Patent SG-11201805794X-A (SG)
Title: Webinterface generation and testing using artificial neural networks
Inventor: MIIKKULAINEN RISTO
IPC: G06N5/00
Filed: 2017-01-05
Abstract: INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2017/118937 Al 13 July 2017 (13.07.2017) WIPO I PCT (51) International Patent Classification: G06N 3/02 (2006.01) G06N 3/12 (2006.01) G06N 5/00 (2006.01) G06F 17/30 (2006.01) (21) International Application Number: PCT/IB2017/050044 (22) International Filing Date: 5 January 2017 (05.01.2017) (25) Filing Language: English (26) Publication Language: English (30) Priority Data: 62/275,058 5 January 2016 (05.01.2016) US 62/275,074 5 January 2016 (05.01.2016) US (71) Applicant: SENTIENT TECHNOLOGIES (BARBA- DOS) LIMITED [BB/BB]; 13, 8th Avenue, Belleville (BB). (72) Inventors: MIIKULAINEN, Risto; 339 Olmsted Road, Stanford, CA 94305 (US). ISCOE, Neil; 3415 Mt. Barker, Austin, TX 78731 (US). (74) Agent: WOLFELD, Warren S.; Haynes Beffel & Wolfeld LLP, PO Box 366, 637 Main Street, Half Moon Bay, CA 94019 (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, 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)) before the expiration of the time limit for amending the claims and to be republished in the event of receipt of amendments (Rule 48.2(h)) (54) Title: WEBINTERFACE GENERATION AND TESTING USING ARTIFICIAL NEURAL NETWORKS GE NOME InpuL I .ryar I filial !Aqua ompuL I ar , r Exemplary Interpretation 1111111111111101110111111111110101111101110111011111011111101111111111111111110111111 W O 20 17 / 118937 Al Day of 1Mxk 0 . 0 Thu 0 hi 0 tin 0 nge Tex ange CS 0 0 Chang!: (SS I Main I laadlinc Othcr o !Ingo Teal Sub Hmdlinn MG.°, I 0 (j)—o• o Ad Group 2 0 I Ch ang: Ad Group Ad Cana. 0 SSS 1 Ad Gmapa 0 o Changc Too Ad Om, 5 0 C)--' Change (SS Calko-Action 4 FIG. 2 (57) : Roughly described, the technology disclosed provides a so-called machine-learned conversion optimization (MLCO) system that uses artificial neural networks and evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Neural networks are represented as genomes. Neural networks map user attributes from live user traffic to different dimensions and dimension values of output funnels that are presented to the users in real time. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well.