Generalized and adjusted models for repair recommendations.

Abstract

To provide generalized and alignment models for repair recommendation.SOLUTION: Systems and methods described herein can involve: training a first generative artificial intelligence (AI) model for a general domain, the first generative AI model being trained using standard information components of the general domain; training a second AI model for a specific domain from the first generative AI model, the training of the second AI model being based on the use of the standard information components, non-standard information components of the specific domain and available label data of the specific domain; and fine-tuning the second AI model to align with preferences of the specific domain to maximize reward and minimize error.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates generally to repair systems, and more particularly to generalized and consistent models for repair recommendations. [Background technology]

[0002] Maintenance is a process whose objective is to keep equipment in an operable, efficient, and cost-effective condition. The maintenance process is accomplished by performing actions on the equipment necessary to achieve one or more of these objectives. These actions include, but are not limited to, inspection, adjustment, repair, and overhaul of the equipment or its components.

[0003] When complex equipment breaks down, it can be difficult and time-consuming to diagnose the equipment, recognize the root cause of the problem, and determine what repair actions need to be taken to fix the equipment. This problem is solved using a data-driven system for predictive maintenance (also known as a repair recommendation system) that guides maintenance personnel on how to repair equipment when it is brought in for repair. Summary of the Invention [Problem to be solved by the invention]

[0004] Data-driven repair recommendations using machine learning models have traditionally relied on the availability of large amounts of data for every instance on which they are created and developed. Such a requirement makes the system, as well as the machine learning models trained using the data, non-portable. Related art implementations lack the ability to learn from different customers / instances in similar fields, which makes such implementations unusable for customers / instances with less data. Furthermore, related art repair recommendation models lack the ability to adapt to customer preferences (i.e., no room for personalization or adjustment to customer preferences).

[0005] Figure 1 shows an example of a related art data-driven repair recommendation system. Data-driven repair recommendation (i.e., recommending a course of repair actions based on multi-modal input) follows the flow shown in Figure 1. In this related art flow, a model is trained using historical repair data. [Means for solving the problem]

[0006] An exemplary implementation solves problems in the related art by developing a generative model in standard information components that are transferable for different customers / instances of the system and developing a data-driven model update mechanism based on engineer feedback to personalize / tune the model to customer preferences. The proposed algorithmic system accommodates multiple data modalities, including high-dimensional and sparse data components. The algorithm includes a training mechanism for consistent structured output predictions. The algorithm leverages the standardized information components to train the generative model. The algorithm includes a mechanism for instantiating the generative model and training the fine-tuned model using available data for the customer / instance. The algorithm also includes a mechanism for personalizing the model by using customer feedback to update the model parameters to match customer preferences.

[0007] Data-driven repair recommendation systems have the following problems:

[0008] Lack of knowledge reuse: In repair recommendation systems, there is an information component for an asset class that is standardized and used to train a model. For example, fault codes generated in the automotive field that follow Society of Automotive Engineers (SAE) standards, such as the J1939 protocol, can be used. In related art repair recommendation systems, models are trained using such standard information as well as other information available about an instance / customer. The standard information has a wealth of knowledge associated with it and is not limited to a specific instance or customer. Such knowledge should be reused and transferred to different instances or customers. Currently, no specialized modeling techniques are used to handle standard information and develop generalized AI for knowledge intended for reuse and transfer to different instances / customers.

[0009] Model inapplicability to small amounts of data: Existing repair recommendation systems assume that there is a large amount of data available for training the model per instance or customer. However, this assumption is often incorrect, and many customers / use cases cannot utilize the power of the repair recommendation system. Current systems do not apply unique techniques to solve this problem. Knowledge reuse, as described above, and the methods developed in the implementations described herein, alleviate this situation.

[0010] Lack of personalization and tuning: Repair recommendation models trained using a lot of historical data are never perfect and can make mistakes. Instances / use cases / customers can have preferences regarding model behavior. These preferences map to personalization. Current repair recommendation systems leave no algorithmic room to tune model parameters for instance / use case / customer-specific personalization. The problem of personalization is further exacerbated when little historical data is available to train the model.

[0011] The implementation described herein solves all of the above problems by developing generalized models for standard information components in the form of generative models and making such models transferable from one instance / customer to another, thereby enabling knowledge reuse. The proposed implementation also mitigates the problem of scarce data by using the generalized models and other data available from the customer to develop custom model instances for each instance / customer. Finally, the system allows for updating model parameters, leading to personalization and adjustment to customer / instance / use case preferences.

[0012] Aspects of the present disclosure may include a method, the method including training a first generation artificial intelligence (AI) model for a general domain, where the first generation AI model is trained using standard information components for the general domain; training a second AI model for a specific domain from the first generation AI model, where training the second AI model is based on use of standard information components, non-standard information components for the specific domain, and available label data for the specific domain; and fine-tuning the second AI model to match preferences for the specific domain to maximize reward and minimize error.

[0013] Aspects of the present disclosure may include a computer program, the computer program including instructions for training a first generative artificial intelligence (AI) model for a general domain, where the first generative AI model is trained using standard information components for the general domain; training a second AI model for a specific domain from the first generative AI model, where training the second AI model is based on use of standard information components, non-standard information components for the specific domain, and available label data for the specific domain; and fine-tuning the second AI model to match preferences for the specific domain to maximize reward and minimize error. The computer program and instructions may be stored on a non-transitory computer-readable medium and executed by one or more processors.

[0014] Aspects of the present disclosure may include a system, the system including: means for training a first generative artificial intelligence (AI) model for a general domain, where the first generative AI model is trained using standard information components for the general domain; means for training a second AI model for a specific domain from the first generative AI model, where training the second AI model is based on use of standard information components, non-standard information components for the specific domain, and available label data for the specific domain; and means for fine-tuning the second AI model to match preferences for the specific domain to maximize reward and minimize error.

[0015] Aspects of the present disclosure may include an apparatus, the apparatus including a processor configured to: train a first generative artificial intelligence (AI) model for a general domain, where the first generative AI model is trained using standard information components for the general domain; train a second AI model for a specific domain from the first generative AI model, where the training of the second AI model is based on use of standard information components, non-standard information components for the specific domain, and available label data for the specific domain; and fine-tune the second AI model to match preferences for the specific domain to maximize reward and minimize error. [Brief explanation of the drawings]

[0016] [Figure 1] FIG. 1 shows an example of a related art data-driven repair recommendation system.

[0017] [Figure 2] FIG. 2 illustrates an example of the components and flow of an example implementation described herein.

[0018] [Figure 3] FIG. 3 shows the system flow according to one implementation example.

[0019] [Figure 4] FIG. 4 illustrates multiple physical systems networked to a management device according to one implementation.

[0020] [Figure 5] FIG. 5 illustrates an example computing environment with an example computing device suitable for use in some implementations. DETAILED DESCRIPTION OF THE INVENTION

[0021] The following detailed description provides details of the drawings and implementation examples of the present application. Redundant element reference numbers and descriptions between drawings have been omitted for clarity. Terminology used throughout the description is provided by way of example and is not intended to be limiting. For example, the use of the term "automatic" may include a fully automatic implementation or a semi-automatic implementation that includes user or administrator control over certain aspects of the implementation, depending on the desired implementation of one skilled in the art practicing the implementation of the present application. Selections may be made by a user through a user interface or other input means, or may be implemented by a desired algorithm. The implementations described herein may be utilized alone or in combination, and the functionality of the implementations may be implemented by any means depending on the desired implementation.

[0022] FIG. 2 illustrates an example of the components and flow of an example implementation described herein.

[0023] Generalized Models for Standard Information Sets: The first component (Component 1) develops generalized models for standardized information sets. An example of a standardized information set is automotive fault codes that follow SAE standards (such as the J1939 protocol). While each fault code is standard, a set of fault codes can lead to various faults and associated repairs. Every customer may have the same set of fault codes, but different faults with different sequences of fault codes and therefore associated repairs. Generative models trained on such standard information sets allow for transfer of the model to different customers / instances, knowledge reuse across instances / customers, and combination with custom information available for fine-tuning and instantiation.

[0024] Instance / Customer Scaling: In the second component (Component 2), to instantiate a repair recommendation model, the generalized model is combined with information available on the use case / customer / instance to form a fine-tuned custom repair recommendation model, thereby achieving knowledge reuse of standardized information, reduced dependency on the availability of large amounts of historical data per instance / customer, and scaling of repair recommendation model instances.

[0025] Personalization / Preference Alignment: The third component (Component 3) involves personalizing each instance of the repair recommendation model by aligning it with customer preferences. An example of a preference might be that if the repair recommended by the model is incorrect, a penalty of $100 will be incurred for Customer 1, while a penalty of $10 will be incurred for Customer 2. Preferences can have various attributes, such as the cost of an incorrect repair, the safety aspect of an incorrect repair, etc., depending on the desired implementation.

[0026] Figure 3 shows the system flow according to one implementation example. The following are the detailed steps, as shown in Figure 3: Steps S301 and S304 relate to component 1, steps S302 and S303 relate to component 2, and steps S305 to S309 relate to component 3.

[0027] Step S301: In step S301, this implementation trains a first generative artificial intelligence (AI) model for a general domain, where the first generative AI model is trained using standard information components for the general domain. Using a standard information repository, this step trains a generalized model for each piece of information in the set. The generalized model is a generative model and is trained in an unsupervised / self-supervised manner.

[0028] In one implementation, training can be performed by inputting partial information (e.g., a partial sequence) from known information of a standard information component of a general domain, using a first generative AI model to output a prediction of the remaining information of the known information from the partial information, and utilizing unsupervised learning to reduce the error between the predicted remaining information and the known information. Specifically, a method for training the generative model can be one in which a portion of the sequence is masked to become partial information, and the unmasked sequence is used to predict the masked portion of the sequence. This error is used to train the generalized model. There may be other methods for training such generalized models, and given their generative capabilities, their learned feature space is rich. The standardized information set is continuously evolving, with information from various customers / instances being added to the set. Given that this is industry-standard information related to an asset class (e.g., automobiles), the data does not violate any privacy or ethical and financial guidelines. The generalized model, trained in an unsupervised / self-supervised manner, is transferable to different instances, thus enabling knowledge reuse and transfer. This first generative AI model is configured to output a first repair recommendation for the general domain. Such repair recommendations may include a series of repair actions, each repair action in the series including a location for repair and a repair action.

[0029] The generalized model is continuously trained based on the schedule and the availability of new information from new instances / customers.

[0030] Steps S302 and S303: In steps S302 and S303, this implementation trains a second AI model for the specific domain from the first generative AI model, where the training of the second AI model is based on using standard information components, non-standard information components for the specific domain, and available label data for the specific domain. Such available label data can include a repair code indicating the system to be repaired and a repair action associated with the system to be repaired.

[0031] These steps, taken together, comprise creating a repair recommendation model instance. The repair recommendation model instance depends on the availability of data related to the use case / customer. The available data can be in various formats in addition to standard information. Furthermore, the available data does not need to be large-scale; even a small amount of data can be applied to creating a repair recommendation model. The available standard information undergoes the same data processing steps as creating a generalized model, while other data may have their own data processing steps. Thus, to train a second AI model for a specific domain from the first generative AI model, feature engineering can be performed on non-standard information components for the specific domain, features of the non-standard information components can be encoded, and the second AI model can be generated by combining the first generative AI model with the encoded non-standard information components using available label data.

[0032] By combining the first generative AI model with the encoded non-standard information component using available label data, the repair recommendation model is composed of an instance of the generalized model and another model for handling other additional data modalities. The output of this model is a set of repair actions. During training, this combined model (i.e., the repair recommendation model) uses learning, such as supervised learning, to reduce the error of the second AI model from the available label information. Such an implementation allows for the error between predictions and a ground truth to be utilized to optimize model parameters. This second AI model can be configured to output second repair recommendations for a specific area. Such repair recommendations can include a set of repair activities, each of which includes a location for repair and a repair action.

[0033] The use of composite models allows for knowledge reuse of standardized information. Note that the standard information is collected from all customers / use cases / instances and continues to grow (S304), so that collective knowledge is used for the generalized model and the specific repair recommendation model instance.

[0034] Step S304: This step involves archiving new standards information and adding it to the existing standards information repository set. This new data is used to retrain the generalization model (S301).

[0035] Step S305: Deploy the repair recommendation model instance to the field. By doing this, the model is used to generate repair recommendations that can be used by technicians. Additionally, the actual repairs performed by the technicians are also recorded in case the recommendations are incorrect. To fine-tune the second AI model, step S305 deploys the second AI model for a certain period of time to collect model predictions of the second AI model and actual repairs performed during the certain period.

[0036] Step S306: This step involves collecting adjustment feedback between the predictions and actual repairs made in the field. This step involves collecting the model predictions of the second AI model and the actual repairs made over a period of time. The adjustment feedback can be simply true or false, or a composite such as the distance between them in the coding space.

[0037] Step S307: This step determines domain-specific preference attributes from the actual repairs. Customers have preferences regarding repair recommendations. For example, for Customer 1, the cost of repeated repairs in case of an inaccurate prediction may be large, and therefore Customer 1 is sensitive to that. For Customer 2, it may be a safety issue. Preferences can have multiple attributes, which, when combined, can provide an understanding of whether a particular prediction creates good or bad value for the customer. Step S307 also includes training a reward model using the model predictions, the actual repairs performed, and the preference attributes. Values are encoded by the reward model in the form of a reward score. The reward model can be a deterministic model or a machine learning model. The reward model considers the recommendations, the actual repairs, and the attributes and generates a scalar score.

[0038] Step S308: This step includes generating a reward score using a reward model. Depending on the desired implementation, the error of the reward model may be determined from the difference between the model prediction and the selected actual repair. In such a case, the reward model may be configured to generate a reward if the model prediction is the same as the selected actual repair.

[0039] Step S309: The reward score is used to update the repair recommendation model parameters. The second AI model is then fine-tuned from the rewards generated from the reward model. Iteratively performing steps S305 through S309 results in a model that is personalized and tailored to the customer / instance / use case preferences. This implementation can thus fine-tune the second AI model to match domain-specific preferences in order to maximize rewards and minimize error. The steps described in S301 through S309 enable the implementation to transfer general domain knowledge to a domain-specific model, enable model training even when limited data is available from the domain-specific model, and enable domain-specific model customization with reduced error compared to training from scratch, thereby providing a technical improvement over related art implementations.

[0040] The current state of the art of data-driven repair recommendation methods relies on the availability of large amounts of data per instantiation, leaving no room for knowledge reuse and no room for personalization. The implementations described herein reduce the dependency on the availability of large amounts of data, thus opening up deployment opportunities to customers who do not have large amounts of data.

[0041] Furthermore, given the modular nature of modeling, the implementation example emphasizes knowledge reuse of standard information across different instantiations / customers. Knowledge reuse can benefit smaller customers by using the knowledge and experience gained by established customers without violating any contractual obligations. On the other hand, knowledge reuse also assists model service providers by scaling deployments seamlessly across many customers.

[0042] Data-driven repair recommendation models are prone to errors. Existing related art repair recommendation model algorithms lack the ability to adjust for personalization. The implementations described herein introduce a personalization component that is customizable and adapts the model to customer needs and preferences, creating operational value.

[0043] 4 illustrates multiple physical systems networked to a management device according to one implementation. One or more physical systems 421, which manage a customer database having repair code information with their specific fields, are communicatively connected to a network 420 (e.g., a local area network (LAN), a wide area network (WAN)) via corresponding network interfaces of the physical systems 421 connected to the management device 422. The one or more physical systems 421 may or may not be associated with sensors for identifying repair codes for a given task, depending on the desired implementation. The management device 422 manages a database 423 containing repair codes for general fields. Models may be trained or deployed on the management device 422 on behalf of the physical systems 421, or may be provided to the physical systems 421 for execution, depending on the desired implementation.

[0044] Figure 5 illustrates an example computing environment having an example computing device suitable for use in some implementations, such as management device 422 shown in Figure 4. The computing device 505 in computing environment 500 can include one or more processing units, cores, or processors 510, memory 515 (e.g., RAM, ROM, etc.), internal storage 520 (e.g., magnetic, optical, solid-state storage, and / or organic), and / or I / O interface 525, any of which can be connected to a communication mechanism or bus 530 for communicating information or can be embedded in the computing device 505. The I / O interface 525 can also be configured to receive images from a camera or to provide images to a projector or display, depending on the desired implementation.

[0045] The computing device 505 may be communicatively coupled to an input / user interface 535 and an output device / interface 540. Either or both of the input / user interface 535 and the output device / interface 540 may be wired or wireless interfaces and may be detachable. The input / user interface 535 may include any physical or virtual device, component, sensor, or interface that can be used to provide input (e.g., buttons, touchscreen interface, keyboard, pointing / cursor control, microphone, camera, Braille, motion sensor, optical reader, etc.). The output device / interface 540 may include a display, television, monitor, printer, speakers, Braille, etc. In some implementations, the input / user interface 535 and the output device / interface 540 may be embedded in the computing device 505 or may be physically connected to the computing device 505. In other implementations, other computing devices can function as or provide the functionality of the input / user interface 535 and the output device / interface 540 for the computing device 505.

[0046] Examples of computing devices 505 may include, but are not limited to, highly mobile devices (e.g., smartphones, devices in vehicles and other machines, devices carried by humans and animals, etc.), mobile devices (e.g., tablets, notebooks, laptops, personal computers, portable televisions, radios, etc.), and devices not designed for mobility (e.g., desktop computers, other computers, information kiosks, televisions, radios with one or more embedded and / or connected processors, etc.).

[0047] Computing device 505 may be communicatively connected (e.g., via I / O interface 525) to external storage 545 and network 550 for communicating with any number of networked components, devices, and systems, including one or more computing devices of the same or different configurations. Computing device 505 or any connected computing device may function, provide services, or be referred to as a server, client, thin server, general-purpose machine, special-purpose machine, or another label.

[0048] I / O interface 525 may include, but is not limited to, wired and / or wireless interfaces using any communication or I / O protocol or standard (e.g., Ethernet, 802.11x, Universal System Bus, WiMax, modem, cellular network protocol, etc.) to exchange information with at least all connected components, devices, and networks in computing environment 500. Network 550 may be any network or combination of networks (e.g., the Internet, a local area network, a wide area network, a telephone network, a cellular network, a satellite network, etc.).

[0049] The computing device 505 uses and / or can communicate using computer-usable or computer-readable media, including transitory and non-transitory media. Transitory media include transmission media (e.g., metallic cables, optical fibers), signals, carrier waves, etc. Non-transitory media include magnetic media (e.g., disks and tape), optical media (e.g., CD ROM, digital video disks, Blu-ray disks), solid-state media (e.g., RAM, ROM, flash memory, solid-state storage), and other non-volatile storage or memory.

[0050] In some examples of computing environments, the computing device 505 may be used to implement techniques, methods, applications, processes, or computer-executable instructions. The computer-executable instructions may be obtained from transitory media or stored on and retrieved from non-transitory media. The executable instructions may be from one or more of any programming, scripting, and machine language (e.g., C, C++, C#, Java, Visual Basic, Python, Perl, JavaScript, etc.).

[0051] The processor 510 can execute under any operating system (OS) (not shown) in a native or virtual environment. One or more applications can be implemented, including a logic unit 560, an application programming interface (API) unit 565, an input unit 570, an output unit 575, and an inter-unit communication mechanism 595 through which the various units communicate with each other, with the OS, and with other applications (not shown). The described units and elements can be modified in design, function, configuration, or implementation and are not limited to the provided description. The processor 510 can be in the form of a hardware processor, such as a central processing unit (CPU), or a combination of hardware and software units.

[0052] In some implementations, when information or instructions to execute are received by API unit 565, they may be communicated to one or more other units (e.g., logic unit 560, input unit 570, output unit 575). In some implementations described above, logic unit 560 may be configured to control the flow of information between units and direct the services provided by API unit 565, input unit 570, and output unit 575. For example, the flow of one or more processes or implementations may be controlled by logic unit 560 alone or in combination with API unit 565. Input unit 570 may be configured to obtain inputs for the calculations described in the implementations, and output unit 575 may be configured to provide outputs based on the calculations described in the implementations.

[0053] The processor 510 may be configured to execute a method or instructions, which may include training a first generative artificial intelligence (AI) model for a general domain, where the first generative AI model is trained using standard information components for the general domain, training a second AI model for a specific domain from the first generative AI model, where training the second AI model is based on the use of standard information components, non-standard information components for the specific domain, and available label data for the specific domain, as described with respect to S301 to S305 of FIG. 3 ; and fine-tuning the second AI model to match preferences for the specific domain to maximize reward and minimize error.

[0054] Depending on the desired implementation, the first generative AI model is configured to output first repair recommendations for a general domain, and the second AI model is configured to output second repair recommendations for a specific domain. Such first and second repair recommendations can include a series of repair activities, and each repair activity in the series can include a location (e.g., on the object to be repaired) for repair and a repair action.

[0055] As described with respect to S301 to S303 of FIG. 3, the processor 510 may be configured to execute the above-described method or instructions, wherein training the first generative AI model for the general domain includes inputting partial information (e.g., a sequence) from known information of a standard information component of the general domain, using the first generative AI model to output a prediction of the remaining information of the known information from the partial information, and utilizing unsupervised learning to reduce an error between the predicted remaining information and the known information.

[0056] The processor 510 may be configured to execute the above-described methods or instructions, and as described with respect to S301 to S304 of FIG. 3, training a second AI model for a specific domain from a first generative AI model may include performing feature engineering on non-standard information components of the specific domain (e.g., by any feature extraction process), encoding features of the non-standard information components (e.g., from features generated from the feature engineering), combining the first generative AI model and the encoded non-standard information components using available label data to generate a second AI model, and using supervised learning to reduce errors of the second AI model from the available label information.

[0057] 3, fine-tuning the second AI model may include deploying the second AI model for a period of time, collecting model predictions of the second AI model and actual repairs performed during the period of time, determining preference attributes for a particular area from the actual repairs, training a reward model using the model predictions, the actual repairs performed, and the preference attributes, and fine-tuning the second AI model from rewards generated from the reward model. Depending on the desired implementation, an error in the reward model is determined from the difference between the model predictions and the actual repairs, and the reward model is configured to generate rewards for cases where the model predictions are the same as the actual repairs to ensure that the reward model provides rewards in response to "correct" predictions.

[0058] As described herein, the available label data may include a repair code indicative of the system being repaired and the repair action associated with the system being repaired.

[0059] Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations within a computer. These algorithmic descriptions and symbolic representations are the means used by those skilled in the data processing arts to convey the essence of their innovations to others skilled in the art. An algorithm is a sequence of defined steps leading to a desired end state or result. In one implementation, the steps performed require physical manipulations of tangible quantities to achieve a tangible result.

[0060] Unless otherwise specified, as is apparent from the description, the description utilizing terms such as "processing," "calculating," "operating," "determining," "displaying," and the like throughout the description will be understood to include the actions and processes of a computer system or other information processing device that manipulates and converts data represented as physical (electronic) quantities in the computer system's registers and memory into other data similarly represented as physical quantities in the computer system's memory or registers, or other information storage, transmission, or display device.

[0061] Implementations may also relate to apparatuses for performing the operations described herein. This apparatus may be specially constructed for the required purposes, or it may comprise one or more general-purpose computers selectively activated or reconfigured by one or more computer programs. Such computer programs may be stored in a computer-readable medium, such as a computer-readable storage medium or a computer-readable signal medium. Computer-readable storage media may include tangible media, such as, but not limited to, optical disks, magnetic disks, read-only memory, random-access memory, solid-state devices and drives, or any other type of tangible or non-transitory medium suitable for storing electronic information. Computer-readable signal media may include media such as carrier waves. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. A computer program may include a pure software implementation containing instructions for performing the operations of a desired implementation.

[0062] Various general-purpose systems may be used with the programs and modules according to the examples herein, or it may prove convenient to construct more specialized apparatus to perform the desired method steps. Additionally, the implementations are not described with reference to any particular programming language. It will be understood that a wide variety of programming languages may be used to implement the techniques of the implementations described herein. Instructions in the programming language may be executed by one or more processing devices, such as a central processing unit (CPU), processor, or controller.

[0063] As is known in the art, the operations described above may be performed by hardware, software, or some combination of software and hardware. Various aspects of the implementations may be implemented using circuits and logic devices (hardware), while other aspects may be implemented using instructions stored on a machine-readable medium (software), which, when executed by a processor, cause the processor to perform methods for carrying out the implementations of the present application. Furthermore, some implementations of the present application may be performed solely in hardware, while other implementations may be performed solely in software. Furthermore, the various functions described may be performed in a single unit or distributed among several components in any number of ways. When performed by software, the methods may be executed by a processor, such as a general-purpose computer, based on instructions stored on a computer-readable medium. If desired, the instructions may be stored on the medium in compressed and / or encrypted format.

[0064] Furthermore, other implementations of the present application will be apparent to those skilled in the art from a consideration of this specification and practice of the present techniques. Various aspects and / or components of the described implementations may be used alone or in any combination. It is intended that the specification and implementations be considered as examples only, with the true scope and spirit of the present application being indicated by the following claims. [Explanation of symbols]

[0065] 420 Networks, 421 Physical Systems, 422 Management Equipment, 423 Databases, 500 Computing Environments, 505 Computer Devices, 510 Processors, 515 Memory

Claims

1. A method executed by a computing device, comprising: training a first generation artificial intelligence (AI) model for a general domain, the general domain being a non-customer specific domain, the first generation AI model being trained using standard information components, the standardized information components for the general domain; training a second AI model for a specific domain, which is a domain for a specific customer, from the first generative AI model, wherein training the second AI model includes performing feature engineering on a non-standard information component, which is an information component that is not standardized, encoding features of the non-standard information component, and combining the first generative AI model and the encoded non-standard information component using available label data to generate a second AI model; and training based on the use of the standard information component, the non-standard information component, and available label data for the specific domain; fine-tuning the second AI model to match the preferences of the particular field to maximize rewards for a reward model that inputs the model predictions of the second AI model and calculates rewards for repairs, and to minimize errors in the reward model between the model predictions of the second AI model and actual repairs; Including, Fine-tuning the second AI model includes: deploying the second AI model for a period of time; collecting the model predictions of the second AI model and actual repairs performed during the period; determining preference attributes for said particular field from said actual repairs; training the reward model using the model predictions, the actual repairs made, and the preference attributes; and fine-tuning the second AI model from the rewards generated from the reward model. A method comprising:

2. 2. The method of claim 1, wherein the first generative AI model is configured to output first repair recommendations for the general domain and the second AI model is configured to output second repair recommendations for the specific domain.

3. The method of claim 2 , wherein the first repair recommendation and the second repair recommendation include a series of repair activities, each repair activity in the series including a location for repair and a repair action.

4. training the first generation AI model for the general domain, inputting partial information from known information of said standard information component of said general field; outputting a prediction of the remaining information of the known information from the partial information using the first generative AI model; Using unsupervised learning to reduce the error between the predicted residual information and the known information. The method of claim 1 , comprising:

5. training the second AI model for the particular domain from the first generative AI model, performing feature engineering on the non-standard information components of the specific domain; encoding characteristics of said non-standard information component; combining the first generated AI model with the encoded non-standard information component using the available label data to generate the second AI model; using supervised learning to reduce the error of the second AI model from the available label information. The method of claim 1 , comprising:

6. The method described in claim 1, wherein the reward model is configured to generate a reward for cases where the model prediction is the same as the actual repair.

7. The method of claim 1 , wherein the available label data includes a repair code indicative of a system to be repaired and a repair action associated with the system to be repaired.

8. A non-transitory computer-readable medium storing instructions for carrying out a process, comprising: The instruction: training a first generation artificial intelligence (AI) model for a general domain, the general domain being a non-customer specific domain, the first generation AI model being trained using standard information components, the standardized information components for the general domain; training a second AI model for a specific domain, which is a domain for a particular customer, from the first generative AI model, wherein training the second AI model includes performing feature engineering on a non-standard information component, which is an information component that is not standardized, encoding features of the non-standard information component, and combining the first generative AI model and the encoded non-standard information component using available label data to generate a second AI model; training based on the use of the standard information component, the non-standard information component, and available label data for the specific domain; and and fine-tuning the second AI model to match the preferences of the particular field to maximize rewards for a reward model that inputs the model predictions of the second AI model and calculates rewards for repairs and minimize errors in the reward model between the model predictions of the second AI model and actual repairs. Including, Fine-tuning the second AI model includes: deploying the second AI model for a period of time; collecting model predictions of the second AI model and actual repairs performed during the period of time; determining preference attributes for said particular field from said actual repairs; training a reward model using the model predictions, the actual repairs made, and the preference attributes; and fine-tuning the second AI model from the rewards generated from the reward model.

1. A non-transitory computer-readable medium comprising:

9. 9. The non-transitory computer-readable medium of claim 8, wherein the first generative AI model is configured to output first repair recommendations for the general domain and the second AI model is configured to output second repair recommendations for the specific domain.

10. 10. The non-transitory computer-readable medium of claim 9, wherein the first repair recommendation and the second repair recommendation comprise a series of repair actions, each repair action in the series of repair actions comprising a location for repair and a repair action.

11. training the first generation AI model for the general domain, inputting partial information from known information of said standard information component of said general field; outputting a prediction of the remaining information of the known information from the partial information using the first generative AI model; Using unsupervised learning to reduce the error between the predicted residual information and the known information.

9. The non-transitory computer-readable medium of claim 8, comprising:

12. training the second AI model for the particular domain from the first generative AI model, performing feature engineering on the non-standard information components of the specific domain; encoding characteristics of said non-standard information component; combining the first generated AI model with the encoded non-standard information component using the available label data to generate the second AI model; using supervised learning to reduce the error of the second AI model from the available label information.

9. The non-transitory computer-readable medium of claim 8, comprising:

13. The non-transitory computer-readable medium of claim 8, wherein the reward model is configured to generate a reward for cases where the model prediction is the same as the actual repair.

14. The non-transitory computer-readable medium of claim 8 , wherein the available label data includes a repair code indicative of a system to be repaired and a repair action associated with the system to be repaired.

15. Training a first generation artificial intelligence (AI) model for a general domain, which is a domain that does not limit customers, wherein the first generation AI model is trained using standard information components, which are standardized information components for the general domain; training a second AI model for a specific domain, which is a domain for a specific customer, from the first generative AI model, wherein training the second AI model includes performing feature engineering on a non-standard information component, which is an information component that is not standardized, encoding features of the non-standard information component, and combining the first generative AI model and the encoded non-standard information component using available label data to generate a second AI model; training based on the use of the standard information component, the non-standard information component, and available label data for the specific domain; and and fine-tuning the second AI model to match the preferences of the particular field to maximize rewards for a reward model that inputs the model predictions of the second AI model and calculates rewards for repairs and minimize errors in the reward model between the model predictions of the second AI model and actual repairs. and Fine-tuning the second AI model includes: deploying the second AI model for a period of time; collecting model predictions of the second AI model and actual repairs performed during the period of time; determining preference attributes for said particular field from said actual repairs; training a reward model using the model predictions, the actual repairs made, and the preference attributes; and fine-tuning the second AI model from the rewards generated from the reward model. a processor configured to include 1. An apparatus comprising:

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