METHODS AND SYSTEMS FOR CONDUCTING WEIGHT-BASED TRANSACTIONS

FIELD OF THE INVENTION

The present invention relates to weighing devices and assemblies, including weighing devices and assemblies for shelves on which non-homogeneous assortments of products can be arranged, and methods for their use in conducting transactions with respect to the products by tracking the weights, locations and identifications of products added to and removed from shelves.

BACKGROUND

Unattended or autonomous retail and inventory management are examples of areas that can benefit from the use of methods for weighing and tracking products on shelves. Technical solutions have been suggested for intelligent shelving arrangements that would track the weight of products on a shelf, including changes in the weight resulting from the addition of products or the removal of products. An example of such a suggested solution is a shelf segment assembly with load cells attached to the underside so that when the shelf segment is placed atop an existing ‘regular’ shelf, weights of the products on the shelf can be tracked. Such solutions are lacking in terms of being able to disambiguate unique products in diverse collections of products, instead dedicating each small shelf or shelf insert to a single product or stock-keeping unit (SKU).

Examples of shelving arrangements include connected shelving bays and standalone shelving arrangements. Connected shelving bays use a familiar type of shelving unit common in supermarkets and other retail stores. Standalone shelving arrangements are usually not connected to other shelving units and are often used in smaller retail environments such as, for example, kiosks, convenience stores, public areas of shopping malls, or shops in public venues such as train stations or airports. Either type of shelving arrangement can be suitable for practicing the embodiments disclosed herein.

SUMMARY

Embodiments of the present invention relate to methods and systems for conducting retail transactions by tracking the weights of non-homogeneous products on shelves, and identifying, from detected changes in weights and in weight distributions, which products are being added to or removed from shelves, or moved from place to place on a single shelf. Some of the embodiments relate to methods and systems for applying statistical analyses and/or probability distributions and other mathematical functions with relation to weights and products (including weights and products jointly), and machine learning algorithms including, without limitation, clustering algorithms, in the disambiguation of product identifications and of user actions taken with respect to those products.

According to embodiments of the invention, a method is disclosed for conducting a retail transaction, using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon. The method comprises: monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points; responsively to a change over time in the values of said weight measurement data-points and contingent upon said values reaching respective steady states according to a stability-tracking rule, determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products; and performing at least one of: (i) recording information about the results of the determining in a non-transient, computer-readable medium, and (ii) displaying information about the results of the determining on a display device. According to the method, the weight measurement data-points comprise at least one type of data selected from the group comprising calculated weights and voltage inputs thereto.

In some embodiments, the method can additionally comprise receiving an indication of a transaction-initiation, and said monitoring can be in response to the receiving.

In some embodiments, the stability-tracking rule can include that said respective steady states for the streams of weight measurement data-points are defined by respective response-amplitude thresholds.

In some embodiments, applying the stability tracking rule can include estimating bias in the weight measurement data-points and compensating for said bias.

In some embodiments, the weight measurement data-points can either comprise voltage inputs or solely comprise voltage inputs.

In some embodiments, said determining can include estimating a joint weight-location event-based product classifier. In some embodiments, estimating the joint weight-location event-based product classifier can include estimating a joint weight-location probability density. In some embodiments, estimating the joint weight-location event-based product classifier can include applying a statistical classification mechanism trained by weight and location information to perform a statistical inference.

In some embodiments, the weight measurement data-points can be the only inputs to said determining that are generated by sensors during the transaction.

In some embodiments, the method can additionally comprise, before said determining: responsively to a change over time in the values of transmitted weight measurement data-points, analyzing each of the streams of weight measurement data-points to detect at least one of noise and drift; and in response to the detection of said at least one of noise and drift, performing at least one of (A) at least partially filtering out the noise and/or drift and (B) at least partially compensating for the noise and/or drift in the weight measurement data points, such that the performing generates revised weight measurement data.

According to embodiments of the invention, a method is disclosed for conducting a retail transaction, using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of non-homogeneous products arranged thereupon. The method comprises: monitoring weight measurement data corresponding to the weight of the shelf and of the products arranged thereupon, said weight measurement data transmitted from a plurality of weighing assemblies as respective streams of weight measurement data points; and responsively to a change over time in the values of said weight measurement data, determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products. According to the method, the determining comprises: identifying one or more supported sets of weight-event parameters in a weight-location space, and applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

In some embodiments, applying the joint weight-location event-based classification function can include estimating a joint weight-location probability density. According to embodiments of the invention, a method is disclosed for conducting applying the joint weight-location event-based classification function can include applying a statistical classification mechanism trained by weight and location information to perform a statistical inference.

In some embodiments, the determining can be based on product weight-distribution data retrieved from a product database. In some embodiments, the determining is based on a product positioning plan.

In some embodiments, the estimating of the joint weight-location probability density can include iteratively improving initial weight and/or location estimations.

In some embodiments, the applying of the classification function can include Bayesian hierarchical modelling.

In some embodiments, the weight measurement data-points can be the only inputs to said determining that are generated by sensors during the transaction.

In some embodiments, the determining can be carried out responsively to an absolute value of the change over time in the values of said weight measurement data exceeding a pre-determined threshold.

In some embodiments, the weight measurement data-points can either comprise voltage inputs or solely comprise voltage inputs.

In some embodiments, the updating the estimation of bias can include accessing historical bias data.

In some embodiments, the compensating for the estimated bias can include correcting an estimation of weight and/or location.

In some embodiments, the determined set of weight-event parameters can be changed because of the compensating for the estimated bias.

In some embodiments, the determining can comprise: identifying one or more supported sets of weight-event parameters in a weight-location space, and applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

In some embodiments, applying the joint weight-location event-based classification function can include estimating a joint weight-location probability density. In some embodiments, applying the joint weight-location event-based classification function can include applying a statistical classification mechanism trained by weight and location information to perform a statistical inference.

In some embodiments, the method can additionally comprise, before said determining: responsively to a change over time in the values of transmitted weight measurement data-points, analyzing each of the streams of weight measurement data points to detect at least one of noise and drift; and in response to the detection of said at least one of noise and drift, performing at least one of (A) at least partially filtering out the noise and/or drift and (B) at least partially compensating for the noise and/or drift in the weight measurement data points, such that the performing generates revised weight measurement data, wherein said determining is based on a change in values in said revised weight measurement data.

According to embodiments of the invention, a method is disclosed for conducting a retail transaction, using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon. The method comprises: receiving an indication of a transaction-initiation; in response to said receiving, monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points; responsively to a change over time in the values of said weight measurement data-points, determining a set of weight-event parameters of a weight event using at least one stability rule, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products; and performing at least one of: (i) recording information about the results of the determining in a non-transient, computer-readable medium, and (ii) displaying information about the results of the determining on a display device.

In some embodiments, the method can additionally comprise receiving an indication of a transaction-completion.

In some embodiments, a first stability-tracking rule can be based on tracking a dynamic response of a weighing assembly to said weight event. In some embodiments, a second stability-tracking rule can be based on a stability attribute of a product. In some embodiments, a third stability-tracking rule is based on a stability attribute of the shelf. In some embodiments, said transaction-initiation can include opening and/or closing a door, and a fourth stability-tracking rule can include tracking a shock response to said transaction-initiation.

In some embodiments, said determining can comprise: identifying one or more supported sets of weight-event parameters in a weight-location space, and applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

In some embodiments, the method can additionally comprise before said determining: responsively to a change over time in the values of transmitted weight measurement data-points, analyzing each of the streams of weight measurement data points to detect at least one of noise and drift; in response to the detection of said at least one of noise and drift, performing at least one of (A) at least partially filtering out the noise and/or drift and (B) at least partially compensating for the noise and/or drift in the weight measurement data points, such that the performing generates revised weight measurement data, wherein said determining is based on a change in values in said revised weight measurement data.

According to embodiments of the invention, a method is disclosed for conducting a retail transaction, using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon. The method comprises: receiving an indication of a transaction-initiation; in response to said receiving, monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points; and responsively to a change over time in the values of said weight measurement data-points, determining a set of weight-event parameters of a weight event using at least one stability rule, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products. According to the method, said indication of the transaction-initiation includes one of (i) a mechanical shock indicating a door opening and/or closing, (ii) a lidar or radar reading of a hand reaching to the shelf, (iii) an electronic or optical reading of a payment or subscription medium, and (iv) a biometric reading of a user.

In some embodiments, said determining comprises: identifying one or more supported sets of weight-event parameters in a weight-location space, and applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

According to embodiments of the invention, a system for conducting a retail transaction comprises: a plurality of weighing assemblies in contact with a shelf and jointly operable to measure a combined weight of a shelf and of products arranged thereupon; one or more computer processors; and a non-transient computer-readable storage medium comprising program instructions, which when executed by the one or more computer processors, cause the one or more computer processors to carry out the following steps: monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points; responsively to a change over time in the values of said weight measurement data-points and contingent upon said values reaching respective steady states according to a stability-tracking rule, determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products; and performing at least one of: (A) recording information about the results of the determining in a non-transient, computer-readable medium, and (B) displaying information about the results of the determining on a display device. According to the program instructions, the weight measurement data-points comprise at least one type of data selected from the group comprising calculated weights and voltage inputs thereto.

In some embodiments, the program instructions, when executed by the one or more computer processors, can additionally cause the one or more computer processors to carry out the following step: receiving an indication of a transaction-initiation, and the carrying out of the monitoring step can be in response to the receiving.

In some embodiments, applying the stability tracking rule can include estimating bias in the weight measurement data-points and compensating for said bias.

In some embodiments, the weight measurement data-points can either comprise voltage inputs or solely comprise voltage inputs.

In some embodiments, the determining step can include estimating a joint weight-location event-based product classifier.

In some embodiments, estimating the joint weight-location event-based product classifier can include estimating a joint weight-location probability density.

In some embodiments, estimating the joint weight-location event-based product classifier can include applying a statistical classification mechanism trained by weight and location information to perform a statistical inference.

In some embodiments, the weight measurement data-points can be the only inputs to the determining step that are generated by sensors during the transaction.

In some embodiments, the program instructions, when executed by the one or more computer processors, can additionally cause the one or more computer processors to carry out the following steps before the determining step: responsively to a change over time in the values of transmitted weight measurement data-points, analyzing each of the streams of weight measurement data-points to detect at least one of noise and drift; and in response to the detection of said at least one of noise and drift, performing at least one of (A) at least partially filtering out the noise and/or drift and (B) at least partially compensating for the noise and/or drift in the weight measurement data points, such that the performing generates revised weight measurement data.

According to embodiments of the invention, a system for conducting a retail transaction comprises: a plurality of weighing assemblies in contact with a shelf and jointly operable to measure a combined weight of a shelf and of products arranged thereupon; one or more computer processors; and a non-transient computer-readable storage medium comprising program instructions, which when executed by the one or more computer processors, cause the one or more computer processors to carry out the following steps: monitoring weight measurement data corresponding to the weight of the shelf and of the products arranged thereupon, said weight measurement data transmitted from a plurality of weighing assemblies as respective streams of weight measurement data points; and responsively to a change over time in the values of said weight measurement data, determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products, wherein the determining comprises: identifying one or more supported sets of weight-event parameters in a weight-location space, and applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

In some embodiments, applying the joint weight-location event-based classification function can include estimating a joint weight-location probability density. In some embodiments, applying the joint weight-location event-based classification function includes applying a statistical classification mechanism trained by weight and location information to perform a statistical inference.

In some embodiments, the determining can be based on product weight-distribution data retrieved from a product database. In some embodiments, the determining can be based on a product positioning plan.

In some embodiments, the estimating of the joint weight-location probability density can include iteratively improving initial weight and/or location estimations.

In some embodiments, the applying of the classification function can include Bayesian hierarchical modelling.

In some embodiments, the weight measurement data-points can be the only inputs to said determining that are generated by sensors during the transaction.

In some embodiments, the determining step can be carried out responsively to an absolute value of the change over time in the values of said weight measurement data exceeding a pre-determined threshold.

In some embodiments, the weight measurement data-points can either comprise voltage inputs or solely comprise voltage inputs.

According to embodiments of the invention, a system for conducting a retail transaction comprises: a plurality of weighing assemblies in contact with a shelf and jointly operable to measure a combined weight of a shelf and of products arranged thereupon; one or more computer processors; and a non-transient computer-readable storage medium comprising program instructions, which when executed by the one or more computer processors, cause the one or more computer processors to carry out the following steps: receiving respective time-series of weight measurement data points from a plurality of weighing assemblies; updating an estimation of bias in the weight measurement data points received from one or more weighing assemblies of the plurality of weighing assemblies, by applying a clustering algorithm to said time-series; and determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products. According to the program instructions, the determining includes compensating for the estimated bias.

In some embodiments, updating the estimation of bias can include accessing historical bias data.

In some embodiments, the compensating for the estimated bias can include correcting an estimation of weight and/or location.

In some embodiments, the determined set of weight-event parameters can be changed because of the compensating for the estimated bias.

In some embodiments, the determining step can comprise: identifying one or more supported sets of weight-event parameters in a weight-location space, and applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets. In some embodiments, applying the joint weight-location event-based classification function can include estimating a joint weight-location probability density. In some embodiments, applying the joint weight-location event-based classification function can include applying a statistical classification mechanism trained by weight and location information to perform a statistical inference.

In some embodiments, the program instructions, when executed by the one or more computer processors, can additionally cause the one or more computer processors to carry out the following steps before the determining step: responsively to a change over time in the values of transmitted weight measurement data-points, analyzing each of the streams of weight measurement data points to detect at least one of noise and drift; and in response to the detection of said at least one of noise and drift, performing at least one of (A) at least partially filtering out the noise and/or drift and (B) at least partially compensating for the noise and/or drift in the weight measurement data points, such that the performing generates revised weight measurement data, wherein said determining is based on a change in values in said revised weight measurement data.

According to embodiments of the invention, a system for conducting a retail transaction comprises: a plurality of weighing assemblies in contact with a shelf and jointly operable to measure a combined weight of a shelf and of products arranged thereupon; one or more computer processors; and a non-transient computer-readable storage medium comprising program instructions, which when executed by the one or more computer processors, cause the one or more computer processors to carry out the following steps: receiving an indication of a transaction-initiation; in response to said receiving, monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points; responsively to a change over time in the values of said weight measurement data-points, determining a set of weight-event parameters of a weight event using at least one stability rule, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products; and performing at least one of: (i) recording information about the results of the determining in a non-transient, computer-readable medium, and (ii) displaying information about the results of the determining on a display device.

In some embodiments, a first stability-tracking rule can be based on tracking a dynamic response of a weighing assembly to said weight event. In some embodiments, a second stability-tracking rule can be based on a stability attribute of a product. In some embodiments, a third stability-tracking rule can be based on a stability attribute of the shelf. In some embodiments, said transaction-initiation can include opening and/or closing a door, and a fourth stability-tracking rule can include tracking a shock response to said transaction-initiation.

In some embodiments, the program instructions, when executed by the one or more computer processors, can additionally cause the one or more computer processors to carry out the following steps before the determining step: responsively to a change over time in the values of transmitted weight measurement data-points, analyzing each of the streams of weight measurement data points to detect at least one of noise and drift; and in response to the detection of said at least one of noise and drift, performing at least one of (A) at least partially filtering out the noise and/or drift and (B) at least partially compensating for the noise and/or drift in the weight measurement data points, such that the performing generates revised weight measurement data wherein said determining is based on a change in values in said revised weight measurement data.

According to embodiments of the invention, a system for conducting a retail transaction comprises: a plurality of weighing assemblies in contact with a shelf and jointly operable to measure a combined weight of a shelf and of products arranged thereupon; one or more computer processors; and a non-transient computer-readable storage medium comprising program instructions, which when executed by the one or more computer processors, cause the one or more computer processors to carry out the following steps: receiving an indication of a transaction-initiation; in response to said receiving, monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points; and responsively to a change over time in the values of said weight measurement data-points, determining a set of weight-event parameters of a weight event using at least one stability rule, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products. According to the program instructions, said indication of the transaction-initiation includes one of (i) a mechanical shock indicating a door opening and/or closing, (ii) a lidar or radar reading of a hand reaching to the shelf, (iii) an electronic or optical reading of a payment or subscription medium, and (iv) a biometric reading of a user.

In some embodiments the determining step can comprises: identifying one or more supported sets of weight-event parameters in a weight-location space, and applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

A method is disclosed for tracking non-homogeneous products on a shelf by using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of the products arranged thereupon, wherein the method comprises: monitoring weight measurement data corresponding to the weight of the shelf and the products arranged thereupon, said weight measurement data measured by the plurality of weighing assemblies and transmitted therefrom as respective streams of weight measurement data points; responsively to a change over time in the values of said weight measurement data, determining a set of weight-event parameters of a weight event, the set of weight-event parameters comprising a product identification and an action taken with respect to the product; and performing at least one of: recording information about the results of the determining in a non-transient, computer-readable medium, and displaying information about the results of the determining on a display device.

In some embodiments, the method can additionally comprise receiving an indication of a transaction-initiation, and said monitoring can be in response to the receiving.

In some embodiments, applying the stability tracking rule can include estimating bias in the weight measurement data-points and compensating for said bias.

In some embodiments, the weight measurement data-points can either comprise voltage inputs or solely comprise voltage inputs.

In some embodiments, the weight measurement data-points can be the only inputs to said determining that are generated by sensors during the transaction.

In some embodiments, the determining can be based on product weight-distribution data retrieved from a product database. In some embodiments, the determining is based on a product positioning plan.

In some embodiments, the estimating of the joint weight-location probability density can include iteratively improving initial weight and/or location estimations.

In some embodiments, the applying of the classification function can include Bayesian hierarchical modelling.

According to the method the determining includes compensating for the estimated bias.

In some embodiments, the updating the estimation of bias can include accessing historical bias data.

In some embodiments, the compensating for the estimated bias can include correcting an estimation of weight and/or location.

In some embodiments, the determined set of weight-event parameters can be changed because of the compensating for the estimated bias.

In some embodiments, the method can additionally comprise receiving an indication of a transaction-completion.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described further, by way of example, with reference to the accompanying drawings, in which the dimensions of components and features shown in the figures are chosen for convenience and clarity of presentation and not necessarily to scale. In the drawings:

FIG. 1A is a schematic perspective view of a shelf with a diverse plurality of products arranged thereupon, according to embodiments of the present invention;

FIG. 1B is a schematic perspective view of a shelf and products of FIG. 1A, the shelf being in contact with a shelf base comprising a plurality of weighing assemblies, according to embodiments of the present invention;

FIG. 2A is a schematic perspective view of a bracket assembly for a shelving bay, comprising a plurality of weighing assemblies, according to embodiments of the present invention;

FIG. 2B is a schematic perspective view of a prior art double-sided gondola-type shelving arrangement comprising three shelving units;

FIGS. 3A and 3B are respectively perspective and exploded schematic perspective views of a weighing assembly including two shelf brackets, according to embodiments of the present invention;

FIG. 4A is a schematic perspective view of a weighing-enabled shelving unit and system, showing a detail of an attachment element, according to embodiments of the present invention;

FIG. 4B is a schematic perspective view of a weighing-enabled refrigerator according to embodiments of the present invention;

FIGS. 5A and 5B are respective assembled and exploded schematic perspective views of a shelving unit, according to embodiments of the present invention;

FIG. 5C shows a protruding element and receiving element according to the prior art;

FIG. 5D shows a top-perspective schematic view of a shelf assembly according to embodiments of the present invention.

FIG. 5E shows a weighing base of the shelf assembly of FIG. 5C.

FIG. 5F shows a bottom-perspective schematic view of the shelf assembly of FIG. 5C.

FIG. 6 is a schematic perspective view of a shelf assembly according to another embodiment of the present invention;

FIG. 7 shows a block diagram of a first system for conducting a retail transaction, based on the shelving arrangement of FIG. 2B, according to embodiments of the present invention;

FIGS. 8A and 8B show block diagrams of examples of a second system for conducting a retail transaction, based on the shelving unit and system of FIG. 4A, according to embodiments of the present invention;

FIG. 8C shows a block diagram of a third example of a second system for conducting a retail transaction, based on the shelving unit and system of FIG. 4B, according to embodiments of the present invention;

FIG. 9 shows a flowchart of a method for conducting a retail transaction, according to embodiments of the present invention;

FIG. 10 is a graph showing dynamic load cell response to a hypothetical weight event;

FIG. 11 is a schematic plan view of a shelf, showing the product location of a weight event, according to embodiments of the present invention;

FIG. 12 shows a schematic perspective view of the shelf of FIG. 11 with a weight-location probability distribution function mapped to the shelf, according to embodiments of the present invention;

FIGS. 13-20 show flowcharts of methods for conducting a retail transaction, according to embodiments of the present invention; and

FIG. 21 shows a block diagram of a system for conducting a retail transaction in respect of a non-homogeneous assortment of products on a shelf, according to embodiments of the present invention.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS

The invention is herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of the preferred embodiments of the present invention only, and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the invention. In this regard, no attempt is made to show structural details of the invention in more detail than is necessary for a fundamental understanding of the invention, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice. Throughout the drawings, like-referenced characters are generally used to designate like elements. Subscripted reference numbers (e.g., 10₁ or 101_L) and number-letter combinations (e.g. 130a) are used to designate multiple separate appearances of elements in a single drawing, e.g. 101₁ is a single appearance (out of a plurality of appearances) of element 10, 101_L is a left-side appearance (out of a plurality of appearances) of element 101, and 130a is a single appearance (out of a plurality of appearances) of element 130.

In accordance with embodiments of the invention, methods and systems are disclosed for conducting transactions, including autonomous retail transactions. The transactions are conducted using weighing systems capable of identifying products added to, removed from, or moved within a shelf, using historical and derived data such as weight distributions for specific products, product placement plans and signal bias, by analyzing signals containing streams of weight measurement data points to detect changes in values over time as well as noise, drift (including periodic drift) and bias, and by building joint weight-location classifying functions for mapping product weight-events to products, events and locations.

We now refer to FIGS. 1A and 1B. In embodiments, a shelf 90 is provided for storing or displaying non-homogeneous products 70. Products can be diverse, e.g., 70₁ and 70₂ are different products, and any number of each type of different products can be placed on a shelf 90. While shown as differing in size and shape, they can also differ in weight or contents, and can be distinguished by having different SKUs, i.e., stock-keeping unit identification numbers, or by other unique identifiers. While shown as differing in size and shape, they can also differ in weight, dimensions, contents or weight history (e.g., a histogram of past weight values for each respective product), and can be distinguished by having different SKUs, i.e., stock-keeping unit identification numbers, or by other unique identifiers. As used herein, the term “SKU” means stock-keeping unit. The use of SKU-identifiers is a standard means of identifying unique products across industries. Unique products can be, for example, products defined by unique combinations of physical characteristics, e.g., weight (whether nominal or average), volume, dimensions, etc. and/or non-physical characteristics, e.g., brand or packaging design. It can be that two products can be similar in physical characteristics but have different SKU-identifiers; in some embodiments they can be considered as ‘non-homogeneous’ and in other embodiments they may not. However, any use of the term ‘products’ in this disclosure or in the claims attached thereto includes the concept of ‘non-homogeneous products.’ In an example, a particular brand of cookies may offer products with a number of different SKU-identifiers: a first SKU for the brand’s large package of large chocolate cookies, a second SKU for the brand’s small package of the same large chocolate cookies, and a third SKU for the brand’s large package of small chocolate cookies, and so on. The term “non-homogeneous”, as applied herein to a group of products, means that the products in the group do not all share the same SKU-identifier, but should not be understood to imply that each product in a group has a unique SKU-identifier. For example, a group of non-homogeneous products might include: (a) 10 large packages of large chocolate cookies bearing a first brand and having a first SKU-identifier, and (b) 2 large packages of small chocolate cookies from a second brand and having a second SKU-identifier, or, without limitation any combination of products having, in combination, two or more SKU-identifiers. A group of products having, in combination, two or more SKU-identifiers can be considered ‘non-homogeneous’ with respect to one another. Thus, within any group of non-homogeneous products, products can differ from other members of the group in terms of, and not exhaustively: product packaging design and/or materials, weight, size, one or more external dimensions, brand, contents, list of ingredients, size and number of sub-divisions within a product packaging, and product-weight history such as can be represented by a database of past weight data, where the past weight data can encompass not only total weight but also the distribution of weight over the footprint of the product.

As shown in FIG. 1B, the shelf 90 can be in contact, at least indirectly, with weighing assemblies 101, so that the weighing assemblies 101 can measure the weight of the shelf 70 and of products 70 on the shelf 90. In the non-limiting example of FIG. 1B, the weighing assemblies 101 are provided as part of a shelf base 91 which supports the shelf 90 when installed. In another non-limiting example illustrated in FIG. 2A, weighing assemblies 101 are provided in a bracket assembly 10 attached to an upright 85 of a shelving unit 300 of a connected retail-type shelving bay, and a shelf 90 can be supported by two such bracket assemblies 10 provided at opposite ends of the shelf 90. The weighing assemblies 101 are illustrated as planar load cell assemblies, but any suitable weight sensor can be used, although preferably one with fast response time and high levels of precision.

Weighing assemblies 101 can include internal processors (not shown), which can be configured, for example, to sample continuous or discrete weight measurements and transmit streams of weight measurement data points to an external processor, using internal communications arrangements (not shown). The sampling rate is preferably at least 50 Hz, or at least 100 Hz, or at least 200 Hz, or higher. A high sampling rate can be helpful, for example, if it is desired to filter out noise. An electronic signal transmitting a stream of weight measurement data points can be analyzed to detect noise, for example by decomposing the signal into component frequencies using a Fourier transform, as is known in the art. Noise in the signal can come from mechanical and/or environmental sources, for example from vibrations due to mechanical equipment in the area. As is known in the art, strain gauge load cells output voltages that correspond to forces acting upon the load cell, or, equivalently to reaction forces of the load cell. In some embodiments, the weight measurement data points solely comprise voltage measurements. In some embodiments, the weight measurement data points additionally or alternatively comprise weight data calculated from the voltages which are inputs thereto.

Any planar load cell assembly can be suitable for use herein. A planar load cell assembly suitable for use with the present invention is disclosed in co-pending PCT application PCT/IB2019/055488, titled “Systems and Methods for Weighing Products on a Shelf” and filed on Jun. 28, 2019, said PCT application incorporated herein by reference in its entirety. It can be desirable to employ a load cell with a ‘high’ ratio of width to thickness, where ‘width’ is the dimension across a plan view of the planar load cell assembly, and thickness is the dimension across a side view. Exemplary suitable load cell assemblies can have a width-to-thickness ratio of more than 10. In some embodiments, the ‘high’ width-to-thickness ratio can be more than 2, more than 3, more than 5, or more than 10.

Examples Of Retailing Units

With reference to FIGS. 2B, 3A-B, 4, 5A-C, and 6, several non-limiting illustrative examples of retailing units suitable for use in connection with the present invention are now described.

A concatenated assembly of three shelving units 300 is shown schematically in FIG. 2B as a non-limiting example of a plurality of weighing assemblies mounted in shelving units. A variety of products 70 are displayed in shelving unit 300₁, including, on shelf 90_1-1 (the double subscript 1-1 indicating the first shelf of the first shelving unit 300₁) supported by shelf bracket 12_1-1, a first plurality of products 70₁ with a first SKU-identifier and a second plurality of products 70₂ with a second SKU-identifier.

Reference is now made to FIGS. 3A and 3B, which respectively, show an assembled weighing assembly 89 comprising two shelf brackets 12_L and 12_R according to embodiments of the present invention, and an exploded view of the weighing assembly. The weighing assembly 89 of FIGS. 3A and 3B is self-stabilizing, i.e., does not require the use of an additional stabilizing element or connection to a back wall of a shelving unit, and can be installed in a shelving unit (e.g., shelving unit 300) without any tools and by a single employee.

Substantially as shown, each of the two shelf brackets 12_L and 12_R may comprise a vertical member 21 which includes industry-standard bracket hooks 13 for engaging with uprights 85, and a horizontal member 22. Planar load cells are fixed to the shelf bracket 12 by anchoring them on a ‘base’ which, according to embodiments, can include the shelf bracket 12 and a shim (adapter plate) 130. Thus, load cell assemblies 101a, 101b can be attached (by screw or rivet or any other appropriate attaching method) to a respective shim 130a, 130b and, in this way, complete the installation of the load cell assemblies on the ‘base’.

The two shelf brackets 12_L and 12_R are joined mechanically by a shelf frame 190 which, although illustrated as a simple frame, can include any member(s) that, when joined with the shelf brackets 12_L and 12_R, provide rigidity. The shelf frame 190 can be an ‘open structural member’ as shown in non-limiting example shown in FIG. 3B, as the ‘openness’ serves to reduce the weight and cost of the illustrated structural member, but this only is for purposes of illustration and the shelf frame need not be open if it is deemed desirable by a designer to use a solid, non-open member or assembly of members that provides structural rigidity at an acceptable weight and cost. Shelf frame 190 can be fabricated from any material such as a metal or a plastic deemed suitable in terms of rigidity, weight and cost.

As discussed earlier, protruding elements 51a, 51b, together with the joining elements 52a, 52b, can function to transfer the load (weight) of a shelf 90 and any products displayed thereupon to the load cell assemblies 101a, 101b. In embodiments, the protruding elements 51 can transfer the load directly by having a lower end positioned in a receptacle in the load cell assembly 101 and in other embodiments the protruding elements function to ensure the positioning of the joining elements 52 on the load cell assemblies 101 so as to transfer the load to the load cell assemblies 101 via the joining elements 52. In some embodiments, protruding elements 51 and joining elements 52 can be threaded (e.g., a threaded bolt and respective nut) and in other embodiments they can be unthreaded (e.g., a simple bolt and respective washer). In some embodiments both a threaded nut and a washer may be provided. One of ordinary skill in the art will appreciate that various conventional arrangements can be employed for coupling the load (shelf 90) to the load cell assemblies 101a, 101b.

In the non-limiting example of FIG. 3B, a processor 161 is provided on-board the weighing assembly 89 in order to simplify communication with load cell assemblies. In the illustrated example, processor 161 is affixed to the shelf frame 190 with upper fasteners 165 and lower fasteners 163. A processor cover 162 can be provided, e.g., to protect the processor from dust, moisture or detritus, and spacers 164 may be used to isolate the processor from a metallic shelf frame 190.

In embodiments, a weighing-enabled shelving arrangement can be a standalone unit adapted for retail sales transactions. In a non-limiting example shown in FIG. 4A, a weighing-enabled standalone shelving unit 200 includes a shelving volume 210 defined by shelving housing 205, the shelving volume 210 enclosed by left and right walls 281_L and 281_R, and back wall 280. In some embodiments any one or more of left and right walls 281_L and 281_R, and back wall 280 can be a partial wall. The weighing capabilities and weighing-relevant components of shelf assemblies 290 are discussed hereinbelow in connection with FIG. 5B. A door 220 can be provided on or near the front boundary of the shelving unit 200 (front being the direction open for access to a shelving volume 210 enclosed on three sides), the door being operative in some embodiments to preserve the interior temperature of the shelving unit 200 in the case that it is a refrigerated unit, and in some embodiments being operative for any one or more of improving hygiene, limiting entry of dust and dirt, limiting customer interaction with the products while choosing, and having a locking mechanism so as to control access to the products within the shelving unit for commercial reasons: As is known in the art, a locking mechanism can be adapted to allow opening of the door upon receipt of an electronic signal, for example from a computer system with a retail module, where the signal is part of the retail sales transaction process, e.g., allowing opening upon swiping of a card or a screen input from a user or cashier. Together with the door 220, a shelving volume 210 can be enclosed on all four sides.

A shelving unit 200 includes at least one shelf assembly 290 of FIGS. 5A and 5B, or shelf assembly 490 of FIG. 6, both of which are further described hereinbelow. In FIG. 4A, five shelf assemblies 290 are shown. The number of shelf assemblies 290 in a shelving unit 200 can be as few as one and as many as practicably can fit in the shelving unit while allowing access for shelf-stockers and customers to the products 70 stocked and displayed thereupon. Products can be stocked and displayed homogeneously, i.e., in groups of identical products taking up part or all of a shelf assembly 290, or mixed with non-identical products, as illustrated in FIG. 4A by the display on the two lower shelf assemblies 290 of products 70₁, 70₂, 70₃ and 70₄.

Shelf assemblies 290 are attached to one or two or three of left and right walls 281_L, 281_R and back wall 280. The shelf assembly 290 can be attached directly to any of the walls and preferably is by employing one or more attachment elements 285 such as, for example, the attachment elements 285_L1, 285_L2, 285_L3 in FIG. 4A. In some embodiments attachment elements 285 share similarities with uprights 85 used in open shelving bays, in that each comprises a plurality of attachment elements (e.g., recesses or holes) designed for the easy insertion and removal of shelf bracket hooks along a continuous strip, and are different from uprights 85 in that they are fixedly attached to a side wall 281 or back wall 280 of an enclosed shelving unit 200. In other embodiments (not shown) attachment elements 285 can be only as large as necessary for having a single attachment arrangement (e.g., recess, hole, peg, hook, etc.) for use by a single shelf, or can have several such attachment arrangements so as to allow flexibility in the height-placement of shelf assemblies 290, but without having the continuous strip configuration of the attachment arrangements shown in FIG. 4A. The detail inset of FIG. 4A shows a close-up of attachment element 285_L1 which includes attachment-element-points 287. Attachment-element-points 287 are shown as holes, but in other embodiments they can be, for example, protruding members, recesses, or slots, which mate with corresponding attachments points 286. An attachment element 285 can also include fastening arrangements 288 for fastening the attachment element 285 to a wall 281 of the shelving unit 200.

FIG. 4A shows three attachment elements 285_L1, 285_L2, 285_L3 on the left wall 281_L of the shelving unit 200, and a skilled artisan can easily understand that in such an example there can be three corresponding and similar attachment elements 285_R1, 285_R2, 285_R3 on the right wall 281_R. However, this presentation of 3 (or, as can be reasonably extrapolated: 6) attachment elements 285 is by way of illustrative example only, and the actual number of attachment elements 285 and their respective placement is merely a design choice where the design goal is providing sufficient support in the right places for each shelf assembly 290 so that shelf assemblies 290 are substantially immobilized in a horizontal position and maximally resistant to rolling or pitching from forces reasonably applied to any of the parts of the shelf assembly 290. Such forces can be generated by, for example, uneven distribution of products, employees or customers leaning on or against a shelf assembly 290 or a child pulling himself up by grasping the front edge of a shelf assembly 290.

In some embodiments, left and right walls 281_L, 281_R can be partial walls or not be present at all, in which case the lack of a front-edge attachment element on the side wall (e.g., 285_L1 on the front edge of left wall 281_L), or even no side wall attachment elements, in which the designer can put in additional structural elements for stabilizing and immobilizing the shelf assemblies 290 without deviating from the spirit of the invention.

As shown in FIG. 4A, shelving unit 200 can include a refrigeration unit 250 for chilling products 70 and keeping them at a desired temperature.

A shelving unit can also include a retail transaction apparatus 230. A retail transaction apparatus 230 can include any combination of credit card reader, cash and coin slots, and a user interface including, for example, a display screen, and be provided for the purpose of enacting payment for products 70 selected and removed from the shelving unit 200. The retail transaction apparatus need not be installed on the shelving unit 200 itself and instead can be a distance away, for example, at a cashier’s position. In another example, there can be one retail transaction apparatus for a plurality of shelving units 200.

According to embodiments, a shelf assembly includes a plurality of planar load cell assemblies 101 (not shown in FIG. 4A; shown, e.g., in FIG. 5B) which track the weight of products on the shelf assemblies 290, as well as changes in the weight, e.g., from the addition of products 70 on the shelf assembly 290 or the removal of products on the shelf assembly 290.

FIG. 4B shows an example of a shelving unit 200 similar in function to that of FIG. 4A but definitively configured as a display refrigerator. To ensure adequate internal airflow, shelf assemblies 390 can be provided with open spaces horizontal surfaces so as to be at least partly open for a vertical airflow.

Referring now to FIGS. 5A and 5B, an example of a shelf assembly 290 according to an embodiment is shown in both assembled and exploded views. A shelf assembly 290 is a type of weighing assembly that comprises a weighing base 299 and a shelf or shelf tray 291. The weighing base 299 can comprise a shelf base 295 and a plurality of load cell installation assemblies 101. In this example four load cell installation assemblies 101_L1, 101_L2 (not shown, blocked by shelf tray 291), 101_R1, 101_R2 are provided, but a higher or lower number of load cell installation assemblies can be provided while meeting the design goal of providing accurate weight indications of products 70 on a shelf assembly 290 or added thereto or removed therefrom. Thus, load cell assemblies 101 can be attached (by screw or rivet or any other appropriate attaching method) and, in this way, complete the installation of the load cell installation assemblies on the weighing base 299.

The shelf tray 291 can include a receiving bracket (not shown) for securing and stabilizing a shelf tray 291 on a weighing base 299. In some embodiments a shelf tray 291 can be attached in other ways to a weighing bracket 299. A plurality of prior-art protruding elements 251 and a plurality of joining elements 252 (shown in FIG. 5C) vertically aligned with respective protruding elements 251 for receiving the respective protruding elements 251 can be provided for transferring load to the load cell assemblies 101. It should be noted that the number of respective protruding elements 251 and joining elements 252 will be the same as the number of load cell assemblies 101 for any given shelf assembly 290. For, example, in the non-limiting example shown in FIG. 5B, the number of load cell assemblies 101 is four, and thus four respective protruding elements 251 and four joining elements 252 are used.

As mentioned in the preceding paragraph, the protruding elements 251 together with the joining elements 252, can function to transfer load (the weight of the shelf tray 291 and of products 70 displayed thereupon) to the load cell assemblies 101. In some embodiments the protruding elements 251 can transfer the load directly by having a lower end positioned in a receptacle in the load cell assembly 101, and in other embodiments the protruding elements function to ensure the positioning of the joining elements 252 around the holes 140 (in FIG. 4A) on the load cell assemblies 101 so as to transfer the load to the load cell assemblies 101 via the joining elements 252. They can also function to inhibit movement of the aforementioned receiving bracket (not shown) or of the shelf tray 291 in the horizontal plane, for example by being installed in or through the holes 140 in respective load cell assemblies 101. In some embodiments, protruding elements 251 and joining elements 252 can be threaded (e.g., a threaded bolt and respective nut) and in other embodiments they can be unthreaded (e.g., a simple bolt and respective washer). In some embodiments both a threaded nut and a washer may be provided. A protruding element 251 can be deployed in any one of a number of approaches. For example, a protruding element 251 can be disposed on a receiving bracket. As another example, a protruding element 251 can be disposed on joining element 252. As yet another example, a protruding element 251 can be disposed on the shelf tray 291 (preferably flush with the upper surface of the shelf tray 251), the respective joining element 252 holding it in its place on the shelf tray 291. In another example, not shown, a weight distributor in the form of a button or disk can be provided to transfer load to joining element 251.

It should be noted that use of the term ‘shelf tray’ should not be taken to literally mean a tray, e.g., as illustrated in the non-limiting example of FIGS. 5A and 5B wherein shelf tray 291 includes tray rim 292. Front flange 294 of shelf tray 291 is optional and has both aesthetic and functional purposes, e.g., obscuring the shelf base 295, the load cell installation assemblies 101 and the miscellaneous elements that might be provided for attachments. In other embodiments, shelf tray 291 can be flat without a tray rim 292, and if additional structural support is necessary for the shelf tray 291, e.g., to resist twisting or bending, it is possible to apply other engineering solutions for strengthening the structure.

Still referring to FIGS. 5A and 5B, a shelf assembly 290 can comprise attachment arrangements or points 286 which mate with attachment elements 285 of side walls 281 and/or back wall 280 of shelving unit 200. If attachment elements 285 comprise holes or recesses, then attachment points 286 can comprise protruding members such as hooks or knobs or similar, and vice versa ― if attachment elements 285 comprise protruding members such as hooks or knobs, then attachment points 286 can comprise corresponding recesses or holes. Shelf assembly 290 preferably extends from left wall 281_L to right wall 281_R such that attachment points on the two sides can mate with attachment elements 285 or attachment components 289 that are joined to the attachment elements 285. In some embodiments, shelf assembly 290 has a width that is at least 80% or at least 90% or at least 95% of the distance between left wall 281_L and right wall 281_R. According to embodiments, there is only a single shelf assembly 290 at any given height in shelving unit 220.

We now refer to FIGS. 5D-F.

A shelf assembly 390 for a refrigerator includes a weighing base 195 and a shelf 391. The shelf 391 can include a peripheral rim 292 to reduce the likelihood that a product leans against the internal wall of the refrigerator 200, which would reduce the force measurable on the shelf 391. Weighing base 195 includes opposing load-cell bases 191_L, 191_R detachedly attachable to respective left and right internal walls of the refrigerator. As shown in FIG. 5F, the bottom of the weighing base 195 (and therefore the bottom of the shelf assembly 390) includes attachment arrangements 286. Each of the load-cell bases 191 includes a plurality of load cells 101 (of any type, not necessarily planar load cells). Thus, there are at least two load cells 101 on each side, or at least 4 load cells for each weighing base 195. The two load-cell bases 191 are joined to form a rigid, e.g., stable, resistant to twisting, and/or not flexible, frame. A single-member beam 190 is shown as joining the two load-cell bases 191 but any appropriate design and number of left-to-right beams can be used. The beam can be used to support electronic communication arrangements 60, for example transmitted via a communications channel 61, as shown in FIG. 5E. In some embodiments, the load-cell bases 191 and/or the beam are covered with covers 192. It can be desirable for the weighing base 195 to allow at least a minimum amount of vertical airflow to flow freely within the interior of the refrigerator/shelving unit 200, and for this purpose a portion of the horizontal surface of the weighing base 195 can be ‘open’ to a vertical airflow, the term ‘vertical’ meaning any airflow within the refrigerator, which in many implementations is vertical or predominantly vertical, e.g., within ±10° of vertical, or within ±20° of vertical, or within ±30° of vertical, or within ±40° of vertical, or within ±45° of vertical. Preferably, at least 25% of the horizontal surface area of the weighing base 195 to vertical airflow, or at least 30%, or at least 35%, or at least 40%, or at least 45%, or at least 50%. In some embodiments, as much as 80% or as much as 70% or as much as 60% of the horizontal surface area of the weighing base 195 can be open to vertical airflow.

The horizontal area of the shelf 391 is also at least partly open to vertical airflow. In embodiments, the horizontal surface area of the shelf 391 can be at least 40% open or at least 50% open or at least 60% open or at least 70% open or at least 80% open or at least 90% open. In embodiments, the shelf 391 can utilize a wire grid design. A wire grid design is mostly open, and airflow passing through the open horizontal areas of the weighing base 195 is not be substantially blocked by the wires of the grid, which generally create minor turbulence as the air passes therethrough without a substantial pressure drop. In some embodiments, a wire-grid shelf can include both thinner wires, e.g., front-to-back wires deployed across the shelf 391 for supporting products, and thicker wires, e.g., left-to-right wires for structural support. As shown in FIG. 5D, left-to-right wires 395 are spaced so as to transmit force, e.g., the weight of the shelf and of products displayed thereupon, to the load cells assemblies 101 in the load-cell bases 191. Thus, the load cell assemblies 101 mediate between the weighing base 195 and the shelf 391, and the shelf 391 does not sit directly on the weighing base 195 or on the cover 192. The left-to-right wires 395 are illustrated in FIG. 5E as being thicker than the front-to-back wires, but in other examples they can all be the same thickness and weight. The skilled artisan will understand that a selection criterion for the left-to-right wires is sufficient rigidity, e.g., resistance to twisting, sagging, etc.

Reference is now made to FIG. 6, in which another embodiment of a shelf assembly 490 is shown. Respective left and right weighing bars 495_L, 495_R are provided as fixed attachments to left and right side-walls 281_L, 281_R of shelving unit 200. Obviously, there can be multiple pairs of weighing bars provided in a single shelving unit 200 ― one for each shelf assembly 490 desired to be installed in the shelving unit 200. Each weighing bar comprises a pair of load cell installation assemblies 101. The shelf assembly 490 further comprises a shelf tray 493, which can be attached to the respective left and right weighing bars 495_L, 495_R by a receiving bracket (now shown) or by other fastening methods known in the art. Protruding elements 251 and receiving elements 252 can be used here in the same manner as in the embodiment illustrated in FIG. 5B.

The embodiments illustrated in FIGS. 3a-3b, 5A-5B and in FIG. 6 are diverse, non-limiting examples of weighing-enabled shelving units and shelf assemblies and do not exhaust the possibilities of a retail display and sales system suitable for use with the present invention. For example, in other embodiments (not illustrated), weighing bars can be deployed in a direction that is orthogonal to the direction of the weighing bars 495 in FIG. 6, such that they each extend from the left wall of a shelving unit to the right wall, one such ‘transverse’ weighing bar proximate to the back wall and secured either to the back wall or to the two side walls, and the other ‘transverse’ weighing bar proximate to the front of the shelf assembly 490 and secured at both ends to the two side walls.

Retailing System Block Diagrams

FIG. 7 includes a block diagram showing details of a system for executing unattended retail sales transactions and/or tracking inventory of products, using, for example, embodiments of weighing assemblies and shelving arrangements disclosed with respect to FIGS. 2B, 3A and 3B. Such a system includes one or more shelving units 300₁ .. 300_N, each of which can include any of the weighing assembly features shown. Each shelving unit include a number of shelves 90, each supported by a left-and-right pair of weighing assemblies 10 including shelf brackets 12. Each pair of weighing assemblies 10 includes a weighing assembly 10 for supporting a different end of the shelf 90. As an example, weighing assemblies 10_L1-1 and 10_R1-1 are respective the left and right assemblies for support a first shelf 90_1-1 in first shelving unit 300₁. Each of the load cell assemblies 101 installed in the system can communicate weight information with computing device 65. Once computing device 65 determines that a product has been added to or removed from a shelf, and further determines which specific product has been added to or removed from a shelf (as discussed earlier in connection with FIG. 15), then the information can be forwarded to a retail sales transaction system 401 and or an inventory tracking system 402. Not all of the elements in the block diagram in FIG. 7 need be present in order to practice the invention.

FIGS. 8A, 8B and 8C include block diagrams showing details of exemplary systems for executing unattended retail sales transactions and/or tracking inventory of products, using, for example, embodiments of shelving units and shelf assemblies disclosed with respect to FIGS. 4, 5A, 5B and 6. Such a system includes a shelving unit 200, which can include any of the shelving unit features shown. Each shelving unit includes a number of shelf assemblies 290, or alternatively, shelf assemblies 490 as discussed above with respect to FIG. 6.

Each shelf assembly 290 includes shelf tray 291, weighing base 299, load cell installation assemblies 101, communications arrangements 60 by which the processors of load cell assemblies can communicate weight information with other system elements, and miscellaneous mechanical elements.

Each shelf assembly 490 includes shelf tray 493, weighing bars 495_L and 495_R, load cell installation assemblies 101, communications arrangements 60 by which the processors of load cell assemblies can communicate with other system elements, and miscellaneous mechanical elements.

Each shelf assembly 390 includes shelf 391, weighing base 195, load cell installation assemblies 101, communications arrangements 60 by which the processors of load cell assemblies can communicate with other system elements, and miscellaneous mechanical elements. In some embodiments (not shown in FIG. 8C) the shelf assembly 390 can include a weighing-base cover.

Each of the load cell assemblies 101 of load cell installation assemblies 101 can communicate weight information with computing device 65. Once computing device 65 determines that a product has been added to or removed from a shelf, and further determines which specific product has been added to or removed from a shelf, then the information can be forwarded to a retail sales transaction system 401 and or an inventory tracking system 402. It will be appreciated by those of skill in the art that not all of the elements in the block diagram in FIG. 8 need be present in order to practice the invention.

Methods for practicing embodiments of the present invention are disclosed in the following sections.

Discussion Of A First Method

A first method for conducting a retail transaction using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon according to embodiments, is now disclosed. The method is suitable for use with any of the embodiments of weighing assemblies and shelving arrangements disclosed herein. As shown in the flowchart in FIG. 9, the method comprises:

Step S01, monitoring weight measurement data transmitted by weighing assemblies as streams of weight measurement data-points. The weight measurement data correspond to the shelf 90 (or, equivalently, shelf 290 or shelf 490) and to a plurality of non-homogeneous products 70 arranged thereupon. The weight measurement can include voltage measurements generated by load cells of the weighing assemblies 101; as is known in the art, as the spring element of a load cell deforms, the cell’s strain gauges also change shape. The resulting alteration to the resistance in the strain gauges can be measured as voltage. In some embodiments, a change in voltage is transmitted. In some embodiments, an estimated voltage-to-gram conversion factor is applied at each of the load cell assemblies 101, producing a first-order estimation of the reaction force of each load cell, i.e., the force in reaction to the force applied by the weight of the shelf and products. Thus, it is possible to transmit the reaction force, or an estimated/calculated weight, from each weighing assembly ― instead of the voltage data points or in addition thereto. In embodiments, the weight measurement data-points (whether weights and/or voltage inputs thereto) are the only sensor-generated data received during the conduct of the transaction, i.e., the eventual identification of the product(s) and respective product-event(s) of a weight-event is made on the basis of the transmitted weight measurement data but no other sensor data, whether from optical sensors (imaging devices), piezoelectric sensors, radar or lidar sensors, etc.
Step S02, determining a set of weight-event parameters of a weight event, responsively to a change in values and contingent upon reaching steady states according to a stability-tracking rule. In some embodiments, a steady state is a state in which, following a transient period, weight measurement data are constant or ‘nearly’ constant according to a threshold defining ‘nearly’. Depending on load cell design, shelf design and other factors, the transient period can be up to a second in length, or up to 5 seconds in length, or up to 10 seconds in length, or even longer. In some embodiments, it can be desirable to define the steady state in accordance with one or more stability-tracking rules. A stability-tracking rule according to embodiments is a rule that governs when the weight measurement data points have adequately reached a ‘steady state’ condition under which a determination of weight-event parameters of a weight event can take place. For example, it can be desirable to define a stability-tracking rule that includes: ‘steady state is reached N seconds after the peak response to a weight-event.’ As another example, it can be desirable to define a stability-tracking rule that includes the following: ‘respective steady states for the streams of weight measurement data-points are defined by respective response-amplitude thresholds.’ As is known in the art, the dynamic response to a weight-event, after an initial ‘shock,’ can have a long dying ‘tail’ of ever-decreasing minimal oscillation about a mean, e.g., if visualized, as in the schematic and illustrative example in FIG. 10, on cartesian axes where the x-axis represents time and the y-axis represents response amplitude. The mean (of a reaction force/weight or of a voltage input thereto) can be easily discernible as the oscillation continues to decrease, and at some point in time there is little additional precision to be gained by waiting for the dynamic response to stop oscillating completely. Thus the stability-tracking rule can be based on a height of the oscillation, i.e., above the mean, reaching a threshold. Examples of suitable thresholds include absolute thresholds (e.g., in grams) and relative thresholds (e.g., as a percentage of the mean).

In some embodiments, applying a stability tracking rule includes estimating bias in the weight measurement data-points and compensating for the estimated bias. The compensation can include cancellation of the bias. As an example, a time-series clustering algorithm can be applied to the received streams of weight measurement data-points in order to identify and quantify bias in the data-points. The results can be compared with historical, i.e., learned and/or stored bias data, and the historical bias data can be updated accordingly so as to create an updated bias estimation.

The purpose of the determining in Step S02 is to produce a deterministically identified set of weight-event parameters that can most reliably be associated with a weight-event and subsequently used for a retail transaction in respect of the weight event. In other words, the ‘determining’ functionality, which receives no real-time sensor-generated information other than weight measurement data-points, is tasked to retroactively identify ‘what happened’ when a non-transient change in weight on the shelf is detected. A set of weight-event parameters includes a pairing of a product and a product-action respective of the product, i.e., one of: a removal of a product from a shelf, addition of a product to a shelf, or a displacement of a product from one location on a shelf to another location on the shelf. If the product is moved from one shelf to another, it would be modeled as a removal from the first shelf and an addition to the second shelf. The determining includes identifying one or more supported sets of weight-event parameters in a weight-location space. This typically involves using a regression model to preliminarily estimate the location of the removed/added/moved product based on the estimated reaction forces which emanate from embodied by the weight measurement data-points (and/or changes therein). For example, the regression model can be a linear regression model; in other non-limiting examples other regression models can be used, e.g., a binomial regression model. The preliminary location estimation can be refined using statistical inference.

In an example illustrated in FIG. 10, a shelf 90 defines an x-y plane having an origin at (0,0) and the diagonally opposite corner at (1,1). A product with weight of W and weight-center coordinates of (X,Y) is removed from the shelf. Theoretically, given a perfect, uniform and balanced shelf 90, no bias, drift or noise, and fully calibrated voltage-to-weight factors, then weighing assemblies (not shown) at (0, 0), (0,1), (1,1), (1,0) could transmit respective weight measurement data points showing respective decreases in reaction forces/weight of (1-X)*(1-Y)*W, (1-X)*Y*W, X*Y*W, X*(1-Y)*W. In other words, the weight would be linearly distributed to the respective weighing assemblies and the total sum of the calculated reaction forces would be W. However, because of the myriad factors affecting reaction forces at the weighing assemblies affecting the predictability of weight measurements, it is more realistic to see weight-location as a probability density function, as shown schematically in FIG. 12 solely for visualization purposes, where the weight decrease from the product removal at (X, Y) is represented as a mapped weight distribution at multiple (x,y) points. The bivariate probability density function shown in FIG. 12 is shown solely as an illustrative example; other suitable probability density functions are discussed hereinbelow.

The weight of the product removed/added/moved, according to embodiments, is estimated by aggregating the estimated reaction forces, where each reaction force at a specific weighing assembly is a product of a voltage input and an estimated voltage-to-weight conversion factor. Weight and location estimations can be iteratively improved, i.e., improved weight information can be used to improve location data, and improved location data can be used to improve weight information. The estimation/improvement iterations can be continued until a ‘stop’ criterion is met, e.g., iteration-over-iteration change reaches a threshold. At that point, a probability density estimation using historical weight data for products, and/or product positioning plan data and/or other external product data as available, can be used to posit a joint weight-location probability density function, and to create an ad hoc group of ‘supported’ event sets in the weight-location space of the post-weight-event shelf. The inventors have found, non-exhaustively, that Gaussian Mixture models, Multinomial Models, Piecewise-Uniform distribution models, Multivariate Beta distribution models and Multivariate Gamma distribution models are suitable for use in modeling joint weight-location probability density functions according to the disclosed embodiments; other probability models as known in the art can also be suitable. ‘Supported’ event sets are those which meet a minimum likelihood criterion for product weight and location. A joint weight-location event-based classification function can be used to select a single set of weight-event parameters from the identified one or more supported sets, and this single set is the ‘determined’ set of weight-event parameters.

In light of the operating requirement in Step S02 that the determining is contingent upon the system reaching a steady state in accordance with a stability-tracking rule, it can become necessary to ‘disambiguate’ or ‘discretize’ multiple discrete weight-events which can make up what appears to be a single weight-event, and this is handled by the joint weight-location event-based classification function. In a first example, a user/customer removes two products that are two items with the same SKU identifier ― simultaneously, e.g. two cans of soda with one hand, or sequentially, where the second product is removed before the system reaches steady-state following the first removal. Although the two products share the same SKU identifier, the weights can be different to the extent that the learned/stored history of weight distribution for that SKU indicates a historical range of product weight. In a second example, the two simultaneously or sequentially removed products have different SKU-identifiers - and may be known to have the same nominal weights, or alternatively may be known to have different nominal weights. Even if they are known to have the same nominal weights, the history of weight distribution can be different for each product. In a third example, a kilogram of product is removed from the shelf, and the classification function determines whether a single one-kg product was removed, or two (or more) products with total weight of 1 kg were removed. In some examples, the estimation, regression and/or iteration of location information, in combination with the weight estimations as described above can be used for assigning different probabilities to different sets of weight-event parameters.

Step S03, recording and/or displaying information about the results of the determining of Step S02. The recording is typically required for conducting/completing the retail transaction. The displaying can be used, for example, in indicating to a user/customer a sub-total or total price for a transaction, along with an identification of a product taken from a shelf (while removing products taken from a shelf and returned to a shelf, even if returned to a different shelf.

In some embodiments, as shown in the flowchart of FIG. 13, the first method additionally comprises:

Step S04, receiving an indication of a transaction-initiation. The indication can include, for example, a physical shock such as from opening and/or closing a door of a retail unit (e.g., retail unit 200 of FIG. 4A or FIG. 4B). The indication can include a sensed movement, such as a hand approaching a shelf, which can be sensed, for example, by an optical sensor or radar or lidar sensor. In some embodiments, the weight-event itself can be the transaction-initiation.

In such embodiments, Step S01 is replaced by Step S01' in which the monitoring of weight measurement data transmitted by weighing assemblies as streams of weight measurement data-points is in response to the receiving of Step S04.

In some embodiments, as shown in the flowchart of FIG. 14, the first method additionally comprises, in addition to Steps S01 .. S03:

Step S05, responsively to a change in the values of weight measurement data-points, analyzing the streams of weight measurement data-points to detect noise and drift. The weight measurement data-points and the changes in their value over time are analyzed for the presence either or both of the two anomalous phenomena of noise and drift. Noise for the purposes of this disclosure comprises high-frequency, i.e., short-lived, changes in values of weight measurement data points in a stream of such data points transmitted by the weighing assemblies. For example, noise can include spikes in value, which can be either ‘plus’ or ‘minus’ with respect to the baseline values, and which are substantially reversed (meaning at least 80% reversed, at least 90% reversed, at least 95% reversed, or at least 99% reversed) within less than 10 seconds after the spike begins, or within less than 5 seconds or within 1 second after the spike begins. In some embodiments, the source of noise can be mechanical and/or environmental. For example, noise can be caused by the vibration of an air conditioning condenser. Noise can be caused by simple mechanical events, such as a customer or employee touching a shelf or a product on the shelf. Opening and closing a door of a retail unit can be consider noise in some embodiments. Drift for the purposes of this disclosure is a low-frequency, i.e., long-lived, change in weight measurement values, usually changes that are relatively minor in magnitude. Examples of causes of drift are daily cycles of indoor temperatures, environmental conditions such as humidity and atmospheric pressure, and artifacts of a power supply. Unlike what is termed herein noise, drift is not quickly reversed, because it is generally caused by a persistent and/or repeating condition. In some embodiments, drift is periodic; for example, the same pattern or trend can repeat itself every day at a certain time, or at the start of every work shift, or even in on an annual cycle in line with seasonal changes in the environment.
Step S06, in response to detection of noise and/or drift in Step S05, at least partially filtering out or compensating for the noise and/or drift, and thus generating revised weight measurement data. Noise and/or drift can mask true changes in weight embodied in the values of the weight measurement data points. Noise and/or drift can also affect the resolution and disambiguation of products and actions (weight-event parameters) by adding uncertainty and skewing probabilities. Therefore, it can be advantageous to filter out, or compensate for, noise and/or drift, at least partially. As is known in the art, a signal can commonly be decomposed into its component frequencies using a Fourier transform. Carrying out this step results in revised weight measurement data that can be generated as a result of the filtering out and/or compensating for noise and drift.

In embodiments, Step S04 and S01' can be combined with steps S02, S03, S05 and S06.

Discussion Of A Second Method

A second method for conducting a retail transaction using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon according to embodiments, is now disclosed. The method is suitable for use with any of the embodiments of weighing assemblies and shelving arrangements disclosed herein. As shown in the flowchart in FIG. 15, the second method comprises:

Step S11, monitoring weight measurement data transmitted by weighing assemblies as streams of weight measurement data-points. Step S11 is identical to Step S01, and the discussion of Step S01 applies as well to Step 511.
Step S12, determining a set of weight-event parameters of a weight event, responsively to a change in values in one or more streams of weight measurement data-points, the determining comprising (i) identifying one or more supported sets of weight-event parameters in a weight-location space, and (ii) applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

The weight of the product removed/added/moved, according to embodiments, is estimated by aggregating the estimated reaction forces, where each reaction force at a specific weighing assembly is a product of a voltage input and an estimated voltage-to-weight conversion factor. Weight and location estimations can be iteratively improved, i.e., improved weight information can be used to improve location data, and improved location data can be used to improve weight information. The estimation/improvement iterations can be continued until a ‘stop’ criterion is met, e.g., iteration-over-iteration change reaches a threshold. At that point, a probability density estimation using historical weight data for products, and/or product positioning plan data and/or other external product data as available, can be used to posit a joint weight-location probability density function, and to create an ad hoc group of ‘supported’ event sets in the weight-location space of the post-weight-event shelf. The inventors have found that Gaussian Mixture models, Multinomial Models, Piecewise-Uniform distribution models, Multivariate Beta distribution models and Multivariate Gamma distribution models are suitable for use in modeling joint weight-location probability density functions; other probability models as known in the art can also be suitable. ‘Supported’ event sets are those which meet a minimum likelihood criterion for product weight and location. A joint weight-location event-based classification function can be used to select a single set of weight-event parameters from the identified one or more supported sets, and this single set is the ‘determined’ set of weight-event parameters. In some embodiments, applying the joint weight-location event-based classification function includes estimating a joint weight-location probability density.

In some embodiments, wherein applying the joint weight-location event-based classification function applying the joint weight-location event-based classification function includes applying a statistical classification mechanism trained by weight and location information to perform a statistical inference. In some embodiments, applying the classification function includes Bayesian hierarchical modelling, including a Bayesian merge of the joint weight-location probability density function with the group of sets of supported events in the weight-location space.

Discussion Of A Third Method

A third method for conducting a retail transaction using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon according to embodiments, is now disclosed. The method is suitable for use with any of the embodiments of weighing assemblies and shelving arrangements disclosed herein. As shown in the flowchart in FIG. 16, the third method comprises:

Step S21, receiving respective time-series of weight measurement data points from a plurality of weighing assemblies. As discussed hereinabove, weight measurement data points can include calculated weight measurements and/or voltage inputs thereto. The data points are received simultaneously as separate time-series from each load cell assembly.
Step S22, updating an estimation of bias in the weight measurement data points by applying a clustering algorithm to each of the time-series of weight measurement data-points. A ‘bias history’ can be maintained by the retail transaction system, and the bias history can be updated when encountering identified bias in ongoing transactions. The bias history is available to be updated on the basis of bias identified in Step S22. The updating includes identifying bias in the time streams. A preferred method of identifying bias is by using a clustering algorithm. The inventors have found, non-exhaustively, that Gaussian Mixture algorithm, K-means clustering, Hard clustering and a mixture of Beta distributions are suitable clustering algorithms according to the disclosed embodiments.
Step S23, determining a set of weight-event parameters of a weight event, wherein the determining includes compensating for the estimated bias. In some embodiments, compensating for the estimated bias includes correcting an estimation of weight and/or location. In some cases, they go together: correcting an estimation of weight can lead to correcting an estimation of location because of the iterative nature of the joint weight-location modeling.

In some embodiments, Step S23 can include features from Step S12 in that the determining can comprise (i) identifying one or more supported sets of weight-event parameters in a weight-location space, and (ii) applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

The third method can additionally include Steps S05 and S06, which were discussed hereinabove in respect of FIG. 14.

Discussion Of A Fourth Method

A fourth method for conducting a retail transaction using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon according to embodiments, is now disclosed. The method is suitable for use with any of the embodiments of weighing assemblies and shelving arrangements disclosed herein. As shown in the flowchart in FIG. 17, the fourth method comprises:

Step S31 (identical to Step S04), receiving an indication of a transaction-initiation. The indication can include, for example, a physical shock such as from opening and/or closing a door of a retail unit (e.g., retail unit 200 of FIG. 4A or FIG. 4B). The indication can include a sensed movement, such as a hand approaching a shelf, which can be sensed, for example, by an optical sensor or radar or lidar sensor. In some embodiments, the weight-event itself can be the transaction-initiation.
Step S32 (identical to Step S01'), in response to the receiving of Step S31, monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points. The weight measurement data correspond to the shelf 90 (or, equivalently, shelf 290 or shelf 490) and to a plurality of non-homogeneous products 70 arranged thereupon.
Step S33, determining a set of weight-event parameters of a weight event, responsively to a change in values and using at least one stability-tracking rule. In some embodiments, Step S33 can include features from Step S12 in that the determining can comprise (i) identifying one or more supported sets of weight-event parameters in a weight-location space, and (ii) applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets. According to embodiments, the at least one stability-tracking rule is/are can be selected from the following non-exhaustive list of stability-tracking rules:
- a first stability-tracking rule based on tracking a dynamic response of a weighing assembly to said weight event. In some embodiments, this rule can be the same as the stability-tracking rule of Step S02;
- a second stability-tracking rule based on a stability attribute of a product;
- a third stability-tracking rule is based on a stability attribute of the shelf;
- a fourth stability-tracking rule is based on tracking a shock response to the transaction-initiation when the transaction-initiation includes a door opening or closing.
Step S34, recording and/or displaying information about the results of the determining of Step S33. The recording is typically required for conducting/completing the retail transaction. The displaying can be used, for example, in indicating to a user/customer a sub-total or total price for a transaction, along with an identification of a product taken from a shelf (while removing products taken from a shelf and returned to a shelf, even if returned to a different shelf.

In some embodiments, as illustrated by the flowchart in FIG. 18, the method can comprise, in addition to Steps S31 .. S34:

Step S35, receiving an indication of a transaction-completion. In some embodiments a transaction-completion can be inferred from context and circumstances, such as a door closing after a product is removed from a shelf and a predetermined amount of time has passed. In other embodiments, there may be a specific and deliberate transaction-completion, such as receiving payment, scanning a user (biometrically) or a user’s transaction card or telephone screen, or simply leaving a store or a specific portion of the store where the shelving unit is located at which the instant transaction is being conducted.

In some embodiments, as shown in the flowchart of FIG. 14, the first method additionally comprises, in addition to Steps S31 .. S34:

Step S36 (identical to Step S05), responsively to a change in the values of weight measurement data-points, analyzing the streams of weight measurement data-points to detect noise and drift, as further discussed hereinabove in respect of Step S05.
Step S37 (identical to Step S06), in response to detection of noise and/or drift in Step S36, at least partially filtering out or compensating for the noise and/or drift, and thus generating revised weight measurement data.

In embodiments, Steps S36 and S37 can be combined with steps S31 .. S35.

Discussion Of A Fifth Method

A fifth method for conducting a retail transaction using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon according to embodiments, is now disclosed. The method is suitable for use with any of the embodiments of weighing assemblies and shelving arrangements disclosed herein. As shown in the flowchart in FIG. 20, the fifth method comprises:

Step S41 (identical to Step S04), receiving an indication of a transaction-initiation. The indication can include, for example, a physical shock such as from opening and/or closing a door of a retail unit (e.g., retail unit 200 of FIG. 4A or FIG. 4B). The indication can include a sensed movement, such as a hand approaching a shelf, which can be sensed, for example, by an optical sensor or radar or lidar sensor. In some embodiments, the weight-event itself can be the transaction-initiation.
Step S42 determining a set of weight-event parameters of a weight event, responsively to a change in values. In some embodiments, Step S42 can include features from Step S12 in that the determining can comprise (i) identifying one or more supported sets of weight-event parameters in a weight-location space, and (ii) applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.
Step S43 (identical to Step S03), recording and/or displaying information about the results of the determining of Step S42.

The skilled artisan will understand that features described with respect to the various methods can be combined to form other methods and combinations of method steps, and any such combination remains within the scope of the invention. In some embodiments, not all steps are required to carry out any one of the described methods.

Referring now to FIG. 21, a block diagram is shown of a system for conducting a retail transaction based on weight-events in respect of a non-homogeneous assortment of products 70 on a shelf 90 (or, equivalently, shelf 290 or shelf 490). The system 100 can include a plurality of weighing assemblies 101 that are in contact with a shelf 90, which as explained above can support a variety of products 70. The system can also include one or more computer processors 66 and at least one non-transient computer-readable storage medium 68, e.g., a mechanical, optical and/or solid-state storage device or a storage device using whatever data storage technology is suitable for the purpose. The one or more computer processors 66 can optionally be included in a computing device 65 that can be a component of the system 100 and which can optionally include other computer hardware such as communications gear 61, a display device 62, and user input accessories (not shown), such as a keyboard and a mouse. The storage medium 68 can include program instructions 50, as well as weight distribution mappings repository 51, a product database 67 and product positioning plan 69, all of which are discussed elsewhere in this disclosure.

Program instructions 65, which when executed by computer processor(s) 66, are effective to cause the computer processor(s) to carry out any of the methods described hereinabove for conducting a retail transaction using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon.

Transmissions of electronic signals from weighing assemblies 101 can be received by the one or more computer processors 66, for example by way of communications gear 61 and used for the purpose of tracking the weights of products 70 on the shelf 90, and especially of actions taken to products. Communications gear 61 can include any kind of wired or wireless communications arrangements, including, without limitation, direct connections, networked connections and internet connections. Actions include adding a product to a shelf, removing a product from a shelf, and moving a product from one place on a shelf to another. If the moving the product includes lifting the product and putting it back down on the same shelf, it may be interpreted as a removal followed by an adding depending on the speed of the actions and the sampling rate of the weight measurement data, e.g., the number of weight measurement data points per second, as well as any stability-tracking rules applied. On the other hand, sliding a product from one place to another on the shelf might not change, at any given moment, the total weight measured by all of the weighing assemblies associated with a single shelf, but can change the weights measured by each of the individual weighing assemblies.

Additional Discussion Of Methods

Inventive Concept 1: A method for conducting a retail transaction, using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon, the method comprising: a. monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points; b. responsively to a change over time in the values of said weight measurement data-points and contingent upon said values reaching respective steady states according to a stability-tracking rule, determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products; and c. performing at least one of: (i) recording information about the results of the determining in a non-transient, computer-readable medium, and (ii) displaying information about the results of the determining on a display device, wherein the weight measurement data-points comprise at least one type of data selected from the group comprising calculated weights and voltage inputs thereto.

Inventive Concept 2: The method of Inventive Concept 1, additionally comprising: receiving an indication of a transaction-initiation, wherein said monitoring is in response to the receiving.

Inventive Concept 3: The method of either of Inventive Concepts 1-2, wherein the stability-tracking rule includes that said respective steady states for the streams of weight measurement data-points are defined by respective response-amplitude thresholds.

Inventive Concept 4: The method of any of Inventive Concepts 1-3, wherein applying the stability tracking rule includes estimating bias in the weight measurement data-points and compensating for said bias.

Inventive Concept 5: The method of any of Inventive Concepts 1-4, wherein the weight measurement data-points either comprise voltage inputs or solely comprise voltage inputs.

Inventive Concept 6: The method of any of Inventive Concepts 1-5, wherein said determining includes estimating a joint weight-location event-based product classifier.

Inventive Concept 7: The method of Inventive Concept 6, wherein estimating the joint weight-location event-based product classifier includes estimating a joint weight-location probability density.

Inventive Concept 8: The method of Inventive Concept 6, wherein estimating the joint weight-location event-based product classifier includes applying a statistical classification mechanism trained by weight and location information to perform a statistical inference.

Inventive Concept 9: The method of any of Inventive Concepts 1-8, wherein the weight measurement data-points are the only inputs to said determining that are generated by sensors during the transaction.

Inventive Concept 10: The method of any of Inventive Concepts 1-9, additionally comprising, before said determining: a. responsively to a change over time in the values of transmitted weight measurement data-points, analyzing each of the streams of weight measurement data-points to detect at least one of noise and drift; and b. in response to the detection of said at least one of noise and drift, performing at least one of (A) at least partially filtering out the noise and/or drift and (B) at least partially compensating for the noise and/or drift in the weight measurement data points, such that the performing generates revised weight measurement data.

Inventive Concept 11: A method for conducting a retail transaction, using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of non-homogeneous products arranged thereupon, the method comprising: a. monitoring weight measurement data corresponding to the weight of the shelf and of the products arranged thereupon, said weight measurement data transmitted from a plurality of weighing assemblies as respective streams of weight measurement data points; and b. responsively to a change over time in the values of said weight measurement data, determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products, wherein the determining comprises: i. identifying one or more supported sets of weight-event parameters in a weight-location space, and ii. applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

Inventive Concept 12: The method of Inventive Concept 11, wherein applying the joint weight-location event-based classification function includes estimating a joint weight-location probability density.

Inventive Concept 13: The method of Inventive Concept 11, wherein applying the joint weight-location event-based classification function includes applying a statistical classification mechanism trained by weight and location information to perform a statistical inference.

Inventive Concept 14: The method of any of Inventive Concepts 11-13, wherein the determining is based on product weight-distribution data retrieved from a product database.

Inventive Concept 15: The method of any of Inventive Concepts 11-14, wherein the determining is based on a product positioning plan.

Inventive Concept 16: The method of any of Inventive Concepts 11-15, wherein the estimating of the joint weight-location probability density includes iteratively improving initial weight and/or location estimations.

Inventive Concept 17: The method of any of Inventive Concepts 11-16, wherein the applying of the classification function includes Bayesian hierarchical modelling.

Inventive Concept 18: The method of any of Inventive Concepts 11-17, wherein the weight measurement data-points are the only inputs to said determining that are generated by sensors during the transaction.

Inventive Concept 19: The method of any of Inventive Concepts 11-18, wherein the determining is carried out responsively to an absolute value of the change over time in the values of said weight measurement data exceeding a pre-determined threshold.

Inventive Concept 20: The method of any of Inventive Concepts 11-19, wherein the weight measurement data-points either comprise voltage inputs or solely comprise voltage inputs.

Inventive Concept 21: A method for conducting a retail transaction, using data received from a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon, the method comprising: a. receiving respective time-series of weight measurement data points from a plurality of weighing assemblies; b. updating an estimation of bias in the weight measurement data points received from one or more weighing assemblies of the plurality of weighing assemblies, by applying a clustering algorithm to said time-series; and c. determining a set of weight-event parameters of a weight event, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products, wherein the determining includes compensating for the estimated bias.

Inventive Concept 22: The method of Inventive Concept 21, wherein the updating the estimation of bias includes accessing historical bias data.

Inventive Concept 23: The method of either one of Inventive Concepts 21-22, wherein the compensating for the estimated bias includes correcting an estimation of weight and/or location.

Inventive Concept 24: The method of any of Inventive Concepts 21-23, wherein the determined set of weight-event parameters is changed because of the compensating for the estimated bias.

Inventive Concept 25: The method of any of Inventive Concepts 21-24, wherein the determining comprises: i. identifying one or more supported sets of weight-event parameters in a weight-location space, and ii. applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

Inventive Concept 26: The method of Inventive Concept 25, wherein applying the joint weight-location event-based classification function includes estimating a joint weight-location probability density.

Inventive Concept 27: The method of Inventive Concept 25, wherein applying the joint weight-location event-based classification function includes applying a statistical classification mechanism trained by weight and location information to perform a statistical inference.

Inventive Concept 28: The method of any of Inventive Concepts 21-27, additionally comprising, before said determining: a. responsively to a change over time in the values of transmitted weight measurement data-points, analyzing each of the streams of weight measurement data points to detect at least one of noise and drift; and b. in response to the detection of said at least one of noise and drift, performing at least one of (A) at least partially filtering out the noise and/or drift and (B) at least partially compensating for the noise and/or drift in the weight measurement data points, such that the performing generates revised weight measurement data, wherein said determining is based on a change in values in said revised weight measurement data.

Inventive Concept 29: A method for conducting a retail transaction, using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon, the method comprising: a. receiving an indication of a transaction-initiation; b. in response to said receiving, monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points; c. responsively to a change over time in the values of said weight measurement data-points, determining a set of weight-event parameters of a weight event using at least one stability rule, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products; and d. performing at least one of: (i) recording information about the results of the determining in a non-transient, computer-readable medium, and (ii) displaying information about the results of the determining on a display device.

Inventive Concept 30: The method of Inventive Concept 29, additionally comprising: receiving an indication of a transaction-completion.

Inventive Concept 31: The method of either of Inventive Concepts 29-30, wherein a first stability-tracking rule is based on tracking a dynamic response of a weighing assembly to said weight event.

Inventive Concept 32: The method of any of Inventive Concepts 29-31, wherein a second stability-tracking rule is based on a stability attribute of a product.

Inventive Concept 33: The method of any of Inventive Concepts 29-32, wherein a third stability-tracking rule is based on a stability attribute of the shelf.

Inventive Concept 34: The method of any of Inventive Concepts 29-33, wherein (i) said transaction-initiation includes opening and/or closing a door, and (ii) a fourth stability-tracking rule includes tracking a shock response to said transaction-initiation.

Inventive Concept 35: The method of any of Inventive Concepts 29 to 34, wherein said determining comprises: iii. identifying one or more supported sets of weight-event parameters in a weight-location space, and iv. applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

Inventive Concept 36: The method of any of Inventive Concepts 29-35, additionally comprising, before said determining: a. responsively to a change over time in the values of transmitted weight measurement data-points, analyzing each of the streams of weight measurement data points to detect at least one of noise and drift; and b. in response to the detection of said at least one of noise and drift, performing at least one of (A) at least partially filtering out the noise and/or drift and (B) at least partially compensating for the noise and/or drift in the weight measurement data points, such that the performing generates revised weight measurement data, wherein said determining is based on a change in values in said revised weight measurement data.

Inventive Concept 37: A method for conducting a retail transaction, using a plurality of weighing assemblies that are jointly operable to measure the combined weight of the shelf and of products arranged thereupon, the method comprising: a. receiving an indication of a transaction-initiation; b. in response to said receiving, monitoring weight measurement data corresponding to the shelf and a plurality of non-homogeneous products arranged thereupon, said weight measurement data transmitted by the plurality of weighing assemblies as respective streams of weight measurement data-points; and c. responsively to a change over time in the values of said weight measurement data-points, determining a set of weight-event parameters of a weight event using at least one stability rule, the determined set of weight-event parameters comprising one or more products and a product-action respective of each one of the one or more products, wherein said indication of the transaction-initiation includes one of (i) a mechanical shock indicating a door opening and/or closing, (ii) a lidar or radar reading of a hand reaching to the shelf, (iii) an electronic or optical reading of a payment or subscription medium, and (iv) a biometric reading of a user.

Inventive Concept 38: The method of Inventive Concept 37, wherein said determining comprises: i. identifying one or more supported sets of weight-event parameters in a weight-location space, and ii. applying a joint weight-location event-based classification function to select a set of weight-event parameters from the identified one or more supported sets.

Unless otherwise defined herein, words and phrases used herein are to be understood in accordance with their usual meaning in normal usage. Some terms used herein are terms of art in the industries that supply and use shelving assemblies, for example (and not exhaustively): An “upright” is a post or rod fixed vertically as a structural support for other components in a shelving unit and to bear the load of the shelves and any goods displayed thereupon, generally including holes or other arrangements along at least two faces for the attachment of shelf brackets. An upright, unless it is at the end of continuous run of shelving, is shared by two adjacent shelving units and therefore a standard “shelving unit” is considered to include only one upright. In the description and claims of the present disclosure, each of the verbs, “comprise”, “include” and “have”, and conjugates thereof, are used to indicate that the object or objects of the verb are not necessarily a complete listing of members, components, elements or parts of the subject or subjects of the verb. As used herein, the singular form “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a shelf” or “at least one shelf” may include a plurality of markings.

The present invention has been described using detailed descriptions of embodiments thereof that are provided by way of example and are not intended to limit the scope of the invention. The described embodiments comprise different features, not all of which are required in all embodiments of the invention. Some embodiments of the present invention utilize only some of the features or possible combinations of the features. Variations of embodiments of the present invention that are described and embodiments of the present invention comprising different combinations of features noted in the described embodiments will occur to persons skilled in the art to which the invention pertains.

METHODS AND SYSTEMS FOR CONDUCTING WEIGHT-BASED TRANSACTIONS

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