DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
This office action is in response to the most recent filings filed by applicants on 03/22/24.
Claims 1, 8 and 15 are amended
No claims are cancelled
No claims are added
Claims 1-20 are pending
Note:
Regarding the claim limitations: “an ingestion layer configured to continuously aggregate data from disparate data sources including a plurality of systems selected from: vendor platforms, CRM systems, ERP systems, and third-party databases” It should be noted that the specification shows in [0146]: Data engine layer which includes data ingestion. However, the specification does not explicitly show an “ingestion layer” as is recited in the claim. The specification shows an integration layer in [0133]. It is unclear which of the two paragraphs the above claim limitation refers to. For the purposes of this application, the above limitation is being reasonably understood as what is discussed in spec. [0146].
In the amended independent claims 1, 8 and 15, the claim limitations discussed below are broad and the specification does not provide enough detailed support to clarify to one of ordinary skill in the art what certain terms in the claims mean. Since applicants haven’t pointed to where in the specification there is support for the amended claims, the paragraphs used by the examiner below seem to be the best match for support. If applicant disagrees and thinks examiner should be looking elsewhere in the specification to fully understand the scope of the claim, support for the amended claims is requested.
For instance, the claim limitation “wherein each purposive datastore of the plurality of purposive datastores is a repository for canonized and/or standardized data dynamically optimized for efficient data retrieval based on one or more specific use cases and requirements;” Here, it is unclear what dynamically optimized means. The specification does not explicitly show “dynamically optimized”. In [0153] of the specification, there is explanation of the data formatting for storage. However, there is no details on what “dynamically optimized” means or how this step is carried out. In light of the specification, “optimized for efficient data retrieval” is broad. This limitation is further part of “a data layer comprising…” limitation. As such, the limitation is no more than storing data on a computer.
Similarly, the claim limitation “wherein the AaS Conversion Module is configured to convert traditional technology products into subscription-based services by integrating data on product specifications, subscription usage patterns, and market trends from the RTDM, analyzing product-service compatibility, and utilizing information from the AAML Module for dynamic pricing” is also broad. Here in light of the specification [0062], the act of converting traditional technology products into subscription-based services is like making a decision to offer. The specification does not offer further details on how the above steps are carried out.
In light of these notes, the amended claims, do not overcome previously presented rejections under 101 and 103. As is discussed below. This note is intended as a conversation starter to help applicants understand the examiner’s perspective. Applicants are welcome to call the examiner to discuss this further.
Claim Rejections - 35 USC § 112F not invoked
In claims 1, 15, 19 and 20, claim limitations recite “configured to”, but the claim and the written description discloses the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function and as such does not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-14 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 15-20 is/are directed to a system which is a statutory category.
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
With respect to the Step 2A, Prong One, the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Claim 1 is directed to an abstract idea, as evidenced by claim limitations “receiving user inputs specifying preferences for technology product conversion; accessing to retrieve data relevant to the user's preferences and market conditions; harmonize the aggregated data into canonical data using a canonical format, the canonical data comprising information selected from one or more of: product specifications, subscription usage patterns, user interaction data, customer profiles, transaction history, pricing, inventory levels, and market trends, harmonized for uniformity across the disparate data sources; analyze the canonical data to forecast demand, identify compatibility with subscription service models, generate predictive recommendations for Anything-as-a-Service (AaS) options, and to refine the AaS options using a feedback engine based on user interactions and fulfillment outcomes; receive the predictive outputs from the AAML Module, apply compatibility and pricing analysis to determine optimal subscription-based conversion configurations, and validate the subscription- based conversion configurations based on rules and quality thresholds derived from the canonical data, to convert traditional technology products into subscription-based services by integrating data on product specifications, subscription usage patterns, and market trends analyzing product-service compatibility, and utilizing information for dynamic pricing;”
Independent Claim 8 is directed to an abstract idea, as evidenced by claim limitations “initiating a subscription request; retrieving user preferences AaS conversion; querying to fetch real-time data relevant to (Anything-as-a-Service) AaS conversion, harmonize the aggregated data into canonical data using a canonical format, the canonical data comprising information selected from one or more of: product specifications, subscription usage patterns, user interaction data, customer profiles, transaction history, pricing, inventory levels, and market trends, harmonized for uniformity across the disparate data sources; analyze the canonical data to forecast demand, identify compatibility with subscription service models, generate predictive recommendations for a subscription package: receive the predictive outputs from the AAML Module, apply compatibility and pricing analysis to determine an optimal subscription-based conversion configuration, and validate the subscription-based conversion configuration based on rules and quality thresholds derived from the canonical data; presenting the subscription package to the user; logging data associated with the AaS conversion for system refinement; initiating a feedback loop within the system for continual improvement of the AaS conversion; to convert traditional technology products into subscription-based services by integrating data on product specifications, subscription usage patterns, and market trends analyzing product-service compatibility, and utilizing information for dynamic pricing”
Independent Claim 15 is directed to an abstract idea, as evidenced by claim limitations “aggregate and disseminate data including user preferences, market trends, and service information, harmonize the aggregated data into canonical data using a canonical format, the canonical data comprising information selected from one or more of: product specifications, subscription usage patterns, user interaction data, customer profiles, transaction history, pricing, inventory levels, and market trends, harmonized for uniformity across the disparate data sources: analyze the canonical data to forecast demand, identify compatibility with subscription service models, generate predictive recommendations for a subscription package, receive the predictive outputs from the AAML Module, apply compatibility and pricing analysis to determine an optimal subscription-based conversion configuration, and validate the subscription-based conversion configuration based on rules and quality thresholds derived from the canonical data, to convert traditional technology products into subscription-based services by integrating data on product specifications, subscription usage patterns, and market trends analyzing product-service compatibility, and utilizing information for dynamic pricing”
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to “managing interaction points between a population of users” for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —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).
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: Claim 1: “A computerized method for executing an (Anything-as-a-Service) AaS model conversion, comprising: a Real-Time Data Mesh (RTDM); an ingestion layer configured to continuously aggregate data from disparate data sources including a plurality of systems selected from: vendor platforms, CRM systems, ERP systems, and third-party databases; and a data layer comprising a data mesh and a plurality of purposive datastores, the data layer configured to; processing the canonical data using an Advanced Analytics and Machine Learning (AAML) Module configured to; generating the AaS options through an AaS Conversion Module configured to; displaying the AaS options to the user via a Single Pane of Glass User Interface (SPoG UI); facilitating the completion of the AaS conversion process and transferring the order to a vendor system; executing the AaS conversion order by integrating data from the SPoG UI, RTDM, and vendor systems, wherein the method is executed by a computer system with a unified platform that integrates data from multiple sources for AaS conversion; wherein each purposive datastore of the plurality of purposive datastores is a repository for canonized and/or standardized data dynamically optimized for efficient data retrieval based on one or more specific use cases and requirements; wherein the AaS Conversion Module is configured to, from the RTDM, from the AAML Module.” Claim 8: “A computerized method for optimizing AaS conversion decisions, comprising: via the SPoG UI, a Real-Time Data Mesh (RTDM), the RTDM comprising: an ingestion layer configured to continuously aggregate data from disparate data sources including a plurality of systems selected from: vendor platforms, CRM systems, ERP systems, and third-party databases; and a data layer comprising a data mesh and a plurality of purposive datastores, the data layer configured to, processing the canonical data using an Advanced Analytics and Machine Learning (AAML) Module configured to, generating the subscription package through an AaS Conversion Module configured to, via the SPoG UI; applying predictive analytics by the AAML Module to determine optimal subscription models; configuring the subscription package based on the subscription-based conversion configuration and the user preferences; validating the subscription configuration using the AAML Module; initiating a feedback loop within the system for continual improvement of the AaS conversion; wherein each purposive datastore of the plurality of purposive datastores is a repository for canonized and/or standardized data dynamically optimized for efficient data retrieval based on one or more specific use cases and requirements; wherein the AaS Conversion Module is configured to, from the RTDM, from the AAML Module.” Claim 15: “A system for automating AaS conversion processes, comprising: a Real-Time Data Mesh (RTDM) configured to, the RTDM comprising: an ingestion layer configured to continuously aggregate data from disparate data sources including a plurality of systems selected from: vendor platforms, CRM systems, ERP systems, and third-party databases; and a data layer comprising a data mesh and a plurality of purposive datastores, the data layer configured to, an Advanced Analytics and Machine Learning (AAML) Module configured to, wherein the AAML Module comprises an (Anything-as-a-Service) AaS Conversion Module, the AaS Conversion Module configured to, a Single Pane of Glass User Interface enabling user interactions and displaying one or more subscription packages; wherein each purposive datastore of the plurality of purposive datastores is a repository for canonized and/or standardized data dynamically optimized for efficient data retrieval based on one or more specific use cases and requirements; wherein the AaS Conversion Module is configured to, from the RTDM, from the AAML Module” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f);
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 8 and 15 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
The additional elements of a “machine learning model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer.
Similarly dependent claims 2-7, 9-14 and 16-20 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recite “further comprising validating the AaS conversion using rules and algorithms within the AAML Module to ensure accuracy and relevance of the conversion recommendations” and dependent claims 4 recite “wherein the RTDM is continuously updated with real-time inventory, user behavior data, and market trends to inform the AaS conversion process”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 9 recites “machine learning algorithms” in the claim limitations “further comprising utilizing machine learning algorithms in the feedback loop to analyze user feedback and system performance for continual optimization of the AaS conversion process”. In this claim, “machine learning algorithms” is an additional element, but it is still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-7, 9-14 and 16-20 are also directed to the abstract idea identified above.
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of Claim 1: “A computerized method for executing an (Anything-as-a-Service) AaS model conversion, comprising: a Real-Time Data Mesh (RTDM); an ingestion layer configured to continuously aggregate data from disparate data sources including a plurality of systems selected from: vendor platforms, CRM systems, ERP systems, and third-party databases; and a data layer comprising a data mesh and a plurality of purposive datastores, the data layer configured to; processing the canonical data using an Advanced Analytics and Machine Learning (AAML) Module configured to; generating the AaS options through an AaS Conversion Module configured to; displaying the AaS options to the user via a Single Pane of Glass User Interface (SPoG UI); facilitating the completion of the AaS conversion process and transferring the order to a vendor system; executing the AaS conversion order by integrating data from the SPoG UI, RTDM, and vendor systems, wherein the method is executed by a computer system with a unified platform that integrates data from multiple sources for AaS conversion; wherein each purposive datastore of the plurality of purposive datastores is a repository for canonized and/or standardized data dynamically optimized for efficient data retrieval based on one or more specific use cases and requirements; wherein the AaS Conversion Module is configured to, from the RTDM, from the AAML Module.” Claim 8: “A computerized method for optimizing AaS conversion decisions, comprising: via the SPoG UI, a Real-Time Data Mesh (RTDM), the RTDM comprising: an ingestion layer configured to continuously aggregate data from disparate data sources including a plurality of systems selected from: vendor platforms, CRM systems, ERP systems, and third-party databases; and a data layer comprising a data mesh and a plurality of purposive datastores, the data layer configured to, processing the canonical data using an Advanced Analytics and Machine Learning (AAML) Module configured to, generating the subscription package through an AaS Conversion Module configured to, via the SPoG UI; applying predictive analytics by the AAML Module to determine optimal subscription models; configuring the subscription package based on the subscription-based conversion configuration and the user preferences; validating the subscription configuration using the AAML Module; initiating a feedback loop within the system for continual improvement of the AaS conversion; wherein each purposive datastore of the plurality of purposive datastores is a repository for canonized and/or standardized data dynamically optimized for efficient data retrieval based on one or more specific use cases and requirements; wherein the AaS Conversion Module is configured to, from the RTDM, from the AAML Module.” Claim 15: “A system for automating AaS conversion processes, comprising: a Real-Time Data Mesh (RTDM) configured to, the RTDM comprising: an ingestion layer configured to continuously aggregate data from disparate data sources including a plurality of systems selected from: vendor platforms, CRM systems, ERP systems, and third-party databases; and a data layer comprising a data mesh and a plurality of purposive datastores, the data layer configured to, an Advanced Analytics and Machine Learning (AAML) Module configured to, wherein the AAML Module comprises an (Anything-as-a-Service) AaS Conversion Module, the AaS Conversion Module configured to, a Single Pane of Glass User Interface enabling user interactions and displaying one or more subscription packages; wherein each purposive datastore of the plurality of purposive datastores is a repository for canonized and/or standardized data dynamically optimized for efficient data retrieval based on one or more specific use cases and requirements; wherein the AaS Conversion Module is configured to, from the RTDM, from the AAML Module” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0028]-[0032], [0057]-[0065]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Applicants originally submitted specification describes the computer components above at least in [0028]-[0032], [0057]-[0065]. In light of the specification, it should be noted that the computer components identified above are well-understood, routine, conventional activities previously known to the industry (see 2106.05(d)). Here, the computer components discussed above are similar to the court case…. site one of the court cases from 2106.05(d), like “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) (see MPEP 2106.05(d) II).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claims 1 and 8 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-7, 9-14 and 16-20 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 8 and 15. As a result, Examiner asserts that dependent claims, such as dependent claims 2-7, 9-14 and 16-20 are also directed to the abstract idea identified above.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Makhija et al. (US 2020/0279200), and further in view of Kadayam (US 11,163,846).
As per claims 1, 8 and 15: Regarding the claim limitations below, Reference Makhija shows:
A computerized method for executing an AaS model conversion, comprising:
Regarding the claim limitations below, Reference Makhija shows:
receiving user inputs specifying preferences for technology product conversion (Makhija shows the above limitation at least in: paragraph 46, 53-54, 61-62, “the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake “… “system layer architecture diagrams with data lake/platform (100B, 100c) of AI based self-driven ERP and SCM system is shown in accordance with an embodiment of the present invention. The system 100a includes a plurality of distinct data source layer 127 to capture all customer, factory, supplier, machine and third-party sources of data (both structured and unstructured), the data lake layer 108 storing all data received from the distinct data source layer 127, an application function layer 128 configured to re-calibrate functions based on data models and scripts generated by a bot. The data models are auto-generated based on change in attribute of the received data to determine the impact of the change on the functions of the one or more applications.” …” a Query language tool (QL) 130, data governance & standardization/protocol layer 131” …” collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc.”);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
accessing a Real-Time Data Mesh (RTDM) to retrieve data relevant to the user's preferences and market conditions, the RTDM comprising
Reference Makhija shows in paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Reference Makhija does not explicitly show “user's preferences and market conditions”.
Reference Kadayam shows the above limitations at least in (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60, “The system is programmed to further store at least some of the collected data in a database. The data can be available at various granularities. For example, for suppliers or buyers, the data can be related to industries, organizations, departments, or individuals; for products, the data can be related to industries, categories, makes, or models. The data can be collected directly from supplier accounts or buyer accounts or from external data sources. The data can be collected in response to processing user queries, as further discussed below, or during ordinary user online activities. The data can be collected from explicit user input through graphical user interfaces, such as a button that when pushed indicates a vote for a particular product as being a good match for another product or a comment box configured to accept supplier reviews, or from implicit user behavior that indicates an affinity for parties or items, such as putting a particular item in a shopping cart or paying invoices to a particular supplier within a specific period of time. (89) In some embodiments, a collective product wisdom dataset may be updated continuously in real-time to reflect the up-to-the-moment selection/purchase activity. The Adaptive Navigation user experience may be built on top of this dynamically changing dataset about the continuously evolving nature of product knowledge and product preferences in the organization. In some embodiments, this may be used to provide a user experience of browsing through a marketplace that is very dynamic and adapts in real-time. Compared to the conventional approach of using all marketplace product data to build a static, and generic product browsing experience, an Adaptive Navigation user experience may be naturally tailored to the company's own product preference and product purchasing behaviors… an Organization Preferences Cognitive Advisor may also have a learning engine inside. This learning engine learns user behavior from actions taken by users to whom recommendations from this Cognitive Advisor have been delivered. These actions may include, for example, that a user clicks on an Organization Preference recommendation, a user selects an Organization Preference recommendation for adding to cart, or a user selects an Organization Preference recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Organization Preferences recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. The Organization Preferences Cognitive Advisor taps into the Collective Intelligence of the organization on product selections and purchases, to provide high quality, reliable recommendations for the user doing the queries. (211) The Best Bets learning engine learns user behavior from actions taken by users to whom the Best Bets recommendations have been delivered. These actions may include, for example, that a user clicks on a Best Bet recommendation, a user selects a Best Bet recommendation for adding to cart, or a user selects a Best Bet recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Best Bets recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization… 215) The learning engine underneath the Bundles Cognitive Advisor 2918 also learns user behavior from actions taken by users to whom the Bundles recommendations have been delivered. These actions may include, for example, that a user clicks on a Bundle recommendation, a user selects a Bundle recommendation for adding to cart, or a user selects a Bundle recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Bundles recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. In addition, the highly active bundles for a given query in a given category in a given region, can be additional data for the Global Item Master, potentially driving recommendations for new Bundle creation or Bundle enhancement for this organization or other organizations overall (244) Often what occurs is that users' shopping patterns don't match with the strategy and contracts set up by Procurement Buyers in different categories; i.e. the user shopping behavior can be said to be non-compliant, and this does not help to tap into procurement rules and expectations, and in turn, the savings are not actualized. A few specific situations would need to be addressed in this regard. For example, procurement buyers would have to provide actual contract information to the system so that the information contained in it can be used in real-time by the system. Systems implemented based on this disclosure may provide the means for the procurement buyers to do so. As another example, Suppliers would need to be carefully organized into categories, and tagged appropriately for their specific attributes (e.g. minority supplier, woman-owned supplier, veteran supplier etc.), so that the guided buying capability of an e-procurement system can operate as expected, suitably rank ordering product selections in the universal search experience. A system implemented based on this disclosure may provide the tools for this to be setup correctly.”
Reference Makhija and Reference Kadayam are analogous prior art to the claimed invention because the references generally relate to field of workflow processing (Makhija: [0015], [0065]. Kadayam: col. 15, lines 28-38, col. 20, lines 60-67, col. 27, lines 53-57, col. 40-67). Further, said references are part of the same classification, i.e., G06Q and G06F. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Kadayam, particularly the ability to customize data insights using additional perspectives like user preference and market conditions information (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60), in the disclosure of Reference Makhija, particularly in the ability to collect real time data (paragraph 62-64, 92, 98, 100-101), in order to provide for a system that the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier as taught by Reference Kadayam (see at least in col. 3, lines 54-65: the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier), so that the process of managing workflow processing can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow processing field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Makhija in view of Reference Kadayam, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
an ingestion layer configured to continuously aggregate data from disparate data sources including a plurality of systems selected from: vendor platforms, CRM systems, ERP systems, and third-party databases (Makhija shows: paragraph 62-64, 92, 98, 10-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Makhija also shows: paragraph 56-60, “The simulation UI 130a enables user to draft statements/query as per underlined model provided through intelligent sensing. The Translator 130 uses NLP and domain specific nomenclature repository, to tokenize query string received from user. Tokenizer takes a sequence of characters and output a sequence of tokens. It will analyze character by character, using multiple levels of lookahead in order to identity what token is currently being examine. The Code Generator 130c extracts Keywords and tokens that are used to generate underlying Machine Learning query and big data query. The Mapper is responsible to generate code and the model 130d utilized domain attributes, Synonyms and tokens.”…” the tool includes an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option/task/action in real time.”…” the tool is configured to attach the recommended task/action to a desired workflow or User interface element or set of rules or validations”.
[0035]: a self-driven system 100 for operating one or more applications including supply chain management (SCM) and enterprise resource planning (ERP) applications is provided in accordance with an embodiment of the present invention. The system 100 includes at least one computing device/entity machine 101 for initiating at least one function to be performed on the one or more applications over a network. The system 100 further includes a server 106 configured to receive input from the entity machine 101. The system 100 includes a support architecture 107 for performing the functions on the one or more applications depending upon the type of input received at the server 106. The system 100 includes a data lake 108 for storing plurality of data from distinct sources, where the data includes, text data, voice data, image data, functional data, data models, scripts etc. to be processed based on Artificial intelligence and machine learning. The system 100 connecting various elements through a network 109. The network 109 enables formation of sub networks depending on the requirement of the function to be performed on the application. [0041]: the server 106 may include electronic circuitry 110 for enabling execution of various steps by a processor of the server 106. The electronic circuity 110 has various elements including but not limited to a plurality of arithmetic logic units (ALU) 111 and floating-point Units (FPU) 112. The ALU 111 enables processing of binary integers to assist in formation of a tables/matrix of variables where a script created by data models is applied to data sets impacting multiple functions like demand planning, supply planning, forecasting, budgeting etc. in applications like ERP or supply chain management (SCM). In an example embodiment, the server electronic circuitry 110 as shown in FIG. 1, may additionally include other processors, memory, storage devices, high-speed interfaces connected through buses for connecting to memory and high-speed expansion ports, and a low speed interface connecting to low speed bus and storage device. Each of the components of the electronic circuitry 110 are interconnected using various busses and may be mounted on a common motherboard or in other manners as appropriate. The processor 114 can process instructions for execution within the server 106, including instructions stored in the elements of the data lake 108 like memory or on the storage devices to display graphical information for a GUI on an external input/output device, such as display coupled to a high-speed interface. In other implementations, multiple processors and/or multiple busses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple servers may be connected, with each server providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system.);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
a data layer comprising a data mesh and a plurality of purposive datastores, the data layer configured to harmonize the aggregated data into canonical data using a canonical format, the canonical data comprising information selected from one or more of: product specifications, subscription usage patterns, user interaction data, customer profiles, transaction history, pricing, inventory levels, and market trends, harmonized for uniformity across the disparate data sources, wherein each purposive datastore of the plurality of purposive datastores is a repository for canonized and/or standardized data dynamically optimized for efficient data retrieval based on one or more specific use cases and requirements (Makhija shows: paragraph 62-64, 92, 98, 10-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Makhija also shows: paragraph 56-60, “The simulation UI 130a enables user to draft statements/query as per underlined model provided through intelligent sensing. The Translator 130 uses NLP and domain specific nomenclature repository, to tokenize query string received from user. Tokenizer takes a sequence of characters and output a sequence of tokens. It will analyze character by character, using multiple levels of lookahead in order to identity what token is currently being examine. The Code Generator 130c extracts Keywords and tokens that are used to generate underlying Machine Learning query and big data query. The Mapper is responsible to generate code and the model 130d utilized domain attributes, Synonyms and tokens.” …” the tool includes an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option/task/action in real time.” …” the tool is configured to attach the recommended task/action to a desired workflow or User interface element or set of rules or validations”.
Makhija further shows: “wherein each purposive datastore of the plurality of purposive datastores is a repository for canonized and/or standardized data dynamically optimized for efficient data retrieval based on one or more specific use cases and requirements” in [0046] In an embodiment, the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake. Also, an audit of the received data and cleansed data is stored in the data lake. Also see [0054]: standardization/protocol layer. [0088]: the at least one server includes a front end web server communicatively coupled to at least one SQL server wherein the front end web server is configured for reprocessing the functions of the one or more applications based on the plurality of data models and script by receiving the recalibrated function processed by the SQL server and applying the AI based dynamic processing logic to the data models and functions using the bot.
[0035]: a self-driven system 100 for operating one or more applications including supply chain management (SCM) and enterprise resource planning (ERP) applications is provided in accordance with an embodiment of the present invention. The system 100 includes at least one computing device/entity machine 101 for initiating at least one function to be performed on the one or more applications over a network. The system 100 further includes a server 106 configured to receive input from the entity machine 101. The system 100 includes a support architecture 107 for performing the functions on the one or more applications depending upon the type of input received at the server 106. The system 100 includes a data lake 108 for storing plurality of data from distinct sources, where the data includes, text data, voice data, image data, functional data, data models, scripts etc. to be processed based on Artificial intelligence and machine learning. The system 100 connecting various elements through a network 109. The network 109 enables formation of sub networks depending on the requirement of the function to be performed on the application. [0041]: the server 106 may include electronic circuitry 110 for enabling execution of various steps by a processor of the server 106. The electronic circuity 110 has various elements including but not limited to a plurality of arithmetic logic units (ALU) 111 and floating-point Units (FPU) 112. The ALU 111 enables processing of binary integers to assist in formation of a tables/matrix of variables where a script created by data models is applied to data sets impacting multiple functions like demand planning, supply planning, forecasting, budgeting etc. in applications like ERP or supply chain management (SCM). In an example embodiment, the server electronic circuitry 110 as shown in FIG. 1, may additionally include other processors, memory, storage devices, high-speed interfaces connected through buses for connecting to memory and high-speed expansion ports, and a low speed interface connecting to low speed bus and storage device. Each of the components of the electronic circuitry 110 are interconnected using various busses and may be mounted on a common motherboard or in other manners as appropriate. The processor 114 can process instructions for execution within the server 106, including instructions stored in the elements of the data lake 108 like memory or on the storage devices to display graphical information for a GUI on an external input/output device, such as display coupled to a high-speed interface. In other implementations, multiple processors and/or multiple busses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple servers may be connected, with each server providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system. [0103] In an exemplary embodiment the present invention includes an intelligence platform for supporting the various functions carried out by AI based self-driven systems. The platform collects data from different sources such as customer data, supplier onboarding data, data from social media into a data repository and enables more accurate forecasting, budgeting, Commodity management including pricing, variance, supplier Risk and performance management and other benefits. More particularly, the intelligence platform enables the prediction algorithm to identify and recommend action/tasks to a user.);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
processing the canonical data using an Advanced Analytics and Machine Learning (AAML) Module configured to analyze the canonical data to forecast demand, identify compatibility with subscription service models, generate predictive recommendations for Anything-as-a-Service (AaS) options, and to refine the AaS options using a feedback engine based on user interactions and fulfillment outcomes (Makhija shows: paragraph 62-64, 92, 98, 10-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Makhija also shows: paragraph 56-60, “The simulation UI 130a enables user to draft statements/query as per underlined model provided through intelligent sensing. The Translator 130 uses NLP and domain specific nomenclature repository, to tokenize query string received from user. Tokenizer takes a sequence of characters and output a sequence of tokens. It will analyze character by character, using multiple levels of lookahead in order to identity what token is currently being examine. The Code Generator 130c extracts Keywords and tokens that are used to generate underlying Machine Learning query and big data query. The Mapper is responsible to generate code and the model 130d utilized domain attributes, Synonyms and tokens.”…” the tool includes an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option/task/action in real time.”…” the tool is configured to attach the recommended task/action to a desired workflow or User interface element or set of rules or validations”.
[0035]: a self-driven system 100 for operating one or more applications including supply chain management (SCM) and enterprise resource planning (ERP) applications is provided in accordance with an embodiment of the present invention. The system 100 includes at least one computing device/entity machine 101 for initiating at least one function to be performed on the one or more applications over a network. The system 100 further includes a server 106 configured to receive input from the entity machine 101. The system 100 includes a support architecture 107 for performing the functions on the one or more applications depending upon the type of input received at the server 106. The system 100 includes a data lake 108 for storing plurality of data from distinct sources, where the data includes, text data, voice data, image data, functional data, data models, scripts etc. to be processed based on Artificial intelligence and machine learning. The system 100 connecting various elements through a network 109. The network 109 enables formation of sub networks depending on the requirement of the function to be performed on the application. [0041]: the server 106 may include electronic circuitry 110 for enabling execution of various steps by a processor of the server 106. The electronic circuity 110 has various elements including but not limited to a plurality of arithmetic logic units (ALU) 111 and floating-point Units (FPU) 112. The ALU 111 enables processing of binary integers to assist in formation of a tables/matrix of variables where a script created by data models is applied to data sets impacting multiple functions like demand planning, supply planning, forecasting, budgeting etc. in applications like ERP or supply chain management (SCM). In an example embodiment, the server electronic circuitry 110 as shown in FIG. 1, may additionally include other processors, memory, storage devices, high-speed interfaces connected through buses for connecting to memory and high-speed expansion ports, and a low speed interface connecting to low speed bus and storage device. Each of the components of the electronic circuitry 110 are interconnected using various busses and may be mounted on a common motherboard or in other manners as appropriate. The processor 114 can process instructions for execution within the server 106, including instructions stored in the elements of the data lake 108 like memory or on the storage devices to display graphical information for a GUI on an external input/output device, such as display coupled to a high-speed interface. In other implementations, multiple processors and/or multiple busses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple servers may be connected, with each server providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system.
[0056]: Tokenizer takes a sequence of characters and output a sequence of tokens. It will analyze character by character, using multiple levels of lookahead in order to identity what token is currently being examine. The Code Generator 130c extracts Keywords and tokens that are used to generate underlying Machine Learning query and big data query. The Mapper is responsible to generate code and the model 130d utilized domain attributes, Synonyms and tokens. [0061]: Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model. The graph store creates a hierarchical tree of relations based on user actions. Further it enables QL tool to search results efficiently. [0090]: The interaction and data exchange between the service provide and subscriber is through the API gateway 133, event management block 134 and routers 137. [0103]: the present invention includes an intelligence platform for supporting the various functions carried out by AI based self-driven systems. The platform collects data from different sources such as customer data, supplier onboarding data, data from social media into a data repository and enables more accurate forecasting, budgeting, Commodity management including pricing, variance, supplier Risk and performance management and other benefits. More particularly, the intelligence platform enables the prediction algorithm to identify and recommend action/tasks to a user.);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
generating the AaS options through an AaS Conversion Module configured to receive the predictive outputs from the AAML Module, apply compatibility and pricing analysis to determine optimal subscription-based conversion configurations, and validate the subscription- based conversion configurations based on rules and quality thresholds derived from the canonical data, wherein the AaS Conversion Module is configured to convert traditional technology products into subscription-based services by integrating data on product specifications, subscription usage patterns, and market trends from the RTDM, analyzing product-service compatibility, and utilizing information from the AAML Module for dynamic pricing (Makhija: paragraph 61-63, 70, “Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model.”…” It collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc. The Curation including selection and organization of data takes place through capturing metadata and lineage and making it available in a data catalog.”…” The data flows in the data lake in real-time processing through event stream layer. Domain Model exposed through the query language (QL) tool 130 enables user to self-serve their data and analytical requirements. Models developed by users are utilized to improve the insights for future purpose.”…”The plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, supplier collaboration platform, invoices, purchase orders (PO), finance modules, inventory management module, contracts and RFx module, supplier module, item master, bill of materials, vendor master, warehouse management module, logistics management module, social media, weather, real time commodity and stock market prices, geo-political news etc. It shall be apparent to a person skilled in the art that the data source may include other source within the scope of the present invention”.
Makhija also shows: “wherein the AaS Conversion Module is configured to convert traditional technology products into subscription-based services by integrating data on product specifications, subscription usage patterns, and market trends from the RTDM, analyzing product-service compatibility, and utilizing information from the AAML Module for dynamic pricing” [0047]: The data lake 108 includes a relational database 122a for storing related data sets received from distinct sources, a non-relational database 122b for storing non-related raw data sets, a functional database 124 for storing a library of functions enabling creation of a plurality of data models for execution of tasks in one or more applications including ERP and SCM, a plurality of registers 125 for temporarily storing data from various sources for determination of characteristic of the data like change in attribute of received data or receipt of a new attribute data itself. The received data may be image data, voice data or text data where the image and voice data can be converted to text data for analysis. The data lake 108 further includes a data model database 126 for storing plurality of data models, where the data models are re-calibrated based on a predicted impact of a new attribute data of the stored data on the one or more applications. [0069]: the Data Lake 108 includes data received from nodes or sources such as customers or retailers, distributors, factories, productions, suppliers etc. It also includes data from outside sources such as financial markets, weather, social media, geo-economics etc. On this Data Lake the executional platform is built that includes functions or products such as planning, Production, Procurement, Suppliers etc. This enables the system to build a real time machine learning or AI based recommendations that guide the user to conduct his or her work on daily basis with more accurate data, with higher confidence and from a system that is easier to use and intelligent. [0093]: The self-driven system and method of operating on ERP and SCM application of the present invention enables, reflection of any change in any of these attributes across other modules/functions. Further, the change is also considered in the ML (machine learning) models driving specific actions in real-time. In case the supplier is a critical supplier who supplies specific materials critical to the manufacturing line operations, any change would be very critical. If the system senses that the deliveries of this supplier have been consistently delayed over the last 3-4 cycles and these changes are used by the data models or machine learning algorithms to determine the new lead time for the specific products, then this insight is extremely valuable to the organization in many ways to take action or perform tasks based on recommendation. [0104]: In S503, processing of the changed data across the application, and the identified functions as part of a self-driven ERP and SCM system for faster processing is initiated. Consider some of the functions as sub processes demand planning in S504, supply planning in S505, production planning in S506, forecasting in S507 and fulfillment planning in S508. Consider the data received at the data lake related to a product or item that moves through the ERP and SCM applications. Demand planning S504 allows determination of a demand for the item or product considering various factors like customer base, consumption, density of population is a geographic location etc. Supply planning in S505 determines actions to fulfill the requirements created from the demand planning with an objective to balance supply and demand in manner that achieves desired objectives of ERP. Production planning S506 enables computation based on availability of items and capacities to meet customer demand by balancing the load on the manufacturing resources after considering the high throughput capacity of a plant. Forecasting S507 determines estimate for demand of item, supply of item, and production of the item. Fulfillment planning S508 ensures receiving of the item, packaging and shipping for eventual fulfillment of the order. Any change in characteristic of data or attributes related to the item/product will affect all the processes in different manner. In case, the change is not reflected at any of the functions, it shall lead to error and inaccuracy in that function. In case of combining of the functions like demand planning and supply planning for fulfillment of order, certain factors are considered related to characteristic of the item itself. When these functions act independently, the supply planning may not consider the change in the item characteristic. Also, during the Production planning S506, a user may wish to restrict the material composition of the item based on the demand of the item S504, thereby saving time on manufacturing items with undesired material characteristics. Makhija: paragraph 38, 61-63, 70, 72, 90, “the recommended task/action includes auto adjust data for the plurality of functions, risk mitigation, removing duplicate entry, or direct interactions with the plurality of nodes. Further, the duplicate entry can be of any data existing in the EA and SCM applications, including but not limited to supplier, invoice, contract etc. “… “It also provides ability for end users to track life cycle and relation of entities in the system. Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model.” …” It collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc. The Curation including selection and organization of data takes place through capturing metadata and lineage and making it available in a data catalog.” …” The data flows in the data lake in real-time processing through event stream layer. Domain Model exposed through the query language (QL) tool 130 enables user to self-serve their data and analytical requirements. Models developed by users are utilized to improve the insights for future purpose.”…” the plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, supplier collaboration platform, invoices, purchase orders (PO), finance modules, inventory management module, contracts and RFx module, supplier module, item master, bill of materials, vendor master, warehouse management module, logistics management module, social media, weather, real time commodity and stock market prices, geo-political news etc. It shall be apparent to a person skilled in the art that the data source may include other source within the scope of the present invention” …” the EA and SCM applications include a plurality of nodes at the data source layer 127 like inventory, logistics, warehouse, procurement, customers, supplier, retailers, distributors, resellers, co-packers and transportation wherein the nodes interact with each other to structure the plurality of functions associated with the applications.”…” The interaction and data exchange between the service provide and subscriber is through the API gateway 133, event management block 134 and routers 137.”). Makhija: paragraph 46, 53-54, 61-62, “the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake “…“system layer architecture diagrams with data lake/platform (100B, 100c) of AI based self-driven ERP and SCM system is shown in accordance with an embodiment of the present invention. The system 100a includes a plurality of distinct data source layer 127 to capture all customer, factory, supplier, machine and third-party sources of data (both structured and unstructured), the data lake layer 108 storing all data received from the distinct data source layer 127, an application function layer 128 configured to re-calibrate functions based on data models and scripts generated by a bot. The data models are auto-generated based on change in attribute of the received data to determine the impact of the change on the functions of the one or more applications.” …” a Query language tool (QL) 130, data governance & standardization/protocol layer 131” …” collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc.”);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
displaying the AaS options to the user via a Single Pane of Glass User Interface (SPoG UI) (Makhija shows: paragraph 41-42, 61, “Information will feed into Analytics and Dashboard 129, with a view getting mode insights.”);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
facilitating the completion of the AaS conversion process and transferring the order to a vendor system (Makhija shows: paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
executing the AaS conversion order by integrating data from the SPoG UI, RTDM, and vendor systems, wherein the method is executed by a computer system with a unified platform that integrates data from multiple sources for AaS conversion (Makhija: paragraph 56-60, “The simulation UI 130a enables user to draft statements/query as per underlined model provided through intelligent sensing. The Translator 130 uses NLP and domain specific nomenclature repository, to tokenize query string received from user. Tokenizer takes a sequence of characters and output a sequence of tokens. It will analyze character by character, using multiple levels of lookahead in order to identity what token is currently being examine. The Code Generator 130c extracts Keywords and tokens that are used to generate underlying Machine Learning query and big data query. The Mapper is responsible to generate code and the model 130d utilized domain attributes, Synonyms and tokens.” …” the tool includes an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option/task/action in real time.” …” the tool is configured to attach the recommended task/action to a desired workflow or User interface element or set of rules or validations”).
Additional limitations discussed in claim 8, that are different from claim 1:
As per claim 8: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
A computerized method for optimizing AaS conversion decisions, comprising:
Regarding the claim limitations below, Reference Makhija shows:
initiating a subscription request via the SPoG UI (Makhija shows the above limitation at least in: paragraph 46, 53-54, 61-62, “the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake “… “system layer architecture diagrams with data lake/platform (100B, 100c) of AI based self-driven ERP and SCM system is shown in accordance with an embodiment of the present invention. The system 100a includes a plurality of distinct data source layer 127 to capture all customer, factory, supplier, machine and third-party sources of data (both structured and unstructured), the data lake layer 108 storing all data received from the distinct data source layer 127, an application function layer 128 configured to re-calibrate functions based on data models and scripts generated by a bot. The data models are auto-generated based on change in attribute of the received data to determine the impact of the change on the functions of the one or more applications.” …” a Query language tool (QL) 130, data governance & standardization/protocol layer 131” …” collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc.”. Also, see [0062]-[0065]: shows subscribers, [0090]-[0091]);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
retrieving user preferences AaS conversion
Reference Makhija shows in paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Reference Makhija does not explicitly show “user preferences” and historical data.
Reference Kadayam shows the above limitations at least in (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60, “The system is programmed to further store at least some of the collected data in a database. The data can be available at various granularities. For example, for suppliers or buyers, the data can be related to industries, organizations, departments, or individuals; for products, the data can be related to industries, categories, makes, or models. The data can be collected directly from supplier accounts or buyer accounts or from external data sources. The data can be collected in response to processing user queries, as further discussed below, or during ordinary user online activities. The data can be collected from explicit user input through graphical user interfaces, such as a button that when pushed indicates a vote for a particular product as being a good match for another product or a comment box configured to accept supplier reviews, or from implicit user behavior that indicates an affinity for parties or items, such as putting a particular item in a shopping cart or paying invoices to a particular supplier within a specific period of time. (89) In some embodiments, a collective product wisdom dataset may be updated continuously in real-time to reflect the up-to-the-moment selection/purchase activity. The Adaptive Navigation user experience may be built on top of this dynamically changing dataset about the continuously evolving nature of product knowledge and product preferences in the organization. In some embodiments, this may be used to provide a user experience of browsing through a marketplace that is very dynamic and adapts in real-time. Compared to the conventional approach of using all marketplace product data to build a static, and generic product browsing experience, an Adaptive Navigation user experience may be naturally tailored to the company's own product preference and product purchasing behaviors… an Organization Preferences Cognitive Advisor may also have a learning engine inside. This learning engine learns user behavior from actions taken by users to whom recommendations from this Cognitive Advisor have been delivered. These actions may include, for example, that a user clicks on an Organization Preference recommendation, a user selects an Organization Preference recommendation for adding to cart, or a user selects an Organization Preference recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Organization Preferences recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. The Organization Preferences Cognitive Advisor taps into the Collective Intelligence of the organization on product selections and purchases, to provide high quality, reliable recommendations for the user doing the queries. (211) The Best Bets learning engine learns user behavior from actions taken by users to whom the Best Bets recommendations have been delivered. These actions may include, for example, that a user clicks on a Best Bet recommendation, a user selects a Best Bet recommendation for adding to cart, or a user selects a Best Bet recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Best Bets recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization… 215) The learning engine underneath the Bundles Cognitive Advisor 2918 also learns user behavior from actions taken by users to whom the Bundles recommendations have been delivered. These actions may include, for example, that a user clicks on a Bundle recommendation, a user selects a Bundle recommendation for adding to cart, or a user selects a Bundle recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Bundles recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. In addition, the highly active bundles for a given query in a given category in a given region, can be additional data for the Global Item Master, potentially driving recommendations for new Bundle creation or Bundle enhancement for this organization or other organizations overall (244) Often what occurs is that users' shopping patterns don't match with the strategy and contracts set up by Procurement Buyers in different categories; i.e. the user shopping behavior can be said to be non-compliant, and this does not help to tap into procurement rules and expectations, and in turn, the savings are not actualized. A few specific situations would need to be addressed in this regard. For example, procurement buyers would have to provide actual contract information to the system so that the information contained in it can be used in real-time by the system. Systems implemented based on this disclosure may provide the means for the procurement buyers to do so. As another example, Suppliers would need to be carefully organized into categories, and tagged appropriately for their specific attributes (e.g. minority supplier, woman-owned supplier, veteran supplier etc.), so that the guided buying capability of an e-procurement system can operate as expected, suitably rank ordering product selections in the universal search experience. A system implemented based on this disclosure may provide the tools for this to be setup correctly.”
Reference Makhija and Reference Kadayam are analogous prior art to the claimed invention because the references generally relate to field of workflow processing (Makhija: [0015], [0065]. Kadayam: col. 15, lines 28-38, col. 20, lines 60-67, col. 27, lines 53-57, col. 40-67). Further, said references are part of the same classification, i.e., G06Q and G06F. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Kadayam, particularly the ability to customize data insights using additional perspectives like user preference and market conditions information (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60), in the disclosure of Reference Makhija, particularly in the ability to collect real time data (paragraph 62-64, 92, 98, 100-101), in order to provide for a system that the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier as taught by Reference Kadayam (see at least in col. 3, lines 54-65: the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier), so that the process of managing workflow processing can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow processing field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Makhija in view of Reference Kadayam, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
generating the subscription package through an AaS Conversion Module configured to receive the predictive outputs from the AAML Module (Reference Makhija shows in paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
apply compatibility and pricing analysis to determine an optimal subscription-based conversion configuration, and validate the subscription-based conversion configuration based _on rules and quality thresholds derived from the canonical data (Makhija shows: paragraph 62-64, 92, 98, 10-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Makhija also shows: paragraph 56-60, “The simulation UI 130a enables user to draft statements/query as per underlined model provided through intelligent sensing. The Translator 130 uses NLP and domain specific nomenclature repository, to tokenize query string received from user. Tokenizer takes a sequence of characters and output a sequence of tokens. It will analyze character by character, using multiple levels of lookahead in order to identity what token is currently being examine. The Code Generator 130c extracts Keywords and tokens that are used to generate underlying Machine Learning query and big data query. The Mapper is responsible to generate code and the model 130d utilized domain attributes, Synonyms and tokens.”…” the tool includes an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option/task/action in real time.”…” the tool is configured to attach the recommended task/action to a desired workflow or User interface element or set of rules or validations”);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
configuring the subscription package based on the subscription-based conversion configuration and the user preferences
Reference Makhija shows in paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Reference Makhija does not explicitly show “user preferences and historical data”.
Reference Kadayam shows the above limitations at least in (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60, “The system is programmed to further store at least some of the collected data in a database. The data can be available at various granularities. For example, for suppliers or buyers, the data can be related to industries, organizations, departments, or individuals; for products, the data can be related to industries, categories, makes, or models. The data can be collected directly from supplier accounts or buyer accounts or from external data sources. The data can be collected in response to processing user queries, as further discussed below, or during ordinary user online activities. The data can be collected from explicit user input through graphical user interfaces, such as a button that when pushed indicates a vote for a particular product as being a good match for another product or a comment box configured to accept supplier reviews, or from implicit user behavior that indicates an affinity for parties or items, such as putting a particular item in a shopping cart or paying invoices to a particular supplier within a specific period of time. (89) In some embodiments, a collective product wisdom dataset may be updated continuously in real-time to reflect the up-to-the-moment selection/purchase activity. The Adaptive Navigation user experience may be built on top of this dynamically changing dataset about the continuously evolving nature of product knowledge and product preferences in the organization. In some embodiments, this may be used to provide a user experience of browsing through a marketplace that is very dynamic and adapts in real-time. Compared to the conventional approach of using all marketplace product data to build a static, and generic product browsing experience, an Adaptive Navigation user experience may be naturally tailored to the company's own product preference and product purchasing behaviors… an Organization Preferences Cognitive Advisor may also have a learning engine inside. This learning engine learns user behavior from actions taken by users to whom recommendations from this Cognitive Advisor have been delivered. These actions may include, for example, that a user clicks on an Organization Preference recommendation, a user selects an Organization Preference recommendation for adding to cart, or a user selects an Organization Preference recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Organization Preferences recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. The Organization Preferences Cognitive Advisor taps into the Collective Intelligence of the organization on product selections and purchases, to provide high quality, reliable recommendations for the user doing the queries. (211) The Best Bets learning engine learns user behavior from actions taken by users to whom the Best Bets recommendations have been delivered. These actions may include, for example, that a user clicks on a Best Bet recommendation, a user selects a Best Bet recommendation for adding to cart, or a user selects a Best Bet recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Best Bets recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization… 215) The learning engine underneath the Bundles Cognitive Advisor 2918 also learns user behavior from actions taken by users to whom the Bundles recommendations have been delivered. These actions may include, for example, that a user clicks on a Bundle recommendation, a user selects a Bundle recommendation for adding to cart, or a user selects a Bundle recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Bundles recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. In addition, the highly active bundles for a given query in a given category in a given region, can be additional data for the Global Item Master, potentially driving recommendations for new Bundle creation or Bundle enhancement for this organization or other organizations overall (244) Often what occurs is that users' shopping patterns don't match with the strategy and contracts set up by Procurement Buyers in different categories; i.e. the user shopping behavior can be said to be non-compliant, and this does not help to tap into procurement rules and expectations, and in turn, the savings are not actualized. A few specific situations would need to be addressed in this regard. For example, procurement buyers would have to provide actual contract information to the system so that the information contained in it can be used in real-time by the system. Systems implemented based on this disclosure may provide the means for the procurement buyers to do so. As another example, Suppliers would need to be carefully organized into categories, and tagged appropriately for their specific attributes (e.g. minority supplier, woman-owned supplier, veteran supplier etc.), so that the guided buying capability of an e-procurement system can operate as expected, suitably rank ordering product selections in the universal search experience. A system implemented based on this disclosure may provide the tools for this to be setup correctly.”
Reference Makhija and Reference Kadayam are analogous prior art to the claimed invention because the references generally relate to field of workflow processing (Makhija: [0015], [0065]. Kadayam: col. 15, lines 28-38, col. 20, lines 60-67, col. 27, lines 53-57, col. 40-67). Further, said references are part of the same classification, i.e., G06Q and G06F. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Kadayam, particularly the ability to customize data insights using additional perspectives like user preference and market conditions information (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60), in the disclosure of Reference Makhija, particularly in the ability to collect real time data (paragraph 62-64, 92, 98, 100-101), in order to provide for a system that the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier as taught by Reference Kadayam (see at least in col. 3, lines 54-65: the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier), so that the process of managing workflow processing can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow processing field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Makhija in view of Reference Kadayam, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
configuring the subscription package based on the subscription-based conversion configuration and the user preferences (Makhija shows: paragraph 62-64, 92, 98, 10-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Makhija also shows: paragraph 56-60, “The simulation UI 130a enables user to draft statements/query as per underlined model provided through intelligent sensing. The Translator 130 uses NLP and domain specific nomenclature repository, to tokenize query string received from user. Tokenizer takes a sequence of characters and output a sequence of tokens. It will analyze character by character, using multiple levels of lookahead in order to identity what token is currently being examine. The Code Generator 130c extracts Keywords and tokens that are used to generate underlying Machine Learning query and big data query. The Mapper is responsible to generate code and the model 130d utilized domain attributes, Synonyms and tokens.”…” the tool includes an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option/task/action in real time.”…” the tool is configured to attach the recommended task/action to a desired workflow or User interface element or set of rules or validations”. Makhija: paragraph 61-63, 70, “Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model.”…” It collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc. The Curation including selection and organization of data takes place through capturing metadata and lineage and making it available in a data catalog.”…” The data flows in the data lake in real-time processing through event stream layer. Domain Model exposed through the query language (QL) tool 130 enables user to self-serve their data and analytical requirements. Models developed by users are utilized to improve the insights for future purpose.”…”The plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, supplier collaboration platform, invoices, purchase orders (PO), finance modules, inventory management module, contracts and RFx module, supplier module, item master, bill of materials, vendor master, warehouse management module, logistics management module, social media, weather, real time commodity and stock market prices, geo-political news etc. It shall be apparent to a person skilled in the art that the data source may include other source within the scope of the present invention”);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
presenting the subscription package to the user via the SPoG UI (Makhija shows: paragraph 41-42, 61, “Information will feed into Analytics and Dashboard 129, with a view getting mode insights.”);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
logging data associated with the AaS conversion for system refinement (Makhija shows: paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
initiating a feedback loop within the system for continual improvement of the AaS conversion (Makhija: paragraph 56-60, “The simulation UI 130a enables user to draft statements/query as per underlined model provided through intelligent sensing. The Translator 130 uses NLP and domain specific nomenclature repository, to tokenize query string received from user. Tokenizer takes a sequence of characters and output a sequence of tokens. It will analyze character by character, using multiple levels of lookahead in order to identity what token is currently being examine. The Code Generator 130c extracts Keywords and tokens that are used to generate underlying Machine Learning query and big data query. The Mapper is responsible to generate code and the model 130d utilized domain attributes, Synonyms and tokens.”…” the tool includes an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option/task/action in real time.”…” the tool is configured to attach the recommended task/action to a desired workflow or User interface element or set of rules or validations”).
Additional limitations discussed in claim 15, that are different from claim 1:
As per claim 15: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
A system for automating AaS conversion processes, comprising:
a Real-Time Data Mesh (RTDM) configured to aggregate and disseminate data including user preferences, market trends, and service information, the RTDM comprising:
Reference Makhija shows in paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Makhija shows the above limitation at least in: paragraph 46, 53-54, 61-62, “the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake “… “system layer architecture diagrams with data lake/platform (100B, 100c) of AI based self-driven ERP and SCM system is shown in accordance with an embodiment of the present invention. The system 100a includes a plurality of distinct data source layer 127 to capture all customer, factory, supplier, machine and third-party sources of data (both structured and unstructured), the data lake layer 108 storing all data received from the distinct data source layer 127, an application function layer 128 configured to re-calibrate functions based on data models and scripts generated by a bot. The data models are auto-generated based on change in attribute of the received data to determine the impact of the change on the functions of the one or more applications.” …” a Query language tool (QL) 130, data governance & standardization/protocol layer 131” …” collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc.”
Reference Makhija does not explicitly show “user's preferences and market conditions”. Reference Kadayam shows the above limitations at least in (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60, “The system is programmed to further store at least some of the collected data in a database. The data can be available at various granularities. For example, for suppliers or buyers, the data can be related to industries, organizations, departments, or individuals; for products, the data can be related to industries, categories, makes, or models. The data can be collected directly from supplier accounts or buyer accounts or from external data sources. The data can be collected in response to processing user queries, as further discussed below, or during ordinary user online activities. The data can be collected from explicit user input through graphical user interfaces, such as a button that when pushed indicates a vote for a particular product as being a good match for another product or a comment box configured to accept supplier reviews, or from implicit user behavior that indicates an affinity for parties or items, such as putting a particular item in a shopping cart or paying invoices to a particular supplier within a specific period of time. (89) In some embodiments, a collective product wisdom dataset may be updated continuously in real-time to reflect the up-to-the-moment selection/purchase activity. The Adaptive Navigation user experience may be built on top of this dynamically changing dataset about the continuously evolving nature of product knowledge and product preferences in the organization. In some embodiments, this may be used to provide a user experience of browsing through a marketplace that is very dynamic and adapts in real-time. Compared to the conventional approach of using all marketplace product data to build a static, and generic product browsing experience, an Adaptive Navigation user experience may be naturally tailored to the company's own product preference and product purchasing behaviors… an Organization Preferences Cognitive Advisor may also have a learning engine inside. This learning engine learns user behavior from actions taken by users to whom recommendations from this Cognitive Advisor have been delivered. These actions may include, for example, that a user clicks on an Organization Preference recommendation, a user selects an Organization Preference recommendation for adding to cart, or a user selects an Organization Preference recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Organization Preferences recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. The Organization Preferences Cognitive Advisor taps into the Collective Intelligence of the organization on product selections and purchases, to provide high quality, reliable recommendations for the user doing the queries. (211) The Best Bets learning engine learns user behavior from actions taken by users to whom the Best Bets recommendations have been delivered. These actions may include, for example, that a user clicks on a Best Bet recommendation, a user selects a Best Bet recommendation for adding to cart, or a user selects a Best Bet recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Best Bets recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization… 215) The learning engine underneath the Bundles Cognitive Advisor 2918 also learns user behavior from actions taken by users to whom the Bundles recommendations have been delivered. These actions may include, for example, that a user clicks on a Bundle recommendation, a user selects a Bundle recommendation for adding to cart, or a user selects a Bundle recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Bundles recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. In addition, the highly active bundles for a given query in a given category in a given region, can be additional data for the Global Item Master, potentially driving recommendations for new Bundle creation or Bundle enhancement for this organization or other organizations overall (244) Often what occurs is that users' shopping patterns don't match with the strategy and contracts set up by Procurement Buyers in different categories; i.e. the user shopping behavior can be said to be non-compliant, and this does not help to tap into procurement rules and expectations, and in turn, the savings are not actualized. A few specific situations would need to be addressed in this regard. For example, procurement buyers would have to provide actual contract information to the system so that the information contained in it can be used in real-time by the system. Systems implemented based on this disclosure may provide the means for the procurement buyers to do so. As another example, Suppliers would need to be carefully organized into categories, and tagged appropriately for their specific attributes (e.g. minority supplier, woman-owned supplier, veteran supplier etc.), so that the guided buying capability of an e-procurement system can operate as expected, suitably rank ordering product selections in the universal search experience. A system implemented based on this disclosure may provide the tools for this to be setup correctly.”
Reference Makhija and Reference Kadayam are analogous prior art to the claimed invention because the references generally relate to field of workflow processing (Makhija: [0015], [0065]. Kadayam: col. 15, lines 28-38, col. 20, lines 60-67, col. 27, lines 53-57, col. 40-67). Further, said references are part of the same classification, i.e., G06Q and G06F. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Kadayam, particularly the ability to customize data insights using additional perspectives like user preference and market conditions information (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60), in the disclosure of Reference Makhija, particularly in the ability to collect real time data (paragraph 62-64, 92, 98, 100-101), in order to provide for a system that the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier as taught by Reference Kadayam (see at least in col. 3, lines 54-65: the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier), so that the process of managing workflow processing can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow processing field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Makhija in view of Reference Kadayam, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
a Single Pane of Glass User Interface enabling user interactions and displaying subscription options (Makhija shows: paragraph 41-42, 61, “Information will feed into Analytics and Dashboard 129, with a view getting mode insights.”).
As per claim 2: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
further comprising validating the AaS conversion using rules and algorithms within the AAML Module to ensure accuracy and relevance of the conversion recommendations (Makhija: paragraph 46, 53-54, 61-62, “the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake “…“system layer architecture diagrams with data lake/platform (100B, 100c) of AI based self-driven ERP and SCM system is shown in accordance with an embodiment of the present invention. The system 100a includes a plurality of distinct data source layer 127 to capture all customer, factory, supplier, machine and third-party sources of data (both structured and unstructured), the data lake layer 108 storing all data received from the distinct data source layer 127, an application function layer 128 configured to re-calibrate functions based on data models and scripts generated by a bot. The data models are auto-generated based on change in attribute of the received data to determine the impact of the change on the functions of the one or more applications.”…” a Query language tool (QL) 130, data governance & standardization/protocol layer 131”…” collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc.”).
As per claim 3: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein the AAML Module utilizes dynamic machine learning algorithms that adapt to changing user preferences and market conditions for effective AaS conversion.
Reference Makhija shows in paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Reference Makhija does not explicitly show “user's preferences and market conditions”.
Reference Kadayam shows the above limitations at least in (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60, “The system is programmed to further store at least some of the collected data in a database. The data can be available at various granularities. For example, for suppliers or buyers, the data can be related to industries, organizations, departments, or individuals; for products, the data can be related to industries, categories, makes, or models. The data can be collected directly from supplier accounts or buyer accounts or from external data sources. The data can be collected in response to processing user queries, as further discussed below, or during ordinary user online activities. The data can be collected from explicit user input through graphical user interfaces, such as a button that when pushed indicates a vote for a particular product as being a good match for another product or a comment box configured to accept supplier reviews, or from implicit user behavior that indicates an affinity for parties or items, such as putting a particular item in a shopping cart or paying invoices to a particular supplier within a specific period of time. (89) In some embodiments, a collective product wisdom dataset may be updated continuously in real-time to reflect the up-to-the-moment selection/purchase activity. The Adaptive Navigation user experience may be built on top of this dynamically changing dataset about the continuously evolving nature of product knowledge and product preferences in the organization. In some embodiments, this may be used to provide a user experience of browsing through a marketplace that is very dynamic and adapts in real-time. Compared to the conventional approach of using all marketplace product data to build a static, and generic product browsing experience, an Adaptive Navigation user experience may be naturally tailored to the company's own product preference and product purchasing behaviors… an Organization Preferences Cognitive Advisor may also have a learning engine inside. This learning engine learns user behavior from actions taken by users to whom recommendations from this Cognitive Advisor have been delivered. These actions may include, for example, that a user clicks on an Organization Preference recommendation, a user selects an Organization Preference recommendation for adding to cart, or a user selects an Organization Preference recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Organization Preferences recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. The Organization Preferences Cognitive Advisor taps into the Collective Intelligence of the organization on product selections and purchases, to provide high quality, reliable recommendations for the user doing the queries. (211) The Best Bets learning engine learns user behavior from actions taken by users to whom the Best Bets recommendations have been delivered. These actions may include, for example, that a user clicks on a Best Bet recommendation, a user selects a Best Bet recommendation for adding to cart, or a user selects a Best Bet recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Best Bets recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization… 215) The learning engine underneath the Bundles Cognitive Advisor 2918 also learns user behavior from actions taken by users to whom the Bundles recommendations have been delivered. These actions may include, for example, that a user clicks on a Bundle recommendation, a user selects a Bundle recommendation for adding to cart, or a user selects a Bundle recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Bundles recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. In addition, the highly active bundles for a given query in a given category in a given region, can be additional data for the Global Item Master, potentially driving recommendations for new Bundle creation or Bundle enhancement for this organization or other organizations overall (244) Often what occurs is that users' shopping patterns don't match with the strategy and contracts set up by Procurement Buyers in different categories; i.e. the user shopping behavior can be said to be non-compliant, and this does not help to tap into procurement rules and expectations, and in turn, the savings are not actualized. A few specific situations would need to be addressed in this regard. For example, procurement buyers would have to provide actual contract information to the system so that the information contained in it can be used in real-time by the system. Systems implemented based on this disclosure may provide the means for the procurement buyers to do so. As another example, Suppliers would need to be carefully organized into categories, and tagged appropriately for their specific attributes (e.g. minority supplier, woman-owned supplier, veteran supplier etc.), so that the guided buying capability of an e-procurement system can operate as expected, suitably rank ordering product selections in the universal search experience. A system implemented based on this disclosure may provide the tools for this to be setup correctly.”
Reference Makhija and Reference Kadayam are analogous prior art to the claimed invention because the references generally relate to field of workflow processing (Makhija: [0015], [0065]. Kadayam: col. 15, lines 28-38, col. 20, lines 60-67, col. 27, lines 53-57, col. 40-67). Further, said references are part of the same classification, i.e., G06Q and G06F. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Kadayam, particularly the ability to customize data insights using additional perspectives like user preference and market conditions information (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60), in the disclosure of Reference Makhija, particularly in the ability to collect real time data (paragraph 62-64, 92, 98, 100-101), in order to provide for a system that the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier as taught by Reference Kadayam (see at least in col. 3, lines 54-65: the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier), so that the process of managing workflow processing can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow processing field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Makhija in view of Reference Kadayam, the results of the combination were predictable (MPEP 2143 A).
As per claim 4: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein the RTDM is continuously updated with real-time inventory, user behavior data, and market trends to inform the AaS conversion process.
Reference Makhija shows in paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Makhija further shows paragraph 61-63, 70, “Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model.”…” It collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc. The Curation including selection and organization of data takes place through capturing metadata and lineage and making it available in a data catalog.”…” The data flows in the data lake in real-time processing through event stream layer. Domain Model exposed through the query language (QL) tool 130 enables user to self-serve their data and analytical requirements. Models developed by users are utilized to improve the insights for future purpose.”…”The plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, supplier collaboration platform, invoices, purchase orders (PO), finance modules, inventory management module, contracts and RFx module, supplier module, item master, bill of materials, vendor master, warehouse management module, logistics management module, social media, weather, real time commodity and stock market prices, geo-political news etc. It shall be apparent to a person skilled in the art that the data source may include other source within the scope of the present invention”. Here, the inventory management module reads on showing real time updates “with real-time inventory” in the claim above.
Reference Makhija does not explicitly show “user's behavior data and market trends”. Reference Kadayam shows the above limitations at least in (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60, “The system is programmed to further store at least some of the collected data in a database. The data can be available at various granularities. For example, for suppliers or buyers, the data can be related to industries, organizations, departments, or individuals; for products, the data can be related to industries, categories, makes, or models. The data can be collected directly from supplier accounts or buyer accounts or from external data sources. The data can be collected in response to processing user queries, as further discussed below, or during ordinary user online activities. The data can be collected from explicit user input through graphical user interfaces, such as a button that when pushed indicates a vote for a particular product as being a good match for another product or a comment box configured to accept supplier reviews, or from implicit user behavior that indicates an affinity for parties or items, such as putting a particular item in a shopping cart or paying invoices to a particular supplier within a specific period of time. (89) In some embodiments, a collective product wisdom dataset may be updated continuously in real-time to reflect the up-to-the-moment selection/purchase activity. The Adaptive Navigation user experience may be built on top of this dynamically changing dataset about the continuously evolving nature of product knowledge and product preferences in the organization. In some embodiments, this may be used to provide a user experience of browsing through a marketplace that is very dynamic and adapts in real-time. Compared to the conventional approach of using all marketplace product data to build a static, and generic product browsing experience, an Adaptive Navigation user experience may be naturally tailored to the company's own product preference and product purchasing behaviors… an Organization Preferences Cognitive Advisor may also have a learning engine inside. This learning engine learns user behavior from actions taken by users to whom recommendations from this Cognitive Advisor have been delivered. These actions may include, for example, that a user clicks on an Organization Preference recommendation, a user selects an Organization Preference recommendation for adding to cart, or a user selects an Organization Preference recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Organization Preferences recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. The Organization Preferences Cognitive Advisor taps into the Collective Intelligence of the organization on product selections and purchases, to provide high quality, reliable recommendations for the user doing the queries. (211) The Best Bets learning engine learns user behavior from actions taken by users to whom the Best Bets recommendations have been delivered. These actions may include, for example, that a user clicks on a Best Bet recommendation, a user selects a Best Bet recommendation for adding to cart, or a user selects a Best Bet recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Best Bets recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization… 215) The learning engine underneath the Bundles Cognitive Advisor 2918 also learns user behavior from actions taken by users to whom the Bundles recommendations have been delivered. These actions may include, for example, that a user clicks on a Bundle recommendation, a user selects a Bundle recommendation for adding to cart, or a user selects a Bundle recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Bundles recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. In addition, the highly active bundles for a given query in a given category in a given region, can be additional data for the Global Item Master, potentially driving recommendations for new Bundle creation or Bundle enhancement for this organization or other organizations overall (244) Often what occurs is that users' shopping patterns don't match with the strategy and contracts set up by Procurement Buyers in different categories; i.e. the user shopping behavior can be said to be non-compliant, and this does not help to tap into procurement rules and expectations, and in turn, the savings are not actualized. A few specific situations would need to be addressed in this regard. For example, procurement buyers would have to provide actual contract information to the system so that the information contained in it can be used in real-time by the system. Systems implemented based on this disclosure may provide the means for the procurement buyers to do so. As another example, Suppliers would need to be carefully organized into categories, and tagged appropriately for their specific attributes (e.g. minority supplier, woman-owned supplier, veteran supplier etc.), so that the guided buying capability of an e-procurement system can operate as expected, suitably rank ordering product selections in the universal search experience. A system implemented based on this disclosure may provide the tools for this to be setup correctly.”
Reference Makhija and Reference Kadayam are analogous prior art to the claimed invention because the references generally relate to field of workflow processing (Makhija: [0015], [0065]. Kadayam: col. 15, lines 28-38, col. 20, lines 60-67, col. 27, lines 53-57, col. 40-67). Further, said references are part of the same classification, i.e., G06Q and G06F. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Kadayam, particularly the ability to customize data insights using additional perspectives like user preference and market conditions information (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60), in the disclosure of Reference Makhija, particularly in the ability to collect real time data (paragraph 62-64, 92, 98, 100-101), in order to provide for a system that the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier as taught by Reference Kadayam (see at least in col. 3, lines 54-65: the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier), so that the process of managing workflow processing can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow processing field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Makhija in view of Reference Kadayam, the results of the combination were predictable (MPEP 2143 A).
As per claim 5: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
further comprising generating real-time reports related to the AaS conversion process, including user engagement metrics and conversion success rates.
Reference Makhija shows in paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Makhija further shows paragraph 61-63, 70, “Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model.”…” It collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc. The Curation including selection and organization of data takes place through capturing metadata and lineage and making it available in a data catalog.”…” The data flows in the data lake in real-time processing through event stream layer. Domain Model exposed through the query language (QL) tool 130 enables user to self-serve their data and analytical requirements. Models developed by users are utilized to improve the insights for future purpose.”…”The plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, supplier collaboration platform, invoices, purchase orders (PO), finance modules, inventory management module, contracts and RFx module, supplier module, item master, bill of materials, vendor master, warehouse management module, logistics management module, social media, weather, real time commodity and stock market prices, geo-political news etc. It shall be apparent to a person skilled in the art that the data source may include other source within the scope of the present invention”. Here, the Data Relation analytics (using Graph store) reads on showing real time updates “generating real-time reports related to the AaS conversion process” in the claim above.
Reference Makhija does not explicitly show “user's behavior data and market trends” as such reference Makhija does not explicitly show “including user engagement metrics and conversion success rates”. Reference Kadayam shows the above limitations at least in (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60, “The system is programmed to further store at least some of the collected data in a database. The data can be available at various granularities. For example, for suppliers or buyers, the data can be related to industries, organizations, departments, or individuals; for products, the data can be related to industries, categories, makes, or models. The data can be collected directly from supplier accounts or buyer accounts or from external data sources. The data can be collected in response to processing user queries, as further discussed below, or during ordinary user online activities. The data can be collected from explicit user input through graphical user interfaces, such as a button that when pushed indicates a vote for a particular product as being a good match for another product or a comment box configured to accept supplier reviews, or from implicit user behavior that indicates an affinity for parties or items, such as putting a particular item in a shopping cart or paying invoices to a particular supplier within a specific period of time. (89) In some embodiments, a collective product wisdom dataset may be updated continuously in real-time to reflect the up-to-the-moment selection/purchase activity. The Adaptive Navigation user experience may be built on top of this dynamically changing dataset about the continuously evolving nature of product knowledge and product preferences in the organization. In some embodiments, this may be used to provide a user experience of browsing through a marketplace that is very dynamic and adapts in real-time. Compared to the conventional approach of using all marketplace product data to build a static, and generic product browsing experience, an Adaptive Navigation user experience may be naturally tailored to the company's own product preference and product purchasing behaviors… an Organization Preferences Cognitive Advisor may also have a learning engine inside. This learning engine learns user behavior from actions taken by users to whom recommendations from this Cognitive Advisor have been delivered. These actions may include, for example, that a user clicks on an Organization Preference recommendation, a user selects an Organization Preference recommendation for adding to cart, or a user selects an Organization Preference recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Organization Preferences recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. The Organization Preferences Cognitive Advisor taps into the Collective Intelligence of the organization on product selections and purchases, to provide high quality, reliable recommendations for the user doing the queries. (211) The Best Bets learning engine learns user behavior from actions taken by users to whom the Best Bets recommendations have been delivered. These actions may include, for example, that a user clicks on a Best Bet recommendation, a user selects a Best Bet recommendation for adding to cart, or a user selects a Best Bet recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Best Bets recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization… 215) The learning engine underneath the Bundles Cognitive Advisor 2918 also learns user behavior from actions taken by users to whom the Bundles recommendations have been delivered. These actions may include, for example, that a user clicks on a Bundle recommendation, a user selects a Bundle recommendation for adding to cart, or a user selects a Bundle recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Bundles recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. In addition, the highly active bundles for a given query in a given category in a given region, can be additional data for the Global Item Master, potentially driving recommendations for new Bundle creation or Bundle enhancement for this organization or other organizations overall (244) Often what occurs is that users' shopping patterns don't match with the strategy and contracts set up by Procurement Buyers in different categories; i.e. the user shopping behavior can be said to be non-compliant, and this does not help to tap into procurement rules and expectations, and in turn, the savings are not actualized. A few specific situations would need to be addressed in this regard. For example, procurement buyers would have to provide actual contract information to the system so that the information contained in it can be used in real-time by the system. Systems implemented based on this disclosure may provide the means for the procurement buyers to do so. As another example, Suppliers would need to be carefully organized into categories, and tagged appropriately for their specific attributes (e.g. minority supplier, woman-owned supplier, veteran supplier etc.), so that the guided buying capability of an e-procurement system can operate as expected, suitably rank ordering product selections in the universal search experience. A system implemented based on this disclosure may provide the tools for this to be setup correctly.”
Reference Makhija and Reference Kadayam are analogous prior art to the claimed invention because the references generally relate to field of workflow processing (Makhija: [0015], [0065]. Kadayam: col. 15, lines 28-38, col. 20, lines 60-67, col. 27, lines 53-57, col. 40-67). Further, said references are part of the same classification, i.e., G06Q and G06F. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Kadayam, particularly the ability to customize data insights using additional perspectives like user preference and market conditions information (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60), in the disclosure of Reference Makhija, particularly in the ability to collect real time data (paragraph 62-64, 92, 98, 100-101), in order to provide for a system that the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier as taught by Reference Kadayam (see at least in col. 3, lines 54-65: the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier), so that the process of managing workflow processing can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow processing field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Makhija in view of Reference Kadayam, the results of the combination were predictable (MPEP 2143 A).
As per claim 6: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein the vendor system for fulfilling the AaS conversion is selected based on criteria including service availability and capability (Makhija shows in paragraph 69-72: vendor platforms as part of diverse communication channels that are used in the decision making and communication. Makhija also shows (paragraph 61-63, 70, “It also provides ability for end users to track life cycle and relation of entities in the system. Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model.”…” It collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc. The Curation including selection and organization of data takes place through capturing metadata and lineage and making it available in a data catalog.”…” The data flows in the data lake in real-time processing through event stream layer. Domain Model exposed through the query language (QL) tool 130 enables user to self-serve their data and analytical requirements. Models developed by users are utilized to improve the insights for future purpose.”…” the plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, supplier collaboration platform, invoices, purchase orders (PO), finance modules, inventory management module, contracts and RFx module, supplier module, item master, bill of materials, vendor master, warehouse management module, logistics management module, social media, weather, real time commodity and stock market prices, geo-political news etc. It shall be apparent to a person skilled in the art that the data source may include other source within the scope of the present invention”. Also see [0093]: a supplier data is received at the data lake and the impact of the data for carrying out a method of operating on one or more applications. In S302, determining characteristic of at least one attribute of the supplier data amongst (Vendor name, firmographic attributes such as City, address, operating countries, number of employees, financials, products or services offered etc) or other attributes of the supplier such as average lead time for delivery etc, from the received supplier data.).
As per claim 7: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
further comprising sending a notification to the user upon successful completion and confirmation of the AaS conversion order (Makhija shows in paragraph 98, 105: “the system includes pro-active detection algorithms for any record/transactions (items/Suppliers/PO/Invoices etc) being entered by a user (supplier/Customer/Employee etc) at the user interface. These will ensure that the Master tables are clean, accurate, complete and non-fraudulent/non-duplicate at any point in time and the data flowing through every single module or pipeline is clean and accurate. The master tables are stored in relational database 122a”.)
As per claim 9: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
further comprising utilizing machine learning algorithms in the feedback loop to analyze user feedback and system performance for continual optimization of the AaS conversion process (paragraph 69-70, “the Data Lake 108 includes data received from nodes or sources such as customers or retailers, distributors, factories, productions, suppliers etc. It also includes data from outside sources such as financial markets, weather, social media, geo-economics etc.”…” the plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback,”).
As per claim 10: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein the RTDM fetches real-time data based on current market conditions and service availability (Makhija: paragraph 89, “an execution engine for receiving changed data and generating impact data processed from the front-end web server for determining impact of change on plurality of functions of the one or more applications to predict and recommend a task/action to the user for enabling the user to initiate the action through the electronic user interface”).
As per claim 11: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
further comprising generating real-time reports related to the AaS conversion process, including metrics such as user satisfaction and service customization level
Reference Makhija shows in paragraph 62-64, 92, 98, 100-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing. Makhija further shows paragraph 61-63, 70, “Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model.”…” It collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc. The Curation including selection and organization of data takes place through capturing metadata and lineage and making it available in a data catalog.”…” The data flows in the data lake in real-time processing through event stream layer. Domain Model exposed through the query language (QL) tool 130 enables user to self-serve their data and analytical requirements. Models developed by users are utilized to improve the insights for future purpose.”…”The plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, supplier collaboration platform, invoices, purchase orders (PO), finance modules, inventory management module, contracts and RFx module, supplier module, item master, bill of materials, vendor master, warehouse management module, logistics management module, social media, weather, real time commodity and stock market prices, geo-political news etc. It shall be apparent to a person skilled in the art that the data source may include other source within the scope of the present invention”.
Reference Makhija does not explicitly show “user's behavior data and market trends” as such reference Makhija does not explicitly show “user satisfaction and service customization level”. Reference Kadayam shows the above limitations at least in (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60, “The system is programmed to further store at least some of the collected data in a database. The data can be available at various granularities. For example, for suppliers or buyers, the data can be related to industries, organizations, departments, or individuals; for products, the data can be related to industries, categories, makes, or models. The data can be collected directly from supplier accounts or buyer accounts or from external data sources. The data can be collected in response to processing user queries, as further discussed below, or during ordinary user online activities. The data can be collected from explicit user input through graphical user interfaces, such as a button that when pushed indicates a vote for a particular product as being a good match for another product or a comment box configured to accept supplier reviews, or from implicit user behavior that indicates an affinity for parties or items, such as putting a particular item in a shopping cart or paying invoices to a particular supplier within a specific period of time. (89) In some embodiments, a collective product wisdom dataset may be updated continuously in real-time to reflect the up-to-the-moment selection/purchase activity. The Adaptive Navigation user experience may be built on top of this dynamically changing dataset about the continuously evolving nature of product knowledge and product preferences in the organization. In some embodiments, this may be used to provide a user experience of browsing through a marketplace that is very dynamic and adapts in real-time. Compared to the conventional approach of using all marketplace product data to build a static, and generic product browsing experience, an Adaptive Navigation user experience may be naturally tailored to the company's own product preference and product purchasing behaviors… an Organization Preferences Cognitive Advisor may also have a learning engine inside. This learning engine learns user behavior from actions taken by users to whom recommendations from this Cognitive Advisor have been delivered. These actions may include, for example, that a user clicks on an Organization Preference recommendation, a user selects an Organization Preference recommendation for adding to cart, or a user selects an Organization Preference recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Organization Preferences recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. The Organization Preferences Cognitive Advisor taps into the Collective Intelligence of the organization on product selections and purchases, to provide high quality, reliable recommendations for the user doing the queries. (211) The Best Bets learning engine learns user behavior from actions taken by users to whom the Best Bets recommendations have been delivered. These actions may include, for example, that a user clicks on a Best Bet recommendation, a user selects a Best Bet recommendation for adding to cart, or a user selects a Best Bet recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Best Bets recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization… 215) The learning engine underneath the Bundles Cognitive Advisor 2918 also learns user behavior from actions taken by users to whom the Bundles recommendations have been delivered. These actions may include, for example, that a user clicks on a Bundle recommendation, a user selects a Bundle recommendation for adding to cart, or a user selects a Bundle recommendation for adding to cart followed by an actual purchase. The entire query context and user context are taken into account, along with the signals above of user actions from Bundles recommendations, for learning and improving the quality of the recommendations made to all users overall within the organization. In addition, the highly active bundles for a given query in a given category in a given region, can be additional data for the Global Item Master, potentially driving recommendations for new Bundle creation or Bundle enhancement for this organization or other organizations overall (244) Often what occurs is that users' shopping patterns don't match with the strategy and contracts set up by Procurement Buyers in different categories; i.e. the user shopping behavior can be said to be non-compliant, and this does not help to tap into procurement rules and expectations, and in turn, the savings are not actualized. A few specific situations would need to be addressed in this regard. For example, procurement buyers would have to provide actual contract information to the system so that the information contained in it can be used in real-time by the system. Systems implemented based on this disclosure may provide the means for the procurement buyers to do so. As another example, Suppliers would need to be carefully organized into categories, and tagged appropriately for their specific attributes (e.g. minority supplier, woman-owned supplier, veteran supplier etc.), so that the guided buying capability of an e-procurement system can operate as expected, suitably rank ordering product selections in the universal search experience. A system implemented based on this disclosure may provide the tools for this to be setup correctly.”
Reference Makhija and Reference Kadayam are analogous prior art to the claimed invention because the references generally relate to field of workflow processing (Makhija: [0015], [0065]. Kadayam: col. 15, lines 28-38, col. 20, lines 60-67, col. 27, lines 53-57, col. 40-67). Further, said references are part of the same classification, i.e., G06Q and G06F. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Kadayam, particularly the ability to customize data insights using additional perspectives like user preference and market conditions information (Col. 3, lines 31-52, col. 11 lines 54-65, col.12, lines 46-54, col. 15 lines 39-45, col. 29 lines 22-29, col. 32 line 61 to col. 33 line 4, col. 34 lines 48-29, col. 35 lines 3-20 and 50-60), in the disclosure of Reference Makhija, particularly in the ability to collect real time data (paragraph 62-64, 92, 98, 100-101), in order to provide for a system that the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier as taught by Reference Kadayam (see at least in col. 3, lines 54-65: the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier), so that the process of managing workflow processing can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow processing field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Makhija in view of Reference Kadayam, the results of the combination were predictable (MPEP 2143 A).
As per claim 12: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein product selections for AaS subscriptions are made based on predefined criteria including user preferences, market trends, and service compatibility (Makhija shows: paragraph 41-42, 61-63, “Information will feed into Analytics and Dashboard 129, with a view getting mode insights.”…” The data flows in the data lake in real-time processing through event stream layer. Domain Model exposed through the query language (QL) tool 130 enables user to self-serve their data and analytical requirements. Models developed by users are utilized to improve the insights for future purpose.”).
As per claim 13: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
further comprising sending a notification to the user upon successful generation and availability of the AaS subscription package (Makhija shows in 67: the control tower 117 is configured for real time visualization of flows in the one or more applications, switching between data models, setting up alert-notifications, data analytics, ensuring security of the data. wherein the tower enables easy communication between the nodes as well as allows having visibility and manages the plurality of functions across the one or more applications.).
As per claim 14: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein the feedback loop for AaS conversion decisions is conducted within a defined time frame based on user engagement and system analytics (Makhija: paragraph 69-70, “the Data Lake 108 includes data received from nodes or sources such as customers or retailers, distributors, factories, productions, suppliers etc. It also includes data from outside sources such as financial markets, weather, social media, geo-economics etc.”…” the plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback”).
As per claim 16: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein the AaS Conversion Module further comprises a logging mechanism to track user interactions and subscription choices for auditing and analytics purposes (Makhija: paragraph 38, 61-63, 70, 72, 90, “the recommended task/action includes auto adjust data for the plurality of functions, risk mitigation, removing duplicate entry, or direct interactions with the plurality of nodes. Further, the duplicate entry can be of any data existing in the EA and SCM applications, including but not limited to supplier, invoice, contract etc. “… “It also provides ability for end users to track life cycle and relation of entities in the system. Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model.” …” It collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc. The Curation including selection and organization of data takes place through capturing metadata and lineage and making it available in a data catalog.” …” The data flows in the data lake in real-time processing through event stream layer. Domain Model exposed through the query language (QL) tool 130 enables user to self-serve their data and analytical requirements. Models developed by users are utilized to improve the insights for future purpose.”…” the plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, supplier collaboration platform, invoices, purchase orders (PO), finance modules, inventory management module, contracts and RFx module, supplier module, item master, bill of materials, vendor master, warehouse management module, logistics management module, social media, weather, real time commodity and stock market prices, geo-political news etc. It shall be apparent to a person skilled in the art that the data source may include other source within the scope of the present invention” …” the EA and SCM applications include a plurality of nodes at the data source layer 127 like inventory, logistics, warehouse, procurement, customers, supplier, retailers, distributors, resellers, co-packers and transportation wherein the nodes interact with each other to structure the plurality of functions associated with the applications.”…” The interaction and data exchange between the service provide and subscriber is through the API gateway 133, event management block 134 and routers 137.”).
As per claim 17: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein the AaS Conversion Module integrates with the AAML Module for validation and optimization purposes, using algorithms stored in the AAML Module to refine subscription recommendations (Makhija shows (paragraph 56-60, 69, 72, “The simulation UI 130a enables user to draft statements/query as per underlined model provided through intelligent sensing. The Translator 130 uses NLP and domain specific nomenclature repository, to tokenize query string received from user. Tokenizer takes a sequence of characters and output a sequence of tokens. It will analyze character by character, using multiple levels of lookahead in order to identity what token is currently being examine. The Code Generator 130c extracts Keywords and tokens that are used to generate underlying Machine Learning query and big data query. The Mapper is responsible to generate code and the model 130d utilized domain attributes, Synonyms and tokens.” …” the tool includes an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option/task/action in real time.”…” the tool is configured to attach the recommended task/action to a desired workflow or User interface element or set of rules or validations”…” he EA and SCM applications include a plurality of nodes at the data source layer 127 like inventory, logistics, warehouse, procurement, customers, supplier, retailers, distributors, resellers, co-packers and transportation wherein the nodes interact with each other to structure the plurality of functions associated with the applications”)).
As per claim 18: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein the SPoG UI is designed to be accessible and responsive across various devices, providing an integrated user experience for subscription customization (Makhija: paragraph 46, 53-54, 61-62, “the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake “…“system layer architecture diagrams with data lake/platform (100B, 100c) of AI based self-driven ERP and SCM system is shown in accordance with an embodiment of the present invention. The system 100a includes a plurality of distinct data source layer 127 to capture all customer, factory, supplier, machine and third-party sources of data (both structured and unstructured), the data lake layer 108 storing all data received from the distinct data source layer 127, an application function layer 128 configured to re-calibrate functions based on data models and scripts generated by a bot. The data models are auto-generated based on change in attribute of the received data to determine the impact of the change on the functions of the one or more applications.” …” a Query language tool (QL) 130, data governance & standardization/protocol layer 131” …” collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc.”).
As per claim 19: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
wherein the RTDM is configured to standardize and harmonize data from diverse sources, making it suitable for consumption and analysis by the SPoG UI and other system modules (Makhija: paragraph 46, 53-54, 61-62, “the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake “…“system layer architecture diagrams with data lake/platform (100B, 100c) of AI based self-driven ERP and SCM system is shown in accordance with an embodiment of the present invention. The system 100a includes a plurality of distinct data source layer 127 to capture all customer, factory, supplier, machine and third-party sources of data (both structured and unstructured), the data lake layer 108 storing all data received from the distinct data source layer 127, an application function layer 128 configured to re-calibrate functions based on data models and scripts generated by a bot. The data models are auto-generated based on change in attribute of the received data to determine the impact of the change on the functions of the one or more applications.”…” a Query language tool (QL) 130, data governance & standardization/protocol layer 131”…” collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc.” Paragraph 62-64, 92, 98, 10-101, the system detects changes in data which indicates that the data is continuously being received and compared and synchronizing).
As per claim 20: Regarding the claim limitations below, Reference Makhija in view of Reference Kadayam shows:
further comprising machine learning models within the AaS Conversion Module, configured to continually refine the conversion process based on user feedback and evolving market data (Makhija: paragraph 46, 53-54, 61-62, “the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake “…“system layer architecture diagrams with data lake/platform (100B, 100c) of AI based self-driven ERP and SCM system is shown in accordance with an embodiment of the present invention. The system 100a includes a plurality of distinct data source layer 127 to capture all customer, factory, supplier, machine and third-party sources of data (both structured and unstructured), the data lake layer 108 storing all data received from the distinct data source layer 127, an application function layer 128 configured to re-calibrate functions based on data models and scripts generated by a bot. The data models are auto-generated based on change in attribute of the received data to determine the impact of the change on the functions of the one or more applications.”…” a Query language tool (QL) 130, data governance & standardization/protocol layer 131” …” collects data from diverse sources, acts a gateway and identifies data attributes to be extracted from application event. The curator engine 132b with the help of mapper and ingestion module 132a stores the received data in multiple type stores viz, the search store for advance search, graph for data and relations, flat structure for logs purpose etc.”).
Response to Arguments
Applicants' arguments are moot in view of the new grounds of rejection necessitated by the amendments made to previously presented claims.
Applicant’s Argument #1
Applicants argue on page(s) 8-11 of applicants remarks that “The Office Action fails to consider any of the structural or functional details recited in the claims and disclosed in the specification that describe how these modules interoperate to address specific technological problems related to cross-platform data harmonization, real-time analytics, and actionable service configuration. This mode of analysis, reducing multi-component system claims to an oversimplified paraphrase while ignoring the claim’s structural specificity, is exactly what the Federal Circuit condemned in the MPEP and in TecSec, Inc. v. Adobe Inc., 978 F.3d 1278 (Fed. Cir. 2020).” (see applicants remarks for more details).
Response to Argument #1
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
Please see the note above. The claims are broad and the specification is not showing how the steps discussed in the claim are carried out. The breadth of the claims is similar to the breadth in the specification. So, it is hard to understand the metes and bounds of the claim and as such understand the scope of the claim is difficult to understand.
Applicant’s Argument #2
Applicants argue on page(s) 8-11 of applicants remarks that “In this regard, the Office Action makes improper reference to Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016), (Office Action, 10) in merely pasting language of MPEP § 2106.05(d)(II) without even properly citing or applying the case. The MPEP and well-established precedents guide specifically against the Office’s approach here. Indeed, the MPEP might well have been describing the instant Office Action when it cautions … Here, the Office Action has neither considered the function and structure of specific claim elements, nor explained their relation to any of the nominally cited cases. For example, the Office Action’s reliance on Intellectual Ventures I v. Symantec Corp. 1s without merit. That decision involved claims that simply performed long-prevalent human practices, 1.e., filtering email, using generic computing functions. Intellectual Ventures I, the court found the claims ineligible because they recited nothing more than “filtering files/e-mail” using conventional computers, with no disclosed technical improvement to the operation of the computer itself.” (see applicants remarks for more details).
Response to Argument #2
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
It is unclear where applicants are saying Intellectual Ventures is cited by the examiner in the office action. Further details are requested.
Applicant’s Argument #3
Applicants argue on page(s) 8-11 of applicants remarks that “Here, as in McRO, the claims recite a specific architecture configured to generate a defined, non-abstract output. The amended claims require a Real-Time Data Mesh (RTDM) configured to harmonize heterogeneous enterprise data into canonical data using a canonical format, followed by the application of that structured data through an Advanced Analytics and Machine Learning (AAML) Module that generates and refines Anything-as-a-Service (AaS) options using prediction, feedback, and compatibility rules. These limitations do not recite the mere idea of “converting products to subscriptions” or “analyzing user preferences,” but instead claim a concrete, structured system that improves how enterprise systems can adapt product offerings dynamically across CRM, ERP, and vendor databases.” (see applicants remarks for more details).
Response to Argument #3
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
It should be noted that the following discussion is in view of McRO, Inc. v. Bandai Namco Games America Inc. (Fed. Cir. 2016):
In McRO v. Bandai, the courts used the following three criteria to judge if the claims are patent eligible under 35 U.S.C. 101:
“First, the claim’s preemptive effect—the extent to which it would “inhibit further discovery”—informs whether it has satisfied the second step of the Section 101 inquiry. Alice Corp., 134 S. Ct. at 2354. Claims that “do not attempt to preempt every application of the idea” generally satisfy the Section 101 threshold. DDR Holdings, 773 F.3d at 1259. Thus, claims that “recite a specific way” of accomplishing a task— including a specific kind of automation that “resolv[es] [a] particular Internet-centric problem”—do not broadly preempt any asserted abstract idea, and are therefore patent-eligible. Id.” (page 19).
In the case of McRO v. Bandai, the courts found that "the claims here do not preempt a broad building block that would unduly obstruct innovation. Instead, the claims describe a very specific means for providing automatic animation of lip synchronization of three-dimensional characters. There is no risk that the claims could foreclose innovation by others.” In contrast, the present application addresses the managing interaction points between a population of users, which is a broad building block that would unduly obstruct innovation.
“Second, the degree of detail regarding the application or implementation of the innovation informs whether it is eligible. As the Supreme Court has explained, courts must ask whether the limitations in “the patent claims,” taken as a whole, “add enough” in the way of specific, practical application to differentiate the scope of the claimed invention from the underlying abstract idea itself. Mayo, 132 S. Ct. at 1297. “Simply appending conventional steps, specified at a high level of generality, [is] not enough to supply an ‘inventive concept.’” Alice Corp., 134 S. Ct. at 2357. For this reason, merely adding a token reference to the application of a non-technical process “on a computer” is insufficient to establish eligibility.” (page 21).
In the case of McRO v. Bandai, the courts found that the detail contained in the claims strongly suggests they amount to a practical application of an idea, rather than effectively claiming the idea itself. In contrast, the present application does exactly what the courts say not to do….merely adding a token reference to the application of a non-technical process as is discussed in the additional elements of the 101 rejection above is insufficient to establish eligibility.
“Third, claims that improve technology are patent eligible. That is, claims that “improved an existing technological process” fall within the scope of Section 101. Alice Corp., 134 S. Ct. at 2358.” (page 22).
In the case of McRO v. Bandai, the courts found that “the claims here have an obvious technological effect: they aid in the technological goal of generating computer animation. These are not claims that contain mere token references to a computer or its use; instead, these claims are inherently tied to the digital creation of a three-dimensional computer animation. Indeed, the district court itself acknowledged that the claims “are tangible, each covering an approach to automated three dimensional computer animation, which is a specific technological process.” (Page 23). In contrast, the present application does exactly what the courts say not to do…. merely adding a token reference to the application of a non-technical process as is discussed in the additional elements of the 101 rejection above is insufficient to establish eligibility. The claims in the present application do not provide any improvement to existing technological processes.
As such, the previously made rejection under 35 U.S.C. 101 is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
Biswas et al. A proposed architecture for big data driven supply chain analytics. ICFAI University Press (IUP) Journal of Supply Chain Management, Vol XIII, No 3 (2016), pp. 7 – 34. https://arxiv.org/abs/1705.04958.
Foreign Reference:
(KR20070057806A) Beckerle et al. This reference recites an architecture for building and managing data integration processes. The architecture may provide modularity and extensibility to many aspects of the integration design process, including user interfaces, programmatic interfaces, services, components, runtime engines, and external connectors. The architecture may also employ a common integrated metadata sharing approach throughout the design process to enable seamless transitions between various phases of the design and implementation of a data integration process.
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/N.N.P/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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