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 12/15/205, Applicant, on 03/25/2026, amended claims; no new claims added or cancelled. Claims 1-18 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 03/25/2026 have been fully considered, but they are not fully persuasive. The 35 USC § 112(f) rejection 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 Claims Recite an Inventive Concept and Provide Significantly More” (Remarks 03/25/2026)
In response, the Examiner respectfully disagrees. 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.
The claimed subject matter is merely claims a method for calculating and analyzing information regarding reatil 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 ideas (organizing human activity). 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). A knowledge graph is a data structure )nodes and edges), not a particular machine, see MPEP 2106.05(b). “Defining and updating multi-level linings” and “embedding additional association lings” are operations on data, executable by any general-purpose computer running graph software. Further, the efficiency assertions are also not commensurate with the claim. The claims recite functional language at the genus level, with no specific algorithm, training, indexing scheme, memory/query path that would actually produce the advertised reduction.
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 purchase requisitions , 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 product 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.
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 that the claims do not teach the elements of claim 1 (see Remarks 03/25/2026).
In response, the Examiner respectfully disagrees. The Applicant’s principle argument relies that the recited art fails to teach a “knowledge graph” with multi-level linking between embedded, weighted, updated entity nodes. However, this argument imports unclaimed structural attributes from the spec and is inconsistent with the BRI, see MPPEP 2111.01(II). Under BRI, the claim recites a “a knowledge graph between entity nodes, wherein the entity nodes comprise product source nodes each associated with one of multiple different product sources providing products to one or more retailers, recipient nodes each associated with one of multiple intended recipient users recipients that are each associated with a respective one of the product sources, product nodes each associated with a different retail product supplied to the one or more retailers, and anomaly alert nodes.” The claim does not recite edge weighting, any particular embedding scheme, or graph edges; those are only in Applicant arguments. Limitations form the spec cannot be read into the claim to distinguish over the prior art. Brannigan teaches what the claim requires under its proper BRI; heterogenous entity nodes, Brannigan 0044; multi level linking in a structured representation, Brannigan 0130; ML community detection identifying additional relationships, Brannigan 0118-0119; and updating the linking based on recipient feedback, see bates 0025-0060; see complete rejection with citation in the 103 rejection below. Applicants downstream arguments each depend on the same unclaimed narrow reading of “knowledge graph,” and thus all fall with it. Brannigan supplies the structured entity relationship learning while Bates supplies the personalized ML driven anomaly alerting that operates on structure. For the reasons stated above, the Examiner does not find the arguments persuasive, and suggests the Applicant amend the claims to recite a more specific reading.
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.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: “defining and updating multi-level linkings within a knowledge graph between entity nodes comprising product source nodes each associated with one of multiple different product sources providing products to one or more retailers, recipient nodes each associated with one of multiple intended recipient users that are each associated with a respective one of the product sources, product nodes each associated with a different retail product supplied to the one or more retailers, and anomaly alert nodes each associated with an alert corresponding to a category of products relative to a business metric; …presenting different customized anomaly notification information specific to respective intended recipients of numerous different intended recipients as a function of the linkings associated with the respective intended recipient; applying …community detection models, and identifying additional relationships between two or more of the entity nodes, based on feedback data from multiple intended recipients of the numerous different intended recipients each relative to one of the presented different customized anomaly notification information; causing updating of the multi-level linkings to embed one or more additional association links between the two or more of the entity nodes based on the identified additional relationships; …to present first customized anomaly notification information specific to a first intended recipient, of the numerous different intended recipients, associated with a first recipient entity node of the two or more entity nodes based on a first additional association link, of the updated additional association links, of the first recipient entity node.” 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 “controlling different display systems to control respective graphical user interfaces… a set of machine learning… and controlling, based on the updated additional association links, a first graphical user interface…; a circuit-based processor; and machine readable medium storing instructions that, when executed, cause the circuit-based processor to execute a coordinated set of operations comprising…; (as recited in claims 1 and 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: “controlling different display systems to control respective graphical user interfaces… a set of machine learning… and controlling, based on the updated additional association links, a first graphical user interface…; a circuit-based processor; and machine readable medium storing instructions that, when executed, cause the circuit-based processor to execute a coordinated set of operations comprising…; (as recited in claims 1 and 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 [0073]) 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 “evaluating, based on the application of the community detection models, touch points by each of the multiple intended recipients in considering the respective customized anomaly notification information, and identifying associations between two or more of the multiple intended recipients as a function of correlations between respective touch points; obtaining, through the first graphical user interface by the first intended recipient, the feedback comprising a tagging to direct to a second intended recipient a portion of the first customized anomaly notification information corresponding to a first alert entity node that is associated with a second recipient entity node; and applying one or more of the set of community detection models based on the tagging in identifying the additional relationships, wherein the updating the linkages comprises: updating the multi-level linkages linkings to increase a level of association of a first recipient-recipient link between a first recipient entity node associated with the first intended recipient and a second recipient entity node associated with the second intended recipient; embedding a first recipient- alert link between the second recipient entity node and the first alert entity node; and increasing a level of association of alert-attribute links between the first alert entity node and a set of attribute nodes previously associated with the first alert entity node; recommending, based on the application of the set of community detection models, embedding a recipient-alert link between the second recipient entity node and a second alert entity node in response to the increasing of the level of association of the first recipient-recipient link between the first recipient entity node and the second entity recipient node and based on a strength of a level of association of the first recipient-recipient link between the first recipient entity node and the second recipient entity node and based on a strength of a level of association of a second recipient-alert link between the first recipient entity node and the second alert entity node; applying a set of machine learning similarity models to pluralities of different recipient- alert links between different sets of recipient entity nodes and alert entity nodes in relation to the feedback comprising interaction by the first intended recipient with the first customized anomaly notification information; and identifying relative similarity measures associated with each of the respective recipient entity node and the respective alert entity node in predicting potential additional linkages to associate with multiple other recipient entity nodes; wherein the applying the set of similarity models focuses factorization and filtering of associations between entity nodes as a function of embedded links between respective pairs of entity nodes; weighting models relative to the similarity measures over time based on the feedback continuing to be received over time from the multiple intended recipient users to repeatedly modify weightings to identified similarity measures in selecting appropriate similarity measures relative to a particular one of the multiple intended recipient users in predicting the potential additional linkages to associate with the particular one of the multiple other recipient entity nodes; adding a second recipient node in response to a new intended recipient being associated to receive personalized anomaly notification information; identifying, based on the application of one or more of the set of community detection models, that the second intended recipient has a threshold relationship with the first intended recipient; and in response to adding the second recipient node and the identification of the threshold relationship with the first intended recipient, the multi-level linkages linkings embedding multiple initial association links corresponding to a set of association links between the first recipient entity node and two or more other recipient entity nodes; and controlling, based on the initial association links, a second graphical user interface to present second customized anomaly notification information specific to the second intended recipient associated with the second recipient entity node as a function of the initial association links”, 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 customized retail product performance information presented to respective individuals, comprising:
a circuit-based processor; and machine readable medium storing instructions that, when executed, cause the circuit-based processor to execute a coordinated set of operations comprising:Brannigan 0041: “Any suitable computing system 205 may be employed in this manner so long as it is capable of accessing one or more databases 210, running one or more algorithms with respect thereto, so as to effectuate one or more actions, such as for the listing and/or purchasing of goods 116. For instance, the computer system 205 may be implemented in a simple processor running software that allows a user, such as a producer 151, manufacturer 152, retailer 155, and/or consumer 300 to list goods for sale 114 and/or to effectuate the sale of such goods 116, and/or may even function to effectuate the delivery of such goods 118, once purchased. For such purposes, the computer system may be an integrated circuit 205, such as a processor, that is configured for fetching instructions and making commands with respect thereto, or may be a dedicated hardware system that is hardwired for doing the same, such as in a wired configuration, such as for accessing a database and/or associated memory 210 so as to store and/or retrieve information therefrom. As explained herein below, in some instances, the system may also include an artificial intelligence module for analyzing one or more aspects of the system, to determine trends with respect thereto, to make predictions based on such trends, and/or to suggest or implement various configurational changes to the system in response.”
and update multi-level linkings within a knowledge graph between entity nodes, wherein the entity nodes comprise product source nodes each associated with one of multiple different product sources providing products to one or more retailers, recipient nodes each associated with one of multiple intended recipient users that are each associated with a respective one of the product sources, product nodes each associated with a different retail product supplied to the one or more retailers, and anomaly alert nodes each associated with an alert corresponding to a category of products relative to a business metric; Brannigan 0012-0131: “The system may also assess geolocation data associated with the order data to determine whether the order is within a defined delivery area and assigns an identifier to the online order for delivery within the delivery area. Logic in the system may then notify the networked client that the online order should be delivered with other online orders also identified for delivery within the defined delivery area. The system may receive a notification from the client that the physical items in the online order were picked up for delivery. The system may also be in communication with a delivery client (such as a smartphone or tablet), and be able to receive a notification that the physical items associated with the online order were delivered…a method of offering goods for sale via an online marketplace. The method includes the step of accessing a database that is embodied on a non-transitory computer readable medium on a computing device (such as server or bank of servers). The database is configured for storing data on physical items for purchase, such as items in a shop's inventory. Item data regarding each of the physical items may be uploaded via a second computing device that communicates with the first computing device via a network. One or more images from the database may be selected and associated with item data for a particular type of item that has been uploaded to the database. Each of the one or more items and at least some of the associated item data may then be displayed in a streaming loop via a web portal, when the web portal is accessed by a client (such as a smartphone, tablet, or laptop)… an artificial intelligence module (A/I) may be included in the system. For instance, the various devices and systems, as well as their methods of use, as disclosed herein may be employed so as to evaluate different elements of commerce, selling, purchasing, and the like. More particularly, the system may be configured for determining patterns in the behaviors of the various people using the system, e.g., producers and shoppers, from which patterns various relationships may be determined, and one or more actions may be taken by the system in view of the identified relationships and/or determined patterns. For example, once a relationship between the various producers and/or consumers acting upon the system is identified, such as with respect to how certain producers interact with certain products they offer for sale, and a pattern with respect to how the consumer is behaving with respect to their relationship to the producers is determined, the system may take one or more actions, e.g., suggestive measures, to account for that relationship and/or activities of the users.”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.”Note: The art teaches multiple computing system communicate with each other for offering goods for sale. The Al module configure to determine the behavior user behavior for buying goods or product from retailers. The system may receive notification from clients that order has been pick up. Various pattern being determine the consumers buying goods from a certain retailers or producer. The various items from various retailers 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.
control different display systems to control respective graphical user interfaces presenting different customized anomaly notification information specific to respective intended recipients of numerous different intended recipients as a function of the linkings associated with the respective intended recipient; 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.”Note: 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.
apply a set of machine learning community detection models to identify additional relationships between two or more of the entity nodes, based on feedback data from multiple intended recipients of the numerous different intended recipients each relative to one of the presented different customized anomaly notification information, and cause the update the multi-level linkages linkings to embed one or more additional association links between the two or more of the entity nodes based on the identified additional relationships; Brannigan 0118-0132: “the system may automatically determine the relationships between different users and their preferences and habits, e.g., with respect to their shopping and/or producing manners, and in turn the system can determine the same for each connection in the users social network to better determine patterns of behavior, such as with respect to a web-crawler, spider, robot, bot, or skimmer of the Artificial Intelligence Module, that is used to gather and/or harvest online information about users, which information may be employed by the system to make predictions, suggestions, and/or weight, and/or adjust potential usages of the system by the user with respect to the products they supply and/or purchase. This information may be gathered based on what physical or virtual, e.g., web, sites various users of the system visit, how they comment and/or interact with those sites and/or other users on those sites, messages they send, texts or images or other data they post, as well as the types of products they purchase and/or relationships they form thereon. This data may be collected by the system and may be fed into the A/I module, e.g., a machine learning platform, and may then be used as data points to form and/or structure a searchable database of the system…the user's engagement with the system may form regular interactions and/or patterns that may be recorded and tracked within the system, from which patterns the machine learning and/or analytics module of the system may be employed to learn each user's particular pattern(s) of behavior, and determine a range of freedom behind those actions and/or predict future courses of action and outcomes. This is useful when by the user's pattern of engagement with the system appears to coincide or conflict with the patterns of usage of other users of the system. More specifically, determining patterns of usage of the system is useful when determining products to be promoted, prices to be charged for those products, sales to be run, and the like, as well as to predict future trends with respect thereto. Particularly, the system may be configured for not only determining the presence of various factors influencing behavior, such as the presence of collective sales and/or bargaining, but as well for determining which factors, e.g., social, environmental, supply, and demand factors, which may be leading to that influencing, and to what degree.”Note: 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. 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 system may form pattern that track within the system to determine the user behavior such as price, products, retailers selling goods and the like.”
…of the numerous different intended recipients, associated with a first recipient entity node of the two or more entity nodes based on a first additional association link, of the updated additional association links, of the first recipient entity node; Brannigan 0083: “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: 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.
Brannigan may not explicitly teach the following. However, Bates teaches:
control, based on the updated additional association links, a first graphical user interface to present first customized anomaly notification information specific to a first intended recipient…;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:
wherein the community detection system in applying the community detection models is configured to evaluate touch points by each of the multiple intended recipients in considering the respective customized anomaly notification information, and identifying associations between two or more of the multiple intended recipients as a function of correlations between respective touch points;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 3, Brannigan and Bates teach all the limitations of claim 1.
In addition, Brannigan teaches:
obtain, through the first graphical user interface by the first intended recipient, feedback comprising a tagging to direct to a second intended recipient a portion of the first customized anomaly notification information corresponding to a first alert entity node that is associated with a second recipient entity node; and apply one or more of the set of community detection models based on the tagging in identifying the additional relationships and cause, in updating the linkages, the linkage mapping system to update the multi-level linkings to increase a level of association of a first recipient-recipient link between a first recipient entity node associated with the first intended recipient and a second recipient entity node associated with the second intended recipient, embed a first recipient-alert link between the second recipient entity node and the first alert entity node, and increase a level of association of alert-attribute links between the first alert entity node and a set of attribute nodes previously associated with the first alert entity node;See Brannigan, 0043-0083Note: 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 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 fulfilment client as to which orders should be group together for delivery. The retailer or producer access to the web portal and upload images of good to be sold and send notification to the consumer may engage his personal or lap top to view the images of goods to be purchase.
As per claim 4, Brannigan and Bates teach all the limitations of claim 3.
In addition, Brannigan teaches:
wherein the community detection system, in applying the set of community detection models, is further configured to recommend embedding a recipient- alert link between the second recipient entity node and a second alert entity node in response to the increasing of the level of association of the first recipient-recipient link between the first recipient entity node and the second entity recipient node and based on a strength of a level of association of the first recipient-recipient link between the first recipient entity node and the second recipient entity node and based on a strength of a level of association of a second recipient-alert link between the first recipient entity node and the second alert entity node;See Brannigan, 0043, 0047-0083Note: 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 retailer or producer access to the web portal and upload images of good to be sold and send notification to the consumer may engage his personal or lap top to view the images of goods to be purchase.
As per claim 5, Brannigan and Bates teach all the limitations of claim 1.
In addition, Brannigan teaches:
wherein the community detection system, in applying the set of machine learning community detection models, further causes updating of the linkages based on a search through the graphical user interface by the first intended recipient as feedback in response to identifications of links between nodes associated with search criteria;See Brannigan, 0118Note: 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 6, Brannigan and Bates teach all the limitations of claim 3.
In addition, Brannigan teaches:
a similarity evaluation system configured to apply a set of machine learning similarity models to pluralities of different recipient-alert links between different sets of recipient entity nodes and alert entity nodes in relation to the feedback comprising interaction by the first intended recipient with the first customized anomaly notification information, and identify relative similarity measures associated with each of the respective recipient entity node and the respective alert entity node in predicting potential additional linkages to associate with multiple other recipient entity nodes;See Brannigan, 0120-0123Note: The Al module is able to learn the consumer posts about the products, producers, and retailers to the users purchasing the goods. The Al intelligence engine determine and predict the usage stocking and/or pattern base on the trend on the larger market and regional basis.
As per claim 7, Brannigan and Bates teach all the limitations of claim 6.
In addition, Brannigan teaches:
wherein the similarity evaluation system in applying the set of similarity models focuses factorization and filtering of associations between entity nodes as a function of embedded links between respective pairs of entity nodes;See Brannigan, 0123Note: The Al intelligence engine determine and predict the usage stocking and/or pattern base on the trend on the larger market and regional basis.
As per claim 8, Brannigan and Bates teach all the limitations of claim 1.
In addition, Brannigan teaches:
a similarity weighting system configured to apply a set of machine learning weighting models relative to the similarity measures over time based on the feedback continuing to be received over time from the multiple intended recipient users to repeatedly modify weightings to identified similarity measures in selecting appropriate similarity measures relative to a particular one of the multiple intended recipient users in predicting the potential additional linkages to associate with the particular one of the multiple other recipient entity nodes;See Brannigan, 0083Note: Once the order grouping is determined, the delivery application notifies the fulfillment client as to which orders should be group together to get for delivery. 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.
As per claim 9, Brannigan and Bates teach all the limitations of claim 1.
In addition, Brannigan teaches:
the linkage mapping system is configured to add a second recipient node in response to a new intended recipient being associated to receive personalized anomaly notification information; and the community detection system, in applying one or more of the set of community detection models, is configured to identify that the second intended recipient has a threshold relationship with the first intended recipient, and cause the linkage mapping system, in response to adding the second recipient node and the identification of the threshold relationship with the first intended recipient, to update the multi-level linkings to embed multiple initial association links corresponding to a set of association links between the first recipient entity node and two or more other recipient entity nodes; and wherein the personalization recommendation system controls, based on the initial association links, a second graphical user interface to present second customized anomaly notification information specific to the second intended recipient associated with the second recipient entity node as a function of the initial association links;See Brannigan, 0047, 0083-0128Note: Once the order grouping is determined, the delivery application notifies the fulfillment client as to which orders should be group together for delivery. The Al module having one or more learning or training platform where items is selling at a lesser rate where seller stop purchasing more inventory. 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.
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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:VEGI; Rajesh. DETERMINING MACHINE LEARNING MODEL ANOMALIES AND IMPACT ON BUSINESS OUTPUT DATA, .U.S. PGPub 20240330824 Machine learning models are becoming ubiquitous. A large number of such machine learning models are being deployed into production yearly. Many models can process large volumes of data to recognize and correlate data patterns and trends associated with a production system. Some machine learning models may utilize training data for learning, and as training progresses, the machine learning model becomes increasingly accurate. 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.
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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