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 .
Summary
This Final Office Action in response to the communication received on May 26, 2026 has been entered.
Claims 1, 12, and 19 have been amended.
Claim 4 has been cancelled.
Claims 1-3 and 5-21 are pending.
Application filed December 3, 2021 and is a Divisional of Application 16/696922 filed November 26, 2019.
Response to Amendment
Amendments to Claims 1, 12, and 19 are acknowledged.
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 1-3 and 5-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
Step 1
As indicated in the preamble of the claim, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter. Claims 1-3, 5-11 and 19-21 are processes and Claims 12-18 are machines. Accordingly, step 1 is satisfied.
Step 2A
Claim 12 (and similarly Claims 1 and 19) recites the following abstract concepts that are found to include abstract idea. Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
receiving a transaction identifier for a transaction flagged for a rescan and an audit check (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
obtaining transaction features for the transaction and items of the transaction (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
training a machine-learning algorithm on a transaction log associated with known transactions associated with theft and other known transactions that are not associated with any theft, the training including providing basket items for each transaction, transaction features for each transaction, and can indication whether an item was stolen or not for the transaction along with item category associated with any known stolen item as training data, such that the machine-learning algorithm derives an algorithm that produces as output a total number of items to rescan and indicates how the total number of rescan items are to be selected by an attendant for a rescan security check (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); see also MPEP 2106.05(f)(2) TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. (See MPEP 2106.04(a)(2)(I) mathematical concepts, using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979), and July 2024 Subject Matter Eligibility Example 47 Claim 2 analysis wherein trained machine leaning algorithms are mathematical concepts, and MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and July 2024 Subject Matter Eligibility Example 47 Claim 2 analysis wherein using a trained ANN encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of an anomaly in a data set);
identifying a number from a total number of transaction items that are to be processed with the partial rescan using the transaction identifier (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.);
determining, based on the transaction features, item categories associated with a subset of times selected from the items to perform the rescan on for the audit check by utilizing a machine-learning algorithm trained on the transaction features, including at least particular items known to be stolen based at least in part on a respectively probability of theft assigned to each item category (See MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); see also MPEP 2106.05(f)(2) TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. (See MPEP 2106.04(a)(2)(I) mathematical concepts, using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979), and July 2024 Subject Matter Eligibility Example 47 Claim 2 analysis wherein trained machine leaning algorithms are mathematical concepts, and MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and July 2024 Subject Matter Eligibility Example 47 Claim 2 analysis wherein using a trained ANN encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of an anomaly in a data set);
assigning an item category percentage of each item category based on a corresponding probability of theft assigned (MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
determining an item rescan total from a total number of the items based on the transaction features (See MPEP 2106.04(a)(2)(I) mathematical concepts, calculating a number representing an alarm limit value using the mathematical formula ‘‘B1=B0 (1.0–F) + PVL(F)’’, Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978));
providing an indication of the subset of items, corresponding item category percentages, and the item rescan total to an attendant terminal to process the rescan and the audit check against the items of the transaction (MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)); and
instructing the attendant to perform a full rescan of the items for the transaction when any particular transaction item rescanned was unaccounted for in the items scanned for the transaction (MPEP 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
Claim 12 (and similarly Claims 1 and 19) is directed to a series of steps for determining and providing the commercial interaction of a determined number of items and item categories for a partial rescan to an attendant terminal, that uses past observed transaction data to train and implement mathematical algorithms to determine items for a rescan, and thus grouped as mathematical concepts and mental processes. The mere nominal recitation of a non-transitory computer-readable storage medium comprising executable instructions, and an attendant terminal does not take the claim out of the mathematical concept and mental processes. Thus, Claim 1 (and similarly Claims12 and 19) recites an abstract idea.
Step 2A
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using 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 MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
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)
The identified abstract idea of exemplary Claim 12 (and similarly Claims 1 and 19) is not integrated into a practical application. The additional elements are: a server comprising a processor and a non-transitory computer-readable storage medium comprising executable instructions, and an attendant terminal that merely implements the underlying abstract idea. These additional elements are broadly recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to merely using a computer as a tool to perform the abstract idea - see MPEP 2106.05(f).
Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Claim 12 (and similarly Claims 1 and 19) is directed to an abstract idea.
Step 2B
Claim 12 (and similarly Claims 1 and 19) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, utilizing a machine learning algorithm to determine item categories and a total number of items for a partial rescan and providing the number and item categories to an attendant terminal, do not add significantly more to the exception because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Claim 12 (and similarly Claims 1 and 19) is ineligible.
Claim 2 recites the abstract idea of mathematical concepts and mental processes. See MPEP 2106.04(a)(2)(I) and MPEP 2106.04(a)(2)(III).
Claim 3 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 5 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 6 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 7 recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 8 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 9 recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 10 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 11 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 13 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 14 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 15 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 16 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 17 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 18 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 20 recites the additional limitation wherein the attendant device is a transaction terminal, a tablet computer, a laptop computer, a desktop computer, a phone, or a wearable processing device, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 21 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Prior Art
Claims 1-3 and 5-21 in the instant application are allowable over the prior art because the prior arts of record fail to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Exemplary claim 1 recites the following:
A method, comprising:
obtaining a transaction identifier for a transaction designated for a partial rescan;
identifying a set of transaction items associated with the transaction using the transaction identifier;
determining item categories corresponding to the set of transaction items;
training a first machine-learning algorithm on transaction logs for previous transactions, the transaction logs including items known to be stolen and items known to not be associated with theft, to derive an algorithm that predicts, for a given transaction, which item categories have respective probabilities of theft;
utilizing the first machine-learning algorithm to produce a ranked listing of item categories based on the respective probabilities of theft and to identify based at least in part on a respective probability of a theft assigned to each item category a subset of items to rescan;
providing an indication of the subset of items to an attendant terminal to process the partial rescan;
determining, based on results of processing the partial rescan that at least one item of the subset of items was not scanned in connection with the transaction; and
instructing the attendant terminal to perform a full rescan of all basket items. (Emphasis added to highlight features that distinguish over the prior art).
As further explained below, the prior art of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the Applicant’s claimed invention.
US Pat Pub 2003/0102373 "Swartz" discloses a statistical basis for use in a self-scanning checkout system determines how many items to check in a shopper's shopping cart for incorrect or missing scans as well as which particular or types of items to check to determine if they were properly scanned, if the shopper is determined to be audited. However, Swartz fails to disclose utilizing a machine-learning algorithm to identify a subset of items to rescan based on a respective probability of a theft assigned to each item category.
US Pat Pub 2019/0188579 "Manoharan" teaches using machine learning to automatically determine a data loading configuration for a computer-based rule engine. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request. Manoharan fails to teach utilizing a machine-learning algorithm to identify a subset of items to rescan based on a respective probability of a theft assigned to each item category.
US Pat Pub 2019/0287113 "Wright" teaches score-based verification of basket contents. A user trust score is generated based on the user's transaction history, item selection area data, and trust rules. If the per-basket verification score is below the threshold value, the basket of items is selected for a partial verification of the contents of the basket. Wright fails to teach t utilizing a machine-learning algorithm to identify a subset of items to rescan based on a respective probability of a theft assigned to each item category.
Response to Arguments
Applicant's arguments filed May 26, 2026 have been fully considered but they are not persuasive.
35 USC 101
Applicant argues that the claims recite an improvement to how the machine-learning model operates by reciting a specific training methodology for the machine-learning algorithm itself, and that the Ex Parte Desjardins and Ex Parte Kelley decisions supports this argument for eligibility.
Applicant has amended Claim 12, and similarly amended claims 1 and 19, to recite:
training a machine-learning algorithm on a transaction log associated with known transactions associated with theft and other known transactions that are not associated with any theft, the training including providing basket items for each transaction, transaction features for each transaction, and can indication whether an item was stolen or not for the transaction along with item category associated with any known stolen item as training data, such that the machine-learning algorithm derives an algorithm that produces as output a total number of items to rescan and indicates how the total number of rescan items are to be selected by an attendant for a rescan security check.
Using specific types of information to train a machine learning algorithm to determine a specific output is not considered to be details that would improve the technical aspects of the machine learning. Example 47, Claim 2 actually provides more technical information about the training of machine learning by stating that it includes a backpropagation algorithm and a gradient descent algorithm. Teaching specific types of algorithms to be applied to the training data is not considered an improvement to the technical aspects of the claims. An intended purpose of the machine learning provides no additional technical improvements. As such, the claims are found to be analogous to Example 47, Claim 2.
Ex Parte Desjardins found that an improvement to how machine learning itself operates was eligible under 35 USC 101. It is found that the amended claims merely use specific types of information to train a machine learning algorithm to determine a specific output, and this is not considered to be details that would improve the technical aspects of the machine learning.
As noted by the Applicant in their arguments, Ex Parte Kelley found that the steps of modifying a linear proxy constraint based on its relationship to a non-linear constraint, replacing the non-linear constraint with the modified linear proxy constraint, and determining a solution that satisfies a convergence criterion based on the modified linear proxy constraint "improve the training and operation of the machine learning algorithm as described in the Specification." The amended claims fail to provide an improvement to the machine learning itself. Instead, they provide additional detail to the machine learning algorithm for the ML to analyze to come up with an output. Providing additional data is not an improvement to the ML training. It is merely increasing the data provided. Kelley recited limitations on how and why to modify the algorithm itself. This is much different than providing additional training data.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
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