DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Prosecutorial Standing
2. This communication is in response to the Preliminary Amendment filed on 12.11.2024. Claim 1 has been cancelled, and new claims 2-9 have been added. Therefore, claims 2-9 will be subject to further examination and evaluation in due course, and will be presented for examination, as detailed below.
Oath/Declaration
3. The Applicants’ oath/declaration has been reviewed by the Examiner and is found to conform to the requirements prescribed in 37 C.F.R. 1.63.
Priority / Filing Date
4. Applicant’s claim for priority of the DIV of 17/383,576 filed on 07.23.2021 is acknowledged. The Examiner takes the PRO date of 07.23.2021 into consideration.
Claim Rejections - 35 USC § 101
5. 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 2-9 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), an abstract idea without significantly more.
Step 1 Statutory Category: claims 2-9 are directed to a machine-learning model that returns rescan scores based on the real-time transaction records. The claims appear to fall under the grouping of abstract idea related to a fundamental economic practice, mathematical concepts, certain methods of organizing human activity, and/or mental processes.
Step 2A – Prong 1: Judicial Exception Recited: Nevertheless, independent claim 2 recites a judicial exception, namely an abstract idea of automating a business practice using generic computing components without a technological improvement. As the steps describe a business method of managing rescan audits using automation.
Exemplary independent claim 2 recites the following abstract ideas:
receiving rescan settings;
provide rescan scores as predicted values;
receiving real-time transaction records;
providing the real-time transaction records;
determining rescan decisions; and
providing select ones of the rescan decisions to the store to enforce the rescan audit.
These limitations describe collecting information, and making a decision based on the analysis, which constitutes data analysis and decision making. Such concepts fall within the categories of mental processes and certain methods of organizing human activity.
The steps of training a machine learning model, generating scores, and determining rescan decisions merely recite the use of mathematical and statistical techniques to evaluate transaction data and determine whether an audit should be performed. Nonetheless, mathematical concepts, data analysis, and risk evaluation are abstract ideas.
Furthermore, the claimed purpose of selectively enforcing rescan audits for self-service checkout transaction reflects a business practice relating to loss prevention and compliance enforcement, which also an abstract idea.
Accordingly, claim 2 recites an abstract idea.
Step 2A – Prong 2: Practical Application: This judicial exception is not integrated into a practical application because the claim as a whole merely describes the concept of predictive rescan service using generally recited computer elements such as a retail server, an interface, a self-service checkout, and a machine-learning model. These additional elements of a retail server, an interface, a self-service checkout, and a machine-learning model, in these steps are recited at a high-level of generality such that it amounts to more than mere instructions to apply the exception using a generic computer component, and are merely invoked as tools for predictive rescan service. Accordingly, these elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Simply implementing the abstract idea on a generic computing environment is not a practical application of the abstract idea, and does not take the claim out of the Commercial or Business Practices or Legal Interactions subgrouping of Certain Methods of Organizing Human Activity grouping. The claim is directed to an abstract idea.
Step 2B – Inventive Concept: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and as an ordered combination, they do not add significantly more (also known as “inventive concept”) to the exception. These elements (retail server, interface, self-service checkout, and machine-learning model) are generic computer components performing well-understood, routine, and conventional functions (e.g., receiving data, collecting transaction data, data processing, managing rescan audits, training and applying a model, and transmitting decisions).
Mere instructions to apply an exception using a generic computer component cannot integrate into a practical application nor provide an inventive concept. The claim merely uses a generic computer as a tool to implement the abstract idea of predictive rescan service, which fails to add an inventive concept sufficient to transform the abstract idea into patent eligible subject matter. Accordingly, these additional elements, do not change the outcome of the analysis, when considered individually and as an ordered combination as there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. Therefore claim 2 is directed to an abstract idea (e.g., predictive rescan service) without significantly more. Accordingly, claim 2 is not patent eligible.
Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter (see Alice Corp v CLS).
Furthermore, claims 3-9 define the same abstract idea noted above for independent claim 2, are considered to be part of the abstract idea above and merely act to further limit it. In the dependent claims, the additional elements or combination of elements in the claims other than the abstract idea per se amounts to no more than: mere instructions to implement the idea on a computer functioning in a standard mode of operation or matters that are routine and conventional in the field. Therefore, they are considered patent ineligible for the reasons given above.
Additionally, claims 3-9 do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter (see Alice Corp v CLS).
Therefore, the limitations of the claimed invention, when viewed individually and in ordered combination, are directed to ineligible subject matter. To address this rejection, the examiner suggests reviewing the recent Federal Circuit Court decisions and USPTO guidelines related to U.S.C. 101 for guidance on what is considered statutory subject matter.
Claim Rejections - 35 USC § 103
6. 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.
7. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
8. Claims 2-9 are rejected under 35 U.S.C. 103 as being unpatentable over Valouch, Pub. No.: US 2021/0365676 in view of Sumpter, Pub., No.: US 2022/0230173.
As per claims 2, 8, and 9, Valouch discloses a method, comprising: providing a rescan interface to a retailer server [see at least ¶0139 (e.g., User interface 1200 is also shown to include a “Rescan” button, a “Submit” button, and an “Assign to Report” element. A user may be enabled to process certain objects again by selecting the “Rescan.”), and illustrated in FIG. 12];
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receiving rescan settings through the rescan interface from a retailer associated with a store [see at least ¶0005 (e.g., Processing the frame may include receiving, from the server, a third candidate data item predicted from the first text data item according to the third model)];
training a machine-learning model to provide rescan scores as predicted values that any given self-service checkout transaction at the store should be subject to or not subject to a rescan audit [see at least ¶0061 (e.g., model 112 is a machine learning model that has been trained with machine-generated text data such as printed characters. As such, text-to-token model 112 may predict candidate data items from printed text data with relatively high confidence scores. Conversely, text-to-token model 112 may predict candidate data items from handwritten text data with relatively lower confidence scores)];
receiving real-time transaction records for self-service checkout transactions from the store [see at least the abstract (e.g., a method that predicts data items from a real-world object in real-time)];
providing the real-time transaction records [see at least ¶0068 (e.g., Record manager 122 serves to find the best data items out of the candidate data items and populates a record with those data items. As used herein, a data item may be a candidate data item that has been selected by record manager record manager 122. In the example shown, record manager 122 is configured to select one or more candidate data items from the priority queues of consolidation engine 120 as a set of data items. Once selected, record manager 122 may populate a record with the set of data items. In some embodiments, record manager 122 may select candidate data items from each of the priority queues associated with the highest confidence score. Record manager 122 may also select a frame from the frames 106 to associate with the set of data items and to populate the record with. For example, if object 101 is a receipt, the text data items are line items, and the record is an expense report, record manager 122 may populate the expense report with line items from the receipt. In this example, record manager selects a frame displaying the receipt to populate the expense report with. As shown, record manager 122 communicates the set of data items and a selected frame to user interface 124 for display to a user. Record manager 122 may also receive requests to assign data items to particular records from user interface 124. In response, record manager 122 may assign data items to those particular records. Furthermore, record manager 122 may communicate the populated records to a remote server that maintains and processes the records)] to the machine-learning model and receiving the corresponding rescan scores as output from the machine-learning model [see at least claim 5 (e.g., model generates a first confidence score associated with the first candidate data item, the second model generates a second confidence score associated with the second candidate data item, and the third model generates a third confidence score associated with the third candidate data item, wherein said selecting the first candidate data item, the second candidate data item, or the third candidate data item further comprises: selecting the first candidate data item, the second candidate data item, or the third candidate data item based on which of the first confidence score, the second confidence score, and the third confidence score is highest)].
Valouch discloses all elements per claimed invention as explained above. Valouch does not explicitly disclose determining rescan decisions for the self-service checkout transactions based on enforcement of the rescan settings and evaluation of the rescan scores; and providing select ones of the rescan decisions to the store to enforce the rescan audit against the corresponding self-service checkout transactions at the store. However, Sumpter discloses determining rescan decisions for the self-service checkout transactions based on enforcement of the rescan settings and evaluation of the rescan scores [see at least ¶0044 (e.g., a third MLA is trained to receive as input the customer grade outputted for a given item-return transaction by the first MLA and the fraud score outputted by the second MLA and produce as output a decision or a value to item return manager 114 for item return manager 114 to compare against a model value in determining whether a given item-return transaction can proceed without audit intervention or whether audit intervention is needed)]; and providing select ones of the rescan decisions to the store to enforce the rescan audit against the corresponding self-service checkout transactions at the store [see at least the abstract (e.g., fraud score and a customer-return grade for the customer are processed to determine whether the transaction can complete at the terminal without assistance or whether the transaction is to be held in abeyance for an audit (onsite audit or remote network-based audit))].
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to incorporate the teaching of Sumpter in order to provide a system for Self-Service Terminal item return anti-fraud [Sumpter: ¶0006].
As per claims 3 and 4, Valouch discloses wherein training further includes delaying initiation of the training based on a silent-mode setting being enabled in the rescan settings, and wherein during a silent-mode of operation with the silent-mode setting enabled, the real-time transaction records are collected along with indications during any rescans as to whether each corresponding self- service checkout transaction was or was not identified as fraudulent; and wherein delaying further includes initiating the training when the silent-mode setting is disabled in the rescan settings and providing the real-time transaction records and the indications as input to the machine-learning model [see at least the abstract (e.g., method also processes the frame using a plurality of models, wherein each model in the plurality of models is configured to predict a set of candidate data items associated with the object), and refer to the rejection of claim 2 above. In light of the preceding examination, claims 3 and 4 is hereby rejected on grounds substantially similar to those articulated in the rejection of claim 2. As detailed in the prior rejection, the rationale and basis for rejecting claim 1 are applicable to claims 3 and 4. For a comprehensive understanding of the rejection grounds, reference is made to the detailed explanation provided in the rejection of claim 2, which is incorporated herein by reference]
As per claim 5, Valouch discloses wherein determining further includes making a first decision to recommend the rescan audit for a given self-service checkout transaction and overriding that first decision to not recommend the rescan audit based on a trusted identifier defined in the rescan settings that matches a customer identifier for a customer who is performing the given self-service checkout transaction at the store [refer to the rejection of claim 2 above. In light of the preceding examination, claim 5 is hereby rejected on grounds substantially similar to those articulated in the rejection of claim 2. As detailed in the prior rejection, the rationale and basis for rejecting claim 1 are applicable to claim 5. For a comprehensive understanding of the rejection grounds, reference is made to the detailed explanation provided in the rejection of claim 2, which is incorporated herein by reference].
As per claim 6, Valouch discloses wherein determining further includes making a first decision to recommend the rescan audit for a given self-service checkout transaction and overriding that first decision to not recommend the rescan audit based on a maximum number of concurrent and ongoing rescan audits being defined in the rescan settings and being reached at the store when the first decision was determined [refer to the rejection of claim 2 above. In light of the preceding examination, claim 6 is hereby rejected on grounds substantially similar to those articulated in the rejection of claim 2. As detailed in the prior rejection, the rationale and basis for rejecting claim 2 are applicable to claim 6. For a comprehensive understanding of the rejection grounds, reference is made to the detailed explanation provided in the rejection of claim 2, which is incorporated herein by reference].
As per claim 7, Valouch discloses rendering a dashboard through the rescan interface that graphically depicts information selectively mined from the real-time transaction records, the rescan settings, and the rescan decisions based on retailer-defined criteria [see at least ¶0166 (e.g., Display 1710 is configured to output visual information (e.g., a graphical user interface (GUI) generated and/or rendered by processors 1704))]; and providing custom reports through the rescan interface when requested by the retailer through the interface based on the real-time transaction records, the rescan settings, and the rescan decisions [see at least ¶0050 (e.g., record keeping and reporting purposes), and ¶0103 (e.g., FIG. 4, In FIG. 4, consolidation engine 120 is configured to ensure that distinct expenses are reported distinctly)].
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO 2020219487, SUN YIN: discloses a method for operating a self-service shopping system.
US 2006/0043175, Fu: discloses a checkout system and checkout method for a retail environment.
US 2024/0311788, Mattison: discloses a self-service kiosk inventory control.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Garcia Ade whose telephone number is (571)272-5586. The examiner can normally be reached on Monday - Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian Zeender can be reached on 517-272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Garcia Ade/Primary Examiner, Art Unit 3627
GARCIA ADE
Primary Examiner
Art Unit 3687
/GA/Primary Examiner, Art Unit 3627