Patent SG-11201900979V-A (SG)
Title: Network-based automated prediction modeling
Inventor: GRIGG ALASTAIR
IPC: G06Q30/00
Filed: 2017-08-03
Abstract: WO 18/0 27 06 4 Al (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (43) International Publication Date 08 February 2018 (08.02.2018) WIP0 I PCT 01E1°1°1a °nolo omoiolomoiloiloommovoimIE (10) International Publication Number WO 2018/027064 Al (51) International Patent Classification: G06Q 30/00 (2012.01) (21) International Application Number: PCT/US2017/045351 (22) International Filing Date: 03 August 2017 (03.08.2017) (25) Filing Language: English (26) Publication Language: English (30) Priority Data: 62/371,168 04 August 2016 (04.08.2016) US 62/371,161 04 August 2016 (04.08.2016) US 15/667,278 02 August 2017 (02.08.2017) US (63) Related by continuation (CON) or continuation-in-part (CIP) to earlier application: US 15/667,278 (CON) Filed on 02 August 2017 (02.08.2017) (71) Applicant: XERO LIMITED [US/US]; 101 Green St, 5th Floor, San Francisco, California 94111 (US). (72) Inventors: GRIGG, Alastair; 295C Sainsbury Rd, RD1, Hamilton, 3281 (NZ). KEMKA, Martin; 807/8 McCrae street, Melbourne, Victoria 3008 (AU). (74) Agent: SCHEER, Bradley, W. et al.; P.O. Box 2938, Min- neapolis, Minnesota 55402 (US). (81) Designated States (unless otherwise indicated, for every kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG). Published: — with international search report (Art. 21(3)) (54) Title: NETWORK-BASED AUTOMATED PREDICTION MODELING (57) : Methods, systems, and computer programs are presented for generating action scores based on the performance of entities to determine action r —loo probabilities. One method includes an operation for accessing data comprising transactions exchanged between entities. The method further includes generat- ing performance values for each entity based on the data, and generating, for each entity, an action score that is an indication of past performance of the entity with respect to the performance values. The method further includes receiving a request, from a user, for action probabilities regarding an event associated with 470 a first entity, each action probability being a probability that another entity re- sponds to the event within a predetermined time frame, determining related en- tities associated with the event, and determining the action probability for each entity based on the action score of the related entity. The action probabilities are presented within a graphical user interface. 522 DETERMINE RELATED ENTITIE FOR THE EVENT 523 ANALYZE ACTION SCORES OF RELATED ENTITIES 524 I DETERMINE ACTION PROBABILITY OF EACH RELATED ENTITY 525 H PRESENT INFORMATION ABOUT ACTION PROBABILITIES FIG. 5B ACTION HISTORY SYSTEM TRANSAC- PERFOR- ACTION TIONS MANCE SCORES 000 000 DOD 000000 => 000 900 512 514 516 502 n ENTITY 503 EXTERNAL ENTITY OTHER ENTITIES 000 000 DOD 000 000 DOD 000 000 Lyn 521 Ld REQUEST ACTION PROBABILITIES FOR AN EVENT