CTFR 17/796,744 CTFR 85117 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Drawings The drawings as submitted by Applicant on August 1,2022 have been accepted. Disposition of Claims Claims 1-20 are pending in the instant application. No claims have been cancelled. No claims have been added. No claims have been amended. The rejection of the pending claims is hereby made final. Response to Remarks 101 Regarding the rejection of the pending claims under 35 USC 101, as stated in the prior Office Action, the examiner has considered Applicant’s arguments and amendments, but does not find them to be persuasive. If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement (see at least MPEP 2106.05(a) Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016). The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr , 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field. Examples that the courts have indicated may show an improvement in computer-functionality: i. A modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, DDR Holdings, 773 F.3d at 1258-59, 113 USPQ2d at 1106-07; ii. Inventive distribution of functionality within a network to filter Internet content, BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016); iii. A method of rendering a halftone digital image, Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 868-69, 97 USPQ2d 1274, 1380 (Fed. Cir. 2010); iv. A distributed network architecture operating in an unconventional fashion to reduce network congestion while generating networking accounting data records, Amdocs (Israel), Ltd. v. Openet Telecom, Inc., 841 F.3d 1288, 1300-01, 120 USPQ2d 1527, 1536-37 (Fed. Cir. 2016); v. A memory system having programmable operational characteristics that are configurable based on the type of processor, which can be used with different types of processors without a tradeoff in processor performance, Visual Memory, LLC v. NVIDIA Corp., 867 F.3d 1253, 1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017); vi. Technical details as to how to transmit images over a cellular network or append classification information to digital image data, TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614-15, 118 USPQ2d 1744, 1749-50 (Fed. Cir. 2016) (holding the claims ineligible because they fail to provide requisite technical details necessary to carry out the function); vii. Particular structure of a server that stores organized digital images, TLI Communications, 823 F.3d at 612, 118 USPQ2d at 1747 (finding the use of a generic server insufficient to add inventive concepts to an abstract idea); PNG media_image1.png 27 30 media_image1.png Greyscale viii. A particular way of programming or designing software to create menus, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1241, 120 USPQ2d 1844, 1854 (Fed. Cir. 2016); ix. A method that generates a security profile that identifies both hostile and potentially hostile operations, and can protect the user against both previously unknown viruses and "obfuscated code," which is an improvement over traditional virus scanning. Finjan Inc. v. Blue Coat Systems, 879 F.3d 1299, 1304, 125 USPQ2d 1282, 1286 (Fed. Cir. 2018); x. An improved user interface for electronic devices that displays an application summary of unlaunched applications, where the particular data in the summary is selectable by a user to launch the respective application. Core Wireless Licensing S.A.R.L., v. LG Electronics, Inc., 880 F.3d 1356, 1362-63, 125 USPQ2d 1436, 1440-41 (Fed. Cir. 2018); xi. Specific interface and implementation for navigating complex three-dimensional spreadsheets using techniques unique to computers; Data Engine Techs., LLC v. Google LLC, 906 F.3d 999, 1009, 128 USPQ2d 1381, 1387 (Fed. Cir. 2018); and xii. A specific method of restricting software operation within a license, Ancora Tech., Inc. v. HTC America, Inc., 908 F.3d 1343, 1345-46, 128 USPQ2d 1565, 1567 (Fed. Cir. 2018). PNG media_image1.png 27 30 media_image1.png Greyscale It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016). As currently recited, the pending claims recite sales data analysis and inventory management, which can be performed mentally, in conjunction with a generic computing device and common computer elements. For at least the reasoning provided above, the rejection of the pending claims under 35 USC 101 is hereby maintained and made final. Regarding Applicant’s assertions pertaining to the rejection of the pending claims under 35 USC 103 in view of the applied prior art of record, the examiner has considered said arguments, but does not find them to be persuasive. The examiner submits that the applied prior art of record McConnell et al discloses wherein an item velocity monitoring system is provided which interfaces with a consumer retail store that has several cash registers that are tied into a "point of sale" store controller. The item velocity monitoring system is capable of detecting when sales (or other movement activities) of an item are occurring too quickly, or too slowly. The item velocity monitoring system is first "trained" in a learning mode of operations, during which item patterns and group patterns are evaluated and placed into a pattern database. The system then compares the observed item velocity to its model probability velocity, and if the observed item velocity deviates beyond the statistical model, a "velocity event" is generated, declaring one of the above selling "too quick" or "too slow" conditions. Once a velocity event is detected, an event handling routine displays the event, and can transmit the event information over a network (including the INTERNET) to a remote computer for additional analysis or record keeping. A "Loyalty Out-of-Stock System," (LOSS) is incorporated in the above item velocity monitoring system which automatically detects when items for sale are out-of-stock (OOS), discovers the reasons for these "stock-outs," and determines how customers react to these stock-outs. The LOSS operates on store data and models the expected item movement rate for each item under varying time-of-day, day-of-week, price, promotion, season, holiday, and market conditions; detects items that are moving abnormally slowly, thereby identifying items that may be improperly displayed; provides early warning that an item may go out-of-stock (OOS) by detecting items with abnormally high movement; detects and reports on items that are OOS at retail stores; summarizes OOS events for the store and retail chain management, and for suppliers, thereby identifying items that are over-stocked (too few OOS events), under-stocked (too many events), badly re-stocked (too long events); analyzes the OOS events to find patterns that explain why OOS's are occurring; and determines the impacts of these OOS events on store customers, thereby measuring losses to the retailer and supplier, and establishing the loyalty of consumers to the item, brand, and chain. The examiner submits that the term “unavailability effect” is disclosed in at least paragraph [0015] of Applicant’s specification, which recites “In contrast, in one embodiment, the present description is directed to a method that can use inventory data gathered in situ, along with customer purchase patterns, to naturally observe how the availability or unavailability of a product in the course of retail operations effects consumer behavior, e.g., consumer purchase patterns. This method provides a more direct measure of assortment change (as represented by product availability or unavailability) on consumer behavior in a dynamic and naturally occurring retail environment, without the need for expensive, time-consuming, and ultimately data-poor A/B testing processes currently employed. As such, in this manner, the methods of this disclosure improve data precision in the field of assortment optimization as compared to existing techniques.” It is clear to one of ordinary skill that the disclosure of the applied prior art is analogous to what is described in Applicant’s disclosure as originally filed, and at minimum, obviates the language of the pending claims. The rejection of the pending claims is hereby maintained and made final. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Step 2A – 1: The claims recite a Judicial Exception. Exemplary claim 1 recites the limitations of: determining a first time period during which a first product is available in an inventory at a point of purchase according to a model that uses (a) sales data, (b) inventory data, or (c) both sales data and inventory data, wherein the inventory data comprises data from an inventory management system, sampled during the first time period, as an input; determining a second time period during which the first product is unavailable in the inventory according to the model; comparing a first time period sales data to a second time period sales data to determine a product unavailability effect; and using the product unavailability effect to change an assortment at the point of purchase. These limitations, as drafted, are a process that, under its broadest reasonable interpretation, covers certain methods of organizing human activity (e.g., agreements between people in the form of contracts, legal obligations, and business relations; managing relationships or transactions between people) 1 by performance of the limitation in the human mind (noting that the claim recites no computer or hardware components). For example, the claim encompasses inventory management for the purpose of product recommendation and sales. Step 2A – 2: This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the additional limitation of determin[ing] a product unavailability effect…to change the assortment at the point of purchase , which is merely collecting or receiving data, and is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Claim 1 and 17 are therefore directed to an abstract idea. The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims, and it has been held that “[i]n defining the excluded categories, the Court has ruled that the exclusion applies if a claim involves a natural law or phenomenon or abstract idea, even if the particular natural law or phenomenon or abstract idea at issue is narrow.” ( buySAFE, Inc. v. Google, Inc ., 765 F.3d 1350. ) Turning to the dependent claims, none of the claimed features of the dependent claims further limit the claimed invention in such a way to direct the claimed invention to statutory subject matter (e.g. change the scope of the claimed invention as to no longer be directed towards an abstract idea, or include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements or combination of elements in the claims other than the abstract idea per se ), nor do they add limitations that, when taken as a combination, result in the claim as a whole amounting to significantly more than the judicial exception. Step 2B : The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, with respect to claims 2-16, the claims recite no additional elements; and with respect to claim 17, the additional elements or combination of elements in the claims other than the abstract idea per se [e.g. inventory management system and point of purchase ] amount to no more than mere instructions to implement the idea on a computer, or the recitation of generic computer structure that serves to perform generic computer functions previously known to the industry 2 [e.g. performing repetitive calculations; receiving, processing, and storing data; electronically scanning or extracting data from a physical document; electronic recordkeeping; automating mental tasks; receiving or transmitting data over a network, e.g., using the Internet to gather data] . Viewed as a whole, these additional claim elements, both individually and in combination, do not provide meaningful limitations to transform the above identified abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more (e.g. improvements to another technology or technical fields, improvements to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment) than the abstract idea itself. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation 3 . Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, 573 U.S. No. 13–298 . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-6, 8-9, and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over McConnell et al (US 2001/0049690) in view of Desai et al (US 2010/0145773) . Regarding claim 1, the prior art reference McConnell et al and Desai et al, in combination, disclose a method, comprising: determining a first time period during which a first product is available in an inventory at a point of purchase according to a model that uses (a) sales data, (b) inventory data, or (c) both sales data and inventory data, wherein the inventory data comprises data from an inventory management system (see at least paragraph [0052] to McConnell et al, wherein point-of-sale (POS) data from one or more stores for a retailer to train the system so that it can detect OOS events. Henceforth, when the POS data is referenced, it is to be understood that this means either data for individual POS transactions, or for hourly or daily summaries of movement for individual items in each store, and that references to communicating with a POS controller may also mean communicating with a data warehouse or other data storage system that holds POS data ) , sampled during the first time period, as an input (see at least paragraph [0044] to McConnell et al, wherein modeling the expected item movement rate for each item under varying time -of-day, day-of-week, price, promotion, season, holiday, and market conditions is provided in the system and method as disclosed) ; determining a second time period during which the first product is unavailable in the inventory according to the model (see at least paragraph [0052] to McConnell et al, wherein a system for automatically detecting time periods during which an item for sale is not in stock and automatically analyzing the likely causes and impacts of these out-of-stocks (OOS's) is provided) ; comparing a first time period sales data to a second time period sales data to determine a product unavailability effect ((see at least paragraph [0078] to McConnell et al, the normal selling rate for the item in a particular store, as adjusted for past out-of-stock conditions; the effect of its current price and promotions; the effect of store and/or category traffic; the effect of competition in the regional market; and the effect of time-of-day, day-of-week, and week-of-year. As noted above, these factors are all parameters in the statistical model of the probability velocity for the arrival of particular items at the transaction points, such as the point-of-sale cash registers) ; and using the product unavailability effect to change an assortment at the point of purchase (see at least Figure 40 to Desai et al, product assortment generator 4020) . The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine , 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones , 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc. , 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the method and apparatus for monitoring the effective velocity of items through a store or warehouse, as taught by McConnell et al, and the system and method for generating product decisions, as disclosed by Desai et al, in order to provide active feedback to merchants to effectuate more effective product substitutions and marketing strategies to increase the probability of sales (see at least paragraph [0003] to Desai et al) , could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing. Regarding claim 2, the prior art discloses the method of claim 1, further comprising determining a third time period during which the first product is available in the inventory, and comparing a third time period sales data to (a) the first time period sales data, (b) the second time period sales data, or (c) both the first time period sales data and the second time period sales data, to determine the product unavailability effect (see at least paragraph [0097] to Desai et al, wherein customer is predicted based on POS data analysis) . Regarding claim 3, the prior art discloses the method of claim 1, wherein the model further uses demographic attributes of customers of the point of purchase as an input (see at least paragraph [0171] to Desai et al wherein customer demographic data may be garnered for the individual) . Regarding claim 4, the prior art discloses the method of any of claim 1, wherein the inventory data further comprises inventory data from an audit of product inventory at the point of purchase as an input (see at least paragraph [0202] to Desai et al wherein historical data may include point-of-sales data) . Regarding claim 5, the prior art discloses the method of claim 4, wherein the audit is (a) performed by a person, (b) performed by a robot, or (c) performed by a robot and validated by human review (see at least paragraph [0202] to Desai et al wherein the business decision system 3800 utilizes POS data) . Regarding claim 6, the prior art discloses the method of claim 1, wherein changing the assortment comprises one or more of (a) changing an amount of the product in the assortment, (b) changing a characteristic of the product in the assortment, (c) changing an amount of a second product in the assortment, or (d) changing a characteristic of the second product in the assortment (see at least paragraph [0264] to Desai et al, wherein the assortment rules may cause restrictions on product types, brands, and quantities of products) . Regarding claim 8, the prior art discloses the method of claim 1, wherein determining a product unavailability effect comprises performing a multivariate analysis of covariance to identify items {Y} having rolling average daily sales that covary with the first product going from the first time period to the second time period, and applying a random forest regression to items {Y} as a function of daily sales of other items {X} (see at least paragraph [0129] to Desai et al, wherein price behavior 1450 can be compared with sales behavior 1460 during various time periods) . Regarding claim 9, the prior art discloses the method of claim 1, wherein determining the second time period comprises inferring that the first product is unavailable even though the first product is shown as available in the inventory management system (see at least paragraphs [0108] and [0110] to Desai et al, wherein for the time periods having no records the product was considered as a stock out) . Regarding claim 11, the prior art discloses the method of claim 10, further comprising evaluating the accuracy of the availability inference of the first product, wherein the evaluating comprises auditing the availability of the first product at the point of purchase (see at least paragraphs [0128] and [0129] to Desai et al) . Regarding claim 12, the prior art discloses the method of claim 9, wherein inferring that the first product is unavailable comprises determining a random forest regression of time-varying sales data, the random forest regression being used by a support vector machine classification to classify the first product as being unavailable (see at least paragraph [0139] to Desai et al, wherein regression analysis may be used to predict sales volume) . Regarding claim 13, the prior art discloses the method comprising creating a synthetic world, comprising using the product unavailability effect determined by the method of claim 1 to determine a first predicted sales data for a proposed product assortment (see at least paragraph [0139] to Desai et al wherein the system obtains a model for predicting sales volume) . Regarding claim 14, the prior art discloses the method of claim 13, further comprising using customer purchase preferences to determine the first predicted sales data, wherein the customer purchase preferences are based on statistical interactions amongst one or more product attributes and customer attribute multiples (see at least paragraph [0139] to Desai et al, wherein the predicted sales volume is calculated) . Regarding claim 15, the prior art discloses the method of claim 1, wherein the product unavailability effect comprises a consumer leaving the point of purchase without making a purchase due to unavailability of the first product (see at least paragraph [0097] to Desai et al, predicting customer loss) . Regarding claim 16, the prior art discloses the method of claim 1, further comprising changing the assortment of the first product at a plurality of points of purchase (see at least Figure 51 to Desai et al “generate recommendations for product assortments” 5106) . Regarding claim 17, the prior art discloses a system comprising: an inventory management system that tracks inventory data of a first product at a point of purchase (see at least paragraph [0052] to McConnell et al, wherein point-of-sale (POS) data from one or more stores for a retailer to train the system so that it can detect OOS events. Henceforth, when the POS data is referenced, it is to be understood that this means either data for individual POS transactions, or for hourly or daily summaries of movement for individual items in each store, and that references to communicating with a POS controller may also mean communicating with a data warehouse or other data storage system that holds POS data ) ; an inventory prediction model operable via a processor and configured to: predict periods of unavailability of the first product using the inventory data, the periods of unavailability based on a probability that the product is unavailable (see at least paragraphs [0088] and [0089] to McConnell et al, wherein Once the LOSS is trained, it detects OOS events for individual items in each store and creates a database of these OOS events. This database is subsequently analyzed by LOSS to summarize events, determine their causes, and measure their impacts. Some of the important attributes of an OOS event are used as described below to find patterns that help in understanding OOS's. There attributes include the following event descriptors: Probability that item is actually OOS) ; based on a relationship between changes in the inventory of a second product during the periods of unavailability of the first product, form a prediction of a product unavailability effect in sales data of the first product and the second product (see at least paragraph [0116] to McConnel et al, Normal selling rate for the item in a particular store, adjusted for past out-of-stocks) ; and a user interface configured to receive input from a user entered via the user interface (see at least paragraph [0306] to McConnell et al, wherein step 554 then appends the OOS event to the OOS Event Database. A step 556 now triggers any alarms and/or displays the event as defined by an application (POS Monitor) user interface ) and operable to facilitate predicting, via the inventory prediction model, the product unavailability effect (see at least paragraph [0326] to McConnell et al, out of stock pattern discovery module) . Regarding claim 18, the prior art discloses the system of claim 17, wherein the inventory prediction model is further configured to cluster points of purchase to maximize similarity among user-provided variables comprising customer demographics and point of purchase characteristics (see at least paragraph [0014] to Desai et al, wherein Customer segments may include groupings of customers who have simil ar attributes and, in particular, simil ar shopping behaviors. From these customer segments may be generated `customer insights`. Examples of such insights may include shopping frequency, category penetration, shopping recency, aggregate spend, average purchase, average spend and categories shopped) to determine customer substitution behavior based upon variables being aligned with each point of purchase by utilizing a k-means clustering algorithm to identify clusters of points of purchase that are similar in a many- dimensional space (see at least paragraph [0270] to Desai et al, wherein maximization may utilize the Bayesian statistical techniques described above, or any other statistical maximization algorithms) . Regarding claim 19, the prior art discloses the system of claim 17, further comprising determining the impact of adding a product in terms of how many customers are expected to be lost based upon a substitution probability for each of a plurality of products (see at least paragraphs [0212] and [0213] to Desai et al, wherein The Assortment Plan Data Assortment Data 4022 may include a plan for the entire assortment of products to be offered by the retailer to maximize a particular goal, or may include an impact report on the change to a particular product offering. Thus, for example, assume a retailer is approached by the manufacturer of a new product. The product may be analyzed by the Product Assortment Generator 4020 to determine what impact its inclusion in the store assortment will have on overall profit. Thus, a retailer may have an informed method of determining if the change to an assortment is desirable) . Regarding claim 20, the prior art discloses the system of claim 17, further comprising identifying specific points of purchase and products for which inventory audits would maximally inform inventory availability predictions and assortment change recommendations (see at least paragraphs [0232] and [0233] to Desai et al, wherein historical transaction data may include point-of-sales (POS) data either directly or as transaction log reports. Additional information may be included in the transaction data, including product loss through theft, damage and/or expiration) . 07-21-aia AIA Claim s 7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over McConnell et al (US 2001/0049690) in view of Desai et al (US 2010/0145773)and further in view of Kliper (US 2016/0140490) . Regarding claim 7, the prior art discloses the method of claim 1, wherein determining a product unavailability effect comprises forming a hidden Markov model using one or more consumer substitution probabilities, and using the hidden Markov model in a computer simulation to predict changes is sales based on changing the assortment (see at least paragraphs [0073] and [0074] to Kliper et al, wherein the probability is calculated whether an item is in stock in a particular store using the Markov model) . Regarding claim 10, the prior art discloses the method of claim 9, wherein inferring that the first product is unavailable comprises determining a covariance between (a) a change in a first product inventory value at a first time T 1 and a first product inventory value at a second time T 2 and (b) a change in a first product replenished inventory value, using a Markov chain, to provide an availability inference (see at least paragraph [0073] to Kliper et al wherein “ based on the estimated current inventory of the product in the store, a probability that the store has a certain quantity of the product in stock is determined. In an embodiment, the probability that the store has a particular quantity of the product in stock may be calculated via a Markov model that accounts for the current estimated inventory level”) . The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine , 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones , 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc. , 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the method and apparatus for monitoring the effective velocity of items through a store or warehouse, as taught by McConnell et al, the system and method for generating product decisions, as disclosed by Desai et al, and the inventory management system and method as taught by Kliper et al, in order to provide active feedback to merchants to effectuate more effective product substitutions and marketing strategies to increase the probability of sales (see at least paragraph [0003] to Desai et al) , could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing . Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The examiner has considered all references listed on the Notice of References Cited, PTO-892. The examiner has considered all references cited on the Information Disclosure Statement submitted by Applicant, PTO-1449 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM EST. 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, Fahd A Obeid can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TALIA F CRAWLEY/Primary Examiner, Art Unit 3627 Application/Control Number: 17/796,744 Page 2 Art Unit: 3627 Application/Control Number: 17/796,744 Page 3 Art Unit: 3627 Application/Control Number: 17/796,744 Page 4 Art Unit: 3627 Application/Control Number: 17/796,744 Page 5 Art Unit: 3627 Application/Control Number: 17/796,744 Page 6 Art Unit: 3627 Application/Control Number: 17/796,744 Page 7 Art Unit: 3627 Application/Control Number: 17/796,744 Page 8 Art Unit: 3627 Application/Control Number: 17/796,744 Page 9 Art Unit: 3627 Application/Control Number: 17/796,744 Page 10 Art Unit: 3627 Application/Control Number: 17/796,744 Page 11 Art Unit: 3627 Application/Control Number: 17/796,744 Page 12 Art Unit: 3627 Application/Control Number: 17/796,744 Page 13 Art Unit: 3627 Application/Control Number: 17/796,744 Page 14 Art Unit: 3627 Application/Control Number: 17/796,744 Page 15 Art Unit: 3627 Application/Control Number: 17/796,744 Page 16 Art Unit: 3627 Application/Control Number: 17/796,744 Page 17 Art Unit: 3627 Application/Control Number: 17/796,744 Page 18 Art Unit: 3627 Application/Control Number: 17/796,744 Page 19 Art Unit: 3627 Application/Control Number: 17/796,744 Page 20 Art Unit: 3627 Application/Control Number: 17/796,744 Page 21 Art Unit: 3627 Application/Control Number: 17/796,744 Page 22 Art Unit: 3627 Application/Control Number: 17/796,744 Page 23 Art Unit: 3627 Application/Control Number: 17/796,744 Page 24 Art Unit: 3627 Application/Control Number: 17/796,744 Page 25 Art Unit: 3627 Application/Control Number: 17/796,744 Page 26 Art Unit: 3627 1 See MPEP 2106.04(a)(2) Abstract Idea Groupings [R-10.2019]. II. CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY B. Commercial or Legal Interactions, and III. MENTAL PROCESSES. 2 “It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea. Rather, the components must involve more than performance of “‘well understood, routine, conventional activit[ies]’ previously known to the industry .” Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)”. Id, pages 10-11. “Likewise, the server fails to add an inventive concept because it is simply a generic computer that “administer[ s]” digital images using a known “arbitrary data bank system.” Id. at col. 5 ll. 45–46 . But “[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of ‘well-understood, routine, [and] conventional activities previously known to the industry .’” Content Extraction, 776 F.3d at 1347–48 (quoting Alice, 134 S. Ct at 2359). “These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (“Nearly every computer will include a ‘communications controller’ and a ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”); Content Extraction, 776 F.3d at 1345, 1348 (“storing information” into memory, and using a computer to “translate the shapes on a physical page into typeface characters,” insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324–25 (generic computer components such as an “interface,” “network,” and “database,” fail to satisfy the inventive concept requirement); Intellectual Ventures I , 792 F.3d at 1368 (a “database” and “a communication medium” “are all generic computer elements”); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.”)” . TLI Communications LLC v. AV Automotive L.L.C., (No. 15-1372, (Fed. Cir. May 17, 2016)) , at *12-13 3 “Nor, in addressing the second step of Alice , does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. , 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp. , 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d , 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted))”. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 U.S.P.Q.2d 1636 (Fed. Cir. 2015).
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