Method and apparatus for providing recommendations for com pletion of an engineering project

The invention relates to a method and apparatus for providing automatically recommendations for the completion of a complex engineering project, in particular an automation system.

An engineering project such as an automated system can be complex and comprise a multitude of different components. The configuration of the complex engineering projects may com prise an iterative process, in which a user incrementally se lects components. The combination of these selected compo nents can fulfill functional requirements of the engineering projects while being also compatible with one another. The configuration of a complex engineering process is not an easy task and requires time, effort, experience, and a certain amount of domain-specific knowledge to be completed correctly by a user.

Accordingly, it is an object of the present invention to pro vide a method and apparatus providing automatically recommen dations for the completion of an engineering project.

This object is achieved according to a first aspect of the present invention by a recommendation engine comprising the features of claim 1.

The invention provides according to the first aspect a recom mendation engine to provide automatically recommendations for the completion of an engineering project, said recommendation engine comprising a first artificial intelligence module adapted to provide la tent representations of a set of items and a second artificial intelligence module adapted to process the latent representations of the set of items provided by said first artificial intelligence module to generate at least one sequence of complementary items required to comple ment the set of items to provide a complete sequence of items output via an interface as a recommendation to complete said engineering project.

Providing recommendations for completing a partially config ured engineering project reduces the time required to select components. Further, the process of selecting items associat ed with components of the engineering process can be per formed by less experienced users with less domain-specific knowledge. The recommendation engine can be used for any kind of engineering project, in particular for different kinds of automated complex systems comprising a plurality of different components, i.e. hardware and/or software components.

In a possible embodiment of the recommendation engine accord ing to the first aspect of the present invention, the items are selected from a set of available items corresponding to hardware and/or software components usable for the respective engineering project.

Each item can correspond to an associated hardware component such as a controller or to a software component such as an application program. Accordingly, the recommendation engine according to the present invention can be used for a wide range of different engineering projects encompassing not only hardware components but also software components.

The information about the order of selected items in the se quence provides additional contextual information supporting the completion of the required items for the respective engi neering project.

In a further possible embodiment of the recommendation engine according to the first aspect of the present invention, the set of selected items is stored at least temporarily in a memory connected to the recommendation engine. Consequently, loss of selected items can be avoided. In a further possible embodiment of the recommendation engine according to the first aspect of the present invention, the first artificial intelligence module comprises a trained fea ture learning module adapted to calculate the latent repre sentations of the set of items.

Latent representations calculated by the first artificial in telligence module can encode technical information about the components of the engineering project.

In a further possible embodiment of the recommendation engine according to the first aspect of the present invention, the second artificial intelligence module comprises a trained se quential model adapted to calculate at least one sequence of complementary items output as a recommendation to complete said engineering project.

The trained sequential model can exploit the temporal depend encies between items selected during engineering projects.

In a further possible embodiment of the recommendation engine according to the first aspect of the present invention, the items are selected by a user via a user interface having a screen adapted to output available items to the user. This facilitates the selection of available items.

In a further possible embodiment of the recommendation engine according to the first aspect of the present invention, one or more sequences of complementary items generated by the second artificial intelligence module are output on the screen of the user interface for selection of a next item from one of the sequences of complementary items or for se lection of one or more items (not necessarily appearing one after the other) from one of the sequences of complementary items or for selection of a whole sequence of complementary items by the user. This provides the advantage that the user has a choice wheth er to select a single next item or the whole sequence of com plementary items for finalizing the selection at once. Ac cordingly, there is an automation mechanism for auto completion of a partially configured engineering project.

In a further possible embodiment of the recommendation engine according to the first aspect of the present invention, the first artificial intelligence module and the second artifi cial intelligence module comprise artificial neural networks trained on technical features or properties of components and a plurality of sequences of previously selected items. The artificial intelligence modules can be trained on item fea tures and historical click-stream data.

In a further possible embodiment of the recommendation engine according to the first aspect of the present invention, the first artificial intelligence module comprises a trained au toencoder.

In an alternative embodiment of the recommendation engine ac cording to the first aspect of the present invention, the first artificial intelligence module comprises a tensor fac torization model.

Other artificial intelligence modules can be used comprising models capable of generating latent representations of items.

In a further possible embodiment of the recommendation engine according to the first aspect of the present invention, the second artificial intelligence module comprises a trained re current neural network.

In a further alternative embodiment of the recommendation en gine according to the first aspect of the present invention, the second artificial intelligence module comprises a trained convolutional neural network. The invention further provides according to a further aspect a computer-implemented method comprising the features of claim 11.

The invention provides according to the second aspect a com puter-implemented method for providing automatically recom mendations for the completion of an engineering project, the method comprising the steps of: calculating by a first artificial intelligence module latent representations of a set of items, processing by a second artificial intelligence module the la tent representations of the set of items to generate at least one sequence of complementary items required to complete the set of items and outputting via an interface the at least one sequence of com plementary items as a recommendation to complete the engi neering project.

In a possible embodiment of the computer-implemented method according to the second aspect of the present invention, one or more sequences of complementary items generated by the second artificial intelligence module are output on a screen of a user interface for selection of a next item from one of the sequences of complementary items or for selection of one or more items (not necessarily appearing one after the other) from one of the sequences of complementary items or for se lection of a whole sequence of complementary items by the us er.

In a further possible embodiment of the computer-implemented method according to the second aspect of the present inven tion, the selection of one of the complementary items or the selection of a whole sequence of complementary items by the user via the user interface triggers automatically an order ing command to order associated components for said engineer ing project. This facilitates the provision of components required for the engineering project.

The invention further provides according to a further aspect a software tool comprising the features of claim 14.

The invention provides according to this aspect a software tool comprising a program code executable to perform the com puter-implemented method according to the second aspect of the present invention.

The invention further provides according to a further aspect a platform comprising the features of claim 15.

The invention provides according to this aspect a platform comprising a recommendation engine according to the first as pect of the present invention.

The platform can comprise a cloud platform.

In the following, possible embodiments of the different as pects of the present invention are described in more detail with reference to the enclosed figures.

Fig. 1 shows a block diagram of a possible exemplary embod iment of a system including a recommendation engine according to the first aspect of the present inven tion;

Fig. 2 shows schematically possible sequences of complemen tary items output as recommendations to a user by a recommendation engine as illustrated in Fig. 1;

Fig. 3 shows a flowchart of a possible exemplary embodiment of a computer-implemented method for providing auto matically recommendations for the completion of an engineering project according to a further aspect of the present invention. As can be seen from the block diagram of Fig. 1, a recommen dation engine 1 according to the first aspect of the present invention can form part of a system used to configure an en gineering project such as an automated system. The automated system can comprise a plurality of hardware and/or software components. The recommendation engine 1 as illustrated in Fig. 1 comprises in the illustrated embodiment a first arti ficial intelligence module 1A and a second artificial intel ligence module IB. The first artificial intelligence module 1A is adapted to provide latent representations of a set of items. The second artificial intelligence module IB is adapted to process the latent representations of the set of items provided by the first artificial intelligence module 1A to generate at least one sequence of complementary items.

This sequence of complementary items is required to comple ment the set of items. To provide a complete sequence of items it can be output via an interface 1C of the recommenda tion engine 1 to complete the respective engineering project. In a possible embodiment, the items are selected from a set of available items wherein each available item does corre spond to a hardware component and/or to a software component which can be used in the engineering project. In the embodi ment illustrated in Fig. 1, the illustrated configuration system comprises a memory 2 connected to a user interface 3. The user interface 3 can be integrated in a user terminal or in a mobile user device. The set of selected items is stored at least temporarily in the memory 2 as shown in Fig. 1. The recommendation engine 1 has access to the memory 2 of the configuration system shown in Fig. 1. In the illustrated em bodiment of Fig. 1, three selectable items Ii, I2, I3 are se lected one after the other at times ti, t2, t3 by the user U by means of the user interface 3. Accordingly, in this exam ple, the set of selected items comprises a sequence of item II followed by item 12 followed by item 13. Each item I cor responds to a component of an engineering project. Each item I can comprise one or more item features. In a possible em bodiment, the recommendation engine 1 has access to a data- base 4 storing item features of different items. Further, the recommendation engine 1 can have access to a further database 5 where a plurality of completed sequences of items are stored. Each item I corresponds to a hardware component such as a controller or a display panel. Each component can com prise one or more features or properties. For instance, a controller may comprise as technical features a supply volt age, a fail-safe compatibility or its power consumption. A display panel may comprise as technical features a supply voltage and the resolution of its screen.

The first artificial intelligence module 1A of the recommen dation engine 1 can comprise in a possible embodiment a trained feature learning module adapted to calculate the la tent representations of the set of items I stored in a selec tion basket of the memory 2 as shown in Fig. 1. Further, the second artificial intelligence module IB of the recommenda tion engine 1 can comprise a trained sequential model adapted to calculate at least one sequence of complementary items output via the data interface 1C of the recommendation engine 1 to the user interface 3 of the user U. In a possible embod iment, the items I are selected by the user U via the user interface 3 having a screen adapted to output available items to the user. One or more sequences of complementary items generated by the second artificial intelligence module IB can be displayed on the screen of the user interface 3 for selec tion of a next item from one of the sequences of complemen tary items or for selection of a whole sequence of complemen tary items by the user U. Further, it is possible that the user selects one or more items I which do not appear one af ter the other from one of the sequences of complementary items.

The first artificial intelligence module 1A as well as the second artificial intelligence module IB can comprise artifi cial neural networks trained on technical features of compo nents and trained on a plurality of sequences of previously selected items. In a possible embodiment, the first artifi- cial intelligence module 1A can comprise a trained autoencod er. In an alternative embodiment, the first artificial intel ligence module 1A comprises a tensor factorization model. In a preferred embodiment, the second artificial intelligence module IB comprises a trained recurrent neural network RNN. The recurrent neural network RNN is designed to exploit the temporal dependencies between the selected items within the engineering project. Further artificial neural networks can also be used for the second artificial intelligence module IB. In a possible embodiment, the second artificial intelli gence module IB comprises a trained convolutional neural net work.

The recommendation system as illustrated in Fig. 1 comprises a recommendation engine 1 which can be used for completing an engineering project that is partially configured by a user U. The recommendation engine 1 reduces the time required for completing the configuration of the engineering project. The recommendation engine 1 exploits the sequential nature of the configuration process of an engineering project. It can also exploit the complex relationships between the underlying com ponents for generating project completion suggestions or rec ommendations. The information about the order in which compo nents or items have been introduced into the recommendation system 1 comprise contextual information for completing the respective engineering project based on historical examples of previously configured engineering projects for which the same information is available. Introducing the technical in formation about the components can additionally ensure their compatibility. Further, the information can be used as the basis for recommendation when there is not enough historical data available. The recommendation engine 1 according to the present invention can suggest to a user U the next item to be added to its engineering project. The recommendation engine 1 can also be used to provide automatic project completion, i.e. it can provide a sequence of items associated with com ponents that remain to be added in order to fulfill all func tional requirements of the engineering project. An automation mechanism can be installed for auto-completion of the par tially configured engineering project.

Fig. 2 shows an example illustrating the operation of a rec ommendation engine 1 according to the present invention. In the example, the user U has performed a partial configuration of an engineering project such as an automated system by se lecting consecutively three items Ii, I ₂ , I ₃ from the availa ble items. Each item I corresponds to an associated component C usable in the engineering project. The partial configura tion, i.e. the set of selected items Ii, I ₂ , I ₃ can be stored in the memory 2 of the recommendation system illustrated in Fig. 1. The set of selected items Ii, I ₂ , 1 ₃ is applied to the first artificial intelligence module 1A of the recommendation engine 1. The first artificial intelligence module 1A is adapted to provide latent representations of the set of se lected items Ii, I ₂ , I ₃ . Latent representations for the se lected items can be calculated automatically by the trained first artificial intelligence module 1A. The second artifi cial intelligence module IB receives the calculated latent representations of the set of selected items Ii, I ₂ , I ₃ pro vided by the first artificial intelligence module 1A. The second artificial intelligence module IB can process the re ceived latent representations of the set of selected items provided by the first artificial intelligence module 1A to calculate at least one sequence of complementary items re quired to complement the set of selected items Ii, I ₂ , I ₃ to provide a complete sequence of items required to complete the whole engineering project. In the illustrated example of Fig. 2, the second artificial intelligence module IB does generate different sequences of complementary items to com plete the engineering project. These different sequences of complementary items form different completion scenarios CompSc which can be output as recommendations to the user U.

A first completion scenario recommendation CompScl comprises in the illustrated example of Fig. 2 items In, I ₁₂ , I ₁₃ , I ₁₄ ,

115, 116. Accordingly, the first sequence of complementary items comprises six items In to In which can be selected by the user U to provide a complete sequence of items required to complete the already partially configured (p config) engi neering project. A second completion scenario CompSc2 com prises a sequence of complementary items including items I21, I22, I23, I24· A third completion scenario CompSc3 comprises in the illustrated example a sequence of complementary items I31, 132, I33. Accordingly, after having selected the third item I3 at time t3 to provide a partial configuration (p con- fig) of the engineering project, on request three different completion scenarios CompSc can be output via the user inter face 3 to the user U as possible recommendations to complete the partially configured engineering project. The user U has now a choice either to select only a single item I of one of the recommended sequences of complementary items CompSc or to select one of the sequences of complementary items CompSc completely to finalize the whole engineering project. Differ ent sequences of complementary items CompSc generated by the second artificial intelligence module IB can be displayed on a screen of the user interface 3 for the selection by the us er U. The user U may select using a selection command of a first type (e.g. clicking on item) a next single item I from one of the sequences of complementary items or may select with a selection command of a second type (e.g. clicking on CompSc) a whole sequence of complementary items. For example, the user U can select the first completion scenario CompScl as a whole to select automatically all remaining complemen tary items In to item Ii ₆ to complete the configuration of the engineering project. Alternatively, the user U may select completion scenario CompSc2 including four items I21, I22, I23, I24 or completion scenario CompSc3 including items I31, I32,

I33. Alternatively, the user U can select with an associated selection command only a single item to continue iteratively the selection process. The selection of a single item can e.g. be performed by clicking on the displayed item. For in stance, the user U may select only item I31 from the third displayed sequence of complementary items CompSc3 by clicking on item I31. In the illustrated example of Fig. 2, the user U can then select in the next step item 132 to get a further completion scenario CompSc4 displayed on the screen of the user interface 3 including items I ₄₁ , I ₄₂ , I ₄₃ . In a further step, the user U may then select the whole completed scenario CompSc4 including the three items I ₄₁ , I ₄₂ , I43 to complete the engineering project or may select only the next single item of the third completion scenario CompSc3 (i.e. 133) or the next single item of the new completion scenario, i.e. I ₄₁ alone.

Fig. 3 shows a flowchart of a possible exemplary embodiment of a computer-implemented method according to the further as pect of the present invention. In the illustrated embodiment, the computer-implemented method is used for providing auto matically recommendations for the completion of an engineer ing project, in particular an automated system comprising a plurality of hardware and/or software components. In the il lustrated embodiment, the computer-implemented method com prises three main steps.

In a first step SI, latent representations of a set of items I are calculated by a first artificial intelligence module 1A.

In a further step S2, latent representations of the of items I are processed by a second artificial intelligence module IB to generate at least one sequence of complementary items re quired to complete the sequence of selected items.

In a further step S3, the at least one sequence of complemen tary items are output as a recommendation to complete the en gineering project. In the example as illustrated in Fig. 2, four sequences of complementary items forming different com pletion scenarios CompScl to CompSc4 can be output via a user interface 3 to a user U for further selection. In the illus trated example of Fig. 2, for the partial configuration at time 13, the first three completion scenarios CompScl to CompSc3 are displayed for selection. The fourth completion scenario CompSc4 is displayed after the user U has selected items I31 and items 132 in the third completion scenario CompSc3.

The first artificial intelligence module 1A calculates for each selected item I a latent representation which comprises a vector v for the different features of the associated com ponent. The recommendation engine 1 of the recommendation system according to the present invention has the advantage that it is less reliant on manually defined rules. When pro vided with sufficiently rich contextual information and enough training examples, the recommendation system can dis cover substantially more complex dependencies among the com ponents than those that can be specified by a domain expert. The performance of the recommendation system as illustrated in Fig. 1 comprising a recommendation engine 1 according to the first aspect of the present invention does improve with time based on the collected training data.

A further advantage of the recommendation system according to the present invention is that the system does not only pro vide recommendations to the user U explicitly but can also suggest to the user U on how to complete the full engineering project rather than just selecting the next item or compo nent. The computer-implemented method as illustrated in the flowchart of Fig. 3 can be implemented in a software tool comprising a program code executable to perform the steps il lustrated in Fig. 3.

The recommendation engine 1 as illustrated in the system of Fig. 1 can be implemented in a possible embodiment as a desk top solution. In this embodiment, the computer-implemented method can be executed by a processor of a user terminal. In an alternative embodiment, the recommendation engine 1 can also be implemented on a web server of a cloud platform con nected via a data network to a plurality of different user terminals. In this embodiment, the computer-implemented meth od can be executed by one or more processors of a server of the cloud platform. Further embodiments of the computer-implemented method ac cording to the present invention are possible. For example, in the example of Fig. 2, a user U may select also an item which is not suggested by a completion scenario CompSc, i.e. a sequence of complementary items. The system can then check automatically whether the selected item does lead to a com pletion of the engineering project or not. If the selected item leads to the completion of the engineering project it can be accepted and on request, new completion scenarios CompSc can be calculated on the extended partial configura tion including the added item. In a further exemplary embodi ment, after having selected an item I suggested by a calcu lated completion scenario CompSc, the user U can also step back to the initial partial configuration, i.e. cancel the selected suggested complementary item. For instance, in the example of Fig. 2, after having selected item In from the first completion scenario, the user U can delete the selec tion and fall back to the partial configuration including items Ii, I ₂ , I ₃ and request a new calculation of completion scenarios and/or select another item such as item I ₂₁ of the second completion scenario CompSc2. After the selection of a completion scenario CompSc, the user U may in a possible im plementation acknowledge the selection to trigger an automat ic ordering process of the associated components. For exam ple, after having selected completion scenario CompSc2 in cluding items I ₂₁ , I ₂₂ , I ₂₃ , I ₂₄ , the system can ask the user U whether he wants to finalize the engineering project and to trigger an automatic ordering of the physical components (hardware and/or software components) associated with the se lected items I ₂₁ , I ₂₂ , I ₂₃ , I ₂₄ of the selected completion sce nario 2. Accordingly, the selecting of a next item or the se lection of a whole sequence of complementary items by the us er U via the user interface 3 can trigger in a possible im plementation automatically an ordering command to order the associated components for completion of the engineering pro- ject. The recommendation system according to the present invention can employ the temporal dependencies between selected items associated with the components of the engineering project. After the user U has completed the set of items to finalize the project, the selected completion scenario can be used to update a content of the database 5 comprising the plurality of historical completed item sequences. Consequently, the performance of the recommendation system can improve over time with the increasing number of completed projects. The recommendation system can be used by one or more users U.

The recommendation engine 1 and method according to the pre sent invention can be used for a wide range of different ap plication and use cases and are not restricted to the embodi- ments illustrated in Fig. 1 and 2. The recommendation engine 1 and computer-implemented method according to the present invention can be used for any complex system which require configuration of its functional components. The computer- implemented method can be implemented in software tools such as TIA Selection Tool, TIA Portal or NX Designer. The inven tion provides a sequential recommendation system for comple tion of any kind of complex engineering projects.