US-20260127658-A1 - SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE ARCHITECTURE PLATFORMS

US20260127658A1US 20260127658 A1US20260127658 A1US 20260127658A1US-20260127658-A1

Abstract

A computer-implemented method including: receiving, by a controller and from a first electronic application associated with a first electronic device, user data associated with an interaction; determining, based on the user data, user behavior associated with the interaction; generating, based on first criteria, a first operator associated with the user behavior and user data; generating, based on the first operator and second criteria, a first sequence; transmitting, based on the generating, the first sequence to the first electronic application; generating, based on first user feedback and the first criteria, a second operator; generating, based on the second operator and the second criteria, a second sequence; transmitting, based on the generating, the second sequence to the first electronic application; encoding, based on second user feedback, a first payload, the first payload including one or more factors for modifying the interaction; and transmit the payload to at least one remote system.

Inventors

Michael TERPIN
Jacques Voorhees
Krista OLSON-BLUNDELL
Alex VOORHEES

Assignees

ICECAPAI, INC.

Dates

Publication Date: 20260507
Application Date: 20251103

Claims (20)

1 . A computer-implemented method comprising: receiving, by a controller and from a first electronic application associated with a first electronic device, user data associated with an interaction; determining, based on analyzing the user data, user behavior associated with the interaction, wherein the analyzing is performed by specialized hardware accelerators configured for real-time behavioral pattern detection; generating, based on first criteria, a first operator associated with the user behavior and user data, wherein the first operator includes one or more first parameters, and wherein the first operator is generated using a multi-layered convolutional neural network with temporal pattern recognition capabilities; generating, based on the first operator and second criteria, a first sequence, wherein the first sequence maps to the one or more first parameters of the first operator; transmitting, based on the generating, the first sequence to the first electronic application; generating, based on first user feedback and the first criteria, a second operator, wherein the second operator includes an incremental modification of the first operator; generating, based on the second operator and the second criteria, a second sequence, wherein the second sequence maps to one or more second parameters of the second operator; transmitting, based on the generating, the second sequence to the first electronic application; encoding, based on second user feedback, a first payload, the first payload including one or more factors for modifying the interaction; displaying, via the user device, an indication of the one or more factors of the first payload; and in response to a request to complete the interaction and an indication that the one or more factors are satisfied, transmit the payload to at least one remote system.
2 . The computer-implemented method of claim 1 , wherein the controller includes an artificial intelligence (AI) orchestration layer.
3 . The computer-implemented method of claim 1 , wherein generating the second operator includes contextual data.
4 . The computer-implemented method of claim 1 , wherein transmitting the second sequence includes displaying the second sequence via a pop-up user interface on the first electronic device.
5 . The computer-implemented method of claim 1 , wherein analyzing the user data includes determining whether a user behavior metric surpasses a predetermined threshold.
6 . The computer-implemented method of claim 1 , wherein the wherein the at least one remote system includes an application programming interface associated with a second electronic device.
7 . The computer-implemented method of claim 1 , further comprising transmitting the first payload in response to receiving an authenticated payload over a standard secure protocol.
8 . A non-transitory computer readable medium comprising one or more programming instructions, which, when executed by a processor, causes a computing system to perform operations comprising: receiving, by a controller and from a first electronic application associated with a first electronic device, user data associated with an interaction; determining, based on analyzing the user data, user behavior associated with the interaction, wherein the analyzing is performed by specialized hardware accelerators configured for real-time behavioral pattern detection; generating, based on first criteria, a first operator associated with the user behavior and user data, wherein the first operator includes one or more first parameters, and wherein the first operator is generated using a multi-layered convolutional neural network with temporal pattern recognition capabilities; generating, based on the first operator and second criteria, a first sequence, wherein the first sequence maps to the one or more first parameters of the first operator; transmitting, based on the generating, the first sequence to the first electronic application; encoding, based on first user feedback, a first payload, the first payload including one or more factors for modifying the interaction; displaying, via the user device, an indication of the one or more factors of the first payload; and in response to a request to complete the interaction and an indication that the one or more factors satisfies a predetermined threshold, transmit the payload to at least one remote system.
9 . The non-transitory computer readable medium of claim 8 , wherein the first electronic application is a browser extension, and the interaction is associated with a website visited by a web browser also operating on the first electronic device.
10 . The non-transitory computer readable medium of claim 8 , the controller includes an artificial intelligence (AI) module that controls one or more conversational AI agents.
11 . The non-transitory computer readable medium of claim 8 , wherein generating the first operator includes contextual data.
12 . The non-transitory computer readable medium of claim 8 , wherein transmitting the first sequence includes displaying the first sequence via a pop-up user interface on the first electronic device.
13 . The non-transitory computer readable medium of claim 8 , wherein encoding the first payload includes encrypting the first payload.
14 . The non-transitory computer readable medium of claim 8 , wherein the wherein the at least one remote system includes a representational state transfer application programming interface associated with a second electronic device.
15 . A system, comprising: a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising: receiving, by a controller and from a first electronic application associated with a first electronic device, user data associated with an interaction; generating, based on first criteria, a first operator, wherein the first operator includes one or more first parameters, and wherein the first operator is generated using a multi-layered convolutional neural network with temporal pattern recognition capabilities; generating, based on the first operator and second criteria, a first sequence, wherein the first sequence maps to the one or more first parameters of the first operator; transmitting, based on the generating, the first sequence to the first electronic application; generating, based on first user feedback and the first criteria, a second operator, wherein the second operator includes an incremental modification of the first operator associated with the one or more first parameters; generating, based on the second operator and the second criteria, a second sequence, wherein the second sequence maps to one or more second parameters of the second operator; transmitting, based on the generating, the second sequence to the first electronic application; encoding, based on second user feedback, a first payload, the first payload including one or more factors for modifying the interaction; displaying, via the user device, an indication of the one or more factors of the first payload; and in response to a request to complete the interaction, transmit the payload to at least one remote system.
16 . The system of claim 15 , wherein the controller includes an artificial intelligence (AI) orchestration layer including a large language model.
17 . The system of claim 15 , wherein the controller determines one or more rules associated with the first sequence or the second sequence.
18 . The system of claim 15 , wherein the controller monitors user behavior during the interaction.
19 . The system of claim 15 , wherein the first user feedback and the second user feedback is real-time feedback.
20 . The system of claim 15 , wherein the first operator or the second operator is based on dynamically populated templates.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 63/716,030, filed Nov. 4, 2024, which is incorporated herein by reference in its entirety. TECHNICAL FIELD Various embodiments of the present disclosure relate generally to artificial intelligence architecture platforms and, more particularly, to systems and methods for employing artificial intelligence platforms as a negotiating AI agents during a user interaction. INTRODUCTION Recently, consumer purchasing behavior has shifted significantly toward online shopping platforms rather than traditional in-person retail environments. This transition may have limited the ability of merchants to engage directly with customers during the purchasing process. In physical retail settings, sales personnel are able to observe a customer's body language, tone, and emotional cues to assess client satisfaction or hesitation, allowing for real-time adjustments such as personalized product recommendations, assistance in decision-making, or negotiation of terms. Online retail systems, however, generally lack this human-to-human feedback loop. As a result, merchants are unable to dynamically interpret a client's intent or emotional state, which may lead to lost sales opportunities and reduced customer satisfaction due to unmet needs or insufficient support during the transaction process. Unless otherwise indicated herein, the techniques and information described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section. SUMMARY In some aspects, the techniques described herein relate to a computer-implemented method including: receiving, by a controller and from a first electronic application associated with a first electronic device, user data associated with an interaction; determining, based on analyzing the user data, user behavior associated with the interaction, wherein the analyzing is performed by specialized hardware accelerators configured for real-time behavioral pattern detection; generating, based on first criteria, a first operator associated with the user behavior and user data, wherein the first operator includes one or more first parameters, and wherein the first operator is generated using a multi-layered convolutional neural network with temporal pattern recognition capabilities; generating, based on the first operator and second criteria, a first sequence, wherein the first sequence maps to the one or more first parameters of the first operator; transmitting, based on the generating, the first sequence to the first electronic application; generating, based on first user feedback and the first criteria, a second operator, wherein the second operator includes an incremental modification of the first operator; generating, based on the second operator and the second criteria, a second sequence, wherein the second sequence maps to one or more second parameters of the second operator; transmitting, based on the generating, the second sequence to the first electronic application; encoding, based on second user feedback, a first payload, the first payload including one or more factors for modifying the interaction; displaying, via the user device, an indication of the one or more factors of the first payload; and in response to a request to complete the interaction and an indication that the one or more factors are satisfied, transmit the payload to at least one remote system. In some aspects, the techniques described herein relate to a computer-implemented method, wherein the controller includes an artificial intelligence (AI) orchestration layer. In some aspects, the techniques described herein relate to a computer-implemented method, wherein generating the second operator includes contextual data. In some aspects, the techniques described herein relate to a computer-implemented method, wherein transmitting the second sequence includes displaying the second sequence via a pop-up user interface on the first electronic device. In some aspects, the techniques described herein relate to a computer-implemented method, wherein analyzing the user data includes determining whether a user behavior metric surpasses a predetermined threshold. In some aspects, the techniques described herein relate to a computer-implemented method, wherein the wherein the at least one remote system includes an application programming interface associated with a second electronic device. In some aspects, the techniques described herein relate to a computer-implemented method, further including transmitting the first payload in response to receiving an authenticated payload over a standard secure protocol. In some aspects, the techniques described herein relate to a non-transitory computer readable medium including one or more programming instructions, which, when executed by a processor, causes a computing system