PATENT CLAIM ANALYSIS

Application Number: 15924083
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["705", "007140"]

Abstract:
Disclosed herein are aspects of a staff scheduling system for preparing a staffing schedule report for secondary education campuses of a school district. In one embodiment, a staff scheduler comprises at least one interface for receiving a plurality of inputs from at least one external computing device; and a processor configured to perform a staff scheduling algorithm to generate a staffing schedule report for the secondary education campuses, wherein the staff scheduling algorithm generates a series of input prompts and decisions based on the plurality of inputs. The plurality of inputs includes at least courses requested by students at each campus, current teachers available in the district, current teachers' qualifications, campus facility information, which courses may be shared at multiple campuses, and which staff may be shared by multiple campuses.

Claim (Index 15):
A method for preparing a staffing schedule report for secondary education campuses of a school district, the method comprising:\n receiving data for the secondary campuses of the school district from at least one external source, at least one interface for receiving a plurality of inputs from at least one external computing device, wherein at least a portion of the inputs are received on a regular periodic basis, the data including at least courses requested by students at each campus, current teachers available in the district, current teachers' qualifications, campus facility information, which courses may be shared at multiple campuses, and which staff may be shared by multiple campuses; periodically preparing a staffing schedule report using a staff scheduling algorithm for the secondary education campuses in the school district using the received data; wherein the processing and periodically preparing the staffing schedule report is performed by a processor, wherein at least one legal restriction is also periodically evaluated and employed by the processor to determine teaching staffing to be shared by multiple campuses, wherein said at least one legal restriction includes at least one of:\n a maximum teaching time each teacher is available; \n a travel time between the multiple campuses and teacher compensation therefor for each teacher; and \n a union constraint, \n wherein the staff scheduling algorithm includes at least the steps of:\n determining the total number of students requesting any course, wherein the total number of students is the number of students requesting said course on said multiple campuses; \n dividing an enrollment of each course by an enrollment factor to determine a quotient having an integer and remainder; \n dropping the remainder from the quotient, and dividing the enrollment by the integer of the quotient to determine an average class size; \n comparing the average class size with a maximum class size; \n if the average class size exceeds the maximum class size, increasing the integer by one and re-dividing the enrollment by the integer; \n aggregating a total number of scheduled sections for each course and aggregating a number of teachers currently assigned for each course category in a department to determine a number of teacher sections for each course; \n subtracting the total number of scheduled sections from the number of teacher sections; and \n generating the staffing schedule report for each of the secondary education campuses, wherein said staffing report is employed to periodically update teacher staffing schedules , and wherein the teacher staffing schedules are employed by teachers for staffing for each campus.

Metadata:
- Claim Count in Document: 2.0
- Percentile: 90.0
- Lexical Diversity: 1.91892
- Patent Class: 705.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['10934652', '11929256', '11076398', '13776857', '11323229']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1040048419815077
- 35 USC 102 Novelty (BERT): 0.5158013860842218
- Combined Prediction Score: 0.1451844963917791
- Mean Citation Score: 259.40238
- Max Citation Score: 282.37863
- Similarity Product: 187.68760829707324

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