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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 10, 12, 17, 19 and 20 are rejected under 35 U.S.C. 102 a1 as being anticipated by Minkin (US Pub. No. 20180165604) (hereinafter, Minkin).
As per claim 1,
Minken teaches,
a query language (QL) tool configured for receiving, translating and extracting data
related to a plurality of functions of the one or more applications, the tool comprises:
(claim 3 and paragraph 87, claim 3 discussing receiving and extracting data related to a plurality of functions of the applications; paragraphs 87 discussing the translation of the same)
an electronic user interface configured to receive a query data from the user.
(Claims 1-3, noting on claim 1: “steps for machine learning via a computer network using analytical workflows on a dataset that can adapt to user inputs and automatically suggest possibilities for further analysis…”)
a translator/interpreter for translating the query data into characters using
natural language processing (NLP) and generating a plurality of tokens.
(paragraphs 169 and 253, noting on paragraph 169 “…The question can be in the form of natural language or packaged as more complex user interface interactions…” ; noting on 253, “…semantic layers of AC processing may be defined for: raw data, published contracts, content profiles, raw semantic descriptions, ontology tokenizes into system analytic domain features, vocabulary tokens in a deep learning model that may produce output by analyzing a group of tables, and others…")
a code generator configured to receive the tokens from the translator/interpreter and generating generate a code using a data mapper and ingestion module for creating an Al based machine learning query
(Paragraphs 162 and 210, on paragraph 210 noting “…The Query Interface step includes steps for parsing user interaction, parsing user questions, generating queries, and performing predictions. Query generation can include steps for simply query construction, model selection, and model narration…”)
at least one data model created based on at least one attribute of the query data and the tokens, wherein the machine learning query is processed to extract a recommendation based on to-the query data received from the user, wherein a bot creates at least one script based on the at least one data models, the machine learning query, the at least one attribute of the query data and AI based processing logic for recommending an action/task to automatically re-calibrate the plurality of functions of the one or more applications
(Paragraphs 158 and 223, noting on paragraph 158 “…An exercise step 134 can include initial training, monitoring and measuring raw performance, determining or adjusting model content, and performing visualizations over data…”; noting on paragraph 223, "…Decision recommendations from the expert system and machine learning system can be constructed for each step in the AC workflow. At each step in the AC goal and task workflow hierarchy a specialized agent can be constructed that is responsible for combining workflow recommendations arising from the expert system and machine learning system…”)
As per claim 2,
Minken teaches,
the system of claim 1, wherein the tool is configured to attach the recommended
task/action to a desired workflow or User interface element or set of rules or validations
(paragraph 223; see above notations).
As per claim 3,
Minken teaches,
the system of claim 2, wherein the tool is configured to generate custom query
for accessing a data lake in real-time
(paragraph 159).
As per claim 10,
Minken teaches,
The system of claim 1, further comprises a test module configured to test the at
At least one data model and apply the at least one tested data model to the one or more
applications in real time wherein at least one tested model enables real time switching
between at least one data model each for different applications based on the functions
and efficiency of the tested data models wherein the switching occurs in real time using AI
based analysis of performance data of the data models, the received data, and the
functions related to one or more applications
(paragraphs 4, 143, 144, 158, 207,208, 215).
As per claim 17,
Claim 17 discloses similar limitations to claim 10, however, in a method form. Minken teaches their invention in such a form, see at least Abstract. Therefore, claim 17 is rejected based on similar citations/rationale as claim 10.
As per claim 12,
Claim 12 discloses similar limitations to claim 1, however, in a method form. Minken teaches their invention in such a form, see at least Abstract. Therefore, claim 12 is rejected based on similar citations/rationale as claim 1.
As per claim 19,
Claim 19 teaches similar limitations to claim 1, however, in a non-transitory computer readable storage medium. Minken teaches their invention in such a form as well, see claims at least. Therefore, claim 19 is rejected under similar limitation as claim 1 rejection and rationale.
As per claim 20,
Minken teaches,
the non-transitory computer readable storage medium of claim 19,
wherein the method is operations are performed in a cloud or cloud-based computing
environment
(paragraph 172).
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 should not be negated by the manner in which the invention was made.
Claims 4-6, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Minkin as applied to claims 1, 3, and 9 above, and further in view of Katz (US Pub. No. 2003/0033179) (Hereinafter, Katz).
As per claim 4,
Minken does not explicitly teach, however, Katz does teach,
the system of claim 1 wherein the one or more applications include enterprise
applications (EA) and supply chain management (SCM) applications
(paragraphs 40-42 discussing both applications)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Katz within the invention of Minkin with the motivation of a more reliable and efficient EA and SCM applications.
As per claim 5,
Minken does not explicitly teach, however, Katz does teach,
The system of claim 3, wherein the enterprise applications include finance
applications like automated billing applications and payment processing applications,
Customer relationship management applications (CRM) and enterprise resource planning
applications (ERP)
(paragraphs 40-42).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Katz within the invention of Minkin with the motivation of a more reliable, efficient, and accurate financing applications.
As per claim 6,
Minken does not explicitly teach, however, Katz does teach,
the system of claim 5, wherein the EA and SCM applications include a plurality
of nodes like inventory, logistics, warehouse, procurement, customers, supplier, retailers,
distributors, resellers, co-packers and transportation…
(paragraphs 40-42)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Katz within the invention of Minkin with the motivation of a more reliable and efficient EA and SCM applications.
Minken teaches,
… wherein the nodes interact with each other to structure the plurality of functions associated with the applications
(Figs. 1 & 4 and corresponding text).
As per claim 9,
Minken/Siebel do not explicitly teach; however, Katz does teach,
The system of claim 8, further comprises interconnected data across the
plurality of functions connecting demand with supply by combining customer and supplier
data in real time and thereby structuring a collaborative platform for multiple entities like
supplier, customer, factories and warehouses
(paragraphs 40-42)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Katz within the invention of Minkin with the motivation of a more reliable, efficient, and accurate data in real time.
As per claim16,
Claim 16 discloses similar limitations to claim 9, however, in a method form. Minken teaches their invention in such a form, see at least Abstract. Therefore, claim 16 is rejected based on similar citations/rationale as claim 9.
Claims 7-8, 11, 14, 15, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Minkin as applied to the claims above, and further in view of Siebel (US Pub. No. 2018/0191867) (Hereinafter, Siebel).
7. (Original)
Minken does not teach, However, Siebel does teach,
The system of claim 1, wherein the plurality of functions includes demand
planning, supply planning, production planning, forecasting, smart factory and fulfillment
planning.
(paragraph 521)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Siebel within the invention of Minkin with the motivation of adjusting the AI data for better results.
As per claim 8,
Minken does not teach, however, Siebel does teach,
the system of claim 7, wherein the recommended task/action includes auto
adjust data for the plurality of functions, risk mitigation, or direct interaction with the
plurality of nodes
(paragraph 481).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Siebel within the invention of Minkin with the motivation of adjusting the AI data for better results.
As per claim 11,
Minken does not teach, however, Siebel does teach,
the system of claim 1, wherein a data curator engine is configured to collect
data from distinct sources and act as a gateway to identify at least one data attribute
from the received query data that is to be extracted as assessed from one or more
applications
(paragraph 87).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Siebel within the invention of Minkin with the motivation of adjusting the AI data for better results.
As per claim 14,
Claim 14 discloses similar limitations to claim 7, however, in a method form. Minken teaches their invention in such a form, see at least Abstract. Therefore, claim 14 is rejected based on similar citations/rationale as claim 7.
As per claim 15,
Claim 15 discloses similar limitations to claim 8, however, in a method form. Minken teaches their invention in such a form, see at least Abstract. Therefore, claim 15 is rejected based on similar citations/rationale as claim 8.
As per claim 18,
Minken does not teach, however, Siebel does teach,
(Original) The method of claim 17, wherein the data lake further comprises a graph store
with data relations analytics for providing real-time recommendation based on a historical
data and relations wherein the graph store utilize a graph store library to detect complex
patterns and structures in the data model
(paragraph 235).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Siebel within the invention of Minkin with the motivation of adjusting the AI data for better results.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Minkin as applied to the claims above, and further in view of Khute (WO 2018/234741) (Hereinafter, Khute).
As per claim 13,
Minken does not explicitly teach, however, Khute does teach,
the method of claim 12, wherein the at least one data model includes
proactive detection algorithms for detecting any record/transactions being entered by the
user at a -the user interface, thereby ensuring a plurality of master tables of at least one
data models are clean, accurate, complete and non-fraudulent/non-duplicate at any point in
time and the data flowing through the one or more applications is clean and accurate
(Page 77).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Khute within the inventio of Minkin with the motivation of identifying “clean, accurate, complete” data models to use in the AI applications for better results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAHRA ELKASSABGI whose telephone number is (571)270-7943. The examiner can normally be reached Monday through Friday 11:30 to 8:00.
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ZAHRA. ELKASSABGI
Examiner
Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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