DETAILED ACTION
Status of Claims
The following is a Non-Final Office Action in response to applicant’s arguments filed on 07/14/2026.
Claim 1 is amended. Claims 1-10 are currently considered. Claims 1-10 are currently pending.
Response to Arguments
Applicant's arguments and amendments necessitated the new ground(s) of rejection presented in this Office action.
Response to § 112 (b) arguments: Applicant's amendments with respect to the 35 U.S.C. §112(b) rejection to claims have been considered, applicant’s amendments overcome the 112(b) rejection in part. An updated the 35 U.S.C. §112(b) rejection will address applicant's amendments.
Response to § 101 arguments: Applicant's arguments and amendments with respect to the 35 U.S.C. §101 rejection to claims have been considered, however they are not persuasive.
Applicant asserts that applicant's claims 1-10 recite a specific, graph-based analytical architecture employing coordinated driver graph modules, hierarchical graph processing, a task controller, and a specially configured LLM operating together with non-generic computational machinery to produce autonomous analytical outputs. The Examiner's characterization of the claims as reciting a "mental process" or involving organization of human activity demonstrates both an over-generalization of the claimed invention and a departure from current 35 U.S.C. § 101 eligibility guidelines.
The examiner respectfully disagrees. The claims recite an abstract idea of collecting/organizing business data, analyzing the data (including variance/anomaly identification), and presenting results(graphs/summary/insights), which falls into “mental process” and “certain methods of organizing human activity”. (See MPEP 2106.04(a)(2)). Examiner notes claim recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). The examiner next notes a system, data store, a pair of encoding keys for encoding and decoding the at least one set of data(recited at high level), a large language module (LLM) comprising a specially configured processor; a task controller in communication with the LLM, a primary driver graph module, secondary driver graph module, the primary and secondary driver graph modules are in communication with a data store and the task controller, the system for providing predictive analysis is performed using a computational machine, and wherein the LLM comprising a specially configured processor are considered as additional elements and further evaluated under Step 2A Prong II.
The examiner further notes The additional elements are directed to a system, data store, a pair of encoding keys for encoding and decoding the at least one set of data(recited at high level), a large language module (LLM) comprising a specially configured processor; a task controller in communication with the LLM, a primary driver graph module, secondary driver graph module, the primary and secondary driver graph modules are in communication with a data store and the task controller, the system for providing predictive analysis is performed using a computational machine, and wherein the LLM comprising a specially configured processor. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification paragraph [0090] describes high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Further, in accordance to MPEP 2106.05(f), the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Further, the "large language module" merely represents computer/ processor environment automatically executing predefined models per changes in the input data/parameters, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)). Further, the use of the LLM as described in the claimed invention does not necessarily alter the technology such as the computing device implementing these models or algorithms; and a possible improvement to the accuracy of resulting optimized model merely represent business model improvement rather than a technological one. Therefore, the argued improvements do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea. Similar analysis applies to Step 2B, and the claims do not provide significantly more.
The additional limitations are directed to: a system, data store, a pair of encoding keys for encoding and decoding the at least one set of data(recited at high level), a large language module (LLM) comprising a specially configured processor; a task controller in communication with the LLM, a primary driver graph module, secondary driver graph module, the primary and secondary driver graph modules are in communication with a data store and the task controller, the system for providing predictive analysis is performed using a computational machine, and wherein the LLM comprising a specially configured processor. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (paragraph [0090]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. The claims do not impose any limits on how a pair of encoding keys for encoding and decoding the at least one set of data. The claims also do not impose any limits on how the analysis is accomplished, and thus it can be performed in any way known to those of ordinary skill in the art. Additionally, with respect to the Berkheimer court case, below can be found evidence provided by the Examiner that provides, based on 2B analysis, how the claims are viewed as well-understood, routine, and conventional activity for consistency with the Federal Circuit’s decision in Berkheimer and MPEP 2106.5(d). This is supported by the fact that the disclosure does not provide the details necessary to provide significantly more than the abstract idea performed on a general-purpose computer and therefore not significantly more. Prior art references teach the limitations of encoding and decoding the at least one set of data is a known technique. Thus, the use of encoding and decoding data, as recognized in art, which predate Applicant’s invention. As disclosed in Remein et al. (US Pub. No 2007/0269212 A1) “[0024] Other OLT logic 126 represents logic elements, such as processors, memories, data encoders and decoders, etc., that are conventional and typically included in prior OLTs of the type known in the art. The structure and function of such elements are well known in the art and therefore not described herein in further detail”. Therefore, as shown by cited prior art references, the 2B features of the invention are “routine and conventional.” It is when the claims are wholly directed to the abstract idea without anything significantly more in the claims that the claims are deemed to preempt or monopolize the exception (i.e. the abstract idea).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of concepts of certain methods of organizing human activity and mental process, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Accordingly, an updated the 35 U.S.C. §101 rejection will address applicant's amendment.
Response to § 103 arguments: Applicant's arguments and amendments with respect to the 35 U.S.C. §103 rejection to claims have been considered, an updated the 35 U.S.C. §103 rejection will address applicant's arguments and amendments.
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.
Claim 1-10 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
In regard to claim 1, the claim introduces “a specially configured processor” in “a large language module (LLM) comprising a specially configured processor”, then later recites another “a specially configured processor” in “wherein the LLM comprising a specially configured processor”, which renders the claim indefinite. It is unclear if the later recited specially configured processor refers to the first specially configured processor or a different one. Applicant is advised to clarify if the LLM comprises two distinct specially configured processors by amending the claim to further differentiate the first and second specially configured processor.
Further claim 1, the claim introduces “a data store” in “a data store comprising at least one set of data”, then later recites another “data store” in “wherein the primary and secondary driver graph modules are in communication with a data store and the task controller”, which renders the claim indefinite. It is unclear if the later recited data store refers to the first recited data store or a different one. Applicant is advised to clarify if a different data store is intended, amending the claim to further differentiate the first and second data store.
Claims 2-10 depend from one of claims rejected and fail to cure the deficiency noted above, and are therefore rejected based on dependency.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “Patent Subject Matter Eligibility Guidance”.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-10) is directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea of collecting/organizing business data, analyzing the data (including variance/anomaly identification), and presenting results(graphs/summary/insights), which falls into “mental process” and “certain methods of organizing human activity”. (See MPEP 2106.04(a)(2)). The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 1, are: A system for providing predictive analysis based upon one or more datasets, comprising: a data store comprising at least one set of data; a pair of encoding keys for encoding and decoding the at least one set of data; a large language module (LLM) comprising a specially configured processor; a task controller in communication with the LLM; a primary driver graph module configured to capture and present the relationships among the at least one set of data; a secondary driver graph module configured to capture the hierarchical relationships between various and different dimensional hierarchies within the at least one set of data; wherein the primary and secondary driver graph modules are in communication with a data store and the task controller; wherein the system for providing predictive analysis is performed using a computational machine specifically configured to process the at least one set of data using the LLM to provide one or more graphs; and wherein the LLM comprising a specially configured processor is specifically configured to provide: anomalies contained in the one or more graphs; a summary of the results displayed in the one or more graph; and insights based on the one or more graphs. Examiner notes claim recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to a system, data store, a pair of encoding keys for encoding and decoding the at least one set of data(recited at high level), a large language module (LLM) comprising a specially configured processor; a task controller in communication with the LLM, a primary driver graph module, secondary driver graph module, the primary and secondary driver graph modules are in communication with a data store and the task controller, the system for providing predictive analysis is performed using a computational machine, and wherein the LLM comprising a specially configured processor. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification paragraph [0090] describes high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. Further, the "large language module" merely represents computer/ processor environment automatically executing predefined models per changes in the input data/parameters, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)).
Further, in accordance to MPEP 2106.05(f), the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: a system, data store, a pair of encoding keys for encoding and decoding the at least one set of data(recited at high level), a large language module (LLM) comprising a specially configured processor; a task controller in communication with the LLM, a primary driver graph module, secondary driver graph module, the primary and secondary driver graph modules are in communication with a data store and the task controller, the system for providing predictive analysis is performed using a computational machine, and wherein the LLM comprising a specially configured processor. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (paragraph [0090]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. The claims do not impose any limits on how a pair of encoding keys for encoding and decoding the at least one set of data. The claims also do not impose any limits on how the analysis is accomplished, and thus it can be performed in any way known to those of ordinary skill in the art. Additionally, with respect to the Berkheimer court case, below can be found evidence provided by the Examiner that provides, based on 2B analysis, how the claims are viewed as well-understood, routine, and conventional activity for consistency with the Federal Circuit’s decision in Berkheimer and MPEP 2106.5(d). This is supported by the fact that the disclosure does not provide the details necessary to provide significantly more than the abstract idea performed on a general-purpose computer and therefore not significantly more. Prior art references teach the limitations of encoding and decoding the at least one set of data is a known technique. Thus, the use of encoding and decoding data, as recognized in art, which predate Applicant’s invention. As disclosed in Remein et al. (US Pub. No 2007/0269212 A1) “[0024] Other OLT logic 126 represents logic elements, such as processors, memories, data encoders and decoders, etc., that are conventional and typically included in prior OLTs of the type known in the art. The structure and function of such elements are well known in the art and therefore not described herein in further detail”. Therefore, as shown by cited prior art references, the 2B features of the invention are “routine and conventional.” It is when the claims are wholly directed to the abstract idea without anything significantly more in the claims that the claims are deemed to preempt or monopolize the exception (i.e. the abstract idea).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of concepts of certain methods of organizing human activity and mental process, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Robert Brian Seigel (US 2020/0250231 A1, hereinafter “Seigel”) in view of Charles Howard Cella (US 2022/0036302 A1, hereinafter “Cella”) in view of Jeb Linton (US 20210273954 A1, hereinafter “Linton”).
Claim 1
Seigel teaches:
A system for providing predictive analysis based upon one or more datasets, comprising: a data store comprising at least one set of data ([0026] The term “database”, “data source” or “data repository” as used herein refers to any one or more of a device, media, component, portion of a component, collection of components, and/or other structure capable of storing data accessible to a processor. [0045] the system comprises source data 20, which may represent data provided by or associated with a business or organization);
a module comprising a specially configured processor ([0018] a processor operating on specially configured computational machinery. [0043] the analytical module(s) possesses the capability to engage in natural language dialog with one or more users and receive and understand various inquiries, instructions and commands);
a task controller in communication with the module ([0061] the system may comprise one or more applications, which may be in communication with analytical modules through one or several other discrete modules);
a primary driver graph module configured to capture and present the relationships among the at least one set of data ([0053] the systems and methods further comprise a Primary Driver Graph Generation 200 module or step. the Primary Driver Graph 70 refers to a set of business measures, outcomes or metrics, and their respective relationships with each other);
a secondary driver graph module configured to capture the hierarchical relationships between various and different dimensional hierarchies within the at least one set of data([0055] the systems and methods further comprise a Dimensional Hierarchy Expansion 220 module or step, which comprises the processing of structured data received from the Source Data 20, following the Data Transformation 30, to generate a structure of “nodes” and “edges” that comports with the hierarchy of the organization's data. The Dimensional Hierarchy Expansion 200 associate’s metrics with nodes, at appropriate levels within the hierarchy);
wherein the primary and secondary driver graph modules are in communication with a data store and the task controller([0063]The main application 110 is preferably in direct communication with the Driver Graph 70 and Dimensional Hierarchy Database 60, via the Driver Graph API 90, to read, write and process driver-related information and application data, while [0055] the systems and methods further comprise a Dimensional Hierarchy Expansion 220 module or step, which comprises the processing of structured data received from the Source Data 20. The Dimensional Hierarchy Expansion 200 associates metrics with nodes, at appropriate levels within the hierarchy, so that the system can efficiently aggregate those metrics across all nodes in the Primary Driver Graph 70 and, eventually, an inflated Driver Graph 80);
wherein the foregoing steps are performed using a computational machine specifically configured to process the at least one set of data using the module to provide one or more graphs ([0018] a processor operating on specially configured computational machinery. [0043] the analytical module(s) may further comprise the capability to supply the user with specific reports, graphs, analysis and insights in a predetermined or independent manner);
and wherein the module is specifically configured to provide: anomalies contained in the one or more graphs; a summary of the results displayed in the one or more graph; and insights based on the one or more graphs( [0043] the analytical module(s) may further comprise the capability to supply the user with specific reports, graphs, analysis and insights in a predetermined or independent manner, while [0069] each Driver Graph is preferably configured to be responsive to system anomalies and other events to provide dynamic insights to a user. Once the system detects anomalies with the performance or state of specific nodes, the Driver Graph is configured to determine the root cause of such anomalies to generate a useful business insight).
While Seigel teaches in [0026] The term “database”, “data source” or “data repository” as used herein refers to any one or more of a device, media, component, portion of a component, collection of components, and/or other structure capable of storing data accessible to a processor. [0045] the system comprises source data 20, which may represent data provided by or associated with a business or organization, Seigel does not explicitly teach the following, however, analogues reference in data analysis and management, Cella teaches:
a pair of encoding keys for encoding and decoding the at least one set of data ([0360] In embodiments, each data handling layer 608 has a set of application programming connectivity facilities 642 for automating data exchange with each of the other data handling layers 624. These may include data integration capabilities, such as for extracting, transforming, loading, normalizing, compression, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information as it exchanged among the layers and/or the applications 630, such as transforming data from one format or protocol to another as needed in order for one layer to consume output from another).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cella with Seigel, because the references are analogous and compatible since they are directed to the same field of endeavor of data analysis and management, to include a pair of encoding keys for encoding and decoding the at least one set of data as part of the analytical module taught in Seigel. Doing so would provide the system with security level by obscuring data from intruders.
While Seigel teaches in [0018] a processor operating on specially configured computational machinery. [0043] the analytical module(s) possesses the capability to engage in natural language dialog with one or more users and receive and understand various inquiries, instructions and commands, Seigel does not explicitly teach the following, however, analogues reference in data analysis and management, Linton teaches:
a large language module (LLM)( [0066] At step 500, the SIEM system 410 collects system and network activity events in bulk to form the corpus 430. In embodiments, the SIEM program module 420 of the SIEM system 410 collects the system and network activity events in bulk from each of the at least one monitored computing environment 460 using a full raw feed (e.g., from QRadar Collectors, etc.). [0070] the SIEM program module 420 uses self-supervision comparable to that used to train very large language models such and BERT and GPT-2 to train the deep neural network backbone to predict a next network or system event in a time series. Examiner Notes: Linton shows using LLM to train and analyze the large/bulk collected data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Linton with Seigel and Cella, because the references are analogous and compatible since they are directed to the same field of endeavor of data analysis and management, to include a large language module (LLM) as part of data processing taught in Seigel. Doing so would provide the system with a efficient and accurate model that analyses large amount of data ([0070]).
Claim 2
Seigel further teaches:
The system of claim 1, wherein the one or more graphs comprise at least one business metric ([0053] the Primary Driver Graph 70 refers to a set of business measures, outcomes or metrics, and their respective relationships with each other, preferably without consideration of the different dimensions of the business (including by way of example but not limitation, product(s), market(s), revenue, costs, distribution channel(s), customer segment(s), administrative unit(s), etc.) The Primary Driver Graph 70 is formulated via the Primary Driver Graph Generation 200 module, preferably incorporating metrics derived from the source data. Thus, according to embodiments, the Primary Driver Graph 70 is based on an organization's metrics and fundamentally defines how those metrics, which in turn capture the performance of a business or other organization, relate to each other.).
Claim 3
Seigel further teaches:
The system of claim 2, wherein the insights provided by the LLM comprise projections for the at least one business metric ([0014] the analytical module may substitute for or otherwise provide the equivalent functions of a financial or business analyst, with the capabilities to interpret, analyze, compare, contrast, extrapolate, project or otherwise process information to provide the user with valuable business intelligence in a convenient, useable format.).
Claim 4
Seigel further teaches:
The system of claim 2, wherein the insights provided by the LLM comprise a comparison of the at least one business metric during a first time period to the at least one business metric during a second time period([0014] the analytical module may substitute for or otherwise provide the equivalent functions of a financial or business analyst, with the capabilities to interpret, analyze, compare, contrast, extrapolate, project or otherwise process information to provide the user with valuable business intelligence in a convenient, useable format. [0050]-[0052] In the example where periods differ, if one period can be mapped to another (e.g., daily to monthly periods), the dataset may be consolidated at the coarsest level (monthly) and treated as a single dataset. Conversely, if the periods cannot be matched (week-of-year vs month), then the two datasets can still be loaded across the same level of nodes while still being treated independently (i.e., each with its own set of periods and measures). e system and method may utilize an organization's transactional or other datasets 501. [0074] The organization's datasets 501 may comprise third party data and may further include structured and unstructured data. The datasets 501 may comprise a period or time field, which may represent the day, week, month and year associated with each row of data in the dataset. In preferred embodiments, one or more dimensions are included with the dataset, such as market1, market2, product2 and product2. Certain datasets 501 may comprise multiple dimensional levels (i.e., market and product), while other dimensions may comprise only a single level, although any combination and number of dimensions may be included in a dataset 501. The dataset 501 may also comprise specific metrics, such as revenue (Rev) and expenses (Exp), as reflected in FIG. 5).
Claim 5
Seigel further teaches:
The system of claim 1, wherein the one or more graphs are provided based on an inputted metric and an inputted dimension ([0047] the dataset comprises a unique identifier (uid), which may be used to trace any individual line of data within the dataset. The dataset also preferably comprises a period field, which in Table 1 represents the month and year associated with each row of data in the dataset. Multiple dimensions (dimension1, dimension2 and dimension3) may also be included with the dataset and reflect multiple variable, such as market1, market2, product1, product2 and segment in Table 1. As shown, certain dimensions may comprise multiple dimensional levels (i.e., market and product), while other dimensions may comprise only a single level (i.e., segment). However, any combination and number of dimensions may be included in a dataset, regardless of whether they are multi-dimensional or singular. Table 1 also depicts the individual metrics, such as revenue and sales units. Metrics are important to the Data Transformation 30 process because they represent important business or organizational values and, once mapped, may be aggregated, associated with or compared to other metrics. The systems and methods described herein also permit visually representing individual and aggregate metrics despite the typically large quantities of data obtained from the Source Data 20. By completing the Data Transformation 30 and formatting the datasets in this manner, individual or aggregated metrics may be queried, polled, sorted, filtered, manipulated and displayed in a meaningful manner).
Claim 6
Seigel further teaches:
The system of claim 5, wherein the inputted metric and inputted dimension are analyzed by the LLM to provide a variance attributable to the at least one data set([0059] During Driver Graph Inflation 240, node statistics and relationships may be evaluated and tested against the Primary Driver Graph 70 and dimensions of the DHDB. For example, once inflation has occurred along primary and dimensional lines, the system may be configured to determine node statistics such as z-score, deviation, last value, mean, etc. The system may also be configured to determine the contribution from a child node to a parent node, or their respective values and/or variances).
Claim 7
Seigel further teaches:
The system of claim 6, wherein the insights comprise potential cause(s) of the variance attributable to the at least one data set([0069] each Driver Graph is preferably configured to be responsive to system anomalies and other events to provide dynamic insights to a user. Once the system detects anomalies with the performance or state of specific nodes, the Driver Graph is configured to determine the root cause of such anomalies to generate a useful business insight. APGD handles this problem of root cause identification efficiently by determining and evaluating a weighted score for detected anomalies and, in certain embodiments, their distance (in the graph) from the metric or node being assessed).
Claim 8
Seigel further teaches:
The system of claim 1, wherein the anomalies, summary of results and the insights are provided autonomously ([0043] the analytical module(s) may further comprise the capability to supply the user with specific reports, graphs, analysis and insights in a predetermined or independent manner. analytical module(s) may be configured to automatically determine the appropriate reporting and analysis to supply to the user in response to an inquiry, instruction or command, including through the use of driver graph logic [0067] The visual representation associated with the driver graph, such as the one shown in FIG. 3A, further enhance the user's ability to understand anomalies in the data set associated with the driver graph. For example, when a metric and its associated node in the driver graph is found the be anomalous, an associated display provides a visual clue or indicia to call the anomaly to the attention of the user. As yet another example, the display may further provide a summary of automatically generated insights derived from the anomalies detected).
Claim 9
Seigel further teaches:
The system of claim 8, wherein the anomalies, summary of results and the insights are updated autonomously as the at least one data set is modified ([0043] the analytical module(s) may further comprise the capability to supply the user with specific reports, graphs, analysis and insights in a predetermined or independent manner. analytical module(s) may be configured to automatically determine the appropriate reporting and analysis to supply to the user in response to an inquiry, instruction or command, including through the use of driver graph logic [0067] The visual representation associated with the driver graph, such as the one shown in FIG. 3A, further enhance the user's ability to understand anomalies in the data set associated with the driver graph. For example, when a metric and its associated node in the driver graph is found the be anomalous, an associated display provides a visual clue or indicia to call the anomaly to the attention of the user. As yet another example, the display may further provide a summary of automatically generated insights derived from the anomalies detected. [0060] The Fully Inflated Driver Graph 270 may be stored with the DHDB and Primary Driver Graph 70, and may be configured to communicate with an Application Server 100 via a Driver Graph API 90. The Fully Inflated Driver Graph 270 may be updated and modified as new Source Data 20 is received or new Dimensional Hierarchies 60 are defined).
Claim 10
Seigel further teaches:
The system of claim 1, wherein a user inputs variables for the one or more graphs by messaging the task controller ([0042] Methods of automatically and near-instantaneously (i.e., near real-time) providing information in response to a user inquiry. one or more analytical module(s), which may be configured to be adaptive and provide new functions/processes or acquire additional knowledge through the course of interactions with a user. [0043]analytical module(s) may be configured to automatically determine the appropriate reporting and analysis to supply to the user in response to an inquiry, instruction or command).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Joey et al. (US 20230065790 A1): relates to a data analysis tool that helps consumers, such as data scientists and engineers, understand datasets better. The disclosed data analysis framework/tool surfaces metrics to data consumers to visually inspect and understand large datasets more efficiently. In addition to the pre-computed and pre-collected metrics on given datasets or tables, the disclosed data analysis tool may also provide a way to detect various anomalies associated with the datasets.
Pierre et al.( US 20190095507 A1): elates to systems for providing information in an automated or semi-automated manner, through use of one or more autonomous virtual analysts. In one embodiment, the autonomous virtual analysts may perform many of the same functions as a human analyst, and provide business intelligence relating to revenue, income, profit, loss, expenses, historical data, projections, trends, comparative analysis, etc. In embodiments, the autonomous virtual analyst may be employed through use of natural language dialog with a user, and further configured to capture and appropriately respond to the context of the dialog by supplying a user with information pertinent to the request. According to varying embodiments, a variety of decision trees comprising a plurality of nodes is disclosed. In varying embodiments, the system may comprise one or more distinct modules, including a driver graph module. Methods of automatically and near-instantaneously providing information in response to a user inquiry are also disclosed.
Sandhya Sree et al. (US 20140149181 A1): a method and a system for conducting a survey of at least one survey item. The method includes obtaining historical survey data of the at least one survey item for T number of data stores. The method also includes determining a sparsity number K associated with the historical survey data of the at least one survey item for the T number of data stores. The method also includes determining a target number M based on the sparsity number K. The target number M is indicative of a reduced number of data stores, present amongst the T number of data stores, for collection of current survey data to estimate current survey data of the at least one survey item for the T number of data stores.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REHAM K ABOUZAHRA whose telephone number is (571)272-0419. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
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, Brian Epstein can be reached at (571)-270-5389. 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.
/REHAM K ABOUZAHRA/Examiner, Art Unit 3625
