PATENT CLAIM ANALYSIS

Application Number: 15860324
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-05
Patent Classification: ["715", "753000"]

Abstract:
Methods and systems are provided for determining the intent of a recommendation made by a user of a mobile application where the application includes a plurality of separable components, any one or more of which the recommendation can apply to. An application in which a user recommendation control is provided for presentation to a user also includes a tag indicating how a recommendation of the application should be interpreted with respect to the components included therein. The tag can be set by the application developer and can be in the form of text (e.g., a keyword or term) or a uniform resource locator (URL). Where a tag references multiple components of an application, a recommending user can be presented with a recommendation intent query. The recommendation intent query allows a user to designate one or more components of the application to which the user's recommendation should be attributed.

Claim (Index 18):
A system, comprising:\n at least one processor; and a computer-readable medium coupled to the at least one processor having instructions stored thereon which, when executed by the at least one processor, cause the at least one processor to: receive an indication that a user selected a user recommendation control in an application running on a user computing device, the indication including that the user recommended the application running on the user computing device; in response to receiving the indication that the user recommended the application:\n traverse a view hierarchy of the application to determine a view of the application containing the selected user recommendation control; \n identify at least two different types of content including portions of text, media, images, or chat interfaces that are presented within the application; \n generate a recommendation intent query in a second user interface, wherein the generated recommendation intent query:\n presents a list of the at least two different types of content that were presented by the application when the user recommended the application; and \n enables the user to choose, from the list of the at least two different types of content, a given type of content that contributed to the user recommendation of the application; and \n \n transmit the recommendation intent query for presentation at the user computing device; \n receive, in response to presentation of the recommendation intent query and from the user computing device that presents the second user interface, an indication of a user selection of at least one of the at least two different types of content from the list of the at least two different types of content that were presented by the recommendation intent query; generate, based on the user recommendation of the application by the user and the user selection of the at least one of the at least two different types of content, at least one social annotation that communicates the recommendation of the application and any of the at least two different types of content that the user selected from the list of the at least two different types of content; and serve, via a network, the at least one social annotation to a second user computing device in a format suitable for presentation on the second user computing device.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 86.0
- Lexical Diversity: 2.20548
- Patent Class: 715.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13451351', '13271079', '13271020', '15047991', '13244882']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3277373057335227
- 35 USC 102 Novelty (BERT): 0.5571686700360272
- Combined Prediction Score: 0.3506804421637732
- Mean Citation Score: 343.135798
- Max Citation Score: 428.78217
- Similarity Product: 390.26260552540185

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

Dataset: test