Compliance information for independently procurable products

Abstract

Users often want to know which digital rules apply to a particular product before engaging in an instance of a relationship with a host entity with respect to the product.SOLUTION: Systems and methods electronically provide information about digital rules associated with potential relationship instances. With the systems and methods described herein, a computing facility can identify an item and receive resource information associated with the item and digital rules applicable to the item.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] Cross-references to related patent applications This patent application claims priority to U.S. Provisional Patent Application No. 63 / 055,830, filed July 23, 2020. In the event of any conflict between this application and any document incorporated by reference, this application controls.

[0002] The technical field relates to computer networks, and in particular to networked automated systems for generating resources based on digital rules. Summary of the Invention

[0003] This specification provides examples of a computer system, a storage medium that can store a program, and a method. System embodiments can use a user device to identify products and generate sensory product data that is used to search for and display resource information about possible relationship instances (or "potential relationship instances") based on the identified products. Displaying resource information about possible relationship instances on the user device allows a user to observe the resource information without having to engage with the possible relationship instances.

[0004] Additionally, users can identify information about a product's compliance with specific digital rules, such as which digital rules apply to the product and what impact those rules have. In various embodiments, the system may compare the digital rules, their effects, etc., to a list of resource usage priorities defined by the user. This allows the system to suggest other products that may better match the resource usage priorities, as well as inform the user about the product and the impact of the digital rules. Additionally, the system allows users to obtain data about the effects of digital rules without the relationship instance actually occurring, thereby reducing the number of relationship instances that are offset by the effects of digital rules that were unknown before the relationship instance began. By reducing the rate of offsetting relationship instances, the system can reduce the amount of resources used to process and offset ongoing relationship instances.

[0005] Thus, the systems and methods described herein for generating product sensory data to identify products and retrieve and display resource information regarding possible relationship instances involving the products improve the capabilities of computers or other hardware, such as by reducing the processing, storage, and / or data transmission resources required to perform various tasks, thereby allowing tasks to be performed by less powerful, less capacity, and / or less expensive hardware devices, performing tasks with less latency, and / or retaining more of the saved resources for use in performing other tasks or additional instances of the same task.

[0006] As indicated above and in more detail throughout this disclosure, the present disclosure provides technical improvements to computer networks in existing computerized systems to provide resources related to proposed relationship instances.

[0007] These and other features and advantages of the claimed invention will become more readily apparent upon examination of the embodiments described and illustrated in this specification, the written specification, and the associated drawings. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure, including an improvement in an automated computer system that obtains product sensory data from products and retrieves resource information based on products identified by using the product sensory data. [Figure 2] 1 is a flowchart illustrating a sample method for generating item identification data and displaying resource information associated with items identified by the item identification data, according to an embodiment of the present disclosure. [Figure 3] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure, including a smartphone that may be used by a user, in accordance with an embodiment of the present disclosure. [Figure 4] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure including an RFID component within a computing device that may be used by a user, in accordance with an embodiment of the present disclosure. [Figure 5] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure including a camera component within a computing device that may be used by a user, in accordance with an embodiment of the present disclosure. [Figure 6] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure including a sensor component within a computing device that may be used by a user, in accordance with an embodiment of the present disclosure. [Figure 7] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure, including a display generated by a computing device that may be presented to a user, in accordance with an embodiment of the present disclosure. [Figure 8] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure, including a display generated by a computing device that may be presented to a user, in accordance with an embodiment of the present disclosure. [Figure 9] 1 illustrates a sample aspect of an embodiment of the present disclosure, including eyeglasses that may be used by a user, in accordance with an embodiment of the present disclosure. [Figure 10] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure including processing of merchandise sensor data, in accordance with an embodiment of the present disclosure. [Figure 11] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure including a computer system at a host facility, in accordance with an embodiment of the present disclosure. [Figure 12] FIG. 10 illustrates a sample aspect of an embodiment of the present disclosure including processing response data, in accordance with an embodiment of the present disclosure. [Figure 13] FIG. 10 illustrates a sample aspect of an embodiment of the present disclosure including displaying response data, according to an embodiment of the present disclosure. [Figure 14] 1A to 1C are diagrams of sample aspects for explaining operation examples and use cases of the embodiment. [Figure 15] 1 is a flowchart illustrating a sample method for obtaining location data, generating item identification data, and displaying resource information associated with the location data and items identified by the item identification data, according to an embodiment of the present disclosure. [Figure 16] 1 is a flowchart illustrating a sample method for obtaining resource usage priorities, obtaining resource information, and comparing the resource information with resource usage priorities according to an embodiment of the present disclosure. [Figure 17] 1 is a flowchart illustrating a sample method for obtaining location data, generating item identification data, and displaying resource information associated with the location data and items identified by the item identification data, according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] As mentioned, the present specification relates to a computer system, a storage medium that can store a program, and a method. Now, embodiments will be described in more detail.

[0010] The following description includes systems, methods, techniques, instruction sequences, and computer program products that embody exemplary embodiments of the present disclosure. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be apparent, however, that embodiments of the inventive subject matter can be practiced without these specific details. In general, well-known structures and methods associated with the underlying technology are not shown or described in detail to avoid unnecessarily obscuring the description of the preferred embodiments.

[0011] FIG. 1 illustrates a sample aspect of an embodiment of the present disclosure, which includes an improvement in an automated computer system that obtains product sensory data 112 from products and retrieves resource information 145 based on products 196 identified using the product sensory data 112.

[0012] 1 illustrates a sample computer system 195 according to an embodiment. Computer system 195 has one or more processors 194 and memory 130. Memory 130 stores programs 131 and data 138. Thus, the one or more processors 194 and memory 130 of computer system 195 implement service engine 183. Additional implementation details of computer system 195 are described later in this document.

[0013] Computer system 195 may be located in the "cloud." Indeed, computer system 195 may optionally be implemented as part of online software platform (OSP) 140. OSP 140 may be configured to perform one or more predefined services, for example, through operation of service engine 183. Such services may include lookup, determination, calculation, validation, notification, transmission of specialized information including data to enable payments, document generation and transmission, online access to other systems to enable enrollment, and the like, as described herein. Such services may be provided as software as a service (SaaS).

[0014] A user 192 may use the system 100, which includes a computing facility 120 interfacing with a user interface (UI) and a display 150 on which display data 151 may be displayed. The display 150 may be part of a broader user interface (UI) 188. The UI 188 may have actuators, such as buttons, through which the system 100 may receive input from the user 192. These inputs may include user settings 189, which may be stored in memory 123. The user settings 189 may be used by the user 192 to quickly select and search for or view preferred products or information.

[0015] System 100 may further include sensors 110. In an embodiment, system 100 is contained within housing 161. Details of additional sample implementations of computing facility 120 are provided later in this document. In some embodiments, user 192 is located at the physical site of the host entity or even is an agent of the host entity, although this is not required. In an embodiment, computing facility 120 or other device of user 192 is a client device of computer system 195. Computing facility 120 includes one or more processors 122 and memory 123. The one or more processors 122 and memory 123 may be used by computing facility 120 to store and execute programs and data to implement the functions of computing facility 120 or system 100.

[0016] Computing facility 120 may access computer system 195 via a communications network 191, such as the Internet. In an embodiment, computing facility 120 may utilize communications module 127 to communicate via communications network 191. In particular, the entities and associated systems of FIG. 1 may communicate via the physical and logical channels of communications network 191. For example, information may be communicated as data using the Internet Protocol (IP) suite over a packet-switched network, such as the Internet or other packet-switched network, which may be included as part of communications network 191. Communications network 191 may include many different types of computer networks and communications media, including those utilized by a variety of different physical and logical communications channels now known or later developed. Non-limiting examples of media and communications channels include one or more, or any operable combination, of fiber optic systems, satellite systems, cable systems, microwave systems, asynchronous transfer mode (ATM) systems, frame relay systems, digital subscriber line (DSL) systems, radio frequency (RF) systems, telephone systems, cellular systems, other wireless systems, and the Internet. In various embodiments, communication network 191 can be or include any type of network, such as a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), or the Internet.

[0017] Downloads or uploads may be permitted from one of these two computer systems to the other, etc. Such access may be performed, for example, by manually uploading a file, such as a spreadsheet file. Such access may also be performed automatically, as shown in the example of Figure 1. Computing facility 120 and computer system 195 may exchange requests and responses with each other, which may be implemented in many architectures.

[0018] In one such architecture, a device remote from the service engine 183, such as the computing facility 120, may have a specific application (not shown) and a connector (not shown) that plugs in on top of the specific application. The connector may obtain details needed for the requested service from the OSP 140 from the remote device, form an object or initial data 133, and then send or push a request 132 carrying the initial data 133 to the service engine 183 via a service call. The service engine 183 may receive the request 132 including the initial data 133. The service engine 183 then determines resource information 145, applies digital rules to the initial data 133 to form response data 135 including the resource information 145, and then pushes, sends, or causes the connector to send a response 141 carrying the response data 135. The connector reads the response 141 and forwards the response data 135 to the specific application.

[0019] In an alternative such architecture, a device remote from the service engine 183, such as the computing facility 120, may have a specific application (not shown). Additionally, the computer system 195 implements a Representational State Transfer (REST) ​​Application Programming Interface (API) (not shown). The design of a REST or RESTful API is designed to leverage existing protocols. REST can be used with almost any protocol, but typically leverages the HyperText Transfer Protocol (HTTP) when used for Web APIs. This alternative architecture allows the system 100 to consume the REST API directly from a specific application without using a connector. The specific application on the remote device can internally obtain the details needed for the desired service from the OSP 140 from the remote device and therefore send or push a request 132 to the REST API. The REST API then communicates with the service engine 183 in the background. Again, the service engine 183 generates response data 135 and sends its aspects back to the REST API. The REST API then sends a response 141 with response data 135 to the particular application.

[0020] In some cases, the user 192 or the system 100 may have a possible relationship instance 198 that includes an item 196. Only one such item 196 is shown. In this example, the system 100 has a possible relationship instance 198 that includes the item 196.

[0021] In some cases, item 196 is within line of sight 175 of system 100 or within sensor 110 used by system 100. Sensor 110 may sense item 196, as indicated by arrow 176. Sensor 110 may then generate sensory data 112 that is provided to computing facility 120. In various embodiments, sensor 110 includes one or more of an RFID reader, a camera, an optical sensor, a machine-readable code sensor, or the like. In an embodiment, housing 161 includes a trigger, switch, button, or the like that may be used to activate sensor 110.

[0022] The sensory data 112 may be used by the computing facility 120 to generate item identification data 125, such as by generating an image of the item 196, to interpret a machine-readable code of the item 196, such as a Universal Product Code (UPC), an Amazon Standard Identification Number (ASIN), or an International Harmonized Item Number, also known as the European Union Item Number or EAN, which is a standard describing a barcode symbology and numbering system used in international commerce, to identify a particular retail product type, and further to generate the item identification data 125. In embodiments, the sensory data 112 may be used to obtain the base value of the item 196. In embodiments, the item identification data 125 may be used to obtain the base value of the item 196. The base value of the item 196 may be obtained by looking up the base value of the item in a list of base values, which may be obtained from the computing facility 120, the OSP 140, a system at the host facility, etc.

[0023] In some cases, user 192 or system 100 may have data regarding one or more secondary entities, for example, through relationship instances therewith. System 100 and / or the secondary entities may simply be referred to as entities. An entity of these entities may have one or more attributes. Such attributes of such an entity may be any one of its name, entity type, physical or geographic location such as an address, contact information elements, affiliation, characteristics of another entity, characteristics by another entity, associations or relationships with another entity (general or specific instances), assets of the entity, filings by or on behalf of the entity, etc.

[0024] In some embodiments, one or more requests may be received by computer system 195 over a network. In this example, request 132 is received by computer system 195 over network 191. Request 132 was sent by remote computing facility 120. The received one or more requests may carry a payload. In this example, request 132 carries initial data 133. In such embodiments, one or more payloads may be parsed by computer system 195 to extract data such as item identification data 125. In this example, initial data 133 may be parsed by computer system 195 to extract item identification data 125. In this example, a single initial data 133 encodes the entire item identification data 125, although this is not required. In fact, a data set may be received from the payloads of multiple requests. In such cases, a single payload may encode only a portion of the data set. Of course, a single request payload may encode multiple data sets. Additional computers may be involved in network 191, some beyond the control of user 192 or OSP 140 and some within such control.

[0025] Item identification data 125 has values ​​that can be numeric, alphanumeric, Boolean, etc., depending on what the value characterizes. For example, an identification value ID may indicate the identity of an item 196 and distinguish the item 196 from other such items.

[0026] In an embodiment, the stored digital rules may be accessed by computer system 195. The rules are digital in that they are implemented for use by software. The stored digital rules are further described in relation to FIG. 14.

[0027] In embodiments, the computing facility 120 may include a learning function 128. The learning function 128 may be implemented in many ways, for example, by artificial intelligence (AI), machine learning (ML), etc. Thus, the learning function 128 may be used by the computing facility to generate item identification data 125 from the item sensing data 112.

[0028] Computing facility 120 may process resource information 145 to generate display data 151 that is displayed using display 150. Display data 151 may include aspects 158, which may include information related to product 196, an image of product 196, obtained resource information 145, etc. In an embodiment, aspect 158 ​​includes an image of product 196 with resource information 145 superimposed on the image. In an embodiment, display 150 is used to provide augmented reality in which aspect 158 ​​is superimposed on product 196. Such superimposition may be a preferred view, for example, according to user settings 189. The system may retrieve stored user settings 189 and superimpose accordingly.

[0029] FIG. 2 is a flowchart illustrating a sample method for generating item identification data and displaying resource information associated with items identified by the item identification data according to an embodiment of the present disclosure.

[0030] In this example, the operations and methods described with reference to the flowcharts illustrated in Figures 2 and 15-17 are described as being performed by system 100, but in various embodiments, one or more of the operations and methods described with reference to the flowcharts shown in Figures 2 and 15-17 may be performed by OSP 140.

[0031] The method 200 begins at 205 .

[0032] At 210, the system 100 captures product sensory data 112 from sensors 110 for products 196 associated with a possible relationship instance 198 between a user 192 and a host entity.

[0033] At 215 , the system 100 generates item identification data 125 for the item 196 based on the item sensory data 112 .

[0034] At 220, the system 100 derives initial data 133 from the item identification data 125 and sends a request 132 including the initial data to the OSP 140.

[0035] At 225 , the system 100 receives a response 141 from the OSP 140 that includes resource information 145 related to the product 196 and possible relationship instances 198 .

[0036] At 230 , the system 100 displays data derived from the resource information 145 by using the display 150 .

[0037] The method ends at 235.

[0038] The system 100 described with reference to FIG. 1 may be used to sense items that may be part of a potential relationship instance and generate item sensing data from the items. The system 100 may generate item identification data that identifies the items from the item sensing data. The system 100 may derive initial data at least in part from the item identification data and transmit the item initial data over a network in a request. The system 100 may receive a response over the network in response to the request based on the initial data, including resource information related to the potential relationship instance. In response to receiving the resource information, the system 100 may derive display data from the resource information and display the display data to show aspects of the potential relationship instance.

[0039] In an embodiment, system 100 is a personal computing device configured to be carried by a person. System 100 may be a smartphone, a tablet, a personal computing device, or the like.

[0040] In an embodiment, the system 100 includes a housing 161. The display 151 may be attached to the housing 161. The sensor 110 may be attached to the housing 161.

[0041] In an embodiment, the system 100 includes a trigger that a user 192 may manually activate. Activation of the trigger may cause the sensor to generate merchandise sensing data 112.

[0042] In embodiments, sensor 110 is part of an RFID reader. Sensory data 112 may be RFID data. In some embodiments, sensor 110 is part of a machine-readable optical code scanner. Sensory data 112 may be machine-readable optical code data. In some embodiments, sensor 110 is part of a camera. Sensory data 112 may be image data.

[0043] In embodiments, display data 151 includes an image of at least a portion of item 196 created from image data. In some embodiments, the image data does not include a machine-readable optical code. Item identification data 125 may be generated, at least in part, by using a learning function, such as learning function 128 described above, to identify item 196 based on the image data.

[0044] In embodiments, the aspect 158 ​​may be superimposed on an image of at least a portion of the product 196. The superimposition may be performed via augmented reality.

[0045] In an embodiment, system 100 includes glasses configured to be worn by user 192. Display 150 may be a heads-up display displayed on the glasses.

[0046] In an embodiment, the merchandise identification data is generated by identifying a product code within the generated merchandise sensory data 112 .

[0047] In an embodiment, the initial data 133 includes item identification data 125 .

[0048] In an embodiment, the sensor 110 is adapted to generate merchandise sensory data 112 .

[0049] In an embodiment, the system 100 obtains information about the entity that offers the product. The initial data 133 may include information about the entity that offers the product.

[0050] In an embodiment, the system 100 obtains location data. The location data may be associated with one or more of the system 100, the product 196, and the entity having the product. The location data may identify a current geographic location that may be associated with one or more of the system 100, the product 196, and the entity having the product. The system may include the location data in the initial data 133. The resource information may be generated based on digital rules obtained from a digital rules database queried using the product query data and the location data. The product query data is generated by the product query database based on the product identification data.

[0051] In an embodiment, resource information is generated by applying digital rules to possible relationship instances 198 based on product identification data 125 and location data. The location data may indicate the current location of the system. The resource information may include a percentage ratio.

[0052] In an embodiment, resource information 145 includes data describing resource quantities sent to a domain as a result of a possible relationship instance 198, the resource quantities being associated with potential relationship instances that include goods. In an embodiment, resource information 145 includes data describing how at least a portion of the resource quantities are used by the domain.

[0053] In an embodiment, system 100 receives input identifying one or more resource usage priorities. System 100 may determine whether the resource amount is to be used for at least one of the resource usage priorities based on data describing how at least a portion of the resource amount is used by the domain. System 100 may display on display 150 that the resource amount is to be used for at least one of the resource usage priorities based on a determination that at least a portion of the resource amount is to be used for at least one of the one or more resource usage priorities.

[0054] In embodiments, system 100 identifies a second product determined to be similar to product 196 based on product identification data 125. System 100 may display additional resource information associated with another potential relationship instance that includes the second product. The additional resource information may include another resource quantity associated with the other potential relationship instance. System 100 may determine whether the other resource quantity is less than, greater than, equal to, etc. the resource quantity of product 196. System 100 may use display 150 to indicate that the second resource quantity is less than, equal to, greater than, etc. the resource quantity of product 196.

[0055] In an embodiment, the item identification data 125 is generated by identifying a product code within the generated item sensory data 112. The system 100 may search for a base value from the product code. The request 132 may include the base value.

[0056] In an embodiment, system 100 stores a list of product codes and base values. System 100 may access another computing device, computing system, etc. to obtain at least a portion of the list of product codes and base values. The base value of an item 196 may be searched for from the list.

[0057] In an embodiment, the system 100 communicates the product code along a communications link to a system at the host facility, and the system 100 may receive the base value of the item 196 in response from the system at the host facility.

[0058] In embodiments, system 100 generates an image of at least a portion of a product 196 based on the product sensing data 112. Product identification data 125 may be generated based on identifying the product in the image. The product in the image may be identified using one or more of object recognition performed by image processing of the image, retrieving a product identification code present in the image, recognizing text in the image indicating the name or brand of the product, recognizing a trademark in the image, etc. The image may be an image of a screen displaying an image of at least a portion of the product.

[0059] In an embodiment, system 100 may obtain information describing a resource quantity exemption certificate associated with product 196. The information describing the resource exemption certificate may be obtained based on at least one of the systems of claim QAS8, the operations of which further include product identification data, resource information, information about the potential recipient of the product, and information about the entity providing the product, etc. System 100 may display the information describing the resource exemption certificate on display 150.

[0060] FIG. 3 illustrates a sample aspect of an embodiment of the present disclosure including a smartphone that may be used by a user, in accordance with an embodiment of the present disclosure.

[0061] 3 is an example of a system that may implement all or a portion of system 100. In operation, smartphone 300 may perform all or a portion of the operations performed by system 100. Smartphone 300 includes a display 350 and a housing 361. Display 350 may display an aspect 358 that includes resource information 145 related to product 196. Aspect 358 may be superimposed on another image, such as an image of product 196. In addition, smartphone 300 may use augmented reality to display aspect 358.

[0062] FIG. 4 is a diagram illustrating a sample aspect of an embodiment of the present disclosure including an RFID component within a computing device that may be used by a user, in accordance with an embodiment of the present disclosure.

[0063] The RFID reader 401 includes an antenna 411 and generates RFID data 412. The RFID reader 401 may be implemented as part of the sensor 110. The antenna 411 may sense an RFID tag 422 that indicates an item 496 as indicated by a sensing signal 476. The RFID tag 422 may be located within, on, near, or in a separate area from the item 496. The item 496 may be associated with a possible relationship instance 498 between the user 192 and an entity, such as a host entity. Data obtained as a result of sensing the RFID tag 422 is then used by the RFID reader 401 to generate the RFID data 412. The system 100 uses the RFID data 412 as item sensing data 112 to generate item identification data 125.

[0064] FIG. 5 illustrates a sample aspect of an embodiment of the present disclosure including a camera component within a computing device that may be used by a user, in accordance with an embodiment of the present disclosure.

[0065] The barcode scanner 501 includes a camera 511 and generates barcode data 512. The camera 511 may be any sensor used to read barcodes or other machine-readable codes. The barcode reader 501 may be implemented as part of the sensor 110. The camera 511 may read a barcode 522 representing an item 596, as indicated by arrow 576. The barcode 522 may be located within, on, near, or in a different area from the item 596. The item 596 may be associated with a possible relationship instance 598 between the user 192 and an entity, such as a host entity. Data obtained as a result of reading the barcode 522 by the camera 511 may be used by the barcode scanner 501 to generate the barcode data 512. The system 100 uses the barcode data 512 as item sensed data 112 to generate item identification data 125.

[0066] FIG. 6 illustrates a sample aspect of an embodiment of the present disclosure including a sensor component within a computing device that may be used by a user, in accordance with an embodiment of the present disclosure.

[0067] System 600 includes housing 661, sensor 610, and trigger 655. When trigger 655 is activated, system 600 activates sensor 610. Trigger 655 can be any type of trigger, button, lever, etc., used to obtain an indication that an action should be performed. In addition, trigger 655 can be electromechanical, mechanical, digital, etc. In embodiments, system 600 functions as trigger 655 because system 600 can obtain a voice command that system 600 can use as an indication to activate sensor 610. In embodiments, multiple actions are taken to activate trigger 655, such as activating multiple triggers or obtaining user input in conjunction with the activation of a trigger. When sensor 610 is activated, it can detect item 696, as indicated by arrow 676. Item 696 can be associated with a possible relationship instance 698 between user 192 and an entity, such as a host entity.

[0068] FIG. 7 illustrates a sample aspect of an embodiment of the present disclosure, including a display generated by a computing device that may be presented to a user, in accordance with an embodiment of the present disclosure.

[0069] Display 750 includes display data 751 and aspects 758. Display 750 retrieves information related to aspects 758 and display data 751 that is used to present aspects 758 and display data 751 to user 192. Aspects 758 may include information related to products 796, as indicated by double arrow 777. Products 796 may be related to possible relationship instances 798 between user 192 and entities, such as host entities.

[0070] FIG. 8 illustrates a sample aspect of an embodiment of the present disclosure, including a display generated by a computing device that may be presented to a user, in accordance with an embodiment of the present disclosure.

[0071] Display 850 includes display data 851 and aspect 858. Display 850 overlays aspect 858 on display data 851. For example, display data 851 may include an image, or a portion of an image, of an item, such as item 896. Aspect 858 may include information related to item 896, as indicated by double arrow 877. Item 896 may be related to a possible relationship instance 898 between user 192 and an entity, such as a host entity.

[0072] FIG. 9 illustrates a sample embodiment of an embodiment of the present disclosure including eyeglasses that may be used by a user, according to an embodiment of the present disclosure.

[0073] User 192 may wear or operate glasses 909, which may include a camera 910. Glasses 909 may implement one or more aspects of system 100. Additionally, glasses 909 may include a display, such as a head-up display, that may be used to display display data 151 and aspect 158. Furthermore, glasses 909 may superimpose aspect 158 ​​on display data 151.

[0074] Camera 910 may be used to generate display data 151 as well as detect product 996. Product 996 may be used to generate aspect 158, as indicated by double arrow 977. Furthermore, aspect 158 ​​may include information related to product 996. Product 996 may be related to a possible relationship instance 998 between user 192 and an entity, such as a host entity.

[0075] FIG. 10 illustrates a sample aspect of an embodiment of the present disclosure including processing merchandise sensing data, according to an embodiment of the present disclosure.

[0076] The product sensory data 1012 may include a product code 1077. The product code 1077 may then be used by the system 100 to generate the product identification data 1025. In an embodiment, the system 100 may communicate with other systems to generate the product identification data 1025 based on the product code 1077. For example, if the product code 1077 is a UPC code, the system 100 may use the product code to identify the product by searching for product data for the product registered to that UPC code. The system 100 may communicate with a system at a host facility to search for the product data based on the UPC code. In another example, if the product code 1077 is an ASIN code, the system 100 may communicate with a system hosting at least a portion of Amazon's product catalog to determine which products correspond to the product code 1077. The system 100 communicates with other systems over a network to obtain a base value for the product based on the product code 1077.

[0077] FIG. 11 illustrates a sample aspect of an embodiment of the present disclosure including a host facility computer system, according to an embodiment of the present disclosure.

[0078] Entity facility 1160 represents a facility of a host entity, through which user 1192 may enter into possible relationship instances 1198 related to product 1196. Entity facility 1160 may also include a host facility computer system 1193. Host facility computer system 1193 may include location data 1178 indicating the location of entity facility 1160. In an embodiment, host facility computer system 1193 includes base value data 1161. Base value data 1161 includes data related to the base values ​​of various products that may be part of possible relationship instances 1198.

[0079] A user 1192 may use the system 100 to obtain resource information related to a possible relationship instance 1198. As shown in FIG. 11 , the system 100 may be within line of sight 1175 of an item 1196. The system 100 may sense the item 1196, as seen by arrow 1176. The sensory data obtained by the system 100 may include a product code 1177, which may be attached to the item, near the item, represent the item, etc. In an embodiment, the system 100 may additionally communicate with a host facility computer system 1193 to obtain location data 1178, as shown by communication link 1133. The system 100 may additionally communicate with a host facility computer system 1193 to obtain base value data 1161 for the item 1196, as shown by communication link 1133. The system 100 may then use the sensed item data to generate item identification data. In an embodiment, the system 100 uses one or more of the product identification data, the location data 1178, and the base value data 1161 to obtain the resource information 145 associated with the possible relationship instance 1198.

[0080] FIG. 12 illustrates a sample aspect of an embodiment of the present disclosure including processing response data, according to an embodiment of the present disclosure.

[0081] Response data 1235 includes resource quantity 1236 and resource quantity usage data 1237. System 100 may utilize resource quantity 1236 and resource quantity usage data 1237 to generate resource information 1245. In embodiments, resource quantity usage data 1237 may be used to compare how a domain uses resource quantity 1236 with resource usage priorities that may be specified by a user. System 100 may display resource information 1245 to user 192.

[0082] FIG. 13 illustrates a sample aspect of an embodiment of the present disclosure including displaying response data, according to an embodiment of the present disclosure.

[0083] Display data 1351 includes resource information 1345 that can be used to identify one or more resource uses, such as resource use 1 1337A and resource use 2 1337B. System 100 may use display data 1351 to display resource information to user 192 by using display 150. Resource use 1337A and resource use 1337B may be used to indicate to the user whether the user's resource use priorities are being met by the possible relationship instances. Display data 1351 may include an image, or portion of an image, of a product, onto which resource information 1345 may be superimposed.

[0084] Figure 14 is a diagram of a sample embodiment illustrating an online software platform (OSP) 1440 that receives initial data and uses the initial data to generate resource information for products related to possible relationship instances. It will be appreciated that aspects of Figure 14 have similarities to aspects of Figure 1. Some of such aspects may be implemented as described for similar aspects of Figure 1.

[0085] Computing device 1420 and its illustrated components operate in a manner similar to computing facility 120 of Figure 1. AI 1428 may operate as described for learning function 128 of Figure 1. Initial data 1433 and response data 1435 are used in a manner similar to initial data 133 and response data 135 of Figure 1. OSP 1440 and its illustrated components operate in a manner similar to OSP 140.

[0086] OSP 1440 receives initial data 1433 and extracts product identification data from the initial data 1433. In an embodiment, OSP 1440 additionally extracts other data related to the product, such as base value data and location data, from the initial data 1433. OSP 1440 uses the extracted data to obtain product digital rules 1437 from a database of digital rules 1470, which are used to obtain resource information, such as the amount of resources sent to the domain and one or more resource usage priorities. OSP 1440 may package product digital rules 1437 into response data 1435 that is sent to computing device 1420. Computing device 1420 processes response data 1435 to obtain resource information 1445. Resource information 1445 is displayed using display 1450.

[0087] In an embodiment, stored digital rules 1470 may be accessed by computer system 1495. These rules 1470 are digital in that they are implemented for use by software. For example, these rules 1470 may be implemented in programs 1431 and data 1438. The data portion of these rules 1470 may alternatively be implemented in memory in another location accessible via network 191. These rules 1470 may be accessed in response to receiving a data set, such as product query data 1436.

[0088] Digital rules 1470 may include main rules that can be accessed by computer system 1495. In this example, three sample digital main rules are explicitly shown: M_RULE5 1475, M_RULE6 1476, and M_RULE7 1477. In this example, digital rules 1470 also include digital priority rules P_RULE2 1472 and P_RULE3 1473, and therefore can be further accessed by computer system 1495. Digital rules 1470 may include additional rules and rule types, as suggested by the vertical ...

[0089] In embodiments, a particular one of the digital main rules may be identified from among the accessed and stored rules by computer system 1495. In particular, the value of the product query data 1436 may be tested against the logical conditions of the digital main rules, as described later in this document, to obtain product digital rules 1437.

[0090] In an embodiment, at least some of the digital main rules include respective conditions and respective results associated with each condition, and for a particular digital main rule, if that particular condition P is met, then that particular result Q will occur or apply. Of course, one or more of the digital rules 1470 may have multiple conditions P, such as both must be met. Some of these digital rules 1470 may then be searched and grouped first according to one of the conditions, and then according to the other conditions.

[0091] Thus, in an embodiment, the identification that a particular condition of a particular one of the accessed digital main rules is satisfied by one or more values ​​of the values ​​of the dataset is performed by recognition by the computer system 1495.

[0092] Many examples are possible for how to recognize that a particular condition of a particular digital rule is satisfied by at least one value of a dataset. For example, the particular condition can define the boundary of a region that exists within a space. The region can be geometric and exist within a larger space. The region can be geographic, within the space of a city, state, country, continent, or globe. The boundary of the region can be defined numerically according to a coordinate system within the space. In a geographic example, the boundary can be defined in terms of a group of longitude and latitude coordinates. In such an embodiment, the particular condition may be satisfied depending on the characterized attribute of the dataset being within the space and within the boundary of the region rather than outside the boundary. For example, the attribute may be the location of an entity, and one or more values ​​in the product query data 1436 that characterize the location may be one or more numbers or addresses, or longitude and latitude. The condition may be satisfied depending on how one or more values ​​compare to the boundary. For example, the comparison may reveal that the location is within the region rather than outside the region. The comparison may be performed by rendering the characterized attribute in units equivalent to the units of the boundary. For example, a characterized attribute can be an address rendered into longitude and latitude coordinates.

[0093] The above embodiments are merely examples and are not limiting.

[0094] If multiple applicable digital main rules are found, further possibilities exist. For example, the computer system 1495 of FIG. 14 can further access at least one stored digital priority rule, such as P_RULE2 1472 or P_RULE3 1473. Thus, a digital main rule can also be identified from the digital priority rules in this manner. In particular, the digital priority rule can determine which of the digital main rules should be applied. Continuing with the previous example, if the value of the product query data 1436 characterizes a location and the location is within multiple overlapping regions according to multiple rules, the digital priority rule can determine whether all of them apply, or less than all of them apply. Equivalent embodiments are also possible that first apply the digital priority rule to limit the iterative search to test the applicability of fewer than all rules.

[0095] Item digital rules 1437 obtained as a result of applying digital rules 1470 to item query data 1436 may include information that is subsequently interpreted by computing device 1420 as resource information 1445. For example, item digital rules 1437 may include the amount of resources to be sent if a relationship instance involving the item occurs, one or more resource usage priorities, data related to the domain associated with the possible relationship instance, resource values ​​for the item, etc.

[0096] Computer system 1495 is similar to computer system 195 of Figure 1. Computer system 1495 includes one or more processors 1494, memory 1430, and service engine 1483. One or more processors 1494 are similar to one or more processors 194. Memory 1430 is similar to memory 130. Service engine 1483 is similar to service engine 183. Service engine 1483 may be used to access digital rules 1470, store digital rules 1470, apply digital rules 1470, etc.

[0097] FIG. 15 is a flowchart illustrating a sample method for obtaining location data, generating product identification data, and displaying resource information associated with the location data and products identified by the product identification data, according to an embodiment of the present disclosure.

[0098] The method 1500 begins at 1505 .

[0099] At 1510, the system 100 captures product sensory data 112 from the sensors 110 for products 196 associated with a possible relationship instance 198 between a user 192 and a host entity.

[0100] At 1515 , the system 100 generates item identification data 125 for the item 196 based on the item sensory data 112 .

[0101] At 1520, the system 100 obtains location data representing the current geographic location of one or more of the system 100, the product 196, the host entity offering the product 196, and the like.

[0102] At 1525, the system 100 derives initial data 133 from the item identification data 125 and the location data and sends a request 132 including the initial data to the OSP 140.

[0103] At 1530, the OSP 140 obtains digital rules 1470 associated with the possible relationship instances 198 based on the item identification data 125 and the location data. The OSP 140 may utilize the digital rules 1470 to generate a response 141.

[0104] At 1535, the system 100 receives from the OSP 140 a response 141 generated based on the digital rules, including resource information 145 related to the product 196 and possible relationship instances 198.

[0105] At 1540 , the system 100 displays the data derived from the resource information 145 by using the display 150 .

[0106] The method ends at 1545.

[0107] FIG. 16 is a flowchart illustrating a sample method for obtaining resource usage priorities, obtaining resource information, and comparing the resource information with resource usage priorities according to an embodiment of the present disclosure.

[0108] The method 1600 begins at 1605 .

[0109] At 1610, the system 100 obtains user input indicating one or more resource usage priorities.

[0110] At 1615, the system 100 captures product sensory data 112 from the sensors 110 for products 196 associated with a possible relationship instance 198 between the user 192 and the host entity.

[0111] At 1620 , the system 100 generates item identification data 125 for the item 196 based on the item sensory data 112 .

[0112] At 1625, the system 100 derives initial data 133 from the item identification data 125 and sends a request 132 containing the initial data to the OSP 140.

[0113] At 1630, the OSP 140 obtains digital rules 1470 associated with the possible relationship instances 198 based on the product identification data 125 and the resource usage priorities. The OSP 140 may utilize the digital rules 1470 to generate the response 141.

[0114] At 1635, the system 100 receives a response 141 generated based on the digital rules from the OSP 140, which includes resource information 145 related to the product 196 and the possible relationship instances 198. The response 141 may additionally include data related to resource usage priorities, such as data indicating how the domain utilizes the resource amounts obtained from the possible relationship instances.

[0115] At 1640 , the system 100 displays the data derived from the resource information 145 by using the display 150 .

[0116] The method ends at 1645.

[0117] FIG. 17 is a flowchart illustrating a sample method for obtaining location data, generating product identification data, and displaying resource information associated with the location data and products identified by the product identification data, according to an embodiment of the present disclosure.

[0118] The method 1700 begins at 1705 .

[0119] At 1710, the system 100 captures product sensory data 112 from the sensors 110 for products 196 associated with a possible relationship instance 198 between a user 192 and a host entity.

[0120] At 1715 , the system 100 generates item identification data 125 for the item 196 based on the item sensory data 112 .

[0121] At 1720, the system 100 derives initial data 133 from the item identification data 125 and sends a request 132 containing the initial data to the OSP 140.

[0122] At 1725 , the OSP 140 obtains digital rules 1470 associated with the possible relationship instances 198 based on the item identification data 125 .

[0123] At 1730, OSP 140 identifies another item similar to the item based on item identification data 125. The OSP obtains second item identification data for the other item.

[0124] At 1735 , the OSP 140 obtains second digital rules associated with the possible relationship instances 198 based on the second item identification data 125 .

[0125] At 1740, OSP 140 identifies a resource quantity associated with digital rule 1470 and a second resource quantity associated with a second digital rule.

[0126] At 1745, the system 100 receives from the OSP 140 a response 141 generated based on the digital rule and the second digital rule, the response 141 including resource information 145 related to the product 196 and possible relationship instances 198. The response 141 may also include second resource information related to another product. The response 141 may also include a resource quantity and a second resource quantity.

[0127] At 1750, system 100 displays data derived from resource information 145 by using display 150. System 100 may additionally display a determination of whether the resource amount is less than the second resource amount.

[0128] The method ends at 1755.

[0129] Example of operation - Use case The above-described embodiments have one or more uses. The following aspects may be implemented as described above for similar aspects. (Some, but not all, of these aspects are labeled with similar reference numbers.)

[0130] Operational and sample use cases are also possible in which an attribute of an entity in a dataset may be any one of the following: the name of the entity, the type of entity, a physical location such as an address, a contact information element, an affiliation, a characteristic of another entity, a characteristic by another entity, an association or relationship with another entity (general or specific instance), an asset of the entity, a claim by or on behalf of the entity, etc. In such cases, various resources, etc. may be generated.

[0131] Computer system 195 may be used to assist customers, such as host entities, with tax compliance. Further, in this example, computer system 195 is part of OSP 140 implemented as a software-as-a-service (SaaS) provider for online access by users 192. Alternatively, the functionality of computer system 195 may be provided locally to users.

[0132] The user 192 may be standalone. The user 192 may use a computing facility 120 that interfaces with the display 150 and the sensor 110. In an embodiment, the user 192 encounters the product 196 inside a host facility. The host facility may be a business, such as a seller, reseller, or buyer of the product. In such a case, the user 192 may be a customer, employee, contractor, or agent of the host entity. In a use case, the host entity is the seller and the user 192 is the customer, and together they are involved in a possible relationship instance 198, such as a possible sales transaction. The possible sales transaction may include an action, such as exchanging data to enter into a contract. This action may be performed directly with a person or over a network 191, for example. In such a case, the host entity may also be an online seller, but that is not required.

[0133] In many cases, a host entity uses software applications to manage business operations such as sales, resource management, production, inventory management, delivery, billing, etc. The host entity may also use accounting applications to manage purchase orders, sales invoices, refunds, payroll, accounts payable, accounts receivable, etc. Such software applications, etc. may be used locally by the host entity, such as on a host facility computer system 1193, or may be used from an online software platform (OSP) 140 that the host entity engages for this purpose. In such use cases, the OSP 140 can be a mobile payment system, a point of sale (POS) system, an accounting application, an enterprise resource planning (ERP) provider, an e-commerce provider, an electronic marketplace, a customer relationship management (CRM) system, etc.

[0134] Businesses owe tax to various tax authorities in different tax jurisdictions. The primary challenge is making the relevant determinations. Tax-related determinations, ultimately aimed at tax compliance, are difficult due to the complexity of the underlying statutes and tax regulations and guidance issued by tax authorities. There are many types of taxes, including sales tax, use tax, excise tax, value-added tax, and cross-border tax issues, including customs and duties. Some types of taxes are industry-specific. Each type of tax has its own unique rules. Additionally, statutes, tax regulations, and tax rates change frequently, and new tax regulations are continually being added. Compliance becomes even more complicated when tax authorities offer temporary tax holidays, during which certain taxes are exempted.

[0135] Tax jurisdictions are primarily defined geographically. Businesses have tax obligations to various tax authorities within each tax jurisdiction. These various tax authorities may be groups of countries, single countries, states, counties, municipalities, cities, or local districts such as local transportation districts. Thus, for example, if a business sells goods in a transaction that may be taxed by a tax authority, the business may have tax obligations to the tax authority. These obligations include requiring the business to a) register with the tax authority's tax agency, b) establish internal processes for collecting sales tax in accordance with the tax authority's sales tax regulations, c) maintain records of sales transactions and, in the event of an audit by the tax authority, the sales tax collected, d) periodically prepare a form ("tax return") containing an accurate determination of the amount owed to the tax authority as sales tax for the sales transactions, e) file the tax return with the tax authority by a deadline determined by the tax authority, and f) pay ("remit") that amount to the tax authority. In such cases, the frequency and deadlines for filing and payment are determined by the tax authority.

[0136] The challenge for businesses is that the software applications mentioned above generally fail to provide businesses with tax information that is accurate enough for tax compliance with all relevant tax authorities. Lack of accuracy can manifest itself as errors in the amounts determined as tax to various tax authorities, and such errors are clearly not a good thing. For example, businesses selling products or services face risk whether they overestimate or underestimate sales tax on sales transactions. On the other hand, if a seller overestimates sales tax, the seller collects more sales tax from the buyer than they should. Of course, the seller does not retain this excess sales tax, but if they cannot refund it to the buyer, they must instead pay it to the tax authorities. If a buyer later discovers that they paid more sales tax than necessary, the seller at least risks reputational damage. While buyers may have the option of sending an explanation and receipt to the state to request a refund for the overpaid tax, this is often cumbersome and often not done. On the other hand, if a seller underestimates the amount of sales tax due, the seller will collect less sales tax from buyers and therefore pay less sales tax to the authorities than they actually owe. This is an underpayment of sales tax that is likely to be discovered later if tax authorities audit the seller. Since ignorance of the law is no excuse, the seller will then be required to pay the difference plus penalties and / or late fees. Furthermore, sales tax is considered a trust fund tax, which means that company management can be personally liable for unpaid sales tax.

[0137] Properly determining sales and use tax is even more difficult, especially in the case of sales. Many factors contribute to the complexity.

[0138] First, some state and local tax authorities have origin-based tax rules, while others have destination-based tax rules. Thus, sales tax may be charged from the seller's location or the buyer's location.

[0139] Second, various taxing authorities assess sales taxes at different, or uneven, percentages of the sales price on purchases and sales of goods involving various taxing jurisdictions. These taxing jurisdictions include various states, counties, cities, municipalities, special taxing jurisdictions, etc. In fact, there are over 10,000 different taxing jurisdictions in the United States, many of which partially overlap.

[0140] Third, in some cases, the type of goods sold may not be subject to sales tax at all. For example, in 2018, the sale of cowboy boots was exempt from sales tax in Texas but not in New York. This heterogeneity results in a multitude of separate tax rules relating to different products and services across various taxing jurisdictions.

[0141] Fourth, in some cases, sales tax may not be paid at all, depending on the identity of the individual purchaser. For example, certain entities are exempt from paying sales tax on their purchases as long as they properly execute and sign an exemption certificate and deliver it to the seller with each purchase. Entities eligible for such exemptions may include wholesalers, resellers, non-profit charitable organizations, educational institutions, and so on. Of course, who is exempt is not exactly the same in each taxing jurisdiction. And even if an entity qualifies for an exemption, different taxing jurisdictions may have different requirements for issuing and / or maintaining the validity of an exemption certificate.

[0142] Fifth, determining to which tax authority a seller owes sales tax can be difficult. A seller can start with the tax jurisdiction where it has a physical presence, such as its headquarters, distribution center or warehouse, or remote employees. Such a nexus with a tax jurisdiction establishes what is known as a physical nexus. However, even tax authorities, such as states and cities, may set their own nexus rules for when a company is considered "engaged in business" and therefore subject to registration and sales tax collection. These nexus rules can include various types of nexus, such as affiliation nexus, click-through nexus, cookie nexus, and threshold economic nexus. For example, an economic nexus may cause a remote seller to be liable to pay sales tax on sales made in a jurisdiction that a) exceeds a set threshold amount and / or b) exceeds a set threshold number of sales transactions.

[0143] Finally, even when a seller may not meet the economic nexus threshold, many states have promulgated marketplace facilitator laws that use such thresholds. Under such laws, intermediaries characterized as marketplace facilitators under state law are obligated to collect and remit sales tax to the state on behalf of, and for, the seller. The situation becomes even more complicated when a seller sells both directly to, and through, a state.

[0144] To assist with such complex determinations, computer system 195 may be specialized for tax compliance. Computer system 195 may have one or more processors and memory, for example, as described for computer system 195 of FIG. 1. Thus, in this example, computer system 195 implements a tax engine to perform tax liability determinations. The tax engine may be as described for service engine 183.

[0145] Computer system 195 may also locally store entity data, i.e., data of user 192 and / or host entity, either of which may be a customer and / or a seller or buyer in a sales transaction. Entity data may include customer profile data and transaction data for which a tax liability determination is desired. In the online implementation of FIG. 1 , OSP 140 has a database for storing host entity data and / or user data. This data may be entered by user 192 or host entity and / or downloaded, uploaded, or retrieved by user 192 or host entity from computing facility 120 or host facility computer system 1193, etc. In other implementations, simpler memory configurations may be sufficient for storing entity and / or user data.

[0146] OSP 140 may access digital rules 1470, which may be digital tax rules, for use by service engine 183, which may be a tax engine. As part of managing the digital tax rules and the tax engine, the digital tax rules may be continually updated with input collected from the setup of different tax authorities. The updates may be performed by a human, a computer, or the like. As noted above, the number of different tax authorities in the setup may be very large.

[0147] For a particular determination of tax liability, computer system 195 may receive one or more data sets including initial data 133. In this example, computing facility 120 transmits request 132 including initial data 133, which is received by computer system 195, which analyzes the received initial data 133. In this example, initial data 133 encodes all of item identification data 125, although, as previously discussed and as with other communicated data, this is not required.

[0148] In this example, initial data 133 has been received because it is desired to determine tax liability, tax usage information, and other tax data resulting from a potential sales transaction. Thus, sample initial data 133 includes values ​​characterizing attributes of a potential sales transaction, as well as item identification data 125. Initial data 133 also includes data indicating the name of a host entity, user, etc., location data, such as address, business location, and prior nexus determinations with various taxing jurisdictions, item identification information, associated data for user 192, such as resource usage settings, location data, and exemption status, a base value for an item 196, such as a base price for the item, and a date 198 of a potential sales transaction. System 100 may use one or more of a variety of methods to obtain the base price or “sale price” of an item, such as reading the base price from a price tag on or nearby the item, by user input, by recognizing a code such as a UPC, ASIN, and the like, and by checking a list of items and base values, which may be located on system 100, on the computer system of the entity selling the item, on a third-party computer system, on OSP 140, etc. In such an embodiment, digital rules may be used to generate one or more resources from a base value, where the one or more resources may correspond to sales taxes, other taxes and assessments, indicate which jurisdictions are funded, etc.

[0149] UPC is a Universal Product Code printed on product packaging to help identify the product. The code consists of two parts: a machine-readable barcode, which is a series of unique black bars, and a unique 12-digit number below it. ASIN is an Amazon Standard Identification Number, a 10-letter and / or numeric unique identifier for products assigned by amazon.com. ASINs are used to identify products within Amazon's product catalog. For books, ASINs are the same as ISBN numbers (International Standard Book Numbers), which can be 10 or 13 digits long. ISBNs are calculated using a specific mathematical formula and utilize a check digit to validate the number.

[0150] Digital rules 1470 may be digital tax rules created to correspond to tax rules promulgated by a set of different tax authorities within the boundaries of their taxing jurisdictions. In such an embodiment, the five digital rules shown in FIG. 14 , namely, P_RULE2 1472, P_RULE3 1473, M_RULE5 1475, M_RULE6 1476, and M_RULE7 1477, may each be a digital tax rule. As in FIG. 14 , some of these digital tax rules may be digital main rules, such as product digital rule 1437, that determine product-specific tax information, while others may be digital priority rules that determine which digital main rule applies in the event of a conflict. Depending on the use case, the digital main rule may relate to determining sales or use tax as a percentage of the purchase price for a potential sales transaction. Digital priority rules could be digital tax rules that determine whether digital tax rules apply to jurisdictions based on origin or destination, temporary tax holidays, how to override the various taxable items of individual goods for exemption from paying sales tax based on who the purchaser is, and on nexus.

[0151] Similar to Figure 14, these digital tax rules can be implemented or constructed in a variety of ways. In some use cases, they can be constructed using conditions and results, as described previously in this document. Such conditions may relate to geographic boundaries, effective dates, etc., to determine when and where the digital tax rules apply. These conditions can be expressed as logical conditions including ranges, dates, other data, etc. Initial data values ​​can be iteratively tested against these logical conditions. In such cases, the results may indicate one or more tax obligations, such as indicating various types of taxes payable, rules, tax rates, exemption requirements, reporting requirements, remittance requirements, etc.

[0152] For example, a particular digital tax rule M_RULE6 may be identified and used. The identification may be performed depending on the value of the product query data 1436 considered for the digital tax rule. For example, it may be recognized that the conditions of the digital tax rule M_RULE6 are satisfied by one or more of the values ​​of the product query data 1436. For example, it may be further determined that at the time of the sale, the user 192 is located within the boundaries of a taxing jurisdiction, the host entity has ties to the taxing jurisdiction, and there is no tax holiday.

[0153] As such, computer system 1495 may generate product digital rules 1437 that match the potential sales transaction and include the product digital rules in a response, such as response data 1435 in FIG. 14. Product digital rules 1437 may be created by computer system 1495 applying specific digital taxation rule M_RULE6. In this example, the identified specific digital taxation rule M_RULE6 may specify that a sales tax is levied, the amount being determined by multiplying the sales price of the product by a specific rate, the form of the tax return that must be prepared and filed, the date that it must be filed, the portion of tax revenue spent by the domain for a particular use, etc.

[0154] The computer system 1495 may then cause response data 1435 to be transmitted. The response data 1435 may include the product digital rules 1437 or information describing the product digital rules 1437. In this example, the response data 1435 is transmitted by the computer system 1495 in response to the received initial data 1433. The response data 1435 may relate to aspects of the product digital rules 1437. In particular, the response data 1435 may inform about aspects of the product digital rules 1437, i.e., that it has been determined, where it can be found, what it is, at least some of its contents or statistics, how the tax amount obtained by the domain will be used, the tax amount obtained by the domain, etc.

[0155] The response data 1435 can be sent to the computing device 1420 from which the initial data 1433 was received. The computing device 1420 can convert the response data 1435 into resource information 1445, which is converted into display data 1451 and aspects 1458 and displayed using the display 1450. The aspects 1458 can be revealed on the display 1450, such as in a graphical user interface (GUI), using augmented reality, or the like. In this example, the single response data 1435 encodes the entire product digital rules 1437, although this is not required, similar to what was described with respect to encoding the product identification data 1425 in the initial data 1433. Of course, it would be advantageous to embed information about the product 196 and possible sales relationships in the response data 1435 along with the product digital rules 1437. This helps the recipient to correlate the response data 1435 with the initial data 1433 and therefore match the received resource information 1445 as an answer to the received product identification data 1425 .

[0156] For such use cases, additional information obtained by embodiments may include tax information for shopping, where a product identified by a user through the augmented reality system is identified as tax-exempt, exempt items are highlighted or suggested, products related to what the user is viewing are suggested if a tax holiday period offers a cheaper alternative, and other products for which a specific holding exemption certificate is applicable are suggested, highlighted, or even verified. If a displayed product is indicated as taxed, the system can display the percentage tax rate, tax amount, and total cost including tax in the augmented reality interface. The system can further indicate which jurisdictions receive the tax and which major categories, such as school districts, local governments, and infrastructure development, are funded by the tax. Thus, customers can select the specific funding categories that interest them most. This data can further be used to suggest alternative product choices that better fit the funding category. This provides customers with a way to make choices based on public funding priorities.

[0157] In the methods described above, each operation can be performed as a positive act or as an action that does or causes something that is described as occurring. Such an act or occurrence can be caused by the system or device as a whole or by one or more components thereof. It is recognized that the methods and operations can be performed in several ways, including using the systems, devices, and implementations described above. Additionally, the order of operations is not limited to that shown, and different orders may be possible according to different embodiments. Examples of such alternative orderings may include overlapping, interleaving, interrupting, reordering, incrementing, preparing, supplementing, simultaneous, inverting, or other variant orderings, unless the context dictates otherwise. Furthermore, in particular embodiments, new operations may be added, or individual operations may be modified or deleted. The added operations may, for example, be from those primarily mentioned in describing a different system, apparatus, device, or method.

[0158] Those skilled in the art will be able to practice the invention in light of this specification, which should be taken as a whole. Details are included to provide a thorough understanding. In other instances, well-known aspects have not been described in order to avoid unnecessarily obscuring this specification.

[0159] Some of the techniques or technologies described herein may be known, but even if so, it is not necessary to follow what is known to apply such techniques or technologies as described herein or for the purposes described herein.

[0160] While the specification includes one or more examples, this fact does not limit how the invention may be practiced. Indeed, examples, instances, versions, or embodiments of the invention may be practiced in accordance with what is described or in other ways, and in conjunction with other current or future technologies. Other such embodiments include combinations and subcombinations of features described herein, including, for example, embodiments that are equivalent to providing or applying features in a different order than the described embodiments, extracting individual features from one embodiment and inserting such features into another embodiment, deleting one or more features from an embodiment, or deleting features from one embodiment and adding features extracted from another embodiment, while providing features incorporated in such combinations and subcombinations.

[0161] Many embodiments are possible, each including various combinations of elements. One or more of the accompanying drawings that are part of this specification, taken together, present some embodiments with their elements in a very compact manner, allowing for a quick overview of these embodiments. This is true even if these elements are described extensively separately in the text, and are only optional in other embodiments.

[0162] In general, this disclosure reflects preferred embodiments of the present invention. However, the careful reader will note that some aspects of the disclosed embodiments go beyond the scope of the claims. To the extent that the disclosed embodiments do go beyond the scope of the claims, the disclosed embodiments should be considered supplemental background information and do not constitute a definition of the claimed invention.

[0163] As used herein, the phrases "constructed to," "adapted to," and / or "configured to" are intended to indicate one or more actual states of construction, adaptation, and / or configuration that are fundamentally linked to the physical characteristics of the element or feature preceding the phrase, and are not merely descriptive of an intended use. Such elements or features can be embodied in many ways beyond the examples shown herein, as will be apparent to those skilled in the art after reviewing this disclosure.

[0164] Parent Patent Application: Any and all parent, parent's parent, parent's parent's parent, etc. patent applications, whether or not mentioned herein or in the Application Data Sheet ("ADS") for this patent application, are incorporated by reference herein as originally disclosed, including any priority claims made in such applications, and the material incorporated by reference, to the extent such subject matter is not inconsistent herewith.

[0165] Reference Number: A single reference number may be used throughout this specification to indicate a single product, aspect, component, or process. Furthermore, in preparing this specification, an effort has been made to use similar but non-identical reference numbers to indicate other versions or embodiments of the same, or at least similar, or related product, aspect, component, or process. Such an effort is not necessary, but has been made unnecessarily to facilitate the reader's understanding. Even if made herein, such an effort may not have been made completely consistently for all versions or embodiments enabled by this specification. Thus, the specification controls the definition of the product, aspect, component, or process, not its reference number. Any similarity of reference numbers is intended to infer similarity within the present text, but may be used to avoid confusing aspects when the present text or other context indicates otherwise.

[0166] The claims herein define particular combinations and subcombinations of elements, features, and acts or operations that are believed to be novel and unobvious. The claims also include equivalent elements, features, acts, or operations to those explicitly recited. Additional claims for other such combinations and subcombinations may be presented in this or related documents. These claims are intended to encompass within their scope all changes and modifications that fall within the true spirit and scope of the subject matter described herein. The terms used in this specification, including the claims, are generally intended to be "open" terms. For example, the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," etc. When a specific number is assigned in the description of a claim, this number is a minimum value and not a maximum value unless otherwise stated. For example, if a claim recites "one" component or "one" product, it means that the claim can include one or more of that component or product.

[0167] In construing the claims herein, 35 U.S.C. §112(f) shall be invoked by the inventor only if the terms "means for" or "step for" are expressly used in a claim. Therefore, if these terms are not used in a claim, the claim is not intended to be construed by the inventor in accordance with 35 U.S.C. §112(f).

Claims

1. 1. A method in a system for use by a person, the method comprising: generating, by one or more processors, item identification data from the item sensing data generated by the sensors, the item identification data identifying the item; transmitting, by the one or more processors over a network, a request including the item identification data to a computer system, the request requesting conditions for determining the amount of tax on the item; receiving a response from the computer system, in response to sending the request by the one or more processors over the network, the response including the condition; and displaying, by the one or more processors, in response to receiving the conditions, display data on a display, the display data including the conditions and information describing an exemption certificate to be used to obtain an exemption from payment of tax on the goods; the one or more processors receive a response including the condition before the computer system executes a process for determining the amount of the tax; the computer system executes a process for determining the amount of the tax, resulting in the tax being paid to a taxing authority; method.

2. The method of claim 1 , wherein the one or more processors are part of a personal computing device configured to be carried by the person.

3. The method of claim 1 , wherein the sensor and the display are mounted in a housing.

4. The method of claim 1 , further comprising activating, by the one or more processors, a trigger configured to cause the sensor to generate the merchandise sensing data.

5. The method of claim 1 , wherein the system for use by a person is a smartphone.

6. the sensor is part of an RFID reader; The method of claim 1 , wherein the product sensing data is RFID data.

7. the sensor is part of a machine-readable optical code scanner; The method of claim 1 , wherein the merchandise sensing data is machine-readable optical code data.

8. the sensor is part of a camera; The method of claim 1 , wherein the merchandise sensory data is image data.

9. The method of claim 8 , wherein the display data further comprises an image of at least a portion of the product created from the image data.

10. Further comprising a learning function, wherein the learning function the image data does not include a machine-readable optical code; 10. The method of claim 9, wherein generating the item identification data includes using the learning function by the one or more processors to identify the item based on the image data.

11. The method of claim 9 , wherein the condition is superimposed on an image of at least a portion of the product.

12. The method of claim 11 , wherein the overlay is performed via augmented reality.

13. the system for use by the person includes eyeglasses configured to be worn by the person; The method of claim 1 , wherein the display is a head-up display based on the glasses.

14. The method of claim 1 , wherein the merchandise identification data is generated by identifying a product code within the generated merchandise sensory data.

15. The method of claim 1 , further comprising causing the one or more processors to cause the sensor to generate the merchandise sensing data.

16. obtaining, by the one or more processors, location data associated with one or more of a system for use by the person, the product, and a business having the product, wherein the location data identifies a current geographic location associated with one or more of a system for use by the person, the product, and a business having the product; the request includes the location data; The method of claim 1 , wherein the condition corresponds to the location data and the item identification data.

17. The method of claim 16 , wherein the conditions are generated based on the item identification data and the location data.

18. The method of claim 17 , wherein the location data indicates a current location of a system for use by the person.

19. The method of claim 1 , wherein the condition includes a tax rate in percentage terms.

20. the merchandise identification data is generated by identifying a product code within the generated merchandise sensing data; The method further includes looking up, by the one or more processors, a price of the item identified by the item identification data from the product code; The method of claim 1 , wherein the request includes a price for the item.

21. storing, by one or more non-transitory computer-readable storage media, the list of product codes and prices; The method of claim 20, wherein the price of the product is looked up from the list.

22. communicating, by the one or more processors, the product code along a communications link to a host facility system; 21. The method of claim 20, further comprising receiving, by the one or more processors, a price for the commodity over the communication link in response to the communication.

23. The method of claim 1 , wherein the conditions further include data describing how at least a portion of the tax is to be used by the taxing authority.

24. identifying, by the one or more processors, a second product determined to be similar to the product based on the product identification data; displaying, by the one or more processors, in response to identifying the second item, conditions that determine the amount of tax on the second item; determining, by the one or more processors, whether the tax amount for the second item is less than the tax amount for the item; 2. The method of claim 1, further comprising: based on a determination that the amount of tax on the second item is less than the amount of tax on the item, displaying on the display, by the one or more processors, that the amount of tax on the second item is less than the amount of tax on the item.

25. generating the product identification data generating, by the one or more processors, an image of at least a portion of the merchandise based on the merchandise sensing data; 10. The method of claim 1, wherein the product identification data is generated based on identifying the product in the image based on one or more of object recognition performed by image processing of the image, reading out product identification codes present in the image, recognizing text in the image that indicates the name or brand of the product, and recognizing trademarks in the image.

26. 26. The method of claim 25, wherein the image is an image of a screen displaying an image of at least a portion of the product.

27. One or more processors included in a system for use by a human, generating product identification data that identifies the product from the product detection data generated by the sensor; transmitting, via a network, a request including the item identification data to a computer system, the request requesting conditions for determining the amount of tax on the item; receiving, over the network, from the computer system in response to sending the request, a response including the condition; storing a plurality of computer-executable instructions for causing the computer to perform operations including, in response to receiving the conditions, displaying on a display display data including the conditions and information describing an exemption certificate to be used to obtain an exemption from payment of taxes on the product; the one or more processors receive a response including the condition before the computer system executes a process for determining the amount of the tax; the computer system executes a process for determining the amount of the tax, resulting in the tax being paid to a taxing authority; A computer-readable storage medium.

28. 30. The computer-readable storage medium of claim 27, wherein the one or more processors are part of a personal computing device configured to be carried by the person.

29. 28. The computer-readable storage medium of claim 27, wherein the sensor and the display are attached to a housing.

30. 30. The computer-readable storage medium of claim 27, wherein the action further comprises activating a trigger configured to cause the sensor to generate the merchandise sensing data.

31. 30. The computer-readable storage medium of claim 27, wherein the system for use by a person is a smartphone.

32. the sensor is part of an RFID reader; 30. The computer-readable storage medium of claim 27, wherein the product sensing data is RFID data.

33. the sensor is part of a machine-readable optical code scanner; 30. The computer-readable storage medium of claim 27, wherein the merchandise sensing data is machine-readable optical code data.

34. the sensor is part of a camera; 30. The computer-readable storage medium of claim 27, wherein the merchandise sensing data is image data.

35. 35. The computer-readable storage medium of claim 34, wherein the display data further comprises an image of at least a portion of the product created from the image data.

36. the image data does not include a machine-readable optical code; 36. The computer-readable storage medium of claim 35, wherein generating the item identification data includes using a learning function of a system for use by the person to identify the item based on the image data.

37. 36. The computer-readable storage medium of claim 35, wherein the condition is superimposed on an image of at least a portion of the product.

38. 38. The computer-readable storage medium of claim 37, wherein the overlay is performed via augmented reality.

39. the system for use by the person includes eyeglasses configured to be worn by the person; 28. The computer-readable storage medium of claim 27, wherein the display is a head-up display based on the glasses.

40. 30. The computer-readable storage medium of claim 27, wherein the merchandise identification data is generated by identifying a product code within the generated merchandise sensory data.

41. 30. The computer-readable storage medium of claim 27, wherein the actions further include causing the sensor to generate the merchandise sensing data.

42. The operations further include obtaining location data associated with one or more of a system for use by the person, the product, and a business having the product, the location data identifying a current geographic location associated with one or more of a system for use by the person, the product, and a business having the product; the request includes the location data; 28. The computer-readable storage medium of claim 27, wherein the condition corresponds to the location data and the item identification data.

43. 43. The computer-readable storage medium of claim 42, wherein the condition is generated based on the item identification data and the location data.

44. 44. The computer-readable storage medium of claim 43, wherein the location data indicates a current location of a system for use by the person.

45. 28. The computer-readable storage medium of claim 27, wherein the condition includes a tax rate in percentage.

46. the merchandise identification data is generated by identifying a product code within the generated merchandise sensing data; the operations further include looking up, from the product code, a price for the item identified by the item identification data; 30. The computer-readable storage medium of claim 27, wherein the request includes a price for the item.

47. one or more non-transitory computer-readable storage media storing a list of product codes and prices; 47. The computer-readable storage medium of claim 46, wherein the price of the product is looked up from the list.

48. The operation is communicating said product code to a host facility system along a communications link; 47. The computer-readable storage medium of claim 46, further comprising receiving, via the communication link, a price for the commodity in response to the communication.

49. 30. The computer-readable storage medium of claim 27, wherein the conditions further include data describing how at least a portion of the tax is to be used by the taxing authority.

50. The operation is identifying a second product determined to be similar to the product based on the product identification data; In response to identifying the second product, displaying conditions that determine the amount of tax on the second product; determining whether the tax amount of the second product is less than the tax amount of the first product; 28. The computer-readable storage medium of claim 27, further comprising: based on a determination that the amount of tax on the second product is less than the amount of tax on the product, displaying on the display that the amount of tax on the second product is less than the amount of tax on the product.

51. generating the product identification data generating an image of at least a portion of the merchandise based on the merchandise sensing data; 28. The computer-readable storage medium of claim 27, wherein the product identification data is generated based on identifying the product in the image based on one or more of object recognition performed by image processing of the image, reading out product identification codes present in the image, recognizing text in the image that indicates the name or brand of the product, and recognizing trademarks in the image.

52. 52. The computer-readable storage medium of claim 51, wherein the image is an image of a screen displaying an image of at least a portion of the product.

Images