PATENT CLAIM ANALYSIS

Application Number: 16038790
Application Type: Utility
Filing Date: 2018-07
Publication Date: 2018-11
Patent Classification: ["702", "019000"]

Abstract:
Compositions, methods and kits are disclosed for high-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules by attachment of a diverse set of labels. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. Detection may be by a variety of methods including hybridization based or sequencing. Molecules that would otherwise be identical in information content can be labeled to create a separately detectable product that is unique or approximately unique in a collection. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed labels are present. The methods may be used, for example, to estimate the number of separate molecules of a given type or types within a sample.

Claim (Index 4):
The method of  claim 1 , wherein labels with t different label sequences are attached to the occurrences of first target molecule or the second molecule exact x times,\n wherein n is n 1  or n 2 , wherein m and n are related by E(t), wherein E \ue8a0 ( t ) = m \u00b7 n ! x ! \ue89e ( n - x ) ! \ue89e ( 1 m ) x \ue89e ( 1 - 1 m ) n \ue89e - x , and wherein E(t) denotes the mean of t.

Metadata:
- Claim Count in Document: 71.0
- Percentile: 95.0
- Lexical Diversity: 1.71765
- Patent Class: 702.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15224460', '16038832', '15217896', '15217886', '14540018']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.256784902077778
- 35 USC 102 Novelty (BERT): 0.626419376846881
- Combined Prediction Score: 0.2937483495546883
- Mean Citation Score: 501.69868
- Max Citation Score: 529.1959
- Similarity Product: 526.2101153085412

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