Qualcomm Incorporated

Yembo, Inc.

Siddharth Mohan

Sharad Deepak Sambhwani

Zachary Rattner

Rohit Kapoor

Maciej Halber

Marc Eder

Haitong Sun

Mehmet Yavuz

Aziz Gholmieh

Farhad Meshkati

Christophe Chevallier

Devin Waltman

Anoop Jakka

Vikram Makam Gupta

Arjun Bharadwaj

William T Frantz

Shriram Ganesh

Timothy K Kerssen

Arnaud Meylan

Ravi Agarwal

Pavan Kumar Vitthaladevuni

Venkata Ramanan Venkatachalam Jayaraman

Michael-David Nakayoshi Canoy

Anne Katrin Konertz

Kiet Tuan Chau

Sridhar Ramanujam

Brandon Allen Burdge

Kyle Babinowich

Janpreet Singh

Zach Rattner

An interactive 3D electronic representation of a physical scene is executed in a browser. The browser has a limited computing capability compared to a native application or hardware usable by the computer. The interactive 3D representation is configured to minimize overall computing resources and processing time. Attributes of data items corresponding to surfaces and/or contents in the physical scene are extracted from the interactive 3D representation. Interactive verification of the attributes of the subset of data items is performed in the browser by: flattening a selected view of a ceiling, floor, or wall two dimensions; receiving user adjustments (if needed) to the dimensions and/or locations of the selected ceiling, floor, or wall; receiving user indications (if needed) of cut outs in the selected ceiling, floor, or wall; and updating the interactive 3D representation based on adjustments to the dimensions and/or locations, and/or the indications of cut outs.

Browser optimized interactive electronic model based determination of attributes of a structure

The process of obtaining an insurance quote is currently based on information provided by filing online forms. Insurance companies use the consumer input data from online the forms to provide a quote for property insurance and for insurance underwriting. To complete the underwriting, the insurance companies have to make assumptions on various factors; for example, the structure and layout of a consumer's home, what type of content the consumer owns, its value, and its condition, among several others. It is desirable to get more accurate data on the consumer content, the structure and layout of the home, as well as other factors (e.g., hazards such as whether a house is on a canyon) in an automated fashion which would let insurance companies underwrite policies with a more accurate risk profile as well as save costs by not having to send an onsite inspector.

Capacity optimized electronic model based prediction of changing physical hazards and inventory items

Systems, methods, and non-transitory computer readable media are disclosed that include operations to generate a virtual representation of a physical location with spatially localized information related to elements within the location being embedded in the virtual representation. The operations includes receiving description data (e.g., a plurality of images and videos) of the location, the description data being generated via at least one of a camera, a user interface; receive metadata associated elements within the location; generating (e.g., offline or in real-time), via a machine learning model and/or a geometric model, a 3-dimensional (3D) model of the location and elements therein; and generating, based on the 3D model of the location, an information-rich virtual representation of the location by annotating the 3D model with spatially localized metadata associated with the elements within the location and semantic information of the elements.

Systems and methods for building a virtual representation of a location

Disclosed is a method for using a virtual representation of an indoor environment to identify contents that have been damaged (e.g., by flooding). A virtual representation of a physical scene of an indoor environment is processed to identify a list of contents in the physical scene. The virtual representation may include 2-dimensional representations of the physical scene (e.g., images or video) or a 3-dimensional representation of the physical scene (e.g., 3D digital model). A reference line is determined in the virtual representation that is indicative of a maximum vertical extent of the damage in the physical scene. The position of the reference line is compared with the position of the identified contents in the virtual representation to determine contents that are likely to be damaged. For example, the contents that are at or below a plane represented by the reference line in the virtual representation may be identified as damaged.

Identifying flood damage to an indoor environment using a virtual representation

Systems and methods for providing AI-based cost estimates for services are disclosed. The method may comprise receiving, at one or more processors, data from a scanning of a location, the scanning performed by one or more of a camera, a computer vision device, an inertial measurement unit, or a depth sensor. Data may be received, at one or more processors, related to the identification of one or more key elements at the location. An itemized statement and quote of work to be performed may be generated at one or more processors.

Artificial intelligence generation of an itemized property and renters insurance inventory list for communication to a property and renters insurance company

Systems and methods for providing AI-based relocation cost estimates for services and products are disclosed. The method may comprise receiving description data of a location, the description data generated via at least one or more of a camera, a user interface, and/or one or more environment sensors. The method may generate an inventory list comprising a plurality of items, utilizing the machine learning model, based on the description data, the inventory list further comprising inventory attributes associated with the plurality of items related to a context of the plurality of items in the location. The method may generate, utilizing the machine learning models, one or more interactive quotes for relocation cost estimates comprising at least one or more offers for products and/or services based on the plurality of items in the inventory list and the inventory attributes.

Systems and methods for providing AI-based cost estimates for services

Artificial intellegence prediction algorithm for generating an itemized statement of work and quote for home services based on two dimensional images, text, and audio

Apparatus and methods of mobility management include identifying a target cell as an active set candidate. The apparatus and methods further include determining that a trigger adjustment condition exists, wherein the trigger adjustment condition triggers an adjustment of a timing value that indicates a sending time of a target cell add message to a serving cell. Moreover, the apparatus and methods include sending the target cell add message including the target cell to the serving cell based on determining that the trigger adjustment condition exists.

Apparatus and methods of enhanced mobility management

Disclosed is an apparatus, system, and method for a wireless device to select a network. The wireless device receives a request from a user to access a network site. The wireless device then selects a network based upon a predefined weighted criteria setting that was previously selected by the user.

Selecting a network for a wireless device

Aspects related to pre-configuring for a serving cell change to neighbor cells are described. In one example, a user equipment (UE) may camp on a first cell. The UE may receive at least one communication including target cell pre-configuration information for one or more neighbor cells eligible for pre-configuration. The UE may determine that the first cell is no longer providing adequate service and identify a target cell, which may be one of the one or more neighbor cells, but is not part of an active set for the UE. The UE may transmit a message (e.g., Event 1d) requesting a serving cell change to the target cell. The UE may configure to receive service from the target cell based on the pre-configuration information associated with the target cell. The UE may receive an indication to perform the serving cell change and perform the serving cell change.

Method and apparatus for pre-configuring for a serving cell change to neighbor cells

Various embodiments for conducting proxy teaching for learning devices within a decentralized system, including an embodiment method with operations for obtaining, by a teacher signaling device, objectives data related to activities of one or more of the learning devices, generating, by the teacher signaling device, teaching routines based on the obtained objectives data, and broadcasting, by the teacher signaling device, teaching signals configured to teach one or more of the learning devices based on the generated teaching routines. Other embodiments may obtain objectives data by requesting reflex information from the learning devices or intercepting event report messages transmitted by the learning devices. Other embodiments may include broadcasting discovery signals to identify nearby learning devices and modifying teaching routines when objectives of the generated teaching routines cannot be achieved. Other embodiments may include transmitting an authorization request to a user device to determine whether to broadcast teaching signals.

Signaling device for teaching learning devices

Methods and apparatus are described for opportunistic small cell triggering. The methods and apparatus include determining, by a user equipment (UE) connected to a cell, a change of location of the UE. Further, the methods and apparatus include requesting small cell information from a database based at least in part on the change of location of the UE. Moreover, the methods and apparatus include attempting to detect a small cell based on the small cell information. Additionally, the methods and apparatus include transmitting a message to turn on the small cell when no the small cell is not detected.

Methods and apparatus for opportunistic small cell triggering

A small base node such as a Home Base Node (HNB), or femto cell, may reduce its transmit power in order to prevent co-channel or adjacent channel interference, or to limit its coverage area. Once the power is set, the HNB signal to a served Home User Equipment (HUE) its transmit Common Pilot Channel (CPICH) transmit power for accurate path loss estimation. When this power is outside of the permissible range, the HNB adjusts other parameters (such as Random Access Channel (RACH) constant value) to compensate for the error in signaled CPICH power, and thus compensate in that process the error in determining path loss. Similarly, if the uplink sensitivity is adjusted, to prevent interference, parameters would also be adjusted and signaled to the HUE to reflect the link imbalance.

Growing community of inventors

San Diego, CA, United States of America

Siddharth Mohan