associated with the identified discrepancy. If a notice-override condition does not exist, the processor issues the user-notification associated with the identified discrepancy.

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Kfar Shmuel, Israel

Yair Adato