US-12617419-B2 - Guiding a user to interact with an intelligent computing system using best practices

US12617419B2US 12617419 B2US12617419 B2US 12617419B2US-12617419-B2

Abstract

A plurality of best practice rules pertaining to best practices for interacting with a system can be received. A first user interaction from a user can received. A best practice determiner can determine whether the first user interaction fits at least one of the best practice rules. Responsive to determining, by the best practice determiner, that the first user interaction does not fit the at least one of the best practice rules, a modified user interaction can be generated by modifying the first user interaction based, at least in part, on the received plurality of best practice rules. The modified user interaction can be presented to the user.

Inventors

Michelle Brachman
Zahra Ashktorab
Michael Desmond
Hyo Jin Do
Casey Dugan
James Johnson
Qian Pan
Raj Sanjay Shah

Assignees

INTERNATIONAL BUSINESS MACHINES CORPORATION

Dates

Publication Date: 20260505
Application Date: 20231215

Claims (20)

1 . A method, comprising: receiving a plurality of best practice rules pertaining to best practices for interacting with a system, wherein the system is a smart vehicle system in a vehicle; receiving a first user interaction from a user, wherein the first user interaction is a user control of a steering system changing a steering angle, and wherein the first user interaction is detected using a steering sensor; determining, by a best practice determiner, whether the first user interaction fits at least one of the best practice rules; responsive to determining, by the best practice determiner, that the first user interaction does not fit the at least one of the best practice rules, generating, using a processor, a modified user interaction by modifying the first user interaction based, at least in part, on the received plurality of best practice rules; and presenting to the user the modified user interaction.
2 . The method of claim 1 , further comprising: receiving a second user interaction from the user; determining, by the best practice determiner, whether the second user interaction fits at least one of the best practice rules; and responsive to determining, by the best practice determiner, that the second user interaction fits the at least one of the best practice rules, storing the second user interaction as a sample interaction comprising training data.
3 . The method of claim 1 , further comprising: identifying system documentation for at least one other system; determining best practices for the at least one other system by the best practice determiner analyzing the system documentation for the at least one other system; determining whether the best practices for the at least one other system fit the first user interaction by comparing the first user interaction to the best practices for the at least one other system; responsive to determining that the best practices for the at least one other system fit the first user interaction, generating a rationale explaining why the best practices for the at least one other system fit the first user interaction; and presenting to the user the rationale explaining why the best practices for the at least one other system fit the first user interaction.
4 . The method of claim 1 , further comprising: determining differences between the first user interaction and the best practice rules; and outputting the determined differences to best practice descriptions of best practices for the system.
5 . The method of claim 1 , further comprising: responsive to the steering sensor detecting the first user interaction, determining, by the best practice determiner, whether an adaptive cruise control for the vehicle is enabled while the first user interaction is detected; and responsive to determining, by the best practice determiner, that the adaptive cruise control for the vehicle is enabled while the first user interaction is detected, determining, in real time based on the best practice rules, that best practice during use of the adaptive cruise control is for the user to keep control of steering system and for the vehicle not to drift into other lanes, wherein the modified user interaction comprises a suggestion pertaining to safe operation of the vehicle.
6 . The method of claim 5 , further comprising: accessing, using a transceiver, best practices for at least one other smart vehicle system; determining whether the best practices for the at least one other smart vehicle system fit the first user interaction by comparing the first user interaction to the best practices for at least one other smart vehicle system; responsive to determining that the best practices for the at least one other smart vehicle system fit the first user interaction, generating a rationale explaining why the best practices for the at least one other smart vehicle system fit the first user interaction; and presenting to the user the rationale explaining why the best practices for the at least one other smart vehicle system fit the first user interaction.
7 . The method of claim 1 , wherein the best practice determiner comprises a large language model.
8 . A system, comprising: a processor programmed to initiate executable operations comprising: receiving a plurality of best practice rules pertaining to best practices for interacting with the system; receiving a first user interaction from a user; determining, by a best practice determiner, whether the first user interaction fits at least one of the best practice rules; responsive to determining, by the best practice determiner, that the first user interaction does not fit the at least one of the best practice rules, generating a modified user interaction by modifying the first user interaction based, at least in part, on the received plurality of best practice rules; presenting to the user the modified user interaction; identifying system documentation for at least one other system; determining best practices for the at least one other system by the best practice determiner analyzing the system documentation for the at least one other system; determining whether the best practices for the at least one other system fit the first user interaction by comparing the first user interaction to the best practices for the at least one other system; responsive to determining that the best practices for the at least one other system fit the first user interaction, generating a rationale explaining why the best practices for the at least one other system fit the first user interaction; and presenting to the user the rationale explaining why the best practices for the at least one other system fit the first user interaction.
9 . The system of claim 8 , the executable operations further comprising: receiving a second user interaction from the user; determining, by the best practice determiner, whether the second user interaction fits at least one of the best practice rules; and responsive to determining, by the best practice determiner, that the second user interaction fits the at least one of the best practice rules, storing the second user interaction as a sample interaction comprising training data.
10 . The system of claim 8 , the executable operations further comprising: determining differences between the first user interaction and the best practice rules; and outputting the determined differences to best practice descriptions of best practices for the system.
11 . The system of claim 8 , wherein; the system is a smart vehicle system in a vehicle; the first user interaction from the user is a user control of a steering system changing a steering angle; and the executable operations further comprise detecting, using a steering sensor, the first user interaction.
12 . The system of claim 11 , the executable operations further comprising: responsive to the steering sensor detecting the first user interaction, determining, by the best practice determiner, whether an adaptive cruise control for the vehicle is enabled while the first user interaction is detected; and responsive to determining, by the best practice determiner, that adaptive cruise control for the vehicle is enabled while the first user interaction is detected, determining, in real time based on the best practice rules, that best practice during use of the adaptive cruise control is for the user to keep control of steering system and for the vehicle not to drift into other lanes, wherein the modified user interaction comprises a suggestion pertaining to safe operation of the vehicle.
13 . The system of claim 12 , the executable operations further comprising: accessing, using a transceiver, best practices for at least one other smart vehicle system; determining whether the best practices for the at least one other smart vehicle system fit the first user interaction by comparing the first user interaction to the best practices for at least one other smart vehicle system; responsive to determining that the best practices for the at least one other smart vehicle system fit the first user interaction, generating a rationale explaining why the best practices for the at least one other smart vehicle system fit the first user interaction; and presenting to the user the rationale explaining why the best practices for the at least one other smart vehicle system fit the first user interaction.
14 . The system of claim 8 , wherein the best practice determiner uses a transformer model architecture.
15 . A computer program product, comprising: one or more computer readable storage mediums having program code stored thereon, the program code stored on the one or more computer readable storage mediums collectively executable by a data processing system to initiate operations including: receiving a plurality of best practice rules pertaining to best practices for interacting with a system, wherein the system is a smart vehicle system in a vehicle; receiving a first user interaction from a user, wherein the first user interaction from the user is a user control of a steering system changing a steering angle, and wherein the first user interaction is detected using a steering sensor; determining, by a best practice determiner, whether the first user interaction fits at least one of the best practice rules; responsive to determining, by the best practice determiner, that the first user interaction does not fit the at least one of the best practice rules, generating a modified user interaction by modifying the first user interaction based, at least in part, on the received plurality of best practice rules; and presenting to the user the modified user interaction.
16 . The computer program product of claim 15 , wherein the program code is executable by the data processing system to initiate operations further comprising: receiving a second user interaction from the user; determining, by the best practice determiner, whether the second user interaction fits at least one of the best practice rules; and responsive to determining, by the best practice determiner, that the second user interaction fits the at least one of the best practice rules, storing the second user interaction as a sample interaction comprising training data.
17 . The computer program product of claim 15 , wherein the program code is executable by the data processing system to initiate operations further comprising: identifying system documentation for at least one other system; determining best practices for the at least one other system by the best practice determiner analyzing the system documentation for the at least one other system; determining whether the best practices for the at least one other system fit the first user interaction by comparing the first user interaction to the best practices for the at least one other system; responsive to determining that the best practices for the at least one other system fit the first user interaction, generating a rationale explaining why the best practices for the at least one other system fit the first user interaction; and presenting to the user the rationale explaining why the best practices for the at least one other system fit the first user interaction.
18 . The computer program product of claim 15 , wherein the program code is executable by the data processing system to initiate operations further comprising: determining differences between the first user interaction and the best practice rules; and outputting the determined differences to best practice descriptions of best practices for the system.
19 . The computer program product of claim 15 , wherein the program code is executable by the data processing system to initiate operations further comprising: responsive to the steering sensor detecting the first user interaction, determining, by the best practice determiner, whether an adaptive cruise control for the vehicle is enabled while the first user interaction is detected; and responsive to determining, by the best practice determiner, that adaptive cruise control for the vehicle is enabled while the first user interaction is detected, determining, in real time based on the best practice rules, that best practice during use of the adaptive cruise control is for the user to keep control of steering system and for the vehicle not to drift into other lanes, wherein the modified user interaction comprises a suggestion pertaining to safe operation of the vehicle.
20 . The computer program product of claim 15 , wherein the best practice determiner determines whether adaptive cruise control is enabled.

Description

BACKGROUND The present invention relates to data processing systems, and more specifically, to intelligent computing systems. An intelligent computing system is an advanced computer system that can gather, analyze and respond to data it collects. An intelligent computing system gains artificial intelligence by training on large amounts of training data using a variety of computational models, and uses the artificial intelligence to interpret and reason about gathered data. Rather than following a set of fixed rules, an intelligent computing system can learn hidden structures in data, extract useful patterns, and learn strategies and actions. SUMMARY A method includes receiving a plurality of best practice rules pertaining to best practices for interacting with a system. The method also can include receiving a first user interaction from a user. The method also can include determining, by a best practice determiner, whether the first user interaction fits at least one of the best practice rules. The method also can include, responsive to determining, by the best practice determiner, that the first user interaction does not fit the at least one of the best practice rules, generating, using a processor, a modified user interaction by modifying the first user interaction based, at least in part, on the received plurality of best practice rules. The method also can include presenting to the user the modified user interaction. A system includes a processor programmed to initiate executable operations implementing the method. A computer program product includes a computer readable storage medium having program code stored thereon. The program code is executable by a data processing system to initiate operations implementing the method. This Summary section is provided merely to introduce certain concepts and not to identify any key or essential features of the claimed subject matter. Other features of the inventive arrangements will be apparent from the accompanying drawings and from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 depicts a cloud computing environment according to an embodiment of the present invention. FIG. 2 is a block diagram illustrating example architecture for an intelligent computing system. FIG. 3 is a block diagram illustrating example architecture for a best practice rule creator. FIG. 4 is a block diagram illustrating example architecture for a best practice determiner. FIG. 5 is a block diagram illustrating example architecture for an input modifier. FIG. 6 is a block diagram illustrating example architecture for a comparer. FIGS. 7A and 7B depict an example of a user interface of an intelligent computing system. FIG. 8 depicts an example of a conversation between a user and an intelligent computing system using spoken utterances. FIG. 9 is a block diagram illustrating an example of an intelligent computing system integrated into a smart vehicle system. FIG. 10 is a flowchart illustrating an example of a method of guiding a user to interact with an intelligent computing system using best practices. DETAILED DESCRIPTION This disclosure relates to data processing systems, and more specifically, to intelligent computing systems. The arrangements described herein are directed to computer technology, and provide an improvement to computer technology. Specifically, the present arrangements improve the efficiency of intelligent computing systems to respond to user inputs. Oftentimes user inputs to an intelligent computing system do not fit best practices for interfacing with the intelligent computing system. For example, user inputs may be nebulous. When the intelligent computing system attempts to process such user inputs, computing resources (e.g., processor and memory resources) are tied up attempting to determine an appropriate response to an unclear, vague or ill-defined request. This process can iterate, tying up computing resources even more, when subsequent user inputs are received that still do not fit best practices for the intelligent computing system. The arrangements described herein address this issue by guiding users to interact with intelligent computing systems using best practices. For instance, a modified user input, which conforms to the best practices for the intelligent computing system, can be presented to a user. The user may consider submitting the modified user input a next user input. Accordingly, the next user input likely will fit best practices, thereby enabling the intelligent computing system to efficiently generate an appropriate response, thereby reducing usage of the computing resources. The present arrangements also improve safety. In this regard, intelligent computing systems can be used in vehicles in conjunction with various control mechanisms/systems. Sometimes user inputs, for example steering inputs, can result in dangerous actions, such as departing a lane while adaptive cruise control is enabled. The present arrangements