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
Election/Restrictions
2. Applicant’s election with traverse of Group 1: claims 1-12 in the reply filed on 12.01.2025 is acknowledged. The traversal is on the ground(s) that “all three groups are directed to the same invention, share the same classification, require the same field of search, and cannot be practiced independently.” This is not found persuasive because the claims are related as sub-combinations as usable together in a single combination.
The requirement is still deemed proper and is therefore made FINAL.
3. Claims 13-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected Groups 2 and 3, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 12.01.2025.
Therefore, claims 1-12 will be subject to further examination and evaluation in due course, and will be presented for examination, as detailed below.
Oath/Declaration
4. The Applicant’s oath/declaration has been reviewed by the Examiner and is found to conform to the requirements prescribed in 37 C.F.R. 1.63.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-12 are directed to “data-driven model for detecting out-of-shelf events in retail environments in real-time.”
The Examiner has identified independent method claim 1 as the claim that represents the claimed invention for analysis. Claim 1 is directed to a method, which fall within a statutory category of invention (process), comprising:
collecting historical transaction data from a plurality or retail stores;
generating a probability distribution function based on the historical transaction data;
receiving real-time transaction data relevant to sales of one item from a particular retail store;
comparing real-time sales data for the one item against the PDF to detect deviations; and
triggering an alert when the real-time sales data deviates according to the PDF with respect to the one item at the particular retail store.
Under the broadest reasonable interpretation, these limitations are directed to the abstract idea of analyzing sales data using a probability distribution to detect deviations and trigger an alert (a mathematical concept and/or method of organizing human activity). In particular, the limitations “generating a probability distribution function” and “comparing real-time sales data against the PDF to detect deviations” constitute mathematical calculations and statistical analysis.
Mathematical concepts, including formulas, probability distributions, and data comparison operations are abstract ideas. Additionally, the claim is directed to monitoring retail sales and detecting interruption in item sales, which constitutes managing commercial activity and organizing human economic behavior. Such activity falls within certain methods of organizing human activity. Therefore, the claim recites a judicial exception in the form of:
Mathematical concepts (probability distribution and deviation analysis, and
Certain methods of organizing human activity (retail sales monitoring and interruption detection).
Further, evidence is cited to: Alice Corp. v. CLS Bank, Electric Power Group v. Alston, and Intellectual Ventures v. Capital One. Accordingly, claim 1 recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: collecting data from retail stores, receiving real-time data, and triggering an alert. These elements are recited at a high-level of generality and function as generic data gathering and output functions (e.g., receiving data, generating a PDF, collecting data, comparing data, and etc.) such that it amounts no more than instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea without a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality, and do not improve the functioning of a computer, do not provide a particular machine configuration, and do not define a particular technical mechanism for detecting out-of-shelf events. Instead, the claim merely applies statistical analysis to business data and outputs an alert when deviation is detected.
Merely implementing and abstract idea on a generic computer does not integrate the exception into a practical application. The claimed method uses a computer as a tool to perform calculations and issue notifications, which amounts to instructions to apply the abstract idea using conventional computer activity.
Accordingly, the claim fails to integrate the judicial exception into a practical application. Therefore, claim 1 is directed to an abstract idea without a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. The additional elements (e.g., data collection, data comparison, and generating alert), represent well-understood, routine, and conventional activities when performed by generic computing system.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, which are well-understood, routine, and conventional, comprise: data collection, data comparison, and generating alert to aid in performing the aforementioned steps and thus amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Limiting an abstract idea to a particular technological environment (detecting out-of-shelf events) does not render the claim patent eligible. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. The claim does not amount to provide significantly more than the abstract idea itself. Thus, claim 1 is not patent eligible.
Claim 1 is directed to an abstract idea, and does not integrate the exception into a practical application. The additional elements constitute routine data gathering and do not amount to significantly more than the abstract idea. Further, the claim does not include an inventive concept and therefore is not patent eligible under 35 USC § 101.
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. Consequently, the claim is directed to: a mathematical concept (probability distribution and deviation), and/or a method of organizing human activity (retail sales monitoring), implemented using generic computer functionality, without reciting significantly more.
Therefore, the claim is rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter (see Alice Corp v CLS).
Dependent claims 2-12, further define the abstract idea that is present in their respective independent claim 1. In addition, they recite additional limitations: updating, implementing, providing, identifying, collecting, generating, and receiving, and triggering are recited at a high-level of generality such that that it amounts no more than mere instructions to apply the exception using a generic computer component. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. These claims are not patent eligible.
Additionally, the dependent claims 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 dependent claims are directed to an abstract idea.
Thus, claims 1-12 are not patent-eligible, and are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter (see Alice Corp v CLS). 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 1, 3-5, 8, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al., Pub. No.: US 2009/0024450 in view of LI Wei, WO 2018/132118.
As per claim 1, Chen discloses collecting historical transaction data from a plurality of retail stores [see at least ¶0006 (e.g., Examples of such data sources are point-of-sale (POS) data and perpetual inventory (PI) data), ¶0013 (e.g., a point-of-sale system 104 (providing point of sale data to the prediction engine 102), as illustrated in FIG. 1, which is a block diagram illustrating an example system 100, and presented below];
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generating a probability distribution function (PDF) for at least one item based on the historical transaction data [see at least ¶0031 (e.g., the prediction engine 102 can then determine an appropriate probability distribution P (e.g., Poisson, geometric, etc.) that fits these sample statistics), as illustrated in FIG. 3, and presented below]; and comparing sales data for the at least one item against the PDF to detect deviations in item sales totals, and triggering an alert when the sales data deviates according to the PDF beyond a predetermined threshold indicating a potential item sales interruption event with respect to the at least one item at the particular retail store [see at least ¶¶0016-0021 and ¶¶0029-0030, and also illustrated in FIG. 3 below]:
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Chen discloses all elements per claimed invention as explained above. Chen does not explicitly discloses continuously receiving real-time transaction data relevant to sales of the at least one item from a particular retail store. However, LI Wei discloses continuously receiving real-time transaction data relevant to sales of the at least one item from a particular retail store [see at least ¶0010 (e.g., systems and methods to support streaming aggregation for analysis of electronic transactions. First, a plurality of metrics to be measured/analyzed for a stream of real-life events, such as processing steps of the electronic transactions, are defined and converted to one or more generic metrics for aggregation. In some embodiments, the plurality of metrics flexibly include ad-hoc aggregation measures as well as various user-defined functions (UDFs), which allow a user/processor/evaluator of the electronic transactions to define and collect various types of information of the electronic transactions for analysis. Once converted, the generic metrics of the stream of real-life events are aggregated by an aggregation engine in real time. The aggregation results are then saved in an aggregation database, which is queried by the user for real time analysis of the electronic transaction), and ¶0011].
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 LI Wei in order to improves scalability and reduces latency of real time analysis of the electronic transactions without having any negative impact on the processing of the electronic transactions for business [LI Wei: ¶0011].
As per claim 3, Chen discloses implementing and providing the method as a cloud-based service to retail systems associated with the retail stores [see at least ¶0041 (e.g., a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of what is disclosed here, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network ("LAN") and a wide area network ("WAN"), e.g., the Internet)].
As per claim 4, Chen discloses wherein collecting further includes identifying from the historical transaction data and for each transaction in the historical transaction data, an item identifier, item quantity sold, store identifier, date, time of day, and day of week [refer to the rejection of claim 1 above. In light of the preceding examination, claim 4 is hereby rejected on grounds substantially similar to those articulated in the rejection of claim 1. As detailed in the prior rejection, the rationale and basis for rejecting claim 1 are applicable to claim 4. For a comprehensive understanding of the rejection grounds, reference is made to the detailed explanation provided in the rejection of claim 1, which is incorporated herein by reference].
As per claim 5, Chen discloses wherein collecting further includes collecting the historical transaction data from a previous period relative to a current date and extending back at least three months [see at least ¶0020 (e.g., Lost sales can be detected when the net sales are depressed for extended periods of time. In some implementations, when sales are zero for consecutive periods or for a predetermined amount of time (e.g., days), there is a probability of lost sales occurring. The system 100 can be configured to find these time periods (e.g., days) when the probability that a SKU has incurred lost sales is relatively high)].
As per claim 8, Chen discloses wherein continuously receiving further includes receiving the real-time transaction data every 15 minutes [refer to the rejection of claim 1 above. In light of the preceding examination, claim 8 is hereby rejected on grounds substantially similar to those articulated in the rejection of claim 1. As detailed in the prior rejection, the rationale and basis for rejecting claim 1 are applicable to claim 8. For a comprehensive understanding of the rejection grounds, reference is made to the detailed explanation provided in the rejection of claim 1, which is incorporated herein by reference].
As per claim 10, Chen discloses wherein triggering further includes causing an automatic inventory check on the at least one item with an inventory system associated with the particular store based on the alert [see at least ¶0014 (e.g., Detection and estimation of lost sales can be done for two scenarios related to inventory at the end of an analysis cycle. Lost sales can be estimated for a scenario where the end-of-day store inventory is zero and a scenario where the end-of-day inventory is positive (greater than zero)), and ¶¶0015-0019].
As per claim 11, Chen discloses wherein triggering further includes sending the alert to at least one retail system associated with the particular store [see at least ¶0037 (e.g., propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus)].
9. Claims 2, 7, 9, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Chen, in view of LI Wei, and further in view of Zhang et al., Patent No.: US 10,445,152.
As per claims 2, 7, 9, and 12, the combination of Chen and LI Wei does not explicitly discloses: updating the PDF dynamically when the historical transaction data is updated; wherein generating further includes updating the PDF dynamically based on newly received historical transaction data; Chen discloses wherein triggering further includes setting the predetermined threshold to an operating parameter, wherein the operating parameter is 5% or less; wherein triggering further includes providing alert data with the alert, wherein the alert data includes an item identifier for the at least one item, time of the alert, and an expected item sales total versus an actual item sales total.
However, Zhang discloses: updating the PDF dynamically when the historical transaction data is updated [see at least column 12: lines 12-21 (e.g., A distribution update generator 156 may be included in the transaction distribution module 150 to update previously-generated user base and user level distributions with new transaction data)]; wherein generating further includes updating the PDF dynamically based on newly received historical transaction data [see at least column 12: lines 12-21 (e.g., The transaction frequency distribution models generated and updated by the user base distribution generator 152, the user level distribution generator 154, and the distribution update generator 156 can be stored on a transaction frequency distribution model data store 158 which is in data communication with the generators 152, 154, and 156)]; Chen discloses wherein triggering further includes setting the predetermined threshold to an operating parameter, wherein the operating parameter is 5% or less [see at least column 15: lines 43-48 (e.g., transactions within a predetermined period of time—can trigger a process to update the transaction frequency distribution model. If a predetermined gap limit (i.e., a maximum expected time between transactions) is exceeded, an alert may be generated), and At block 506, the process 500 determines whether the time period (i.e., gap) since the user's most recent transaction is greater than a predetermined threshold time period for alert generation (i.e., gap limit), such as for example, the time period associated with a ninety-five percent (95%) likelihood that the user will make a transaction, as determined by the user's transaction frequency distribution model]; wherein triggering further includes providing alert data with the alert, wherein the alert data includes an item identifier for the at least one item, time of the alert, and an expected item sales total versus an actual item sales total [see at least column 4: lines 55-59 (e.g., event change alert includes an identification of an event category associated with the event change alert, a number of days since a last event by the particular user occurred, and a number of events performed by the particular user within the preceding two months), and via engine 136 using the transaction data such as an item identifier, an item name, merchant name, a merchant code, or a merchant category code, to name a few as illustrated in FIG. 1].
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 Zhang for accessing and traversing disparate, complex, and multi-dimensional data structures to dynamically and interactively generate reports based on automated modeling of complex and non-uniformly formatted data [Zhang: abstract].
10. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Chen, in view of LI Wei, in view of Zhang, and further in view of Official Notice.
As per claim 6, the applied references do not disclose wherein generating further includes using a Gaussian Kernel-Density Estimate algorithm to generate the PDF. However, Official Notice is taken that Kernel Density Estimation algorithm, is a well-known statistical technique for estimating probability density functions from practical data samples. Evidence supporting this statement can be found, for example, in Schneiderman, Pub. No.: US 2006/0088207, which describes KDE algorithm techniques used to estimate probability density functions (PDF) from data samples [see ¶0113 and ¶0150].
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to use a well-known density estimation technique such as Gaussian Kernel density estimation to generate the probability density function based on historical data transaction data.
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2014/0039951, Appel: discloses a system for detecting a lost sale due to an out-of-shelf condition in a retail environment includes a plurality of sensors distributed throughout the retail environment for monitoring a current behavior of a customer in the retail environment, a database for storing a purchasing history of the customer, and a matching system for automatically detecting when the customer fails to purchase an expected product, based at least in part on the current behavior and on the purchasing history, and for inferring, based on the automatically detecting, that the expected product is out-of-shelf.
US 8,321,303, Krishnamurthy: discloses a method of detecting out-of-stock conditions for retail products, and for dynamically updating associated replenishment plans, sales forecasts, and event scripts for product stocking events such as turn stock products, promotional products, new product introductions and modular resets.
US 2025/0131484, Bronicki: discloses Systems and methods are provided for triggering actions in response to point of sales data. The systems and methods may comprise obtaining point of sale data from a retail store; analyzing the point of sale data to identify at least one anomalous transaction; in response to the identified at least one anomalous transaction, providing information configured to cause capturing of image data from the retail store; analyzing the image data relating to the at least one anomalous transaction to determine at least one condition associated with the at least one anomalous transaction in the retail store; and based on the analyzed image data relating to the at least one anomalous transaction, generating an indicator associated with the at least one condition.
12. 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