DETAILED ACTION
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 .
Status
This action is in response to the amendment filed on 3/20/2026. Claims 1, 3, 6, 8, 10, 11, 13, 16, 18, 20, 21, are pending. Claims 1, 11, and 21, are amended. No claims have been added. No claims are currently cancelled.
Response to Arguments
Applicant's arguments filed 3/20/2026 have been fully considered but they are not persuasive. The applicant has argued that the claims are not directed to an abstract idea. Specifically “First, amended independent claim 1 is not directed to a judicial exception because it does not merely recite a fundamental economic practice, a method of organizing human activity, or a mental process. Rather, amended independent claim 1 recites a specific, network-based consumption estimation system that performs structured, multi-layered computational modeling on distributed product data received from multiple computing devices. Amended independent claim 1 requires generating aggregated product consumption data comprising a transaction probability function for product shelf life prediction, user clustering computations for segmentation and reorder rate determination, frequency-based logic computations for brand affinity, and attribute-driven product popularity modeling. These operations are technical data processing steps performed in a computing environment and cannot reasonably be characterized as fundamental human activity." The examiner respectfully disagrees. The claims are directed to collecting information about what products customers buy, analyzing that information to determine when they are likely to run out of those products, estimating how much they should reorder, and recommending products to restock. This is a fundamental and longstanding commercial inventory management and product replenishment practice that business and merchants have performed for many year prior to the filing of applicant’s invention. The invention is seen as determining what goods customers need, when they need them, and how much to order. The mere fact that the invention is being implemented using general purpose technology does not change its fundamental nature.
The applicant has argued “Second, the Examiner's characterization of the claimed steps as "mathematical concepts" improperly oversimplifies the claim. While certain computational models may involve mathematical operations, amended independent claim 1 does not recite a mathematical formula in the abstract. Instead, it applies multiple computational models within a specifically configured system to process large-scale product and user data received over a network. The recited clustering computations, transaction probability modeling, and weighted parameter learning require iterative machine-based data analysis that cannot be practically performed in the human mind. Amended independent claim 1 therefore does not recite a "mental process" under its broadest reasonable interpretation.” The examiner respectfully disagrees the only step that appears to be tied to anything machine based is the recommending a product stock out data. This step appears to be making a recommendation using previously computed reorder estimation and optimal quantity estimation. Although the applicant does have language of an AI engine the limitation of recommending by the processor using an artificial intelligence engine is merely using an AI engine which is recited as being performed by a computer. The computer is recited at a high level of generality. In the limitation the computer and an AI engine are used as a tool to perform the generic computer function of receiving data in a manner of apply it. In limitations the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Further, the transaction probability function for shelf life prediction is a mathematical calculation. It is a probability distribution applied to transaction data to generate a predictive output. Generating a probability distribution from historical transaction data is a mathematical concept regardless of the computer environment in which it is performed. User clustering computations for segmentation and reorder rate determination involve grouping data points into clusters based on shared characteristics and computing associated rates. Clustering is a well-recognized mathematical technique and computing a reorder rate from clustered user data is a mathematical calculation. The claims recite the mathematical result of clustering computation at a high level of generality.
The applicant has argued “Third, amended independent claim 1 further recites an artificial intelligence (AI) engine trained using merchant feedback to dynamically learn and update specific weighting parameters (wl, w2, w3) corresponding to article capacitance score, merchant affinity score, and pin-code product popularity score. This adaptive feedback-based learning mechanism reflects a machine learning implementation that improves predictive performance over time. Such a structured AI training and parameter updating mechanism is a technical implementation executed within a computing system, not an abstract idea performed mentally or a mere method of organizing human activity. Accordingly, amended independent claim 1 does not recite a judicial exception under Step 2A, Prong 1.” The examiner respectfully disagrees. The applicant at most is using an AI engine for making a recommendation. The step uses the AI engine for recommending a product stock based on given data. While the disclosure states that the AI engine makes recommendations, however, there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of determining a consumption estimation rather than to any technology. See MPEP 2106.05(a). Unlike the claimed invention in McRO that improved how the physical display operated to produce better quality images, the claimed invention here receives data, computes data, recommends data, and generates further data. This generic computer implementation is not only directed to mental processes, but also does not improve a display mechanism as was the case in McRO. See SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) (distinguishing McRO). The claimed invention describes the concept of managing inventory and order fulfillment operations in a warehouse specifically, computing product consumption data, reorder estimation, and optimal quantity estimation data, which constitutes an abstract idea. In addition to encompassing commercial activity, the concept of inventory management and order fulfillment is a fundamental commercial and business practice long prevalent in our system of commerce. The recitation of generic learnable parameters w1, w2, w3, that are updated through an unspecified learning process does not constitute a specific technical improvement. It merely describes an abstract result without specifying any particular technical means of achieving that result. The weighting parameters correspond to a commercial concept, not a technical one. Weighing the 3 metrics against each other and adjusting those weights based on merchant feedback is the commercial practice of refining a product scoring system based on commercial results. At most the invention is improving predictive performance which is not a technical improvement.
The applicant has argued “Even assuming, arguendo, that certain elements could be viewed as involving mathematical concepts, amended independent claim 1 integrates any such concept into a practical technological application. The claimed system does not merely calculate data and display results; rather, it processes distributed product data to generate reorder estimation, optimal quantity estimation, and product stock-out data recommendations that are actionable within a merchant inventory ecosystem. The claimed system therefore applies computational modeling in a concrete commercial inventory management environment.” The examiner respectfully disagrees. The applicant is merely stating that manipulating data which is common to stock and inventory such as shelf life, user segmentation, brand affinity, and popularity data are directed to a technical problem. Even if the applicant is using an AI engine the claimed invention does not provide a technical solution to a problem unique to a technical field, thereby providing an improvement to the technical field, applicant’s claim 1 merely uses generic computers to improve the abstract idea of generating a consumption recommendation. (“[T]he claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity.”). See also Spec. ¶¶ 25, 35, 53, 67, 74, (describing “artificial intelligence (AI)” generically). Thus, claim 1’s recitation of AI is no more than the use of generic computing technology as a tool to implement the abstract idea of using computed data to generate a consumption estimation, and does not integrate the abstract idea into a practical application. Any improvement would be to the abstract idea and not to the technology. It is unclear how applying known data points into a computer a technological improvement. Applicant’s arguments are not persuasive.
The applicant has argued “Additionally, amended independent claim 1 recites a specific integration of multiple predictive components into a unified consumption estimation architecture. The transaction probability function, clustering-based user segmentation, frequency-based affinity modeling, and attribute-based popularity modeling are not isolated calculations; they collectively form aggregated product consumption data that is further processed to generate reorder and stock-out predictions. This ordered combination reflects a structured technical workflow that transforms raw network-received data into actionable system outputs, thereby integrating any alleged abstract idea into a real-world system.” The examiner respectfully disagrees. First, the applicant is arguing features that are not claimed for example transforms aggregated data into optimized reorder and stock-out predictions, enabling reduced network data overhead and more efficient inventory decisions. Second, previously computed data points applied into a generic processor would not be a technological improvement. At most it would be an improvement to the abstract idea. Using additional data points would not be a technological improvement. Third, using a computer and applying a computer to stock/inventory prediction management would merely be adding a generically recited AI engine to a general purpose processor. It is unclear what might be novel or non-conventional in the process of using data points to predict information about a product. The Al engine does not perform complex data modeling. It appears as though the AI engine merely makes a recommendation based on received data. There are no specifics in the claim nor in the originally filed disclosure for these argued limitations. Further, the argued technological improvements would not be an improvement to the technology at most it would be the an improvement to managing consumption and supply in the data (an improvement to the abstract idea). Not only does executing the claimed AI-based instructions stored in memory mean that the claimed system architecture is generic the applicant is arguing limitations that cannot be found in the claims. Although the applicant is claiming specific data points, the data itself would not integrate the claims into a practical application. The additional elements in the claims do no more than use the computer components as a tool and there is no change to the computers and other technology that is recited in the claims. The use of a computer as a tool to compute and recommend the data is merely a use of a computer as a tool, and does not reflect a technological improvement but merely a choice in terms of how the data is processed.
The applicant has argued “Furthermore, the AI engine trained using merchant feedback to update learnable weighting parameters provides an additional layer of practical application. The feedback loop modifies system behavior based on real-world merchant interactions, thereby improving prediction accuracy and stock-out forecasting over time. This adaptive learning mechanism demonstrates that the claim applies any alleged mathematical concepts in a manner that meaningfully limits and integrates them into a practical machine-based application. Therefore, amended independent claim 1 satisfies Step 2A, Prong 2.” The examiner respectfully disagrees. An improvement to the prediction accuracy and stock-out forecasting are improvements to the commercial outcome of the claimed inventory management system, they are not improvements to the functioning of the computer or network itself. The test is whether the claim is an improvement to the technology, not whether it improves the business result the technology is used to achieve. At most claim 1 improves the abstract idea by improving the accuracy of commercial product recommendations, which is a commercial benefit not a technical improvement. The claimed practical application is merely linking the abstract idea to a technological environment. Improving the data in inventory stock management is not a technological improvement. The examiner maintains that the claims do not integrate the alleged abstract idea into a practical application.
The applicant has argued “Under Step 2B, amended independent claim 1 recites significantly more than any alleged abstract idea. Amended independent claim 1 does not merely invoke a generic computer to perform routine calculations. Instead, it specifies a particular combination of data ingestion, multi-factor predictive modeling, AI-based parameter weighting, and stock-out recommendation generation within a network-connected system. The ordered combination of these elements reflects a specific technical architecture rather than the mere automation of a fundamental economic practice.” The examiner respectfully disagrees. The limitations that the applicant is arguing are non-generic are generic transaction probability functions, clustering-based segmentation, and frequency-based brand affinity logic. It is clear that the applicant agrees that the claimed limitations were previously done manually, therefore the applicant is merely adding a computer as a tool to perform the steps of the invention. The argument of improving not only efficiency but also reliability and adaptability in high-volume consumption systems is also non-persuasive. The use of a computer rather than a manual system would increase efficiency and reliability, however adding a computer in a manner of apply it does not satisfy the “significantly more” requirement. Using a computer to receive a known benefit of a computer is not a technological improvement. In both the claims and the original disclosure there is not support for an Al engine that is responsible for learning and generating context-sensitive, user-specific recommendations based on aggregated data inputs. There is also no support for pattern recognition and adaptive modeling that cannot be accomplished by a human or generic computer following standard instructions. These elements are claimed and described as generic components that link the abstract idea to a particular technological environment and perform generic functions of the abstract idea by receiving product data, computing product consumption data, a reorder estimation, and an optimal quantity estimation, and recommending a product stock out data before generating the results. As such, they do not improve computers, another technology, or a technical field. They do not implement the abstract idea on a particular machine that is integral to the claim. They do not transform or reduce a particular article to a different state or thing. Nor do they apply the abstract idea in a meaningful way beyond linking its use to a particular technological environment.
The applicant has argued “Moreover, the recited AI engine trained using merchant feedback and configured to update learnable parameters (wl, w2, w3) represents a non-conventional and non-generic improvement over simple rule-based estimation systems. The dynamic weighting of article capacitance score, merchant affinity score, and geographic product popularity score demonstrates a structured predictive framework that enhances system performance. This adaptive learning mechanism goes beyond merely implementing an abstract idea on a general-purpose computer.” The examiner respectfully disagrees. Training machine learning models on feedback data to update weighting parameters is a known technique in the field of machine learning. The use of feedback-based learning to update model parameters including the claimed techniques are foundational machine learning techniques. Claim 1 recites the AI engine only at a high level of generality, as it is trained using merchant feedback to learn and update parameters w1, w2, w3, without specifying the particular technical means of achieving the result. Each additional element of claim 1 is not anything significantly more that the judicial exception.
The applicant has argued “Finally, when viewed as an ordered combination, the claimed elements provide an improvement in the technological field of automated consumption prediction and inventory estimation systems. The integration of probability modeling, clustering, frequency-based logic, attribute-driven popularity scoring, and feedback-based AI weighting creates a system that improves prediction precision and inventory optimization. Such a coordinated and adaptive predictive system constitutes significantly more than a judicial exception and therefore satisfies the requirements of Step 2B.” The examiner respectfully disagrees. First, the claims do not appear to involve probabilistic modeling, merely taking computed values and plugging them into a (black box) AI engine to output a recommendation. That recommendation is later used to generate an estimate. It appears as though the claim is merely a connection of math problems. At most the claims involve mathematical modeling, this however, would not be significantly more than the abstract idea. “If a claim’s only ‘inventive concept’ is the application of an abstract idea using conventional and well-understood techniques, the claim has not been transformed into a patent-eligible application of an abstract idea.” BSG, 899 F.3d at 1290–91. “It has been clear since Alice that a claimed invention’s use of the ineligible concept to which it is directed cannot supply the inventive concept that renders the invention ‘significantly more’ than that ineligible concept.” Using a computer to compute data, use an AI model, and analyze data is not significantly more than the abstract idea. The claimed limitations amount to “nothing significantly more” than an instruction to apply an abstract idea’ using generic computer technology”). The use of these additional elements does no more than employ a computer as a tool to automate and/or implement the abstract idea, which cannot provide significantly more than the abstract idea itself. Applicant’s arguments are not found persuasive and the previous 101 rejection is maintained and updated below.
The applicant has argued that “The combination of Govindan and Ohlsson therefore fails to teach or suggest "compute an aggregated product consumption data based on the received one or more product data, wherein the aggregated product consumption data comprises a product shelf life data generated based on a transaction probability function associated with the received one or more product data, a user segmentation data generated based on a computation of one or more user clusters and an associated user reorder rate from the received one or more product data, a user brand affinity data generated based on a computation of a frequency based logic of one or more parameters, and a product popularity data computed using the received one or more product data and an associated attribute input," as recited in amended independent claim 1.” The examiner respectfully disagrees. Govindan explicitly teaches tracking duration data for each item in a catalog based on purchase histories of users, wherein the duration data measures a reorder rate for each item within one or more periods of time, see Govindan, paragraph 44. Govindan further teaches generating a Weibull distribution for each item using this duration data, wherein the Weibull distribution models the probability that an item will be reordered after a given number of days, see Govindan, paragraph 46 ("a Weibull distribution for an item can include a probability that an item will likely be reordered after 'n' number of days"). Govindan expressly ties this Weibull distribution to the lifespan and consumption patterns of the item, stating that "a lifespan of an item can be a period of time when an item is most often consumed and/or based on an expiration date for an item." see Govindan, paragraph 46. AWeibull distribution is itself a probability function specifically, a continuous probability distribution that models the probability of an event (reordering) occurring as a function of time. Govindan explicitly describes it as a "Weibull Probability Density function (Weibull PDF) model." See Govindan, paragraph 46. This Weibull PDF is generated from transaction data specifically, reorder rate data derived from purchase histories. See Govindan, paragraphs 44-46. The transaction probability function recited in claim 1 is therefore taught by Govindan's Weibull PDF model applied to transaction-based duration data. Second, the output of Govindan's Weibull PDF model a probability distribution predicting when an item will be reordered based on its consumption lifespan is substantively equivalent to product shelf life data. Govindan's Weibull distribution predicts reorder timing based on item lifespan and consumption patterns, which is precisely what product shelf life data represents. The claim does not require any particular mathematical form for the transaction probability function, and Govindan's Weibull PDF satisfies this limitation under the broadest reasonable interpretation. Third, Govindan's Weibull distribution is generated from and associated with product data specifically, the purchase history data and duration data for each item in the catalog. See Govindan, paragraphs 44-47. This satisfies the requirement that the product shelf life data be generated based on a transaction probability function associated with the received one or more product data. Govindan teaches grouping users into crowds based on demographic features including geographical location, age, gender, and ethnicity, see Govindan, paragraph 49. Govindan further teaches generating a respective Weibull distribution for each crowd of users, which models the reorder rate for items within that crowd. See Govindan, paragraph 49. The grouping of users into crowds based on shared characteristics is a form of user clustering the claim does not require any particular clustering algorithm or methodology, and Govindan's demographic-based crowd grouping satisfies the limitation of computing one or more user clusters under the broadest reasonable interpretation. Furthermore, each crowd in Govindan is associated with a respective Weibull distribution that models item reorder rates for that crowd, which corresponds to the associated user reorder rate recited in the claim. Govindan's crowd grouping produces user segments that are associated with reorder rates derived from product purchase data, satisfying this limitation. Govindan's crowd grouping is not based solely on demographics it is used in conjunction with product purchase history data to generate Weibull distributions that model reorder behavior for each crowd. See Govindan, paragraphs 49 and 70. The combination of demographic grouping with product-based reorder rate computation satisfies the claimed limitation of user segmentation data generated based on user clusters and an associated user reorder rate from product data. Third, to the extent Govindan's crowd grouping is viewed as not fully teaching computation of user clusters from product data, it would have been obvious to one of ordinary skill in the art to supplement Govindan's demographic-based grouping with product purchase behavior-based clustering, as such behavioral clustering was a well-known and routine technique in the field of recommendation systems and inventory management at the time of the invention. Govindan explicitly teaches determining brand affinities for a user based on purchase histories, specifically by measuring the number of times a brand of an item has been reordered over another brand within a period of time. See Govindan, paragraph 51 ("determining a brand affinity for a user can be generated using previous order histories that can include a number of times a brand of an item has been reordered over another brand of the item . . . over a period of time"). Govindan further teaches that a brand affinity is confirmed when the number of times a brand was reordered exceeds a predetermined threshold. See Govindan, paragraph 51. This teaching directly and explicitly discloses user brand affinity data generated based on a computation of frequency based logic of one or more parameters. The frequency based logic is the counting and comparison of reorder frequencies across the parameters of user, brand, and time period. The parameters are the user, the brand of the item, the competing brand, and the time period over which reorders are counted. Govindan's brand affinity computation is precisely a frequency based logic applied to one or more parameters it counts how frequently a user reorders one brand over another across a defined time period and derives an affinity value therefrom. The claim does not require any particular mathematical form for the frequency based logic beyond this, and Govindan's teaching satisfies this limitation under the broadest reasonable interpretation. Govindan teaches grouping items by item features such as product category, department, size, price, and other attributes listed within a retailer's catalog, see paragraph 50. Govindan further teaches computing product type affinities for users based on purchase histories and product type data, wherein product type data is determined by categorizing items as product types, see Govindan, paragraphs 52-53. Govindan additionally teaches using these product type groupings and affinities as input features for its machine learning model, where the frequency of reordering within each product type category reflects the relative popularity of that product type, see Govindan, paragraph 54. The product popularity data recited in claim 1 is computed using received product data and an associated attribute input. Govindan's product type affinity data is computed using product purchase history data, which is received product data, and product attribute inputs including category, department, size, and price, see Govindan, paragraphs 50-53. The frequency with which users reorder products within a given attribute category reflects the popularity of those products within that category, and Govindan's product type affinity computation captures precisely this information. The claims merely require popularity data be computed from product data and attribute inputs, which Govindan explicitly teaches. Govindan explicitly teaches combining all of the above data components the Weibull distribution, the crowd-based user segmentation with associated reorder rates, the brand affinity data, and the product type affinity data as unified input data for a single machine learning model that generates reorder predictions and quantity estimations, see Govindan, paragraph 54 ("input data for the machine learning model comprises the Weibull distribution for the each respective item of the items, brand affinities for the respective user, and product type affinities for the respective user" and "output data for the machine learning model comprises respective indications of whether the respective user ordered the each respective item, respective quantities associated with the each respective item, and a basket size of the each respective order"). This unified input comprising all four data components, used together to generate both reorder predictions and quantity estimations, directly corresponds to the claimed aggregated product consumption data used to compute both reorder estimation and optimal quantity estimation. Applicant’s argument that Govindan structures these inputs as separate model features rather than as a single pre-computed aggregated dataset identifies a difference in implementation detail rather than a substantive structural distinction. The claim recites computing an aggregated product consumption data comprising the four components, and then using that aggregated data to compute reorder estimation and optimal quantity estimation. Govindan teaches computing each of the four data components and combining them as unified input to a predictive model that generates reorder and quantity outputs. The logical difference between pre-aggregating these components into a labeled dataset before feeding them to a model versus combining them as simultaneous model inputs is a trivial implementation choice that would have been obvious to one of ordinary skill in the art. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) ("the combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results").
Applicant’s arguments with respect to the previous 103 rejections in view of applicant’s amendments have been considered but are moot because the applicant has amended the claims to narrow previous limitations and an updated search was conducted. The previous 103 rejection is updated below.
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, 6, 8, 10, 11, 13, 16, 18, 20, 21, are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1, 3, 6, 8, 10, are directed to a system, claims 11, 13, 16, 18, 20, are directed to a method, and claim 21 is directed to user equipment. Therefore, claims 1, 3, 6, 8, 10, 11, 13, 16, 18, 20, 21, are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 11, and 21 recite estimating consumption, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to managing personal behavior or interactions between individuals including social activities and using Mathematical Concepts specifically a mathematical calculation to compute an aggregated product consumption and a reorder estimation. Claim 1 recites abstract limitations including “receive one or more product data; compute an aggregated product consumption data based on the received one or more product data; compute an aggregated product consumption data based on the received one or more product data, wherein the aggregated product consumption data comprises a product shelf life data generated based on a transaction probability function associated with the received one or more product data, a user segmentation data generated based on a computation of one or more user clusters and an associated user reorder rate from the received one or more product data, a user brand affinity data generated based on a computation of a frequency based logic of one or more parameters, and a product popularity data computed using the received one or more product data and an associated attribute input; recommend… using feedback from merchants to learn and update learnable parameters (wl, w2, w3) that weight an article capacitance score, a merchant affinity score, and a pin-code product popularity score a product stock out data to the one or more users based on the reorder estimation and the optimal quantity estimation; and generate the consumption estimation for the one or more users based on the recommendation. Claim 11 recites abstract limitations including “receiving one or more product data; computing an aggregated product consumption data based on the received one or more product data, wherein the aggregated product consumption data comprises a product shelf life data generated based on a transaction probability function associated with the received one or more product data, a user segmentation data generated based on a computation of one or more user clusters and an associated user reorder rate from the received one or more product data, a user brand affinity data generated based on a computation of a frequency based logic of one or more parameters, and a product popularity data computed using the received one or more product data and an associated attribute input; computing, a reorder estimation and an optimal quantity estimation for the received one or more product data based on the computed aggregated product consumption data; recommending… using feedback from merchants to learn and update learnable parameters (wl, w2, w3) that weight an article capacitance score, a merchant affinity score, and a pin-code product a product stock out data to the one or more users based on the reorder estimation and the optimal quantity estimation; and generating the consumption estimation for the one or more users based on the recommendation. Claim 21 recites abstract limitations including “transmit one or more product data; receive the one or more product data; compute an aggregated product consumption data based on the received one or more product data, wherein the aggregated product consumption data comprises a product shelf life data generated based on a transaction probability function associated with the received one or more product data, a user segmentation data generated based on a computation of one or more user clusters and an associated user reorder rate from the received one or more product data, a user brand affinity data generated based on a computation of a frequency based logic of one or more parameters, and a product popularity data computed using the received one or more product data and an associated attribute input; compute a reorder estimation and an optimal quantity estimation for the received one or more product data based on the computed aggregated product consumption data; recommend… using feedback from merchants to learn and update learnable parameters (wl, w2, w3) that weight an article capacitance score, a merchant affinity score, and a pin-code product a product stock out data to the one or more users based on the reorder estimation and the optimal quantity estimation; and generate the consumption estimation for the one or more users based on the recommendation.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “by the processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “by the processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “by the processor” language, the claim steps in the context of the claim encompass an abstract idea directed to a “Mental Process”, “Mathematical Concept” and “Certain Methods of Organizing Human Activity.”
Dependent claims 3, 6, 8, 10, 13, 16, 18, 20, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Step 2A, Prong 2: Independent Claims 1, 11, and 21 do not integrate the judicial exception into a practical application. Claim 1 is a system comprising “a processor; and a memory coupled to the processor, wherein the memory comprises processor-executable instructions, one or more computing devices, wherein the one or more computing devices are operated by one or more users and are connected to the processor via a network; using an artificial intelligence (AI) engine.” Claim 11 is a method that recites limitations performed using “a processor, one or more computing devices, wherein the one or more computing devices are operated by one or more users and are connected to the processor via a network; an artificial intelligence (AI) engine.” Claim 21 is an equipment that comprises “A user equipment (UE) for consumption estimation, the UE comprising: one or more processors communicatively coupled to a processor of a system, wherein the one or more processors are coupled to a memory, and wherein said memory stores instructions which when executed by the one or more processors cause the UE to: the processor via a network, wherein one or more users operate the UE, wherein the processor is configured to: using an artificial intelligence (AI) engine.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, compute, recommend, and generate data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). Further, this limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claims 3, 6, 8, 10, 13, 16, 18, 20, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 11, and 21 do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, claim 1 is a system comprising “a processor; and a memory coupled to the processor, wherein the memory comprises processor-executable instructions, one or more computing devices, wherein the one or more computing devices are operated by one or more users and are connected to the processor via a network; using an artificial intelligence (AI) engine.” Claim 11 is a method that recites limitations performed using “a processor, one or more computing devices, wherein the one or more computing devices are operated by one or more users and are connected to the processor via a network; an artificial intelligence (AI) engine.” Claim 21 is an equipment that comprises “A user equipment (UE) for consumption estimation, the UE comprising: one or more processors communicatively coupled to a processor of a system, wherein the one or more processors are coupled to a memory, and wherein said memory stores instructions which when executed by the one or more processors cause the UE to: the processor via a network, wherein one or more users operate the UE, wherein the processor is configured to: using an artificial intelligence (AI) engine.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, compute, recommend, and generate data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claims 3, 6, 8, 10, 13, 16, 18, 20, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception. Accordingly, claims 1, 3, 6, 8, 10, 11, 13, 16, 18, 20, 21, are rejected under 35 USC 101.
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(s) 1, 3, 6, 8, 10, 11, 13, 16, 18, 20, 21, is/are rejected under 35 U.S.C. 103 as being unpatentable over Govindan et al. (US 20210233148 A1) in view of Ohlsson et al. (US 20210390498 A1) in view of Keren at al. (US 20210248624 A1).
Regarding claim 1, Govindan teaches a processor (¶ 22, 32, 113-114);
and a memory coupled to the processor, wherein the memory comprises processor- executable instructions, which on execution, cause the processor (¶ 32, 38, 43, 113-114):
receive one or more product data from one or more computing devices, wherein the one or more computing devices are operated by one or more users and are connected to the processor via a network (abstract, ¶ 43, 113, 114, disclose using more than one processor. ¶ 39-40, 88, 101, discloses a product database including product data. ¶ 21-24, 31, 41, 112, disclose a network for receiving data);
compute an aggregated product consumption data based on the received one or more product data (¶ 46, 95, disclose product consumption data), wherein the aggregated product consumption data comprises a product shelf life data generated based on a transaction probability function associated with the received one or more product data (¶ 19, 46, 67, 95, disclose product consumption data that includes an expiration data. The data also have modeling frequency and probability of behavior over time. ¶ 28, 42, 47, 59, 80-84, disclose transaction probabilities), a user segmentation data generated based on a computation of one or more user clusters and an associated user reorder rate from the received one or more product data (¶ 42, 48-49, 50, 79-80, 89, 91, discloses grouping the users and includes user reorder rates), a user brand affinity data generated based on a computation of a frequency based logic of one or more parameters (¶ 51, 54, 59, 88-91, 97, 113-114 , discloses brand affinity in view of a parameter related to frequency), and a product popularity data computed using the received one or more product data and an associated attribute input (¶ 65-67, 70, 73, 74, 108, discloses how frequently an item is ordered.);
compute a reorder estimation and an optimal quantity estimation for the received one or more product data based on the computed aggregated product consumption data (¶ 44-45, 59, 67, 79, 95-100, 109, discloses determining a reorder rate);
machine learning trained using feedback from merchants to learn and update learnable parameters (w1, w2, w3) an article capacitance score, a merchant affinity score, and a pin-code product popularity score (¶ 50-54 discloses an XGBoost model as a machine learning model that uses an algorithm to build and refine predictions. ¶ 97-101, discloses new and incoming data. ¶ 56-57, discloses tuning the decision trees during training. ¶ 58-59, discloses weighing the contribution of the future prediction. ¶ 46, 54, disclose how much of a product a user is likely to consumer and reorder within a given period of time which corresponds to the article capacitance score. ¶ 51, 54, 56-59, disclose a brand affinity score. ¶ 50-53, 56-59, disclose a pin-code product popularity score (the combination of geographic crowd grouping and computation information discloses a popularity feature based on geography).
Govindan does not specifically teach recommend, using an artificial intelligence (AI) engine, a product stock out data to the one or more users based on the computed reorder estimation and the optimal quantity estimation.
However, Ohlsson teaches recommend, a product stock out data to the one or more users based on the computed reorder estimation and the optimal quantity estimation (¶ 47, In view of these challenges in inventory management systems, a “big data” artificial intelligence platform may be necessary to aggregate data from multiple disparate source systems (e.g., enterprise system, IoT sensors, and third-party data providers) and apply artificial intelligence-based techniques that continually incorporate and learn from new or updated datasets. For example, the types of data useful for optimizing inventory may include demand forecast, supplier orders, production orders, bill of materials (time-varying), change history of re-order parameters, and inventory movement data. ¶ 9-10, 50-51, 100, 124);
generate the consumption estimation for the one or more users based on the recommendation (¶ 86-88, The optimization module 130 can alternatively or additionally project future stochastic inventory variables. For every combination of values, we can run a series of forward-projections to evaluate the performance of the combination. The performance criterion may be operational costs and obtained service level. The optimization module 130 may use binary search, grid search, random search or Bayesian optimization to pick what combinations to evaluate. In this case, the optimization module 130 may not need to evaluate different combinations and may be able to derive suitable combinations from the obtained distribution of forward projected stochastic variables. ¶ 94, 118, 53-54).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Govindan to include/perform use of a quantity estimation as taught/suggested by Ohlsson. This known technique is applicable to the system of Govindan as they both share characteristics and capabilities, namely, they are directed to inventory management. One of ordinary skill in the art would have recognized that applying the known technique of Ohlsson would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ohlsson to the teachings of Govindan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate quantity features into similar systems. Further, applying an optimal quantity estimation would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user the ability to have additional data points to increase the accuracy when generating the consumption estimation.
Govindan does not specifically teach the weighing of the features as claimed.
However, Keren teaches recommend, using an artificial intelligence (AI) engine trained using feedback from merchants to learn and update learnable parameters (wl, w2, w3) that weight an merchant affinity score, and a pin-code product popularity score (abstract, discloses weighing a formula to apply to the score values. ¶ 117, discloses weighing the various factors. ¶ 447-448, 454, 460, 481, discloses merchant brand score, ¶ 460, discloses a location or geo-location based score. ¶ 180, 341, 400, discloses learning from the feedback of others.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Govindan to include/perform learnable parameters that weight various scores as taught/suggested by Keren. This known technique is applicable to the system of Govindan as they both share characteristics and capabilities, namely, they are directed to brand based recommendations. One of ordinary skill in the art would have recognized that applying the known technique of Keren would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Keren to the teachings of Govindan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate weighing features into similar systems. Further, applying learnable parameters that weight a handful of score would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user to decide which inputs matter most.
Regarding claims 3 and 13, Govindan discloses wherein the one or more product data comprises at least one of a user order data, and a product catalog data (¶ 45, 61-62, 80, 97, 108, discloses user order history. ¶ 28, 44-47, 78, 86, 90-91, 113, discloses product catalog data).
Regarding claims 6 and 16, Govindan discloses wherein the associated attribute input comprises at least one of: a user address, an ordered quantity, a category level, or a popularity attribute (¶ 65-67, 70, 73, 74, 108, discloses how frequently an item is ordered, a popularity attribute. ¶ 49, discloses further user information including location).
Regarding claims 8 and 18, Govindan discloses wherein the one or more parameters comprise at least one of: a user, a brand, and a category associated with the received one or more product data (¶ 95, 46, 49, 50, 52-54, 73, 74, 86, disclose user data and a category. ¶ 51, 54, 59, 78, 79, 88-91, disclose a brand).
Regarding claims 10 and 20, Govindan discloses wherein the processor is configured to generate a look ahead reorder based on the user segmentation data and the user brand affinity data (abstract, ¶ 44, 59, 79, 95-100, discloses reordering predictions and brand affinity. ¶ 51, 54, 59, 88-91, 97, 113-114 , discloses brand affinity in view of a parameter related to frequency).
Regarding claim 11, Govindan teaches
receiving, by a processor, one or more product data from one or more computing devices, wherein the one or more computing devices are operated by one or more users and are connected to the processor via a network (abstract, ¶ 43, 113, 114, disclose using more than one processor. ¶ 22, 32, 113-114, discloses a processor. ¶ 39-40, 88, 101, discloses a product database including product data. ¶ 21-24, 31, 41, 112, disclose a network for receiving data);
computing by the processor, an aggregated product consumption data based on the received one or more product data (¶ 46, 95, disclose product consumption data), wherein the aggregated product consumption data comprises a product shelf life data generated based on a transaction probability function associated with the received one or more product data (¶ 19, 46, 67, 95, disclose product consumption data that includes an expiration data. The data also have modeling frequency and probability of behavior over time. ¶ 28, 42, 47, 59, 80-84, disclose transaction probabilities), a user segmentation data generated based on a computation of one or more user clusters and an associated user reorder rate from the received one or more product data (¶ 42, 48-49, 50, 79-80, 89, 91, discloses grouping the users and includes user reorder rates), a user brand affinity data generated based on a computation of a frequency based logic of one or more parameters (¶ 51, 54, 59, 88-91, 97, 113-114 , discloses brand affinity in view of a parameter related to frequency), and a product popularity data computed using the received one or more product data and an associated attribute input (¶ 65-67, 70, 73, 74, 108, discloses how frequently an item is ordered.);
computing by the processor, a reorder estimation and an optimal quantity estimation for the received one or more product data based on the computed aggregated product consumption data (¶ 44-45, 59, 67, 79, 95-100, 109, discloses determining a reorder rate);
machine learning trained using feedback from merchants to learn and update learnable parameters (w1, w2, w3) an article capacitance score, a merchant affinity score, and a pin-code product popularity score (¶ 50-54 discloses an XGBoost model as a machine learning model that uses an algorithm to build and refine predictions. ¶ 97-101, discloses new and incoming data. ¶ 56-57, discloses tuning the decision trees during training. ¶ 58-59, discloses weighing the contribution of the future prediction. ¶ 46, 54, disclose how much of a product a user is likely to consumer and reorder within a given period of time which corresponds to the article capacitance score. ¶ 51, 54, 56-59, disclose a brand affinity score. ¶ 50-53, 56-59, disclose a pin-code product popularity score (the combination of geographic crowd grouping and computation information discloses a popularity feature based on geography).
Govindan does not specifically teach recommend, using an artificial intelligence (AI) engine, a product stock out data to the one or more users based on the computed reorder estimation and the optimal quantity estimation.
However, Ohlsson teaches recommending, by the processor, using an artificial intelligence (AI) engine, a product stock out data to the one or more users based on the computed reorder estimation and the optimal quantity estimation (¶ 47, In view of these challenges in inventory management systems, a “big data” artificial intelligence platform may be necessary to aggregate data from multiple disparate source systems (e.g., enterprise system, IoT sensors, and third-party data providers) and apply artificial intelligence-based techniques that continually incorporate and learn from new or updated datasets. For example, the types of data useful for optimizing inventory may include demand forecast, supplier orders, production orders, bill of materials (time-varying), change history of re-order parameters, and inventory movement data. ¶ 50-51, 124);
and generating, by the processor, the consumption estimation for the one or more users (108) based on the recommendation (¶ 86-88, The optimization module 130 can alternatively or additionally project future stochastic inventory variables. For every combination of values, we can run a series of forward-projections to evaluate the performance of the combination. The performance criterion may be operational costs and obtained service level. The optimization module 130 may use binary search, grid search, random search or Bayesian optimization to pick what combinations to evaluate. In this case, the optimization module 130 may not need to evaluate different combinations and may be able to derive suitable combinations from the obtained distribution of forward projected stochastic variables. ¶ 94, 118, 53-54).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Govindan to include/perform use of a quantity estimation as taught/suggested by Ohlsson. This known technique is applicable to the system of Govindan as they both share characteristics and capabilities, namely, they are directed to inventory management. One of ordinary skill in the art would have recognized that applying the known technique of Ohlsson would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ohlsson to the teachings of Govindan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate quantity features into similar systems. Further, applying an optimal quantity estimation would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user the ability to have additional data points to increase the accuracy when generating the consumption estimation.
Govindan does not specifically teach the weighing of the features as claimed.
However, Keren teaches recommend, using an artificial intelligence (AI) engine trained using feedback from merchants to learn and update learnable parameters (wl, w2, w3) that weight an merchant affinity score, and a pin-code product popularity score (abstract, discloses weighing a formula to apply to the score values. ¶ 117, discloses weighing the various factors. ¶ 447-448, 454, 460, 481, discloses merchant brand score, ¶ 460, discloses a location or geo-location based score. ¶ 180, 341, 400, discloses learning from the feedback of others.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Govindan to include/perform learnable parameters that weight various scores as taught/suggested by Keren. This known technique is applicable to the system of Govindan as they both share characteristics and capabilities, namely, they are directed to brand based recommendations. One of ordinary skill in the art would have recognized that applying the known technique of Keren would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Keren to the teachings of Govindan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate weighing features into similar systems. Further, applying learnable parameters that weight a handful of score would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user to decide which inputs matter most.
Regarding claim 21, Govindan teaches a user equipment (UE) for consumption estimation, the UE comprising: one or more processors communicatively coupled to a processor of a system, wherein the one or more processors are coupled to a memory, and wherein said memory stores instructions which when executed by the one or more processors cause the UE to transmit one or more product data to the processor via a network, wherein one or more users operate the UE, wherein the processor is configured to: receive the one or more product data from the UE (abstract, ¶ 43, 113, 114, disclose using more than one processor. ¶ 22, 32, 113-114, discloses a processor. ¶ 39-40, 88, 101, discloses a product database including product data. ¶ 21-24, 31, 41, 112, disclose a network for receiving data, ¶ 22, 32, 38, 43, 113-114);
compute an aggregated product consumption data based on the received one or more product data (¶ 46, 95, disclose product consumption data), wherein the aggregated product consumption data comprises a product shelf life data generated based on a transaction probability function associated with the received one or more product data (¶ 19, 46, 67, 95, disclose product consumption data that includes an expiration data. The data also have modeling frequency and probability of behavior over time. ¶ 28, 42, 47, 59, 80-84, disclose transaction probabilities), a user segmentation data generated based on a computation of one or more user clusters and an associated user reorder rate from the received one or more product data (¶ 42, 48-49, 50, 79-80, 89, 91, discloses grouping the users and includes user reorder rates), a user brand affinity data generated based on a computation of a frequency based logic of one or more parameters (¶ 51, 54, 59, 88-91, 97, 113-114 , discloses brand affinity in view of a parameter related to frequency), and a product popularity data computed using the received one or more product data and an associated attribute input (¶ 65-67, 70, 73, 74, 108, discloses how frequently an item is ordered.);
compute a reorder estimation and an optimal quantity estimation for the received one or more product data based on the computed aggregated product consumption data (¶ 44-45, 59, 67, 79, 95-100, 109, discloses determining a reorder rate);
machine learning trained using feedback from merchants to learn and update learnable parameters (w1, w2, w3) an article capacitance score, a merchant affinity score, and a pin-code product popularity score (¶ 50-54 discloses an XGBoost model as a machine learning model that uses an algorithm to build and refine predictions. ¶ 97-101, discloses new and incoming data. ¶ 56-57, discloses tuning the decision trees during training. ¶ 58-59, discloses weighing the contribution of the future prediction. ¶ 46, 54, disclose how much of a product a user is likely to consumer and reorder within a given period of time which corresponds to the article capacitance score. ¶ 51, 54, 56-59, disclose a brand affinity score. ¶ 50-53, 56-59, disclose a pin-code product popularity score (the combination of geographic crowd grouping and computation information discloses a popularity feature based on geography).
Govindan does not specifically teach recommend, using an artificial intelligence (AI) engine, a product stock out data to the one or more users based on the computed reorder estimation and the optimal quantity estimation.
However, Ohlsson teaches recommend, using an artificial intelligence (AI) engine, a product stock out data to the one or more users based on the computed reorder estimation and the optimal quantity estimation (¶ 47, In view of these challenges in inventory management systems, a “big data” artificial intelligence platform may be necessary to aggregate data from multiple disparate source systems (e.g., enterprise system, IoT sensors, and third-party data providers) and apply artificial intelligence-based techniques that continually incorporate and learn from new or updated datasets. For example, the types of data useful for optimizing inventory may include demand forecast, supplier orders, production orders, bill of materials (time-varying), change history of re-order parameters, and inventory movement data. ¶ 50-51, 124);
generate the consumption estimation for the one or more users based on the recommendation (¶ 86-88, The optimization module 130 can alternatively or additionally project future stochastic inventory variables. For every combination of values, we can run a series of forward-projections to evaluate the performance of the combination. The performance criterion may be operational costs and obtained service level. The optimization module 130 may use binary search, grid search, random search or Bayesian optimization to pick what combinations to evaluate. In this case, the optimization module 130 may not need to evaluate different combinations and may be able to derive suitable combinations from the obtained distribution of forward projected stochastic variables. ¶ 94, 118, 53-54).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Govindan to include/perform use of a quantity estimation as taught/suggested by Ohlsson. This known technique is applicable to the system of Govindan as they both share characteristics and capabilities, namely, they are directed to inventory management. One of ordinary skill in the art would have recognized that applying the known technique of Ohlsson would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ohlsson to the teachings of Govindan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate quantity features into similar systems. Further, applying an optimal quantity estimation would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user the ability to have additional data points to increase the accuracy when generating the consumption estimation.
Govindan does not specifically teach the weighing of the features as claimed.
However, Keren teaches recommend, using an artificial intelligence (AI) engine trained using feedback from merchants to learn and update learnable parameters (wl, w2, w3) that weight an merchant affinity score, and a pin-code product popularity score (abstract, discloses weighing a formula to apply to the score values. ¶ 117, discloses weighing the various factors. ¶ 447-448, 454, 460, 481, discloses merchant brand score, ¶ 460, discloses a location or geo-location based score. ¶ 180, 341, 400, discloses learning from the feedback of others.).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Govindan to include/perform learnable parameters that weight various scores as taught/suggested by Keren. This known technique is applicable to the system of Govindan as they both share characteristics and capabilities, namely, they are directed to brand based recommendations. One of ordinary skill in the art would have recognized that applying the known technique of Keren would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Keren to the teachings of Govindan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate weighing features into similar systems. Further, applying learnable parameters that weight a handful of score would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user to decide which inputs matter most.
Other pertinent prior art includes Radhakrishnan (US 20170357940 A1) which teaches dynamic inventory control in a warehouse. Nguyen (US 20210256440 A1) teaches remembering food expiration dates during store checkout for end user food inventory management and consumption. Raina (US 11157526 B1) which discloses data segmentation and, more specifically, to data segmentation using machine learning.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
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