DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Introduction
The following is a final Office Action in response to Applicant’s communications received on March 26, 2026. Claims 1, 3, 4, 11, 21-23 and 25 have been amended, claims 3, 5, 12-13, 15 and 24 have been canceled and claim 26 has been added.
Currently claims 1-2, 4, 6-11, 14, 16-23 and 25-26 are pending. Claims 1 and 11 are independent.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/26/2026 appears to be in compliance with the previsions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Response to Amendments
Applicant’s amendments necessitated the new ground(s) of rejection in this Office Action.
Applicant’s amendments to the Specification received on 08/22/2025 is acknowledged, however, a clean copy of the amended Specification is required.
The 35 U.S.C. § 112(a) rejection to claims 1-2, 4, 6-11, 14 and 16-25 as set forth in the previous Office Action is withdrawn in response to Applicant’s amendments.
Applicant’s amendments to claims 1, 3, 4, 11, 21-23 and 25 are NOT sufficient to overcome the 35 U.S.C. § 101 rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 101 rejection to claims 1-2, 4, 6-11, 14, 16-23 and 25-26 has been maintained.
Response to Arguments
Applicant’s arguments filed on March 26, 2026 have been fully considered but are not persuasive.
In the Remarks on page 13, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that claims 1 and 11 now integrate any purported abstract idea into a practical application because the claim provide specific technical steps for training and updating of a machine learning model.
In response to Applicant’s argument, the Examiner respectfully disagrees. Indeed the claims recite “training a machine learning model of the predictive engine to generate machine-predicted data patterns based on enterprise data associated with the enterprise data model, by utilizing the Bayesian predictive algorithm, the machine-predicted data patterns indicating a proposed modification to either (1) a business process of an enterprise associated with the enterprise data computing platform, or (2) the persona associated with the user account.” However, the machine learning model does not use the trained function, and the function used by the machine learning model was not actually trained. Therefore, using the machine learning to analyze the enterprise data against the machine-predicted data patterns is merely adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement an abstract idea on a computer is NOT enough to qualify as “significantly more”. With this regard, the courts have held that a process defined simply as using a computer and memory to perform a series of mental steps that people can and regularly do perform in their heads. See CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011); see also In re Grams, 888 F.2d 835, 840-41 (Fed. Cir. 1989); In re Meyer, 688 F.2d 789, 794-95 (C.C.P.A. 1982).
In the Remarks on page 14, Applicant argues that the combination of references cited in the Office Action do not teach all the claimed element of claim 1, as amended. However, Applicant’s arguments are directed to the newly amended claims, and therefore, the newly amended claims will be fully addressed in this Office Action.
Claim Objection
Claims 1 and 11 are objected to because of the following informalities: claim 1 and 11 recite “Baysian” which appears to be a misspelling word. Examiner interpreted the claim to read “Bayesian” for the purpose of examination.
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 1-2, 4, 6-11, 14, 16-23 and 25-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1, 2, 4, 6-10, 21-23 and 25-26 are directed to a method for generating and managing component data objects representing enterprise task and processes, which falls within the statutory category of a process. Claims 11, 14 and 16-20 are directed to a system comprising a data store (memory) storing executable instructions and a processor, which falls within the statutory category of a machine.
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking the method as representative, the claims recite the limitations of “activating an application…to analyze and modify enterprise data…and to implement a machine learning model, determining a user account, accessing an enterprise data model including one or more component data objects, training the machine learning model of the predictive engine to generate machine-predicted data patterns based on enterprise data associated with the enterprise data model, using the machine learning model to analyze the enterprise data against the machine-predicted data patterns, and updating the machine learning model with the optimized subset of the enterprise data”, the dependent claims further narrowing the limitations including: “employing a machine learning algorithm…to analyze the enterprise data model to identify pattern among data objects relevant to an enterprise function, receiving configuration data and generating code to identify another API to access data for which to perform the function, identifying data representing at least one enterprise function associated with the user account, implementing the persona based on user attributes associated with the user account linked to a subset of permissions, and accessing a configurable navigation component configured to implement a hierarchical data structure to display configurable data links”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to methods, that allow users to generate a computerized tools include a processor and a machine learning algorithm to analyze and modify enterprise data, and to manage commercial interactions including sales activities and business relations, by using an enterprise computing device (e.g., CRM), which are certain methods of organizing human activity. The Specification supports this view, for example: “the enterprise computing devices, which may be configured to perform or facilitate any number of business functions for an enterprise, such as sales, marketing, project planning, finance, accounting, procurement, inventory management, human resource management, supply chain management, and the like”(see ¶ 44). Thus, the claims fall within the certain methods of organizing human activity grouping. The mere nominal recitation of “by a processor”, “at an enterprise computing platform”, “via an application programming interface (“API”), and “employing a machine learning algorithm” do not take the claim out of the certain methods of organizing human activity grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, the claims recite the additional elements of “a processor”, “an enterprise computing platform”, and “a machine learning algorithm”. The Specification describes that “any process or device described herein, can be implemented in one or more computing devices (i.e., any mobile computing device, such as a wearable device, such as a hat or headband, or mobile phone, whether worn or carried) that may include one or more processors configured to execute one or more algorithms in memory.”(See ¶ 148). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are recited at a high level of generality and merely invoked as tools to perform generic computer functions. In particular, using a machine learning model is to no more than adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. The Supreme Court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). As to learning per se, such an argument overlooks the entire education system. Reciting machine learning is placing such learning in a computer context, offering no technological implementation details beyond the conceptual idea to use a machine for learning. Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). Again, automating an abstract process does not convert it into a practical application. See Credit Acceptance v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017) (“Our prior cases have made clear that mere automation of manual processes using generic computers does not constitute a patentable improvement in computer technology.”); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). The Federal Circuit has also indicated that mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, nothing in the claims that reflects an improvement to the functioning of a computer itself or another technology, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effect designed to monopolize the exception. Therefore, the additional elements do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
The claims recite the additional elements of “a processor”, “an enterprise computing platform”, and “a machine learning algorithm”. The Specification describes that “any process or device described herein, can be implemented in one or more computing devices (i.e., any mobile computing device, such as a wearable device, such as a hat or headband, or mobile phone, whether worn or carried) that may include one or more processors configured to execute one or more algorithms in memory.”(See ¶ 148). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are recited at a high level of generality and merely invoked as tools to perform generic computer functions. In particular, using a machine learning model is to no more than adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. The Supreme Court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). As to learning per se, such an argument overlooks the entire education system. Reciting machine learning is placing such learning in a computer context, offering no technological implementation details beyond the conceptual idea to use a machine for learning. The additional elements, when taken individually and as an ordered combination, the enterprise computing device, at best, may perform the generic computer functions including receiving, manipulating, and transmitting information over a network. However, using a generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1326-27, 122 USPQ2d 1377, 1379-80 (Fed. Cir. 2017) (claim reciting multiple abstract ideas, i.e., the manipulation of information through a series of mental steps and a mathematical calculation, was held directed to an abstract idea)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1, 2, 4, 6-10, 21-23 and 25-26 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims 11, 14 and 16-20 parallel claims 1, 2, 4, 6-10, 21-23 and 25-26 —similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, 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. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 1-2, 4, 6-11, 14, 16-23 and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Reynolds et al., (US 2021/0390507, hereinafter: Reynolds), and in view of Gasper et al., (US 2022/0327119, hereinafter: Gasper).
Regarding claim 1, Reynolds discloses the a method comprising:
activating an application at an enterprise data computing platform configured to generate a computerized tool implementable via an interface to analyze and modify enterprise data (see Abstract; Fig. 3, # 302; ¶ 27, ¶ 30-31, ¶ 35, ¶ 46, ¶ 64), the enterprise data computing platform including a predictive engine that is configured to implement a machine learning model that utilizes a Bayesian predictive algorithm;
determining, by the processor, a user account for which the computerized tool is implemented (see Abstract; ¶ 27, ¶ 30, ¶ 53, ¶ 56, ¶ 59);
accessing, by the processor, an enterprise data model including one or more component data objects configured to implement a component within a plurality of components with which to include in the computerized tool, the component being accessible based on data representing a persona associated with the user account (see Abstract; ¶ 20, ¶ 27, ¶ 41-43, ¶ 51-53, ¶ 56-59);
using the machine learning model to analyze the enterprise data against the machine-predicted data patterns, resulting in an optimized subset of the enterprise data and optimized associations among the one or more component objects in the enterprise data model (see ¶ 18, ¶ 23, ¶ 29-30, ¶ 36, ¶ 66); and
updating the machine learning model with the optimized subset of the enterprise data as the updated training data set of the machine learning model, to improve the functionality of the machine learning model to generate the machine-predicted data patterns when executed by the processor (see ¶ 6, ¶ 30, ¶ 79).
Reynolds discloses the applications are configured to apply machine learning algorithms or deep learning algorithms to generate one or more predictive data models, and further configured to implement Bayesian models, neural network algorithms, linear regression algorithms, etc. (see ¶ 23); a computerized tool to facilitate interoperability of canonical datasets with other databases in different formats with various external computerized analysis tools (e.g., via application programming interface or APIs) to procure, inspect, analyze, generate, manipulate, and share databases (see ¶ 27, ¶ 55).
Reynolds does not explicitly disclose the following limitations; however, Gasper in an analogous art for generating data model discloses
the enterprise data computing platform including a predictive engine that is configured to implement a machine learning model that utilizes a Bayesian predictive algorithm (see ¶ 56, ¶ 60, ¶ 69, ¶ 72, ¶ 128, ¶ 156); and
training the machine learning model of the predictive engine to generate machine-predicted data patterns based on enterprise data associated with the enterprise data model, by utilizing the Bayesian predictive algorithm (see ¶ 54-56, ¶ 60, ¶ 156, ¶ 167-168), the machine-predicted data patterns indicating a proposed modification to either (1) a business process of an enterprise associated with the enterprise data computing platform (see ¶ 107, ¶ 132, ¶ 203), or (2) the persona associated with the user account (see ¶ 138, ¶ 150-151).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Reynolds to include teaching of Gasper in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computation efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Reynolds discloses the method of claim 1 further comprising:
utilizing the predictive engine employing a machine learning algorithm, the machine learning algorithm being configured (see ¶ 23-25, ¶ 41, ¶ 48-49) to:
Reynolds does not explicitly disclose the following limitations; however, Gasper discloses
analyze the enterprise data model to identify patterns among data objects and data object relationships relevant to the enterprise function (see ¶ 59, ¶ 69, ¶ 84-86).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Reynolds to include teaching of Gasper in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computation efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 4, Reynolds discloses the method of claim 1 comprising:
configuring, by the processor, the component to form a portion of the workspace adapted to the data representing the persona (see Fig. 1, # 194; ¶ 25, ¶ 32, ¶ 45) by:
receiving configuration data to configure the component to perform a function (see Abstract; ¶ 27, ¶ 43, ¶ 65, ¶ 72); and
generating code to identify another API to access data for which to perform the function (see ¶ 21, ¶ 27, ¶ 60-61).
Regarding claim 6, Reynolds discloses the method of claim 1 wherein determining the user account comprises:
identifying data representing at least one enterprise function associated with the user account (see ¶ 26, ¶ 53).
Regarding claim 7, Reynolds discloses the method of claim 6 wherein the least one enterprise function comprises a role (see ¶ 29, ¶ 57).
In addition, claim 3 merely describing the characteristic of the enterprise function is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 8, Reynolds discloses the method of claim 1 wherein accessing the enterprise data model including the component being accessible based on the data representing the persona comprises:
implementing the persona based on user attributes associated with the user account linked to a subset of permissions (see ¶ 32-33, ¶ 53).
Regarding claim 9, Reynolds discloses the method of claim 1 wherein the component being accessible based on data representing the persona is based on a subset of permissions (see ¶ 30, ¶ 53).
Regarding claim 10, Reynolds discloses the method of claim 1 wherein accessing the enterprise data model including one or more component data objects comprises:
accessing a configurable navigation component configured to implement a hierarchical data structure to display configurable data links (see ¶ 43, ¶ 51-53, ¶ 64).
Regarding claim 11, Reynolds discloses a system comprising:
a data store (storage, memory) configured to store executable instructions (see Fig. 8, # 800; ¶ 39); and
a processor configured to implement the executable instructions (see Fig. 8, # 800; ¶ 67) to implement an application configured to:
activate an application at an enterprise data computing platform configured to generate a computerized tool implementable via an interface to analyze and modify enterprise data (see Abstract; Fig. 3, # 302; ¶ 27, ¶ 30-31, ¶ 35, ¶ 46, ¶ 64), the enterprise data computing platform including a predictive engine that is configured to implement a machine learning model that utilizes a Bayesian predictive algorithm;
determine a user account for which the computerized tool is implemented (see Abstract; ¶ 27, ¶ 30, ¶ 53, ¶ 56, ¶ 59);
access an enterprise data model including one or more component data objects configured to implement a component within a plurality of components with which to include in the computerized tool, the component being accessible based on data representing a persona associated with the user account (see Abstract; ¶ 20, ¶ 27, ¶ 41-43, ¶ 51-53, ¶ 56-59);
use the machine learning model to analyze the enterprise data against the machine-predicted data patterns, resulting in an optimized subset of the enterprise data and optimized associations among the one or more component objects in the enterprise data model (see ¶ 18, ¶ 23, ¶ 29-30, ¶ 36, ¶ 66); and
update the machine learning model with the optimized subset of the enterprise data as the updated training data set of the machine learning model, to improve the functionality of the machine learning model to generate the machine-predicted data patterns when executed by the processor (see ¶ 6, ¶ 30, ¶ 79).
Reynolds discloses the applications are configured to apply machine learning algorithms or deep learning algorithms to generate one or more predictive data models, and further configured to implement Bayesian models, neural network algorithms, linear regression algorithms, etc. (see ¶ 23); a computerized tool to facilitate interoperability of canonical datasets with other databases in different formats with various external computerized analysis tools (e.g., via application programming interface or APIs) to procure, inspect, analyze, generate, manipulate, and share databases (see ¶ 27, ¶ 55).
Reynolds does not explicitly disclose the following limitations; however, Gasper in an analogous art for generating data model discloses
the enterprise data computing platform including a predictive engine that is configured to implement a machine learning model that utilizes a Bayesian predictive algorithm (see ¶ 56, ¶ 60, ¶ 69, ¶ 72, ¶ 128, ¶ 156); and
train the machine learning model of the predictive engine to generate machine-predicted data patterns based on enterprise data associated with the enterprise data model, by utilizing the Bayesian predictive algorithm (see ¶ 54-56, ¶ 60, ¶ 156, ¶ 167-168), the machine-predicted data patterns indicating a proposed modification to either (1) a business process of an enterprise associated with the enterprise data computing platform (see ¶ 107, ¶ 132, ¶ 203), or (2) the persona associated with the user account (see ¶ 138, ¶ 150-151).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Reynolds to include teaching of Gasper in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computation efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 14, Reynolds discloses the system of claim 11 wherein the processor configured to configure the component to form the portion of the workspace (see Fig. 1, # 194; ¶ 25, ¶ 32, ¶ 45) is further configured to:
receive configuration data to configure the component to perform a function (see Abstract; ¶ 27, ¶ 43, ¶ 65, ¶ 72); and
generate code to identify another API to access data for which to perform the function (see ¶ 21, ¶ 27, ¶ 60-61).
Regarding claim 16, Reynolds discloses the system of claim 11 wherein the processor configured to determine the user account is further configured to:
identify data representing at least one enterprise function associated with the user account (see ¶ 26, ¶ 53).
Regarding claim 17, Reynolds discloses the system of claim 16 wherein the least one enterprise function comprises a role (see ¶ 29, ¶ 57).
In addition, claim 17 merely describing the characteristic of the enterprise function is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 18, Reynolds discloses the system of claim 11 wherein the processor configured to access the enterprise data model including the component being accessible based on the data representing the persona is further configured to:
implement the persona based on user attributes associated with the user account linked to a subset of permissions (see ¶ 32-33, ¶ 53).
Regarding claim 19, Reynolds discloses the system of claim 11 wherein the component being accessible based on data representing the persona is based on a subset of permissions (see ¶ 30, ¶ 53).
Regarding claim 20, Reynolds discloses the system of claim 11 wherein the processor configured to access the enterprise data model including one or more component data objects is further configured to:
access a configurable navigation component configured to implement a hierarchical data structure to display configurable data links (see ¶ 43, ¶ 51-53, ¶ 63-64).
Regarding claim 21, Reynolds discloses the method of claim 1, further comprising utilizing a predictive engine employing a machine learning algorithm configured to analyze the enterprise data model (see Fig. 3, # 302-304; ¶ 18, ¶ 23, ¶ 27, ¶ 30-32, ¶ 70).
Regarding claim 22, Reynolds discloses the method of claim 21, wherein the machine learning algorithm comprises a neural network (see ¶ 23-24).
In addition, claim 22 merely describing the attribute of the machine learning is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 23, Reynolds discloses the method of claim 21, wherein the machine learning algorithm includes a regression model (see ¶ 23).
In addition, claim 23 merely describing the attribute of the machine learning is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 25, Reynolds discloses the method of claim 1, further comprising tailoring the workspace component configuration to the user account by employing a machine learning (see ¶ 23, ¶ 25, ¶ 29-30, ¶ 61).
Regarding claim 26, Reynolds discloses the method of claim 1, wherein the enterprise data is included in one or more enterprise data streams associated with the enterprise data computing platform, and the proposed modification to the business process of the enterprise or the persona is implemented by the enterprise data computing platform (see ¶ 35, ¶ 44, ¶ 53-55).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Boutros et al., (US 2019/0066052) discloses a system provides computerized tools to facilitate data project development via data access layering logic in a networked computing platform including collaborative datasets.
Shiba et al., (WO 2022209574) discloses an apparatus that processes image data using a convolutional neural network and detecting the boundary of each layer based on the mapping result.
Rao et al., (US 2021/0342723) discloses a method involves receiving historical process data, applying process mining techniques and generating process models.
Von Kaenel et al., (US 2004/0117358) discloses a method for providing access to enterprise spatial data and their third party data in response to user request.
Xu et al., (US 10812482) discloses a method for managing permissions to resources of a resource provider has an orthogonal relationship to the other resources.
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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/PAN G CHOY/Primary Examiner, Art Unit 3624
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