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1. WO2022006344 - METHOD FOR DYNAMICALLY RECOMMENDING FORECAST ADJUSTMENTS THAT COLLECTIVELY OPTIMIZE OBJECTIVE FACTOR USING AUTOMATED ML SYSTEMS

Publication Number WO/2022/006344
Publication Date 06.01.2022
International Application No. PCT/US2021/039999
International Filing Date 30.06.2021
IPC
G06Q 10/04 2012.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
10Administration; Management
04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
G06N 20/00 2019.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G06Q 10/00 2012.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
10Administration; Management
G06Q 10/06 2012.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
10Administration; Management
06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
G06Q 30/02 2012.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
30Commerce, e.g. shopping or e-commerce
02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
Applicants
  • SAMYA.AI INC, [US]/[US]
Inventors
  • DHINGRA, Deepinder, Singh
  • VERMA, Ankur
  • GUPTA, Yadunath
  • SHAHI, Siddharth
  • SRIVASTAVA, Rajat
  • KUMAR, Rohit
Agents
  • HANER, Ronald, Lambert
Priority Data
20204102787330.06.2020IN
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) METHOD FOR DYNAMICALLY RECOMMENDING FORECAST ADJUSTMENTS THAT COLLECTIVELY OPTIMIZE OBJECTIVE FACTOR USING AUTOMATED ML SYSTEMS
(FR) PROCÉDÉ DE RECOMMANDATION DYNAMIQUE DE RÉGLAGES DE PRÉVISION QUI OPTIMISENT COLLECTIVEMENT LE FACTEUR D'OBJECTIF À L'AIDE DE SYSTÈMES DE ML AUTOMATISÉS
Abstract
(EN) A method for integrating a machine learning (ML) model that impacts different factor groups for generating a dynamic recommendation to collectively optimize an objective factor is provided. The method includes (i) obtaining external and historical data associated with use- case including historical data of a forecasted, objective factor and historical data of at least one factor group associated with the use-case, (ii) generating a probabilistic forecasting model for forecasted factor, (iii) generating a relationship model based on a relationship between the forecasted factor, model of objective factor and one or more factors, (iv) determining an optimization model based on the probabilistic forecasting model and the relationship model by at least one adjustment of the existing forecast of the forecasted factor, (v) dynamically generating a recommendation to adjust an existing forecast based on the optimization function, and (vi) applying the recommendation at the use-case to collectively optimize objective factor.
(FR) L'invention concerne un procédé d'intégration d'un modèle d'apprentissage automatique (ML) ayant un impact sur différents groupes de facteurs pour générer une recommandation dynamique servant à optimiser collectivement un facteur d'objectif. Le procédé consiste à (i) obtenir des données externes et historiques associées à un cas d'utilisation comprenant des données historiques d'un facteur d'objectif prévu et des données historiques d'au moins un groupe de facteurs associé au cas d'utilisation, à (ii) générer un modèle de prévision probabiliste du facteur prévu, à (iii) générer un modèle de relation sur la base d'une relation entre le facteur prévu, le modèle du facteur d'objectif et un ou plusieurs facteurs, à (iv) déterminer un modèle d'optimisation sur la base du modèle de prévision probabiliste et du modèle de relation par au moins un réglage de la prévision existante du facteur prévu, à (v) générer de manière dynamique une recommandation pour régler une prévision existante sur la base de la fonction d'optimisation, et à (vi) appliquer la recommandation au niveau du cas d'utilisation pour optimiser collectivement le facteur d'objectif.
Latest bibliographic data on file with the International Bureau