Abstract:
A shopper is presented with a customized online store whose inventory is defined by the shopper. In one embodiment, specification of the inventory is conducted in a bricks and mortar store—either during checkout, or by the shopper walking the aisles and scanning items with a barcode scanner pen or the like. The inventory may be defined—at least in part—by scanning items in the shopper's home. A variety of other novel features are also disclosed.
Abstract:
The disclosure relates to accessing computer resources by sensing audio with a microphone. One claim recites a method comprising: obtaining data in a first user's cell phone, the data corresponding to microphone-captured audio; responsive to the data, the first user's cell phone receiving a service provided by a first party, the service comprising facilitating access to a computer resource over a network; in which the first user does not provide a fee to the first party for the service, as the first party bills a charge connected with the service to a sponsoring party. Of course, other claims and combinations are provided as well.
Abstract:
The present technology concerns cell phones and similar devices, and their use in conjunction with media content (electronic and physical) and other systems (e.g., televisions, digital video recorders, and electronic program directories). Some aspects of the technology particularly concern “second screen” applications that sense a television program being watched by a user, and present menus of complementary content on the phone touchscreen from which the user can select. This complementary content can include other video content, associated web pages, opportunities to buy merchandise related to the program, etc. This complementary content can be identified by a provider of the television program, or can be identified otherwise (e.g., by crowd-sourcing). In some embodiments, the phone instructs a remote DVR to record content of interest for later viewing. The technology also provides features for making TV watching a social experience—involving remote friends. A great number of other arrangements and details are also disclosed.
Abstract:
Deterministic identifiers fuel reliable efficient capture of product discovery, purchase and consumption events, which in turn enable more reliable product recommendation, more accurate shopping list generation and in-store navigation. A mobile device, equipped with image and audio detectors, extracts product identifiers from objects, display screens and ambient audio. In conjunction with a cloud-based service, a mobile device application obtains product information and logs product events for extracted identifiers. The cloud service generates recommendations, and mapping for in-store navigation. The detectors also provide reliable and efficient product identification for purchase events, and post shopping product consumption events.
Abstract:
In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views—further minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and used—or ignored—in product identification. A great variety of other features and arrangements are also detailed.
Abstract:
Arrangements involving portable devices (e.g., smartphones and tablet computers) are disclosed. One arrangement enables a content creator to select software with which that creator's content should be rendered—assuring continuity between artistic intention and delivery. Another utilizes a device camera to identify nearby subjects, and take actions based thereon. Others rely on near field chip (RFID) identification of objects, or on identification of audio streams (e.g., music, voice). Some technologies concern improvements to the user interfaces associated with such devices. For example, some arrangements enable discovery of both audio and visual content, without any user requirement to switch modes. Other technologies involve use of these devices in connection with shopping, text entry, and vision-based discovery. Still other improvements are architectural in nature, e.g., relating to evidence-based state machines, and blackboard systems. Yet other technologies concern computational photography. A great variety of other features and arrangements are also detailed.
Abstract:
Deterministic identifiers fuel reliable efficient capture of product discovery, purchase and consumption events, which in turn enable more reliable product recommendation, more accurate shopping list generation and in-store navigation. A mobile device, equipped with image and audio detectors, extracts product identifiers from objects, display screens and ambient audio. In conjunction with a cloud-based service, a mobile device application obtains product information and logs product events for extracted identifiers. The cloud service generates recommendations, and mapping for in-store navigation. The detectors also provide reliable and efficient product identification for purchase events, and post shopping product consumption events.
Abstract:
In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views—further minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and used—or ignored—in product identification. A great variety of other features and arrangements are also detailed.
Abstract:
In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views—further minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and used—or ignored—in product identification. A great variety of other features and arrangements are also detailed.
Abstract:
A variety of technologies having practical application in retail stores are detailed. One is an improved method of identifying items selected by customers. This method includes receiving sensor data from plural sensors, including (a) ceiling-mounted cameras that monitor tracks of customers through aisles of the store, and (b) inventory sensors that are positioned to monitor removal of stock from store shelves. This received sensor data is employed in evaluating plural alternate item identification hypotheses. These hypotheses include a first hypothesis that a customer selected an item having a first identity, and a second hypothesis that the customer selected an item having a second identity. A confidence score is associated with each of the first and second item selection hypotheses. These confidence scores are refined as sensor data is received, e.g., increasing a confidence score of one hypothesis, and reducing a confidence score of another. Such refining continues until one of the hypotheses becomes a winner, due to an associated confidence score fulfilling a predetermined criterion (e.g., reaching a threshold value), at which time the item can be added to a tally for that individual. The winning item identification hypothesis may identify a barcoded item, without that item's barcode ever having been read by a barcode reader. A great number of other features and arrangements are also detailed.