US-20260129045-A1 - DECENTRALIZED TRUST ESTABLISHMENT USING SENTIMENT

US20260129045A1US 20260129045 A1US20260129045 A1US 20260129045A1US-20260129045-A1

Abstract

Decentralized trust establishment using sentiment documents is described. In an implementation, a decentralized network of nodes is generated. A first entity is associated with a select node of the nodes. A user interface is presented including one or more options at an edge device of the first entity. The options support inputs specifying identification of a second entity, sentiment regarding the second entity, and supporting information describing why the sentiment is expressed towards the second entity. A plurality of sentiment documents are collected, respectively, from the plurality of nodes of the decentralized network. A sentiment is determined as associated with an entity by processing the plurality of sentiment documents. The determined sentiment is output.

Inventors

Daniel Buchner

Assignees

BLOCK, INC.

Dates

Publication Date: 20260507
Application Date: 20251231

Claims (20)

1 . (canceled)
2 . A computer-implemented method comprising: receiving, by a processing device, an input via a user interface to initiate a transaction with an entity involving a resource transfer; collecting, by the processing device, a plurality of sentiment documents from respective nodes of a plurality of nodes of a decentralized network; quantifying, by the processing device, a sentiment indicative of an amount of trust associated with the entity, the quantifying performed by processing the plurality of sentiment documents; and displaying the sentiment as associated with the entity in the user interface.
3 . The computer-implemented method of claim 2 , wherein the user interface includes an option that is user selectable to continue the resource transfer of the transaction.
4 . The computer-implemented method of claim 2 , wherein collecting the plurality of sentiment documents from the respective nodes is performed using a plurality of decentralized identifiers associated with the plurality of nodes of the decentralized network.
5 . The computer-implemented method of claim 2 , wherein the quantifying is performed using generative artificial intelligence implemented using one or more machine-learning models.
6 . The computer-implemented method of claim 2 , further comprising: parsing the plurality of sentiment documents using a sentiment schema to generate parsed sentiment data corresponding to the entity, wherein the quantifying is based on the parsed sentiment data.
7 . The computer-implemented method of claim 6 , wherein quantifying the sentiment comprises: determining, using a first machine-learning model, one or more expressed sentiments expressed in the parsed sentiment data; and generating a sentiment score for the entity based on the one or more expressed sentiments.
8 . The computer-implemented method of claim 7 , wherein generating the sentiment score comprises: applying, using a weighting module, respective weights to a plurality of different types of sentiments including one or more of entity mentions, transaction history, transaction feedback, order timeliness, cost effectiveness, and quality of service.
9 . The computer-implemented method of claim 2 , wherein collecting the plurality of sentiment documents comprises: generating a sentiment search query that identifies the entity; resolving, using a decentralized identifier resolver and based on the sentiment search query, decentralized identifiers corresponding to nodes that maintain sentiment documents associated with the entity; and obtaining, using the decentralized identifiers, the plurality of sentiment documents from the nodes that maintain the sentiment documents associated with the entity.
10 . The computer-implemented method of claim 2 , further comprising: in response to receiving the input to initiate the transaction, determining whether a threshold number of sentiment documents have been collected.
11 . The computer-implemented method of claim 2 , further comprising: based on the quantified sentiment, causing the user interface to present an option to continue the resource transfer or cancel the transaction.
12 . A system comprising: one or more processors; and memory coupled to the one or more processors with instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform or control performance of operations comprising: collecting a plurality of sentiment documents from respective nodes of a plurality of nodes of a decentralized network using, respectively, a plurality of decentralized identifiers; determining a sentiment associated with an entity by processing the plurality of sentiment documents; and outputting the determined sentiment in a user interface.
13 . The system of claim 12 , wherein the instructions cause the one or more processors to perform or control performance of further operations comprising: receiving an input via the user interface to initiate a transaction to transfer resources with the entity; and initiating the transaction.
14 . The system of claim 13 , wherein the instructions cause the one or more processors to perform or control performance of a further operation comprising: receiving an identifier of the entity via the user interface and resolving the plurality of decentralized identifiers using the identifier of the entity.
15 . The system of claim 12 , wherein the determining the sentiment is performed using generative artificial intelligence implemented using a machine-learning model.
16 . The system of claim 12 , wherein the instructions cause the one or more processors to perform or control performance of a further operation comprising: configuring the user interface based on the plurality of sentiment documents using a machine-learning model.
17 . A non-transitory computer-readable medium with instructions stored thereon that, when executed by a processor, cause the processor to perform or control performance of operations comprising: receiving, via a user interface, an input to initiate a transaction with an entity, the transaction involving a resource transfer; collecting a plurality of sentiment documents from respective nodes of a plurality of nodes of a decentralized network; parsing the plurality of sentiment documents according to a sentiment schema to generate parsed sentiment data; generating, based on the parsed sentiment data, a sentiment score that is indicative of an amount of trust associated with the entity; and displaying, in the user interface, the sentiment score as associated with the entity.
18 . The non-transitory computer-readable medium of claim 17 , wherein displaying the sentiment score comprises presenting an option in the user interface that is user selectable to continue the resource transfer of the transaction.
19 . The non-transitory computer-readable medium of claim 17 , wherein collecting the plurality of sentiment documents comprises collecting the plurality of sentiment documents using a plurality of decentralized identifiers.
20 . The non-transitory computer-readable medium of claim 17 , wherein generating the sentiment score is performed using generative artificial intelligence implemented using one or more machine-learning models.

Description

RELATED APPLICATION This application is a divisional of U.S. patent application Ser. No. 18/322,796, filed May 24, 2023, and titled “Decentralized Trust Establishment Using Sentiment,” the entire disclosure of which is hereby incorporated by reference. TECHNICAL FIELD Decentralized networks provide a variety of functionality in connection with implementing and securely transferring data, examples of which include cryptocurrencies and cryptographic-based tokens, such as tokens for decentralized web applications implemented as part of a distributed state machine. Additional functionality has been developed that leverages decentralized networks. BRIEF DESCRIPTION OF THE DRAWINGS One or more embodiments of the disclosed technologies are illustrated by way of example and are not limited by the figures of the accompanying drawings, in which like references indicate similar elements. FIG. 1 is a non-limiting illustration of an example system that is operable to implement decentralized trust establishment techniques using sentiment documents as described herein according to an implementation of the present subject matter. FIG. 2 is a non-limiting illustration of an example system depicting a topology of nodes as part of a decentralized network that are configurable to implement sentiment documents according to an implementation of the present subject matter. FIG. 3 is a non-limiting illustration of an example system that is operable to implement a resource transfer communication protocol and trust establishment techniques described herein according to an implementation of the present subject matter. FIG. 4 is a non-limiting example showing operation of a sentiment application as supporting input of sentiment document data according to an implementation of the present subject matter. FIG. 5 is a non-limiting example showing a sentiment user interface of FIG. 4 in greater detail as supporting input of sentiment document data according to an implementation of the present subject matter. FIG. 6 is a non-limiting example showing operation of a sentiment document generation module of a sentiment application as supporting input of sentiment document data using a sentiment schema according to an implementation of the present subject matter. FIG. 7 is a non-limiting example showing operation of a sentiment application as performing an upload of the sentiment document data as generated via FIG. 6 for inclusion as part a sentiment document maintained by a node manager module of a node according to an implementation of the present subject matter. FIG. 8 is a non-limiting example showing operation of a sentiment document, as executed by a node, configured to control exposure of sentiment document data using respective application programming interfaces according to an implementation of the present subject matter. FIG. 9 is a flow diagram depicting a procedure in a non-limiting example of use of options in a user interface to generate a sentiment document of an entity at a selected decentralized node according to an implementation of the present subject matter. FIG. 10 is a non-limiting example showing operation of a second edge device to obtain sentiment documents usable to quantify a sentiment towards another entity according to an implementation of the present subject matter. FIG. 11 is a non-limiting example showing operation of a second edge device to quantify a sentiment towards another entity based on access to sentiment documents as described in relation to FIG. 10 according to an implementation of the present subject matter. FIG. 12 is a non-limiting example showing operation of a machine-learning model to generate sentiment scores to quantify a sentiment towards another entity as an ensemble model implementing multi-label classification according to an implementation of the present subject matter. FIG. 13 is a non-limiting example showing operation of a sentiment application as outputting a sentiment user interface to quantify a sentiment towards another entity according to an implementation of the present subject matter. FIG. 14 is a non-limiting example showing operation of a sentiment application as outputting a sentiment user interface as part of decentralized trust establishment using sentiment documents according to an implementation of the present subject matter. FIG. 15 is a non-limiting example showing operation of a sentiment application as outputting a review analyzer user interface to quantify a sentiment towards an entity, itself, according to an implementation of the present subject matter. FIG. 16 is a flow diagram depicting a procedure in a non-limiting example of decentralized trust establishment using sentiment documents according to an implementation of the present subject matter. FIG. 17 is an example environment with which techniques described herein can be implemented, according to an embodiment described herein. FIG. 18 is an example environment with which techniques described herein can be imple