DETAILED ACTION
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 .
Notice to Applicant
A restriction with regard to claims 1-20 was mailed on October 1, 2025. Applicant elected Group II, claims 11-18. Claims 1-10 and 19-20 have been withdrawn. Claims 11-18 are pending and examined hereinbelow.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 12, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 11-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 11-18 are drawn to a method which is one of the statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 11 recites a method comprising the following:
organizing store data for a store into featured labeled data for features indicative of shrink events at the store;
processing the feature labeled data and obtaining sets of scores that predict future shrink events at the store in intervals of time over a given period, wherein each score is associated with a certain feature or a certain combination of features, and wherein each score is associated with one or more prescriptive actions to take to avoid a corresponding future shrink event; and
integrating the sets of scores into one or more of a store application, a store workflow, or a store system.
These steps are directed to predicting risk associated with retail shrink, which amounts to certain methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people).
Dependent claim 12 recites, in part, iterating to the organizing at an end of each interval of time.
Dependent claim 13 recites, in part, wherein organizing further includes obtaining a first portion of the featured labeled data by providing the store data as input to an initial MLM and receiving as output from the initial MLM the portion.
Dependent claim 14 recites, in part, generating a second portion of the featured labeled data by mapping each shrink event to one or more certain prescriptive actions.
Dependent claim 15 recites, in part, providing the first portion of the featured labeled data as input to a heatmap interface.
Dependent claim 16 recites, in part, extending the heatmap interface with the second portion to provide the corresponding one or more certain prescriptive actions within the heatmap interface.
Dependent claim 17 recites, in part, wherein integrating further includes mapping a certain prescriptive action associated with a certain score for a particular feature or a particular combination of features to an instruction for a security application of the store and sending the instruction to the security application prior to a given interval of time associated with the certain score.
Dependent claims 18 recites, in part, wherein integrating further includes mapping a certain prescriptive action to a manager recommendation for a store department, a store item, a store item classification, a store employee, or a store customer, and sending the manager recommendation to a device operated by a store manager.
Each of these steps of the preceding dependent claims 12-18 only serve to further limit or specify the features of independent claim 11 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements already analyzed in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Independent Claim 11 recites, in part, a machine-learning model (MLM) and a store application. The specification defines a machine-learning model (MLM) as trained on the training data set to produce predictive shrink risk scores for each of the features and combinations of features and to produce prescriptive action identifiers for any given feature/combination score above a threshold value, (Specification in ¶ 0004), and a store application as the MLM-provided prescriptive action identifiers are provided to store applications, via an application programming interface (API), as a cloud-based service, (Specification in ¶ 0022). The machine-learning model (MLM) limitation is only recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). The store application limitation is only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Dependent claim 13 recites, in part, a machine-learning model (MLM). The machine-learning model (MLM) limitation is only recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Dependent claims 15 and 16, recite in part, a heatmap interface. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
Dependent claim 17 recites in part, a security application. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Dependent claim 18, recites in part, a device. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Independent Claim 11 recites, in part, a machine-learning model (MLM) and a store application. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as use of a machine-learning model (MLM) to predict future shrink events and use of a store application to enter and store data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Dependent claim 13, recites in part, a machine-learning model (MLM). These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claims 15 and 16 recite in part, a heatmap interface. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Dependent claim 17, recites in part, a security application. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Dependent claim 18, recites in part, a device. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 11-18 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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, 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 11-14, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication Number 2022/0343349, Subramanian, et al., hereinafter Subramanian in view of United States Patent Application Publication Number 2019/0027003, Lobo, et al., hereinafter Lobo.
Regarding claim 11, Subramanian discloses a method, comprising:
organizing store data for a store into featured labeled data for features indicative of shrink events at the store, (Figures 1 and 2A, para. 18, The shrink databases 105 may include real-time information for inventory, traffic information that calculates the number of customers that may be present in any one store at any given time, and/or shrink information (e.g., number of items that may be stolen or lost from inventory), para. 22, Once the dataset has been formatted and normalized across the plurality of databases, the formatted dataset 110 may be fed into the feature generator and the feature generator 115 in collaboration with the feature generation component 345 may generate or identify attributes within the training dataset of the formatted data that are associated with retail theft);
processing a machine-learning model (MLM) with the feature labeled data and obtaining sets of scores that predict future shrink events at the store in intervals of time over a given period, wherein each score is associated with a certain feature or a certain combination of features, and wherein each score is associated with one or more prescriptive actions to take to avoid a corresponding future shrink event, (para. 23, The generated features may then be input into a plurality of machine learning algorithms 120, para. 24, the ML algorithms 120 may output predictions of risk factors and likelihood of an item being subject to retail theft for any particular day or time, para. 25, the plurality of machine learning algorithms to form a hybrid machine learning model, and para. 28, the array of predictions provided by the hybrid machine learning model 130 and organize the information to be fed into a recommendation engine 145. In turn, the recommendation engine 145, in collaboration with the recommendation development component 355 of the network device 300, may generate actionable recommendations that maximize the use of available resources. For example, the recommendation engine 145 may provide instructions to move the display of certain items away from exit doors as they may be susceptible to retail theft in coming days and/or or place security in the high risk zones).
Subramanian does not explicitly disclose integrating the sets of scores into one or more of a store application, a store workflow, or a store system.
However, Lobo teaches integrating the sets of scores into one or more of a store application, a store workflow, or a store system, (Fig. 5, para. 30, Machine learning engine 150 is communicatively coupled with the first shrinkage database 120, the second shrinkage database 130, and the analytics engine 140. Machine learning engine 150 is able to receive and utilize retail shrinkage data 122, external data 132, and the issuance of an alert 142 to conduct predictive modeling. In one example, a thousand items could be present at one location, where 30% of the items are deemed high risk, 20% of the items are deemed medium risk, and all the rest of the items are considered low risk. Based on historical data, the times and locations when shrink happened are known. Accordingly, it is possible to use a time series prediction or another machine learning algorithm to find any trends present. This enables prediction of when high risk situations will happen for those items which are high or medium risk. Further, the machine learning engine 150 can cause the analytics engine 140 to issue a further alert if the predictive modeling determines that a high shrinkage risk situation is likely to occur).
One having ordinary skill in the art at the time the invention was filed would combine the techniques of Subramanian with the system of Lobo with the motivation of providing new effective systems and methods for predicting and identifying retail shrinkage activity (Lobo in the Summary para. 4).
11. Regarding claim 12, Subramanian teaches the method of claim 11 as described above. Subramanian further discloses further comprising, iterating to the organizing at an end of each interval of time, (para. 22, Other features that may be generated include, but are not limited to, item shrink frequency ratio during the week across all zones for weekdays and hours or number of times an item shrank on holidays across all days, hours, weeks, and zones. Additionally or alternatively, the feature generation component 345 may identify total number of items that were subject to “grab and run” in particular zones, days, and hours, para. 24, the ML algorithms 120 may output predictions of risk factors and likelihood of an item being subject to retail theft for any particular day or time, and para. 28, the prediction analyzer 140 may extract the array of predictions provided by the hybrid machine learning model 130 and organize the information to be fed into a recommendation engine).
12. Regarding claim 13, Subramanian teaches the method of claim 11 as described above. Subramanian further discloses wherein organizing further includes obtaining a first portion of the featured labeled data by providing the store data as input to an initial MLM and receiving as output from the initial MLM the portion, (para. 24, the ML algorithms 120 may output predictions of risk factors and likelihood of an item being subject to retail theft for any particular day or time. In some aspects, the predictions may identify the items that are at risk for theft, the number of items that is expected to be shrunk on one or more of the upcoming days or weeks. The outputs of each of the plurality of ML algorithms 120 may be measured against known testing dataset (e.g., historical data that was set aside from the training dataset). The margin of error for each of the plurality of ML algorithms 120 outputs against the testing dataset may be measured as a mean absolute error, root mean square error, or broadly the difference between the predictions against actual shrink for any particular day).
13. Regarding claim 14, Subramanian teaches the method of claim 11 as described above. Subramanian further discloses generating a second portion of the featured labeled data by mapping each shrink event to one or more certain prescriptive actions, (para. 19, in order to develop the hybrid machine learning model that may accurately identify the risk factors and prescriptions that impact shrink, the data extraction component 335 may extract relevant data from the shrink databases).
14. Regarding claim 17, Subramanian teaches the method of claim 11 as described above. Subramanian further discloses wherein integrating further includes mapping a certain prescriptive action associated with a certain score for a particular feature or a particular combination of features to an instruction for a security application of the store and sending the instruction to the security application prior to a given interval of time associated with the certain score, (para. 28, the recommendation engine 145 may provide instructions to move the display of certain items away from exit doors as they may be susceptible to retail theft in coming days and/or or place security in the high risk zones. The recommendation engine 145 may also generate information about securing certain zones or products with EAS or directing one or more cameras to be adjusted based on the risk factors. The recommendation engine 145 output is important because the items that may be at risk for shrink are not always constant year around or even day-to-day or hour-to-hour, and para. 39, the method 250 may include generating a shrink control plan that identifies actionable steps to minimize risk for shrinkage for the one or more products identified, wherein the shrink control plan includes actionable steps to implement for securing the one or more products. In some examples, the shrink control plan may include information regarding allocation of resources that a user may implement to minimize risk of shrinkage for the one or more products, including but not limited to relocating the high risk items to different part of the store. The resources may also be one or both of human resources or electronic article surveillance (EAS) that are deployed in order to prevent or minimize the risk of shrinkage for the one or more products. Aspects of block 265 may also be performed by the recommendation development component 355 described with reference to FIG. 3).
15. Regarding claim 18, Subramanian teaches the method of claim 11 as described above. Subramanian further discloses wherein integrating further includes mapping a certain prescriptive action to a manager recommendation for a store department, a store item, a store item classification, a store employee, or a store customer, and sending the manager recommendation to a device operated by a store manager, (para. 3, The shrinkage control plan may then be displayed to the user on a display device (or user interface) in order to allow for the user (e.g., store manager) to implement the recommendations developed by the recommendation engine and para. 21, As such the data pre-processing component 340 may utilize a user interface component (see FIG. 3) to select items within the extracted data and allocate different priorities to each item (e.g., low priority, medium priority, and high priority). As such, the data pre-processing component 340 may allocate different weights to each item based on the input from the retailer via the user interface component 340. The formatted dataset may also be subdivided into either training dataset used to train the machine learning algorithm and testing dataset in order to verify the accuracy of the ML model against historical data).
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication Number 2022/0343349, Subramanian, et al., hereinafter Subramanian in view of United States Patent Application Publication Number 2019/0027003, Lobo, et al., hereinafter Lobo and further in view of United States Patent Application Publication Number 2015/0289111, Ozkan, et al., hereinafter Ozkan.
Regarding claim 15, Subramanian teaches the method of claim 11 as described above. Subramanian in view of Lobo does not explicitly disclose further comprising, providing the first portion of the featured labeled data as input to a heatmap interface.
However, Ozkan teaches further comprising, providing the first portion of the featured labeled data as input to a heatmap interface, (para. 58, The heat map data 142 can include data associated with customer heat maps. Customer heat maps are used herein to identify hot spots, dead areas and bottlenecks of customer traffic within the indoor environment 102. Customer heat maps can aid companies to optimize store performance, to improve customer service and to improve marketing and promotion results. Companies can use heat maps to visualize the impact of changes to the indoor environment in terms of customer flows, sold items, average sales values, and the like. The heat map data 142 can include previously recorded customer coordinate during a given time interval and/or real-time customer coordinate information. It should be understood that the heat map data 142 can include any combination of the aforementioned data and other data associated with heat maps that is not specified herein).
One having ordinary skill in the art at the time the invention was filed would combine the techniques of Subramanian in view of the system of Lobo and further in view of the method of Ozkan with the motivation to aid companies to optimize store performance, to improve customer service and to improve marketing and promotion results Ozkan para. 58).
Regarding claim 16, Subramanian teaches the method of claim 11 as described above. Subramanian in view of Lobo does not explicitly teach further comprising, extending the heatmap interface with the second portion to provide the corresponding one or more certain prescriptive actions within the heatmap interface.
However, Ozkan teaches further comprising, extending the heatmap interface with the second portion to provide the corresponding one or more certain prescriptive actions within the heatmap interface, (para. 58, The heat map data 142 can include data associated with customer heat maps. Customer heat maps are used herein to identify hot spots, dead areas and bottlenecks of customer traffic within the indoor environment 102. Customer heat maps can aid companies to optimize store performance, to improve customer service and to improve marketing and promotion results. Companies can use heat maps to visualize the impact of changes to the indoor environment in terms of customer flows, sold items, average sales values, and the like. The heat map data 142 can include previously recorded customer coordinate during a given time interval and/or real-time customer coordinate information. It should be understood that the heat map data 142 can include any combination of the aforementioned data and other data associated with heat maps that is not specified herein).
One having ordinary skill in the art at the time the invention was filed would combine the techniques of Subramanian in view of the system of Lobo and further in view of the method of Ozkan with the motivation to aid companies to optimize store performance, to improve customer service and to improve marketing and promotion results Ozkan para. 58).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Multi-service business platform system having entity resolution systems and methods (US 11775494 B2) teaches the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amber Misiaszek whose telephone number is 571-270-1362. The examiner can normally be reached M-F 8:00-5:30, First Friday Off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached on 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682