Abstract: Described in detail herein are systems and methods for network environment for a dynamic learning system for object matching and substitution using hand-held electronic devices. In exemplary embodiments, a hand-held electronic device may receive a request for physical objects of which a first physical object may be unavailable. The hand-held electronic device may transmit an identifier of the first physical object to a central computing system. The central computing system may query the database to retrieve data associated with the first physical object. The centrally computing system may attempt to match the first physical object to an available physical object based on dynamically learned matching behavior using the retrieved data.

Abstract: Described in detail herein are systems and methods for network environment for a dynamic learning system for object matching and substitution using hand-held electronic devices. In exemplary embodiments, a hand-held electronic device may receive a request for physical objects of which a first physical object may be unavailable. The hand-held electronic device may transmit an identifier of the first physical object to a central computing system. The central computing system may query the database to retrieve data associated with the first physical object. The centrally computing system may attempt to match the first physical object to an available physical object based on dynamically learned matching behavior using the retrieved data.

Abstract: Methods, apparatus, and processor-readable storage media for automatically generating inventory-related information forecasts using machine learning techniques are provided herein.

Abstract: Described in detail herein are systems and methods for network environment for a dynamic learning system for object matching and substitution using hand-held electronic devices. In exemplary embodiments, a hand-held electronic device may receive a request for physical objects of which a first physical object may be unavailable. The hand-held electronic device may transmit an identifier of the first physical object to a central computing system. The central computing system may query the database to retrieve data associated with the first physical object. The centrally computing system may attempt to match the first physical object to an available physical object based on dynamically learned matching behavior using the retrieved data.

Abstract: Described in detail herein are systems and methods for network environment for a dynamic learning system for object matching and substitution using hand-held electronic devices. In exemplary embodiments, a hand-held electronic device may receive a request for physical objects of which a first physical object may be unavailable. The hand-held electronic device may transmit an identifier of the first physical object to a central computing system. The central computing system may query the database to retrieve data associated with the first physical object. The centrally computing system may attempt to match the first physical object to an available physical object based on dynamically learned matching behavior using the retrieved data.

Abstract: Described in detail herein are systems and methods for network environment for a dynamic learning system for object matching and substitution using hand-held electronic devices. In exemplary embodiments, a hand-held electronic device may receive a request for physical objects of which a first physical object may be unavailable. The hand-held electronic device may transmit an identifier of the first physical object to a central computing system. The central computing system may query the database to retrieve data associated with the first physical object. The centrally computing system may attempt to match the first physical object to an available physical object based on dynamically learned matching behavior using the retrieved data.