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
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 2/26/2026, Applicant, on 5/26/2026, amended claims 1, 7 and 13. Claims 1-4, 6-10, 12-16, and 18-23 are pending in this application and have been rejected below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on
5/26/2026 has been entered.
Response to Arguments
Applicant’s arguments filed May 26, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed May 26, 2026.
On Pg. 10-13 of the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 101, Applicant states the machine learning algorithms and data collected from hardware ( point-of-sale devices/ EAS) . In response, Examiner respectfully disagrees. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea/ Examiner asserts, regardless of the complexity of the data analysis and/or processing, without recitation of improvements to the functioning of the technology, technological field and/or computer-related technology (i.e. software), the steps outlined in the claimed invention to create competency learning maps amount to no more than mere instructions to implement the idea on a general purpose computer. The additional elements of point-of-sale devices and EAS are devices for collecting data into the database which is MPEP 2106.05(h)- field of use.
On Pg. 14-17 of the Remarks, with respect to the claim rejection(s) under 35 U.S.C. § 101, Applicant states the amended independent claims cannot be considered abstract because they offer a technologically-rooted solution for a problem specifically arising in the realm of computer technology similar to DDR Holdings. In response, In regards to DDR Holdings, the Court went on to distinguish the invention from that in Ultramercial: Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. . . When the limitations of the '399 patent's asserted claims are taken together as an ordered combination, the claims recite an invention that is not merely the routine or conventional use of the Internet.
The present claim involves an invention more similar to that in Ultramercial than DDR Holdings. Examiner finds the present claims do not recite significantly more than the abstract idea and include words equivalent to “apply it” as described in MPEP2106.05(f). The “database”, “memory”, “processor”, and “computer readable medium” performs functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); and (storing and retrieving information in memory), Versata Dev. Group, Inc. v. SAP Ant, Inc.,193 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Examiner finds, the improvements are directed towards the judicial exception, in particular, extracting, and analyzing data. The claims contain little more than a directive to use computer elements to implement the abstract idea recited by the claims. In contrast, the patent claims in DDR Holdings, described by the Court, "specify how interactions with the Internet are manipulated to yield a desired result." DDR Holdings, 773 F.3d at 1258. Please review updated 101 rejection below for updated analysis.
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 1-4, 6-10, 12-16, and 18-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-4, 6 and 19-23 are directed to an apparatus for performing analytics using machine learning for shrink prevention. Claims 7-10 and 12 are directed to a method for performing analytics using machine learning for shrink prevention. Claims 13-16 and 18 are directed to an article of manufacture for performing analytics using machine learning for shrink prevention.
Claim 1 recites an apparatus for performing analytics using machine learning for shrink prevention, Claim 7 recites a method for performing analytics using machine learning for shrink prevention and Claim 13 recites an article of manufacture for performing analytics using machine learning for shrink prevention, which include extracting a dataset that comprise one or more of inventory information, traffic information, or shrink information associated with a retailer the one or more shrink databases further comprise real-time information updated from point-of-sale devices and electronic article surveillance (EAS) that tracks items that exit a geofence perimeter in the absence of a corresponding point-of-sale transaction ; formatting the dataset that is extracted, and summarize the dataset to increase a granularity of the collected data by aggregating data points across one or more time intervals or other dimensions to produce a reduced dataset to be processed, wherein a portion of the formatted dataset is subdivided into a training dataset and testing dataset; generating one or more shrink features from the training dataset by identifying attributes within the training dataset that are associated with retail theft; and storing shrink predictions generated from the hybrid machine learning model.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes”- evaluation. The recitation of "database”, “memory”, “processor”, and “computer readable medium”, provide nothing in the claim elements to preclude the step from being Mental Processes-evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “database”, “memory”, “processor”, and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). The additional elements of point-of-sale devices and electronic article surveillance (EAS) is MPEP 2106.05(h)- field of use. Regarding the additional element of machine learning - testing combinations of plurality of machine learning algorithms based on the one or more shrink features such that each combination of the plurality of machine learning algorithms outputs a predictive result associated with the retail theft, wherein the testing of combinations of the plurality of machine learning algorithms comprises feeding outputs of a first machine learning algorithm as inputs to a second machine learning algorithm in a particular order; selecting two or more machine learning algorithms from the plurality of machine learning algorithms to form a hybrid machine learning model, wherein the hybrid machine learning model feeds the output of a first selected machine learning algorithm as inputs to a second selected machine learning algorithm in a particular order and the hybrid machine learning model provides a lowest margin of error than a margin of error achieved from any one of the plurality of machine learning algorithms individually. The specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in data analytics.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “database”, “memory”, “processor”, and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to extracting and testing data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Regarding Step 2B and additional elements of point-of-sale devices and electronic article surveillance (EAS) is MPEP 2106.05(h)- field of use . Regarding the additional element of machine learning and Step 2B- the specification discloses the machine learning at a high-level of generality, providing examples of different techniques (linear regression, logistic regression, decision tree, random forest, dimensionality reduction algorithms, or gradient boosting algorithms) that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-4,6, 8-10, 12 and 14-16, 18-23 recite process the dataset in order to expand granularity of information associated with the one or more of inventory information, the traffic information, or the shrink information for the retailer included in the one or more databases; identify data points within the dataset that identify one or more of types of items that the retailer has identified as high priority items; and allocate weights to each of the one or more types of items based on input from the retailer; determine a pattern during a time period that directly correlates against increase in the retail theft for the time period; determine the margin of error that is achieved from the plurality of machine learning algorithms against the testing dataset that reflects the actual shrink for a time period, wherein the margin of error comprises one or both of mean absolute error or root mean square error for the time period; modify at least one of the two or more machine learning algorithms that are selected for the hybrid machine learning model; select two or more machine learning algorithms from the plurality of machine learning algorithms to form a hybrid machine learning model comprises an order to apply the two or more selected machine learning algorithms; wherein the one or more shrink databases include weather information; wherein the shrink predictions generated from the hybrid machine learning model include predictions of risk factors and likelihood of an item being subject to retail theft for any particular day or time; wherein the one or more shrink features comprise at least one of patterns and trends of how a day of a week or weather impacts retail theft of specific items, an item shrink frequency ratio during a week across zones for weekdays and hours, or a number of times an item was subject to retail theft on holidays across days, hours, weeks, and zones; wherein testing the combinations of the plurality of machine learning algorithms further comprises testing a plurality of combinations of the machine learning algorithms taken together in different ordered sequences and modifying at least one machine learning algorithm by applying a dimensionality reduction algorithm to at least one other machine learning algorithm and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 7 and 13. Regarding Claim 2, 8, 14 and 20 and the additional element of “database” – it is storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Regarding claims 4, 6, 10,12, and 16,19, 21, 23 and the additional element of machine learning - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1-4, 6-10, 12-16, and 18-23 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 7 and Claim 13), the prior art does not teach or fairly suggest:
“… wherein the one or more shrink databases further comprise real-time information updated from point-of-sale devices and electronic article surveillance (EAS) that tracks items that exit a geofence perimeter in the absence of a corresponding point-of-sale transaction; formatting the dataset that is extracted from the one or more shrink databases and summarizing the dataset to increase a granularity of the collected data by aggregating data points across one or more time intervals or other dimensions to produce a reduced dataset to be processed, wherein a portion of the formatted dataset is subdivided into a training dataset and testing dataset”.
Examiner finds that Lobo, US Publication No. 20190027003A1 teaches A retail shrinkage activity prediction and identification system that includes: a sensor control system, a first shrinkage database, a second shrinkage database, an analytics engine, and a machine learning engine. The sensor control system is communicatively coupled with a plurality of sensors arranged in a retail environment. The sensor control system is configured to control a setting of each of the plurality of sensors. The first shrinkage database includes retail shrinkage data for at least the retail environment. The retail shrinkage data includes at least one item at high risk for shrinkage or at least one time at high risk for shrinkage activity. The second shrinkage database includes external data related to shrinkage in a geographic area of the retail environment. The analytics engine is communicatively coupled with the first shrinkage database, the second shrinkage database, and the sensor control system (see Abstract). In particular, Lobo discloses a method of predicting or identifying retail shrinkage activity includes: accessing retail shrinkage data comprising at least one item at high risk for shrinkage or at least one time at high risk for shrinkage activity in a retail environment; accessing external data related to shrinkage in a geographic area of the retail environment; receiving real-time sensor data from a plurality of sensors arranged in the retail environment; comparing the real-time sensor data with the external data to identify a high shrinkage risk situation and if a high shrinkage risk situation is identified, issuing an alert, causing a sensor control system to alter a setting of at least one of the plurality of sensors, and updating at least one of the retail shrinkage data or the external data; conducting predictive modeling using the retail shrinkage data, the external data, and the issuance of an alert; and issuing an alert if the predictive modeling determines that a high shrinkage risk situation is likely to occur. (see par. 0006-0008).
Austin et al., US Publication No. 20210174130A1 teaches hybrid ML system 204 includes a ranking process 208, a select hybrid components process 210, training data 212 and test data 214. In the illustrated embodiment, the hybrid ML system 204 includes a leaderboard 216 and an application programming interface (API) 218. The hybrid ML system 204 operates to develop one or more ML solutions to ML problems such as business problem 220. The hybrid ML system 204 in exemplary embodiments is implemented using a processing system including at least one processor and a memory storing instructions to control operations of the processing system. The hybrid ML system 204 may receive information about the business problem 220 including the dataset and scoring method from the user 202 and develop an initial ML solution from these inputs. In alternative embodiments, the hybrid ML system 204 may receive the initial ML solution along with the information about the business problem 220 and the dataset and the scoring method from the user 202. (see par. 0052-0057).
Lei et al., US Publication No. 20190188536A1 teaches generating a model of demand of a product that includes an optimized feature set. Embodiments receive sales history for the product and receive a set of relevant features for the product and designate a subset of the relevant features as mandatory features. From the sales history, embodiments form a training dataset and a validation dataset and randomly select from the set of relevant features one or more optional features. Embodiments include the selected optional features with the mandatory features to create a feature test set. Embodiments train an algorithm using the training dataset and the feature test set to generate a trained algorithm and calculate an early stopping metric using the trained algorithm and the validation dataset. When the early stopping metric is below a predefined threshold, the feature test set is the optimized feature set. (see Abstract). In particular, Lei discloses Each feature set can be used as input into a forecasting algorithm to generate forecasting trained models. The multiple trained models can then be aggregated to generate a demand forecast, as disclosed in detail below in conjunction with FIG. 5. The output of the functionality of FIG. 2 is one or more optimized feature sets (see par. 0062, 0072-0074).
Although Lobo, Austin and Lei teaches the analysis elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the extraction and modeling.
Additionally, Examiner finds Cash et al., US Publication No. 20200265437A1 teaches systems, methods, and software for in situ and network-based transaction classification. Such embodiments use advanced data analytics and machine learning techniques of consumer's transaction attributes to reduce shrink at checkout. One embodiment, in the form of a method, includes processing a dataset of transactions to identify normal transaction patterns and processing a dataset of transactions that included known fraud to identify variation patterns between the identified normal transaction patterns and the data of each transaction. The method further includes generating at least one pattern model based on the identified normal transaction patterns and the identified variation patterns. In such embodiments, each pattern model typically includes classification values for determining a likelihood of fraud in transactions. The method continues by applying the model to a current transaction to calculate a score indicative of a likelihood of fraud and outputs the score. (see Abstract). In particular, Cash discloses Once transaction patterns are generated 104 into a model which is then tested and validated, the model is provided 106 to a processing engine that evaluates transaction data in near or actual real-time to identify when a shrink event is occurring. The near or actual real-time monitoring of transaction may be implemented as an add-on application to a retailers POS software systems, through a network accessible cloud application or mobile application to alert a self-checkout attendant or a front-end supervisor to potential transactions where shrink may be occurring using the current item and transaction characteristics. Detection of a possible shrinkage event may also or alternatively be transmitted to another shrink-prevention solution, such as an image or video processing system that processes images or video to identify or confirm attempted theft or other system involved in shrinkage prevention. The cloud based solution may be implemented on a network accessible server that is located in a store, in a backend system of a store or a chain of stores, be hosted by a shirk detection service provider, and the like. (see par. 0039).
The dependent claims 2-4, 6, 8-12, 14-16, 18-23 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1 that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Patent No. 11461690 B2 to Szeto et al. Abstract-“ A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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