DETAILED ACTION
This is a Non-Final office action in reply to claims on 04/21/2026. Claims 1, 3-4, 9, 12-20, 31, 34, 37-38, 41, and 47-48 are pending. The effective filing date is 12/14/22.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/21/2026 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-4, 9, 12-20, 31, 34, 37-38, 41, and 47-48 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1- Claims 1, 3-4, 9, 12-20, 34, 37-38, 41 and 47-48 are directed to a method, and are subject eligible matter. Claims 1, 3-4, 9, 12-20, 34, 37-38, 41 and 47-48 pass step 1.
Step 2A, Prong 1-The independent claim 1, and similarly claim 12, recites:
obtaining, from said shelf area, a sequence of actual facing counts (AFCs) during a specified sample window for each of a plurality of stock keeping units (SKUs), wherein each SKU is associated with one of a plurality of products (obtaining information is a method of collecting information that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
identifying a subset of the sequence of AFCs as candidate restock events at which it is assumed the product associated with an SKU has been replenished in a shelving area since a previous AFC observation (the identification for a need to restock is a step of managing personal behavior, in that watching for restock is typically performed by persons in stores, and the claim limitation is directed to the action of observation for business purposes, see MPEP 2106.04(a)(2)(II)(C) An example of a claim reciting following rules or instructions is In re Marco Guldenaar Holding B.V., 911 F.3d 1157, 1161, 129 USPQ2d 1008, 1011 (Fed. Cir. 2018). The patentee claimed a method of playing a dice game including placing wagers on whether certain die faces will appear face up. 911 F.3d at 1160; 129 USPQ2d at 1011. The Federal Circuit determined that the claims were directed to the abstract idea of "rules for playing games", which the court characterized as a certain method of organizing human activity. 911 F.3d at 1160-61; 129 USPQ2d at 1011);
selecting a set of restock events that are most likely to represent the intentional restock level (EFC) for that SKU (selection a specific restock event to enact, is another method of managing personal behavior, in that the action is standard business practice to monitor restocking, see MPEP 2106.04(a)(2)(II)(C));
preparing a plurality of EFC lists, wherein each of said plurality of EFC lists contains the EFCs for all the SKUs in a specified shelf area (preparing a list is a method of managing items with specific SKU numbers, the management of product is a way to follow the commercial interactions that occur in a business, see MPEP 2106.04(a)(2)(II)(B));
periodically updating the plurality of EFC lists, thereby obtaining a plurality of updated EFC lists (updating lists of restock items is a method of managing items, and updating sales activities, which is part of commercial interactions, see MPEP 2106.04(a)(2)(II)(B)); and
using the plurality of updated EFC lists to compute at least one Key Performance Indicator (KPI) for said at least one shelf area (computing a performance indictor is the use of an calculation to make a business decision, using algorithms are mathematical calculations under MPEP 2106.04(a)(2)(I)(C) and the calculation is used to make sales activity decisions under MPEP 2106.04(a)(2)(II)(B)).
Overall, the method is directed to obtaining a computed expected facing count. The steps described to are obtaining the current shelf count, identifying possible restock events, selecting restock events, preparing lists, updating lists, updating the facing count and creating a key performance indicator for the shelf. All of the steps relate to identification of items on a shelf, using the information to identify specific products and restock events to determine both the performance and candidate list. The act of creating lists and predications about restocking is part of running a business, and is a commercial interaction. A commercial interaction is part of the enumerated grouping of a certain method of organizing human activity. MPEP 2106.04(a)(2) describes when the steps recites an idea, having no tangible form, it is directed to an abstract idea, and therefore when the claims obtain information, create lists and make determination, the ideas are described in the abstract.
Step 2A, Prong 2-The independent claim 1, and similarly claim 12, do not include any additional elements. The entirety of the claims are directed to steps that are ideas, without being implemented by any element.
This judicial exception is not integrated into a practical application because the claims are directed to ideas, and ideas are not connected to an element that would integrate the judicial exception into a practical application. The steps describe obtaining a previously determined CEFC, but does not include where this information is obtained from. It could be from a person’s memory, a computer, a filing cabinet, but without a limitation to how this is happening, the claims are an abstract idea. The additional steps also comprise receiving, creating and determining based on the information. The act of receiving, creating and determining are all actions that a person working for a business would need to in order to run a proper business, and therefore, in order for the ideas to be more than just an idea, it must integrate the judicial exception. MPEP 2106.05(e) discusses that other meaningful limitations may be enough to make the claims eligible, but in order for this to be true, the claims must sufficiently limit the abstract idea to the practical application. The practical application in MPEP 2106.05(e) is the physical molding of rubber products, however the claimed application is for the management of inventory, and management of inventory is in itself an idea, and not a practical application.
Step 2B-The independent claims 1, and similarly claim 12, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the totality of the claims are all steps that by themselves are ideas, and as a whole combine different ideas to create the overarching concept of restock patterns, and therefore the claims remain an abstract idea.
Dependent claims
Claims 3-4, 13-20, 37-38 and 41 further describe information processing. Adding more steps about the idea of stock management does not show how the idea of stock management would be integrated into a practical application. The claims remain ineligible for the same reasons as above.
Claim 9 describe that the obtaining of SKUs is from a plurality of images. Using images compared to a person visually looking at the shelves may use a camera to obtain images, but using a computer element as a tool to implement the abstract idea, such as collecting information, it would not be anything more than a drafting measure to attempt to link the idea to an element.
Dependent claim 31 further describe using deep learning to make the updates. The deep learning is used as a tool to create the lists, and therefore fails to integrate the abstract idea, and remains an abstract idea under MPEP 2106.05(f).
Dependent claim 34, further describes human review of the calculated lists. Review of calculated lists is another way to direct a person to complete the business task, and therefore, is another abstract step that fails to integrate the abstract idea.
Dependent Claims 47-48, further describes making selections and making identifications, which are analysis steps to be performed by a person when selecting shelf items, which is a mental process that is used for giving directions to a person in a business environment (a certain method of organizing human activity), and remain an abstract idea under MPEP 2106.04(a)(2). There are not additional elements presented in these independent claims, and therefore the computer elements presented in the independent claims are used as tools to perform the abstract idea under MPEP 2106.05(f).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-4, 9, 12-20, 31, 34, 38 41 and 47-48 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2021/0398068 A1 Adato et al. (hereinafter Adato).
Regarding claim 1, Adato teaches a method for obtaining a computed expected facing count (CEFC) for a shelf area containing a plurality of consumer packaged goods (CPGs) (Adato Abstract, observing a shelf area with consumer packaged goods, and determining the stock is below the expected level and alerting; Fig. 1), the method comprising:
obtaining, from said shelf area, a sequence of actual facing counts (AFCs) during a specified sample window for each of a plurality of stock keeping units (SKUs), wherein each SKU is associated with one of a plurality of products (Adato Para. [0035] the computer program processes images to identify the quantity at a specific time; Para. [0119-0120] images captured of shelves to identify products, including the type of product and quantity; Para. [0230] the category may be region or SKU);
identifying a subset of the sequence of AFCs as candidate restock events at which it is assumed the product associated with an SKU has been replenished in a shelving area since a previous AFC observation (Adato Para. [0206-0208] the sensors identify when movement around the shelves, and create a first subset of products placed during that detection);
selecting a set of restock events that are most likely to represent the intentional restock level (EFC) for that SKU (Adato Para. [0422] management of restock, so the server may identify when there was an intentional restock);
preparing a plurality of EFC lists, wherein each of said plurality of EFC lists contains the EFCs for all the SKUs in a specified shelf area (Adato Para. [0545] each particular product type includes a restocking schedule; the schedule will be the amount of products, the intentional restock level, required to restock);
periodically updating the plurality of EFC lists, thereby obtaining a plurality of updated EFC lists (Adato Para. [0234] displaying in real time a list of all available items within a store, this showcases updating the in stock list to what is available on shelf, and what is running low stock; [0570] alerting when shelves are low stock, to indicate which items need to be added to the restock list); and
using the plurality of updated EFC lists to compute at least one Key Performance Indicator (KPI) for said at least one shelf area (Adato Para. [0230] the server generates reports to summarize performance; the performance is used to determine the restock level, which can be updated based on the recognition of low stock patterns).
Regarding claim 3, Adato teaches the method of claim 1, wherein the previous AFC observation is the most recent previous AFC observation (Adato Para. [0035] the computer program processes images to identify the quantity at a specific time; Para. [0135] the memory device stores information about the inventory level and updated after alerts).
Regarding claim 4, Adato teaches the method of claim 1, further comprising:
using the plurality of updated EFC lists to compute at least one item selected from the group consisting of;
(a) at least one Key Performance Indicator (KPI) for each of a plurality of shelf areas (Adato Para. [0230] the server generates reports to summarize performance; the performance is used to determine the restock level, which can be updated based on the recognition of low stock patterns of an item within a shelf area; Para. [0483] notification may be for a shelf area);
(b) a plurality of Key Performance Indicators (KPIs) for at least one shelf area; and
(c) a plurality of Key Performance Indicators (KPIs) for each of a plurality of shelf areas.
Regarding claim 9, Adato teaches the method of claim 1, wherein obtaining a sequence of AFCs during a specified sample window for each of a plurality of SKUs includes obtaining a plurality of images of a plurality of shelf areas using an imaging device mounted on a movable platform (Adato Para. [0015] the images are captured at two different time and the current inventory at each time is determined to compare; Para. [0157] the cameras may be mounted on shelved, and shelved are movable to change the required height if the store layout needs to change).
Regarding claim 12, Adato teaches a method for determining a computed expected facing count (CEFC) (Adato Abstract, observing a shelf area with consumer packaged goods, and determining the stock is below the expected level and alerting; Fig. 1), comprising:
obtaining a previously determined CEFC (Adato Para. [0479] compliance is when the product facing is the same as the stored inventory amount; to be uncompliant, the quantity measured by the product facing image does not match the prior expected threshold amount; Para. [0034] the facing event is associated with a specific location in the store, and compared to determine if the current stock is above or below the expected inventory; in order to be compared the previous expected inventory needs to be stored);
receiving a plurality of shelf observations which report the actual facing counts (AFCs) of a plurality of SKUs or of products associated with the plurality of SKUs (Adato Para. [0035] the computer program processes images to identify the quantity at a specific time; Para. [0119-0120] images captured of shelves to identify products, including the type of product and quantity; Para. [0230] the category may be region or SKU);
creating a set of the plurality of AFCs to include in a sample window (Adato Para. [0206-0208] the sensors identify when movement around the shelves, and creates the actual product count of each shelf area and saving to a database; Para. [0035] the computer program processes images to identify the quantity at a specific time);
determining a set of potential restock events (Adato Para. [0550] when monitoring shelves, and the product are below the expected inventory, a low stock alert is sent, triggering a potential restock);
using the set of potential restock events to determine whether to update the CEFC for the SKUs in a given shelf area (Adato Para. [0422] management of restock, so the server may identify when there was an intentional restock; Para. [0552] detecting a low stock event and then assign restocking to replenish to the expected amount);
if a determination is made to update the CEFC for the SKUs in a given shelf area, calculating CEFCs for all of the SKUs in the given shelf area, thereby obtaining a set of calculated CEFCs (Adato Para. [0552] two different facing events are recorded and a determination is made if the amount replenished meets the expected inventory; Para. [0507] the low stock alerts are recorded on a server, and an algorithm may update the restock level based on the new trend of low stock; when the low stock events are happening more frequently, due to the product performing better than in previous re-stocking, the expected amount of inventory may be updated to a higher level);
creating a candidate expected facing count (EFC) list from the set of calculated CEFCs (Adato Para. [0230] the server generates reports to summarize performance; the performance is used to determine a new restock level to select); and
if publication criteria are met, publishing the candidate EFC list (Adato Para. [0570-0571] when the low stock alert is different than the predicted time frame, the algorithm is updated when the confidence score can be improved; the confidence score being the criteria to publish a new expected inventory).
Regarding claim 13, Adato teaches the method of claim 12, wherein the publication criteria are met if the proportion of EFCs which changed since a previous iteration of the method exceeds a predetermined threshold value (Adato Para. [0570-0571] when the low stock alert is different than the predicted time frame, the algorithm is updated when the confidence score can be improved; the confidence score being the criteria to publish a new expected inventory).
Regarding claim 14, Adato teaches the method of claim 12, wherein the set of shelf observations included in the sample window are all AFCs occurring after a specified point in time (Adato Para. [0015] the images are captured at two different time and the current inventory at each time is determined to compare; Para. [0157] the cameras may be mounted on shelved, and shelved are movable to change the required height if the store layout needs to change).
Regarding claim 15, Adato teaches the method of claim 12, wherein the set of shelf observations included in the sample window is the maximum number of AFCs (Adato Para. [0516] the imaging is able to determine the maximum number of products for specific shelf and time).
Regarding claim 16, Adato teaches the method of claim 12, wherein the sample window is a data structure in which the shelf observations are sequenced chronologically (Adato Fig. 40A, see the events and windows are taken over time in chronological order).
Regarding claim 17, Adato teaches the method of claim 12, wherein the EFC is recomputed only if there is evidence of restocking behavior (Adato Para. [0034] the restocking event starts the analysis; Fig. 40A, restocking events starts the window).
Regarding claim 18, Adato teaches the method of claim 12, wherein creating a set of the plurality of AFCs to include in a sample window includes determining which of the AFCs occurred in a fixed period of time (Adato Para. [0551] information is monitored over time, the events can be identified between a first and second time period; Fig. 40A).
Regarding claim 19, Adato teaches the method of claim 12, wherein creating a set of the plurality of AFCs to include in a sample window includes selecting the n most recent AFCs, wherein n is an integer, and wherein n > 0 (Adato Para. [0551] information is monitored over time, the events can be identified between a first and second time period; Fig. 40A, each time period has more than one facing event).
Regarding claim 20, Adato teaches the method of claim 12, wherein using the set of potential restock events to determine whether to update the CEFC for the SKUs in a given shelf area includes at least one step selected from the group consisting of:
(a) determining whether an AFC changed from a lower value to a higher value between two consecutive observations;
(b) identifying potential restock events changes in packaging and changes in the physical positioning of a product on a shelf;
(c) utilizing prior knowledge of restock patterns (Adato Para. [0554] pattern determination based on prior restock events and low stock alerts);
(d) utilizing prior knowledge of which products are typically restocked at night from an on-premises or warehouse inventory, versus those that are typically restocked during the day by Direct Store Delivery (DSD) by CPG Distributors;
(e) computing a statistical mode over all the events where a product's AFC has changed from a smaller value to a larger one; and
(f) finding the AFC value most often observed immediately after a product is potentially restocked on the shelf.
Regarding claim 31, Adato teaches the method of claim 13, wherein calculating CEFCs for all of the SKUs in the given shelf area includes at least one step selected from the group consisting of: (a) using a statistical mode of the potential restock levels in order to discover the most often used restock levels; and (b) keeping the current value of the EFC for a SKU info such mode exists; and (c) using at least one machine learning model that is trained using longitudinal EFC data in combination with at least one item selected from the group consisting of day-of-week, season, holidays, weather conditions, supply chain disruptions and stock-related disruptions (Adato Para. [0324] the expected inventory may change based on seasonal preferences, and therefore the restock events associated with the new baseline can be used; Para. [0481] machine learning model trained to determine restock actions and image identification).
Regarding claim 34, Adato teaches the method of claim 12, wherein the publication criteria includes at least one item selected from the group consisting of: (a) the proportion of SKUs in the shelf area that have changed their CEFC values (Adato Para. [0570-0571] when the low stock alert is different than the predicted time frame, the algorithm is updated when the confidence score can be improved; the confidence score being the criteria to publish a new expected inventory); and (b) human-assisted review and approval process of the candidate EFC list; (c) human-assisted review and approval process of the candidate EFC list.
Regarding claim 38, Adato teaches the method of claim 12, wherein the decision to add a new product to the EFC list is based on at least one criteria selected from the group consisting of: (a) the number of observations in the sample window that found that product on the shelf (Adato Para. [0227] new product may be placed in the store, and market research is done to see the amount of time within a window the new product is changed, and to record popularity);_(b) whether the new product belongs to the same product category as the other products in the same shelf area; (c) an accounting for equivalency classes of SKUs that are commonly substituted for one another during restocking.
Regarding claim 41, Adato teaches the method of claim 12, wherein the decision to remove a product from the EFC list is based on at least one criteria selected from the group consisting of: (a) whether the product has a zero AFC for some minimum number of consecutive observations; (b) whether the product has a zero AFC for some minimum contiguous time periods; (c) correlation analyses with other EFCs in the candidate EFC List; (d) prior knowledge that a planogram reset has occurred (Adato Para. [0230] the products are monitored, and when a product underperforms the store may update a planogram to remove the item); and (e) confirmation from a human auditor.
Regarding claim 47, Adato teaches the method of claim 1, further comprising: selecting, from the identified restock events, a subset of restock events based on a consistency criterion across multiple restock events, the consistency criterion identifying restock events that converge toward a common post-restock facing count, thereby representing an intentional restock level [0358-0359] common products may be grouped together, and monitored their change in time and restocking watched additionally; [0552] identifying restocking events and the amount of items restocked, and determining the post restock count to calculate a replenishment percentage).
Regarding claim 48, Adato teaches the method of claim 1, wherein identifying restock events comprises detecting increases in facing counts between successive observations within a defined temporal window, each restock event corresponding to a transition from a first observed facing count to a higher second observed facing count (Adato Para. [0206-0208] the sensors identify when movement around the shelves, and creates the actual product count of each shelf area and saving to a database; [0552] a first time period for a restocking event, recognizing a second restocking event at a different time, and determining the count of items and replenishment percentage).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim 37 is rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0398068 A1 Adato et al. (hereinafter Adato) in view of US 2008/0154693 A1 Bateni et al. (hereinafter Bateni).
Regarding claim 37, Adato teaches the method of claim 12. Adato fails to explicitly disclose wherein the publication criteria is selected to minimize the frequency of EFC list publication by requiring a certain minimum number of days has elapsed since the last publication. Bateni is in the field of forecasting inventory needs (Bateni Abstract, modeling product demand) and teaches wherein the publication criteria is selected to minimize the frequency of EFC list publication by requiring a certain minimum number of days has elapsed since the last publication (Bateni Para. [0028-0029] the coefficients used to determine prediction and expected inventory are updated on a weekly basis, but may also be chosen to update less frequently). It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the publication of new expectations in Adato with the frequency of update as taught by Bateni. The motivation for doing so would be to only update the lists when enough sales data has been collected to make a relevant change (Bateni Para. [0029] results are saved weekly once new sales data becomes available).
Response to Arguments
Applicant's arguments filed 04/21/2026 have been fully considered but they are not persuasive.
Regarding 101,
Step 2A, Prong 1-Applicant states that the claims are not directed to simply mathematical formulas, mental processors or a method of organizing human activity. This is done by restating the claim limitations that are about obtaining information, identifying events, selecting events, preparing lists and using lists to make calculations. All of these steps are methods that can be performed in the human mind, calculation that can be performed using pen and paper and the overall analysis is for restocking events, which is accomplished by people in a business setting. This is not a specific way to gather information, because the specificity is that it is “obtained”, which is a general instruction. Therefore, the claims recite an abstract idea.
Step 2A, Prong 2- The abstract idea is then analyzed to determine if it was incorporated into a practical application. Applicant cites that the abstract idea can be integrated if it affects a particular treatment or prophylaxis for a disease or medical condition, or to effect a change in functionality of a computer or other technology. Examiner would note that the steps do not affect a particular treatment, because the steps are recited to “obtain”, “identify”, “prepare” without any details about how this is accomplished that would indicate a change in how a CEFC is obtained. The problem of time-consuming planograms can be a technical problem, in that the manner in which the recognition and calculation is accomplished, however, the improvement claims is based on observations, identifications and updating lists, which are all acts that can be performed by a person, made faster by its application on a computer. Application on a computer, and the improvement to the idea being improved efficiency on time solely based on its application on a computer, there is not integration, but the use of the computer as a tool to implement the abstract idea. See MPEP 2106.05(f). The specification can contemplate how images are captured, but Examiner cannot read elements into the claim limitations, and therefore drone and cart camera are not claimed.
Step 2B-The particular arrangement and data flow claimed by the Applicant is not found in the claims. Examiner notes that there is specific language about what information is being sent, but this does not make a particular flow of that information. The information is “obtained”, “selected”, “updated” which is not particular. Additionally, he computer based imaging and analysis is stated as being more than “pencil-and-paper” but nothing is stopping a person from reading and writing down a SKU, and therefore, the claim does not provide limitations that would make this an unreasonable interpretation. The no generic post-solution activity applicant describes transforming real-world shelf images based on the computation, but again, examiner notes that the claimed invention simply states transformation, without details about how a computer would implement this action.
Regarding 102
Claim 1
Determining an intentional restock level. When the claim recites “most likely restock level” is computed, and that there is an intentional restock level, Examiner is interpreting this as being the same as excepted inventory. They are the same because the expected inventory is the level of items that are expected to be on the shelves, and compared to an intentional restock level, which is also the amount of items needed to restock the shelves to the amount expected to be on the shelves. Therefore, when the items are below the threshold in Adato, there is a need to then calculate the amount to replace and restock, which is an intentional restock.
The claim recites multiple steps for creating an expected facing count (EFC) list. This is being interpreted by the Examiner to mean that the expected count is the expected amount of stock on the shelves, and therefore when the shelves are analyzed, and the stock is belonging the expected or threshold value, there is a restocking event that takes place to bring the current stock to the expected stock level.
Periodically updating a plurality of EFC lists. When the claims are updated periodically, Adato teaches a regularly scheduled scan of the shelves, therefore the lists of items in stock on the shelves are periodically updated.
Computing Shelf-Level Performance Metrics. The claim uses stock lists to make a calculation about the performance of that product, and therefore, when Adato [0230] generates reports on the performance of items based on the restock level, therefore teaches making a determination about the performance of a specific item.
Temporal transition identification of Restock Events. The analysis is done in specific windows, and this may be every Thursday at the same time, and therefore, would be based on specific temporal events. When Adato teaches in [0324] the inventory changes are determined based on the seasons, which showcases specific time restocking events.
Regarding 103
Claim 37
The claim requires time-sequenced observations, selecting an event to restock and making a SKU list. The combination of which was not found in Adato, but when Adato is able to identify forward facing shelf counts, and the amount of items for a specific season, this showcases the time based observations. Secondary reference Bateni was introduced to showcase a weekly regression, and the goal is to update less frequently, so that they are completing the same objective. Overall, claim 27 is rejected under 103, and Bateni teaches a shorter amount of frequency updated, shown in Bateni as [0028-0029] to predict on a weekly basis, compared to Adato’s seasonal updates.
Therefore, the claims remain rejected under 102 and 103 for the above cited reasoning.
Not Properly Combinable
A reasoned explanation is used to create a combination, and therefore when Adato uses seasonal updating of shelves, to create an expectation of shelving counts, and to combine with Bateni to teach a shorter time frame analysis would have been obvious, as shown in Bateni [0029] to analyze stores sales data to keep updated on trends.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2018/0005174 A1 Dixon et al. teaches managing product inventory (Abstract); US 2022/0292442 A1 Watanabe teaches management of shelves (Abstract); US 2003/0216969 A1 Bauer et al. teaches inventory management (Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA E SULLIVAN whose telephone number is (571)272-9501. The examiner can normally be reached M-Th; 9:00 AM-5PM EST.
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/JESSICA E SULLIVAN/Examiner, Art Unit 3627
/AARON TUTOR/Primary Examiner, Art Unit 3627
