Automated service design using AI/ML to suggest process blocks for inclusion in service design structure
A system of one or more computers can be configured to facilitate the design of a service. The disclosed system may operate to add a process block to a service design structure for the service. The process block is provided to a trained AI/ML process prediction model. The trained AI/ML process prediction model suggests one or more further process blocks for addition to the service design structure based, at least in part, on the addition of the process block to the service design structure. In certain embodiments, a process block is selected from the suggested one or more further process blocks and added to the service design structure. Other embodiments of this aspect of the disclosure include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
1. A computer-implemented method for use in designing a service, the method comprising:
adding a process block to a service design structure for a service, wherein the process block corresponds to a plurality of process block in a category divided according to corresponding attributes in a tree-type structure;
providing the process block to a trained AI (Artificial Intelligence)/ML (Machine Learning) process prediction model;
using the trained AI/ML process prediction model to suggest one or more further process blocks for addition to the service design structure based, at least in part, on the addition of the process block to the service design structure, wherein the trained AI/ML process prediction model suggests the one or more further process blocks based on a comparison of the process blocks in the service design structure with process blocks in other service design structures constructed for similar services; and
adding one or more suggested process blocks to the service design structure.
2. The computer-implemented method of claim 1 , wherein
the trained AI/ML process prediction model is trained using a process block catalog, wherein the process block catalog includes process blocks that are categorized by parametric and/or technical attributes of the process blocks.
3. The computer-implemented method of claim 2 , wherein
the trained AI/ML process prediction model is trained using service design structures of a plurality of services.
4. The computer-implemented method of claim 1 , wherein
the trained AI/ML process prediction model suggests the one or more further process blocks based on a context of the process blocks added to the service design structure from the suggested process blocks.
5. The computer-implemented method of claim 1 , further comprising:
determining a deviation value between the process blocks of the service design structure and the process blocks of the other service design structures; and
displaying the deviation value on a graphical user interface.
6. The computer-implemented method of claim 1 , further comprising:
assigning a confidence level value to process blocks in the one or more suggested process blocks using the trained AI/ML process prediction model; and
displaying the confidence level value for each of the one or more further process blocks on a graphical user interface.
7. A computer system comprising:
one or more information handling systems, wherein the one or more information handling systems include:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus;
wherein the computer program code included in one or more of the information handling systems is executable by the processor of the information handling system so that the information handling system, alone or in combination with other information handling systems, executes operations comprising:
adding a process block to a service design structure for a service, wherein the process block corresponds to a plurality of process block in a category divided according to corresponding attributes in a tree-type structure;
providing the process block to a trained AI (Artificial Intelligence)/ML (Machine Learning) process prediction model;
using the trained AI/ML process prediction model to suggest one or more further process blocks for addition to the service design structure based, at least in part, on the addition of the process block to the service design structure, wherein the trained AI/ML process prediction model suggests the one or more further process blocks based on a comparison of process blocks in the service design structure with process blocks in other service design structures constructed for similar services; and
adding one or more suggested process blocks to the service design structure.
8. The computer system of claim 7 , wherein
the trained AI/ML process prediction model is trained using a process block catalog, wherein the process block catalog includes process blocks that are categorized by parametric and/or technical attributes of the process blocks.
9. The computer system of claim 8 , wherein
the trained AI/ML process prediction model is trained using service design structures of a plurality of services.
10. The computer system of claim 7 , wherein
the trained AI/ML process prediction model suggests the one or more further process blocks based on a context of the process blocks added to the service design structure from the suggested process blocks.
11. The computer system of claim 7 , further comprising:
determining a deviation value between the process blocks of the service design structure and the process blocks of the other service design structures; and
displaying the deviation value on a graphical user interface.
12. The computer system of claim 7 , further comprising:
assigning a confidence level value to process blocks in the one or more suggested process blocks using the trained AI/ML process prediction model; and
displaying the confidence level value for each of the one or more further process blocks on a graphical user interface.
13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer-executable instructions configured for:
adding a process block to a service design structure for a service, wherein the process block corresponds to a plurality of process block in a category divided according to corresponding attributes in a tree-type structure;
providing the process block to a trained AI (Artificial Intelligence)/ML (Machine Learning) process prediction model;
using the trained AI/ML process prediction model to suggest one or more further process blocks for addition to the service design structure based, at least in part, on the addition of the process block to the service design structure, wherein the trained AI/ML process prediction model suggests the one or more further process blocks based on a comparison of the process blocks in the service design structure with process blocks in other service design structures constructed for similar services; and
adding one or more suggested process blocks to the service design structure.
14. The non-transitory, computer-readable storage medium of claim 13 , wherein the instructions are further configured for:
training the AI/ML process prediction model using a process block catalog, wherein the process block catalog includes process blocks that are categorized by parametric and/or technical attributes of the process blocks.
15. The non-transitory, computer-readable storage medium of claim 14 , wherein the instructions are further configured for:
training the AI/ML process prediction model using service design structures of a plurality of services.
16. The non-transitory, computer-readable storage medium of claim 13 , wherein
the trained AI/ML process prediction model suggests the one or more further process blocks based on a context of the process blocks added to the service design structure from the suggested process blocks.
17. The non-transitory, computer-readable storage medium of claim 13 , wherein the instructions are further configured for:
determining a deviation value between the process blocks of the service design structure and the process blocks of the other service design structures; and
displaying the deviation value on a graphic user interface.