DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/3/2025 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 11/3/2025.
Claims 1, 12, and 19 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Response to Amendments
The previously pending 35 USC 112a rejection has been withdrawn in response to Applicant’s claim amendments.
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 103 and 35 USC 101 rejections.
Examiner recommendation
The Examiner strongly recommends amending the claims to be more mirrored, which would bolster Applicant’s arguments. Each independent claim is an obvious variant of one another, which gives motivation to combine the cited prior art. In addition the random omission of certain claim features in the different claim sets weakens Applicant’s arguments on eligibility by showing that some steps really don’t matter for the invention. In addition, Applicant does not argue against independent claim 1 or 19 on eligibility or prior art because those specific features are not even recited. The Examiner strongly recommends amending the claims to be more mirrored, which would bolster Applicant’s position. If there is a reason for mixing the claims up as is currently done the Examiner would love specific reasoning as to how this helps the invention become eligible or overcome prior art.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are patent eligible under 35 USC 101 via a technical solution to a technical problem. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The claims recite specific analysis of human cashiers to make determinations about what to do with the human cashiers, which is an abstract idea. The Applicant’s claims are analyzing how humans use and manage cashiers, which is 100% human analysis. There is no improvement to the actual hardware, but rather analysis of human interactions to make recommendations to improve those human interactions which is all abstract. The Examiner strongly recommends reading MPEP 2106 for more details on 101. Applicant’s arguments are not persuasive.
The use of machine learning is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106). Applicant’s arguments are not persuasive.
The Examiner further asserts Applicant’s claims being written to include and not include certain steps shows how the combination of steps does not matter and shows how it really is just how a human user would interact with a general purpose computer for implementing the abstract idea (e.g. cashier analysis). For example, how does a heatmap improve a POS system? Applicant’s claims recite generic computing hardware being used at a high level of generality for implementing the abstract idea, which does not make the claims eligible (See MPEP 2106). The Examiner strongly recommends reading MPEP 2106 for more details on 101. Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are allowable over 35 USC 103 because the claim amendments overcome the current art rejection. The Examiner respectfully disagrees. Please see the updated rejection below since amendments by Applicant require additional reference to the Examiner’s art rejection.
Applicant does not properly argue against the prior art rejection. The limitations are all mixed up and each independent claim does not contain what is argued. Applicant makes the claims recite different steps, but does not properly identify what is not taught from each set of claims. For example, Applicant makes no arguments against independent claim 1. Applicant’s arguments are not persuasive.
The Applicant argues that there is no motivation to combine the cited prior art references, but Applicant specifically has each obvious variant of independent claims reciting different features. The arguments contradict what Applicant has claimed. The Examiner asserts that each patent application can only have one invention and Applicant has worded the claims in a manner showing that there is only one invention claimed as obvious variants of one another. The omitting and addition of certain features within the different independent claims shows that the Examiner does have motivation to combine because it is specifically recited by Applicant. If the Applicant does not want this interpretation then the Examiner strongly recommends amending the claims to be closer to mirrored. The Examiner further specifically points to each of the cited prior art references which all disclose analyzing POS locations to make determinations about what actions to take in a retail environment, which is motivation to combine. Applicant’s arguments are not persuasive.
As a further example, the Examiner specifically points to claim 12 which goes into further definition of a generic heatmap disclosing how “darker shades of red” are used on a heatmap. The Examiner strongly recommends claiming what the actual invention is not generic functions of a heatmap such as color, if the Applicant wants to overcome the prior art rejection. Applicant’s arguments are not persuasive.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-18 are directed toward a process and claims 19-20 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claim 1 is directed toward a method, comprising: labeling features relevant to non-deliberate cashier shrink within transaction data for a current interval of time; providing the labeled features to a machine-learning model (MLM) as input; receiving, as output from the MLM, predictions corresponding to a next interval of time, each prediction indicative of a likelihood that a corresponding cashier will be associated with a non- deliberate shrink event during the next interval of time; and providing the predictions to a device operated by a manager of a store; wherein providing comprises: generating prescriptive recommendations comprising at least one of training of relevant cashiers; presenting real-time notifications within a point-of-sale interface prior to payment processing asking cashiers whether they scanned each item of a transaction wherein the real-time notifications are presented when a transaction during the interval of time moves to a payment state and the corresponding cashier has to acknowledge a message before entering the payment state; wherein the transaction interface of the point-of-sale interface is enhanced to receive the prescriptive recommendations via an application programming interface from a shrink prediction manager that informs the transaction interface to display the notification through the transaction interface for every transaction that takes place during the interval of time; providing training focused on notifying a cashier that an end of their shift will include a recorded training session; and performing one or more of: sending notifications to store managers to increase security and monitoring of certain cashiers during pre-defined times of day; delivering customized training of certain cashiers to facilitate memorization of different types of produce and deli item codes; or providing customized training of certain cashiers to demonstrate how to locate hard- to-find barcodes for various items (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing transaction data and features of a store during certain time periods to make determinations about shrink based on actions taken by cashiers (humans) within a store and displaying the results/recommendations on an interface for a human to interpret and providing further analysis of the cashiers, which is managing how humans interact for commercial purposes. The claims are generically watching human cashiers to see if they make errors while processing checkouts.
Independent claim 12 is directed toward a method, comprising: training a machine-learning model (MLM) on features relevant to non-deliberate shrink events to generate predictions for a next interval of future time as to whether cashiers of a store are likely or not likely to cause a given non-deliberate shrink event in the next interval of future time; obtaining transaction data for a most-recent past interval of time for the store; labeling the transaction data with the features; providing the labeled features as input to the MLM; receiving current predictions for a next interval of time as output from the MLM; and providing the current predictions through an interface to a manager of the store to manage the next interval of time and mitigate occurrences of any of the non-deliberate shrink events by the cashiers; wherein providing comprises: displaying the predictions within the interface as an interactive heatmap comprising a layout of the store that details locations of point-of-sale terminals operated by cashiers of the store, each cashier labeled via a name or cashier identifier on corresponding terminals, the predictions are color coded based on their values on top of or adjacent to each cashier name identifier, and the interactive heatmap animated to illustrate the predictions of each cashier at each terminal within the store over a future interval of time, wherein darker shades of red correlate to a high probability of non-deliberate shrink; wherein each terminal and cashier depicted within the heatmap is selectable to obtain corresponding assigned prescriptive recommendations (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing transaction data and features of a store during certain time periods to make determinations about shrink based on actions taken by cashiers (humans) within a store and displaying the results/recommendations on an interface for a human to interpret and providing further analysis of the cashiers, which is managing how humans interact for commercial purposes. The claims are generically watching human cashiers to see if they make errors while processing checkouts.
Independent claim 19 is directed toward a system, comprising: a cloud processing environment comprising at least one server; the at least one server comprising a processor and a non-transitory computer-readable storage medium; the non-transitory computer-readable storage medium comprises executable instructions; and the executable instructions when executed on the processor cause the processor to perform operations comprising: training a machine-learning model (MLM) on features relevant to non-deliberate shrink events and on metrics relevant to the non-deliberate shrink events to generate predictions and prescriptive recommendations for avoiding the non-deliberate shrink events; obtaining transaction data for a store for a most-recent past interval of time; updating the metrics based on the transaction data for the most-recent past interval of time; labeling the features and providing the labeled features and the updated metrics as input to the MLM; receiving current predictions and corresponding prescriptive recommendations as output from the MLM for a next interval of time; and providing the current predictions and the corresponding prescriptive recommendations through an interface to a manager of the store for the manager to mitigate occurrences of any of the non-deliberate shrink events; wherein providing comprises: generating reports at preconfigured intervals of time providing a summary of statistical metrics by cashier, by all cashiers as a whole of the store, and by categories assigned to the cashiers, and comparing each cashier against categories of cashiers or cashiers of the store as a whole within the reports, wherein the categories include new cashiers, experienced cashiers, and low-performing cashiers; wherein the reports include shrink event types so that the manager can identify specific issues that are problematic each cashier (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing transaction data and features of a store during certain time periods to make determinations about shrink based on actions taken by cashiers (humans) within a store and displaying the results/recommendations on an interface for a human to interpret and providing further analysis of the cashiers, which is managing how humans interact for commercial purposes. The claims are generically watching human cashiers to see if they make errors while processing checkouts.
Dependent claims 2-11, 13-18, and 20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the Independent claims additionally recite “a machine-learning model (MLM) as input; from the MLM; to a device operated by a manager of a store; within a point-of-sale interface the transaction interface of the point-of-sale interface; via an application programming interface (claim 1)”; “a machine-learning model (MLM); through an interface to a manager of the store; point-of-sale terminals operated by cashiers of the store (claim 12)”; “a system, comprising: a cloud processing environment comprising at least one server; the at least one server comprising a processor and a non-transitory computer-readable storage medium; the non-transitory computer-readable storage medium comprises executable instructions; and the executable instructions when executed on the processor cause the processor to perform operations comprising: a machine-learning model (MLM); input to the MLM; through an interface to a manager of the store (claim 19)”, which are additional elements that would not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
The Examiner further notes that the machine learning is recited so generically and at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05).
In addition, dependent claims 2-11, 13-18, and 20 further narrow the abstract idea and dependent claims 8-9 additionally recite “via an application programming interface; a device”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, method; and System Independent claims 1-20 recite “a machine-learning model (MLM) as input; from the MLM; to a device operated by a manager of a store; within a point-of-sale interface the transaction interface of the point-of-sale interface; via an application programming interface (claim 1)”; “a machine-learning model (MLM); through an interface to a manager of the store; point-of-sale terminals operated by cashiers of the store (claim 12)”; “a system, comprising: a cloud processing environment comprising at least one server; the at least one server comprising a processor and a non-transitory computer-readable storage medium; the non-transitory computer-readable storage medium comprises executable instructions; and the executable instructions when executed on the processor cause the processor to perform operations comprising: a machine-learning model (MLM); input to the MLM; through an interface to a manager of the store (claim 19)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0015-0019 and Figures 1. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed 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.
In addition, claims 2-11, 13-18, and 20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 8-9 additionally recite “via an application programming interface; a device”, which are additional elements that do not amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1-11: Claim 1 recites “the transaction interface”. There is insufficient antecedent basis for this claim limitation. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (US 2021/0319420 A1) in view of Morales Saiki et al. (US 2024/0193497 A1), which claims priority of Provisional Application 63/428373 filed 11/28/2022.
Regarding Claim 1: Yu et al. teach a system, comprising: a cloud processing environment comprising at least one server; the at least one server comprising a processor and a non-transitory computer-readable storage medium; the non-transitory computer-readable storage medium comprises executable instructions; and the executable instructions when executed on the processor cause the processor to perform operations comprising (See Figure 9 and Paragraphs 0114-0119):
training a machine learning model (model) on features relevant to non-deliberate shrink events and on metrics relevant to the non-deliberate shrink events to generate predictions and prescriptive recommendations for avoiding the non-deliberate shrink events (See Figure 7, Paragraphs 0024-0026 – “the disclosed system uses various visual object tracking technologies to track, e.g., a hand, a product, a shopping cart, etc., and accordingly to perform transaction recognition tasks or loss prevention tasks based on the tracked location information or an object at a tracked location”, Paragraph 0050 – “a trained neural network”, Paragraph 0058, Paragraph 0075 – “SRPN 560 takes a pair of convolutional features computed from the two branches of Siamese networks and outputs dense prediction maps, including a set of target proposals with corresponding bounding boxes and class scores”, and claim 1 – “generating a message for loss prevention”);
obtaining transaction data for a store for a most-recent past interval of time; updating the metrics based on the transaction data for the most-recent past interval of time (See Figure 3, Figure 7, Paragraph 0027 – “the disclosed technologies are used to perform various practical applications related to transaction recognition and loss prevention”, Paragraph 0029 – “associate an account with a shopping cart for a transaction”, Paragraph 0050 – “MLMs and image data (e.g., image data captured by camera 322, product data associated with the high-dimensional feature space, etc.) may be stored in data store 390 and accessible in real-time via network 370”, and Paragraph 0107);
labeling the features and providing the labeled features and the updated metrics as input to the model (See Paragraph 0043 – “label those objects”, Paragraph 0051 – “The input layer neurons pass data to neurons in the hidden layer. Neurons in the hidden layer pass data to neurons in the output layer. The output layer then produces a classification”, Paragraph 0058, Paragraph 0075, and Paragraph 0086);
receiving current predictions and corresponding prescriptive recommendations as output from the model for a next interval of time (See Figure 7, Figure 8, Paragraph 0050 – “real-time”, Paragraph 0082, and Paragraph 0107 – “For some regular event types, e.g., a regular product scan event at a checkout machine, process 810 may generate a null response … For some irregular event types, e.g., a concealment of a product in a shopping zone, process 810 may generate an alert to a loss prevention person”);
and providing the current predictions and the corresponding prescriptive recommendations through an interface to a “person” of the store for the manager to mitigate occurrences of any of the non-deliberate shrink events (See Figure 7, Figure 8, Paragraph 0050 – “real-time”, Paragraph 0082, and Paragraph 0107 – “For some regular event types, e.g., a regular product scan event at a checkout machine, process 810 may generate a null response … For some irregular event types, e.g., a concealment of a product in a shopping zone, process 810 may generate an alert to a loss prevention person”);
wherein providing comprises: generating prescriptive recommendations comprising at least one of training of relevant cashiers: presenting real-time notifications within a point-of-sale interface prior to payment processing asking cashiers whether they scanned each item of a transaction wherein the real-time notifications are presented when a transaction during the interval of time moves to a payment state and the corresponding cashier has to acknowledge a message before entering the payment state; wherein the transaction interface of the point-of-sale interface is enhanced to receive the prescriptive recommendations via an application programming interface from a shrink prediction manager that informs the transaction interface to display the notification through the transaction interface for every transaction that takes place during the interval of time (See Figure 2, Figure 3, Figure 8, Paragraph 0022 – “finalize the retail transaction”, Paragraph 0024 – “Inadvertent human actions, such as poorly executed business processes, may be mitigated by enacting or improving employee training”, Paragraph 0031 – “complete a payment for a product in a virtual shopping cart that corresponds to the physical cart”, Paragraph 0042 – “generating an alert and distribute the alert to a loss-prevention person”, and Paragraph 0107);
providing training focused on notifying a cashier that an end of their shift will include a recorded training session: and performing one or more of: sending notifications to store managers to increase security and monitoring of certain cashiers during pre-defined times of day: delivering customized training of certain cashiers to facilitate memorization of different types of produce and deli item codes: or providing customized training of certain cashiers to demonstrate how to locate hard-to-find barcodes for various items (claim 1) (See Figure 2, Figure 3, Figure 7, Figure 8, Paragraph 0022 – “finalize the retail transaction”, Paragraph 0024 – “Inadvertent human actions, such as poorly executed business processes, may be mitigated by enacting or improving employee training”, Paragraph 0031 – “complete a payment for a product in a virtual shopping cart that corresponds to the physical cart”, Paragraph 0042 – “generating an alert and distribute the alert to a loss-prevention person”, and Paragraph 0107).
Yu et al. do not specifically disclose a manager of the store. However, Morales Saiki et al. further teach:
a manager of the store (See Paragraph 0121, Paragraph 0224, Paragraph 0227, Paragraph 0247, and Paragraph 0303).
The teachings of Yu et al. and Morales Saiki et al. are related because both are analyzing shoppers in a retail location to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the shrink prevention recommendation system of Yu et al. to incorporate the manager of Morales Saiki et al. in order to ensure the data collected is received by the right person.
Regarding Claim 2: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 1. Yu et al. further teach wherein labeling further includes updating metrics maintained for non-deliberate cashier shrink based on the transaction data for the current interval of time (See Figure 7, Figure 8, Paragraph 0024 – “Inadvertent human actions”, Paragraph 0050, and Paragraph 0107).
Regarding Claim 3: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 2. Yu et al. further teach wherein updating further includes maintaining the metrics on at least one of a per-cashier basis, a per-cashier category basis, or across all cashiers of the store (See Figure 7, Figure 8, Paragraphs 0025-0026, Paragraph 0108, and claim 1).
Regarding Claim 4: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 3. Yu et al. further teach wherein labeling further includes labeling the features from the transaction data associated with a size of a basket of items for each transaction within the transaction data, a time of day associated with each transaction, a cashier identifier associated with each transaction, and an item category associated with each item of each transaction (See Figure 2, Figure 7, Figure 8, Paragraph 0043 – “label those objects”, Paragraph 0051 – “The input layer neurons pass data to neurons in the hidden layer. Neurons in the hidden layer pass data to neurons in the output layer. The output layer then produces a classification”, Paragraph 0058, Paragraph 0075, and Paragraph 0086).
Regarding Claim 5: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 4. Yu et al. further teach wherein providing the labeled features further includes providing the metrics as additional input to the model (See Paragraph 0043 – “label those objects”, Paragraph 0051, Paragraph 0058, Paragraph 0075, and Paragraph 0086).
Regarding Claim 6: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 1. Yu et al. further teach wherein receiving the predictions further includes receiving at least one prescriptive recommendation for each prediction that exceeds a predefined value as additional output from the model (See Figure 7, Figure 8, Paragraph 0050, Paragraph 0082, Paragraph 0101 – “greater than a threshold”, Paragraph 0107, and Paragraph 0127).
Regarding Claim 7: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 6. Yu et al. further teach wherein receiving the at least one prescriptive recommendation further includes providing “a location” for the store that includes each prediction and a corresponding prescriptive recommendation (See Figure 7, Figure 8, Paragraph 0026, Paragraph 0044, Paragraph 0075, and Paragraph 0126).
Yu et al. do not specifically disclose an interactive heatmap for the store. However, Morales Saiki et al. teach an interactive heatmap for the store (See Figures 5A-6E, Paragraph 0103, and Paragraphs 0158-0161).
The teachings of Yu et al. and Morales Saiki et al. are related because both are analyzing shoppers in a retail location to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the shrink prevention recommendation system of Yu et al. to incorporate the heatmap of Morales Saiki et al. in order to better visualize where the data and information is coming from location wise.
Regarding Claim 8: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 1. Yu et al. further teach wherein providing the predictions further include providing the predictions through an application programming interface (See Paragraph 0065, Paragraph 0121, Paragraph 0123, and claim 1).
Yu et al. do not specifically disclose an interactive heatmap for the store. However, Morales Saiki et al. teach an interactive heatmap for the store (See Figures 5A-6E, Paragraph 0103, and Paragraphs 0158-0161).
The teachings of Yu et al. and Morales Saiki et al. are related because both are analyzing shoppers in a retail location to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the shrink prevention recommendation system of Yu et al. to incorporate the heatmap of Morales Saiki et al. in order to better visualize where the data and information is coming from location wise.
Regarding Claim 9: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 1. Yu et al. further teach wherein providing the predictions further includes pushing a device notification to the device for at least one of the predictions when the at least one of the predictions exceeds a predefined value (See Figure 7, Figure 8, Paragraph 0042, Paragraph 0082, Paragraph 0101 – “greater than a threshold”, Paragraph 0107, and Paragraph 0127).
Regarding Claim 10: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 1. Yu et al. further teach further comprising, iterating to the labeling when the next interval of time expires (See Figure 2, Figure 7, Figure 8, Paragraph 0043 – “label those objects”, Paragraph 0051, Paragraph 0058, Paragraph 0075, and Paragraph 0086).
Regarding Claim 11: Yu et al. in view of Morales Saiki et al. teach the limitations of claim 1. Yu et al. further teach: periodically reporting metrics relevant to the non-deliberate shrink on at least one of a per cashier basis, a per cashier category basis or across all cashiers of the store (See Figure 7, Figure 8, Paragraphs 0025-0026, Paragraph 0108, and claim 1).
Claims 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (US 2021/0319420 A1) in view of Morales Saiki et al. (US 2024/0193497 A1), which claims priority of Provisional Application 63/428373 filed 11/28/2022 and further in view of Jamtgaard et al. (US 2018/0158063 A1).
Regarding Claims 12: Claims 12 recite limitations already addressed by the rejections of claims 1-11 above; therefore the same rejections apply. Yu et al. do not specifically disclose wherein providing comprises: displaying the predictions within the interface as an interactive heatmap comprising a layout associated with the store that details locations of point-of-sale terminals operated by cashiers of the store; labeling each cashier via a name or cashier identifier on corresponding terminals; color coding the predictions based on their values on top of or adjacent to each cashier name identifier; and animating the interactive heatmap to illustrate the predictions of each cashier at each terminal within the store over a future interval of time. However, Morales Saiki et al. further teach an interactive heatmap (See Figure 4A, Figures 5A-6E, Paragraph 0103, Paragraphs 0158-0161, and Paragraph 0170).
The teachings of Yu et al. and Morales Saiki et al. are related because both are analyzing shoppers in a retail location to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the shrink prevention recommendation system of Yu et al. to incorporate the manager of Morales Saiki et al. in order to ensure the data collected is received by the right person.
Yu et al. in view of Morales Saiki et al. do not specifically disclose wherein providing comprises: displaying the predictions within the interface as an interactive heatmap comprising a layout of the store that details locations of point-of-sale terminals operated by cashiers of the store, each cashier labeled via a name or cashier identifier on corresponding terminals, the predictions are color coded based on their values on top of or adjacent to each cashier name identifier, and the interactive heatmap animated to illustrate the predictions of each cashier at each terminal within the store over a future interval of time, wherein darker shades of red correlate to a high probability of non-deliberate shrink; wherein each terminal and cashier depicted within the heatmap is selectable to obtain corresponding assigned prescriptive recommendations. However, Jamtgaard et al. further teach wherein providing comprises: displaying the predictions within the interface as an interactive heatmap comprising a layout of the store that details locations of point-of-sale terminals operated by cashiers of the store, each cashier labeled via a name or cashier identifier on corresponding terminals, the predictions are color coded based on their values on top of or adjacent to each cashier name identifier, and the interactive heatmap animated to illustrate the predictions of each cashier at each terminal within the store over a future interval of time, wherein darker shades of red correlate to a high probability of non-deliberate shrink; wherein each terminal and cashier depicted within the heatmap is selectable to obtain corresponding assigned prescriptive recommendations (See Figure 2, Figure 3A, Figure 3B, Paragraph 0024 – “records 214 of Employee D can be augmented with visual indicia (e.g., highlighting, color, badges, background glowing, animation, shading, text) to indicate priority for review”, and Paragraph 0029);
The teachings of Yu et al., Morales Saiki et al., and Jamtgaard et al. are related because all are analyzing shoppers/cashiers in a retail location to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the shrink prevention recommendation system of Yu et al. in view of Morales Saiki et al. to incorporate the manager of Jamtgaard et al. in order to ensure employees are performing there job duties correctly.
Regarding Claim 13: Yu et al. in view of Morales Saiki et al. and further in view of Jamtgaard et al. teach the limitations of claim 12. Yu et al. further teach maintaining up-to-date metrics for the non-deliberate shrink events on at least one of per cashier basis, a per cashier category basis or across all cashiers of the store (See Figure 7, Figure 8, Paragraphs 0025-0026, Paragraph 0108, and claim 1).
Regarding Claim 14: Yu et al. in view of Morales Saiki et al. and further in view of Jamtgaard et al. teach the limitations of claim 13. Yu et al. further teach periodically generating a report for the metrics and providing the report through the interface to the “person” of the store (See Figure 7, Figure 8, and Paragraph 0042).
Yu et al. do not specifically disclose a manager of the store. However, Morales Saiki et al. further teach a manager of the store (See Paragraph 0121, Paragraph 0224, Paragraph 0227, Paragraph 0247, and Paragraph 0303).
The teachings of Yu et al. and Morales Saiki et al. are related because both are analyzing shoppers in a retail location to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the shrink prevention recommendation system of Yu et al. to incorporate the manager of Morales Saiki et al. in order to ensure the data collected is received by the right person.
Regarding Claim 15: Yu et al. in view of Morales Saiki et al. and further in view of Jamtgaard et al. teach the limitations of claim 12. Yu et al. further teach wherein providing the labeled features further includes providing up-to-date metrics for the non-deliberate shrink events by cashier as additional input to the model (See Figure 7, Figure 8, Paragraphs 0025-0026, Paragraph 0108, and claim 1).
Regarding Claim 16: Yu et al. in view of Morales Saiki et al. and further in view of Jamtgaard et al. teach the limitations of claim 15. Yu et al. further teach wherein receiving further includes receiving at least one prescriptive recommendation per prediction as additional output from the model (See Figure 7, Figure 8, Paragraph 0042, Paragraph 0082, Paragraph 0101, Paragraph 0107, and Paragraph 0127).
Regarding Claim 17: Yu et al. in view of Morales Saiki et al. and further in view of Jamtgaard et al. teach the limitations of claim 16. Yu et al. further teach wherein providing the current predictions further includes providing “a location” for the predictions and corresponding prescriptive recommendations through the interface (See Figure 7, Figure 8, Paragraph 0026, Paragraph 0044, Paragraph 0075, and Paragraph 0126).
Yu et al. do not specifically disclose an interactive heatmap for the store. However, Morales Saiki et al. teach an interactive heatmap for the store (See Figures 5A-6E, Paragraph 0103, and Paragraphs 0158-0161).
The teachings of Yu et al. and Morales Saiki et al. are related because both are analyzing shoppers in a retail location to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the shrink prevention recommendation system of Yu et al. to incorporate the heatmap of Morales Saiki et al. in order to better visualize where the data and information is coming from location wise.
Regarding Claim 18: Yu et al. in view of Morales Saiki et al. and further in view of Jamtgaard et al. teach the limitations of claim 17. Yu et al. further teach wherein providing the interactive heatmap further includes displaying over the next interval of time within the interface (See Figure 7, Figure 8, Paragraph 0026, Paragraph 0044, Paragraph 0075, and Paragraph 0126).
Yu et al. do not specifically disclose animating the interactive heatmap for the store. However, Morales Saiki et al. teach animating the interactive heatmap for the store (See Figures 5A-6E, Paragraph 0103, and Paragraphs 0158-0161).
The teachings of Yu et al. and Morales Saiki et al. are related because both are analyzing shoppers in a retail location to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the shrink prevention recommendation system of Yu et al. to incorporate the heatmap of Morales Saiki et al. in order to better visualize where the data and information is coming from location wise.
Regarding Claims 19: Claims 19 recite limitations already addressed by the rejections of claims 1-18 above; therefore the same rejections apply. Yu et al. in view of Morales Saiki et al. do not specifically disclose wherein providing comprises: generating reports at preconfigured intervals of time providing a summary of statistical metrics by cashier, by all cashiers as a whole of the store, and by categories assigned to the cashiers, and comparing each cashier against categories of cashiers or cashiers of the store as a whole within the reports, wherein the categories include new cashiers, experienced cashiers, and low-performing cashiers; wherein the reports include shrink event types so that the manager can identify specific issues that are problematic each cashier. However, Jamtgaard et al. further teach “wherein providing comprises: generating reports at preconfigured intervals of time providing a summary of statistical metrics by cashier, by all cashiers as a whole of the store, and by categories assigned to the cashiers, and comparing each cashier against categories of cashiers or cashiers of the store as a whole within the reports, wherein the categories include new cashiers, experienced cashiers, and low-performing cashiers; wherein the reports include shrink event types so that the manager can identify specific issues that are problematic each cashier” (See Figure 3B, Figure 3C, and claim 1 – “obtaining, by the computer, statistical data representing past behaviors of the identified employees; identifying, by the computer, and based on the statistical data, the POS transaction data and the video data, one or more particular employees as possibly participating in fraudulent activity during one or more of the POS transactions; and causing to display, on a display device communicatively coupled to the computer, data identifying the one or more particular employees”).
The teachings of Yu et al., Morales Saiki et al., and Jamtgaard et al. are related because all are analyzing shoppers/cashiers in a retail location to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the shrink prevention recommendation system of Yu et al. in view of Morales Saiki et al. to incorporate the manager of Jamtgaard et al. in order to ensure employees are performing there job duties correctly.
Regarding Claims 20: Claims 20 recite limitations already addressed by the rejections of claims 1-19 above; therefore the same rejections apply.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625