Abstract: Real-time offer negotiation assistance is provided. In examples, an offer negotiation engine receives a proposed offer from a user. An offer grading model trained on historical data regarding listings, offers, and sales of homes evaluates the proposed offer and generates an offer grade. An offer guidance generator generates guidance for the proposed offer, such as recommendations to improve the proposed offer. In further examples, the offer negotiation engine receives an offer prompt from the user. An offer generation model trained on the historical data generates a proposed offer based on the offer prompt.

Abstract: Comps processing systems and methods for processing embeddings of properties and candidate comparable properties thereof to learn an implicit ranking of the candidate comparable properties are disclosed. The comps processing system uses embeddings, predicted values, and actual values of particular properties and candidate comparable properties thereof to train an implicit comparable-ranking model to produce comparable-adjusted predicted values and intermediate outputs. The comps processing system can then extract the intermediate outputs from the trained implicit comparable-ranking model, when applied to subject properties. The intermediate outputs can include compatibility probabilities, which represent a degree of compatibility between the subject property and a candidate comparable property thereof. Accordingly, the comps processing system can learn the similarities between a subject property and candidate comparable properties thereof when learning to value the subject property.

Abstract: Embodiments of the present disclosure pertain to extracting data from images using neural networks. In one embodiment, an image is fit to a predetermined bounding window. The image is then processed with a convolutional neural network to produce a three dimensional data cube. Slices of the cube are processed by an encoder RNN, and the results concatenated. The concatenated results are processed by an attention layer with input from a downstream decoder RNN. The attention layer output is provided to the decoder RNN to generate a probability array where values in the probability array correspond to particular characters in a character set. The maximum value is selected, and translated into an output character. In one embodiment, an amount may be extracted from an image of a receipt.

Abstract: Embodiments of the present disclosure pertain to extracting data from images using neural networks. In one embodiment, an image is fit to a predetermined bounding window. The image is then processed with a convolutional neural network to produce a three dimensional data cube. Slices of the cube are processed by an encoder RNN, and the results concatenated. The concatenated results are processed by an attention layer with input from a downstream decoder RNN. The attention layer output is provided to the decoder RNN to generate a probability array where values in the probability array correspond to particular characters in a character set. The maximum value is selected, and translated into an output character. In one embodiment, an amount may be extracted from an image of a receipt.

Abstract: Secure and efficient methods and systems of providing multiple access interference constraints for frequency hopped spread spectrum communications are presented. The methods and systems leverage a cryptographic keystream generator and iterative indexing into shuffled lists to generate a constrained collision-free frequency hopping sequence. This flexible frequency hopping access (FFHA) can support varying hop frequency and bandwidth resources including secure operation for applications where the number of channels (hopping sequences) exceeds the number of hop frequency resources available.

Abstract: A wireless communication network is provided that includes a plurality of communication cells each defined by at least one communication coverage area and a plurality of access points each corresponding to a different one of the plurality of communication cells. Each access point is configured to communicate on a first frequency with at least one of (i) a subscriber in the at least one communication coverage area in a first communication cell and (ii) a subscriber in a second communication cell. Each access point is further configured to communicate on a second frequency with an access point in a third communication cell.