DETAILED ACTION
Status of the Application
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status
This action is a Final Action on the merits in response to the application filed on 04/06/2026.
Claims 1, 8, 11, 12, and 17 have been amended.
Claims 7, 15, and 18 have been cancelled.
Claims 21 and 22 have been added
Claims 1-6, 8-14, 16, 17, and 19-22 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office action have been maintained.
The 35 U.S.C. 103 rejections of claims in the previous office action are withdrawn in light of applicant’s amendments, however a new 103 rejections was added.
Claim 21 is objected to because of the following informalities of Claim 21:
Claim 21 recites, “the method of claim 14” and it should read as “the cooler retrofit kit of claim 14”
and should be revised.
Some of the words used lacks proper use, sentence structure, and should be written clearly and concisely. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 and 8-11 are directed towards a method, claims 12-14, 16 and 21 are directed towards a cooler retrofit kit, and claims 17, 19, 20, 22 are directed towards a cooler it, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-6, 8-14, 16, 17, and 19-22, the independent claims (claims 1, 7, 12, and 17) are directed to managing the usage of a cooler, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, a method of optimizing usage of coolers, the method comprising:
aggregating data stored on a plurality of the data modules;
using a machine learning model to derive an optimization for the preexisting coolers from the aggregated data, wherein the optimization includes at least one of optimizing operating parameters of refrigeration systems of the coolers to maximize energy efficiency, optimizing schedules for restocking product in the coolers to maximize profit, optimizing maintenance protocols for the coolers to minimize downtime of the coolers.
these steps fall within the commercial interaction such as sales activities; managing personal behavior such as activities (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions relating behaviors and business relations, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of coolers, sensor suite, refrigeration system monitor, traffic monitor, stock monitoring, module, machine learning (Claim 12 cooler retrofit kit, coolers, sensor suite, refrigeration system monitor, traffic monitor, stock monitoring, module, machine learning; Claim 17 coolers, sensor suite, traffic monitor, stock monitoring, module, machine learning). The claims recite the steps are performed by the coolers, sensor suite, refrigeration system monitor, traffic monitor, stock monitoring, module, machine learning.
The limitations of
retrofitting preexisting coolers, wherein retrofitting each of the preexisting coolers comprises:
installing a sensor suite on the cooler, wherein the sensor suite comprises at least one selected from a group consisting of:
a refrigeration system monitor configured to monitor power usage by a refrigeration system of the cooler,
a traffic monitor configured to detect presence of individuals in a vicinity of the cooler, and
a stock monitoring system; and
installing a data module on the cooler, wherein the data module is configured to store data acquired by the sensor suite;
are mere data gathering and processing recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by coolers, sensor suite, refrigeration system monitor, traffic monitor, stock monitoring, module, machine learning. The coolers, sensor suite, refrigeration system monitor, traffic monitor, stock monitoring, module, machine learning are recited at a high level of generality. In limitation (a), the machine learning model is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The machine learning model is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites machine learning model. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the coolers, sensor suite, refrigeration system monitor, traffic monitor, stock monitoring, module, machine learning. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and processing. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0068 “the generation of optimizations and the analysis of the collected information can be performed by human analysts, computer programs such as, for example, machine learning models, or both.]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
retrofitting preexisting coolers, wherein retrofitting each of the preexisting coolers comprises:
installing a sensor suite on the cooler, wherein the sensor suite comprises at least one selected from a group consisting of:
a refrigeration system monitor configured to monitor power usage by a refrigeration system of the cooler,
a traffic monitor configured to detect presence of individuals in a vicinity of the cooler, and
a stock monitoring system; and
installing a data module on the cooler, wherein the data module is configured to store data acquired by the sensor suite;
are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a processor to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-6, 8-11, 13, 14,16, 19, and 20 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claim 2 recites a sensor; claims 3, 4, 14, 20 recite load cells to measure weight; claim 5 installing a data module; claims 6, 9, 13 recite a module for controlling the cooler; claims 8, 10, 19 recites module and sensor for optimizing data. The dependent claims 2-6, 8-11, 13, 14,16, 19, and 20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-6, 8-11, 13, 14,16, 19, and 20 recites sensor suite, refrigeration system monitor, traffic monitor, stock monitoring, module which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-6, 8-11, 13, 14,16, 19, and 20 recites coolers, sensor suite, refrigeration system monitor, traffic monitor, stock monitoring, module, machine learning which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-6, 8-11, 13, 14,16, 19, and 20 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 12, and 17. Therefore claims 2-6, 8-11, 13, 14,16, 19, and 20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 11, 12, 15, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20240240853, Avakian, et al. to hereinafter Avakian in view of United States Patent Publication US 20110168290, Breitenbach, et al. to hereinafter Breitenbach in view of United States Patent Publication US 20220398647, Hisham, et al.
Referring to Claim 1, Avakian teaches, the method comprising:
a sensor suite on the cooler (
Avakian: Sec. 0006, One general aspect includes a method for generating advertisement content on a display screen affixed to a movable door attached to a retail product container that may include an internal storage volume. The method may also include determining a planogram mapping of the internal storage volume and a description of a plurality of products, where the description may include shape, color, and/or dimension of each of the plurality of products. The method also includes post-processing one or more images captured by an optical sensor, which is installed on (e.g., on, in, or about) the movable door, into a composite image.
Avakian: Sec. 0048, A plurality of retail product containers (similar to retail product container 102) and associated displays may be arranged side-by-side down an aisle of a retail store (for example, a grocery store). Edge processor (controller) 101 (as shown in FIG. 1 ) may be configured to operate the display 416 depending on what is detected, e.g., by the customer detector 418 and/or the inventory tracker 420.),
wherein the sensor suite comprises at least one selected from a group consisting of:
a traffic monitor configured to detect presence of individuals in a vicinity of the cooler (
Avakian: Sec. 0051, Each retail product container 102 may further comprise customer detector 418 that may detect any or specific individuals (e.g., customers versus employees), motion (e.g., of a customer), a human form (e.g., a human-shaped form or generic face of a customer), heat, etc. (For example, customers may be individuals in the vicinity of a retail product container as detected by customer detector 418.) With some embodiments, the customer detector may utilize one or more of a proximity sensor (e.g., via a heat map), an image sensor, a sensor that detects human form/features, a scanner, an eye-sensor (e.g., an iris-tracking sensor), etc. In addition to customer detector 418, the retail product container 102 may comprise other sensors 423 configured to detect activity in/around/on the retail product container 102. Although for illustrative purposes customer detector 418 has been described as an input to trigger/activate further steps (e.g., in FIG. 8 , refs. 802 and 804), the customer detector 418 may be substituted with and/or supplemented with one or more other sensors 423, including but not limited to the following examples: a sensor configured to detect an open door; a sensor configured to detect movement of the door from one position to another (e.g., from closed to open, or from open to closed, whether the door was slammed closed, the angular speed of the movement of the door, and the like);), and
a stock monitoring system (
Avakian: Sec. 0051, a weight sensor on a shelf in the retail product container 102 to detect a change in inventory;
Avakian: Sec. 0053, Each retail product container 102 may further comprise inventory tracker 420, e.g., to identify, quantify, and/or otherwise track stored retail products. In some aspects, the inventory tracker 420 may utilize additional cameras and/or sensors 423 that may be disposed inside retail product container 102 and face the stored products. In some aspects, the inventory tracker 420 may comprise processors, memory, and/or computer-readable instructions for post-processing images and/or other data captured by the cameras and/or sensors. In some aspects, the processors and/or computer-readable instructions may be integrated with the controller 101, and the controller 101 may perform post-processing and analysis of images/data to identify, quantify, and/or otherwise track the stored retail products.);
installing a data module on the cooler, wherein the data module is configured to store data acquired by the sensor suite (
Avakian: Sec. 0039, Referring to FIG. 1 , with some embodiments, controller 101 may communicate with retail product container via a wired or wireless communication channel (for example, Wi-Fi, Bluetooth®, Zigbee®, and so forth) and may be near the retail product container or at any place in the world through the Internet. As illustrated in FIG. 1B, in some embodiments, the controller 101 may be built-into/integrated into an edge computing device 110 in a retail product container. Of course, the computing platform 100 may include one or more routers (e.g., wireless routers) to communicatively connect one or more edge processors 101 corresponding to retail product containers 102, 103 with cloud computing services 104.
Avakian: Sec. 0055, As shown in FIG. 1 , controller 101 may be networked with retail product container 102 through the Internet, Bluetooth, and so forth via wired Ethernet, wireless LAN, a cellular network, and the like. Controller 101 may be configured to control the display 416 of retail product container 102 as well as may be configured to receive information from retail product container 102, including information from the display 416 (for example, information regarding touchscreen interactions) as well as information from the customer detector 418 and inventory tracker 420.
Avakian: Sec. 0056, Each retail product container 102 may also include interface 422 that may be configured to facilitate, among other things, the networking and transfer of information between the controller 101 and retail product container 102 and control of display 416, customer detector 418, and inventory tracker 420.
Avakian: Sec. 0057, With some embodiments, controller 101 may comprise a server having one or more processors, memory storage, a user interface, and so forth and may be configured to instruct what is displayed on the display 416 and to receive information and data from the retail product container 102. Additionally, the controller 101 may be configured to perform analytics based on the received information and data.).
Avakian does not explicitly teach a method of optimizing usage of coolers, the method comprising:
installing a sensor suite on the cooler; schedules for restocking product in the coolers.
However, Breitenbach teaches a method of optimizing usage of coolers, the method comprising:
installing a sensor suite on the cooler (
Breitenbach: Sec. 0016, According to some embodiments, for example, methods may include (i) intercepting (e.g., by a retrofit device coupled to a machine) an input signal from an input device of a machine, (ii) transmitting (e.g., by the retrofit device and/or to a remote electronic processing device) an indication of the intercepted input, (iii) receiving (e.g., in response to the transmitting of the indication of the intercepted input, by the retrofit device, and/or from the remote electronic processing device) an indication of a desired function to be activated at the machine in response to the input signal, and/or (iv) causing (e.g., via the retrofit device and/or based on the indication of the desired function of the machine) the machine to execute the desired function.
Breitenbach: Sec. 0031, In some embodiments, the machine 108 may comprise any type or configuration of mechanical, electrical, and/or electro-mechanical device or system that is or becomes known or desirable. The machine 108 may, for example, comprise a vending machine, a visi-cooler (and/or a standard cooler, refrigerator, freezer, warmer, and/or oven), and/or a soda fountain. In some embodiments, the machine 108 may be associated with and/or capable of initiating and/or conducting certain functions standard to the machine 108 (e.g., dispensing a product in the case of a vending machine or allowing the opening of a temperature-controlled product storage area in the case of a visi-cooler). According to some embodiments, the coupling of the retrofit device 120 to the machine 108 (and/or portions or components thereof) may alter the standard functionality of the machine 108 (such as by adding additional functionality and/or changing standard functionality).)
schedules for restocking schedules for restocking product in the coolers (
Breitenbach: Sec. 0090, The stored, tracked, and/or reported data may, in some embodiments, be utilized by the controller and/or by an establishment in which the interactive fountain resides (e.g., by a POS system thereof) to perform revenue management, restocking, and/or marketing analysis based on products sold via the interactive fountain.)
optimizing maintenance protocols for the coolers to minimize downtime of the coolers (
Breitenbach: Sec. 0082, The transmission at 790-4 may comprise, for example, a confirmation of the dispensing of the desired beverage, sales data associated with the transaction conducted in accordance with the process 700, sales data from other transactions, inventory levels of syrup, carbon dioxide, and/or ice, and/or settings and/or maintenance conditions or diagnostics of the interactive soda fountain 708 (and/or components thereof). In some embodiments, such as in the case that the transmission at 790-4 is not directed to the customer device 704, the controller 702 may transmit and the customer device 704 may receive, an indication of the transaction such as a confirmation of the dispensing of the desired product, a transaction receipt, and/or another indication that the customer has properly acquired the desired beverage (e.g., and is accordingly permitted to leave an associated restaurant establishment with the filled beverage cup 780), at 790-5.)
Breitenbach describes the use of maintenance for optimizing the operating of the machines which inherently will include downtime.
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian with the teaching of retrofitting the cooler comprising installing a sensor suite on the cooler as disclosed by Breitenbach as this would have provided the advantage for modifying the standard functions and improving the operations of the cooler (see Breitenbach - para [0015]-[0016], [0031]).
Avakian in view Breitenbach of does not explicitly teach aggregating data stored on a plurality of the data modules; and using a machine learning model to derive an optimization for the preexisting coolers from the aggregated data, wherein the optimization includes at least one of optimizing operating parameters of refrigeration systems of the coolers to maximize energy efficiency, optimizing schedules for restocking product in the coolers to maximize profit.
However, Hisham teaches these limitations
aggregating data stored on a plurality of the data modules (
Hisham: Claim 1, receiving initial planogram data and one or more of traffic data, inventory data, sales data, staff data, or shrink data… generating updated planogram data);
and using a machine learning model to derive an optimization for the preexisting coolers (See Breitenbach) from the aggregated data, wherein the optimization includes at least one of optimizing operating parameters of refrigeration systems of the coolers (See Breitenbach) to maximize energy efficiency, optimizing schedules for restocking product in the coolers (See Breitenbach) to maximize profit, (
Hisham: Sec. 0014, Retailers may change planogram layouts to boost visibility, which in turn increases the sales. But retailers may lack insights to the effectiveness of their planogram setups. Since planogram changes occur continuously and may not be captured correctly, the retailers may lose sight of optimum placements which were done in the past before being changed. Also, store employees may collate data from different sources to plan for layouts. In some instances, the lack of a predictive planogram tool may be a time consuming process to plan for organized and/or data-backed layout changes within the store.
Hisham: Sec. 0016, Aspects of the present disclosure includes a solution that may be used in a retail sector, which uses planograms and radio frequency identification (RFID) based inventory solutions to intelligently place products. Some aspects of the present disclosure may include one or more machine learning systems to prescribe an optimum planogram setup for a day, a month, and/or a sales season. The planogram system may be designed to continuously capture changes in store layouts, traffic, point of sales, shrink, staff allocation, and/or prescribe a future layout that may increase sales and reduce costs of staff distribution. Some aspects of the present disclosure may include partially modifying a planogram through movement of blocks.
Hisham: Sec. 0034, an updated sales revenue associated with the updated merchandise placements is projected to be higher than an initial sales revenue associated with the initial merchandise placements.),
Hisham teaches that planogram optimization using traffic/inventory/sales data to improve sales. Hisham teaches that updated planogram data is generated and discloses applying one or more machine learning algorithms and predictive models to traffic and sales data to prescribe a future layout that increases sales and reduces costs uses inventory data and sales data as inputs and generates updated merchandise placement to increase sales volume; such systems inherently affect replenishment practices and profits.
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach with the teaching of aggregating traffic, inventory, sales, staff, and shrink data and using machine learning to derive merchandising optimizations for retail display structures, as the teaching of Avakian in view of Breitenbach teaches the problem of optimizing usage of refrigerated machines using data-driven techniques, would have found it obvious to apply the ML-based planogram optimization framework of Hisham (see Hisham - para [0024)-(0031)).
Referring to Claim 2, Avakian teaches the method of claim 1, wherein the sensor suite comprises a traffic monitor, and the traffic monitor comprises a radio frequency sensor (
Avakian: Sec. 0049, With some embodiments, customer detector 418 may detect a customer when the detected object is deemed to be a human (person) (rather than, for example, a shopping cart). Customer detector 418 may further determine whether the detected person is an employee (for example, by a uniform of the employee or by a RFID tag on the employee). In such a situation, customer detector 418 may ignore the employee so that a display at a retail product container is not updated.).
Referring to Claim 11, Avakian teaches the method of claim 1, Avakian in view of Breitenbach does not explicitly teach wherein the optimization includes an optimization of a planogram for the coolers to maximize a conversion rate of foot traffic in the vicinity of one of coolers (See Breitenbach) to removal of product from the one of the coolers.
However, Hisham teaches these wherein the optimization includes an optimization of a planogram for the coolers to maximize a conversion rate of foot traffic in the vicinity of one of coolers (See Breitenbach) to removal of product from the one of the coolers. (
Hisham: Claim 1, receiving initial planogram data and one or more of traffic data, inventory data, sales data, staff data, or shrink data, wherein the initial planogram data includes initial merchandise placements in a retail store; generating updated planogram data including updated merchandise placements in the retail store, wherein an updated sales revenue associated with the updated merchandise placements is projected to be higher than an initial sales revenue associated with the initial merchandise placements; and
outputting the updated planogram data.
Hisham: Sec. 0023, The traffic data may include customer traffic (e.g., number of customers in each block at a given time, durations of each customer in each block, demographic of customers in each block, predicted traffic data, etc.) in the retail store. The shrink data may include loss of merchandise in the retail store due to theft, accidents, or other causes. The staff data may include work hours, sales made, locations of staff at various times, and/or other information related to staff of the retail store. The initial planogram data may include initial merchandise placements, actual block(s) to be swapped, predicted block(s) to be swapped, and/or other information associated with the retail store.
Controller 101 may be configured to control the display 416 of each retail product container 102 to provide a planogram (for example, as shown in advertisements 801-805 in FIG. 8 ). In some aspects, the planogram may relate to retail products physically contained in the internal storage volume of retail product container 102. The retail products may not necessarily be viewable through the display 416. For example, products stored in the retail product container 102 may not be neatly arranged or may be blocked from view by other products stored therein. However, the planogram may indicate (e.g., to a customer) the retail products stored within the internal storage volume, based on inventory information provided by inventory tracker 420. Consequently, the displayed planogram may effectively optimize what is presented to the customer.
Hisham: Claim 8, the traffic data includes one or more of a number of customers in each block of the retail store, durations of each customer in each block, demographic information of the customers in each block, or predicted traffic data.
Hisham: Claim 9, the sales data includes one or more of a number of merchandise sold, sales revenues generated, sales date of the merchandise, sales times of merchandise, or predicted sales data).
Hisham describes optimization of planogram data using traffic and sales data to increase sales revenue.
Hisham teaches that traffic and sales metrics per block are used as ML training data to generate updated planograms that improve sales performance, effectively maximizing conversion rate of customer traffic near merchandise blocks into product removal/purchase.
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach with the teaching of aggregating traffic, inventory, sales, staff, and shrink data and using machine learning to derive merchandising optimizations for retail display structures, as the teaching of Avakian in view of Breitenbach teaches the problem of optimizing usage of refrigerated machines using data-driven techniques, would have found it obvious to apply the ML-based planogram optimization framework of Hisham (see Hisham - para [0024)-(0031)).
Referring to Claim 12, Avakian teaches a cooler (
Avakian: Sec. 0047, FIG. 4 shows retail product container 102 in accordance with an embodiment. Each retail product container 102 comprises at least one display 416, such as a display on a door of retail product container 102.
Avakian: Sec. 0037, Retail product containers 102 and 103 may comprise a cooler, freezer, vending machine, and so forth and typically store different types of products (for example, milk, frozen meals, beer, ice cream, and so forth) in corresponding internal volumes.)
comprising: a sensor suite (
Avakian: Sec. 0048, A plurality of retail product containers (similar to retail product container 102) and associated displays may be arranged side-by-side down an aisle of a retail store (for example, a grocery store). Edge processor (controller) 101 (as shown in FIG. 1 ) may be configured to operate the display 416 depending on what is detected, e.g., by the customer detector 418 and/or the inventory tracker 420.)
comprising at least two selected from a group consisting of:
a traffic monitor configured to detect presence of individuals in a vicinity of the cooler (
Avakian: Sec. 0051, Each retail product container 102 may further comprise customer detector 418 that may detect any or specific individuals (e.g., customers versus employees), motion (e.g., of a customer), a human form (e.g., a human-shaped form or generic face of a customer), heat, etc. (For example, customers may be individuals in the vicinity of a retail product container as detected by customer detector 418.) With some embodiments, the customer detector may utilize one or more of a proximity sensor (e.g., via a heat map), an image sensor, a sensor that detects human form/features, a scanner, an eye-sensor (e.g., an iris-tracking sensor), etc. In addition to customer detector 418, the retail product container 102 may comprise other sensors 423 configured to detect activity in/around/on the retail product container 102. Although for illustrative purposes customer detector 418 has been described as an input to trigger/activate further steps (e.g., in FIG. 8 , refs. 802 and 804), the customer detector 418 may be substituted with and/or supplemented with one or more other sensors 423, including but not limited to the following examples: a sensor configured to detect an open door; a sensor configured to detect movement of the door from one position to another (e.g., from closed to open, or from open to closed, whether the door was slammed closed, the angular speed of the movement of the door, and the like);), and
a stock monitoring system (
Avakian: Sec. 0051, a weight sensor on a shelf in the retail product container 102 to detect a change in inventory;
Avakian: Sec. 0053, Each retail product container 102 may further comprise inventory tracker 420, e.g., to identify, quantify, and/or otherwise track stored retail products. In some aspects, the inventory tracker 420 may utilize additional cameras and/or sensors 423 that may be disposed inside retail product container 102 and face the stored products. In some aspects, the inventory tracker 420 may comprise processors, memory, and/or computer-readable instructions for post-processing images and/or other data captured by the cameras and/or sensors. In some aspects, the processors and/or computer-readable instructions may be integrated with the controller 101, and the controller 101 may perform post-processing and analysis of images/data to identify, quantify, and/or otherwise track the stored retail products.);
a data module configured to store data acquired by the sensor suite (
Avakian: Sec. 0039, Referring to FIG. 1 , with some embodiments, controller 101 may communicate with retail product container via a wired or wireless communication channel (for example, Wi-Fi, Bluetooth®, Zigbee®, and so forth) and may be near the retail product container or at any place in the world through the Internet. As illustrated in FIG. 1B, in some embodiments, the controller 101 may be built-into/integrated into an edge computing device 110 in a retail product container. Of course, the computing platform 100 may include one or more routers (e.g., wireless routers) to communicatively connect one or more edge processors 101 corresponding to retail product containers 102, 103 with cloud computing services 104.
Avakian: Sec. 0055, As shown in FIG. 1 , controller 101 may be networked with retail product container 102 through the Internet, Bluetooth, and so forth via wired Ethernet, wireless LAN, a cellular network, and the like. Controller 101 may be configured to control the display 416 of retail product container 102 as well as may be configured to receive information from retail product container 102, including information from the display 416 (for example, information regarding touchscreen interactions) as well as information from the customer detector 418 and inventory tracker 420.
Avakian: Sec. 0056, Each retail product container 102 may also include interface 422 that may be configured to facilitate, among other things, the networking and transfer of information between the controller 101 and retail product container 102 and control of display 416, customer detector 418, and inventory tracker 420.
Avakian: Sec. 0057, With some embodiments, controller 101 may comprise a server having one or more processors, memory storage, a user interface, and so forth and may be configured to instruct what is displayed on the display 416 and to receive information and data from the retail product container 102. Additionally, the controller 101 may be configured to perform analytics based on the received information and data.).
Avakian does not explicitly teach cooler retrofit kit comprising: a sensor suite.
However, Breitenbach teaches cooler retrofit kit comprising: a sensor suite (
Breitenbach: Sec. 0015, Embodiments presented herein are descriptive of systems, apparatus, methods, and articles of manufacture for machine retrofits and for interactive fountains. In some embodiments, for example, a retrofit device may be coupled to a conventional vending machine, visi-cooler, and/or soda fountain to facilitate remote, wireless, cashless, and/or account-based sales. According to some embodiments, the retrofit device may allow customers to utilize a variety of enhanced services via what would otherwise be standard machines.
Breitenbach: Sec. 0031, In some embodiments, the machine 108 may comprise any type or configuration of mechanical, electrical, and/or electro-mechanical device or system that is or becomes known or desirable. The machine 108 may, for example, comprise a vending machine, a visi-cooler (and/or a standard cooler, refrigerator, freezer, warmer, and/or oven), and/or a soda fountain. In some embodiments, the machine 108 may be associated with and/or capable of initiating and/or conducting certain functions standard to the machine 108 (e.g., dispensing a product in the case of a vending machine or allowing the opening of a temperature-controlled product storage area in the case of a visi-cooler). According to some embodiments, the coupling of the retrofit device 120 to the machine 108 (and/or portions or components thereof) may alter the standard functionality of the machine 108 (such as by adding additional functionality and/or changing standard functionality).)
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian with the teaching of retrofitting the cooler comprising installing a sensor suite on the cooler as disclosed by Breitenbach as this would have provided the advantage for modifying the standard functions and improving the operations of the cooler (see Breitenbach - para [0015]-[0016], [0031]).
Claims 16 recite limitations that stand rejected via the art citations and rationale applied to claim 11.
Claims 17 recite limitations that stand rejected via the art citations and rationale applied to claim 1.
Claims 3, 4, 14, 20 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20240240853, Avakian, et al. to hereinafter Avakian in view of United States Patent Publication US 20110168290, Breitenbach, et al. to hereinafter Breitenbach in view of United States Patent Publication US 20220398647, Hisham, et al. to hereinafter Hisham in view of United States Patent Publication US 20180189720, Henderson, et al.
Referring to Claim 3, Avakian teaches the method of claim 1, wherein the sensor suite comprises a stock monitoring system (
Avakian: Sec. 0053, Each retail product container 102 may further comprise inventory tracker 420, e.g., to identify, quantify, and/or otherwise track stored retail products. In some aspects, the inventory tracker 420 may utilize additional cameras and/or sensors 423 that may be disposed inside retail product container 102 and face the stored products. In some aspects, the inventory tracker 420 may comprise processors, memory, and/or computer-readable instructions for post-processing images and/or other data captured by the cameras and/or sensors. In some aspects, the processors and/or computer-readable instructions may be integrated with the controller 101, and the controller 101 may perform post-processing and analysis of images/data to identify, quantify, and/or otherwise track the stored retail products.),
Avakian in view of Breitenbach does not explicitly teach and the stock monitoring system comprises load cells.
However, Henderson teaches and the stock monitoring system comprises load cells (
Henderson: Sec. 0097, FIG. 1 shows an example of the main components associated with a single inventory location, e.g. shelf or bay for example, of an inventory management system. The inventory location typically comprises a load sensor 10, such as a strain gauge based load cell, that is mounted or coupled to or into the base surface of the bay such that it detects the load or weight of the inventory items within the bay. It generates a raw sensor signal, such as a voltage signal, normally in the range of millivolts, that is proportional to the sensed load or weight impinging on the load cell.
Henderson: Sec. 0098, In this example, each inventory location comprises its own respective event detection module 12 that is configured to receive and process a raw sensor signal 14 from the load sensor and generate representative output event data 16 that is then transmitted to a central server or data processor 18 for further analysis or processing by the overall inventory management system. In this example, the load sensor 10 may be hardwired to the event detection module 12, which may be in the form of electronic circuitry provided on a printed circuit (PCB) board mounted to or associated with the inventory location. Likewise, the event detection module 12 may be hardwired to the central server 18 or alternatively may transmit or communicate with the server 18 over a network or wireless data link such as in applications where the server or data processor 18 is remote to the inventory locations being monitored. It will be appreciated that in alternative embodiments the raw sensor signals 14 from the load sensors of each inventory location may alternatively be sent directly, over a hardwired connection or network or wireless data link, to the central server 18 which is configured to execute or implement the event detection algorithm in a centralised fashion on all incoming load signals in parallel, rather than having dedicated event detection modules 12 on-board at each inventory location.
Henderson: Sec. 0099, In this example, the load sensor is a resistor-based strain gauge load cell. In one example, the load cell is a 700 gram load cell with a 1 mV/V output rating, provided at an excitation of 3.67 volts results in a full scale output range of ±3.67 mV.).
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach with the teaching of the stock monitoring system comprises load cells as disclosed by Henderson as this would have provided the advantage for accurately determining the inventory at a location (see Henderson - para [0097)-[0099]).
Referring to Claim 4, Avakian teaches the method of claim 3, Avakian in view of Breitenbach does not explicitly teach wherein installing the sensor suite comprises configuring the load cells to measure load applied to shelves of the cooler.
However, Henderson teaches wherein installing the sensor suite comprises configuring the load cells to measure load applied to shelves of the cooler (
Henderson: Sec. 0097, FIG. 1 shows an example of the main components associated with a single inventory location, e.g. shelf or bay for example, of an inventory management system. The inventory location typically comprises a load sensor 10, such as a strain gauge based load cell, that is mounted or coupled to or into the base surface of the bay such that it detects the load or weight of the inventory items within the bay. It generates a raw sensor signal, such as a voltage signal, normally in the range of millivolts, that is proportional to the sensed load or weight impinging on the load cell.
Henderson: Sec. 0099, In this example, the load sensor is a resistor-based strain gauge load cell. In one example, the load cell is a 700 gram load cell with a 1 mV/V output rating, provided at an excitation of 3.67 volts results in a full scale output range of ±3.67 mV.).
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach with the teaching of the stock monitoring system comprises load cells as disclosed by Henderson as this would have provided the advantage for accurately determining the inventory at a location (see Henderson - para [0097)-[0099]).
Claims 14 recite limitations that stand rejected via the art citations and rationale applied to claims 3.
Referring to Claim 20, Avakian teaches the cooler of claim 17, comprising a refrigerated storage compartment and shelves located within the storage compartment, wherein the stock monitoring system comprises load cells (see Henderson) configured to measure load on the shelves (
Avakian: Sec. 0051, a sensor configured to detect an open door; a sensor configured to detect movement of the door from one position to another (e.g., from closed to open, or from open to closed, whether the door was slammed closed, the angular speed of the movement of the door, and the like); a weight sensor on a shelf in the retail product container 102 to detect a change in inventory; an optical sensor (e.g., a camera) configured to detect out-of-stock products in the retail product container 102; and/or other kinds of sensor/camera operations.).
Avakian in view of Breitenbach does not explicitly teach the stock monitoring system comprises load cells.
However, Henderson teaches the stock monitoring system comprises load cells (
Henderson: Sec. 0097, FIG. 1 shows an example of the main components associated with a single inventory location, e.g. shelf or bay for example, of an inventory management system. The inventory location typically comprises a load sensor 10, such as a strain gauge based load cell, that is mounted or coupled to or into the base surface of the bay such that it detects the load or weight of the inventory items within the bay. It generates a raw sensor signal, such as a voltage signal, normally in the range of millivolts, that is proportional to the sensed load or weight impinging on the load cell.
Henderson: Sec. 0098, In this example, each inventory location comprises its own respective event detection module 12 that is configured to receive and process a raw sensor signal 14 from the load sensor and generate representative output event data 16 that is then transmitted to a central server or data processor 18 for further analysis or processing by the overall inventory management system. In this example, the load sensor 10 may be hardwired to the event detection module 12, which may be in the form of electronic circuitry provided on a printed circuit (PCB) board mounted to or associated with the inventory location. Likewise, the event detection module 12 may be hardwired to the central server 18 or alternatively may transmit or communicate with the server 18 over a network or wireless data link such as in applications where the server or data processor 18 is remote to the inventory locations being monitored. It will be appreciated that in alternative embodiments the raw sensor signals 14 from the load sensors of each inventory location may alternatively be sent directly, over a hardwired connection or network or wireless data link, to the central server 18 which is configured to execute or implement the event detection algorithm in a centralised fashion on all incoming load signals in parallel, rather than having dedicated event detection modules 12 on-board at each inventory location.
Henderson: Sec. 0099, In this example, the load sensor is a resistor-based strain gauge load cell. In one example, the load cell is a 700 gram load cell with a 1 mV/V output rating, provided at an excitation of 3.67 volts results in a full scale output range of ±3.67 mV.).
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach with the teaching of the stock monitoring system comprises load cells as disclosed by Henderson as this would have provided the advantage for accurately determining the inventory at a location (see Henderson - para [0097)-[0099]).
Claims 5, 6, 8-10, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20240240853, Avakian, et al. to hereinafter Avakian in view of United States Patent Publication US 20110168290, Breitenbach, et al. to hereinafter Breitenbach in view of United States Patent Publication US 20220398647, Hisham, et al. to hereinafter Hisham in view of United States Patent Publication US 20090306817, Antao, et al.
Referring to Claim 5, Avakian teaches the method of claim 1, wherein Avakian in view of Breitenbach does not explicitly teach installing the data module comprises replacing a preexisting controller of the cooler with the data module.
However, Antao teaches installing the data module comprises replacing a preexisting controller of the cooler with the data module (
Antao: Sec. 0043, The vending machine 102 may include, but is not limited to, a product or beverage dispenser, a vending machine, a snack dispenser, a device capable of dispensing or providing a consumable food or drink item, a device capable of dispensing or providing a non-consumable item, or a device capable of facilitating the purchase of a good and/or service. The vending machine 102 may include a vending machine 102A, a cooler 102B, a fountain dispenser 102C, and similar devices. The vending machine 102 also may be referred to as immediate consumption equipment, immediate consumption equipment 102, a virtual vending machine 102, equipment 102, cooler equipment 102, fountain equipment 102, or vending equipment 102. Vending, cooler, and fountain equipment also may be referred to as the vending machine 102.
Antao: Sec. 0044, In an exemplary embodiment, a virtual equipment module replaces the electronic hardware in the known vending machines with networked virtual equipment modules that reside external to and remote from the vending machine 102. As an example, a known vending machine controller 202 may be replaced with a virtual vending machine controller 708A. In operation, the vending bridge 500 may be in data communication with the virtual vending machine controller 708A, resident external to and remote from the vending machine 102. The virtual vending machine controller 708A may receive data, determine sold out status, manage space to sales dispensing, account for consumer payment, reconcile consumer selection to product location, and remotely send data communications to effectuate the dispensing of products or services from vending machine 102.).
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach with the teaching of replacing a preexisting controller of the cooler with the data module as disclosed by Antao as this would have provided the advantage for improving the functions and operation of the cooler (see Antao - para [0043)-(0044)).
Referring to Claim 6, Avakian teaches the method of claim 5, Avakian in view of Breitenbach does not explicitly teach wherein replacing the preexisting controller of the cooler with the data module comprises configuring the data module to assume control functions previously executed by the controller in the cooler.
However, Antao teaches wherein replacing the preexisting controller of the cooler with the data module comprises configuring the data module to assume control functions previously executed by the controller in the cooler (
Antao: Sec. 0044, In an exemplary embodiment, a virtual equipment module replaces the electronic hardware in the known vending machines with networked virtual equipment modules that reside external to and remote from the vending machine 102. As an example, a known vending machine controller 202 may be replaced with a virtual vending machine controller 708A. In operation, the vending bridge 500 may be in data communication with the virtual vending machine controller 708A, resident external to and remote from the vending machine 102. The virtual vending machine controller 708A may receive data, determine sold out status, manage space to sales dispensing, account for consumer payment, reconcile consumer selection to product location, and remotely send data communications to effectuate the dispensing of products or services from vending machine 102.).
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach with the teaching of replacing a preexisting controller of the cooler with the data module as disclosed by Antao as this would have provided the advantage for improving the functions and operation of the cooler (see Antao - para [0043)-(0044)).
Referring to Claim 8, Avakian teaches the method of claim 7, comprising transmitting the optimization to at least one of the data modules (
Avakian: Sec. 0112, In an example, if the controller is unable to identify a product, the controller may send a notification (e.g., to the wireless device 106 associated with an employee or the computing device 115 associated with an expert user in a remote location). The notification may comprise an identity of the retail product container 102, the composite image, and/or attributes (e.g., shape, color, dimensions, etc.) of the unidentified product for identification by the employee/expert user. The employee/expert user may identify the product and manually enter an identity of the product using the wireless device 106/computing device 115. The wireless device 106 or the computing device 115 may send the identity of the product to the controller. In an example, if the controller is unable to identify the product, the controller may send determined attributes of the product to a central cloud-based processing system (e.g., the cloud services 104) for identification. Based on identification of the product (e.g., by an employee/expert user or by the cloud-based processing system), the controller may update the planogram mapping 902 in the data store 111 and/or generate an advertisement including an artwork associated with the product. The controller may, for example, add a new entry to the planogram mapping 902 comprising information associated with the product (e.g., product indicator, location indicator, description, etc.)
Avakian: Sec. 0054, Controller 101 may be configured to control the display 416 of each retail product container 102 to provide a planogram (for example, as shown in advertisements 801-805 in FIG. 8 ). In some aspects, the planogram may relate to retail products physically contained in the internal storage volume of retail product container 102. The retail products may not necessarily be viewable through the display 416. For example, products stored in the retail product container 102 may not be neatly arranged or may be blocked from view by other products stored therein. However, the planogram may indicate (e.g., to a customer) the retail products stored within the internal storage volume, based on inventory information provided by inventory tracker 420. Consequently, the displayed planogram may effectively optimize what is presented to the customer.).
Referring to Claim 9, Avakian teaches the method of claim 8, wherein the at least one of the data modules is configured to execute control functions in at least one of the coolers (
Avakian: Sec. 0039, As illustrated in FIG. 1B, in some embodiments, the controller 101 may be built-into/integrated into an edge computing device 110 in a retail product container. Of course, the computing platform 100 may include one or more routers (e.g., wireless routers) to communicatively connect one or more edge processors 101 corresponding to retail product containers 102, 103 with cloud computing services 104. In one system 150, a wireless router (not shown in FIG. 1A) may be used to connect the edge processor 101 with a cloud computing services 104; the wireless router may provide a shared, wireless network for a plurality of Internet of Things (IoT) devices at a retail location, including retail product containers 102, 103.
Avakian: Sec. 0055, As shown in FIG. 1 , controller 101 may be networked with retail product container 102 through the Internet, Bluetooth, and so forth via wired Ethernet, wireless LAN, a cellular network, and the like. Controller 101 may be configured to control the display 416 of retail product container 102 as well as may be configured to receive information from retail product container 102, including information from the display 416 (for example, information regarding touchscreen interactions) as well as information from the customer detector 418 and inventory tracker 420.
Avakian: Sec. 0056, Each retail product container 102 may also include interface 422 that may be configured to facilitate, among other things, the networking and transfer of information between the controller 101 and retail product container 102 and control of display 416, customer detector 418, and inventory tracker 420.).
Referring to Claim 10, Avakian teaches the method of claim 9, Avakian in view of Breitenbach does not explicitly teach wherein the at least one of the data modules is configured to alter operating parameters of the refrigeration system of the at least one of the coolers in accordance with the optimization.
However, Antao teaches wherein the at least one of the data modules is configured to alter operating parameters of the refrigeration system of the at least one of the coolers in accordance with the optimization (
Antao: Sec. 0068, Remote data processing resources may determine the state and status of the vending machine 102 by employing virtual software modules to effectuate refrigeration control, energy management optimization, vending machine control functionality, and other types and kinds of virtual equipment modules. Furthermore, consumer selections may be determined at the remote data processing resources so as to validate payments, and send the appropriate commands to cause the correct vending machine item to be vended or otherwise dispensed.
Antao: Sec. 0074, The heating/refrigeration control 528 controls the vending machine refrigeration system. The remote data processing resources may monitor refrigeration system operation, control the compressor “ON” and “OFF” cycles, and optimize energy savings aspects of the refrigeration system. As illustrated in FIG. 2D, the heating/refrigeration control 528 may further include a compressor interface 528A, an evaporator fan interface 528B, a line power interface 528C, a reverse relay 528D, a high temperature sensor 528E, a return air sensor 528F, a remote motion detector 528G, a user interface 528H, a general purpose input/output (I/O) 528I, an evaporator sensor interface 528J, an independent condenser fan interface 528K, an electronic evaporator valve (EEV) interface 528L, a general purpose input/output night mode button 528M, a variable speed evaporator fan interface 528N, and other components.).
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach with the teaching of replacing a preexisting controller of the cooler with the data module as disclosed by Antao as this would have provided the advantage for improving the functions and operation of the cooler (see Antao - para [0043)-(0044)).
Claims 13 recite limitations that stand rejected via the art citations and rationale applied to claims 6.
Claims 19 recite limitations that stand rejected via the art citations and rationale applied to claim 10.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20240240853, Avakian, et al. to hereinafter Avakian in view of United States Patent Publication US 20110168290, Breitenbach, et al. to hereinafter Breitenbach in view of United States Patent Publication US 20220398647, Hisham, et al. to hereinafter Hisham in view of United States Patent Publication US 20180189720, Henderson, et al. to hereinafter Henderson in view of United States Patent Publication US 20220204015, Chari, et al
Referring to Claim 21, Avakian teaches the method of claim 14, wherein:
the stock monitoring system further comprises a camera (
Avakian: Sec. 0051, a weight sensor on a shelf in the retail product container 102 to detect a change in inventory;
Avakian: Sec. 0053, Each retail product container 102 may further comprise inventory tracker 420, e.g., to identify, quantify, and/or otherwise track stored retail products. In some aspects, the inventory tracker 420 may utilize additional cameras and/or sensors 423 that may be disposed inside retail product container 102 and face the stored products. In some aspects, the inventory tracker 420 may comprise processors, memory, and/or computer-readable instructions for post-processing images and/or other data captured by the cameras and/or sensors. In some aspects, the processors and/or computer-readable instructions may be integrated with the controller 101, and the controller 101 may perform post-processing and analysis of images/data to identify, quantify, and/or otherwise track the stored retail products.);
the data module comprises a memory configured to store information acquired by the sensor suite (
Avakian: Sec. 0039, Referring to FIG. 1 , with some embodiments, controller 101 may communicate with retail product container via a wired or wireless communication channel (for example, Wi-Fi, Bluetooth®, Zigbee®, and so forth) and may be near the retail product container or at any place in the world through the Internet. As illustrated in FIG. 1B, in some embodiments, the controller 101 may be built-into/integrated into an edge computing device 110 in a retail product container. Of course, the computing platform 100 may include one or more routers (e.g., wireless routers) to communicatively connect one or more edge processors 101 corresponding to retail product containers 102, 103 with cloud computing services 104.
Avakian: Sec. 0055, As shown in FIG. 1 , controller 101 may be networked with retail product container 102 through the Internet, Bluetooth, and so forth via wired Ethernet, wireless LAN, a cellular network, and the like. Controller 101 may be configured to control the display 416 of retail product container 102 as well as may be configured to receive information from retail product container 102, including information from the display 416 (for example, information regarding touchscreen interactions) as well as information from the customer detector 418 and inventory tracker 420.
Avakian: Sec. 0056, Each retail product container 102 may also include interface 422 that may be configured to facilitate, among other things, the networking and transfer of information between the controller 101 and retail product container 102 and control of display 416, customer detector 418, and inventory tracker 420.
Avakian: Sec. 0057, With some embodiments, controller 101 may comprise a server having one or more processors, memory storage, a user interface, and so forth and may be configured to instruct what is displayed on the display 416 and to receive information and data from the retail product container 102. Additionally, the controller 101 may be configured to perform analytics based on the received information and data.);
Avakian in view of Breitenbach in view of Hisham does not explicitly teach the data module is configured to cease recording data from the camera and continue record data from the load cells when less than a predetermined amount of the memory remains available for storing information acquired by the sensor suite.
However, Chari teaches the data module is configured to cease recording data from the camera and continue record data from the load cells when less than a predetermined amount of the memory remains available for storing information acquired by the sensor suite (
Chari: Sec. 0023, The system 102 includes a plurality of sensors 110, e.g., the first sensor 110 a, a second sensor 110 b, a third sensor 110 c, a fourth sensor 110 d, etc. The sensors 110 can be optical sensors 110. For example, the sensor 110 can be cameras and can detect electromagnetic radiation in some range of wavelengths.
Chari: Sec. 0006, The vehicle control module is programmed to receive sensor data from at least one sensor, execute a machine-learning program trained to determine whether the sensor data satisfies at least one criterion, and transmit the sensor data satisfying the at least one criterion to the vehicle gateway module.
Chari: Sec. 0036, In the block 330, the vehicle gateway module 106 stores the sensor data in memory and adds metadata to the sensor data. The metadata includes location data, time data, and which vehicle control module 104 transmitted the sensor data. The location data can be, e.g., global positioning system (GPS) data reported by a GPS sensor over the wired vehicle communications network 108.
Chari: Sec. 0007, The vehicle control module may be further programmed to delete the sensor data after transmitting the sensor data to the vehicle gateway module.
Chari: Sec. 0008, may be further programmed to delete the sensor data after executing the machine-learning program to determine that the sensor data fails to satisfy the at least one criterion.).
Chari teaches a sensor side control module and gateway with memory that store and manage sensor data, including camera data. Chari discloses a system where cameras produce unstructured data that is processed by ML in local modules and then stored in gateway memory.
Chari: Sec. 0037, remote server 112, the process 300 returns to the decision block 315 to continue monitoring for prespecified events and additional sensor data while waiting for the connection to be established. By storing the sensor data, which is complicated unstructured data, the vehicle gateway module 106 frees up the vehicle control module 104 to delete the sensor data. If a connection is established, the process 300 proceeds to a block 340.
Chari teaches selective retention, deletion, and management of sensor data in the gateway, in view of memory and bandwidth constraints.
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach in view of Hisham with the teaching of aggregating traffic, inventory, sales, staff, and shrink data and using machine learning to derive merchandising optimizations for retail display structures, as the teaching of Avakian in view of Breitenbach in view of Hisham teaches the problem of optimizing usage of refrigerated machines using data-driven techniques, would have found it obvious to apply Chari teaches detailed mechanisms for handling camera-based sensor data with ML programs, storing such data in modules with memory, and deleting data to manage memory and bandwidth (see Chari - para [(0018)(0037)-(0039)).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20240240853, Avakian, et al. to hereinafter Avakian in view of United States Patent Publication US 20110168290, Breitenbach, et al. to hereinafter Breitenbach in view of United States Patent Publication US 20220398647, Hisham, et al. to hereinafter Hisham in view of United States Patent Publication US 20220204015, Chari, et al
Referring to Claim 22, Avakian teaches the cooler of claim 17, wherein the data module is configured to derive conclusions from information acquired by the sensor suite, store the conclusions(
Avakian: Sec. 0097, In one example, an artificial intelligence network on which one or more machine learning algorithms/models are executing is included in the system disclosed herein. A framework for a machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality
Avakian: Sec. 0112, In an example, if the controller is unable to identify a product, the controller may send a notification (e.g., to the wireless device 106 associated with an employee or the computing device 115 associated with an expert user in a remote location). The notification may comprise an identity of the retail product container 102, the composite image, and/or attributes (e.g., shape, color, dimensions, etc.) of the unidentified product for identification by the employee/expert user. The employee/expert user may identify the product and manually enter an identity of the product using the wireless device 106/computing device 115. The wireless device 106 or the computing device 115 may send the identity of the product to the controller. In an example, if the controller is unable to identify the product, the controller may send determined attributes of the product to a central cloud-based processing system (e.g., the cloud services 104) for identification. Based on identification of the product (e.g., by an employee/expert user or by the cloud-based processing system), the controller may update the planogram mapping 902 in the data store 111 and/or generate an advertisement including an artwork associated with the product. The controller may, for example, add a new entry to the planogram mapping 902 comprising information associated with the product (e.g., product indicator, location indicator, description, etc.)),
Avakian in view of Breitenbach in view of Hisham does not explicitly teach remove the information used to derive the conclusions from memory after deriving the conclusions.
However, Chari teaches remove the information used to derive the conclusions from memory after deriving the conclusions (
Chari: Sec. 0007, The vehicle control module may be further programmed to delete the sensor data after transmitting the sensor data to the vehicle gateway module.
Chari: Sec. 0008, may be further programmed to delete the sensor data after executing the machine-learning program to determine that the sensor data fails to satisfy the at least one criterion.
Chari: Sec. 0038, the vehicle gateway module 106 transmits the sensor data, along with the metadata, to the remote server 112. The vehicle gateway module 106 does not transform the sensor data or extract a portion of the sensor data before transmitting the sensor data. After transmitting the sensor data, the vehicle gateway module 106 can delete the sensor data and metadata. After the block 340, the process 300 ends.).
Chari teaches deleting raw and stored sensor data and metadata after it has been used to make ML-based decisions or after transmission in the gateway. Chari discloses that once sensor data has been stored, used, and transmitted to a remote server for training/update, the gateway can delete the stored sensor data and memory in the control module, preserving only higher-level model state or conclusions at the remote server.
It would have been obvious to a person having ordinary skill in the art to modify the method of Avakian and Breitenbach in view of Hisham with the teaching of aggregating traffic, inventory, sales, staff, and shrink data and using machine learning to derive merchandising optimizations for retail display structures, as the teaching of Avakian in view of Breitenbach in view of Hisham teaches the problem of optimizing usage of refrigerated machines using data-driven techniques, would have found it obvious to apply Chari teaches detailed mechanisms for handling camera-based sensor data with ML programs, storing such data in modules with memory, and deleting data to manage memory and bandwidth (see Chari - para [(0018)(0037)-(0039)).
Response to Arguments
Applicant’s arguments filed 04/06/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 04/06/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 7-11 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
a method of optimizing usage of coolers, the method comprising:
retrofitting preexisting coolers, wherein retrofitting each of the preexisting coolers comprises:
installing a sensor suite on the cooler, wherein the sensor suite comprises at least one selected from a group consisting of:
a refrigeration system monitor configured to monitor power usage by a refrigeration system of the cooler,
a traffic monitor configured to detect presence of individuals in a vicinity of the cooler, and
a stock monitoring system; and
installing a data module on the cooler, wherein the data module is configured to store data acquired by the sensor suite;
aggregating data stored on a plurality of the data modules;
using a machine learning model to derive an optimization for the preexisting coolers from the aggregated data, wherein the optimization includes at least one of optimizing operating parameters of refrigeration systems of the coolers to maximize energy efficiency, optimizing schedules for restocking product in the coolers to maximize profit, optimizing maintenance protocols for the coolers to minimize downtime of the coolers.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations managing the usage of a cooler which constitutes methods related to fundamental economic principles or practices, as well as, commercial interaction such as sales activities; managing personal behavior such as activities, which are still considered an abstract idea under the 2019 PEG. The commercial interaction such as sales activities; managing personal behavior such as activities, are disclosed in Applicant’s specs as followed: 0037-0039 that teaches customer interactions with the cooler and inventory, as well as, 0049, 0076, 0095 that teaches determining the customer activities, experiences, and sales. Additionally, the claims teach the actions of individuals. Therefore, Applicant’s present case is directed towards: a. Sales activities and b. Managing person behavior.
The managing the usage of a cooler is comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 11 that Applicant points to as “significantly more” are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps of:
pg. 10-11, “some embodiments, load cell 116 data usable for stock monitoring can require significantly less memory to store than image data sufficient to enable stock monitoring of similar accuracy... Given the low costs or acquiring and installing load cells, embodiments wherein cooler 10 includes load cells 116 can also be advantageous where minimizing cost is at a premium" and "[e]mbodiments wherein cooler 10 includes load cells 116 can also be advantageous when seeking to use less energy." (Specification, [0047]). The specification therefore establishes that at least claims 3, 4, 14, and 20 (all relating to the usage of load cells) include additional elements that improve coolers by providing stock monitoring capabilities with less memory, cost, and memory consumption than alternative hardware.”
These arguments at pg. 11 seems to describe a “particular way” of managing the usage of a coolers and inventory. “
Moreover, at pg. 11 the Applicant admission that the application is directed to improving the user’s experience and not the computer itself,
(at pg. 24, “The specification therefore establishes that at least claims 3, 4, 14, and 20 (all relating to the usage of load cells) include additional elements that improve coolers by providing stock monitoring capabilities with less memory, cost, and memory consumption than alternative hardware"”)
this argument the Applicant is admitting that the application is directed to improving the user’s experience, saving money, and not the computing system or any type of computer or structure, .
Moreover, the Examiner would like to point the Applicant to the 2019 PEG, in which ” managing the usage of a cooler and inventory”. will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Therefore, the additional elements do not integrate into a practical application.
Regarding the 35 U.S.C. 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Credle ., U.S. 5992685, (discussing the retrofitting of beverage dispensers.).
Avakian et al., W.O. Pub. 2021150406, (discussing the updating of pop machines).
Brinkley, Exploring The Needs, Preferences, And Concerns Of Persons With Visual Impairments Regarding Autonomous Vehicles, https://dl.acm.org/doi/pdf/10.1145/3372280, ACM Transactions on Accessible Computing (TACCESS), 2020 (discussing the modernizing of machinery that includes pop machines).
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/UCHE BYRD/Examiner, Art Unit 3624
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