Systems and methods for providing electronic components based on specification

Abstract

Systems and methods for providing electronic components based on specification. The method includes receiving, by a processor, a set of data related to the one or more electronics components from a database; receiving, by the processor, one or more inputs in relation to the one or more electronics components; generating, by the processor, a set of electronics components based on the set of data and the one or more inputs; generating, on an online platform, the set of electronic components based on the set of data and the one or more inputs; and outputting, to an output interface, the set of electronic components.

Description

SYSTEMS AND METHODS FOR PROVIDING ELECTRONIC COMPONENTS BASED ON SPECIFICATION TECHNICAL FIELD

[0001] Example embodiments relate to data management in the electronics industry, in particular to systems and methods for optimizing selection and purchase of electronic components. BACKGROUND

[0002] Common electronics products such as integrated circuits, resistors, capacitors, transistors, and diodes are used in a wide range of electronic devices, such as computers, smartphones, televisions, automobiles, and more. Given their importance, these products are offered by a large variety of manufacturers and businesses, often for varying prices and of varying qualities.

[0003] However, due to the abundance of supply, it can be difficult for an engineer or designer to find the best deal for a component. Further, some deals can be too good to be true, with overly cheap parts often being of low quality. As some manufacturers may price certain parts lower than others, designers or hobbyists often have to look to multiple different manufacturers to get the best deal on each individual part. This can lead to difficulties in sourcing the proper materials needed for a project while staying within the budget. Junior or rookie designers may also not know what parts are needed to build a certain project, or do not know where to find quality electronic components, even if they have a specification sheet or bill of materials for the project.

[0004] There is thus a need in the electronics market for an application which can allow technical developers, designers, engineers, hobbyists, and innovators to access prices, deals, availability, and order information from multiple manufacturers or businesses at once, without having to view each supplier separately. There is further a need in the electronics market for an application which allows less experienced users to easily and quickly figure out what components are needed for certain projects and find out the best way to get the required components. There is further a need in the electronics market for an application which allows 1users to conveniently locate all parts in multi-component projects, without having to manually search databases or manufacturer websites. SUMMARY

[0005] An example embodiment is a computer implemented method for determining one or more electronics components, the method comprising receiving, by a processor, a set of data related to the one or more electronics components from a database; receiving, by the processor, one or more inputs in relation to the one or more electronics components; generating, by the processor, a set of electronics components based on the set of data and the one or more inputs; generating, on an online platform, the set of electronic components based on the set of data and the one or more inputs; and outputting, to an output interface, the set of electronic components.

[0006] Another example embodiment is a system, comprising a database comprising a set of data related to one or more electronic components; an electronic device for receiving inputs and producing outputs; a server for receiving the set of data from the database, the server configured to: receive an input from the electronic device; generate a list of one or more electronic components based on the input and the set of data; and output the list of one or more electronic components to the electronic device. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Reference will now be made, by way of example, to the accompanying drawings which show example embodiments of the present application, and in which:

[0008] Figure 1 is a schematic diagram of a system for managing, viewing and organizing data in the electronics industry, according to an example embodiment;

[0009] Figure 2 is a flow chart illustrating a method for using artificial intelligence to propose electronic components to users based on specifications and material lists;

[0010] Figure 3 is a flow chart illustrating an exemplary process for registering and using a platform to view and purchase electronic components as a buyer; and 2

[0011] Figure 4 is a flow chart illustrating an exemplary process for registering and using a platform to list electronic components for sale as a business or supplier. DESCRIPTION OF EXAMPLE EMBODIMENTS

[0012] Figure 1 illustrates a system 10 for optimizing an electronics marketplace, according to an example embodiment. The system 10 may be for use by electronics manufacturers, suppliers, and vendors (referred to as suppliers) and buyers, such as hobbyists, enthusiasts, engineers, designers, developers, researchers, and innovators. The system 10 can include a server 100, a database 102, and one or more electronic devices 108.

[0013] The server 100 is configured to output specific electronics components based on attributes of the components inputted by suppliers. For example, as will be discussed in greater detail in Figure 2, the server 100 is configured to generate a list of potential electronics components for buyers, based on a specification sheet or bill of materials uploaded by the buyer, using an online platform (Figures 3 and 4). In some examples, the server 100 is configured to generate an ordered list of potential electronics components based on a ranking of match to the buyer’s preferences. The server 100 is also configured to receive input from users through the electronic devices 108, such as additional information regarding the specification and to identify, from the data stored in the database 102, whether the proposed electronics components satisfy the buyer’s requirements. In some examples, the server 100 is configured to output specific relevant information related to a plurality of electronic components which is customized to a buyer.

[0014] In some examples, the server 100 can be a cloud server. The system 10 illustrated in this example is a cloud platform, which allows both the data stored in the database 102 and the outputs of the server 100 to be accessed and stored in various locations globally.

[0015] The database 102 is configured to receive data related to various electronics components. The database 102 can receive the data from inputs from the supplier of the electronics components. The database 102 can also receive the data directly from a remote server 112, for example, from a manufacturer’s specification or website listing. The database 102 can 3communicate with a remote server 112, such as via communication link 116, to receive information related to a plurality of electronics components.

[0016] In some examples, the supplier or an associated party can upload a 3D model of an electronics component to the database 102. The model may include information such as dimensions, weight, materials, and price, among other things. The model may be accompanied by a specification sheet which may include even more specific information relating to each device, such as recommended usage, voltage, current, resistance, capacitance, or any other relevant electrical measurements. The server 100 may be able to extract the relevant details from the model, and display them in a different manner, such as plain text associated with the component. For example, for a sophisticated scale 3D model, the server 100 may be able to extract features relating to dimensions and materials. In an embodiment, the server 100 uses machine learning to determine the relevant information, even if it is not readily available.

[0017] In some examples, the information related to each electronic component is received through an API. In other examples, the information related to each electronic component is received from a third-party database. In other examples, the information relating to the electronic component is manually entered by the supplier or an associated party.

[0018] The information stored on the database 102 may further include supplier details such as street address, city, province or state, zip code or postal code, or country. In an embodiment, the server 100 may estimate the shipping costs and delivery time for an electronic component based on features of the component, such as size and weight, features of the order, such as number of components, and features of the supplier, such as location and available inventory. The information stored on the database 102 can also include details such as name and location of the manufacturer (if being sold by a third-party), and may include hyperlinks to online manufacturer specification pages, or online listings of the electronics component offered by the third-party.

[0019] In some examples, the database 102 can include any other information relevant to the electronic component. In some examples, the information may be received from various sources, such as inputs from users of the system 10, inputs generated automatically from the electronic devices 108 or information derived directly from the Internet. By consolidating all of 4the data from different sources into the database 102, the server 100 can conveniently access relevant information in relation to the users and the electronics components.

[0020] The electronic device 108 can include a microphone for receiving voice inputs from a user and a speaker for communicating the outputs of the server 100. The electronic device 108 may also include a screen or touchscreen for interaction with a user interface. The electronic device 108 may also include a keyboard for receiving text inputs from a user and a display screen for communicating the outputs of the server 100. The electronic device 108 can be a desktop, a laptop, or a mobile communication device, such as a smart phone or a tablet. The electronic device 108 can be a stationary loT device having a microphone and a speaker, such as a smart speaker. The electronic devices 108 can be connected to the server 100 via a communication link 114. With the electronic device 108, the system 10 is suitable for use by electronics purchasers. Other parties in the process such as a manufacturer, supplier, or business can interact within the system 10 using an electronic device.

[0021] The system 10 allows the supplier to compile all relevant data about the electronics components in one location and allows buyers to quickly and efficiently view information related to the components, among other tasks.

[0022] Figure 2 is a flow chart illustrating a method 200 for using artificial intelligence to propose electronic components to buyers based on specifications or bills of materials related to an electronics project.

[0023] In method 200, at step 202 the server receives a set of data related to a plurality of electronic components. The data may be obtained from database 102 and can include any of the information stored on the database 102. As mentioned previously, this data can include information about the electronic components, or information about the manufacturer or supplier of the electronic components.

[0024] At step 204 the server 100 receives an input from a user. The user can provide this input via the electronic device 108. In an embodiment, the user input may be a bill of materials (BOM) for a project they are working on. The BOM may include one or more electronics components that are necessary for the project. In an embodiment, the BOM may include non- 5electronic components, such as mechanical assemblies or individual parts. The BOM may specify the exact type of electronic component required, or may generally state what is needed (e.g., 3 resistors). Alternatively, the user may upload a schematic of a circuit. The schematic may or may not include a listing of the parts present in the schematic. The schematic may or may not include labels on electrical components, or values such as voltages, resistances, capacitances or any other relevant values. Alternatively, the user may upload a specification sheet detailing required performance metrics for a project. The performance metrics may include values such as size, weight, voltage, current, or any other values that the user deems important to the performance of the final product. Alternatively, the user may upload a 3D model or exploded view of the project including all of the components that are required for the project.

[0025] At this step, the user may also input preferences relating to sourcing and purchase of the electronic components. The preferences may be related to cost, manufacturer, manufacturer location, supplier location, minimum order quantity, shipping times, and availability. The user may further indicate a preference for a supplier that can complete multiple aspects of the transaction at once. For example, even if a supplier has a good price on one type of electronic component, the user may specify that they would prefer a supplier that can supply at least a certain percentage of all the components necessary.

[0026] At step 206 the server 100 determines the relevant electronics components based on the user input. For example, if the user provides a BOM, the server 100 may extract the relevant details from the BOM using optical character recognition to determine the specific component required and the quantity in which it is required. In another example, if the user provides a schematic of the circuit, the server 100 may extract the relevant details from the schematic using image recognition on common symbols such as resistors, voltage sources, capacitors, diodes, op-amps, and any other electrical components. If details are present in the schematic such as voltages, currents, resistances, and capacitances, the server 100 may extract the relevant numbers / labels using optical character recognition and pair it to the extracted component using a machine learning model. For example, if the values 25£2 and 5V are listed near a battery and a resistor in close proximity to each other, the server 100 may recognize that the 25 £2 is associated with the resistor, and the 5V is associated with the battery. 6

[0027] The machine learning model can include a neural network, such as a convolutional neural network. A neural network consists of neurons. A neuron is a computational unit that uses xs and an intercept of 1 as inputs. An output from the computational unit may be: s=l, 2,... n, n is a natural number greater than 1, Ws is a weight of xs, b is an offset (i.e. bias) of the neuron and f is an activation function (activation functions) of the neuron and used to introduce a nonlinear feature to the neural network, to convert an input of the neuron to an output. The output of the activation function may be used as an input to a neuron of a following convolutional layer in the neural network. The activation function may be a sigmoid function. The neural network is formed by joining a plurality of the foregoing single neurons. In other words, an output from one neuron may be an input to another neuron. An input of each neuron may be associated with a local receiving area of a previous layer, to extract a feature of the local receiving area. The local receiving area may be an area consisting of several neurons.

[0028] A deep neural network (DNN) is also referred to as a multi-layer neural network and may be understood as a neural network that includes a first layer (generally referred to as an input layer), a plurality of hidden layers, and a final layer (generally referred to as an output layer). A layer is considered to be a fully connected layer when there is a full connection between two adjacent layers of the neural network. To be specific, all neurons at an i*11 layer is connected to any neuron at an (i+l)*11 layer. In the DNN, more hidden layers enable the DNN to depict a complex situation in the real world. Training of the deep neural network is a weight matrix learning process. A final purpose of the training is to obtain a trained weight matrix (a weight matrix consisting of learned weights W of a plurality of layers) of all layers of the deep neural network.

[0029] In a further example, if the user uploads a specification sheet detailing the required performance metrics for a project, the server 100 may determine the performance metrics using optical character recognition, and then automatically determine the components that would be required for the project. In a simple example, if the user specifies that the required 7voltage is 100V, and the current should be no more than 5A, the server 100 may automatically determine that a resistor of 20£2 or more is suitable for the project. In a further example, if the user uploads a 3D model or exploded view of the project, the server 100 may use image recognition to determine what components are present in the model.

[0030] At step 208 the server 100 determines whether the information provided by the user is enough to determine the proper electronics components. For example, in a BOM, if the specific components, values, and quantity required are listed (e.g., 3 25£2 resistors), the server 100 can easily determine the component required using the methods provided above. However, if the user does not provide enough information to narrow the required component down, the server 100 may prompt the user for more information. The prompt may be in the form of a choice between several possible options that the server 100 has determined are equally probable. The prompt may alternatively be a prompt for the user to manually enter more information related to a certain component. The prompt may alternatively be an open-ended prompt for the user to provide any information necessary. The server 100 may display the prompt to the user on the display screen of electronic device 108. The server 100 may receive input from the user via the electronic device 108.

[0031] At step 210, once the server 100 has confirmed all of the electronics components required, the server 100 compares the available electronic components on the database 102 with the user preferences input in step 204. At this step, the server 100 may determine which manufacturers, suppliers, or components do not fit the user preferences, and are thus not shown. For example, if the user specifies a preference for shipping costs under $10, overseas suppliers with an estimated shipping cost of over $10 will be removed from being potential candidates.

[0032] At step 212, the server 100 outputs the electronic components that meet the users needs and preferences for review by the user. These components may be displayed using an interactive user interface displayed on electronic device 108, in the form of plain text on the display screen of electronic device 108, via the speakers of electronic device 108, or on an online website or platform accessible by electronic device 108. The electronics components may be provided in an unordered list, or they may be ranked by other characteristics, such as price, manufacturer, minimum order quantity, availability for customization, or available inventory. If 8the user specifies a part from a certain manufacturer or supplier in the BOM or schematic, but the part is unavailable, the server 100 may suggest alternatives or replacements that can fill the gap.

[0033] The user may be able to provide their own ranking for which characteristics are most important, which can change the ranking of the components. The server 100 may automatically make recommendations to combat redundancy, such as suggesting one component which can do the job of multiple other components combined. The server 100 may provide the user with a complete summary with all information for each component, or may only provide information that is relevant to the user in order to prevent the user from being overwhelmed with information. For example, if a user does not specify a preference for a certain location of supplier, the component summary may include information such as shipping times and cost, but not the actual location of the supplier. In some examples, certain characteristics from the component summaries may be pre-selected to be highlighted or more prominently displayed.

[0034] The method 200 may be an iterative process, in which the server 100 generates a second set of characteristics, similar to the first set of characteristics. The second set of characteristics may include some or all of the same information that was included in the first set of characteristics. Alternatively, the second set of characteristics can contain different information than the first set of characteristics. The second set of characteristics can be determined based on the information stored in the database 102, information previously provided to the user, and information obtained from the user based on the first set of electronic components outputted to the user. For example, a user may have been previously provided with the technical specifications of an electronic component as a result of the first search. The user may further indicate that they are interested in the materials present in the electronic component. The second set of characteristics may then not include the technical specifications, but instead include the materials used in the component. In some examples, the second set of characteristics can include any information that is relevant to the buyer and that may assist the buyer in making the decision to purchase the electronic components.

[0035] Having generated the second set of characteristics, the server 100 can then generate another list of components, unordered, or organized by various filters relating to data 9from the second set of characteristics. These listings may be displayed by the same methods mentioned previously.

[0036] By automatically generating a first list and following lists of electronic components based on user input and preferences, the server 100 provides a more efficient and cost-effective method of providing users with options for electronic components that meet their needs from a variety of suppliers. Without such a system, a buyer would have to manually search for and find components that match the user preferences, and would have a lower degree of accuracy as a human would likely not be able to compare components from various suppliers with every single one of the user preferences. Further, a human would not be able to efficiently extrapolate information that was not explicitly provided in a specification sheet or schematic. Further, the process of searching for each electronic component individually is extremely time consuming in comparison to being able to search for all components required all at once, simply by uploading a BOM, schematic, specification sheet, or model.

[0037] Figure 3 shows a flow chart illustrating an example workflow 300 of a user registering on a platform to view and purchase electronic components, using the elements of Figures 1 and 2.

[0038] At step 302, the user registers on a platform, providing some personal contact information. The contact information may include information relating to name, place of work, and location. The platform may incorporate features to verify the user’s identity, in an attempt to combat fraud or scams. Before the user inputs preferences or requirements for electronic components, they may be able to access resources on market trends and best practices through an internal knowledge center.

[0039] At step 304a, the user may have the option to search for specific parts or components, similar to a typical search. However, the platform will automatically compile results from several different suppliers, saving the user from having to search each website or database individually. In an embodiment, the search results can be filtered based on user preferences or requirements, as discussed previously. 10

[0040] At step 306a, the list of all available parts are shown to the user. In an embodiment, the suppliers are pre-screened and vetted by the platform, to ensure quality of goods and proper business practices. The available components may be displayed in the manners discussed previously. In an embodiment, alongside the rest of the details relating to the manufacturer, supplier, and component, the user can view ratings and reviews of each supplier from other orders. In an embodiment, the ratings and reviews can be specified into categories, such as quality of goods, shipping speed, customer support, or any other relevant categories in which a manufacturer or supplier may be ranked.

[0041] Alternatively, at step 304b, the user may search for a general technology, such as a drone, automobile, robot, or any other electronics project. In an embodiment, the platform may display various types of technologies or projects related to the search term. For example, if the user searches for ‘drone’, the platform may show the user different types of drones, such as multi rotor drones, fixed wing drones, single rotor drones, or any other type of drone listed on the platform. Once the user specifies the type, the platform may further show the user specific results related to their specification.

[0042] At step 306b, once the user selects a specific project, the platform provides the user with everything required for the project. This may include a bill of materials listing all parts necessary and prices for each part, as well as schematics, specification sheets, and 3D models if available. The information provided may be supplied to the platform by the manufacturer, or may be automatically developed by a machine learning model.

[0043] At step 308, the user can further receive manuals from the suppliers or manufacturers. The manuals may be related to building, operation, specifications, or best practices related to the technical project. Once the user has received the manuals, BOM, specifications sheets, schematics, 3D models, or any other documentation required for the project, they can view available parts from various vendors as discussed previously.

[0044] Alternatively, at step 304c, the user can upload a bill of materials, specification sheet, schematic, or 3D model to the platform through the methods discussed previously. Similarly, at step 306c, the user receives a list of all available parts from the vetted suppliers through the methods discussed previously. 11

[0045] At step 310, regardless of what path the user has taken, the user browses the listings of electronic components provided by the platform via electronic device 108, and has the option to easily add components to their cart. The platform provides the opportunity for the user to quickly and easily add parts from multiple suppliers to a centralized cart, where the user can review and compare parts without having to change between pages for manufacturers or suppliers. Additionally, the user can complete the process in one checkout, and receive a single order confirmation and receipt / invoice, making it easier to keep track of all parts purchased rather than having to track various transactions from various suppliers.

[0046] Finally, at step 312, the user may book a research and design (R&D) test facility through the platform, or may use an R&D facility associated with or owned by the platform. In an embodiment, the user may choose to have materials purchased from the platform shipped to the R&D facility to save them the trouble of transportation.

[0047] Figure 4 shows a flow chart illustrating an example workflow 400 of a supplier registering on the platform and listing electronic components or technologies available for sale.

[0048] At step 402, the manufacturer, supplier, or vendor registers on the platform and lists available electronic components for sale. Similar to the user, the platform may incorporate features to verify the supplier’s identity, in an attempt to combat fraud and scams. In an embodiment, the platform may require samples of parts to be analyzed to ensure quality of goods sold. The components available for sale may be kept separate from each other on the platform, or may be aggregated under the supplier’s profile.

[0049] At step 404, the supplier uploads a full list of electronic components available for sale, along with information related to the components, such as product codes, price, quantity available, minimum order quantity, weight, dimensions, technical specifications, and shipping costs. In an embodiment, the platform can connect through an API to an inventory management system of the supplier to automatically update availability in real time as the supplier receives and sells various electronic components.

[0050] At step 406, the supplier may additionally upload technology or products alongside specification sheets, schematics, or a BOM to create the product. The BOM or 12schematic may be linked to their active inventory, so that users viewing the BOM can quickly and easily find which parts are necessary. The supplier may further upload manuals, reference sheets, or any other information necessary for a buyer to properly use the technology.

[0051] At step 408, the supplier publishes their profile and active inventory, and accepts and fulfills orders through the platform. In an embodiment, the supplier can interact with users during the order fulfillment process to determine if any changes or customizations need to be made to the order before it is shipped.

[0052] The various embodiments presented above are merely examples and are in no way meant to limit the scope of this disclosure. Variations of the innovations described herein will be apparent to persons of ordinary skill in the art, such variations being within the intended scope of the present disclosure. In particular, features from one or more of the above-described embodiments may be selected to create alternative embodiments comprises of a sub-combination of features which may not be explicitly described above. In addition, features from one or more of the above-described embodiments may be selected and combined to create alternative embodiments comprised of a combination of features which may not be explicitly described above. Features suitable for such combinations and sub-combinations would be readily apparent to persons skilled in the art upon review of the present disclosure as a whole. The subject matter described herein intends to cover all suitable changes in technology.

[0053] Certain adaptations and modifications of the described embodiments can be made. Therefore, the above discussed embodiments are considered to be illustrative and not restrictive. 13

Claims

WHAT IS CLAIMED IS:

1. A computer implemented method for determining one or more electronics components, the method comprising: receiving, by a processor, a set of data related to the one or more electronics components from a database; receiving, by the processor, one or more inputs in relation to the one or more electronics components; generating, by the processor, a set of electronics components based on the set of data and the one or more inputs; generating, on an online platform, the set of electronic components based on the set of data and the one or more inputs; and outputting, to an output interface, the set of electronic components.

2. The method of claim 1, wherein the one or more inputs are a bill of materials.

3. The method of claim 2, wherein a set of data related to the one or more inputs is generated through a machine learning model, using optical character recognition to generate data from text fields.

4. The method of claim 1, wherein the one or more inputs are a circuit schematic.

5. The method of claim 4, wherein a set of data related to the one or more inputs is generated through a machine learning model, using optical character recognition to generate data from text fields and image processing to generate data from symbols in the circuit schematic or one or more sections of the circuit schematic without text.

6. The method of claim 1, wherein the one or more inputs are a specifications sheet.

7. The method of claim 6, wherein a set of data related to the one or more inputs is generated through a machine learning model, using optical character recognition to generate data from text fields.

148. The method of any one of claims 3, 5, or 7, wherein the set of data related to the one or more inputs is compared to the set of data related to the one or more electronics components to generate the set of electronics components.

9. The method of claim 8, wherein the set of data related to the one or more electronic components are voltage, current, resistance, capacitance, price, weight, dimensions, minimum order quantity, inventory availability, material, manufacturer, supplier, or opportunity for customization.

10. The method of claim 9, wherein the set of data related to the one or more electronic components is at least partially generated through a machine learning model, using optical character recognition to generate data from text fields of a manufacturer’s specifications sheet and using image recognition to generate data from one or more sections of the manufacturer’s specifications sheet without text.

11. The method of claim 9, wherein the set of data related to the one or more electronic components is at least partially generated through a machine learning model by gathering data from online websites or databases.

12. The method of any one of claim 10 or 11, wherein the set of data is linked to an inventory database of a supplier such that the set of data is automatically updated as soon as the inventory database of the supplier is updated.

13. The method of claim 1, wherein the set of electronic components is viewable on a display screen, touchscreen device, or interactable user interface.

14. The method of claim 1, wherein the one or more inputs are manually entered through a text input using an electronic device, wherein the electronic device is equipped with a touch screen or a keyboard, and wherein a set of data relating to the one or more inputs is generated by extrapolating the one or more manually entered inputs, wherein the set of data relating to the one or more inputs is based on the manually entered inputs.

15. The method of claim 1, further comprising: selecting, from the set of electronics components, a subset of electronics components; 15creating, by the processor, a purchase order for the subset of electronics components; and shipping the subset of electronics components to a research and design facility.

16. A non-transitory memory containing instructions and statements which, when executed by a processor, cause the processor to perform the method in any one of claims 1 to 15.

17. A system, comprising: a database comprising a set of data related to one or more electronic components; an electronic device for receiving inputs and producing outputs; a server for receiving the set of data from the database, the server configured to: receive an input from the electronic device; generate a list of one or more electronic components based on the input and the set of data; and output the list of one or more electronic components to the electronic device.

18. The system of claim 17, wherein the input is a bill of materials, circuit schematic, or specifications sheet.

19. The system of claim 18, wherein a set of data related to the input is generated through a machine learning model, using optical character recognition to generate data from text fields and image processing to generate data from one or more sections of the input without text.

20. The system of claim 19, wherein the electronic device is a device equipped with a touchscreen, display screen, or interactable user interface, wherein the output is displayed on the touchscreen, display screen, or interactable user interface. 16