Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the amendments filed 01/27/2026. Claims 1, 5, 8-11, 14-16, and 18 have been amended, claims 1-20 are currently pending.
Response to Arguments
Applicant’s arguments regarding the 101 rejection have been fully considered but they are not persuasive. Applicant argues that the claimed features “solve a technical problem of preparing a bid for a contract, where users engage in numerous rounds of revision in order to develop a proposal and accompanying documents” and further argues that “the interaction between the planning system and the data lake as well as the user device is not a mere mental process nor an abstract idea that can be performed in the mind”. Examiner respectfully disagrees and notes that at least the noted claim limitations directed to the planning system, receiving outcome indications from a user device, storing outcome indications in a data lake, communicatively coupling a user device and a data lake, providing a stored outcome indication to an updated machine learning model, and retraining a machine learning model were not interpreted as abstract ideas or mental steps, but as additional elements. These additional elements, as noted in analysis provided in the 101 rejection, were interpreted as at least aspects of the technological environment in which other claim limitations that were interpreted as abstract ideas were performed and as well-understood, routine, conventional activity; therefore these additional elements do not integrate those abstract ideas into a practical application or amount to significantly more.
Examiner also notes that claim 1 does not recite limitations directed to generating scatter plots, inferring or generating replacement values for missing features from the unified set of data, using a generative adversarial network or a multi-class neural network, using reducing a number of dimensions, selecting relevant features and discarding irrelevant features, testing a model using the unified set of data, tuning hyperparameters, and “containerizing” deployment of a model. Use of multi-class neural networks was rejected as well-understood, routine, conventional activity in the rejection of claim 5 in light of the evidence provided by the Cha reference.
Applicant also argues that the limitations related to updating the machine learning model by analyzing exploratory data, generating a report indicating feature values associated with a unified set of data and providing a coefficient of variation with this report integrate any claimed judicial exceptions into a practical application. Examiner respectfully disagrees and notes that the claims do not describe these steps in such a way that distinguishes any intended technical computer functions from the way a person could analyze exploratory data and generate a report with feature values and a coefficient of variation in their mind, potentially assisted by pen and paper. Updating the machine learning model would therefore interpreted as merely applying these abstract ideas with a generic computer component (see MPEP 2106.05(f)). Applicant argues that “the machine learning model can recommend more accurate modifications to the bid”; however, Examiner notes that claim 1 does not discuss bid modifications. The limitation in claim 8 directed to generating modifications to an input was interpreted as a mental step, as the claim does not describe this process in a way that distinguishes the required process from the way a person could generate modifications in their mind. Examiner respectfully disagrees with Applicant’s comparison of the claims to DDR Holdings, LLC v. Hotels.com Federal Circuit 2014, as Applicant’s claims are directed to automation of a composite webpage. Applicant’s claims are more suitably compared to Recentive Analytics, Inc v. Fox Corp., No. 23-2437 (Fed. Cir. 2025), as the claims merely recite use of a machine learning model in a new environment (see page 13 of Recentive). Page 15 of Recentive further states “the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by a human with greater speed and efficiency than could previously be achieved. Whether the issue is raised at step one or step two, the increased speed and efficiency resulting from use of computers (with no improve computer technique) do not themselves create eligibility”. Similarly, Applicant has argued that the claims reflect an improvement to a task previously performed by users, but Applicant has not shown how the claims reflect an improved machine learning model or other computer technique. The 101 rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
Applicant’s arguments regarding the prior art rejection have been fully considered but are moot because of the new ground(s) of rejection. Applicant argues that the cited references do not teach the amended features of claim 1. Examiner notes that the Xiao reference has been brought in to teach the limitation directed to providing a coefficient of variation, and that the Sekar reference has been brought in to teach limitations related to a data lake and retraining a machine learning model based on an outcome from a previous training operation. The prior art rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
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 8-20 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.
Claim 8 recites the limitations “wherein the outcome indication is stored in the data lake” and “provide, by the data lake, the stored outcome indication as feedback to a consequent training cycle of the updated machine learning model, wherein the planning system is communicatively coupled to the data lake. There is insufficient antecedent basis for “the data lake” in the claim.
Claim 14 recites similar limitations and is rejected for the same reasons. Dependent claims 9-13 and 15-20 are also rejected because they fail to correct the deficiencies of the independent claims on which they depend.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 6 and 12 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 6 recites the limitation “wherein the phase associated with the current contract comprises a planning phase, a constructing phase, or a finalization phase”; however, Examiner notes that claim 1 on which claim 6 depends already recites this limitation.
Claim 12 recites a similar limitation to claim 6, which is also recited in independent claim 8, and so claim 12 is rejected for the same reasons. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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. Claims 1-7 are directed to a method, claims 8-13 are directed to a system, and claims 14-20 are directed to a non-transitory computer-readable medium; therefore, claims 1-20 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). However, claims 1-20 fall within the judicial exception of an abstract idea, specifically the abstract ideas of “Mental Processes” (including observation, evaluation, and opinion) and “Mathematical Concepts (including mathematical calculations and relationships)”.
Claim 1:
Claim 1 is directed to a method; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 1 recites the following abstract ideas:
analyzing exploratory data for converting the unified set of data to a set of feature values (mental step directed to observation, evaluation – a person could analyze observed exploratory data in their mind. Examiner notes that this limitation states the intended use of the exploratory data, but does not actively convert unified data to a set of feature values);
generating a report indicating the feature values associated with the unified set of data, wherein a coefficient of variation associated with each feature is provided based on the generated report (mental step directed to observation, evaluation – a person could generate a report in their mind to indicate feature values associated with an observed or mentally determined unified set of data and provide a coefficient of variation in their mind associated with each feature based on the mentally generated report);
selecting a set of factors, from a plurality of sets of factors, based on a phase associated with the current contract, wherein the phase associated with the current contract comprises at least one of a planning phase, a constructing phase, and a finalization phase (selecting a set of factors is interpreted as a mental step directed to observation, evaluation – a person could selected a set of observed or determined factors in their mind based on an observed or determined contract planning, constructing, or finalization phase);
applying the updated machine learning model to the input to generate a probability associated with the current contract (generating a probability associated with a contract is interpreted as a mental step directed to observation, evaluation – a person could generate a probability in their mind associated with an observed current contract. As the claim does not recite any particular technical details describing the machine learning model nor how it is applied to generate such a probability, the machine learning model is interpreted as a generic computer component used to merely apply the mental step as interpreted above (see MPEP 2106.05(f)).
Claim 1 recites the following additional elements:
a planning system interacting with a plurality of data sources, a data lake, and a user device; receiving, from the plurality of data sources, a plurality of files in a plurality of formats and associated with historical contracting information, wherein the planning system is communicatively coupled to the plurality of data sources; converting the plurality of files into a unified data format, to generate a unified set of data, using one or more scripts; updating a machine learning model based on the unified set of data; receiving input associated with a current contract; and providing instructions for a user interface (UI) that visually depicts the probability; and receiving an outcome indication from the user device associated with the user interface, wherein the outcome indication is stored in the data lake, and wherein the planning system is communicatively coupled to the user device and the data lake; providing, the stored outcome indication as feedback to a consequent training cycle of the updated machine learning model; and retraining the updated machine learning model based on the stored outcome indication.
The planning system interacting with and communicatively coupled to the plurality of data sources, the data lake, and the user device are all interpreted as generic computer components used to merely apply the claimed abstract ideas.
Receiving a plurality of files in a plurality of formats from a plurality of data sources, updating a machine learning model with a unified set of data, receiving input, providing instructions for a user interface; receiving an outcome indication from the user device associated with the user interface; and providing, the stored outcome indication as feedback to a consequent training cycle of the updated machine learning model are all interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. Wherein the received files and input data are associated with historical and current contracting information is interpreted as the technological environment or field of use in which the abstract ideas are applied.
Converting files into a unified data format using one or more scripts is interpreted as well-understood, routine, conventional activity in the art in light of US 20060047780 A1 (Patnude), paragraphs [0015]-[0016] of which recite “high-level programming languages that support object-oriented programming techniques and practices contain internal data structures, methods, and variables is well known. In the object-oriented paradigm, various languages use a fairly consistent approach to data types, and that data types can be cast, coerced, or otherwise converted from one type to another is well known”. Paragraph [0022] of Patnude also recites “most modern internet browser software supports and executes embedded scripting languages and scripted instructions in various forms, including VBScript, JavaScript, and ECMA-262 compliant languages is a well-known fact and widely used as a client-side technology on the internet”.
Wherein the outcome indication is stored in the data lake interpreted as well-understood, routine, conventional activity directed to storing information in memory.
Retraining the updated machine learning model based on the stored outcome indication is interpreted as well-understood, routine, conventional activity in light of US 5477444 A (Bhat et al), column 14, lines 60-64 of which recite “The actual retraining task 450 is illustrated in FIG. 13. The task 450 commences at step 452 where the neural network 108 is retrained using conventional techniques, preferably back propagation techniques, as well known to those skilled in the art”. Using a stored outcome indication is interpreted as selecting a particular type of data to be manipulated.
These additional elements do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(h)).
Claim 8:
Claim 8 is directed to a system; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 8 recites the following abstract ideas:
analyzing exploratory data for converting the unified set of data to a set of feature values (mental step directed to observation, evaluation – a person could analyze observed exploratory data in their mind. Examiner notes that this limitation states the intended use of the exploratory data, but does not actively convert unified data to a set of feature values);
generating a report indicating the feature values associated with the unified set of data, wherein a coefficient of variation associated with each feature is provided based on the generated report (mental step directed to observation, evaluation – a person could generate a report in their mind to indicate feature values associated with an observed or mentally determined unified set of data and provide a coefficient of variation in their mind associated with each feature based on the mentally generated report);
select a set of factors, from a plurality of sets of factors, based on a phase associated with the current contract, wherein the phase associated with the current contract comprises at least one of a planning phase, a constructing phase, and a finalization phase (selecting a set of factors is interpreted as a mental step directed to observation, evaluation – a person could selected a set of observed or determined factors in their mind based on an observed or determined contract planning, constructing, or finalization phase);
apply the machine learning model to the input to generate a probability associated with the current contract (generating a probability associated with a contract is interpreted as a mental step directed to observation, evaluation – a person could generate a probability in their mind associated with an observed current contract. As the claim does not recite any particular technical details describing the machine learning model nor how it is applied to generate such a probability, the machine learning model is interpreted as a generic computer component used to merely apply the mental step as interpreted above (see MPEP 2106.05(f));
generate one or more modifications to the input based on the probability failing to satisfy a threshold (mental step directed to observation, evaluation – a person could generate modifications to an observed input in their mind based on observing or determining that a probability fails to satisfy an observed or determined threshold).
Claim 8 recites the following additional elements:
a device, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, and a planning system; receive, from a plurality of data sources, a plurality of files in a plurality of formats and associated with historical contracting information, wherein the planning system is communicatively coupled to the plurality of data sources; convert the plurality of files into a unified data format, to generate a unified set of data, using one or more scripts; update a machine learning model based on the unified set of data; receiving input associated with a current contract; and transmit, to a user device, one or more files encoding the one or more modifications; receiving an outcome indication from the user device associated with the user interface, wherein the outcome indication is stored in the data lake, and wherein the planning system is communicatively coupled to the user device and the data lake; providing, the stored outcome indication as feedback to a consequent training cycle of the updated machine learning model; and retraining the updated machine learning model based on the stored outcome indication.
The device comprising memories and processors and the planning system communicatively coupled to a plurality of data sources and a user device are all interpreted as generic computer components merely used to implement the claimed abstract ideas.
Receiving a plurality of files in a plurality of formats from a plurality of data sources, updating a machine learning model with a unified set of data, receiving input associated with a current contract, transmitting encoded modifications to a user device, and providing a stored outcome indication as feedback to a consequent training cycle of an updated machine learning model are all interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. Wherein the received files and input data are associated with historical and current contracting information is interpreted as the technological environment or field of use in which the abstract ideas are applied.
Converting files into a unified data format using one or more scripts is interpreted as well-understood, routine, conventional activity in the art in light of US 20060047780 A1 (Patnude), paragraphs [0015]-[0016] of which recite “high-level programming languages that support object-oriented programming techniques and practices contain internal data structures, methods, and variables is well known. In the object-oriented paradigm, various languages use a fairly consistent approach to data types, and that data types can be cast, coerced, or otherwise converted from one type to another is well known”. Paragraph [0022] of Patnude also recites “most modern internet browser software supports and executes embedded scripting languages and scripted instructions in various forms, including VBScript, JavaScript, and ECMA-262 compliant languages is a well-known fact and widely used as a client-side technology on the internet”.
Wherein the outcome indication is stored in the data lake interpreted as well-understood, routine, conventional activity directed to storing information in memory.
Retraining the updated machine learning model based on the stored outcome indication is interpreted as well-understood, routine, conventional activity in light of US 5477444 A (Bhat et al), column 14, lines 60-64 of which recite “The actual retraining task 450 is illustrated in FIG. 13. The task 450 commences at step 452 where the neural network 108 is retrained using conventional techniques, preferably back propagation techniques, as well known to those skilled in the art”. Using a stored outcome indication is interpreted as selecting a particular type of data to be manipulated.
These additional elements do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(h)).
Claim 14:
Claim 14 is directed to a non-transitory computer-readable medium; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 14 recites the following abstract ideas:
analyzing exploratory data for converting the unified set of data to a set of feature values (mental step directed to observation, evaluation – a person could analyze observed exploratory data in their mind. Examiner notes that this limitation states the intended use of the exploratory data, but does not actively convert unified data to a set of feature values);
generating a report indicating the feature values associated with the unified set of data, wherein a coefficient of variation associated with each feature is provided based on the generated report (mental step directed to observation, evaluation – a person could generate a report in their mind to indicate feature values associated with an observed or mentally determined unified set of data and provide a coefficient of variation in their mind associated with each feature based on the mentally generated report);
select a set of factors, from a plurality of sets of factors, based on a phase associated with the current contract, wherein the phase associated with the current contract comprises at least one of a planning phase, a constructing phase, and a finalization phase (selecting a set of factors is interpreted as a mental step directed to observation, evaluation – a person could selected a set of observed or determined factors in their mind based on an observed or determined contract planning, constructing, or finalization phase);
apply the updated machine learning model to the input to generate one or more recommended parameters for the current contract (generating a probability associated with a contract is interpreted as a mental step directed to observation, evaluation – a person could generate a probability in their mind associated with an observed current contract. As the claim does not recite any particular technical details describing the updated machine learning model nor how it is applied to generate such a probability, the updated machine learning model is interpreted as a generic computer component used to merely apply the mental step as interpreted above (see MPEP 2106.05(f)).
Claim 14 recites the following additional elements:
one or more processors of a device, and a planning system communicatively coupled to a plurality of data sources and a data device; receive, from a plurality of data sources, a plurality of files in a plurality of formats and associated with historical contracting information, wherein the planning system is communicatively coupled to the plurality of data sources; convert the plurality of files into a unified data format, to generate a unified set of data, using one or more scripts; updating a machine learning model based on the unified set of data; receive input associated with a current contract; provide instructions for a user interface (UI) that visually depicts the recommended parameters; and transmit, from the planning system to a user device associated with the user interface (UI), one or more files encoding the one or more recommended parameters, wherein the planning system is communicatively coupled to the user device; receiving an outcome indication from the user device associated with the user interface, wherein the outcome indication is stored in the data lake, and wherein the planning system is communicatively coupled to the user device and the data lake; providing, the stored outcome indication as feedback to a consequent training cycle of the updated machine learning model; and retraining the updated machine learning model based on the stored outcome indication.
Receiving a plurality of files in a plurality of formats from a plurality of data sources, updating a machine learning model with a unified set of data, receiving input associated with a current contract, providing instructions for a user interface, transmitting files encoding the recommended parameters, and receiving an outcome indication from a user device are all interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. Wherein the received files and input data are associated with historical and current contracting information is interpreted as the technological environment or field of use in which the abstract ideas are applied.
Converting files into a unified data format using one or more scripts is interpreted as well-understood, routine, conventional activity in the art in light of US 20060047780 A1 (Patnude), paragraphs [0015]-[0016] of which recite “high-level programming languages that support object-oriented programming techniques and practices contain internal data structures, methods, and variables is well known. In the object-oriented paradigm, various languages use a fairly consistent approach to data types, and that data types can be cast, coerced, or otherwise converted from one type to another is well known”. Paragraph [0022] of Patnude also recites “most modern internet browser software supports and executes embedded scripting languages and scripted instructions in various forms, including VBScript, JavaScript, and ECMA-262 compliant languages is a well-known fact and widely used as a client-side technology on the internet”.
Wherein the outcome indication is stored in the data lake interpreted as well-understood, routine, conventional activity directed to storing information in memory.
Retraining the updated machine learning model based on the stored outcome indication is interpreted as well-understood, routine, conventional activity in light of US 5477444 A (Bhat et al), column 14, lines 60-64 of which recite “The actual retraining task 450 is illustrated in FIG. 13. The task 450 commences at step 452 where the neural network 108 is retrained using conventional techniques, preferably back propagation techniques, as well known to those skilled in the art”. Using a stored outcome indication is interpreted as selecting a particular type of data to be manipulated.
These additional elements do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d) and MPEP 2106.05(h)).
The independent claims are not patent eligible.
Dependent claims 2-7, 8-13, and 15-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea, as they recite further embellishment of the judicial exception.
Claim 2 recites wherein the plurality of formats includes two or more of an application outsourcing format, a systems integration format, a strategy and consulting format, an infrastructure outsourcing format, a business process outsourcing format, or a spreadsheet format. This limitation is interpreted as further description of the technological environment or field of use in which the files, or data, are being manipulated, and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(h)).
Claim 3 recites wherein the one or more scripts comprise Python scripts that convert files to structured query language data. Using scripts to convert files to SQL is interpreted as well-understood, routine, conventional activity in light of US 20060047780 A1 (Patnude), paragraphs [0005]-[0006] of which recite “Relational database systems or RDBMS's are known to exist. Most RDBMS's support or conform to published ANSI SQL standards and that SQL-based databases and the tables and structures contained therein are well known in the art”. Paragraphs [0015]-[0016] of Patnude additionally recite “high-level programming languages that support object-oriented programming techniques and practices contain internal data structures, methods, and variables is well known. In the object-oriented paradigm, various languages use a fairly consistent approach to data types, and that data types can be cast, coerced, or otherwise converted from one type to another is well known”. Paragraph [0022] of Patnude also recites “most modern internet browser software supports and executes embedded scripting languages and scripted instructions in various forms, including VBScript, JavaScript, and ECMA-262 compliant languages is a well-known fact and widely used as a client-side technology on the internet”. Using Python scripts is additionally interpreted as well-understood, routine, conventional activity in light of US 20030163552 A1 (Savitzky et al), paragraph [0053] of which recites “Some of the more well known and commonly used languages for writing CGI scripts include: C, C++, Perl, Python, TCL and shells”.
Claim 4 recites wherein updating the machine learning model comprises: performing a retraining using the unified set of data. Retraining a machine learning model is interpreted as well-understood, routine, conventional activity in light of US 5477444 A (Bhat et al), column 14, lines 60-64 of which recite “The actual retraining task 450 is illustrated in FIG. 13. The task 450 commences at step 452 where the neural network 108 is retrained using conventional techniques, preferably back propagation techniques, as well known to those skilled in the art”. Using a unified set of data is interpreted as selecting a particular type of data to be manipulated. These additional elements do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d) and MPEP 2106.05(g)).
Claim 5 recites wherein the updated machine learning model comprises a multi-class neural network. Multi-class neural networks are interpreted as well-understood, routine, conventional activity in light of US 20200175352 A1 (Cha et al), paragraph [0358] of which recites “A softmax layer is a well-known multi-class classifier in CNNs that can predict the class of its input. Softmax normally takes features from a fully-connected layer, calculates the probabilities of each individual class, and then outputs the class with the highest probability as the classification results”.
Claim 6 recites wherein the phase associated with the current contract comprises a planning phase, a constructing phase, or a finalization phase. Examiner notes the 112(d) rejection of claim 6, and further notes that this limitation is interpreted as further description of conditions in the technological environment or field of use to perform the mental steps described in claim 1 and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(h)).
Claim 7 recites wherein the UI includes a pie chart or a bar graph depicting the probability. This limitation is interpreted as further description of way in which data is transmitted over a network by the user interface, and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d)).
Claim 9 recites transmitting the input associated with the current contract and the one or more modifications to a storage associated with the updated machine learning model. Transmitting an input to storage is interpreted as well-understood, routine, conventional activity directed to transmitting data over network and storing information in memory, and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d)(II)).
Claim 10 recites wherein the one or more processors are further configured to: store the one or more modifications in association with a first version indicator; receive updated input associated with the current contract; generate one or more new modifications to the updated input based on applying the updated machine learning model to the updated input; and store the one or more new modifications in association with a second version indicator.
Generating new modifications to an updated input is interpreted as a mental step directed to observation, evaluation – a person could generate new modifications in their mind to observed updated input data. As the claim does not recite any particular technical details describing the updated machine learning model nor how it is applied to generate modifications to input data, wherein this generating step is based on applying the updated machine learning model to updated input is interpreted as a generic computer component used to merely apply the mental step as interpreted above (see MPEP 2106.05(f)). Storing modifications associated with a first version indicator, receiving updated input data, and storing new modifications associated with a second version indicator are all interpreted as well-understood, routine, conventional activity directed to transmitting data over network and storing information in memory, and do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d)(II)).
Claim 11 recites wherein the one or more processors, to generate the one or more modifications, are configured to: apply the updated machine learning model to the input to receive the one or more modifications that are expected to increase the probability. As the claim does not recite any particular technical details describing the updated machine learning model nor how it is applied to generate modifications to input data, using generic computer processors to generate modifications and applying the updated machine learning model to an input to receive modifications are interpreted as generic computer components used to merely apply the mental step of generating modifications to observed input data (see MPEP 2106.05(f)).
Claim 12 recites wherein the phase associated with the current contract comprises a planning phase, a constructing phase, or a finalization phase. Examiner notes the 112(d) rejection of claim 6, and further notes that this limitation is interpreted as further description of conditions in the technological environment or field of use to perform the mental steps described in claim 1 and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(h)).
Claim 13 recites wherein the one or more files comprise a presentation file or a portable document format file. This limitation is interpreted as further description of the files being manipulated in the technological environment or field of use, and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(h)).
Claim 15 recites transmitting the input associated with the current contract and the one or more recommended parameters to a storage associated with the updated machine learning model. Transmitting an input and recommended parameters to storage is interpreted as well-understood, routine, conventional activity directed to transmitting data over network and storing information in memory, and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d)(II)).
Claim 16 recites selecting a set of factors, from a plurality of sets of factors, based on a phase associated with the current contract, wherein the updated machine learning model is applied based on the selected set of factors.
Selecting a set of factors is interpreted as a mental step directed to observation, evaluation – a person could selected a set of observed or determined factors in their mind based on an observed or determined contract phase. As the claim does not recite any particular technical details describing the updated machine learning model nor how it is applied to generate modifications to input data, applying an updated machine learning model based on the selected factors is interpreted as a generic computer component used to merely apply the mental steps of claim 14 on which claim 16 depends (see MPEP 2106.05(f)).
Claim 17 recites wherein the phase associated with the current contract comprises a planning phase, a constructing phase, or a finalization phase. This limitation is interpreted as further description of conditions in the technological environment or field of use to perform the mental steps described in claim 1 and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(h)).
Claim 18 recites wherein the updated machine learning model comprises a multi-class neural network. Multi-class neural networks are interpreted as well-understood, routine, conventional activity in light of US 20200175352 A1 (Cha et al), paragraph [0358] of which recites “A softmax layer is a well-known multi-class classifier in CNNs that can predict the class of its input. Softmax normally takes features from a fully-connected layer, calculates the probabilities of each individual class, and then outputs the class with the highest probability as the classification results”.
Claim 19 recites inputting the one or more recommended parameters to a web-based graph generator. Inputting recommended parameters to a web-based graph generator is interpreted as well-understood, routine, conventional activity directed to transmitting data over a network and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d)).
Claim 20 recites wherein the one or more files comprise a presentation file or a portable document format file. This limitation is interpreted as further description of the files being manipulated in the technological environment or field of use, and does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(h)).
Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 8-9, 11-12, 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Hazen et al (US 20190312793 A1, herein Hazen) in view of Watson et al (“A Unified System for Data Analytics and In Situ Query Processing”, herein Watson), in further view of Xiao et al (“Using Spearman's correlation coefficients for exploratory data analysis on big dataset”, herein Xiao), in further view of Sekar (US 10997195 B1, herein Sekar).
Regarding claim 1, Hazen teaches a method performed using a planning system interacting with a plurality of data sources, [a data lake] and a user device (para. [0003] recites “a method may include receiving a request for a service management plan that is to be used to implement a fully-integrated enterprise resource planning (ERP) system for an organization”. Fig 2B and para. [0016]-[0017] recite “FIGS. 1A-1F are diagrams of one or more example implementations 100 described herein. For example, the one or more example implementations 100 may include a client device, a transformation platform, and a fully-integrated enterprise resource planning (ERP) system. As shown in FIG. 1A, and by reference number 105, a user may submit a request for the service management plan” (i.e., a method performed by a planning system comprising user devices and data sources)), the method comprising:
receiving, by the planning system, from a plurality of data sources, a plurality of files in a plurality of formats and associated with historical contracting information (para. [0021] recites “As shown by reference number 110, the transformation platform may receive organizational data for the organization”. Para. [0050] recites “the transformation platform may receive training data such as historical organizational data of one or more other organizations, historical industry data, historical observations, historical priorities, historical results data that associated particular target states with particular configurations, and/or the like. plan. The organization data may include process data that describes a set of organizational processes that are used for providing goods and/or services to customers, application data that is generated while the organizational processes are being performed. . .”. Para. [0023] recites “The application data may include order management and inventory data, transportation management data, procurement data, asset lifecycle management data, supply chain management data, supplier management data, project and portfolio management data, customer relationship management data, and/or the like”. Para. [0024] recites “the procurement data may include contractual data relating to agreements between the organization and client organizations for particular goods and/or services, such as a final contract between the organization and a client organization, records of negotiations between the organization and the client organization, non-disclosure agreements, and/or the like” (i.e., receiving a plurality of files related to historical contracting information in different formats associated with different areas such as transportation and customer relationship management));
wherein the planning system is communicatively coupled to the plurality of data sources (para. [0093] recites “FIGS. 2A and 2B are diagrams of an example environment 200 in which systems and/or methods described herein may be implemented. As shown in FIG. 2A, environment 200 may include a client device 210, a data storage device 220, a transformation platform 230 hosted within a cloud computing environment 240, a client system 250, and/or a network 260. Devices of environment 200 may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections” (i.e., data storage, or sources, can be communicatively coupled to the planning system));
converting, by the planning system, the plurality of files into a unified data format, to generate a unified set of data (para. [0053] recites “a service management tool provides application data to a data fabric aggregation tool that is part of a fully-integrated ERP system of a particular organization, the data fabric aggregation tool may convert the application data to shared application data and may provide the shared application data to the master data fabric aggregation tool” (i.e., converting files into a unified data format));
updating a machine learning model based on the unified set of data (para. [0055] recites “the transformation platform may provide, as input data to the data model, the organizational data, the industry data, the observation data for the set of observations, the priorities data for the set of priorities, and/or the like” (i.e., inputting, or updating, a model with organization data that has been converted by the data fabric application tool as described in at least para. [0053]));
wherein updating the machine learning model comprises: analysing exploratory data for converting the unified set of data to a set of feature values, and generating a report indicating the feature values associated with the unified set of data (para. [0051] recites “When training the data model, the transformation platform may have identified trends and/or patterns associated with particular combinations of training data” (i.e., the model analyzes exploratory data related to the training data). Para. [0053]-[[0054] recite “the data fabric aggregation tool may convert the application data to shared application data and may provide the shared application data to the master data fabric aggregation tool. The set of candidate configurations that are used by the data model may include different service management tools, differentiation variations of the same service management tool ( e.g., with different features or service offerings, varied levels of customization, and/or the like)”. Para. [0057] recites “the data model may be used to generate a score for each aspect of a configuration. For example, the transformation platform may use the data model to generate a score for each available service management tool, for each available environment, and/or the like” (i.e., the model can generate a score, or report, indicating values associated with features of the unified training data)),
receiving, by the planning system, input associated with a current contract (para. [0085] recites “the transformation platform may analyze the new shared application data to generate new performance indicator data for performance indicators described elsewhere herein” (i.e., receiving new, or current, contract information));
selecting, by the planning system a set of factors, from a plurality of sets of factors, based on a phase associated with the current contract (para. [0045] recites “As shown by reference number 125, the transformation platform may identify a set of priorities that define a target state for the fully-integrated ERP system that is to be generated. For example, the transformation platform may identify the set of priorities based on the set of observations, based on priorities data provided by the organization, and/or the like” (i.e., selecting, or identifying, a set of observations, or factors, associated with the organizational information, which can include information related to the finalization phase of a contract as described in at least para. [0024]));
wherein the phase associated with the current contract comprises at least one of a planning phase, a constructing phase, and a finalization phase (para. [0024] recites “the procurement data may include contractual data relating to agreements between the organization and client organizations for particular goods and/or services, such as a final contract between the organization and a client organization, records of negotiations between the organization and the client organization, non-disclosure agreements, and/or the like” (i.e., the contract information includes at least finalization phase information));
applying the updated machine learning model to the input to generate a probability associated with the current contract (para. [0049] recites “As shown in FIG. 1C, and by reference number 130, the transformation platform may use a data model to generate a set of scores that indicate likelihoods of a set of candidate configurations for the fully-integrated ERP system creating the target state. For example, the transformation platform may have trained a data model (or may be received a trained data model) to score configurations and may use the data model to generate, for each candidate configuration of the set of candidate configurations, a score that indicates a likelihood of causing a state of the fully-integrated ERP system to be the target state” (i.e., applying a machine learning model to the contract input data to generate an associated likelihood, or probability));
providing instructions for a user interface (UI) that visually depicts the probability (para. [0087] recites “the transformation platform may configure and deploy one or more analytics tools to the fully-integrated ERP system and may use the one or more analytics tools to display the performance statistics data via an interface that may be accessed by the client device. This may allow users to view the performance statistics, make decisions based on the performance statistics, and/or the like” (i.e., providing instructions for a user interface to display performance statistics, which can include the likelihoods output from the model in at least para. [0049]));
and receiving, by the planning system, an outcome indication from the user device associated with the user interface, wherein the outcome indication is stored [in the data lake], and wherein the planning system is communicatively coupled to the user device [and the data lake] (para. [0070] recites “As shown by reference number 150, the client device may provide the service management plan for display. For example, the client device may provide the service management plan for display such that a user may view the plan, accept one or more phases of the plan, modify one or more phases of the plan, remove one or more phases of the plan, and/or the like”. Para. [0093] recites “FIGS. 2A and 2B are diagrams of an example environment 200 in which systems and/or methods described herein may be implemented. As shown in FIG. 2A, environment 200 may include a client device 210, a data storage device 220, a transformation platform 230 hosted within a cloud computing environment 240, a client system 250, and/or a network 260. Devices of environment 200 may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections” (i.e., outcome indications can be received from a user device communicatively coupled to the planning system)).
However, while Hazen teaches converting data into a unified format, Hazen does not explicitly teach using one or more scripts to convert data into a unified format.
Watson teaches using one or more scripts to convert data into a unified format (section IV A para. 1 recites “The system architecture of DaskDB incorporates five main components: the SQLParser, QueryPlanner, DaskPlanner, DaskDB Execution Engine, and HDFS. They are shown in Figure 1”. Section IV para. 4-5 recite “The DaskPlanner is used to transform the preliminary physical query plan from the QueryPlanner into Python code that is ready for execution. The first step in this process is for the DaskPlanner to go through the physical plan obtained from QueryPlanner and convert it into a Daskplan. This maps the operators from the physical plan into operators that more closely resemble the Dask API. The DaskPlanner converts the Daskplan into the Python code, which utilizes the Dask API”. Section IV C para. 1 recites “DaskDB supports UDFs in SQL as part of in situ querying. A UDF enables a user to create a function using Python code and embed it into the SQL query. Since DaskDB converts the SQL query, including the UDF back into Python code, the UDFs can reference and utilize features from any of the data science packages available in Anaconda Python API ecosystem” (i.e., executing a method, or script, in Python to convert data from formats associated with specific data packages to a unified format)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by utilizing the scripts from Watson to supplement the document conversion process from Hazen. Hazen and Watson are both directed to document analysis methods, and Hazen states in at least para. [0026] that “any number of different types of application data may be provided to the transformation platform”. Accordingly, one of ordinary skill in the art would understand how to utilize the scripts from Watson to convert documents to a unified format as described by Hazen.
However, the combination of Hazen and Watson does not explicitly teach providing a coefficient of variation associated with each feature.
Xiao teaches providing a coefficient of variation associated with each feature (section 2.1 recites “Suppose a consideration of two sets of N samples, X and Y, the basic correlation analysis is to find out whether the set of samples X is likely to predict another set of samples Y. Given that
X
-
,
Y
-
are the mean value of Xi and Yi, respectively. And the standard deviation terms are sx2, and sy2, therefore, the standard definition of the correlation coefficient r can be expressed as follows (EQ3). As we can see from the aforementioned equation, the correlation coefficient r must be in the range from −1 to 1, which is able to provide help for determining the magnitude and direction of the relationship between two variables in pair. The sign of the correlation coefficient r shows that the direction is positive or negative; however, the numerical value of the correlation coefficient r represents the magnitude of the correlation coefficient. The bigger the absolute value of r is, the greater the relationship of the two variables is. The square value r2, called coefficient of determination, is the measure of the proportion of the explained variance between 0 to 1, and 1 − r2 is the proportion of variance unexplained” (i.e., providing a coefficient of determination as a measure of variance for a given feature)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by applying the exploratory correlation analysis technique from Xiao to the trend analysis component of the planning system from Hazen. Section I of Xiao teaches that correlation analysis is a data exploration technique “for identifying and revealing the degree of association between one dependent variable and another one or more variables in a big or high-dimensional dataset. This information can be used for exploring and simplifying complex multivariate dataset and indicate possible factors that confound a relationship of interest”. One of ordinary skill in the art would be motivated to explore and simplify the multivariate datasets from Hazen using the correlation analysis technique from Xiao.
However, while Hazen teaches providing an outcome as feedback to the planning system (see at least paragraph [0072] of Hazen), the combination of Hazen, Watson, and Xiao does not explicitly teach a data lake, or providing an outcome indication to a consequent training cycle of the updated machine learning model; and retraining a machine learning model based on the stored outcome indication.
Sekar teaches a data lake (col. 3 recites “Regardless of the type of business, and regardless the types of data (structured/unstructured/streaming IoT signals) and whether in real-time or not, all advanced analytics applications follow some or all of these seven stages”. Col. 4-5 recite “Stage 6: Creating artifacts like dashboards, charts and reports. In the case of structured data, these are operational metrics dashboards, created typically using tools like Tableau, Qlik and Power BI by Microsoft. It is noted that these dashboards are created by certified BI developers working on pre-defined data sets, data cubes and data lakes” (i.e., a data lake can be used in connection with a planning system like the one taught by Hazen)),
providing an outcome indication to a consequent training cycle of the updated machine learning model; and retraining a machine learning model based on the outcome indication (col. 23 recites “At act 620, in the case of unstructured data, receiving, by the process device, at least one metric or at least one AI/ML algorithm, or both, defined by a user through the at least one graphical user interface 585 (FIG. 10b) without programming. Thus, through AI/ML algorithm library, the user defines the values for the process device to execute the AI/ML algorithm and generate an output to retrieve a plurality of stored data in the composite data master file”. Col. 26 recites “Together, the graphical representation, the drill down links, and the placeholder links for future collaboration etc., all form a reusable building block. Examples of the graphical presentation of a pie chart are shown on FIG. 11 and a time trends in FIG. 12. The reuse could also be but not limited to: sending the result of AI/ML execution back to the Training Set, to enhance the richness of knowledge for use later in the case of AI/ML supervised learning algorithms” (i.e., enhancing, or retraining a model with the results, or outcome indications of a previous usage, or training cycle of the model)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by utilizing the data lake and model reuse, or retraining methods from Sekar as part of the contract planning system from Hazen (as modified by Watson). Hazen and Sekar are both directed to methods of organizing data from a plurality of different sources and determining metrics associated with the organized data. Columns 3-4 of Sekar states “Regardless of the type of business, and regardless the types of data (structured/unstructured/streaming IoT signals) and whether in real-time or not, all advanced analytics applications follow some or all of these seven stages. . . Stage 4: performing metrics calculations and/or executing AI/ML algorithms”. One of ordinary skill in the art would recognize that the model retraining, or reuse, as described in at least column 26 of Sekar would fall under the category of executing AI/ML algorithms, and that the planning system from Hazen would fall under the category of “advanced analytics applications” such that the model from Hazen could be reused, or retrained, using the method from Sekar.
Regarding claim 2, the combination of Hazen, Watson, Xiao, and Sekar teaches the method of claim 1, wherein the plurality of formats includes two or more of an application outsourcing format, a systems integration format, a strategy and consulting format, an infrastructure outsourcing format, a business process outsourcing format, or a spreadsheet format (Hazen para. [0021] recites “As shown by reference number 110, the transformation platform may receive organizational data for the organization. The organization data may include process data that describes a set of organizational processes that are used for providing goods and/or services to customers, application data that is generated while the organizational processes are being performed, performance indicator data that identifies a set of network performance metrics that the organization has generated for measuring performance within the organization, and/or the like”. Hazen para. [0023] recites “The application data may include order management and inventory data, transportation management data, procurement data, asset lifecycle management data, supply chain management data, supplier management data, project and portfolio management data, customer relationship management data, and/or the like” (i.e., file formats can be related to at least outsourcing business process and application information to the enterprise resource planning platform)).
Regarding claim 3, the combination of Hazen, Watson, Xiao, and Sekar teaches the method of claim 1, wherein the one or more scripts comprise Python scripts that convert files to structured query language data (Watson section IV para. 4-5 recite “The DaskPlanner is used to transform the preliminary physical query plan from the QueryPlanner into Python code that is ready for execution. The first step in this process is for the DaskPlanner to go through the physical plan obtained from QueryPlanner and convert it into a Daskplan. This maps the operators from the physical plan into operators that more closely resemble the Dask API. The DaskPlanner converts the Daskplan into the Python code, which utilizes the Dask API”. Section IV C para. 1 recites “DaskDB supports UDFs in SQL as part of in situ querying. A UDF enables a user to create a function using Python code and embed it into the SQL query.” (i.e., using python scripts to convert data to SQL)).
Regarding claim 4, the combination of Hazen, Watson, Xiao, and Sekar teaches the method of claim 1, wherein updating the machine learning model comprises: performing a retraining using the unified set of data (Hazen para. [0050] recites “the transformation platform may have trained a data model by analyzing the training data using one or more machine learning techniques, such as a regression technique, a classification technique, a technique using a neural network, and/or the like”. Hazen para. [0084] recites “the transformation platform may obtain new shared application data associated with the set of service management tools. In this case, as the organization uses the set of service management tools to generate new shared application data, the new shared application data may be provided to the data fabric aggregation tool within the fully-integrated ERP system, converted to a uniform format, and provided to the master data fabric aggregation tool. This may allow the transformation platform to obtain the new shared application data from the master data fabric aggregation tool”. Hazen para. [0085] recites “As shown by reference number 170-2, the transformation platform may generate new performance indicator data. For example, the transformation platform may analyze the new shared application data to generate new performance indicator data for performance indicators described elsewhere herein” (i.e., analyzing new application data” (i.e., running the model to analyze new application data that has been converted to a unified format, or retraining the model)).
Regarding claim 5, the combination of Hazen, Watson, Xiao, and Sekar teaches the method of claim 1, wherein the updated machine learning model comprises a multi-class neural network (Hazen para. [0050] recites “the transformation platform may have trained a data model by analyzing the training data using one or more machine learning techniques, such as a regression technique, a classification technique, a technique using a neural network, and/or the like” (i.e., the machine learning model can be a neural network capable of classifying organization data into multiple classes associated with at least the different candidate configuration data as described in at least para. [0049])).
Regarding claim 6, the combination of Hazen, Watson, Xiao, and Sekar teaches the method of claim 1, wherein the phase associated with the current contract comprises a planning phase, a constructing phase, or a finalization phase (Examiner notes the 112(d) rejection of claim 6, but for purposes of compact prosecution, at least Hazen para. [0024] recites “the procurement data may include contractual data relating to agreements between the organization and client organizations for particular goods and/or services, such as a final contract between the organization and a client organization, records of negotiations between the organization and the client organization, non-disclosure agreements, and/or the like” (i.e., the contract information includes at least finalization phase information)).
Regarding claim 7, the combination of Hazen, Watson, Xiao, and Sekar teaches the method of claim 1, wherein the UI includes a pie chart or a bar graph depicting the probability (Hazen para. [0087] recites “the transformation platform may configure and deploy one or more analytics tools to the fully-integrated ERP system and may use the one or more analytics tools to display the performance statistics data via an interface that may be accessed by the client device. This may allow users to view the performance statistics, make decisions based on the performance statistics, and/or the like”. Sekar col. 2 lines 25-29 recite “The output and the results of the advanced analytics are presented in the form of graphs, dashboards etc., In the case of operational metrics, the dashboards are created to display weekly/monthly/quarterly time trends, pie charts, pivot charts etc.” (i.e., analytics data, such as the likelihood taught in at least para. [0049] of Hazen, can be depicted on an interface such as the one taught in at least paragraph [0087] of Hazen, as at least a pie chart)).
Regarding claim 8, Hazen teaches a device, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories (para. [0004] recites “According to some implementations, a device may include one or more memories, and one or more processors, operatively coupled to the one or more memories”), configured to:
receive, at a planning system, from a plurality of data sources, a plurality of files in a plurality of formats and associated with historical contracting information (para. [0021] recites “As shown by reference number 110, the transformation platform may receive organizational data for the organization”. Para. [0050] recites “the transformation platform may receive training data such as historical organizational data of one or more other organizations, historical industry data, historical observations, historical priorities, historical results data that associated particular target states with particular configurations, and/or the like. plan. The organization data may include process data that describes a set of organizational processes that are used for providing goods and/or services to customers, application data that is generated while the organizational processes are being performed. . .”. Para. [0023] recites “The application data may include order management and inventory data, transportation management data, procurement data, asset lifecycle management data, supply chain management data, supplier management data, project and portfolio management data, customer relationship management data, and/or the like”. Para. [0024] recites “the procurement data may include contractual data relating to agreements between the organization and client organizations for particular goods and/or services, such as a final contract between the organization and a client organization, records of negotiations between the organization and the client organization, non-disclosure agreements, and/or the like” (i.e., receiving a plurality of files related to historical contracting information in different formats associated with different areas such as transportation and customer relationship management)),
wherein the planning system is communicatively coupled to the plurality of data sources (para. [0093] recites “FIGS. 2A and 2B are diagrams of an example environment 200 in which systems and/or methods described herein may be implemented. As shown in FIG. 2A, environment 200 may include a client device 210, a data storage device 220, a transformation platform 230 hosted within a cloud computing environment 240, a client system 250, and/or a network 260. Devices of environment 200 may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections” (i.e., data storage, or sources, can be communicatively coupled to the planning system));
convert, at the planning system, the plurality of files into a unified data format, to generate a unified set of data (para. [0053] recites “a service management tool provides application data to a data fabric aggregation tool that is part of a fully-integrated ERP system of a particular organization, the data fabric aggregation tool may convert the application data to shared application data and may provide the shared application data to the master data fabric aggregation tool” (i.e., converting files into a unified data format));
update, at the planning system, a machine learning model based on the unified set of data (para. [0055] recites “the transformation platform may provide, as input data to the data model, the organizational data, the industry data, the observation data for the set of observations, the priorities data for the set of priorities, and/or the like” (i.e., inputting, or updating, a model with organization data that has been converted by the data fabric application tool as described in at least para. [0053]));
wherein updating the machine learning model comprises: analysing exploratory data for converting the unified set of data to a set of feature values, and generating a report indicating the feature values associated with the unified set of data (para. [0051] recites “When training the data model, the transformation platform may have identified trends and/or patterns associated with particular combinations of training data” (i.e., the model analyzes exploratory data related to the training data). Para. [0053]-[[0054] recite “the data fabric aggregation tool may convert the application data to shared application data and may provide the shared application data to the master data fabric aggregation tool. The set of candidate configurations that are used by the data model may include different service management tools, differentiation variations of the same service management tool ( e.g., with different features or service offerings, varied levels of customization, and/or the like)”. Para. [0057] recites “the data model may be used to generate a score for each aspect of a configuration. For example, the transformation platform may use the data model to generate a score for each available service management tool, for each available environment, and/or the like” (i.e., the model can generate a score, or report, indicating values associated with features of the unified training data)),
receive, at the planning system, input associated with a current contract (para. [0085] recites “the transformation platform may analyze the new shared application data to generate new performance indicator data for performance indicators described elsewhere herein” (i.e., receiving new, or current, contract information));
select a set of factors, at the planning system, from a plurality of sets of factors, based on a phase associated with the current contract (para. [0045] recites “As shown by reference number 125, the transformation platform may identify a set of priorities that define a target state for the fully-integrated ERP system that is to be generated. For example, the transformation platform may identify the set of priorities based on the set of observations, based on priorities data provided by the organization, and/or the like” (i.e., selecting, or identifying, a set of observations, or factors, associated with the organizational information, which can include information related to the finalization phase of a contract as described in at least para. [0024])),
wherein the phase associated with the current contract comprises at least one of a planning phase, a constructing phase, and a finalization phase (para. [0024] recites “the procurement data may include contractual data relating to agreements between the organization and client organizations for particular goods and/or services, such as a final contract between the organization and a client organization, records of negotiations between the organization and the client organization, non-disclosure agreements, and/or the like” (i.e., the contract information includes at least finalization phase information));
apply, at the planning system, the updated machine learning model, based on the selected set of factors, to the input to generate a probability associated with the current contract (para. [0049] recites “As shown in FIG. 1C, and by reference number 130, the transformation platform may use a data model to generate a set of scores that indicate likelihoods of a set of candidate configurations for the fully-integrated ERP system creating the target state. For example, the transformation platform may have trained a data model (or may be received a trained data model) to score configurations and may use the data model to generate, for each candidate configuration of the set of candidate configurations, a score that indicates a likelihood of causing a state of the fully-integrated ERP system to be the target state” (i.e., applying a machine learning model to the contract input data to generate an associated likelihood, or probability));
generate one or more modifications to the input based on the probability failing to satisfy a threshold (Hazen para. [0088]-[0089] recite “the transformation platform may modify one or more phases of the plan to reflect the increase in time, to mitigate against potential issues that may rise when the time to upgrade the set of service management tools increases, and/or the like. As another example, if a level of customization of the set of service management tools reaches 100% or a threshold percentage, the transformation platform may determine that there is no longer a need to generate performance indicators data relating to customization” (i.e., modifying the plan, which includes the probabilities calculated in at least para. [0049] and [0056], when a threshold is not met));
and transmit, from the planning system to a user device, one or more files encoding the one or more modifications, wherein the planning device is communicatively coupled to the user device (Hazen para. [0069]-[0070] recite “As shown by reference number 145, the transformation platform may provide the plan to the client device. For example, the transformation platform may use a communication interface (e.g., an application programming interface (API), another type of communication interface, etc.) to provide the plan to the client device. The client device may provide the service management plan for display such that a user may view the plan, accept one or more phases of the plan, modify one or more phases of the plan, remove one or more phases of the plan, and/or the like” (i.e., transmitting the plan to a communicatively coupled user device, which includes any modifications from the user));
receiving, by the planning system, an outcome indication from the user device associated with the user interface, wherein the outcome indication is stored [in the data lake], and wherein the planning system is communicatively coupled to the user device [and the data lake] (para. [0070] recites “As shown by reference number 150, the client device may provide the service management plan for display. For example, the client device may provide the service management plan for display such that a user may view the plan, accept one or more phases of the plan, modify one or more phases of the plan, remove one or more phases of the plan, and/or the like”. Para. [0093] recites “FIGS. 2A and 2B are diagrams of an example environment 200 in which systems and/or methods described herein may be implemented. As shown in FIG. 2A, environment 200 may include a client device 210, a data storage device 220, a transformation platform 230 hosted within a cloud computing environment 240, a client system 250, and/or a network 260. Devices of environment 200 may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections” (i.e., outcome indications can be received from a user device communicatively coupled to the planning system)).
However, while Hazen teaches converting data into a unified format, Hazen does not explicitly teach using one or more scripts to convert data into a unified format.
Watson teaches using one or more scripts to convert data into a unified format (section IV A para. 1 recites “The system architecture of DaskDB incorporates five main components: the SQLParser, QueryPlanner, DaskPlanner, DaskDB Execution Engine, and HDFS. They are shown in Figure 1”. Section IV para. 4-5 recite “The DaskPlanner is used to transform the preliminary physical query plan from the QueryPlanner into Python code that is ready for execution. The first step in this process is for the DaskPlanner to go through the physical plan obtained from QueryPlanner and convert it into a Daskplan. This maps the operators from the physical plan into operators that more closely resemble the Dask API. The DaskPlanner converts the Daskplan into the Python code, which utilizes the Dask API”. Section IV C para. 1 recites “DaskDB supports UDFs in SQL as part of in situ querying. A UDF enables a user to create a function using Python code and embed it into the SQL query. Since DaskDB converts the SQL query, including the UDF back into Python code, the UDFs can reference and utilize features from any of the data science packages available in Anaconda Python API ecosystem” (i.e., executing a method, or script, in Python to convert data from formats associated with specific data packages to a unified format)).
See claim 1 for motivation to combine.
However, the combination of Hazen and Watson does not explicitly teach providing a coefficient of variation associated with each feature.
Xiao teaches providing a coefficient of variation associated with each feature (section 2.1 recites “Suppose a consideration of two sets of N samples, X and Y, the basic correlation analysis is to find out whether the set of samples X is likely to predict another set of samples Y. Given that
X
-
,
Y
-
are the mean value of Xi and Yi, respectively. And the standard deviation terms are sx2, and sy2, therefore, the standard definition of the correlation coefficient r can be expressed as follows (EQ3). As we can see from the aforementioned equation, the correlation coefficient r must be in the range from −1 to 1, which is able to provide help for determining the magnitude and direction of the relationship between two variables in pair. The sign of the correlation coefficient r shows that the direction is positive or negative; however, the numerical value of the correlation coefficient r represents the magnitude of the correlation coefficient. The bigger the absolute value of r is, the greater the relationship of the two variables is. The square value r2, called coefficient of determination, is the measure of the proportion of the explained variance between 0 to 1, and 1 − r2 is the proportion of variance unexplained” (i.e., providing a coefficient of determination as a measure of variance for a given feature)).
See claim 1 for motivation to combine.
However, while Hazen teaches providing an outcome as feedback to the planning system (see at least paragraph [0072] of Hazen), the combination of Hazen, Watson, and Xiao does not explicitly teach a data lake, or providing an outcome indication to a consequent training cycle of the updated machine learning model; and retraining a machine learning model based on the stored outcome indication.
Sekar teaches a data lake (col. 3 recites “Regardless of the type of business, and regardless the types of data (structured/unstructured/streaming IoT signals) and whether in real-time or not, all advanced analytics applications follow some or all of these seven stages”. Col. 4-5 recite “Stage 6: Creating artifacts like dashboards, charts and reports. In the case of structured data, these are operational metrics dashboards, created typically using tools like Tableau, Qlik and Power BI by Microsoft. It is noted that these dashboards are created by certified BI developers working on pre-defined data sets, data cubes and data lakes” (i.e., a data lake can be used in connection with a planning system like the one taught by Hazen)),
providing an outcome indication to a consequent training cycle of the updated machine learning model; and retraining a machine learning model based on the outcome indication (col. 23 recites “At act 620, in the case of unstructured data, receiving, by the process device, at least one metric or at least one AI/ML algorithm, or both, defined by a user through the at least one graphical user interface 585 (FIG. 10b) without programming. Thus, through AI/ML algorithm library, the user defines the values for the process device to execute the AI/ML algorithm and generate an output to retrieve a plurality of stored data in the composite data master file”. Col. 26 recites “Together, the graphical representation, the drill down links, and the placeholder links for future collaboration etc., all form a reusable building block. Examples of the graphical presentation of a pie chart are shown on FIG. 11 and a time trends in FIG. 12. The reuse could also be but not limited to: sending the result of AI/ML execution back to the Training Set, to enhance the richness of knowledge for use later in the case of AI/ML supervised learning algorithms” (i.e., enhancing, or retraining a model with the results, or outcome indications of a previous usage, or training cycle of the model)).
See claim 1 for motivation to combine.
Regarding claim 9, the combination of Hazen, Watson, Xiao, and Sekar teaches transmitting the input associated with the current contract and the one or more modifications to a storage associated with the updated machine learning model (Hazen para. [0097] recites “Transformation platform 230 includes one or more devices capable of receiving, storing, generating, determining, and/or providing information associated with a service management plan” (i.e., transmitting plan data to storage, wherein the plan data can be associated with the contract information as described in at least para. [0024] and plan modifications as described in at least para. [0088]-[0089])).
Regarding claim 11, the combination of Hazen, Watson, Xiao, and Sekar teaches the device of claim 8, wherein the one or more processors, to generate the one or more modifications, are configured to: apply the updated machine learning model to the input to receive the one or more modifications that are expected to increase the probability (Hazen para. [0085] recites “As shown by reference number 170-2, the transformation platform may generate new performance indicator data. For example, the transformation platform may analyze the new shared application data to generate new performance indicator data for performance indicators described elsewhere herein”. Hazen para. [0088]-[0089] recite “the transformation platform may modify one or more phases of the plan to reflect the increase in time, to mitigate against potential issues that may rise when the time to upgrade the set of service management tools increases, and/or the like. As another example, if a level of customization of the set of service management tools reaches 100% or a threshold percentage, the transformation platform may determine that there is no longer a need to generate performance indicators data relating to customization” (i.e., applying the model to new data based on modifications that can be made in the manner described in para. [0088]-[0089]. Examiner notes that wherein modifications “that are expected to increase the probability” are received is interpreted as the intended use of applying the machine learning model and does not provide additional patentable weight to this limitation)).
Regarding claim 12, the combination of Hazen, Watson, Xiao, and Sekar teaches the device of claim 8, wherein the phase associated with the current contract comprises a planning phase, a constructing phase, or a finalization phase (Examiner notes the 112(d) rejection of claim 6, but for purposes of compact prosecution, at least Hazen para. [0024] recites “the procurement data may include contractual data relating to agreements between the organization and client organizations for particular goods and/or services, such as a final contract between the organization and a client organization, records of negotiations between the organization and the client organization, non-disclosure agreements, and/or the like” (i.e., the contract information includes at least finalization phase information)).
Regarding claim 14, Hazen teaches a non-transitory computer-readable medium storing a set of instructions (Hazen para. [0005] recites “According to some implementations, a non-transitory computer-readable medium may store one or more instructions”), the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to:
receive, at a planning system, from a plurality of data sources, a plurality of files in a plurality of formats and associated with historical contracting information, wherein the planning system is communicatively coupled to the plurality of data sources (para. [0021] recites “As shown by reference number 110, the transformation platform may receive organizational data for the organization”. Para. [0050] recites “the transformation platform may receive training data such as historical organizational data of one or more other organizations, historical industry data, historical observations, historical priorities, historical results data that associated particular target states with particular configurations, and/or the like. plan. The organization data may include process data that describes a set of organizational processes that are used for providing goods and/or services to customers, application data that is generated while the organizational processes are being performed. . .”. Para. [0023] recites “The application data may include order management and inventory data, transportation management data, procurement data, asset lifecycle management data, supply chain management data, supplier management data, project and portfolio management data, customer relationship management data, and/or the like”. Para. [0024] recites “the procurement data may include contractual data relating to agreements between the organization and client organizations for particular goods and/or services, such as a final contract between the organization and a client organization, records of negotiations between the organization and the client organization, non-disclosure agreements, and/or the like” (i.e., a planning system can receive a plurality of files from communicatively coupled data sources related to historical contracting information in different formats and associated with different areas such as transportation and customer relationship management));
convert, at the planning system, the plurality of files into a unified data format, to generate a unified set of data (para. [0053] recites “a service management tool provides application data to a data fabric aggregation tool that is part of a fully-integrated ERP system of a particular organization, the data fabric aggregation tool may convert the application data to shared application data and may provide the shared application data to the master data fabric aggregation tool” (i.e., converting files into a unified data format));
update, at the planning system, a machine learning model based on the unified set of data (para. [0055] recites “the transformation platform may provide, as input data to the data model, the organizational data, the industry data, the observation data for the set of observations, the priorities data for the set of priorities, and/or the like” (i.e., inputting, or updating, a model with organization data that has been converted by the data fabric application tool as described in at least para. [0053])),
wherein updating the machine learning model comprises: analysing exploratory data for converting the unified set of data to a set of feature values, and generating a report indicating the feature values associated with the unified set of data (para. [0051] recites “When training the data model, the transformation platform may have identified trends and/or patterns associated with particular combinations of training data” (i.e., the model analyzes exploratory data related to the training data). Para. [0053]-[[0054] recite “the data fabric aggregation tool may convert the application data to shared application data and may provide the shared application data to the master data fabric aggregation tool. The set of candidate configurations that are used by the data model may include different service management tools, differentiation variations of the same service management tool ( e.g., with different features or service offerings, varied levels of customization, and/or the like)”. Para. [0057] recites “the data model may be used to generate a score for each aspect of a configuration. For example, the transformation platform may use the data model to generate a score for each available service management tool, for each available environment, and/or the like” (i.e., the model can generate a score, or report, indicating values associated with features of the unified training data)),
receive, at the planning system, input associated with a current contract (para. [0085] recites “the transformation platform may analyze the new shared application data to generate new performance indicator data for performance indicators described elsewhere herein” (i.e., receiving new, or current, contract information));
apply, by the planning system, the machine learning model, based on the selected set of factors, to the input to generate one or more recommended parameters for the current contract (para. [0049] recites “As shown in FIG. 1C, and by reference number 130, the transformation platform may use a data model to generate a set of scores that indicate likelihoods of a set of candidate configurations for the fully-integrated ERP system creating the target state. For example, the transformation platform may have trained a data model (or may be received a trained data model) to score configurations and may use the data model to generate, for each candidate configuration of the set of candidate configurations, a score that indicates a likelihood of causing a state of the fully-integrated ERP system to be the target state”. Para. [0056] recites “the transformation platform may select, as the recommendation, the configuration that is associated with a best available score of the set of scores (e.g., a score with a highest likelihood value, etc.)” (i.e., applying a machine learning model to the contract input data to generate recommended configurations, or parameters, associated with the contract information));
provide, by the planning system, instructions for a user interface (UI) that visually depicts the one or more recommended parameters (para. [0017] recites “a user may use the client device to interact with an interface of an application or a website that allows the user to input a request for the service management plan”. Para. [0087] recites “the transformation platform may configure and deploy one or more analytics tools to the fully-integrated ERP system and may use the one or more analytics tools to display the performance statistics data via an interface that may be accessed by the client device. This may allow users to view the performance statistics, make decisions based on the performance statistics, and/or the like” (i.e., providing instructions for a user interface to display performance statistics, which can include the recommendations based on the output from the model in at least para. [0049] and [0056]));
and transmit, from the planning system to a user device associated with the user interface (UI), one or more files encoding the one or more recommended parameters, wherein the planning system is communicatively coupled to the user device (para. [0017] recites “a user may use the client device to interact with an interface of an application or a website that allows the user to input a request for the service management plan”. Para. [0069] recites “As shown by reference number 145, the transformation platform may provide the plan to the client device. For example, the transformation platform may use a communication interface (e.g., an application programming interface (API), another type of communication interface, etc.) to provide the plan to the client device” (i.e., transmitting the plan to a communicatively coupled user device with a user interface, which includes the recommendations from at least para. [0049] and [0056])); and
receive, using the planning system, an outcome indication from the user device associated with the user interface, wherein the outcome indication is stored [in the data lake], (para. [0093] recites “FIGS. 2A and 2B are diagrams of an example environment 200 in which systems and/or methods described herein may be implemented. As shown in FIG. 2A, environment 200 may include a client device 210, a data storage device 220, a transformation platform 230 hosted within a cloud computing environment 240, a client system 250, and/or a network 260. Devices of environment 200 may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections” (i.e., data storage, or sources, can be communicatively coupled to the planning system). para. [0097] recites “Transformation platform 230 includes one or more devices capable of receiving, storing, generating, determining, and/or providing information associated with a service management plan” (i.e., transmitting plan data to storage, wherein the plan data can be associated with the contract information as described in at least para. [0024] and recommendations as described in at least para. [0049] and [0056]));
receiving, by the planning system, an outcome indication from the user device associated with the user interface, wherein the outcome indication is stored [in the data lake], and wherein the planning system is communicatively coupled to the user device [and the data lake] (para. [0070] recites “As shown by reference number 150, the client device may provide the service management plan for display. For example, the client device may provide the service management plan for display such that a user may view the plan, accept one or more phases of the plan, modify one or more phases of the plan, remove one or more phases of the plan, and/or the like”. Para. [0093] recites “FIGS. 2A and 2B are diagrams of an example environment 200 in which systems and/or methods described herein may be implemented. As shown in FIG. 2A, environment 200 may include a client device 210, a data storage device 220, a transformation platform 230 hosted within a cloud computing environment 240, a client system 250, and/or a network 260. Devices of environment 200 may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections” (i.e., outcome indications can be received from a user device communicatively coupled to the planning system)).
However, while Hazen teaches converting data into a unified format, Hazen does not explicitly teach using one or more scripts to convert data into a unified format.
Watson teaches using one or more scripts to convert data into a unified format (section IV A para. 1 recites “The system architecture of DaskDB incorporates five main components: the SQLParser, QueryPlanner, DaskPlanner, DaskDB Execution Engine, and HDFS. They are shown in Figure 1”. Section IV para. 4-5 recite “The DaskPlanner is used to transform the preliminary physical query plan from the QueryPlanner into Python code that is ready for execution. The first step in this process is for the DaskPlanner to go through the physical plan obtained from QueryPlanner and convert it into a Daskplan. This maps the operators from the physical plan into operators that more closely resemble the Dask API. The DaskPlanner converts the Daskplan into the Python code, which utilizes the Dask API”. Section IV C para. 1 recites “DaskDB supports UDFs in SQL as part of in situ querying. A UDF enables a user to create a function using Python code and embed it into the SQL query. Since DaskDB converts the SQL query, including the UDF back into Python code, the UDFs can reference and utilize features from any of the data science packages available in Anaconda Python API ecosystem” (i.e., executing a method, or script, in Python to convert data from formats associated with specific data packages to a unified format)).
See claim 1 for motivation to combine.
However, the combination of Hazen and Watson does not explicitly teach providing a coefficient of variation associated with each feature.
Xiao teaches providing a coefficient of variation associated with each feature (section 2.1 recites “Suppose a consideration of two sets of N samples, X and Y, the basic correlation analysis is to find out whether the set of samples X is likely to predict another set of samples Y. Given that
X
-
,
Y
-
are the mean value of Xi and Yi, respectively. And the standard deviation terms are sx2, and sy2, therefore, the standard definition of the correlation coefficient r can be expressed as follows (EQ3). As we can see from the aforementioned equation, the correlation coefficient r must be in the range from −1 to 1, which is able to provide help for determining the magnitude and direction of the relationship between two variables in pair. The sign of the correlation coefficient r shows that the direction is positive or negative; however, the numerical value of the correlation coefficient r represents the magnitude of the correlation coefficient. The bigger the absolute value of r is, the greater the relationship of the two variables is. The square value r2, called coefficient of determination, is the measure of the proportion of the explained variance between 0 to 1, and 1 − r2 is the proportion of variance unexplained” (i.e., providing a coefficient of determination as a measure of variance for a given feature)).
See claim 1 for motivation to combine.
However, while Hazen teaches providing an outcome as feedback to the planning system (see at least paragraph [0072] of Hazen), the combination of Hazen, Watson, and Xiao does not explicitly teach a data lake, or providing an outcome indication to a consequent training cycle of the updated machine learning model; and retraining a machine learning model based on the stored outcome indication.
Sekar teaches a data lake (col. 3 recites “Regardless of the type of business, and regardless the types of data (structured/unstructured/streaming IoT signals) and whether in real-time or not, all advanced analytics applications follow some or all of these seven stages”. Col. 4-5 recite “Stage 6: Creating artifacts like dashboards, charts and reports. In the case of structured data, these are operational metrics dashboards, created typically using tools like Tableau, Qlik and Power BI by Microsoft. It is noted that these dashboards are created by certified BI developers working on pre-defined data sets, data cubes and data lakes” (i.e., a data lake can be used in connection with a planning system like the one taught by Hazen)),
providing an outcome indication to a consequent training cycle of the updated machine learning model; and retraining a machine learning model based on the outcome indication (col. 23 recites “At act 620, in the case of unstructured data, receiving, by the process device, at least one metric or at least one AI/ML algorithm, or both, defined by a user through the at least one graphical user interface 585 (FIG. 10b) without programming. Thus, through AI/ML algorithm library, the user defines the values for the process device to execute the AI/ML algorithm and generate an output to retrieve a plurality of stored data in the composite data master file”. Col. 26 recites “Together, the graphical representation, the drill down links, and the placeholder links for future collaboration etc., all form a reusable building block. Examples of the graphical presentation of a pie chart are shown on FIG. 11 and a time trends in FIG. 12. The reuse could also be but not limited to: sending the result of AI/ML execution back to the Training Set, to enhance the richness of knowledge for use later in the case of AI/ML supervised learning algorithms” (i.e., enhancing, or retraining a model with the results, or outcome indications of a previous usage, or training cycle of the model)).
See claim 1 for motivation to combine.
Regarding claim 15, the combination of Hazen, Watson, Xiao, and Sekar teaches non-transitory computer-readable medium of claim 14, wherein the one or more instructions, when executed by the one or more processors, further cause the device to: transmit the input associated with the current contract and the one or more recommended parameters to a storage associated with the updated machine learning model (Hazen para. [0097] recites “Transformation platform 230 includes one or more devices capable of receiving, storing, generating, determining, and/or providing information associated with a service management plan” (i.e., transmitting plan data to storage, wherein the plan data can be associated with the contract information as described in at least para. [0024] and recommendations as described in at least para. [0049] and [0056])).
Regarding claim 16, the combination of Hazen, Watson, Xiao, and Sekar teaches the non-transitory computer-readable medium of claim 14, wherein the one or more instructions, when executed by the one or more processors, further cause the device to: select a set of factors, from a plurality of sets of factors, based on a phase associated with the current contract, wherein the updated machine learning model is applied based on the selected set of factors (Hazen para. [0045] recites “As shown by reference number 125, the transformation platform may identify a set of priorities that define a target state for the fully-integrated ERP system that is to be generated. For example, the transformation platform may identify the set of priorities based on the set of observations, based on priorities data provided by the organization, and/or the like” (i.e., selecting, or identifying, a set of observations, or factors, associated with the organizational information, which can include information related to the finalization phase of a contract as described in at least para. [0024])).
Regarding claim 17, the combination of Hazen, Watson, Xiao, and Sekar teaches the non-transitory computer-readable medium of claim 16, wherein the phase associated with the current contract comprises a planning phase, a constructing phase, or a finalization phase (Hazen para. [0024] recites “the procurement data may include contractual data relating to agreements between the organization and client organizations for particular goods and/or services, such as a final contract between the organization and a client organization, records of negotiations between the organization and the client organization, non-disclosure agreements, and/or the like” (i.e., the contract information includes at least finalization phase information)).
Regarding claim 18, the combination of Hazen, Watson, Xiao, and Sekar teaches the non-transitory computer-readable medium of claim 14, wherein the updated machine learning model comprises a multi-class neural network (Hazen para. [0050] recites “the transformation platform may have trained a data model by analyzing the training data using one or more machine learning techniques, such as a regression technique, a classification technique, a technique using a neural network, and/or the like” (i.e., the machine learning model can be a neural network capable of classifying organization data into multiple classes associated with at least the different candidate configuration data as described in at least para. [0049])).
Regarding claim 19, the combination of Hazen, Watson, Xiao, and Sekar teaches the non-transitory computer-readable medium of claim 14, wherein the one or more instructions, that cause the device to provide instructions for the UI, cause the device to: input the one or more recommended parameters to a web-based graph generator (Sekar col. 4 lines 60-65 recite “In the case of streaming real-time data, dashboards have to display changing trends in real-time, of course with some processing delays. In the case of structured data, these are operational metrics dashboards, created typically using tools like Tableau, Qlik and Power BI by Microsoft” (i.e., a user interface can depict parameters, such as those taught by at least para. [0049] and [0056] of Hazen, using a web-based generator like Microsoft Power BI. Examiner notes that Applicant has cited Microsoft Power BI as an example of a web based graph generator in para. [0032] of Applicant’s specification).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Hazen et al (US 20190312793 A1, herein Hazen) in view of Watson et al (“A Unified System for Data Analytics and In Situ Query Processing”, herein Watson), in further view of Xiao et al (“Using Spearman's correlation coefficients for exploratory data analysis on big dataset”, herein Xiao), in further view of Sekar (US 10997195 B1, herein Sekar), in further view of Savitzky et al (US 20030163552 A1, herein Savitzky).
Regarding claim 10, the combination of Hazen, Watson, Xiao, and Sekar teaches the device of claim 8, wherein the one or more processors are further configured to: store the one or more modifications [in association with a first version indicator] (Hazen para. [0097] recites “Transformation platform 230 includes one or more devices capable of receiving, storing, generating, determining, and/or providing information associated with a service management plan” (i.e., transmitting plan data to storage, wherein the plan data can be associated with the contract information as described in at least para. [0024] and plan modifications as described in at least para. [0088]-[0089]));
receive updated input associated with the current contract (Hazen para. [0085] recites “the transformation platform may analyze the new shared application data to generate new performance indicator data for performance indicators described elsewhere herein” (i.e., receiving new, or updated, contract information));
generate one or more new modifications to the updated input based on applying the updated machine learning model to the updated input (Hazen para. [0085] recites “As shown by reference number 170-2, the transformation platform may generate new performance indicator data. For example, the transformation platform may analyze the new shared application data to generate new performance indicator data for performance indicators described elsewhere herein”. Hazen para. [0088]-[0089] recite “the transformation platform may modify one or more phases of the plan to reflect the increase in time, to mitigate against potential issues that may rise when the time to upgrade the set of service management tools increases, and/or the like. As another example, if a level of customization of the set of service management tools reaches 100% or a threshold percentage, the transformation platform may determine that there is no longer a need to generate performance indicators data relating to customization” (i.e., applying a model to new data, or updating a model, based on modifications that can be made in the manner described in para. [0088]-[0089]));
and store the one or more new modifications [in association with a second version indicator] (Hazen para. [0097] recites “Transformation platform 230 includes one or more devices capable of receiving, storing, generating, determining, and/or providing information associated with a service management plan” (i.e., transmitting plan data to storage, wherein the plan data can be associated with the contract information as described in at least para. [0024] and plan modifications as described in at least para. [0088]-[0089])).
However, the combination of Hazen, Watson, Xiao, and Sekar does not explicitly teach storing file information with associated version indicators.
Savitzky teaches storing file information with associated version indicators (para. [0011] recites “Each version of a document is maintained. The distribution and notification lists can be different from one version of the document to the next”. Para. [0019] recites “FIGS. 6A-6C show a partial file system illustrating the process of layer numbering for version control” (i.e., files can be stored with associated version indicators)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by utilizing the version indicators from Savitzky to better organize the organization information from Hazen (as modified by Watson, Xiao, and Sekar). Savitzky and Hazen are both directed to document management and storage systems. One of ordinary skill in the art would recognize that the organization information from the disparate business areas and sources as taught by Hazen would be more efficiently organized using version indicators such as those taught by Savitzky to avoid determining current performance metrics for old data.
Claims 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hazen et al (US 20190312793 A1, herein Hazen) in view of Watson et al (“A Unified System for Data Analytics and In Situ Query Processing”, herein Watson), in further view of Xiao et al (“Using Spearman's correlation coefficients for exploratory data analysis on big dataset”, herein Xiao), in further view of Sekar (US 10997195 B1, herein Sekar), in further view of Mamou et al (US 20050223109 A1, herein Mamou).
Regarding claim 13, the combination of Hazen, Watson, Xiao, and Sekar teaches the device of claim 8.
However, the combination of Hazen, Watson, Xiao, and Sekar does not explicitly teach wherein the one or more files comprise a presentation file or a portable document format file.
Mamou teaches wherein the one or more files comprise a presentation file or a portable document format file (para. [0195] recites “FIG. 1 represents a platform 100 for facilitating integration of various data of a business enterprise. The platform includes a plurality of business processes, each of which may include a plurality of different computer applications and data sources. The data sources 102 may include files created or used by applications such as Microsoft Outlook, Microsoft Word, Microsoft Excel, Microsoft Access, as well as files in standard formats such as ASCII, CSV, GIF, TIF, PNG, PDF, and so forth”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by including the PDF files from Mamou in the formats available the transformation system from Hazen (as modified by Watson, Xiao, and Sekar). Mamou and Hazen are both directed to data integration systems; accordingly, one of ordinary skill would recognize that data stored in a standard format such as the PDF file format from Mamou would be commonly included in the business organization data from Hazen.
Regarding claim 20, the combination of Hazen, Watson, Xiao, and Sekar teaches the non-transitory computer-readable medium of claim 14.
However, the combination of Hazen, Watson, Xiao, and Sekar does not explicitly teach wherein the one or more files comprise a presentation file or a portable document format file.
Mamou teaches wherein the one or more files comprise a presentation file or a portable document format file (para. [0195] recites “FIG. 1 represents a platform 100 for facilitating integration of various data of a business enterprise. The platform includes a plurality of business processes, each of which may include a plurality of different computer applications and data sources. The data sources 102 may include files created or used by applications such as Microsoft Outlook, Microsoft Word, Microsoft Excel, Microsoft Access, as well as files in standard formats such as ASCII, CSV, GIF, TIF, PNG, PDF, and so forth”).
See claim 13 for motivation to combine.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200279200 A1 (Makhija et al) teaches a method for enterprise application operation management comprising a centralized data lake, which can be analyzed using machine learning models and exploratory data analysis methods.
US 20220156117 A1 (Chen et al) teaches a method for optimizing allocation of computer resources stored in a data lake using iterative machine learning training methods.
“TextTile: An Interactive Visualization Tool for Seamless Exploratory Analysis of Structured Data and Unstructured Text” (Felix et al) teaches a data visualization tool for investigation of datasets using exploratory analysis methods.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/L.M.F./ Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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