PATENT CLAIM ANALYSIS

Application Number: 15941552
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-08
Patent Classification: ["370", "252000"]

Abstract:
A recursive algorithm may be applied to group cells in a service network into a small number of clusters. For each of the clusters, different regression algorithms may be evaluated, and a regression algorithm generating a smallest error is selected. A total error for the clusters may be identified based on the errors from the selected regression algorithms and from degrees of separation associated with the cluster. If the total error is greater than a threshold value, the cells may be grouped into a larger number of clusters and the new clusters may be re-evaluated. A key performance indicator (KPI) may be estimated for a cell based on a regression algorithm selected for the cluster associated with the cell. A resources may be allocated to the cell based on the KPI value.

Claim (Index 15):
A non-transitory computer readable memory to store one or more of instructions that cause a processor to:\n collect usage data related to cells of a service network; group the cells into clusters, wherein the clusters are first clusters, wherein the first clusters include a first quantity of clusters, and wherein one or more instructions further cause the processor, when grouping the cells into the clusters, to:\n calculate degrees of separation between pairs of the first clusters; \n determine a total error for the first clusters based on the degrees of separation; and \n group the cells into second clusters when the total error is greater than a threshold value, wherein the second clusters include a second quantity of clusters that is greater than the first quantity; \n select regression algorithms for the clusters, wherein the regression algorithms are selected from a group of regression algorithms, and wherein the processor, when selecting the regression algorithms, is further configured to:\n identify prediction errors for the group of regression algorithms for each of the clusters, and \n select, as the regression algorithms, regression algorithms of the group of regression algorithms associated with the smallest prediction errors for each of the clusters; \n identify a key performance indicator (KPI) related to a communication resource for a cell of the cells; identify a cluster of the clusters that includes the cell, wherein the cluster is associated with one of the regression algorithms; calculate a value for the KPI based on the usage data and the one of the regression algorithms; and allocate the communication resource to the cell based on the calculated value for the KPI.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 90.0
- Lexical Diversity: 2.23438
- Patent Class: 370.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15087129', '14975116', '14273433', '14248056', '15373177']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5565563847791541
- 35 USC 102 Novelty (BERT): 0.5095853225185244
- Combined Prediction Score: 0.5518592785530911
- Mean Citation Score: 167.14679699999996
- Max Citation Score: 296.3735
- Similarity Product: 233.5173533454835

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