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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 12/15/2025, Applicant, on 04/09/2026, amended claims. Claims 1-18 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 04/09/2026 have been fully considered, but they are not fully persuasive. The 35 USC § 112(f) has been overcome. However, the updated 35 USC § 103 and 101 rejections of claims 1-18 are applied in light of Applicant's amendments.
The Applicant argues the following, form Remarks filed 04/09/2026:
The Applicant argues the 101 does not apply.
The claimed subject matter, is directed to an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas set forth in the MPEP 2106 Patent Subject Matter Eligibility [R-10.2019].
The claimed subject matter is merely claims a method for calculating and analyzing information regarding sales data. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract idea. Additionally, the claimed subject matter can also be categorized as a Mental Process as it recites concepts performed in the human mind (observation and evaluation). The steps of calculating data, training/updating models, and generating a model/trend line can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing models with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The claims do not mention to any use of a specialized computer and/or processor. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for sales data, and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding sales information, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
The use of Artificial Intelligence (AI), machine learning (ML) models, and/or artificial neural networks (ANN) fall within the realm of abstract ideas. They are, at their core, mathematical algorithms implemented on a computer. As highlighted in Examples 47-49 of the 2024 Patent Subject Matter Eligibility Guidance, the USPTO has consistently viewed claims directed to such models as being drawn to abstract ideas. These examples illustrate claims that, while couched in the language of specific applications, ultimately boil down to mathematical relationships and calculations.
Furthermore, even if the claim recites specific steps related to data collection, preprocessing, or post-processing, these steps often represent well-understood, conventional activities. As demonstrated in Examples 47-49, adding such conventional elements to a claim directed to an abstract idea does not necessarily transform it into a patent-eligible application. These examples illustrate situations where the additional steps were deemed insufficient to provide an "inventive concept" that meaningfully narrowed the scope of the abstract idea. In the context of machine learning, simply collecting and preparing data for input into a model, or applying the model's output to a particular problem, falls into this category of conventional activity. The Applicant has not created a new learning algorithm, but rather optimizing existing algorithm(s) or the application of known techniques to a new dataset. Such incremental advancements, while potentially valuable for business, do not automatically confer patent eligibility or a technological improvement. As highlighted in the Alice framework, the mere recitation of known components or processes does not necessarily amount to an inventive concept.
The Applicant argues Brannigan does not teach ML anomaly detection.
In response, the Examiner disagrees. Brannigan 0047: “the system 2 may also be configured to provide feedback to the producer, manufacturer, and/or shop or vendor 150 regarding the sales of goods and/or the need to increase or decrease production of items in inventory 100 that are being sold or to be offered for sale. The feedback may be based on the purchasing trends of the various retailers 155 and consumers 300. For instance, if various items are being sold at a more rapid rate than in the past, the system may be configured so as to instruct the seller 150 to purchase more inventory or raw goods, so as to meet the increased consumer demand. Likewise, if an item is selling at a less rapid rate than in the past, the system 2 may be configured so as to instruct the seller 150 to forestall the purchase of more inventory, e.g., craft goods 100 or raw goods 110 (see FIG. 1), until current inventory has begun to move more rapidly. Further, as described herein in greater detail below, the system may be configured to effectuate or otherwise allow the delivery of the raw products /finished goods /purchased goods from the inventory to the retailer 155 and/or consumer 300. Hence, shops and vendors using the systems 2, apparatuses, and methods herein disclosed will be able to quickly and easily offer various goods in various conditions of production and/or manufacture for sale to a larger consumer base, while also being able to manage the inventory they are selling via the web-based portal 200 (and/or in the shop), track inventory sold online and/or within the shop, as well as receive customer orders for products via the web page, manage payment by customers via the web page, and coordinate delivery of the products to the customer 300 who placed the order.Brannigan identifies the threshold variation “various items are being sold at a more rapid rate than in the apst’ or “selling ta less rapid rate in the past” – each deviation form the historic cales baseline that triggers a system action (instructs seller to increase inventory). A change in sales rate over time relative to a historical baseline teaches a “threshold variation in a business metric over time.”
Brannigan 0128: “ an important aspect of the system is an Artificial Intelligence (A/I) module having one or more of a learning or training platform, including a learning engine, and an analytics or inference platform, including an inference engine. In one instance, the learning platform includes a processing engine that is configured for taking known data, running a learning and/or training protocol on the data, and developing one or more organizing rules therefrom. Likewise, the analytics processing platform includes a processing, e.g., inference, engine that is configured for applying the rules developed by or for the learning platform and applying them to newly or previously acquired data to generate one or more outcomes thereby, such as where the outcome may be a known or inferred relationship, a known or predicted result, and/or a probability of one or more outcomes, and the like. In various instances, the inference engine is configured for continuously running analytics on received data on a daily basis and/or with regard to one or more special or promotional events, such as prior to, during, and/or after the event, e.g., a shopping event, and functions with the purpose of improving the efficacy of the event results, e.g., sales results, such as for the event itself and/or for one or more participants thereof, such as by improving the usefulness of producing, consuming, and/or delivery, of goods and services, being sold and purchased through the system.” Here the art applies ML to the data. The recited ML model is applied to business metric data, see 0128 above.
The Applicant argues Brannigan citations do no apply context ML relative to the identified anomaly.
In response, the Examiner disagrees. Brannigan teaches eights and mines correlations across a plurality of contextual dimensions; see 0123: “those shopping at a particular outlet, the products they are consuming, how they are rating those products, how often they are consuming those products, the frequency of purchase, as well as the brands purchased, from whom, and the like. This data may then be fed into an artificial intelligence engine of the system to determine and/or predict usage stocking and/or usage patterns. Additional information may also be collected and used to understand, evaluate, and characterize usage patterns, make predictions and suggestions, as well as determine trends on a larger market and regional basis, where this data may include an analysis of various social media, e.g., FACEBOOK®, postings of photos, comments, and/or likes or dislikes, what they post on the internet, and/or on another users or third party's webpage. This analysis allows for a great quantity of data to be collected and analyzed so as to derive one or more conclusions, such as a conclusion as related to producing and/or consuming.” Additionally, paragraph 0025 confirms the contextual model, contextual factor identification, 0125: “the data from all various sources may be collected and organized in a structure that is specifically designed to pinpoint correlations between otherwise unknown relationships. Such a relational architecture may take many forms, such as in the form of a Structured Query Language (SQL), Hierarchical Tree, or Knowledge Graph database. Collected information, for example, may be run through one or more computational and/or analytics regimes, as herein described, so as to identify pertinent known or inferred data points from which various relationships between producers, consumers, and delivery agents engaged with the system may be determined, and motifs in their usage may be explored, and future patterns predicted.” Applicant’s arguments reads that the claim as requiring strict temporal segregation between anomaly identification stage and the contextualization stage. The claim does not require this. It requires only that the contextual models operate on data “relative to the first anomaly.” Brannigans AI engine, per 0128, operates continuously on the same data stream that produces the rate variation determination, satisfies this requirement.
The Applicant argues Brannigan is correlative, not causal, and no causal interface.
In response, the Examiner disagrees. Brannigan’s AI module identifies and weights influencing factors against user actions and outcomes, assigns each prediction a 0.0-1.0 confidence level, predicts the influence of external factors affecting system usage, and determines and weights inferred relationships is causal in form. The term causal inference and determination models is not restricted by the spec; under BRI, it encompasses Brannigans weighted inference of influencing factors and their predicted effect on outcomes. See Brannigan 0133: “the artificial intelligence module increasing or decreasing a weighting scale used to weight the connections between various influencing factors and user actions and/or outcomes of those actions. For instance, in such instances, when various patterns are formed, the system may learn these patterns, determine the presence of one or more trends, or other factors of import, and/or predict a likely manner in which the user will behave, and the level of confidence may be given to the predicted outcome, such as from 0.0, not very likely to 1.0 almost completely certain. Accordingly, when the system makes a correct prediction, the connection between the initiating action and the presence of a trend in that action, as well as the connection between the action and a predicted outcome of that action, may be strengthened, such as by giving an estimation of the presence of a trend and/or a predicted outcome in the future, for the same or substantially similar circumstances, more weight…0137: data points between the various branches or nodes in the data structure of the system may be used to generate correlations between the nodes and to weight those correlations so as to build a data structure thereby, such as a knowledge tree or graph, which may then be queried to determine other relationships not previously known and/or to predict the influence of external factors affecting the usage of the system, and/or to predict and weight potential outcomes..0140: from these known fact based relationships, previously unknown, inferred relationships may be determined, and weighted.”
The Applicant argues Brannigan does not personalize on prioritized causal influence factors. In response, the Examiner disagrees. Brannigan assigns predictions a weighted score, such as to the probability of being correct, and uses that score to drive system self correction(s). The weighted score is the relevancy score, and score driven correction over the predictions is prioritized. Brannigan teaches the prioritization mechanism; the AI module increases or decreases a weighting scale sued to weigh the connections between various influencing factors. The weighted score and self correct based on that score paring reads directly onto the claims define relevancy scores and prioritize the influence factors limitation. See Brannigan paragraphs 0141 and 0133.
The Applicant argues neither reference teaches recipient type personalization.In response, the Examiner disagrees. Bates provides tailored alerts and associated thresholds for users based on a variety of factors; alerts vary per user according to type defining factors. Bates then differentiates the display channel by recipient (push/email/SMS for metrics), dependent control of the display system. Brannigan teaches the recipient type; users are grouped together by the system, such as based on the type of factors by which their user of the system in being influenced. Thus, the combination teaches multiple recipient types and recipient specific GUI presentation. See Bates 0016 and 0060. See Brannigan 0136.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-9) and method (claims 10-18) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas set forth in the MPEP 2106 Patent Subject Matter Eligibility [R-10.2019]. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The limitations reciting the abstract idea(s) (Mental process and Certain methods of organizing human activity), as set forth in exemplary claim 10, are: “identifying, based on …anomaly detection models to business metric data relative to products being sold through a retailer, a first anomaly relative to a threshold variation in a business metric over time of a first category of products; identifying, based on …contextual models to non-sales data and sales data relative to the first anomaly, contextual factors associated with the first anomaly relative to different sales channels and geographic hierarchy of sales; determining, based on …causal inference and determination models to sets of relevance data having potential effects on the first category of products as a function of the contextual factors associated with the first anomaly, influence attribution factors that are predicted to have been factors in causing the threshold variation in the business metric of products of the first category of product; defining, based on …attribution prioritization models, relevancy scores to the influence attribution factors and prioritizing the influence attribution factors; and …personalization models to the prioritized influence attribution factors and contextual factors of the first anomaly as a function of a particular first recipient type, of multiple different recipient types, intended to receive personalized anomaly notification information, and …presenting first customized anomaly notification information specific to the first recipient type.”
Independent claim 1 recites the system for performing the method of independent claim 10 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two of the MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to “a circuit-based processor; and machine readable medium storing instructions that, when executed, cause the circuit-based processor to…applying a series of machine learning… applying a set of machine learning… applying a set of machine learning… controlling a display system to control a rendering of a graphical user interface” (as recited in claim 10). The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). The elements mentioned fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: “a circuit-based processor; and machine readable medium storing instructions that, when executed, cause the circuit-based processor to…applying a series of machine learning… applying a set of machine learning… applying a set of machine learning… controlling a display system to control a rendering of a graphical user interface” (as recited in claim 10) implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is well-understood, routine, and conventional activity and thus insufficient to add significantly more to the claims. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, Applicant’s Specification (paragraph [0043]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 11-18 “receiving feedback information through the graphical user interface corresponding to actions by the first recipient interfacing with the graphical user interface based on the rendered first customized anomaly notification information; and retraining, based on the feedback information, one or more of the attribution prioritization models of the set of attribution prioritization models and the personalization models of the set of personalization models providing retrained attribution prioritization models and retrained personalization models; textually identifying the threshold variation in the business metric over time of the first category of products; and textually explaining a first relationship between a first subset of the influence attribute attribution factors and threshold variation in the business metric over time of the first category of products, based on the prioritization and being associated with one or more key performance indicators relevant to the first recipient type; wherein the presenting the first textual summary further comprising textually identifying relevant sales channels and geographic regions causing the threshold variation in the business metric over time of the first category of products; identifying, based on applying a set of machine learning forecast models, a deviation between a forecasted trend of the business metric corresponding to the products of the first category of products relative to an intended goal; and wherein the presenting the first textual summary further comprises textually explaining the deviation between the forecasted trend of the business metric of the products of the first category of products relative to the intended goal; generating and presenting a second customized anomaly notification information intended for a different second recipient type, of the multiple different recipient types, wherein the second customized anomaly notification information comprises a different second textual summary relevant to a second recipient type, wherein the second textual summary textually identifies the threshold variation in the business metric over time of the first category of products, and textually explains a second relationship between a second subset of the influence attribute attribution factors and the threshold variation in the business metric over time of the first category of products, based on the prioritization and being associated with a different second set of one or more key performance indicators relevant to the second recipient type; applying a first sub-set of one or more of the causal inference and determination models of the set of the causal inference and determination models to internal contextual retail factors corresponding to actions managed by the retailer and corresponding to one or more products of the first category of products; and further applying a second sub-set of one or more of the causal inference and determination models of the set of the causal inference and determination models to external contextual factors; wherein the prioritizing the influence attribution factors comprises identifying a sub-set of the influence attribution factors that correspond to actions controllable by an expected first recipient, of the first recipient type, intended to receive the first customized anomaly notification information; and prioritizing the sub-set of the influence attribution factors as more relevant than other attribute factors of the influence attribution factors; wherein the applying the set of contextual models comprises applying historic period filtering relative to multiple different historic durations and statistical range based prioritization, and identifying the contextual factors associated with the first anomaly ”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims.
The remaining dependent claims (2-9) recite the system for performing the method of claims 11-18. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are 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. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
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 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.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20180342007 (hereinafter “Brannigan”) et al., in view of U.S. PGPub 20170083929 to (hereinafter “Bates”) et al.
As per claim 1, Brannigan teaches A system to control retail product allocation, comprising: a circuit-based processor; and machine readable medium storing instructions that, when executed, cause the circuit-based processor to:
apply a series of machine learning anomaly detection models to business metric data relative to products being sold through a retailer to identify a first anomaly relative to a threshold variation in a business metric over time of a first category of products; See Brannigan, 0047-0128: “the system 2 may also be configured to provide feedback to the producer, manufacturer, and/or shop or vendor 150 regarding the sales of goods and/or the need to increase or decrease production of items in inventory 100 that are being sold or to be offered for sale. The feedback may be based on the purchasing trends of the various retailers 155 and consumers 300. For instance, if various items are being sold at a more rapid rate than in the past, the system may be configured so as to instruct the seller 150 to purchase more inventory or raw goods, so as to meet the increased consumer demand. Likewise, if an item is selling at a less rapid rate than in the past, the system 2 may be configured so as to instruct the seller 150 to forestall the purchase of more inventory, e.g., craft goods 100 or raw goods 110 (see FIG. 1), until current inventory has begun to move more rapidly. Further, as described herein in greater detail below, the system may be configured to effectuate or otherwise allow the delivery of the raw products /finished goods /purchased goods from the inventory to the retailer 155 and/or consumer 300. Hence, shops and vendors using the systems 2, apparatuses, and methods herein disclosed will be able to quickly and easily offer various goods in various conditions of production and/or manufacture for sale to a larger consumer base, while also being able to manage the inventory they are selling via the web-based portal 200 (and/or in the shop), track inventory sold online and/or within the shop, as well as receive customer orders for products via the web page, manage payment by customers via the web page, and coordinate delivery of the products to the customer 300 who placed the order… the system is an Artificial Intelligence (A/I) module having one or more of a learning or training platform, including a learning engine, and an analytics or inference platform, including an inference engine. In one instance, the learning platform includes a processing engine that is configured for taking known data, running a learning and/or training protocol on the data, and developing one or more organizing rules therefrom. Likewise, the analytics processing platform includes a processing, e.g., inference, engine that is configured for applying the rules developed by or for the learning platform and applying them to newly or previously acquired data to generate one or more outcomes thereby, such as where the outcome may be a known or inferred relationship, a known or predicted result, and/or a probability of one or more outcomes, and the like. In various instances, the inference engine is configured for continuously running analytics on received data on a daily basis and/or with regard to one or more special or promotional events, such as prior to, during, and/or after the event, e.g., a shopping event, and functions with the purpose of improving the efficacy of the event results, e.g., sales results, such as for the event itself and/or for one or more participants thereof, such as by improving the usefulness of producing, consuming, and/or delivery, of goods and services, being sold and purchased through the system.”Note: Brannigan teaches an Al module having one or more learning or training platform when items are selling at a lesser rate where a seller stops purchasing additional inventory.
apply a set of machine learning contextual models to non-sales data and sales data relative to the first anomaly and identifying contextual factors associated with the first anomaly relative to different sales channels and geographic hierarchy of sales; See Brannigan, 0123-0128: “Such relationships may then be weighted and mined to determine correlations between those shopping at a particular outlet, the products they are consuming, how they are rating those products, how often they are consuming those products, the frequency of purchase, as well as the brands purchased, from whom, and the like. This data may then be fed into an artificial intelligence engine of the system to determine and/or predict usage stocking and/or usage patterns. Additional information may also be collected and used to understand, evaluate, and characterize usage patterns, make predictions and suggestions, as well as determine trends on a larger market and regional basis, where this data may include an analysis of various social media, e.g., FACEBOOK®, postings of photos, comments, and/or likes or dislikes, what they post on the internet, and/or on another users or third party's webpage. This analysis allows for a great quantity of data to be collected and analyzed so as to derive one or more conclusions, such as a conclusion as related to producing and/or consuming… the system is an Artificial Intelligence (A/I) module having one or more of a learning or training platform, including a learning engine, and an analytics or inference platform, including an inference engine. In one instance, the learning platform includes a processing engine that is configured for taking known data, running a learning and/or training protocol on the data, and developing one or more organizing rules therefrom. Likewise, the analytics processing platform includes a processing, e.g., inference, engine that is configured for applying the rules developed by or for the learning platform and applying them to newly or previously acquired data to generate one or more outcomes thereby, such as where the outcome may be a known or inferred relationship, a known or predicted result, and/or a probability of one or more outcomes, and the like. In various instances, the inference engine is configured for continuously running analytics on received data on a daily basis and/or with regard to one or more special or promotional events, such as prior to, during, and/or after the event, e.g., a shopping event, and functions with the purpose of improving the efficacy of the event results, e.g., sales results, such as for the event itself and/or for one or more participants thereof, such as by improving the usefulness of producing, consuming, and/or delivery, of goods and services, being sold and purchased through the system.”Note: Brannigan teaches an Al engine that can determine and predict the usage stocking and/or pattern base on the trend on the larger market and regional basis.
apply a set of machine learning causal inference and determination models to sets of relevance data having potential effects on the first category of products as a function of the contextual factors associated with the first anomaly, determining influence attribution factors that are predicted to have been factors in causing the threshold variation in the business metric of products of the first category of products, and applying a set of machine learning attribution prioritization models to define relevancy scores to the influence attribution factors and prioritize the influence attribution factors; See Brannigan, 0047-0083: “the system 2 may also be configured to provide feedback to the producer, manufacturer, and/or shop or vendor 150 regarding the sales of goods and/or the need to increase or decrease production of items in inventory 100 that are being sold or to be offered for sale. The feedback may be based on the purchasing trends of the various retailers 155 and consumers 300. For instance, if various items are being sold at a more rapid rate than in the past, the system may be configured so as to instruct the seller 150 to purchase more inventory or raw goods, so as to meet the increased consumer demand. Likewise, if an item is selling at a less rapid rate than in the past, the system 2 may be configured so as to instruct the seller 150 to forestall the purchase of more inventory, e.g., craft goods 100 or raw goods 110 (see FIG. 1), until current inventory has begun to move more rapidly. Further, as described herein in greater detail below, the system may be configured to effectuate or otherwise allow the delivery of the raw products /finished goods /purchased goods from the inventory to the retailer 155 and/or consumer 300. Hence, shops and vendors using the systems 2, apparatuses, and methods herein disclosed will be able to quickly and easily offer various goods in various conditions of production and/or manufacture for sale to a larger consumer base, while also being able to manage the inventory they are selling via the web-based portal 200 (and/or in the shop), track inventory sold online and/or within the shop, as well as receive customer orders for products via the web page, manage payment by customers via the web page, and coordinate delivery of the products to the customer 300 who placed the order… delivery application 218 may be executed by server 205 and receive fulfillment data, include geographic location of individual orders, as well as identify and group orders together that may delivered within a specified delivery area. The delivery application, and/or artificial intelligence module, then assesses the orders that need to go out in a given window of time, such as hours or portions of a day (e.g. morning, afternoon). The delivery application then groups orders together for delivery by a given delivery vehicle or service, with a route calculated for an efficient and optimal delivery time. In some instances, predetermined and/or optional stops for the route may be inserted into the route. Exemplary stops may include mandated driver breaks, stops for fueling, etc. Once an order grouping is determined, delivery application 218 notifies fulfillment client 156 as to which orders should be grouped together for delivery (once the individual orders are fulfilled).”Note: Brannigan teaches the ability to identify items that are selling at a less rapid rate than in the past which the system may be configure to instruct the seller to forestall the purchase of more inventory. The various items from various retailors are being sold at more rapid rate which the inventory might not have the supply therefore the system is configure to instruct the seller to increase the goods to meet the consumer demand. The various items from various retailors are being sold at more rapid rate which the inventory might not have the supply therefore the system is configure to instruct the seller to increase the goods to meet the consumer demand.
Brannigan may not explicitly teach the following. However, Bates teaches:
apply a set of machine learning personalization models to the prioritized influence attribution factors and contextual factors of the first anomaly as a function of a particular first recipient type, of multiple different recipient types, intended to receive personalized anomaly notification information and controlling a display system to control a graphical user interface presenting first customized anomaly notification information specific to the first recipient type; See Bates, 0016 0025-0060: “The term “intelligent alerting” refers to an automated process that employs machine learning to create individually tailored alerts and associated thresholds for users based on a variety of factors, including, for example, user behavior, consumption patterns, and anomaly detection. The alerts are communicated to the user and context is automatically provided for the alert. Feedback from the user provides reinforced learning to the machine learning models… “Suggested alerts” represent alerts suggested by intelligent alerting that the user may be interested in but has not yet manually created. The suggested alerts are based on machine learning processes that have analyzed consumption patterns, similar users, anomalies, and the like…The alert component 116 is generally configured to suggest and/or communicate alerts to the user. In various embodiments, the suggested alerts are based on information learned by the behavior component 110, the similar user component 112, and the game component 114. For example, the suggested alerts may be based upon the consumption pattern of a user indicating certain metrics are more important than others. Similarly, the suggested alerts are based upon alerts or slight differences in alerts that similar users utilize. In some embodiments, the suggested alerts are based upon results from the on-demand game that indicate one metric is more critical to the user than another. In some embodiments, the suggested alerts are based upon anomalies in data that have been identified utilizing deep learning models. As described herein, these anomalies are based on statistically significant changes as identified by anomaly detection (i.e., the unknown-unknowns) that have been approved or accepted by the user. In some embodiments, if the user is not consuming the alerts, the alerts are turned off. In these cases, the user is periodically asked if the user would like to opt back in to the alerts. This reduces the amount of noise if the user is not interested in a particular alert(s)... the intelligent alerting monitors metrics identified as being more critical/important for real-time significant changes while less important metrics are analyzed less frequently (e.g, daily). Also, significant changes for more critical/important metrics are communicated to the user through more direct communication channels (e.g., mobile/watch push notification, email, SMS text) while significant changes to less important metrics leverage less intrusive communication channels (e.g., AMC notifications, email, etc.).”
Brannigan and Bates are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Brannigan with the aforementioned teachings from Bates with a reasonable expectation of success, by adding steps that allow the software to utilize data detection with the motivation to more efficiently and accurately communicate and analyze data [Bates 0060 ].
As per claim 2, Brannigan and Bates teach all the limitations of claim 1.
In addition, Brannigan teaches:
a training system configured to receive feedback information through the graphical user interface corresponding to actions by a first recipient interfacing with the graphical user interface based on the rendered first customized anomaly notification information, and retraining based on the feedback information one or more of the attribution prioritization models of the set of attribution prioritization models and the personalization models of the set of personalization models providing retrained attribution prioritization models and retrained personalization models;See Brannigan, 0047-0067Note: The systems may configure to provide feedback to producer, manufacturer, and vendor or shop regarding the sales of goods and the needs to increase or decrease the inventory that are being sold or offered for sale. The item the consumer selected is not available for delivery the system will notify the consumer of the unavailable item. The system may determine a time estimate for the delivery of the item base on the inventory availability.
As per claim 3, Brannigan and Bates teach all the limitations of claim 1.
In addition, Brannigan teaches:
wherein the personalization recommendation system in customizing the anomaly notification information is configured to present a first textual summary identifying the threshold variation in the business metric over time of the first category of products, and explaining a first relationship between a first subset of the influence attribute attribution factors and threshold variation in the business metric over time of the first category of products, based on the prioritization and being associated with one or more key performance indicators relevant to the first recipient type;See Brannigan, 0047-0067Note: The artificial intelligence model assesses the order that need to be go out in a given window of time or group a bunch of orders together to be delivery at one time such as morning, afternoon, or evening. Once the order grouping is determined, the delivery application notifies the fulfillment client as to which orders should be group together for delivery.
As per claim 4, Brannigan and Bates teach all the limitations of claim 3.
In addition, Brannigan teaches:
wherein the personalization recommendation system in presenting the first textual summary further textually identifies relevant sales channels and geographic regions causing the threshold variation in the business metric over time of the first category of products;See Brannigan, 0047-0062Note: The systems may configure to provide feedback to producer, manufacturer, and vendor or shop regarding the sales of goods and the needs to increase or decrease the inventory that are being sold or offered for sale which the delivery information may include city, state, or other form of location
As per claim 5, Brannigan and Bates teach all the limitations of claim 4.
In addition, Brannigan teaches:
a forecast system applying a set of machine learning forecast models to identify a deviation between a forecasted trend of the business metric corresponding to the products of the first category of products relative to an intended goal; wherein the personalization recommendation system in presenting the first textual summary further textually explains the deviation between the forecasted trend of the business metric of the products of the first category of products relative to the intended goal;See Brannigan, 0083Note: The artificial intelligence model assesses the order that need to be go out in a given window of time or group a bunch of orders together to be delivery at one time such as morning, afternoon, or evening. The artificial intelligence model assesses the order that need to be go out in a given window of time or group a bunch of orders together to be delivery at one time such as morning, afternoon, or evening. Once the order grouping is determined, the delivery application notifies the fulfillment client as to which orders should be group together for delivery.
As per claim 6, Brannigan and Bates teach all the limitations of claim 3.
In addition, Brannigan teaches:
wherein the personalization recommendation system in customizing the anomaly notification information is configured generate and present a second customized anomaly notification information intended for a different second recipient type, of the multiple different recipient types, wherein the second customized anomaly notification information comprises a different second textual summary relevant to a second recipient type, wherein the second textual summary identifies the threshold variation in the business metric over time of the first category of products, and explaining a second relationship between a second subset of the influence attribution factors and the threshold variation in the business metric over time of the first category of products, based on the prioritization and being associated with a different second set of one or more key performance indicators relevant to the second recipient type;See Brannigan, 0083-0119Note: The artificial intelligence model assesses the order that need to be go out in a given window of time or group a bunch of orderss together to be delivery at one time such as morning, afternoon, or evening. Once the order grouping is determined, the delivery application notifies the fulfillment client as to which orders should be group together for delivery. The system may notify consumer of particular events such as sale being posted, product being limited, product expired, low inventory and other pertinent information.
As per claim 7, Brannigan and Bates teach all the limitations of claim 1.
In addition, Brannigan teaches:
wherein the causal detection system in applying the set of causal inference and determination models to the sets of relevance data comprises applying a first sub-set of one or more of the causal inference and determination models of the set of the causal inference and determination models to internal contextual retail factors corresponding to actions managed by the retailer and corresponding to one or more products of the first category of products, and further applying a second sub-set of one or more of the causal inference and determination models of the set of the causal inference and determination models to external contextual factors that are independent of actions by the retailer and associated with the one or more products of the first category of products;See Brannigan, 0070-0118Note: The system may be configure to assess changes in the size of inventory one or more times on a daily, weekly, or monthly basis and provide alerts and recommendation to shop or vendor on increase or decreasing the rate of production to maintain an optimal inventory level and amount of raw materials. The system may determine the relationships between different users and their preferences and habit in respect to their shopping to correlate the pattern of behavior on purchasing the product.
As per claim 8, Brannigan and Bates teach all the limitations of claim 1.
In addition, Brannigan teaches:
wherein the causal detection system in applying the set of attribution prioritization models is configured to identify a sub-set of the influence attribution factors that correspond to actions controllable by an expected first recipient, of the first recipient type, intended to receive the first customized anomaly notification information, and prioritize the sub-set of the influence attribution factors as more relevant than other attribute factors of the influence attribution factors;See Brannigan, 0047-0067Note: Items that is selling at a less rapid rate than in the past which the system may be configure to instruct the seller to forestall the purchase of more inventory. THhe various items from various retailers are being sold at more rapid rate which the inventory might not have the supply therefore the system is configured to instruct the seller to increase the goods to meet the consumer demand.
As per claim 9, Brannigan and Bates teach all the limitations of claim 1.
In addition, Brannigan teaches:
wherein the contextualization detection system in applying the set of contextual models is configured to apply historic period filtering relative to multiple different historic durations and statistical range based prioritization in identifying the contextual factors associated with the first anomaly;See Brannigan, 0057Note: Items that is selling at a less rapid rate than in the past which the system may be configure to instruct the seller to forestall the purchase of more inventory.
Claims 10-18 is directed to the method for performing the system of claims 1-9 above. Since Brannigan and Bates teach the method, the same art and rationale apply.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625
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