# US 2018/0322467 A1 · Methods and systems for a job suggestions engine and talent timeline in a talent management application > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0322467 A1 - Document ID: US20180322467A1 - Status: Published application - Kind: A1 - Application number: 16/033,102 - Filed: 2018-07-11 - Published: 2018-11-08 - Earliest claimed: 2012-09-11 - Assignees: Oracle International Corporation - Applicants: Oracle International Corporation - Inventors: Deepak Rammohan - Web page: https://patentgrep.com/patent/US20180322467A1 ## CPC - G06Q 10/1053: Employment or hiring - G06Q 10/10: Office automation; Time management - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Techniques for automatically suggesting one or more employment positions are provided. A method may include receiving, by a computing device, candidate data relating to an employment position candidate. The computing device may then match the employment position candidate with one or more employment positions based on the received candidate data. Once the employment position candidate is matched with the one or more employment positions, the computing device may generate one or more suggested employment positions for the employment position candidate, wherein the one or more suggested employment positions are automatically generated based on the matching of the employment position candidate with the one or more employment positions. ## Claims ### Claim 1 A computer-implemented method, comprising: pre-aggregating training data for a plurality of employment positions associated with a plurality of employment position sources, wherein the training data is in one or more sequence file formats; converting the pre-aggregated training data into a set of sparse vectors; training a classifier model with the set of sparse vectors, wherein the classifier model is configured to determine a employment position source effectiveness for an employment position source, wherein the employment position source effectiveness is associated with a likelihood a job seeker will be hired for an employment position if the job seeker applies through the employment position source; receiving candidate data relating to an employment position candidate; matching the employment position candidate with one or more employment positions based on the received candidate data and the one or more employment positions; determining a suggested employment position for the employment position candidate from the one or more employment positions; and generating, for the suggested employment position, a predicted employment position source effectiveness associated with an employment position source of the plurality of employment position sources, the predicted employment position source effectiveness determined by applying the classifier model to the suggested employment position and to the received candidate data relating to the employment position candidate. ### Claim 2 (depends on 1) The method of claim 1, further comprising: receiving one or more log-in credentials from the employment position candidate; and providing the employment position candidate with access to a job search application in response to receiving the one or more log-in credentials. ### Claim 3 (depends on 2) The method of claim 2, wherein the candidate data is included in a candidate profile, wherein the candidate profile includes data collected based on past activity by the employment position candidate in the job search application. ### Claim 4 (depends on 1) The method of claim 1, wherein the candidate data is included in a candidate profile, wherein the candidate profile includes data supplied by the employment position candidate. ### Claim 5 (depends on 1) The method of claim 1, wherein the candidate data includes demographic data of the employment position candidate. ### Claim 6 (depends on 5) The method of claim 5, wherein the demographic data includes one or more of a residence, an age, a gender, or a hobby of the employment position candidate. ### Claim 7 (depends on 1) The method of claim 1, wherein the candidate data includes one or more past job searches performed by the employment position candidate. ### Claim 8 A job suggestion system, comprising: a memory storing a plurality of instructions; and one or more processors configured to access the memory, wherein the one or more processors are further configured to execute the plurality of instructions to: pre-aggregate training data for a plurality of employment positions associated with a plurality of employment position sources, wherein the training data is in one or more sequence file formats; convert the pre-aggregated training data into a set of sparse vectors; train a classifier model with the set of sparse vectors, wherein the classifier model is configured to determine a employment position source effectiveness for an employment position source, wherein the employment position source effectiveness is associated with a likelihood a job seeker will be hired for an employment position if the job seeker applies through the employment position source; receive candidate data relating to an employment position candidate; match the employment position candidate with one or more employment positions based on the received candidate data and the one or more employment positions; determine a suggested employment position for the employment position candidate from the one or more employment positions; and generate, for the suggested employment position, a predicted employment position source effectiveness associated with an employment position source of the plurality of employment position sources, the predicted employment position source effectiveness determined by applying the classifier model to the suggested employment position and to the received candidate data relating to the employment position candidate. ### Claim 9 (depends on 8) The system of claim 8, wherein the one or more processors are further configured to execute the plurality of instructions to: receive one or more log-in credentials from the employment position candidate; and provide the employment position candidate with access to a job search application in response to receiving the one or more log-in credentials. ### Claim 10 (depends on 9) The system of claim 9, wherein the candidate data is included in a candidate profile, wherein the candidate profile includes data collected based on past activity by the employment position candidate in the job search application. ### Claim 11 (depends on 8) The system of claim 8, wherein the candidate data is included in a candidate profile, wherein the candidate profile includes data supplied by the employment position candidate. ### Claim 12 (depends on 8) The system of claim 8, wherein the candidate data includes demographic data of the employment position candidate. ### Claim 13 (depends on 12) The system of claim 12, wherein the demographic data includes one or more of a residence, an age, a gender, or a hobby of the employment position candidate. ### Claim 14 (depends on 8) The system of claim 8, wherein the candidate data includes one or more past job searches performed by the employment position candidate. ### Claim 15 A non-transitory computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising: instructions that cause the one or more processors to pre-aggregate training data for a plurality of employment positions associated with a plurality of employment position sources, wherein the training data is in one or more sequence file formats; instructions that cause the one or more processors to convert the pre-aggregated training data into a set of sparse vectors; instructions that cause the one or more processors to train a classifier model with the set of sparse vectors, wherein the classifier model is configured to determine a employment position source effectiveness for an employment position source, wherein the employment position source effectiveness is associated with a likelihood a job seeker will be hired for an employment position if the job seeker applies through the employment position source; instructions that cause the one or more processors to receive candidate data relating to an employment position candidate; instructions that cause the one or more processors to match the employment position candidate with one or more employment positions based on the received candidate data and the one or more employment positions; instructions that cause the one or more processors to determine a suggested employment position for the employment position candidate from the one or more employment positions; and instructions that cause the one or more processors to generate, for the suggested employment position, a predicted employment position source effectiveness associated with an employment position source of the plurality of employment position sources, the predicted employment position source effectiveness determined by applying the classifier model to the suggested employment position and to the received candidate data relating to the employment position candidate. ### Claim 16 (depends on 15) The non-transitory computer-readable memory of claim 15, wherein the plurality of instructions further comprise: instructions that cause the one or more processors to receive one or more log-in credentials from the employment position candidate; and instructions that cause the one or more processors to provide the employment position candidate with access to a job search application in response to receiving the one or more log-in credentials. ### Claim 17 (depends on 16) The non-transitory computer-readable memory of claim 16, wherein the candidate data is included in a candidate profile, wherein the candidate profile includes data collected based on past activity by the employment position candidate in the job search application. ### Claim 18 (depends on 15) The non-transitory computer-readable memory of claim 15, wherein the candidate data is included in a candidate profile, wherein the candidate profile includes data supplied by the employment position candidate. ### Claim 19 (depends on 15) The non-transitory computer-readable memory of claim 15, wherein the candidate data includes demographic data of the employment position candidate. ### Claim 20 (depends on 19) The non-transitory computer-readable memory of claim 19, wherein the demographic data includes one or more of a residence, an age, a gender, or a hobby of the employment position candidate.