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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. IN-202311018291, filed on 03/17/2023.
Specification
The disclosure is objected to because of the following informalities:
Para. [0085] details that the clustering algorithm of K-medoids is supervised. K-medoids is an unsupervised learning algorithm. Appropriate correction is required.
Claim Objections
Claims 19-20 are objected to because of the following informalities: Claim 19 recites the storage medium when it should recite the non-transitory computer readable storage medium. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 3 and 12 recite the limitation “the corresponding at least one client.” There is insufficient antecedent basis for this limitation in the claim.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 partly recites the following limitations:
A method for facilitating automated data analytics in real-time via artificial intelligence, the method being implemented...the method comprising:
triggering using a lambda function, a transformation process for the raw data;
persisting...the at least one structured data set in a repository...
determining...using at least one model, at least one predictive output based on the persisted at least one structured data set;
These limitations, as drafted, are a process under Step 1 that that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
by at least one processor
automatically aggregating, by the at least one processor in real-time, raw data from at least one source, the at least one source including a source application that persists the raw data in a data storage container
generating, by the at least one processor based on an output of the transformation process, at least one structured data set from the aggregated raw data;
the repository including a distributed database
and generating, by the at least one processor in real-time, at least one dashboard by using the at least one predictive output and the at least one structured data set.
The additional claim elements of by at least one processor/ by the at least one processor, and the repository including a distributed database are recited at a high-level of generality using generic computer components (i.e., using a generic processor and generic database to perform generic computer functions) such that it does not amount to a particular machine.
The additional claim elements of automatically aggregating, by the at least one processor in real-time, raw data from at least one source, the at least one source including a source application that persists the raw data in a data storage container; generating, by the at least one processor based on an output of the transformation process, at least one structured data set from the aggregated raw data; and generating, by the at least one processor in real-time, at least one dashboard by using the at least one predictive output and the at least one structured data set amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., aggregating and transforming data to be output using a user interface/dashboard).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above, by at least one processor/ by the at least one processor, and the repository including a distributed database are recited at a high-level of generality using generic computer components (i.e., using a generic processor and generic database to perform generic computer functions) such that it does not amount to a particular machine. And automatically aggregating, by the at least one processor in real-time, raw data from at least one source, the at least one source including a source application that persists the raw data in a data storage container; generating, by the at least one processor based on an output of the transformation process, at least one structured data set from the aggregated raw data; and generating, by the at least one processor in real-time, at least one dashboard by using the at least one predictive output and the at least one structured data set are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Versata Dev. Group, Inc, and Symantec as cited in MPEP §2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network along with grouping of data to be displayed using a generic computer are well-understood, routine, and conventional function when claimed in a merely generic manner (as it is here). Even when considered in combination, these additional elements represent mere instructions to apply an exception with well understood, routine, and conventional activities, which does not provide significantly more to the abstract idea.
Accordingly, claim 1 is not patent eligible.
Claim 2 partly recites the following limitations:
The method of claim 1, further comprising: exposing...the at least one predictive output and the at least one structured data set by, automatically generating...at least one documentation based on the at least one predictive output, the at least one structured data set, and at least one entitlement rule, the at least one documentation including a selection of client data that corresponds to each of a plurality of clients; validating...each of the at least one documentation based on a predetermined guideline to confirm content; and publishing...the at least one documentation for a corresponding client.
These limitations, as drafted, are a process under Step 1 that that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
by the at least one processor
The additional claim element of by the at least one processor is recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above, by the at least one processor, is recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine.
Accordingly, claim 2 is not patent eligible.
Claim 3 partly recites the following limitations:
The method of claim 1, further comprising: alerting...at least one client based on the at least one predictive output and the at least one structured data set by, automatically determining...using the at least one model, whether at least one parameter exceeds a corresponding threshold; generating...at least one alert notification based on a result of the automatic determining, the at least one alert notification including a selection of information that corresponds to the at least one parameter, the corresponding threshold, the at least one predictive output, and the at least one structured data set; validating...each of the at least one alert notification based on a predetermined guideline to confirm content;
These limitations, as drafted, are a process under Step 1 that that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
by the at least one processor
and transmitting, by the at least one processor, the at least one alert notification to the corresponding at least one client
The additional claim element of by the at least one processor is recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine.
The additional claim elements of and transmitting, by the at least one processor, the at least one alert notification to the corresponding at least one client amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., transmitting data to client).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above by the at least one processor is recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine. Further, and transmitting, by the at least one processor, the at least one alert notification to the corresponding at least one client are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Versata Dev. Group, Inc, and Symantec as cited in MPEP §2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network along with grouping of data to be displayed using a generic computer are well-understood, routine, and conventional function when claimed in a merely generic manner (as it is here). Even when considered in combination, these additional elements represent mere instructions to apply an exception with well understood, routine, and conventional activities, which does not provide significantly more to the abstract idea.
Accordingly, claim 3 is not patent eligible.
Claim 4 partly recites the following limitations:
The method of claim 1, wherein the transformation process further comprises: normalizing...using the lambda function, the raw data across a plurality of data records and a plurality of data fields;
These limitations, as drafted, are a process under Step 1 that that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
by the at least one processor
and outputting, by the at least one processor, the normalized raw data
The additional claim element of by the at least one processor is recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine.
The additional claim elements of and outputting, by the at least one processor, the normalized raw data amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., outputting normalized data).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above by the at least one processor is recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine. Further, and outputting, by the at least one processor, the normalized raw data are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Versata Dev. Group, Inc, and Symantec as cited in MPEP §2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network along with grouping of data to be displayed using a generic computer are well-understood, routine, and conventional function when claimed in a merely generic manner (as it is here). Even when considered in combination, these additional elements represent mere instructions to apply an exception with well understood, routine, and conventional activities, which does not provide significantly more to the abstract idea.
Accordingly, claim 4 is not patent eligible.
Claim 5 partly recites the following limitations:
The method of claim 4, wherein normalizing the raw data further comprises: identifying...a plurality of data elements in the raw data, each of the plurality of data elements corresponding to an atomic unit of data with a distinct value; mapping...each of the plurality of data elements based on the plurality of data records and the plurality of data fields; and organizing...the plurality of data elements into at least one grouping based on a result of the mapping.
These limitations, as drafted, are a process under Step 1 that that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
by the at least one processor
The additional claim element of by the at least one processor is recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above by the at least one processor is recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine.
Accordingly, claim 5 is not patent eligible.
Claim 6 partly recites the following limitations:
The method of claim 1, further comprising... and wherein the at least one dashboard includes at least one from among a real-time risk view, a drill down view, a multiple currency view, and a forecast view.
These limitations, as drafted, are a process under Step 1 that that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
displaying, by the at least one processor in real-time, the at least one dashboard via a graphical user interface, wherein the at least one dashboard corresponds to a visual representation of the at least one predictive output and the at least one structured data set
The additional claim elements of displaying, by the at least one processor in real-time, the at least one dashboard via a graphical user interface, wherein the at least one dashboard corresponds to a visual representation of the at least one predictive output and the at least one structured data set amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., displaying output using graphical user interface).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above displaying, by the at least one processor in real-time, the at least one dashboard via a graphical user interface, wherein the at least one dashboard corresponds to a visual representation of the at least one predictive output and the at least one structured data set are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Versata Dev. Group, Inc, and Symantec as cited in MPEP §2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network along with grouping of data to be displayed using a generic computer are well-understood, routine, and conventional function when claimed in a merely generic manner (as it is here). Even when considered in combination, these additional elements represent mere instructions to apply an exception with well understood, routine, and conventional activities, which does not provide significantly more to the abstract idea.
Accordingly, claim 6 is not patent eligible.
Claim 7 partly recites the following limitations:
The method of claim 6...based on a corresponding entitlement rule that governs data sharing....
These limitations, as drafted, are a process under Step 1 that that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
wherein the at least one dashboard includes a client dashboard that is displayable for each of a plurality of clients
the client dashboard including a selection of data from the at least one predictive output and the at least one structured data set that corresponds to each of the plurality of clients
The additional claim elements of wherein the at least one dashboard includes a client dashboard that is displayable for each of a plurality of clients; the client dashboard including a selection of data from the at least one predictive output and the at least one structured data set that corresponds to each of the plurality of clients amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., displaying output using a graphical user interface).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above wherein the at least one dashboard includes a client dashboard that is displayable for each of a plurality of clients; the client dashboard including a selection of data from the at least one predictive output and the at least one structured data set that corresponds to each of the plurality of clients are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Versata Dev. Group, Inc, and Symantec as cited in MPEP §2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network along with grouping of data to be displayed using a generic computer are well-understood, routine, and conventional function when claimed in a merely generic manner (as it is here). Even when considered in combination, these additional elements represent mere instructions to apply an exception with well understood, routine, and conventional activities, which does not provide significantly more to the abstract idea.
Accordingly, claim 7 is not patent eligible.
Claim 8 partly recites the following limitations:
The method of claim 6...the application including at least one from among a mobile application and a web application....
These limitations, as drafted, are a process under Step 1 that that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
wherein the graphical user interface corresponds to an application that is compatible with a plurality of mobile computing devices
that is usable to facilitate interactions with the at least one dashboard
The additional claim elements of wherein the graphical user interface corresponds to an application that is compatible with a plurality of mobile computing devices; that is usable to facilitate interactions with the at least one dashboard amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., using a graphical user interface to be interacted with using a generic mobility device).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above wherein the graphical user interface corresponds to an application that is compatible with a plurality of mobile computing devices; that is usable to facilitate interactions with the at least one dashboard are well-understood, routine, conventional activity that court decisions, such as OIP Techs, Versata Dev. Group, Inc, and Symantec as cited in MPEP §2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network along with grouping of data to be displayed using a generic computer are well-understood, routine, and conventional function when claimed in a merely generic manner (as it is here). Even when considered in combination, these additional elements represent mere instructions to apply an exception with well understood, routine, and conventional activities, which does not provide significantly more to the abstract idea.
Accordingly, claim 8 is not patent eligible.
Claim 9 partly recites the following limitations:
The method of claim 1, wherein the at least one model includes at least one from among... a mathematical model, a process model, and a data model.
These limitations, as drafted, are a process under Step 1 that that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
a machine learning model
The additional claim element of a machine learning model amounts to no more than generally linking the use of the judicial exception to a particular technological environment and/or field of use. In this case, no meaningful limitations are attached to the above claim element of a machine learning model other than linking it to the environment of machine learning and/or artificial intelligence, which does not integrate the judicial exception into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above
a machine learning model amounts to no more than generally linking the use of the judicial exception to a particular technological environment and/or field of use. In this case, no meaningful limitations are attached to the above claim element of a machine learning model other than linking it to the environment of machine learning and/or artificial intelligence, which does not recite significantly more than the judicial exception.
Accordingly, claim 9 is not patent eligible.
Claim 10:
Because claim 10 is directed to a machine the claimed invention is directed to statutory subject matter under Step 1, that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements, and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One. The additional claim elements of a processor; a memory; and a communication interface coupled to each of the processor and the memory are not sufficient to amount to a practical application that is significantly more than the judicial exception under Step 2A, Prong Two and Step 2B since these additional claim elements are recited at a high level of generality (i.e. using a generic processor, communication interface and generic memory to perform generic computer functions) and for all other claim limitations of claim 10 they are rejected using the same PEG analysis of claim 1 since they are analogous claims
Claims 11-18:
Because dependent claims 11-18 are directed to a machine the claimed invention is directed to statutory subject matter under Step 1 and are rejected using the same PEG analysis of claims 2-9 since they are analogous claims
Claim 19:
Because claim 19 is directed to a manufacture the claimed invention is directed to statutory subject matter under Step 1, that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements, and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One. The additional claim elements of the storage medium comprising executable code which, when executed by a processor, causes the processor to are not sufficient to amount to a practical application that is significantly more than the judicial exception under Step 2A, Prong Two and Step 2B since these additional claim elements are recited at a high level of generality (i.e. using a generic processor, and generic memory to perform generic computer functions) and for all other claim limitations of claim 19 they are rejected using the same PEG analysis of claim 1 since they are analogous claims.
Claim 20:
Because dependent claim 20 is directed to a manufacture the claimed invention is directed to statutory subject matter under Step 1 and is rejected using the same PEG analysis of claim 15 since they are analogous claims
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Riemer et al., US 2023/0138753 Al(“Riemer”).
Regarding claim 1, Riemer teaches a method for facilitating automated data analytics in real-time via artificial intelligence, the method being implemented by at least one processor(Riemer, para. 0071, see also fig.7, “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”), the method comprising:
automatically aggregating, by the at least one processor in real-time, raw data from at least one source, the at least one source including a source application that persists the raw data in a data storage container(Riemer, paras. 0041-0047, see also figs. 1A(element 110c)[ in a data storage container], 2A, and 2B, “FIG. 2B includes...customer data processing engine 120 having master table 122 and supplemental variables 124...[t]he customer data processing engine 120 processes customer data (including input data). Processing customer data can include aggregating the customer data at different aggregation levels[automatically aggregating, raw data from at least one source, the at least one source including a source application that persists the raw data].”);1
triggering, by the at least one processor using a lambda function, a transformation process for the raw data(Riemer, paras. 0041-0047, see also figs. 1A, 2A, and 2B, “The customer data processing engine 120 processes customer data (including input data)...[d]ata associated with feature variables ( e.g., variables 124) is aggregated and transformed from customer data, where aggregation is associated with different time periods, customer accounts, and data sources[triggering, using a lambda function, a transformation process for the raw data].”);2
generating, by the at least one processor based on an output of the transformation process, at least one structured data set from the aggregated raw data(Riemer, paras. 0041-0047, see also fig. 2B, “Data associated with feature variables ( e.g., variables 124) is aggregated and transformed from customer data... a master table 122 and associated with relevant variables 124 from customer data. The master table 122 that contains data from a variety of data sources ( e.g., customer data, product data, and transaction data). The data is then connected in the master table, where all data associated with a particular customer is then stored for a defined period of time[generating, based on an output of the transformation process, at least one structured data set from the aggregated raw data].”);3
persisting, by the at least one processor, the at least one structured data set in a repository, the repository including a distributed database(Riemer, para. 0043, see also figs., 2A, 2B, and 6, “Aspects of the technical solution can be described by way of examples and with reference to FIGS. 2A and 2B. FIG. 2A is a block diagram of an exemplary technical solution environment, based on example environments described with reference to FIGS. 6 [persisting, the at least one structured data set in a repository, the repository including a distributed database]....”);4
determining, by the at least one processor using at least one model, at least one predictive output based on the persisted at least one structured data set(Riemer, paras. 0034-0036, see also figs. 1A and 1B, “[T]raining the data analytics models of the financial data analytics engine includes generating target variables by checking whether certain conditions are met within the forward window... [t]he model input feature variables are generated using values from within the backward window-i.e., by aggregating the aggregated variables from the master table over the months of the backward window[determining, using at least one model, at least one predictive output based on the persisted at least one structured data set].”);5
and generating, by the at least one processor in real-time, at least one dashboard by using the at least one predictive output and the at least one structured data set(Riemer, paras. 0034-0036, see also figs. 1A and 1B, “[T]raining the data analytics models of the financial data analytics engine includes generating target variables... [t]he model input feature variables are generated using values from within the backward window-i.e., by aggregating the aggregated variables from the master table over the months of the backward window[one structured data set]” & Riemer, paras., 0056-0059 , see also figs. 2C and 2D, “With reference to FIGS. 2C and 2D, FIGS. 2C and 2D illustrate a financial data analytics interface dashboard 200 ("dashboard") that provides at-a-glance views and detail views of key performance indicators relevant to the financial data analytics engine functionality described herein. The dashboard 200 can be used to cause display of information associated with model-generated suggested consumer solutions or alerts of increased risk of attrition[and generating, at least one dashboard by using the at least one predictive output and the at least one structured data set].” ).6
Regarding claim 2, Riemer teaches the method of claim 1, further comprising:
exposing, by the at least one processor, the at least one predictive output and the at least one structured data set by,automatically generating, by the at least one processor, at least one documentation based on the at least one predictive output, the at least one structured data set, and at least one entitlement rule, the at least one documentation including a selection of client data that corresponds to each of a plurality of clients; validating, by the at least one processor, each of the at least one documentation based on a predetermined guideline to confirm content; and publishing, by the at least one processor, the at least one documentation for a corresponding client(Riemer, paras. 0058, see also figs. 2E and 2F, “Turning to FIGS. 2E and 2F, FIG. 2E illustrates a Next Best Solution interface portion 230 that includes revenues from next best solution leads summary data and a visualization 212 associated with the data. FIG. 2F illustrates a retention interface portion 240 that includes revenues
from clients at high or medium risk of attrition summary data 242 and a visualization associated with the data[exposing the at least one predictive output and the at least one structured data set by,automatically generating, at least one documentation based on the at least one predictive output, the at least one structured data set, and at least one entitlement rule, the at least one documentation including a selection of client data that corresponds to each of a plurality of clients]. As shown in FIG. 2G, FIG. 2G displays recommendation data for loans for a first customer (i.e., ABC Group 250; additional loans 252) and a second customer (i.e., DEF
Group 26; first-time loan 262), where the recommendation data includes insights that provide plain text explanations. For example, insight 254 recites "Client's transaction volume has increased by at least 73% in the past year.") and insight 264 "Client uses ACH origination." FIG. 2H illustrates different categories ( e.g., Estimated Annual Revenue, Best in Class, Likelihood and Status) for sorting and presenting the financial data analytics recommendations. The
attributes of the category can be visualizations ( e.g., Likelihood visualization 270A and 270B) or text (e.g., Status: Shortlisted 272A and Status: Open Lead 27B)[validating, each of the at least one documentation based on a predetermined guideline to confirm content; and publishing, the at least one documentation for a corresponding client].”).7
Regarding claim 3, Riemer teaches the method of claim 1, further comprising:
alerting, by the at least one processor, at least one client based on the at least one predictive output and the at least one structured data set by, automatically determining, by the at least one processor using the at least one model, whether at least one parameter exceeds a corresponding threshold(Riemer, paras. 0051-0055, see also figs. 2B, “At block 12, generate a master table comprising the aggregated customer data. At block 14, identify feature variables for a data analytics opportunities recommendation model (e.g., a Next Best Solution Model)... [a]t block 26, select a best model that is used to predict model-based leads (i.e., a likelihood that a customer purchases a product or a likelihood of an alert for increased risk of attrition). At block 28, using one or more thresholds, group leads into different levels indicating a quality of the lead[alerting at least one client based on the at least one predictive output and the at least one structured data set by, automatically determining, using the at least one model, whether at least one parameter exceeds a corresponding threshold].”);8
generating, by the at least one processor, at least one alert notification based on a result of the automatic determining, the at least one alert notification including a selection of information that corresponds to the at least one parameter, the corresponding threshold, the at least one predictive output, and the at least one structured data set(Riemer, paras. 0051-0055, see also figs. 2B, “At block 12, generate a master table comprising the aggregated customer data. At block 14, identify feature variables for a data analytics opportunities recommendation model (e.g., a Next Best Solution Model)... [a]t block 26, select a best model that is used to predict model-based leads (i.e., a likelihood that a customer purchases a product or a likelihood of an alert for increased risk of attrition). At block 28, using one or more thresholds, group leads into different levels indicating a quality of the lead[generating at least one alert notification based on a result of the automatic determining, the at least one alert notification including a selection of information that corresponds to the at least one parameter, the corresponding threshold, the at least one predictive output, and the at least one structured data set].”);9
validating, by the at least one processor, each of the at least one alert notification based on a predetermined guideline to confirm content; and transmitting, by the at least one processor, the at least one alert notification to the corresponding at least one client(Riemer, para. 0056, see also figs. 2C and 2D, “[I]llustrate a financial data analytics interface dashboard 200 ("dashboard") that provides at-a-glance views and detail views of key performance indicators relevant to the financial data analytics engine functionality described herein. The dashboard 200 can be used to cause display of information...alerts of increased risk of attrition[validating each of the at least one alert notification based on a predetermined guideline to confirm content; and transmitting the at least one alert notification to the corresponding at least one client]. The dashboard 200 can include interface elements associated with Next Best Solution (e.g., Next Best Solution icon 202) and Retention (e.g., Retention icon 204).”).10
Regarding claim 4, Riemer teaches the method of claim 1, wherein the transformation process further comprises: normalizing, by the at least one processor using the lambda function, the raw data across a plurality of data records and a plurality of data fields; and outputting, by the at least one processor, the normalized raw data(Riemer, paras. 0047-0049, see also figs. 1A, 2A, and 2B, “The customer data processing engine 120 processes customer data (including input data)...[d]ata associated with feature variables ( e.g., variables 124) is aggregated and transformed from customer data, where aggregation is associated with different time periods, customer accounts, and data sources... [t]he feature variables are created by aggregating the data points in the backward window to one variable ( e.g., taking the highest value of the last six months, taking the last value of the last six months, calculate a trend over the last six months)[ normalizing, using the lambda function, the raw data across a plurality of data records and a plurality of data fields]... [a]t a high level, leads computation and machine learning engine 130 supports detecting patterns in customer data using feature variables (e.g., variables 124) from the customer data processing engine 120[and outputting, by the at least one processor, the normalized raw data].”).11
Regarding claim 5, Riemer teaches the method of claim 4, wherein normalizing the raw data further comprises:
identifying, by the at least one processor, a plurality of data elements in the raw data, each of the plurality of data elements corresponding to an atomic unit of data with a distinct value; mapping, by the at least one processor, each of the plurality of data elements based on the plurality of data records and the plurality of data fields; and organizing, by the at least one processor, the plurality of data elements into at least one grouping based on a result of the mapping(Riemer, paras. 0047-0050, see also figs. 1A, 2A, and 2B, “Data associated with feature variables ( e.g., variables 124) is aggregated and transformed from customer data, where aggregation is associated with different time periods, customer accounts,
and data sources[identifying a plurality of data elements in the raw data, each of the plurality of data elements corresponding to an atomic unit of data with a distinct value;]... the feature variables (e.g., feature table 136) that are used to predict the target variables. Training data (e.g., development sample of the development sample engine 134) is associated with feature variables of a backward window (e.g., a backward window time period) and target variables of a forward window (e.g., a forward window time period)[mapping each of the plurality of data elements based on the plurality of data records and the plurality of data fields; and organizing the plurality of data elements into at least one grouping based on a result of the mapping].”).12
Regarding claim 6, Riemer teaches the method of claim 1, further comprising: displaying, by the at least one processor in real-time, the at least one dashboard via a graphical user interface, wherein the at least one dashboard corresponds to a visual representation of the at least one predictive output and the at least one structured data set; and wherein the at least one dashboard includes at least one from among a real-time risk view, a drill down view, a multiple currency view, and a forecast view(Riemer, paras. 0055-0059, see also figs. 2C-2I, “With reference to FIGS. 2C and 2D, FIGS. 2C and 2D illustrate a financial data analytics interface dashboard 200 ("dashboard") that provides at-a-glance views and detail views of key performance indicators relevant to the financial data analytics engine functionality described herein. The dashboard 200 can be used to cause display of information
associated with model-generated suggested consumer solutions or alerts of increased risk of attrition[displaying in real-time, the at least one dashboard via a graphical user interface, wherein the at least one dashboard corresponds to a visual representation of the at least one predictive output and the at least one structured data set; and wherein the at least one dashboard includes at least one from among a real-time risk view].”).13, 14
Regarding claim 7, Riemer teaches the method of claim 6, wherein the at least one dashboard includes a client dashboard that is displayable for each of a plurality of clients based on a corresponding entitlement rule that governs data sharing, the client dashboard including a selection of data from the at least one predictive output and the at least one structured data set that corresponds to each of the plurality of clients(Riemer, paras. 0045-0046, see also figs. 1C(elements 154A, 154F and 150)[ based on a corresponding entitlement rule that governs data sharing] and 2C-2I, “The financial data analytics engine 110 supports providing financial data analytics recommendations in a customer relationship management system associated with the customer relationship management client device (e.g., customer relationship management client device 110D)...[t]he financial data analytics recommendations can be presented using a financial data analytics engine client (e.g., financial data analytics engine client) that is associated with a financial data analytics interface. The financial data analytics recommendations can include financial product lead information that is associated with a model-generated suggested consumer solution or an alert of increased risk of attrition (or default)... [t]he solution interface data and the retention interface data correspond to interface elements described with reference to FIG. 2C-2I[wherein the at least one dashboard includes a client dashboard that is displayable for each of a plurality of clients the client dashboard including a selection of data from the at least one predictive output and the at least one structured data set that corresponds to each of the plurality of clients].”).
Regarding claim 8, Riemer teaches the method of claim 6, wherein the graphical user interface corresponds to an application that is compatible with a plurality of mobile computing devices, the application including at least one from among a mobile application and a web application that is usable to facilitate interactions with the at least one dashboard(Riemer, para. 0070, “The invention may be practiced in a variety of system configurations, including hand-held devices, consumer electronics, general-purpose computers, more specialty computing devices, etc.”).
Regarding claim 9, Riemer teaches the method of claim 1, wherein the at least one model includes at least one from among a machine learning model, a mathematical model, a process model, and a data model(Riemer, para. 0048, see also fig. 2A, “Operations associated with the backward window can be performed via the backward window computation model 134A and operations associated with the forward window can be performed via the forward window computation model 134B. Statistical models and machine learning models 132 can include but are not limited to-logistic regression or ordered logic models and tree-based machine learning models (e.g., Random Forest or Gradient Boosted Trees) respectively, that can be trained to predict future events[wherein the at least one model includes at least one from among a machine learning model, a mathematical model, a process model, and a data model].”).15
Regarding claim 10, Riemer teaches a computing device configured to implement an execution of a method for facilitating automated data analytics in real-time via artificial intelligence, the computing device comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory(Riemer, para. 0071, see also fig.7, “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714, one or more presentation components 716[a processor; a memory; and a communication interface coupled to each of the processor and the memory], input/output ports 718, input/output components 720, and illustrative power supply 722.”) and for all other claim limitations they are rejected on the same basis as independent claim 1 since they are analogous claims.
Referring to dependent claims 11-18, they are rejected on the same basis as dependent claims 2-9 since they are analogous claims.
Regarding claim 19, Riemer teaches a non-transitory computer readable storage medium storing instructions for facilitating automated data analytics in real-time via artificial intelligence, the storage medium comprising executable code which, when executed by a processor(Riemer, para. 0071, see also fig.7, “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714, [the storage medium comprising executable code which, when executed by a processor] one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”) and for all other claim limitations they are rejected on the same basis as independent claim 1 since they are analogous claims.
Referring to dependent claim 20, it is rejected on the same basis as dependent claim 15 since they are analogous claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11,636,418 B2(details a real-time data analysis system with regard to human resource data to identify errors and anomalies in human resources with respect to currency conversion and job compensation)
US 2021/0342344 Al(details an intelligent augmentation system for dynamic analysis of unstructured data within a GUI framework)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM C STANDKE whose telephone number is (571)270-1806. The examiner can normally be reached Gen. M-F 9-9PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Adam C Standke/
Primary Examiner
Art Unit 2129
1 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
2 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
3 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
4 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
5 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
6 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
7 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
8 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
9 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
10 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
11 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
12 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
13 Riemer, para. 0071, see also fig.7 teaches: “With reference to FIG. 7, computing device 700 includes bus 710 that directly or indirectly couples the following devices: memory 712, one or more processors 714[by the at least one processor], one or more presentation components 716, input/output ports 718, input/output components 720, and illustrative power supply 722.”
14 Examiner remarks: In line with the holding in SuperGuide Corp. v. DirecTV Enters.,Inc., 358 F.3d 870 (Fed. Cir. 2004) Examiner is interpreting the claim elements of a real-time risk view, a drill down view, a multiple currency view, and a forecast view as individual items that may serve as replacements for one another, rather than categories i.e., BRI of the claim limitation connotates disjunctive rather than conjunctive form. Thus, the claim requires one or more of these above claim elements but not all.
15 Examiner remarks: In line with the holding in SuperGuide Corp. v. DirecTV Enters.,Inc., 358 F.3d 870 (Fed. Cir. 2004) Examiner is interpreting the claim elements of a machine learning model, a mathematical model, a process model, and a data model as individual items that may serve as replacements for one another, rather than categories i.e., BRI of the claim limitation connotates disjunctive rather than conjunctive form. Thus, the claim requires one or more of these above claim elements but not all.