Object localization and tracking based on RFID and millimeter wave scanning

Abstract

A method and system relate to identifying, locating, and tracking the location of assets and actors within a local environment. The system includes a millimeter wave (mmWave) antenna circuit coupled with a beam control antenna (BSA). The millimeter wave antenna circuit includes a millimeter wave processing unit (MPU) coupled with the RFID integrated circuit. The RFID reader may be configured to scan and locate RFID tags attached to assets within a particular area / beam of the local environment by communicating with the RFID integrated circuit. The MPU is configured to scan an actor in a particular area / beam, generate a point cloud to determine an actor location within the point cloud, and transmit point cloud location data to the RFID integrated circuit.

Description

Technical Field

[0001] Example embodiments of the present disclosure relate generally to radio frequency identification (RFID) systems, and more particularly to RFID positioning systems and methods. Background Art

[0002] Radio frequency identification (RFID) tags can be affixed to items whose presence is to be detected and / or monitored. The presence of an RFID tag, and therefore the presence of the item to which the tag is affixed, can be wirelessly checked and monitored by an RFID reader. RFID systems are useful in a wide variety of applications. For example, RFID systems can be used to help identify and track assets that need to be reliably monitored within a particular environment, such as a retail store.

[0003] RFID systems may include a large number of tags and readers spread over a potentially wide area, and it may be desirable to obtain the location of items with attached tags. For example, in a retail store, RFID tags may be affixed to merchandise throughout the store. This may be useful not only for monitoring the presence of merchandise, but also for monitoring the location of merchandise in the store to understand shopper behavior / footprints (e.g., a customer may put a shirt in a shopping cart after putting pants in the cart).

[0004] Existing methods and systems have been developed to scan and monitor activity within a store. Applicants have recognized the technical challenges and difficulties associated with such existing methods and systems. Summary of the invention

[0005] Various embodiments described herein relate to components, devices, and systems for tracking objects.

[0006] According to various embodiments of the present disclosure, an antenna unit is provided. In some embodiments, the antenna unit includes: a coupler coupled to a reader computing entity at an input port of the coupler; a radio frequency (RF) switch coupled to a first output port of the coupler; a radio frequency identification (RFID) integrated circuit (IC) coupled to a second output port of the coupler, wherein the RFID IC is configured to select between a local beam steering antenna and a switch antenna for RFID scanning by switching the RF switch based on a read instruction signal from the reader computing entity. In some embodiments, the antenna unit also includes: a millimeter wave processing unit (MPU) coupled to the RFID IC, wherein the MPU is configured to: (i) generate point cloud location data, and (ii) send point cloud location data to the RFID IC. In some embodiments, the antenna unit also includes a millimeter wave antenna element coupled to the MPU.

[0007] In some embodiments, the millimeter wave antenna element includes a four-element millimeter wave radar antenna. In some embodiments, the MPU is coupled to the RFID IC using a serial peripheral interface. In some embodiments, the local beam steering antenna is arranged above the position in the indoor environment. In some embodiments, the millimeter wave antenna element includes a transmitting antenna, a receiving antenna, a frequency synthesizer, and a mixer.

[0008] According to another embodiment, a method is provided. In some embodiments, the method includes: receiving RFID tag data and point cloud location data from one or more reader computing entities by a computing device; associating, by the computing device, a location of a given shopper with a shopping cart RFID tag associated with an unassociated shopping cart proximate to the given shopper based on the RFID tag data and the point cloud location data; determining, by the computing device, that a location change of the given shopper has occurred; determining, by the computing device, that the given shopper has selected one or more additional items; associating, by the computing device, the one or more additional items with the given shopper and the shopping cart RFID tag; determining, by the computing device, one or more previously selected items associated with the given shopper before the location change; and generating a shopping behavior data entry, the shopping behavior data entry including an indication that the given shopper selected one or more additional items after selecting one or more previously selected items and a location change occurred.

[0009] In some embodiments, the RFID tag data includes identification of one or more RFID tags detected in one of the one or more beam zones. In some embodiments, the point cloud location data includes identification of objects within coordinates associated with the one or more beam zones. In some embodiments, the RFID tag data is generated based on RF signals received from one or more RFID tags in response to RFID read signals sent from one or more reader computing entities. In some embodiments, the point cloud location data is generated by a millimeter wave processing unit (MPU).

[0010] In some embodiments, the method further includes: detecting a given shopper based on the point cloud location data; and identifying an unassociated shopping cart based on the RFID tag data. In some embodiments, determining that a given shopper is associated with a shopping cart further includes: determining the location of the shopping cart based on the RFID tag data, and confirming the interaction of the given shopper with the shopping cart based on the point cloud location data. In some embodiments, the location includes an identification of a beam zone and / or point cloud coordinates. In some embodiments, determining that a change in the location of a given shopper has occurred further includes: determining that the current location of the given shopper is different from the location associated with the given shopper and the shopping cart RFID tag. In some embodiments, the current location of the given shopper includes a beam zone and / or point cloud coordinates that are different from the beam zone and / or point cloud coordinates of the location associated with the given shopper and the shopping cart RFID tag. In some embodiments, the method further includes: updating the location of the given shopper based on the location change. In some embodiments, determining that a given shopper has selected one or more additional items further includes: detecting an RFID tag associated with one or more additional items; and determining that one or more additional items are close to the location of the given shopper. In some embodiments, determining the location of one or more additional items proximate to a given shopper also includes determining one or more of an angle, a received signal strength indication, or movement / visibility of one or more RFID tags associated with the one or more additional items based on the RFID tag data and the point cloud location data.

[0011] The above exemplary invention content and other exemplary objects and / or advantages of the present disclosure and the manner of achieving these objects and / or advantages are further explained in the following detailed description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The description of the exemplary embodiments may be read in conjunction with the accompanying drawings. It will be appreciated that, for simplicity and clarity of illustration, the elements illustrated in the figures are not necessarily drawn to scale unless otherwise described. For example, the dimensions of some of the elements may be exaggerated relative to other elements unless otherwise described. Embodiments incorporating the teachings of the present disclosure are shown and described with respect to the drawings presented herein, in which:

[0013] Figure 1 Provides an example overview of an architecture according to some embodiments of the present disclosure;

[0014] Figure 2 Provides an example data analysis computing entity according to some embodiments of the present disclosure;

[0015] Figure 3 An example millimeter wave radio frequency identification reader computing entity is provided according to some embodiments of the present disclosure;

[0016] Figure 4 providing example antenna units according to some embodiments of the present disclosure;

[0017] Figure 5 providing example millimeter wave components according to some embodiments of the present disclosure;

[0018] Figure 6 providing a top view of at least a portion of an example shopper tracking system according to various embodiments of the present disclosure;

[0019] Figure 7 providing a top view of a quadrant representing the scan range coverage of an antenna unit according to some embodiments of the present disclosure; and

[0020] Figure 8 A flow chart is provided of a method for tracking an object according to some embodiments discussed herein. DETAILED DESCRIPTION

[0021] Some embodiments of the present disclosure will be described more fully below with reference to the accompanying drawings, which illustrate some, but not all, embodiments of the present disclosure. In fact, these disclosures may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present disclosure satisfies applicable legal requirements. Throughout the content, similar reference numerals refer to similar elements.

[0022] As used herein, terms such as "front," "rear," "top," and the like are used for illustrative purposes in the examples provided below to describe the relative positions of certain components or portions of components. Additionally, as will be apparent to one of ordinary skill in the art from this disclosure, the terms "substantially" and "approximately" indicate that the referenced element or associated description is accurate within an applicable engineering tolerance.

[0023] As used herein, the term "comprising" means including but not limited to, and should be interpreted in the manner commonly used in the patent context. It should be understood that the use of broad terms such as "comprising," "including," and "having" provides support for narrower terms such as "consisting of," "consisting essentially of," and "consisting essentially of."

[0024] The phrases "in one embodiment," "according to one embodiment," and the like generally mean that the particular feature, structure, or characteristic following the phrase may be included in at least one embodiment of the present disclosure, and may be included in more than one embodiment of the present disclosure (importantly, such phrases are not necessarily referring to the same embodiment).

[0025] The word “example” or “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations.

[0026] If the specification states that a component or feature "may", "can", "could", "should", "will", "preferably", "likely", "usually", "optionally", "for example", "often", or "might" (or other such language) be included or have a property, the specific component or feature is not required to be included or have that property. Such components or features may optionally be included in some embodiments, or may be excluded.

[0027] Retailers are generally interested in the shopping activities, behaviors and / or habits of shoppers in retail stores. Understanding shopper behavior is an area of ​​particular interest to retailers because shopper behavior is critical to optimizing marketing strategies, including store layout planning. Consumer activities related to shopping can be used to correlate product sales with specific shopping behaviors and / or improve the placement of products, advertisements and / or other product-related information in retail stores. Therefore, obtaining information related to the shopping activities, behaviors and / or habits of shoppers in retail stores enables retailers to plan their planograms and plan their inventory supply for promotional days. Real-time positioning systems using technologies such as Bluetooth, ultra-wideband or WiFi can be used to identify and track the location of merchandise and / or shoppers within a contained area in real time. However, the establishment of the infrastructure required to support such a real-time positioning system can be time-consuming and expensive.

[0028] According to various embodiments of the present disclosure, a radio frequency identification (RFID) system is combined with a radio positioning system to identify, locate, and track the location of assets and actors within a local environment. In this way, information from the two systems can be correlated to determine the movement patterns of various assets and actors interacting with various objects. For example, RFID tag data associated with detected merchandise and / or shopping carts (assets) from the RFID system and location data of shoppers (actors) obtained from the radio positioning system can be used together to map and determine where shoppers are in a retail store and which path they are taking.

[0029] In some embodiments, the radio positioning system includes a millimeter wave (mmWave) antenna circuit. The millimeter wave antenna circuit can be configured to be deployed in a radar operating in a millimeter wave band. In some embodiments, the millimeter wave antenna circuit is coupled to a beam steering antenna (BSA). In some embodiments, the millimeter wave antenna circuit includes a four-element millimeter wave radar antenna. In some embodiments, the millimeter wave antenna circuit also includes a millimeter wave processing unit (MPU) coupled to an RFID integrated circuit. In some embodiments, the RFID reader can be configured to scan and locate an RFID tag of an asset within a specific area / beam attached to a local environment by communicating with the RFID integrated circuit. In some embodiments, the MPU is configured to scan an actor in a specific area / beam via a four-element millimeter wave radar antenna, generate a point cloud to determine the actor position within the point cloud, and transmit the point cloud position data to the RFID integrated circuit.

[0030] In some embodiments, the RFID reader sends the identification of the RFID tag and the actor location to the data analysis system. In some embodiments, the data analysis system can associate the RFID tag with the actor location. For example, one or more RFID tags can include identification information, such as a serial number or identification number of an asset to which the one or more RFID tags are attached, and the data analysis system can associate the one or more RFID tags with a specific actor based on a combination of the RFID tag data determined by the RFID system and the actor location within the point cloud determined by the MPU.

[0031] The example methods, systems, and apparatus disclosed herein can be used in a retail environment to determine shopper locations associated with shopper flow and the times at which those shoppers' locations were detected. Additionally, the paths of travel of different shoppers can be determined. The example methods, systems, and apparatus can be used to help retailers better understand shopper flow and shopper flow trends in a retail store. For example, by monitoring the number and flow of shoppers within a store, the example methods, systems, and apparatus can be used to determine when shopper flow is greatest and least and to determine the most frequently visited locations in a retail store.

[0032] Figure 11 is a schematic diagram of an example architecture 100 for tracking assets such as shopping carts and merchandise and actors with respect to the assets such as shoppers. The architecture 100 includes a data analysis system 101 configured to receive RFID tag data associated with one or more RFID tags attached to the assets and point cloud location data including actor locations from a millimeter wave RFID reader computing entity 102, associate the one or more RFID tags to the actor locations, and generate an actor behavior data set (e.g., including the actor's activities with respect to the one or more RFID tags). In some embodiments, the data analysis system 101 can communicate with at least one of the millimeter wave RFID reader computing entities 102 using one or more communication networks. Examples of communication networks include any wired or wireless communication networks, including, for example, wired or wireless local area networks (LANs), personal area networks (PANs), metropolitan area networks (MANs), wide area networks (WANs), etc., and any hardware, software, and / or firmware required to implement the same (such as, for example, network routers, etc.).

[0033] The data analysis system 101 may include a data analysis computing entity 106 and a storage subsystem 108. The data analysis computing entity 106 may be configured to receive RFID tag data associated with one or more RFID tags attached to an asset and point cloud location data including an actor location from the millimeter wave RFID reader computing entity 102, associate the one or more RFID tags to the actor location, and generate an actor behavior data set. The storage subsystem 108 may be configured to store input data used by the data analysis computing entity 106 to perform predictive data analysis and model definition data used by the data analysis computing entity 106 to perform various predictive data analysis tasks. The storage subsystem 108 may include one or more storage units, such as multiple distributed storage units connected by a computer network. Each storage unit in the storage subsystem 108 may store at least one of one or more data assets and / or one or more data about the computational characteristics of the one or more data assets. Moreover, each storage unit in the storage subsystem 108 may include one or more non-volatile storage or memory media, including but not limited to a hard disk, a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory (e.g., serial, NAND, NOR, etc.), a multimedia memory card (MMC), a secure digital (SD) memory card, a smart media card, a compact flash (CF) card, a memory stick, a conductive bridging random access memory (CBRAM), a phase change random access memory (PRAM), a ferroelectric random access memory (FeRAM), a non-volatile random access memory (NVRAM), a magnetoresistive random access memory (MRAM), a resistive random access memory (RRAM), a silicon-oxide-nitride-oxide-silicon memory (SONOS), a floating junction gate random access memory (FJG RAM), a millipede memory, a racetrack memory, etc.

[0034] Figure 2A schematic diagram of a data analysis computing entity 106 according to an embodiment of the present disclosure is provided. In general, the terms computing entity, computer, entity, device, system and / or similar words used interchangeably herein may refer to, for example, one or more computers, computing entities, desktop computers, mobile phones, tablet computers, tablet phones, notebook computers, laptop computers, distributed systems, information kiosks, input terminals, servers or server networks, blade servers, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, etc., and / or any combination of devices or entities suitable for performing the functions, operations and / or processes described herein. Such functions, operations and / or processes may include, for example, sending, receiving, operating, processing, displaying, storing, determining, creating / generating, monitoring, evaluating, comparing and / or similar terms used interchangeably herein. In some embodiments, these functions, operations and / or processes may be performed on data, content, information and / or similar terms used interchangeably herein.

[0035] like Figure 2 As shown, in some embodiments, the data analysis computing entity 106 may include one or more processing elements 205 (also referred to as processors, processing circuits and / or similar terms used interchangeably herein) or communicate with one or more processing elements, which communicate with other elements within the data analysis computing entity 106 via, for example, a bus. It should be understood that the processing element 205 can be embodied in a variety of different ways. For example, the processing element 205 can be embodied as one or more complex programmable logic devices (CPLDs), microprocessors, multi-core processors, co-processing entities, application specific instruction set processors (ASIPs), microcontrollers and / or controllers. In addition, the processing element 205 can be embodied as one or more other processing devices or circuits. The term circuit can refer to a complete hardware embodiment or a combination of hardware and computer program products. Therefore, the processing element 205 can be embodied as an integrated circuit, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic array (PLA), a hardware accelerator, other circuits, etc.

[0036] It should therefore be understood that the processing element 205 may be configured for specific purposes or configured to execute instructions stored in volatile or non-volatile media or otherwise accessible by the processing element 205. Thus, whether configured by hardware or by a computer program product, or by a combination thereof, when configured accordingly, the processing element 205 may be capable of performing steps or operations according to embodiments of the present disclosure.

[0037] In some embodiments, the data analysis computing entity 106 may also include a non-volatile medium (also referred to as a non-volatile storage device, memory, memory storage device, memory circuit and / or similar terms used interchangeably herein) or communicate with a non-volatile medium. In some embodiments, the non-volatile storage device or memory may include one or more non-volatile memories 210, including but not limited to a hard disk, ROM, PROM, EPROM, EEPROM, flash memory, MMC, SD memory card, smart media card, CF card, memory stick, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJGRAM, millipede memory, racetrack memory, etc.

[0038] As will be appreciated, non-volatile storage devices or memory media may store databases, database instances, database management system entities, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, etc. The terms "database," "database instance," "database management system," and / or similar terms used interchangeably herein may refer to a collection of records or data stored in a computer-readable storage medium using one or more database models, such as a hierarchical database model, a network model, a relational model, an entity-relationship model, an object model, a document model, a semantic model, a graph model, etc.

[0039] In some embodiments, the data analysis computing entity 106 may also include or communicate with volatile media (also referred to as volatile storage, memory, memory storage, memory circuits, and / or similar terms used interchangeably herein). In some embodiments, the volatile storage device or memory may include, but is not limited to, one or more volatile memories 215, including, but not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), fast page mode dynamic random access memory (FPM DRAM), extended data output dynamic random access memory (EDO DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), second generation double data rate type synchronous dynamic random access memory (DDR2SDRAM), third generation double data rate type synchronous dynamic random access memory (DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), two-transistor RAM (TTRAM), semiconductor thyristor RAM (T-RAM), zero capacitor (Z-RAM), Rambus embedded memory module (RIMM), dual in-line memory module (DIMM), single in-line memory module (SIMM), video random access memory (VRAM), cache memory, register memory, etc.

[0040] As will be appreciated, volatile storage devices or memory media may be used to store at least portions of a database, database instance, database management system, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, etc., which are executed by, for example, processing element 205. Thus, databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, etc. may be used to control certain aspects of the operation of data analysis computing entity 106 with the assistance of processing element 205 and an operating system.

[0041] As noted, in some embodiments, the data analysis computing entity 106 may also include one or more network interfaces 220 for communicating with various computing entities, such as by transferring data, content, information, and / or similar terms that may be used interchangeably herein, which may be sent, received, manipulated, processed, displayed, stored, etc. Such communications may be performed using a wired data transmission protocol, such as Fiber Distributed Data Interface (FDDI), Digital Subscriber Line (DSL), Ethernet, Asynchronous Transfer Mode (ATM), Frame Relay, Data Over Cable Service Interface Specification (DOCSIS), or any other wired transmission protocol. Similarly, the data analysis computing entity 106 may be configured to communicate via a wireless external communication network using any of a variety of protocols, such as General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA2000 1X (1xRTT), Wideband Code Division Multiple Access (WCDMA), Global System for Mobile Communications (GSM), Enhanced Data Rates for GSM Evolution (EDGE), Time Division Synchronous Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Evolution Data Optimized (EVDO), High Speed ​​Packet Access (HSPA), High Speed ​​Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX), Ultra-Wideband (UWB), Infrared (IR) protocol, Near Field Communication (NFC) protocol, Wibree, Bluetooth protocol, wireless Universal Serial Bus (USB) protocol and / or any other wireless protocol.

[0042] Although not shown, the data analysis computing entity 106 may include or communicate with one or more input elements (such as keyboard input, mouse input, touch screen / display input, motion input, movement input, audio input, pointing device input, joystick input, keypad input, etc.). The data analysis computing entity 106 may also include or communicate with one or more output elements (not shown) (such as audio output, video output, screen / display output, motion output, movement output, etc.).

[0043] Figure 3An illustrative schematic diagram is provided representing a millimeter wave RFID reader computing entity 102 that may be used in conjunction with embodiments of the present disclosure. In general, the terms device, system, computing entity, entity, and / or similar terms used interchangeably herein may refer to, for example, one or more computers, computing entities, desktop computers, mobile phones, tablet computers, tablet phones, notebook computers, laptop computers, distributed systems, kiosks, input terminals, servers or server networks, blade servers, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, etc., and / or any combination of devices or entities suitable for performing the functions, operations, and / or processes described herein. Figure 3 As shown, the millimeter wave RFID reader computing entity 102 may include one or more antenna units 312, one or more transmitters 304 (e.g., radios), one or more receivers 306 (e.g., radios), and a processing element 308 (e.g., a CPLD, a microprocessor, a multi-core processor, a co-processing entity, an ASIP, a microcontroller, and / or a controller) that provides signals to the one or more transmitters 304 and receives signals from the one or more receivers 306, respectively. In some embodiments, the millimeter wave RFID reader computing entity 102, the one or more antenna units 312, the one or more transmitters 304, and the one or more receivers 306 may be dedicated to communication with a specific entity. For example, a first set of one or more antenna units 312, one or more transmitters 304, and one or more receivers 306 may be dedicated to communication with a data analysis computing entity 106, and a second set of one or more antenna units 312, one or more transmitters 304, and one or more receivers 306 may be dedicated to communication with RFID tags and radio positioning sensors (e.g., millimeter waves).

[0044] The signals provided to the one or more transmitters 304 and the signals received from the one or more receivers 306, respectively, may include signaling information / data in accordance with the air interface standard of the applicable wireless system. In this regard, the millimeter wave RFID reader computing entity 102 may be capable of operating with one or more air interface standards, communication protocols, modulation types, and access types. More specifically, the millimeter wave RFID reader computing entity 102 may operate in accordance with any of a variety of wireless communication standards and protocols, such as those described above with respect to the data analysis computing entity 106. In some embodiments, the millimeter wave RFID reader computing entity 102 may operate in accordance with a variety of wireless communication standards and protocols, such as UMTS, CDMA2000, 1xRTT, WCDMA, GSM, EDGE, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB, RFID, and the like. Similarly, the mmWave RFID reader computing entity 102 may operate via the network interface 320 in accordance with a variety of wired communication standards and protocols, such as those described above with respect to the data analysis computing entity 106 .

[0045] Via the above-mentioned communication standards and protocols, the mmWave RFID reader computing entity 102 may communicate with various entities such as the data analysis computing entity 106 using mechanisms such as Unstructured Supplementary Service Data (USSD), Short Message Service (SMS), Multimedia Message Service (MMS), Dual Tone Multi-Frequency Signaling (DTMF), and / or Subscriber Identity Module Dial Pad (SIM Dial Pad). The mmWave RFID reader computing entity 102 may also download changes, additional plug-ins, and updates, for example, to its firmware, software (e.g., including executable instructions, applications, program modules), and operating system.

[0046] According to some embodiments, the millimeter wave RFID reader computing entity 102 may include location determination aspects, devices, modules, functional accessories and / or similar words that may be used interchangeably herein. For example, the millimeter wave RFID reader computing entity 102 may include indoor positioning aspects, such as a positioning module suitable for obtaining, for example, latitude, longitude, altitude, geocoding, route, direction, heading, speed, time, date and / or various other information / data. Some indoor systems may use various positioning (position or location) technologies, including RFID tags, indoor beacons or transmitters, Wi-Fi access points, cellular towers, nearby computing devices (e.g., smart phones, laptops), etc. For example, such technologies may include iBeacon, Gimbal proximity beacons, Bluetooth low energy (BLE) transmitters, NFC transmitters, etc. These indoor positioning aspects can be used in a variety of settings to determine the location of someone or something, accurate to within inches or centimeters.

[0047] The millimeter wave RFID reader computing entity 102 may also include a user interface (which may include a display 316 coupled to the processing element 308) and / or a user input interface (coupled to the processing element 308). For example, the user interface may be a user application, a browser, a user interface, and / or similar words used interchangeably herein executed on and / or accessible via the millimeter wave RFID reader computing entity 102 to interact with the data analysis computing entity 106 and / or cause information / data from the data analysis computing entity to be displayed, as described herein. The user input interface may include any of a number of devices or interfaces that allow the millimeter wave RFID reader computing entity 102 to receive data, such as a keypad 318 (hard keyboard or soft keyboard), a touch display, a sound / voice or motion interface, or other input device. In embodiments that include a keypad 318, the keypad 318 may include (or cause to be displayed) conventional numeric keys (0-9) and related keys (#, *) and other keys used to operate the mmWave RFID reader computing entity 102, and may include a full set of alphabetic keys or a set of keys that may be enabled to provide a full set of alphanumeric keys. In addition to providing input, the user input interface may also be used, for example, to activate or deactivate certain functions, such as a screen saver and / or sleep mode.

[0048] The millimeter wave RFID reader computing entity 102 may also include volatile memory 322 and / or non-volatile memory 324, which may be embedded and / or removable. For example, the non-volatile memory 324 may be ROM, PROM, EPROM, EEPROM, flash memory, MMC, SD memory card, memory stick, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, millipede memory, racetrack memory, etc. The volatile memory 322 may be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory, etc. The volatile and non-volatile memory may store databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, etc. to implement the functionality of the mmWave RFID reader computing entity 102. As indicated, this may include user applications resident on the mmWave RFID reader computing entity 102 or accessible through a browser or other user interface for communicating with the data analysis computing entity 106 and / or various other computing entities.

[0049] In another embodiment, the millimeter wave RFID reader computing entity 102 may include one or more components or functions that are the same or similar to the components or functions of the data analysis computing entity 106, as described in more detail above. As will be appreciated, these architectures and descriptions are provided for exemplary purposes only and are not limiting of the various embodiments.

[0050] In various embodiments, the millimeter wave RFID reader computing entity 102 may be embodied as an artificial intelligence (AI) computing entity, such as Amazon Echo, Amazon Echo Dot, Amazon Show, Google Home, etc. Thus, the millimeter wave RFID reader computing entity 102 may be configured to provide / or receive information / data to a user via an input / output mechanism (such as a display, camera, speaker, voice-activated input, etc.). In certain embodiments, the AI ​​computing entity may include one or more predefined and executable program algorithms stored in an onboard memory storage module and / or accessible via a network. In various embodiments, the AI ​​computing entity may be configured to retrieve and / or execute one or more of these predefined program algorithms when a predefined trigger event occurs.

[0051] Figure 4 An illustrative schematic diagram is provided showing one example of one or more antenna units 312 for receiving RFID tag data and point cloud location data that may be used in conjunction with embodiments of the present disclosure. Antenna unit 312-1 may be configured to communicate with mmWave RFID reader computing entity 102 via an air interface standard such as Electronic Product Code (EPC) Generation 2 or any other RFID standard protocol. Figure 4 As depicted, antenna unit 312-1 includes a coupler 402, a radio frequency (RF) switch 404, and an RFID integrated circuit (IC) 406. Coupler 402 may include a directional coupler configured to (i) split the signal output from mmWave RFID reader computing entity 102 to RF switch 404 and RFID IC 406, and (ii) combine the discrete signals from RF switch 404 and RFID IC 406 to feed a single line to mmWave RFID reader computing entity 102. In some embodiments, mmWave RFID reader computing entity 102 may be coupled to an input port of coupler 402, RF switch 404 may be coupled to a through port of coupler 402, and RFID IC 406 may be coupled to a coupled port of coupler 402.

[0052] The RFID IC 406 may be configured to operate as an antenna controller. In some embodiments, the RFID IC 406 may be configured to select an antenna for RFID scanning based on a read instruction signal from the mm-wave RFID reader computing entity 102. For example, the RFID IC 406 may receive instructions from the mm-wave RFID reader computing entity 102 to cycle between the local beam steering antenna 408 and the switch antenna 414. The RF switch 404 may be switched by the RFID IC 406 to enable the RF signal to be routed through the local beam steering antenna 408 or the switch antenna 414.

[0053] In some embodiments, the millimeter wave RFID reader computing entity 102 can access the antenna unit 312-1 to scan the location covered by the local beam steering antenna 408 for RFID tags. In some embodiments, the local beam steering antenna 408 may include an antenna at a proximate location that coincides with the antenna unit 312-1. For example, the antenna unit 312-1 including the local beam steering antenna 408 can be set above a location in an indoor environment. The location may include one or more beam areas covered by the local beam steering antenna 408. The upper location can be supported vertically or horizontally, mounted to the upper structure or lower structure of a given environment. In addition, the antenna unit 312-1 can be suspended above, supported by a fixture placed on the floor.

[0054] In some embodiments, the millimeter wave RFID reader computing entity 102 can access the antenna unit 312-1 to relay RF signals between the millimeter wave RFID reader computing entity 102 and the switch antenna 414 to scan the locations covered by the switch antenna 414 for RFID tags and point cloud location data. In some embodiments, the switch antenna 414 can be located at a location that includes one or more other beam regions away from the antenna unit 312-1. The switch antenna 414 can include or be coupled to components that are substantially similar to those included in the antenna unit 312-1.

[0055] The antenna unit 312-1 also includes an MPU 410 and a millimeter wave antenna element 412 coupled to the MPU 410. The MPU 410 can use the millimeter wave band to scan the position coincident with the local beam steering antenna 408 via the millimeter wave antenna element 412 to detect objects (e.g., shoppers, merchandise, or shopping carts / baskets) and provide the range, speed, and angle of these objects in the form of point cloud location data. In some embodiments, the millimeter wave antenna element 412 includes a four-element millimeter wave radar antenna. Generally, millimeter waves can refer to electromagnetic waves in the frequency domain of approximately 24 GHz to 300 GHz. The RFID IC 406 can be coupled to the MPU 410 via an interface such as a serial peripheral interface (SPI) to allow the millimeter wave RFID reader computing entity 102 to read data (e.g., point cloud location data) from the MPU 410 through the RFID IC 406.

[0056] Figure 5 Depict example components of MPU 410 and millimeter wave antenna element 412. Millimeter wave antenna element 412 includes a transmit antenna 502, a receive antenna 504, a frequency synthesizer 506, and a mixer 508. The Doppler principle can be used to determine the motion, range, speed, and direction of object 510. In some embodiments, MPU 410 and millimeter wave antenna element 412 can use frequency modulated continuous wave (FMCW). FMCW can include continuously transmitting frequency modulated signals to measure distance as well as angle and speed. Transmit antenna 502 can be configured to transmit a radar signal generated by frequency synthesizer 506, the frequency of which is continuously increased to create a signal scan. Frequency synthesizer 506 may include an electronic circuit that uses an oscillator to generate a set of pre-programmed stable frequencies with minimum phase noise.

[0057] The radar signal transmitted from the transmitting antenna 502 may be reflected by the object 510, and thus, an echo of the signal may be received by the receiving antenna 504. The mixer 508 may combine the radar signal generated by the frequency synthesizer 506 and transmitted from the transmitting antenna 502 with the echo signal received by the receiving antenna 504 to generate an intermediate frequency (IF) signal 512 including a difference (e.g., Doppler shift) between the transmitted signal and the echo signal. The echo signal received by the receiving antenna 504 may include a frequency difference referred to as a Doppler frequency. That is, if the object 510 is not stationary, the echo signal received by the receiving antenna 504 may include a frequency change relative to the frequency of the signal transmitted from the transmitting antenna 502. The Doppler frequency may be used to detect movement, speed, and distance.

[0058] The MPU 410 is coupled to the mmWave antenna element 412 and receives the IF signal 512 from the mixer 508. The MPU 410 includes a low pass (LP) filter 514, an analog to digital converter (ADC) 516, and a fast Fourier transform (FFT) signal processing module 518. The IF signal 512 may pass through the LP filter 514 and the ADC 516 before being processed by the FFT signal processing module 518 to generate point cloud position data 520.

[0059] Figure 6 Depicted is a top view of at least a portion of an example shopper tracking system 600 deployed in a retail store, according to one embodiment of the present disclosure. Figure 6 The shopper tracking system 600 depicted in FIG. 6 can be used to autonomously track the movement of objects (such as shoppers, merchandise, or shopping carts / baskets) at a retail store where shoppers (e.g., actors in a local environment) view, select, and purchase products. The retail store can locate numerous products throughout a sales floor to be sold and / or distributed to shoppers. The retail store can be of any size or form and can include products from one or more merchants. For example, the retail store can be a single store operated by one merchant, a chain of two or more stores operated by one entity, or can be one store in a collection of stores covering multiple merchants.

[0060] The depicted shopper tracking system 600 includes multiple millimeter wave RFID reader computing entities 602 (A, B), multiple antenna units 604 (AH), multiple switch antennas 606, a data analysis computing entity 608, and multiple RFID desktop readers 610 (A, B). The millimeter wave RFID reader computing entity 602 may include an overhead reader coupled to multiple antenna units 604 and switch antennas 606, which may be positioned at fixed locations throughout a retail store for detecting RFID signals from multiple RFID tags and location data of objects within the retail store. Thus, objects moving within the retail store (e.g., people, shopping carts, or baskets) may pass within the scanning range of the multiple antenna units 604 and switch antennas 606. The RFID desktop reader 610 may include a reader for detecting RFID signals from RFID tags within a range shorter than the range of the multiple antenna units 604 and switch antennas 606, and may be used for implicit transactions, such as price checking, checkout, or purchases. RFID tags may be affixed to or incorporated into and / or affixed to items located, for example, on shelves, modules, racks or end caps or to packaging of items sold from a retail store (e.g., boxed foods, canned foods, cleaning supplies, produce, frozen foods, consumer electronics, health products, beauty products, apparel, and other such items).

[0061] The data analysis computing entity 608 may be configured to monitor the path of shoppers through a retail location, where each shopping cart and / or shopping basket at the retail location may be affixed with an RFID tag. For example, during operation, the millimeter wave RFID reader computing entity 602 may scan via the plurality of antenna units 604 to identify from which beam zone the shopping cart RFID tag X1 is read, and generate point cloud location data to identify the shopper X2 in the first zone. Figure 7 The scanning of the beam area is described in more detail below.

[0062] The millimeter wave RFID reader computing entity 602 may send X1 and X2 to the data analysis computing entity 608 to conjugate the location of X2 with the shopping cart RFID tag X1. Shopper X2 may pick up item Y1 read by the antenna unit 604, where item Y1 may be identified as being close to the shopping cart RFID tag X1, and then the data analysis computing entity 608 may assign item Y1 to the shopping cart RFID tag X1. Since the shopping cart RFID tag X1 and the shopper X2 are conjugated, item Y1 is also implicitly assigned to the shopper X2. The millimeter wave RFID reader computing entity 602 may further detect activities associated with the shopper X2 moving to the second beam zone and selecting another item Y2. Such activities may be sent from the millimeter wave RFID reader computing entity 602 to the analysis computing entity 608 and interpreted to generate a shopping behavior dataset of shopper X2 indicating that the shopper X2 selected Y2, for example, after selecting Y1. The shopping behavior datasets of different shoppers may be used to be stored in a database and used by retailers to plan sales offerings and / or store planograms.

[0063] Figure 7 A top view of quadrant 700 representing the scan range coverage of a given one of the plurality of antenna units 604 is depicted in accordance with various embodiments of the present disclosure. Each quadrant can represent a separate beam zone. In some embodiments, a given one of the plurality of antenna units 604 can include a four-beam switched beam-steering antenna above a floor area. An example scan sequence of quadrants (0, 0), (0, k), (k, k), and (k, 0) can be performed by antenna unit 604 to track an RFID tag (e.g., attached to an item or a shopping cart) interacting with a shopper who is simultaneously being tracked using a millimeter wave radar sensor (e.g., MPU 410), such as Figure 7 As depicted in .

[0064] The millimeter wave RFID reader computing entity 602 can be configured to identify which beam zone a shopping cart RFID tag is being read from and monitor the movement of a shopper corresponding to the shopping cart RFID tag via point cloud location data generated by millimeter wave radar detection (e.g., millimeter wave antenna element 412 and MPU 410). For example, the point cloud location data associated with quadrant 700 can be used to confirm the detection of RFID tag data in a particular beam zone. In this way, the millimeter wave RFID reader computing entity 602 can use the point cloud location data to monitor the travel path of a shopper through a retail location, where each shopping cart and / or shopping basket of the retail location is provided with an RFID tag.

[0065] Figure 8A flowchart depicting a method for tracking an object according to an example embodiment of the present disclosure. Note that each block in the flowchart and combinations of blocks in the flowchart may be implemented by various components such as hardware, firmware, circuits, and / or other devices associated with the execution of software including one or more computer program instructions. For example, Figure 8 One or more of the steps / operations described in the flowchart may be embodied by computer program instructions, which may be stored in a non-transitory memory of a device employing an embodiment of the present disclosure and executed by a processor component in the device (such as, but not limited to, the data analysis computing entity 106). For example, these computer program instructions may instruct the processor component to function in a specific manner, so that the instructions stored in the computer-readable storage memory produce an article of manufacture, the execution of which implements the functions specified in the flowchart block.

[0066] exist Figure 8 In the example method 800, the data analysis computing entity 106 may be performed. At step 802, the data analysis computing entity 106 receives RFID tag data and point cloud location data from, for example, the millimeter wave RFID reader computing entity 102. The RFID tag data may include identification of one or more RFID tags detected in one of the one or more beam zones. The point cloud location data may include identification of objects (such as shoppers, shopping carts, and merchandise) within coordinates associated with the one or more beam zones. The RFID tag data may be generated by the millimeter wave RFID reader computing entity 102 based on RF signals received from one or more RFID tags in response to an RFID read signal sent from the millimeter wave RFID reader computing entity 102. The point cloud location data may be generated by an MPU coupled to a millimeter wave antenna element and sent to the millimeter wave RFID reader computing entity 102. The RFID tag data and the point cloud location data may be used together by the data analysis computing entity 106 in a complementary manner.

[0067] In some embodiments, after step 802, the example method proceeds to step 804, where the data analysis computing entity 106 determines whether a given shopper among the plurality of shoppers is associated with a shopping cart. The shopper may be detected based on the point cloud location data, and the shopping cart may be identified by an RFID tag of the shopping cart based on the RFID tag data. In some embodiments, determining whether the shopper is associated with the shopping cart may include: determining the location of the shopping cart based on the RFID tag data, and confirming the interaction of the shopper with the shopping cart based on the point cloud location data. Determining whether the shopper is associated with the shopping cart may also include detecting that the shopper is within a vicinity of an unassociated shopping cart. Determining whether the shopper is associated with the shopping cart may also include determining whether the shopper has previously been associated with the shopping cart.

[0068] In some embodiments, after step 804, if the shopper is not associated with a shopping cart, the example method proceeds to step 806, where the data analysis computing entity 106 associates the location of a given shopper with a shopping cart RFID tag based on the RFID tag data and the point cloud location data. The location may include an identification of a beam zone and / or point cloud coordinates. The shopper may be associated with a shopping cart based on the detection of an unassociated shopping cart in close proximity to the shopper's location. In some embodiments, the association may be confirmed based on observation of the RFID tag data and the point cloud location data over time, which is described in more detail with respect to the description of step 810.

[0069] In some embodiments, after step 806, the example method proceeds to step 808. In some embodiments, after step 804, if the shopper is associated with a shopping cart, the example method also proceeds to step 808. In step 808, the data analysis computing entity 106 determines whether a change in the location of the shopper has occurred. Determining whether a change in the location of the shopper has occurred may include determining that the shopper's current location is different from the location associated with the shopper and shopping cart RFID tags. For example, the shopper's current location may include beam areas and / or point cloud coordinates that are different from the beam areas and / or point cloud coordinates of the previous location associated with the shopper and shopping cart RFID tags (e.g., performed in step 806). In some embodiments, determining whether a change in the location of the shopper has occurred may also include determining whether the current location of the shopping cart RFID tag associated with the shopper has changed relative to the location associated with the shopper and / or shopping cart RFID tags or relative to the shopper's current location. For example, the data analysis computing entity 106 may monitor whether the shopping cart RFID tag is moving in a corresponding manner within one or more beam areas relative to the shopper. Thus, the association of a shopper with a shopping cart may be changed or updated based on this monitoring (eg, whether the shopper remains associated with an unassociated shopping cart).

[0070] In some embodiments, after step 808, if a change in the location of the shopper has occurred, the example method proceeds to step 810, where the data analysis computing entity 106 updates the location associated with the shopper and shopping cart RFID tags. For example, the data analysis computing entity 106 may associate the new location with the shopper and shopping cart RFID tags. In some embodiments, updating the location associated with the shopper and shopping cart RFID tags may include associating the shopper and shopping cart RFID tags with the shopper's new location.

[0071] In some embodiments, updating the location associated with the shopper and the shopping cart RFID tag may include associating the shopper with the new shopper location and associating the shopping cart RFID tag with the new shopping cart location. In some embodiments, a counter or timer component may be used to determine the degree of disassociation between the shopper and the shopping cart RFID tag associated with the shopper. In some embodiments, updating the location associated with the shopper and the shopping cart RFID tag also includes updating the association of the shopper with the shopping cart RFID tag. For example, the data analysis computing entity 106 may determine that the shopper should not be associated with the shopping cart RFID tag based on the counter or timer.

[0072] In some embodiments, after step 810, the example method proceeds to step 812. In some embodiments, after step 808, if the location associated with the shopper has not changed, the example method also proceeds to step 812. In step 812, the data analysis computing entity 106 determines whether the shopper has selected one or more additional items. Determining whether the shopper has selected one or more additional items may include: detecting an RFID tag associated with the one or more additional items, and determining whether the one or more additional items associated with the RFID tag are close to the shopper's location. Determining that the one or more additional items are close to the shopper's location may include: determining one or more of an angle, a received signal strength indication (RSSI), or movement / visibility of an RFID tag associated with one or more additional items in a particular area based on the RFID tag data and the point cloud location data.

[0073] In some embodiments, after step 812, if the shopper has selected one or more additional items, the example method proceeds to step 814, where the data analysis computing entity 106 associates the one or more additional items with the shopper and shopping cart RFID tags. Otherwise, if the shopper has not selected additional items, the example method proceeds to step 802, where the data analysis computing entity 106 receives additional RFID tag data and point cloud location data.

[0074] In some embodiments, after step 814, the example method proceeds to step 816, where the data analytics computing entity 106 determines whether any previously selected items were associated with the shopper before the location associated with the shopper changed.

[0075] In some embodiments, after step 816, if there are any previously selected items associated with the shopper before the location associated with the shopper changes, the example method proceeds to step 818, where the data analysis computing entity 106 generates a shopping behavior data entry. In some embodiments, the shopping behavior data entry may include an indication that the shopper selected one or more additional items after selecting the previously selected items and the shopper's location change occurred. In some embodiments, after step 816, if there are no previously selected items associated with the shopper before the location associated with the shopper changes, the example method proceeds to step 802, where the data analysis computing entity 106 receives additional RFID tag data and point cloud location data.

[0076] Although the example method 800 is described for a single shopper, the example steps of method 800 may be performed for each shopper in a plurality of shoppers. In addition, the disclosed systems and methods are described in the context of retail and commerce, however, embodiments of the present disclosure may be applied to other fields, such as healthcare, defense, industrial or agricultural environments.

[0077] It should be understood that the disclosure is not limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation unless otherwise specified.

Claims

1. An antenna unit, comprising: a coupler that is physically coupled to a reader at an input port of the coupler; a radio frequency (RF) switch coupled to the first output port of the coupler; a radio frequency identification (RFID) integrated circuit (IC) coupled to the second output port of the coupler, the RFID IC configured to select between a local beam steering antenna and a switching antenna for RFID scanning by switching the RF switch based on a read command signal from the reader computing entity; a millimeter wave processing unit (MPU) coupled to the RFID IC, the MPU being configured to: (i) generate point cloud position data, and (ii) transmit the point cloud position data to the RFID IC; as well as A millimeter wave antenna element is coupled to the MPU.

2. A method for tracking an object, the method comprising: Receiving, by a computing device, RFID tag data and point cloud location data from one or more reader computing entities; associating, by the computing device, a location of a given shopper with a shopping cart RFID tag associated with an unassociated shopping cart proximate to the given shopper based on the RFID tag data and the point cloud location data; determining, by the computing device, that a change in location of the given shopper has occurred; determining, by the computing device, that the given shopper has selected one or more additional items; associating, by the computing device, the one or more additional items with the given shopper and the shopping cart RFID tag; determining, by the computing device, one or more previously selected items associated with the given shopper prior to the location change; as well as A shopping behavior data entry is generated, the shopping behavior data entry including an indication that the given shopper selected the one or more additional items after selecting the one or more previously selected items and the change of location occurred.

3. The method of claim 2, wherein the RFID tag data comprises an identification of one or more RFID tags detected in one of the one or more beam zones. 4 . The method of claim 2 , wherein the point cloud position data includes identification of objects within coordinates associated with one or more beam zones.

5. The method according to claim 2, further comprising: detecting the given shopper based on the point cloud location data; as well as The unassociated shopping cart is identified based on the RFID tag data.

6. The method of claim 2, wherein determining that the given shopper is associated with a shopping cart further comprises: The location of the shopping cart is determined based on the RFID tag data, and interaction of the given shopper with the shopping cart is verified based on the point cloud location data.

7. The method of claim 2, wherein determining that a change in location of the given shopper has occurred further comprises: A current location of the given shopper is determined to be different than a location associated with the given shopper and the shopping cart RFID tag.

8. The method of claim 7, wherein the current location of the given shopper comprises a beam area and / or point cloud coordinates that are different from the beam area and / or point cloud coordinates of the location associated with the given shopper and the shopping cart RFID tag.

9. The method of claim 2, wherein determining that the given shopper has selected the one or more additional items further comprises: detecting an RFID tag associated with the one or more additional items; as well as A determination is made as to whether the one or more additional items are proximate to a location of the given shopper.

10. The method of claim 9, wherein determining that the one or more additional items are proximate to the given shopper's location further comprises: One or more of an angle, a received signal strength indication, or movement / visibility of one or more RFID tags associated with the one or more additional items is determined based on the RFID tag data and the point cloud location data.

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