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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 17, 2026 has been entered.
In response to Applicant’s claims filed on February 17, 2026, claims 1-5, 7-20 are now pending for examination in the application.
Response to Arguments
The 112 rejection under USC 112 set forth in the 12/17/25 office action is hereby withdrawn.
This office action is in response to amendment filed 02/17/2026. In this action claim(s) 1, 9-10, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Colcord et al. (US Pub. No. 20220012363) and Siebel et al. (US Pub. No. 20220405775) and Gschwind et al. (US Pub. No. 20190056943) in further view of Zolotow et al. (US Pub. No. 20210004738). The Gschwind et al. reference has been added to address the amendment of testing confidence values of the data entities in comparison to a confidence threshold.
Applicant’s arguments:
In regards to claim 1 on Page(s) 21, applicant argues “The claimed feature increases accuracy and consistency by reducing delay associated with conventional enterprise search methodologies that fail to provide the required recommendations as they fail to provide different types of data entities required for building data products. Enterprise search tools for data entities are not context-aware and rely heavily on humans for contextual validations. As a result, accurate, fast searches are not enabled.”
Examiner’s Reply:
Generating recommendations based in context is a mental process. Using such a process in the context of product provisioning using a computer as a generic tool does not improve the functioning of a computer. Furthermore, Using ML modeling in the assistance and does not improve the functioning of a computer.
Applicant’s arguments:
In regards to claim 1 on Page(s) 26, applicant argues “In particular, the claimed subject matter addresses the technical challenge of providing relevant recommendations to make the process more effective and efficient, incomplete LDP definitions, difficulty in enabling self- service for the users and domain experts, lost opportunities, increased risk, timeline expansions, and high operational costs. The claimed subject matter enables an enhanced, context-aware text search for data entities and recommends context-aware, best-fit, and trustworthy data entities so that LDPs may be created and managed at scale.”
Examiner’s Reply:
Verifying the ability of a data product to be built using context as well as a variety of resources is a mental process. Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application.
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 1-5, 7-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1-5, 7-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the apparatus (claim(s) 1-4 and 6-14), method (claim(s) 15-18), storage media (claim(s) 19-20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mathematical Concepts and Mental Processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 15, and 19 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claim(s) 1 and 19 recites the following limitations directed towards a Mathematical Concepts and Mental Processes:
a user query analyzer that builds a conceptual data product (CDP) listing requirements from a user query requesting information that is to be generated by at least one physical data product (PDP) (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to build a conceptual data product);
a data product verfier that trains a plurality of ML models on labeled training data including user queries expressing different informational needs and a set of different types of PDPs that are marked as responsive to corresponding one of the user queries (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to build a conceptual data product), wherein the recommendation generator further includes:
a data product verifier that selects a ML model from a plurality of ML models based on model-parameter combination with highest performance metrics and the selected ML model determines if the at least one PDP responsive to the user query exists in an enterprise data entity catalog that lists PDPs of a plurality of PDP types, wherein if the at least one PDP cannot be identified in the enterprise data entity catalog (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determine a product’s existence),
the selected ML model identifies a type of the at least one PDP responsive to the user query (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determine a type of product), and
the selected ML model generates a logical data product (LDP) that represents data entities required to build anew the at least one PDP, wherein the LDP is generated based on the type of the at least one PDP to be built (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate logic for a data product), wherein the selected ML model generates the LDP by:
testing confidence values of the data entries in comparison to a confidence threshold (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to testing confidence values); and
extracting entity relationships and features of the data entities, wherein the features comprise recency, rating, data veracity metrics, past user acceptance or rejection statistics, and asset type (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to extracting relationships);
a data product builder that builds the at least one PDP by on a data store accessing the data entities identified in the LDP (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to build a physical data product).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1 and 19:
at least one hardware processor (i.e., as a generic processor/component performing a generic computer function); and
at least one non-transitory processor-readable medium (i.e., as a generic processor/component performing a generic computer function) storing instructions for and the at least one hardware processor executing:
an output generator that outputs as a reply to the user query, one of information from the at least one PDP and the at least one PDP (recites insignificant extra solution activity that amounts to outputting data).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Independent claim(s) 15 recites the following limitations directed towards a Mental Processes:
training, by a processor, a plurality of ML models on labeled training data including user queries expressing different informational needs and a set_of different types of Physical Data Products (PDPs), wherein the PDPs in the set of different types of PDPs are marked as responsive to corresponding one of the user queries (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to training ML models);
Generating, by a processor, an enhanced user query from a received user query, wherein the received user query includes requirements for information to be provided by a physical data product (PDP) (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a query enhancement);
extracting, by the processor, features of the mapped search results (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to extract features);
selecting, by the processor from the plurality of ML models trained on the training data and based on model-parameter combination, a machine learning (ML) model having highest performance metrics (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to select an ML model);
determining, by the processor, based on the features, that the PDP responsive to the user query is not stored on the plurality of data sources (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determining about a data product);
identifying, by the processor, by executing the selected ML model, a type of the PDP to be built based at least on the enhanced user query (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to identifying a type of data product);
automatically generating a logical data product (LDP) for building the PDP, wherein the LDP includes one or more of the data entities required for building the PDP (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a logical data product);
automatically generating, by the processor via the selected ML model, a configuration file for the PDP using natural language processing (NLP) on the LDP (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a configuration file);
automatically generating, by the processor, the code for building the PDP from the configuration file; and building the PDP by executing the code on a target platform (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate code).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 15:
retrieving, by the processor, mapped search results from a plurality of data sources using the enhanced user query, wherein the mapped search results include one or more of data products and data entities that constitute the data products (recites insignificant extra solution activity that amounts to retrieving data);
providing, by the processor, access to one of the PDP and an output of the PDP as a reply to the user query (recites insignificant extra solution activity that amounts to outputting data).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 15, and 19 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2:
Step 2A, prong one of the 2019 PEG:
wherein the user query analyzer implements a Key Bidirectional Encoder Representations from Transformers (KeyBERT) technique for extracting context-based keywords (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a data product).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3:
Step 2A, prong one of the 2019 PEG:
wherein the user query analyzer further generates an enhanced search query by combining the context-based keywords (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate an enhanced query).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4:
Step 2A, prong one of the 2019 PEG:
applying cosine similarity to the context-based keywords and contents of the enterprise data catalog (The limitation recites a mathematical concept; calculating similarities); and
applying sequence matching between the context-based keywords and contents of the enterprise data entity catalog (The limitation recites a mathematical concept; sequencing); and
comparing with respective thresholds to results of the cosine similarity and the sequence matching (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to compare a threshold).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5:
Step 2A, prong one of the 2019 PEG:
applying natural language processing (NLP) for tag based searching of the data entities (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to process natural language); and
extracting the entity relationships and the features of the data entities (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to extract relationships and features).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7:
Step 2A, prong one of the 2019 PEG:
wherein the selected ML model generates the LDP by further applying sentiment analysis to filter the data entities (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to filter data entities).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8:
Step 2A, prong one of the 2019 PEG:
wherein the LDP includes a knowledge graph with nodes representing the data entities and edges representing connections between the data entities (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a logical data product).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 9:
Step 2A, prong one of the 2019 PEG:
wherein the plurality of types of PDP include at least database tables, analytical models, and visualization dashboards (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a physical data product).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 10:
Step 2A, prong one of the 2019 PEG:
wherein to build the at least one PDP the data product builder generates a configuration file based on the type of PDP to be built, wherein the configuration file includes at least details of the data entities required to build the at least one PDP (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a physical data product).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 11:
Step 2A, prong one of the 2019 PEG:
wherein to build the at least one PDP, the data product builder:
automatically creates code from the configuration file (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to create code).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 12:
Step 2A, prong one of the 2019 PEG:
wherein the at least one PDP to be built includes at least one of the database tables and the data product builder:
automatically creates the code including Data Definition Language (DDL) and Data Manipulation Language (DML) statements (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to create code);
creating
Step 2A Prong Two Analysis:
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 13:
Step 2A, prong one of the 2019 PEG:
wherein the at least one PDP to be built is one of the visualization dashboards and the data product builder:
automatically creates the code including application commands associated with an application to build the visualization dashboard (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to create code);
automatically executes the application commands to create
Step 2A Prong Two Analysis:
automatically executes the application commands to
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 14:
Step 2A, prong one of the 2019 PEG:
wherein the PDP to be built is one of the analytical models and the data product builder:
automatically creates the code including a framework with a machine learning (ML) model and feature list (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to create code).
Step 2A Prong Two Analysis:
provides access to the ML model for training, wherein the reply to the user query is generated by the trained ML model (recites insignificant extra solution activity that amounts to accessing a ML model).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 16:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
Providing, by the processor, the mapped search results to the selected ML model (recites insignificant extra solution activity that amounts to providing search results).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 17:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
obtaining a set of the data entities from the mapped search results that are qualified for building the PDP (recites insignificant extra solution activity that amounts to obtaining data entities).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 18:
Step 2A, prong one of the 2019 PEG:
automatically creating, by the processor, the code including Data Definition Language (DDL) and Data Manipulation Language (DML) statements when the PDP includes a table (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to create code);
automatically creating, by the processor, the code including application commands associated with an application to build a visualization dashboard, when the PDP to be built includes the visualization dashboard (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to create code); and
automatically creating the code including a framework with a machine learning (ML) model and feature list wherein the PDP to be built includes the ML model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to create code).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 20:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
receive user feedback to the reply (recites insignificant extra solution activity that amounts to receiving data); and
fine tune parameters of a plurality of machine learning (ML) models accessed by the data product verifier based at least on the user feedback (recites insignificant extra solution activity that amounts to tuning model parameters).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
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, 9-10, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Colcord et al. (US Pub. No. 20220012363) and Siebel et al. (US Pub. No. 20220405775) and Gschwind et al. (US Pub. No. 20190056943) and Chew et al. (US Pub. No. 20220335553) in further view of Zolotow et al. (US Pub. No. 20210004738).
With respect to claim 1, Colcord et al. teaches an Artificial Intelligence (AI) based data product provisioning apparatus, comprising:
at least one hardware processor (Paragraph 35 discloses one or more processors); and
at least one non-transitory processor-readable medium (Paragraph 10 discloses a computer-readable medium) storing instructions for and the at least one hardware processor executing:
a user query analyzer that builds a conceptual data product (CDP) listing requirements from a user query requesting information that is to be generated by at least one physical data product (PDP) (Paragraph 46 discloses analyze the data in data system 110 in response to one or more queries from client device 140 and Paragraph 47 discloses data products can include those generated by data analysis and discovery unit 118). Colcord et al. does not discloses a data product verifier that determines if the at least one PDP responsive to the user query exists in an enterprise data entity catalog that lists PDPs of a plurality of PDP types, wherein if the at least one PDP cannot be identified in the enterprise data entity catalog, the data product verifier identifies a type of the at least one PDP, and the data product verifier generates a logical data product (LDP) that represents data entities required to build the at least one PDP, wherein the LDP is generated based on the type of the at least one PDP to be built. Colcord et al. does not disclose a data product verifier that trains...; a data product verifier that selects…; the selected ML model identifies a type…; the selected ML model generates…
However, Siebel et al. teaches a data product verifier that trains a plurality of ML models on labeled training data including user queries expressing different informational needs and a set of different types of PDPs that are marked as responsive to corresponding one of the user queries (Paragraph 30 discloses Each CRM function is associated with and configured to use one or more trained machine learning models and one or more data models. The method also includes administering, using a model orchestrator, usage of the machine learning models and the data models based on (i) the at least one CRM function of the multiple CRM functions being executed and (ii) a specified use case associated with the at least one CRM function being executed),
wherein the recommendation generator further includes:
a data product verifier that selects a machine learning (ML) model from a plurality of ML models based on model-parameter combination with highest performing metrics and the selected ML model determines if the at least one PDP responsive to the user query exists in an enterprise data entity catalog that lists PDPs of a plurality of PDP types (Paragraph 170 discloses Entity type definitions may include data validation constraints to declare which fields are required, define a permissible list of values, and/or implement indexing to improve performance),
wherein if the at least one PDP cannot be identified in the enterprise data entity catalog,
the selected ML model identifies a type of the at least one PDP responsive to the user query (Paragraph 169 discloses developers of the type system or cyber-physical system may benefit from abstraction between types, functions, or modules within the type system. The type system may be defined by metadata or circuitry within the model-driven architecture for distributed systems. The type system may include a collection of modules and types. The modules may include a collection of types that are grouped based on related types or functionality. The types may include definitions for types, data, data shapes, application logic functions, validation constraints, machine learning classifiers and/or UI layout), and
the selected ML model generates a logical data product (LDP) that represents data entities required to build anew the at least one PDP, wherein the LDP is generated based on the type of the at least one PDP to be built, wherein the selected ML model generates the LDP by (Paragraph 425 discloses Case ML management can be used to manage customer service requests with end-to-end case management workflows for financial services that support complex product and service catalogues across multiple different customer segments and to use machine learning to proactively recommend actions to resolve the cases).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics with Siebel et al.’s artificial intelligence. This would have improved customer relationship management.
Colcord et al. as modified by Siebel et al. does not disclose testing confidence values…; extracting entity relationships and features.
However, Gschwind et al. discloses testing confidence values of the data entities in comparison to a confidence threshold (Paragraph 154 discloses test of whether a prediction confidence exceeds a threshold value).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence with Gschwind et al.’s value derivation. This would have improved provisioning of a product.
Colcord et al. reference as modified by Siebel et al. and Gschwind et al. does not disclose extracting entity relationships and features of the data entities…
However, Chew et al. teaches extracting entity relationships and features of the data entities, wherein the features comprise recency, rating, data veracity metrics, past user acceptance or rejection statistics, and asset type (Paragraph 37 discloses the knowledge graph logic algorithms may learn from the use of the operative data in different digital contract scenarios, and accordingly may enrich the content of the graph database e.g. by creating new relationship links between statements, updating the risk factor value, updating the expected output values for each operative statement and Paragraph 73 discloses operative data may further comprise information relating to the operative statement such as sector, type of transactions involved in the digital contract, type of assets or services involved, type of principals, medium of exchange, and the like).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence and Gschwind et al.’s value derivation with Chew et al.’s digital contracts. This would have improved provisioning of services/products.
Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. does not disclose a data product builder that builds …; and an output generator that outputs…
However, Zolotow et al. teaches a data product builder that builds the at least one PDP by accessing the data entities identified in the LDP (Paragraph 70 discloses the PI triggers the expansion of this existing service (see operations S810 and S816). If there is a high number of similar requests, which are not similar to an existing service in the service catalog, the PI triggers the development of a new service (see operations S812 and S818)); and
an output generator that outputs as a reply to the user query, one of information from the at least one PDP and the at least one PDP (Paragraph 70 discloses actual decision to adapt the catalog will typically be done by a human. Small changes to existing service (for example, a new tier for the size of storage or of a mailbox) could automatically be applied to the corresponding service and entry in the service catalog, that is without human approval. The decision of the approver to approve or reject a specific request flows back to the PI to adapt the corresponding thresholds).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence and Gschwind et al.’s value derivation Chew et al.’s digital contracts with Zolotow et al.’s service catalog. This would have improved service routing.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. teaches all the limitations of claim 1. With respect to claim 9, Colcord et al. teaches the AI-based data product provisioning apparatus of claim 1, wherein the plurality of types of PDP include at least database tables, analytical models, and visualization dashboards (Paragraph 47 discloses Such data products can include those generated by data analysis and discovery unit 118. The data consumers can be or include client device 140. The data products may include, but are not limited to, a data exchange product, a customer engagement product, a data connection product, a data governance product, a data customization product, a data optimization product, a data analysis product, and a data exploration product).
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. teaches all the limitations of claim 1. With respect to claim 10, Colcord et al. teaches the AI-based data product provisioning apparatus of claim 9, wherein to build the at least one PDP the data product builder generates a configuration file based on the type of PDP to be built, wherein the configuration file includes at least details of the data entities required to build the at least one PDP (Paragraph 145 discloses a YAML or JSON file describing the configuration of flow service 1210 can be consumed by an infrastructure-as-code application (e.g., Terraform, AWS CloudFormation, or the like) to create flow service 1210 on a cloud computing platfor).
With respect to claim 19, Colcord et al. teaches a non-transitory processor-readable storage medium storing machine-readable instructions for and a processor executing:
a user query analyzer that builds a conceptual data product (CDP) listing requirements from a user query requesting information that is to be generated by at least one physical data product (PDP) (Paragraph 46 discloses analyze the data in data system 110 in response to one or more queries from client device 140 and Paragrph 47 discloses data products can include those generated by data analysis and discovery unit 118). Colcord et al. does not discloses a data product verifier that determines if the at least one PDP responsive to the user query exists in an enterprise data entity catalog that lists PDPs of a plurality of PDP types, wherein if the at least one PDP cannot be identified in the enterprise data entity catalog, the data product verifier identifies a type of the at least one PDP, and the data product verifier generates a logical data product (LDP) that represents data entities required to build the at least one PDP, wherein the LDP is generated based on the type of the at least one PDP to be built. Colcord et al. does not disclose a data product verifier that trains...; a data product verifier that selects…; the selected ML model identifies a type…; the selected ML model generates…
However, Siebel et al. teaches a data product verifier that trains a plurality of ML models on labeled training data including user queries expressing different informational needs and a set of different types of PDPs that are marked as responsive to corresponding one of the user queries (Paragraph 30 discloses Each CRM function is associated with and configured to use one or more trained machine learning models and one or more data models. The method also includes administering, using a model orchestrator, usage of the machine learning models and the data models based on (i) the at least one CRM function of the multiple CRM functions being executed and (ii) a specified use case associated with the at least one CRM function being executed),
wherein the recommendation generator further includes:
a data product verifier that selects a machine learning (ML) model from a plurality of ML models based on model-parameter combination with highest performing metrics and the selected ML model determines if the at least one PDP responsive to the user query exists in an enterprise data entity catalog that lists PDPs of a plurality of PDP types (Paragraph 170 discloses Entity type definitions may include data validation constraints to declare which fields are required, define a permissible list of values, and/or implement indexing to improve performance),
wherein if the at least one PDP cannot be identified in the enterprise data entity catalog,
the selected ML model identifies a type of the at least one PDP responsive to the user query (Paragraph 169 discloses developers of the type system or cyber-physical system may benefit from abstraction between types, functions, or modules within the type system. The type system may be defined by metadata or circuitry within the model-driven architecture for distributed systems. The type system may include a collection of modules and types. The modules may include a collection of types that are grouped based on related types or functionality. The types may include definitions for types, data, data shapes, application logic functions, validation constraints, machine learning classifiers and/or UI layout), and
the selected ML model generates a logical data product (LDP) that represents data entities required to build anew the at least one PDP, wherein the LDP is generated based on the type of the at least one PDP to be built, wherein the selected ML model generates the LDP by (Paragraph 425 discloses Case ML management can be used to manage customer service requests with end-to-end case management workflows for financial services that support complex product and service catalogues across multiple different customer segments and to use machine learning to proactively recommend actions to resolve the cases).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics with Siebel et al.’s artificial intelligence. This would have improved customer relationship management.
Colcord et al. as modified by Siebel et al. does not disclose testing confidence values…; extracting entity relationships and features.
However, Gschwind et al. discloses testing confidence values of the data entities in comparison to a confidence threshold (Paragraph 154 discloses test of whether a prediction confidence exceeds a threshold value);
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence with Gschwind et al.’s value derivation. This would have improved provisioning of a product.
Colcord et al. reference as modified by Siebel et al. and Gschwind et al. does not disclose extracting entity relationships and features of the data entities…
However, Chew et al. teaches extracting entity relationships and features of the data entities, wherein the features comprise recency, rating, data veracity metrics, past user acceptance or rejection statistics, and asset type (Paragraph 37 discloses the knowledge graph logic algorithms may learn from the use of the operative data in different digital contract scenarios, and accordingly may enrich the content of the graph database e.g. by creating new relationship links between statements, updating the risk factor value, updating the expected output values for each operative statement and Paragraph 73 discloses operative data may further comprise information relating to the operative statement such as sector, type of transactions involved in the digital contract, type of assets or services involved, type of principals, medium of exchange, and the like).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence and Gschwind et al.’s value derivation with Chew et al.’s digital contracts. This would have improved provisioning of services/products.
Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. does not disclose a data product builder that builds …; and an output generator that outputs…
However, Zolotow et al. teaches a data product builder that builds the at least one PDP by accessing the data entities identified in the LDP (Paragraph 70 discloses the PI triggers the expansion of this existing service (see operations S810 and S816). If there is a high number of similar requests, which are not similar to an existing service in the service catalog, the PI triggers the development of a new service (see operations S812 and S818)); and
an output generator that outputs as a reply to the user query, one of information from the at least one PDP and the at least one PDP (Paragraph 70 discloses actual decision to adapt the catalog will typically be done by a human. Small changes to existing service (for example, a new tier for the size of storage or of a mailbox) could automatically be applied to the corresponding service and entry in the service catalog, that is without human approval. The decision of the approver to approve or reject a specific request flows back to the PI to adapt the corresponding thresholds).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence and Gschwind et al.’s value derivation Chew et al.’s digital contracts with Zolotow et al.’s service catalog. This would have improved service routing.
Claim(s) 2-5, 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Colcord et al. (US Pub. No. 20220012363) and Siebel et al. (US Pub. No. 20220405775) and Gschwind et al. (US Pub. No. 20190056943) and Chew et al. (US Pub. No. 20220335553) and Zolotow et al. (US Pub. No. 20210004738) in view of Cella et al. (US Pub. No. 20220245574).
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. teaches all the limitations of claim 1. With respect to claim 2, Colcord et al. as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. does not disclose wherein the user query analyzer implements a Key Bidirectional Encoder Representations from Transformers (KeyBERT) technique for extracting context-based keywords.
However, Cella et al. teaches the AI-based data product provisioning apparatus of claim 1, wherein the user query analyzer implements a Key Bidirectional Encoder Representations from Transformers (KeyBERT) technique for extracting context-based keywords (Paragraph 3606 discloses models include recurrent neural networks (RNNs) such as long short-term memory (LSTM), deep learning models such as transformers, decision trees, support-vector machines, genetic algorithms, Bayesian networks, and regression analysis. Examples of systems based on a transformer model include bidirectional encoder representations from transformers (BERT) and generative pre-trained transformer (GPT). Training a machine-learning model may include supervised learning (for example, based on labelled input data), unsupervised learning, and reinforcement learning).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence and Gschwind et al.’s value derivation and Chew et al.’s digital contracts and Zolotow et al.’s service catalog with Cella et al.’s digital products. This would have improved data product and enterprise management.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. and Cella et al. teaches all the limitations of claim 2. With respect to claim 3, Colcord et al. teaches the AI-based data product provisioning apparatus of claim 2, wherein the user query analyzer further generates an enhanced search query by combining the context-based keywords (Paragraph 46 discloses in response to one or more queries from client device 140. Data analysis and discovery unit 118 may employ a data science lab to analyze the structured or unstructured data from the data access layer in data organization and processing unit 114. Data analysis and discovery unit 118 may also leverage an external data system containing any form of structured or unstructured data, to perform the analysis).
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. and Cella et al. teaches all the limitations of claim 3. With respect to claim 4, Zolotow et al. teaches the AI-based data product provisioning apparatus of claim 3, wherein the user query analyzer determines if the at least one PDP responsive to the user query exists in the enterprise data entity catalog by:
applying cosine similarity to the context-based keywords and contents of the enterprise data catalog (Paragraph 55 discloses the DCUC/PI include a unique method to identify similarity matching patterns on the consumer/enterprise requests to add, update or remove services in a service catalog); and
applying sequence matching between the context-based keywords and contents of the enterprise data entity catalog (Paragraph 55 discloses the DCUC/PI include a unique method to identify similarity matching patterns on the consumer/enterprise requests to add, update or remove services in a service catalog); and
comparing with respective thresholds to results of the cosine similarity and the sequence matching (Paragraph 67 discloses If the usage of specific services of the service catalog is below a certain threshold, it is tested whether there are specific reasons (for example, only seasonal usage). This test can be based on historical data provided by the repository of the DCUC. Based on this analysis, a recommendation to remove this specific service from the catalog could be developed). The motivation to combine statement previously provided in the rejection of dependent claim 3 provided above, combining the Colcord et al. reference and the Zolotow et al. reference is applicable to dependent claim 4.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. and Cella et al. teaches all the limitations of claim 3. With respect to claim 5, Siebel et al. teaches the AI-based data product provisioning apparatus of claim 3, wherein the data product verifier generates the LDP by:
applying natural language processing (NLP) for tag based searching of the data entities (Paragraph 203 discloses one virtual-feature called “CRM Data” might contain features that document how many emails or other communications have been sent to a customer, how long an opportunity has been in a current stage, the tone of the customer emails (as determined by natural language processing), and how many calls have been scheduled with the customer (among many others)); and
extracting the entity relationships and the features of the data entities (Paragraph 350 discloses the machine learning model features can be extracted from outputs of the one or more machine learning models). The motivation to combine statement previously provided in the rejection of dependent claim 3 provided above, combining the Colcord et al. reference and the Siebel et al. reference is applicable to dependent claim 5.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. and Cella et al. teaches all the limitations of claim 5. With respect to claim 7, Cella et al. teaches the AI-based data product provisioning apparatus of claim 5, wherein the data product verifier generates the LDP by further applying sentiment analysis to filter the data entities (Paragraph 1966 discloses he model 10213 may be trained to predict behavior and purchase patterns of one or more customers to provide personalized sales, marketing, advertising, promotion and/or customer service. In embodiments, the machine learning system 10210 may train the model using customer data and one or more outcomes associated with customer response to a personalized campaign, such as using various data sources that provide insight into consumer sentiment, behavior, or the like, including search engines, news sites, websites, behavioral analytic systems and algorithms, consumer sentiment measures, microeconomic measures, macroeconomic measures, and many others). The motivation to combine statement previously provided in the rejection of dependent claim 5 provided above, combining the Colcord et al. reference and the Cella et al. reference is applicable to dependent claim 7.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. and Cella et al. teaches all the limitations of claim 5. With respect to claim 8, Cella et al. teaches the AI-based data product provisioning apparatus of claim 5, wherein the LDP includes a knowledge graph with nodes representing the data entities and edges representing connections between the data entities (Paragraph 323 discloses graph database (e.g., graph database architectures 1124) may include the knowledge graph or the knowledge graph may be an example of the graph database. In example embodiments, the knowledge graph may include ontology and connections (e.g., relationships) between the ontology of the knowledge graph). The motivation to combine statement previously provided in the rejection of dependent claim 5 provided above, combining the Colcord et al. reference and the Siebel et al. reference is applicable to dependent claim 8.
Claim(s) 11-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Colcord et al. (US Pub. No. 20220012363) and Siebel et al. (US Pub. No. 20220405775) and Gschwind et al. (US Pub. No. 20190056943) and Chew et al. (US Pub. No. 20220335553) and Zolotow et al. (US Pub. No. 20210004738) in further view of Ricard et al. (US Pub. No. 20200133932).
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. teaches all the limitations of claim 10. With respect to claim 11, Colcord et al. as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. does not disclose automatically creates code from the configuration file.
However, Ricard et al. teaches AI-based data product provisioning apparatus of claim 10, wherein to build the at least one PDP, the data product builder:
automatically creates code from the configuration file (Paragraph 73 discloses a process for creating a DF table is performed automatically based on the stored information, without intervention or assistance from a customer).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence and Gschwind et al.’s value derivation and Chew et al.’s digital contracts and Zolotow et al.’s service catalog with Ricard et al.’s data foundation fragments. This would have improved data usage in a business enterprise.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. and Ricard et al. teaches all the limitations of claim 11. With respect to claim 12, Ricard et al. teaches the AI-based data product provisioning apparatus of claim 11, wherein the at least one PDP to be built includes at least one of the database tables and the data product builder:
automatically creates the code including Data Definition Language (DDL) and Data Manipulation Language (DML) statements (Paragraph 68 discloses A process referred to as materialization may issue data manipulation language (DML) and data definition language (DDL) SQL on the customer specified database); and language
creating and storing the database table in a target database by automatically executing the DDL and DML statements on the target database (Paragraph 68 discloses A process referred to as materialization may issue data manipulation language (DML) and data definition language (DDL) SQL on the customer specified database). The motivation to combine statement previously provided in the rejection of dependent claim 11 provided above, combining the Colcord et al. reference and the Ricard et al. reference is applicable to dependent claim 12.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. and Ricard et al. teaches all the limitations of claim 11. With respect to claim 13, Ricard et al. teaches the AI-based data product provisioning apparatus of claim 11, wherein the at least one PDP to be built is one of the visualization dashboards and the data product builder:
automatically creates the code including application commands associated with an application to build the visualization dashboard (Paragraph 43 discloses Database 230 may execute instructions corresponding to the SQL statements to generate query results (i.e., data mapped to the objects selected by the user). The query results may be presented to the user in a view including, for example, a report, a dashboard, or other record); and
automatically executes the application commands to create and store the visualization dashboard on a target platform (Paragraph 43 discloses Database 230 may execute instructions corresponding to the SQL statements to generate query results (i.e., data mapped to the objects selected by the user). The query results may be presented to the user in a view including, for example, a report, a dashboard, or other record). The motivation to combine statement previously provided in the rejection of dependent claim 11 provided above, combining the Colcord et al. reference and the Ricard et al. reference is applicable to dependent claim 13.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al. and Chew et al. and Zolotow et al. and Ricard et al. teaches all the limitations of claim 11. With respect to claim 14, Ricard et al. teaches the AI-based data product provisioning apparatus of claim 11, wherein the PDP to be built is one of the analytical models and the data product builder:
automatically creates the code including a framework with a machine learning (ML) model and feature list (Paragraph 82 discloses machine learning because they perform self-learning based on the above mentioned events (including service requests, service usage, human decisions). Starting with an initial catalog, the underlying, mathematical system/algorithm (including patterns and service catalog) is continuously adapted based on the evaluation of the above events); and
provides access to the ML model for training, wherein the reply to the user query is generated by the trained ML model (Paragraph 82 discloses the result is an optimized model of the required service catalogue with optimized corresponding recommendations). The motivation to combine statement previously provided in the rejection of dependent claim 11 provided above, combining the Colcord et al. reference and the Zolotow et al. reference is applicable to dependent claim 13.
With respect to claim 15, Colcord et al. teaches an Artificial Intelligence (AI) based method of automatically provisioning data products including:
Generating, by a processor, an enhanced user query from a received user query, wherein the received user query includes requirements for information to be provided by a physical data product (PDP) (Paragraph 46 discloses in response to one or more queries from client device 140. Data analysis and discovery unit 118 may employ a data science lab to analyze the structured or unstructured data from the data access layer in data organization and processing unit 114. Data analysis and discovery unit 118 may also leverage an external data system containing any form of structured or unstructured data, to perform the analysis);
retrieving, by the processor, mapped search results from a plurality of data sources using the enhanced user query, wherein the mapped search results include one or more of data products and data entities that constitute the data products (Paragrph 47 discloses data products can include those generated by data analysis and discovery unit 118);
extracting, by the processor, features of the mapped search results (Paragraph 8 discloses extract data from an append-only first data store; extract identifying characteristics from the extracted data). Colcord et al. does not disclose training, by a processor, a plurality of ML models…; selecting, by the processor from the plurality of ML models trained on the training data and based _on model-parameter combination, a machine learning (ML) model…
However, Siebel et al. teaches training, by a processor, a plurality of ML models on labeled training data including user queries expressing different informational needs and a set_of different types of Physical Data Products (PDPs), wherein the PDPs in the set of different types of PDPs are marked as responsive to corresponding one of the user queries ();
selecting, by the processor from the plurality of ML models trained on the training data and based _on model-parameter combination, a machine learning (ML) model having highest performance metrics (Paragraph 30 discloses Each CRM function is associated with and configured to use one or more trained machine learning models and one or more data models. The method also includes administering, using a model orchestrator, usage of the machine learning models and the data models based on (i) the at least one CRM function of the multiple CRM functions being executed and (ii) a specified use case associated with the at least one CRM function being executed);
selecting, by the processor from the plurality of ML models trained on the training data and based on model-parameter combination, a machine learning (ML) model having highest performance metrics (Paragraph 170 discloses Entity type definitions may include data validation constraints to declare which fields are required, define a permissible list of values, and/or implement indexing to improve performance).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics with Siebel et al.’s artificial intelligence. This would have improved customer relationship management.
Colcord et al. as modified by Siebel et al. does not disclose determining based on the features, that the PDP responsive to the user query is not store…; identifying, by the processor, by executing the selected ML model, a type of the PDP...; automatically generating, by the processor, via the selected ML model, a logical data product…; automatically, by the processor, generating a configuration file for the PDP…; building, by the processor, the PDP…; and providing, by the processor, access to one of the PDP…
However, Zolotow et al. discloses determining, by the processor, based on the features, that the PDP responsive to the user query is not stored on the plurality of data sources (Paragraph 61 discloses services not part of the catalog block 518);
identifying, by the processor, by executing the selected ML model, a type of the PDP to be built based at least on the enhanced user query (Paragraph 42 discloses environment type and application type. Alternatively, other parameters related to a provision of information technology service);
automatically generating, by the processor, via the selected ML model, a logical data product (LDP) for building the PDP, wherein the LDP includes one or more of the data entities required for building the PDP, wherein the generating the LDP comprises (Paragraph 65 discloses Recommendations of the DCUC to update the service catalog will be approved by the owner of the service catalog. Small changes to existing service (for example a new tier for the size of storage or of a mailbox) could automatically be applied to the corresponding service and entry in the service catalog, that is without human approval and Paragrapg 77 discloses uses AI based mechanisms to enhance a service catalog; and/or (iii) relies on data collection and analytics to derive service catalog optimization);
automatically, by the processor, generating a configuration file for the PDP using natural language processing (NLP) on the LDP (Paragraph 83 discloses cognitive systems include self-learning technologies that use data mining, pattern recognition and natural language processing to mimic the way the human brain works);
building, by the processor, the PDP by executing the code on a target platform (Paragraph 70 discloses the PI triggers the expansion of this existing service (see operations S810 and S816). If there is a high number of similar requests, which are not similar to an existing service in the service catalog, the PI triggers the development of a new service (see operations S812 and S818)); and
providing, by the processor, access to one of the PDP and an output of the PDP as a reply to the user query (Paragraph 70 discloses actual decision to adapt the catalog will typically be done by a human. Small changes to existing service (for example, a new tier for the size of storage or of a mailbox) could automatically be applied to the corresponding service and entry in the service catalog, that is without human approval. The decision of the approver to approve or reject a specific request flows back to the PI to adapt the corresponding thresholds).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence with Zolotow et al.’s service catalog. This would have improved service routing.
Colcord et al. as modified by Siebel et al. and Zolotow et al. does not disclose testing confidence values…; extracting entity relationships and features.
However, Gschwind et al. discloses testing confidence values of the data entities in comparison to a confidence threshold (Paragraph 154 discloses test of whether a prediction confidence exceeds a threshold value).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence with Gschwind et al.’s value derivation. This would have improved provisioning of a product.
Colcord et al. reference as modified by Siebel et al. and Gschwind et al. does not disclose extracting entity relationships and features of the data entities…
However, Allen et al. teaches extracting entity relationships and features of the data entities, wherein the features comprise recency, rating, data veracity metrics, past user acceptance or rejection statistics, and asset type (Paragraph 5 discloses extracting relationships between the terms of each of the cell documents using an extractor configured to perform statistical modelling that takes into account linguistic feature statistics);
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence and Gschwind et al.’s value derivation with Chew et al.’s digital contracts. This would have improved information searching for provisioning.
Colcord et al. as modified by Siebel et al. and Chew et al. and Zolotow et al. and Gschwind et al. does not disclose automatically generating the code for building the PDP from the configuration file…
However, Ricard et al. teaches automatically generating, by the processor, the code for building the PDP from the configuration file (Paragraph 73 discloses a process for creating a DF table is performed automatically based on the stored information, without intervention or assistance from a customer).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Colcord et al.’s data analytics and Siebel et al.’s artificial intelligence and Gschwind et al.’s value derivation and Chew et al.’s digital contracts and Zolotow et al.’s service catalog with Ricard et al.’s data foundation fragments. This would have improved data usage in a business enterprise.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al and Chew et al. and Zolotow et al. and Gschwind et al. and Ricard et al. teaches all the limitations of claim 15. With respect to claim 16, Zolotow et al. teaches the AI-based method of automatically provisioning data products of claim 15, wherein determining that the PDP responsive to the user query is not stored in the plurality of data sources further includes:
Providing, by the processor, the mapped search results to the selected ML model (Paragraph 82 discloses the result is an optimized model of the required service catalogue with optimized corresponding recommendations). The motivation to combine statement previously provided in the rejection of dependent claim 15 provided above, combining the Colcord et al. reference and the Zolotow et al. reference is applicable to dependent claim 16.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al and Chew et al. and Zolotow et al. and Ricard et al. teaches all the limitations of claim 15. With respect to claim 17, Zolotow et al. teaches the AI-based method of automatically provisioning data products of claim 15, wherein generating the LDP for building the PDP further includes:
Obtaining, by the processor, a set of the data entities from the mapped search results that are qualified for building the PDP (Paragraph 65 discloses Recommendations of the DCUC to update the service catalog will be approved by the owner of the service catalog. Small changes to existing service (for example a new tier for the size of storage or of a mailbox) could automatically be applied to the corresponding service and entry in the service catalog, that is without human approval and Paragrapg 77 discloses uses AI based mechanisms to enhance a service catalog; and/or (iii) relies on data collection and analytics to derive service catalog optimization). The motivation to combine statement previously provided in the rejection of dependent claim 15 provided above, combining the Colcord et al. reference and the Zolotow et al. reference is applicable to dependent claim 17.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al and Chew et al. and Zolotow et al. and Ricard et al. teaches all the limitations of claim 17. With respect to claim 18, Ricard et al. teaches the AI-based method of automatically provisioning data products of claim 17, wherein automatically generating the code for building the PDP from the configuration file further includes:
automatically creating, by the processor, the code including Data Definition Language (DDL) and Data Manipulation Language (DML) statements when the PDP includes a table (Paragraph 68 discloses A process referred to as materialization may issue data manipulation language (DML) and data definition language (DDL) SQL on the customer specified database);
automatically creating, by the processor, the code including application commands associated with an application to build a visualization dashboard, when the PDP to be built includes the visualization dashboard (Paragraph 43 discloses Database 230 may execute instructions corresponding to the SQL statements to generate query results (i.e., data mapped to the objects selected by the user). The query results may be presented to the user in a view including, for example, a report, a dashboard, or other record); and
automatically creating the code including a framework with a machine learning (ML) model and feature list wherein the PDP to be built includes the ML model (Paragraph 82 discloses machine learning because they perform self-learning based on the above mentioned events (including service requests, service usage, human decisions). Starting with an initial catalog, the underlying, mathematical system/algorithm (including patterns and service catalog) is continuously adapted based on the evaluation of the above events). The motivation to combine statement previously provided in the rejection of dependent claim 17 provided above, combining the Colcord et al. reference and the Ricard et al. reference is applicable to dependent claim 18.
The Colcord et al. reference as modified by Siebel et al. and Gschwind et al and Chew et al. and Zolotow et al. and Ricard et al. teaches all the limitations of claim 19. With respect to claim 20, Zolotow et al. teaches the non-transitory processor-readable storage medium of claim 19, including further instructions that cause the processor to:
receive user feedback to the reply (Paragraph 51 discloses the user is given an opportunity to communicate whether Service X was satisfactory with respect to the user's needs); and
fine tune parameters of a plurality of machine learning (ML) models accessed by the data product verifier based at least on the user feedback (Paragraph 82 discloses use machine learning because they perform self-learning based on the above mentioned events (including service requests, service usage, human decisions). Starting with an initial catalog, the underlying, mathematical system/algorithm (including patterns and service catalog) is continuously adapted based on the evaluation of the above events. The system/algorithm is permanently trained by service requests, service usage and human decisions. In some embodiments, the result is an optimized model of the required service catalogue with optimized corresponding recommendations).
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-PUB 20220138004 is directed to SYSTEM AND METHOD FOR AUTOMATED PRODUCTION AND DEPLOYMENT OF PACKAGED AI SOLUTIONS: [0063] The AI OS may comprise an execution engine for constructing and/orchestrating the execution of a data processing pipeline and enable automatic provisioning of optimal computing resources for each of the functions, tasks or operations for each block. The AI OS may provide a free standing CLI that is accessible by a user to construct pipelines, utilize local and/or distributed computing systems, and execute all capabilities and functions provided by the AI OS for managing the full lifecycle of ML solutions or application development.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
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