PATENT CLAIM ANALYSIS

Application Number: 15953635
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-12
Patent Classification: ["705", "007310"]

Abstract:
Provided herein are systems, methods and computer readable media for forecasting demand. An example method comprises generating a virtual offer for one or more combinations of a category or sub-category, location, and price range, accessing consumer data comprising one or more users and user data related to each of the one or more users, calculating a probability that a particular user would buy a particular offer in a particular time frame for at least a portion of the plurality of users and for each of the virtual offers, and determining an estimated number of units of to be sold for at least a portion of the one or more virtual offers as a function of at least the probability associated with each of the one or more virtual offers.

Claim (Index 38):
An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to:\n generate, via a virtual offer generation module, a virtual offer for each combination of a plurality of attributes, wherein the attributes comprise price data, one product category or one service category, and location data, wherein the generation of the virtual offer for each combination of the plurality of attributes comprises: accessing both a category taxonomy model and a location taxonomy model to identify a plurality of sub-category and hyper-location pairs, the category taxonomy model defining a hierarchical structure of service categories and sub-categories that is found in one or more local or hyper-local regions and the location taxonomy model defining a hierarchical structure that is based on locations, sub-locations, and hyper-local regions resulting in a set of virtual offers comprised of each combination of a plurality of attributes, and utilize a set of guidelines to match at least one of one or more price ranges to the sub-category and hyper-location pair; access, via a consumer data database, consumer data comprising one or more users and user data related to each of the one or more users; calculate, via a probability generation module, a probability value that a particular user from the one or more users would buy a particular offer from the set of virtual offers comprised of each combination of a plurality of attributes in a particular time frame for at least a portion of the one or more users and for each of the plurality of the virtual offers; determining an estimated number of units to be sold for at least a portion of the plurality of the virtual offers as a function of at least the probability associated with each of the plurality of virtual offers; correlate the at least one of the plurality virtual offers to a real promotion by matching the price data, product category or service category, and the location data of the at least one of the plurality of virtual offers to price data, a product or service category and the location data of the real promotion; access information indicative of the available inventory of the real promotion; calculate a residual demand as a function of the forecasted demand and the available inventory; and dynamically adjust the residual demand, subsequent to a change in the available inventory of the real promotion.

Metadata:
- Claim Count in Document: 62.0
- Percentile: 91.0
- Lexical Diversity: 2.04478
- Patent Class: 705.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13826333', '14316245', '13804403', '14106203', '13826464']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1105353656782449
- 35 USC 102 Novelty (BERT): 0.6047307585513433
- Combined Prediction Score: 0.1599549049655547
- Mean Citation Score: 370.449844
- Max Citation Score: 522.11774
- Similarity Product: 495.260065416844

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test