PATENT CLAIM ANALYSIS

Application Number: 15860470
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-07
Patent Classification: ["706", "012000"]

Abstract:
In one embodiment, a method includes managing delivery of messages to non-stationary mobile clients. A computing system receives a request from a first user to send a message to a second user. The computing system then accesses the location information of the second user which includes the velocity of the mobile client of the second user. The computing system further accesses the activity information of the second user which indicates if the second user is currently active on a social-networking application. If the velocity indicates that the second user is non-stationary, the computing system then determines an activity-level of the second user. If the activity-level is below a threshold activity-level, the computing system confirms with the first user whether the message to the second user should be sent. If the activity-level is above a threshold activity-level, the computing system sends the message to the receiver.

Claim (Index 16):
The method of  claim 13 , further comprising training the machine-learning model, the training comprises:\n accessing a plurality of training samples from a training data set, the training samples comprising one or more of: (1) one or more signals associated with a plurality of users on an online social network, respectively, wherein the one or more signals are associated with one or more timestamps and are collected over a prior time window, (2) known activity-levels on a social-networking application associated with each of the plurality of users corresponding to the one or more signals, respectively, or (3) user feedback associated with each of the plurality of users, wherein the feedback is a user-selected activity-level on a social-networking application corresponding to the timestamp; formulating the one or more signals of the plurality of users into one or more feature vectors for each of the plurality of users; and updating the machine-learning model, the updating comprising:\n generating probabilities of respective activity-levels for the plurality of users based on the respective one or more feature vectors; and \n re-training the machine-learning model based on a comparison between the generated probabilities and the user feedback.

Metadata:
- Claim Count in Document: 53.0
- Percentile: 86.0
- Lexical Diversity: 2.51613
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14868123', '14868110', '13681843', '14836206', '13767724']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3758061143322185
- 35 USC 102 Novelty (BERT): 0.4941675472559002
- Combined Prediction Score: 0.3876422576245867
- Mean Citation Score: 202.958032
- Max Citation Score: 213.84999
- Similarity Product: 138.30502800317288

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

Dataset: test