Field Service Record Search Using AI Query Translation
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Solution Overview
Problem
Field service technicians face challenges in querying work order datastores due to the technical complexity of specialized data query languages, making it difficult and time-consuming to retrieve relevant information without specialized knowledge.
Innovation Solution
Implementing generative AI to translate natural language queries into Structured Query Language (SQL) or Salesforce Object Query Language (SOQL) queries, allowing technicians to search datastores using intuitive utterances, with AI-generated queries resolving variables and formatting results for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data query languages (SQL/SOQL) are used to query work order datastores, then accurate data retrieval is achieved, but the technical complexity and difficulty of operation increase significantly for field service technicians
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates between user-friendly natural language queries and the complex SQL/SOQL query languages. This intermediary system includes a language model that converts natural language into structured query syntax, allowing field service technicians to query datastores without needing to learn specialized query languages, thus resolving the contradiction between accurate data retrieval and ease of operation
Solution Approach 2:
The system creates a simplified copy or representation of the complex query language in the form of natural language. Instead of requiring users to directly manipulate complex SQL/SOQL syntax, the system allows them to use everyday language that is then transformed into the required query format, maintaining accuracy while dramatically improving ease of operation
2Measurement precision
If field service technicians learn specialized data query languages, then query accuracy improves, but the time required for training and onboarding increases
Solution Approach 1:
The natural language processing intermediary eliminates the need for technicians to learn specialized query languages by translating their natural language requests into accurate SQL/SOQL queries automatically. This removes the training time requirement while maintaining query accuracy through the intelligent translation layer
3Ease of operation
If natural language queries are used instead of specialized query languages, then ease of operation improves, but the complexity of the system increases due to AI integration
Solution Approach 1:
The patent extracts the complex AI and natural language processing functionality into a separate, modular service layer that interfaces with the existing datastore infrastructure. This extraction allows the core querying system to remain relatively simple while enabling natural language capabilities through the added service layer, reducing the impact of increased system complexity
4Reliability
If conventional query methods are used, then system reliability is maintained, but productivity decreases due to the time-consuming nature of writing queries
Solution Approach 1:
The system creates a natural language copy of the query interface that is instantly translatable to executable queries. This allows technicians to retrieve data rapidly by simply speaking or typing natural language requests, which are immediately converted and executed, significantly improving productivity while the underlying reliable SQL/SOQL execution maintains system reliability
Data Source
AI summary
Methods, systems, and machine-readable media perform a search of field service records in a datastore using generative artificial intelligence (AI). An utterance requesting a search of field service records is received from a user device. The generative AI selects one or more actions with which to process the user utterance. The generative AI also resolves a datetime range. A field service record data query language (DQL) query is built based on the selected one or more actions and the datetime range. The DQL query is executed on a field service record datastore to receive a list of field service records, which are then transmitted to the user device responsive to the utterance. The generative AI can also be used to format the field service records for display before transmitting to the user device. Generative AI hallucinations and inaccuracies are avoided in the search results.