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 non-final Office Action in response to Applicant’s communications received on August 22, 2025. Claims 1 and 11 have been amended, and claims 3, 5, 12-13 and 15 have been canceled.
Currently claims 1-2, 4, 6-11, 14 and 16-25 are pending. Claims 1 and 11 are independent.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submissions filed on August 22, 2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/29/2025 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 to the drawings is acknowledged.
Applicant’s amendments to the Specification is acknowledged, however, a clean copy of the amended Specification is required.
Applicant’s amendments to claims 1 and 11 are NOT sufficient to overcome the 35 U.S.C. § 112(a) rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 112(a) rejection to claims 2 and 21-25 has been maintained.
Applicant’s amendments to claims 1 and 11 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 and 16-25 has been maintained.
Response to Arguments
Applicant’s arguments filed on August 22, 2025 have been fully considered but are not persuasive.
In the Remarks on page 27, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that claim 1 is more than an abstract idea because it generates API code dynamically based on a system’s current characteristics and relationships between objects determined by analysis.
In response to Applicant’s argument, the Examiner respectfully disagrees. The closest feature recited in independent claims is “automatically generating API endpoint code based on the identified data object and data object relationship, the API endpoint code defining another API for accessing the data”. Here, generating and API to access data for performing the enterprise function is no more than calling an API for data gathering, when taken claim elements individually and as an ordered combination, executing by a processor in a well understood, routine and conventional way do not provide significantly more than the abstract idea itself. For example, the Specification describes that “applications are configured to generate customizable workspaces, persona interfaces, and permissions workbench interfaces that access data from multiple sources of application data, such as CRM application data, ERP application data, external third-party application data, and any other enterprise application data via any number of APIs to access data in an enterprise data computing platform…” (see ¶ 48). Further, “Automating manual and mental processes on generic computers does not make an abstract idea patent eligible.” See Credit Acceptance Corp. v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017) (“[A]utomation of manual processes using generic computers does not constitute a patentable improvement in computer technology.”). Thus, the claims as a whole do not recite significantly more than using APIs in the manner they are normally used in computing technology, on generic computers, accessing data in a database in the normal manner, and applying all of these elements in a computing environment, to facilitate implementation of enterprise data management. As such, it is an abstract idea.
In the Remarks on page 27, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that claim 1 is a technical improvement of the generation of an API endpoint, and claim 1 is a practical application because the claim provides specific technical steps for the generation of an API endpoint..
In response to Applicant’s argument, the Examiner respectfully disagrees. Applicant does not persuasively explain why these amendments improve the functioning of a computer or another technology. Rather, the claim merely adapts the abstract idea to an execution of steps performed by a processor. See Credit Acceptance, 859 F.3d at 1055 ("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 Services, 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."). Beyond the abstract, claim 1 recites the additional elements of “by a processor” for performing the steps including receiving data, display in the interface, and accessing data via an API. The processor is recited at a high level of generality and claim does not purport to improve or invent the API. Whether the additional elements are taken individually or in an ordered combinations of all, nothing in the claim elements reflects an improvement in the functioning of a computer or another technology or technical field. See Memorandum, 84 Fed. Reg. at 55; cf. Trading Techs. Int’l, Inc. v. IBG LLC, No. 2017-2257, 2019 WL 1716242, at *3 (Fed. Cir. Apr. 18, 2019) (“This invention makes the trader faster and more efficient, not the computer. This is not a technical solution to a technical problem.”). Therefore, the additional elements do not integrate the abstract idea into a practical application.
In the Remarks on page 28, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that claim 1 is a practical application because the claim provides specific technical steps for the generation of an API endpoint.
In response to Applicant’s argument, the Examiner respectfully disagrees. The claims do not purport to improve the functioning of the computer itself. Nor do they effect an improvement in any other technology or technical field. But instead, the claims at issue amount to nothing significantly more than instructions to apply the abstract idea of optimizing the business functions for an enterprise, such as sales, marking, project planning, finance, accounting, procurement, inventory management, human resource management, supply chain management, and the like. Thus, Applicant’s invention aims to solve an entrepreneurial problem—management and performance of enterprise functions.
Further, In order for a claim to integrate the exception into a practical application, the additional claimed elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), affect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)). See Revised 2019 Guidance. As discussed above, the claimed additional elements are recited at a high level of generality and merely invoked as tools to preform generic computer functions including receiving, manipulating, and transmitting information over a network. The additional elements do not integrate the abstract idea into a practical application because nothing in the claim limitations reflect an improvement to the functioning of a computer itself, or another technology or technical field. Having a processor automatically generate an API endpoint code is a form of automation (doing with a computer what was previously done manually by a programmer) with data processing. Automating manual and mental processes on generic computers does not make an abstract idea patent eligible. See Cellspin Soft, Inc. v. Fitbit, Inc., 927 F.3d 1306, 1316 (Fed. Cir. 2019) (“But the need to perform tasks automatically is not a unique technical problem.”).
In the Remarks on page 31, Applicant’s arguments regarding the 35 U.S.C. § 112(a) rejection that “proposed modification” is synonymous with “generate recommendations” from paragraph [0050].
To satisfy this requirement, a written “description must ‘clearly allow persons of ordinary skill in the art to recognize that [the inventor] invented what is claimed.’” Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (en banc) (quoting Vas-Cath, 935 F.2d 1555, 1563) (Fed. Cir. 1991). “In other words, the test for sufficiency is whether the disclosure of the application relied upon reasonably conveys to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date.” Id. (citing Vas-Cath, 935 F.2d at 1563).
Here, Applicant pointed to the following paragraphs for teaching the limitations as rejected under 35 U.S.C. § 112(a).
[0043] discloses receive or exchange enterprise data 107a and other data in real time (or near real time)…
[0050] discloses analyzing data patterns that may be matched against machine-predicted patterns or against a set of one or more rules…
[0132] discloses determine optimized flow links among various roles and task object based on analyzing data patterns of interactions among various users or roles…
[0009] highlights the need for what is provided by this invention, “…provide enterprise applications and services that facilitate efficient and expeditious deployment of computerized tools targeted to a corresponding role or process….” And
[0073 ] discloses An input analyzer 452 is configured to receive data representing one or more user inputs, any of which may be configured to activate formation or implementation of a workspace component to present in an interactive computerized tool, such as a workspace. In at least one example, input analyzer 452 may be configured to receive data representing a user, whereby input analyzer 452 may be configured to identify attributes of a user as well as a role or enterprise function with which the user is associated."
However, the Examiner respectfully submits that the above paragraphs fail to describe the claimed limitations in such a manner as to reasonably convey that the inventor has possession of the claimed subject matter as of the filing date.
For example, the specification does not define “a data object relationship”. Nor did it disclose “recommendation”, “historical user interaction”, “predictive insights”, “ongoing data analysis” “user behavior”, “historical data trends”, and etc.
If the term “proposed modification” is synonymous with “generate recommendations”, why not refer to the claim as “proposed modification”.
In the Remarks on page 37, Applicant argues that Maloo does not teach the endpoint code being generated based on: analyzing the enterprise data model to identify data object and data object relationships pertinent to the enterprise function” as provided in the independent claims 1 and 11.
In response to Applicant’s argument, the Examiner respectfully disagrees. Maloo discloses “A user data API refers to a function that retrieves data based on an identifier; a user data API is a user generated code that invokes a data access API provided by the cloud service. In some configuration, a static source code analysis of the user data API verifies that an identifier retrieved from a request container is provided as the identifier argument of the data access API. For example, source code of a data access API may be analyzed to identify parameters that have a “user input” annotation” (see ¶ 12-14); “User data API source code static analysis validator may analyze call sites that invoke the data access API to ensure that values passed to an identifying information parameter originate from a request container.” (see ¶ 56).
Therefore, given the broadest reasonable interpretation to one of ordinary skill in the art, Reynolds and in view of Maloo teaches the limitation in the form of Applicant claimed.
In the Remarks on page 37, Applicant argues that Maloo and the other cited references, Reynolds, Sriharsha, and Bonnin, also fail to teach these newly added limitations. 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 Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2, 4, 6-11, 14 and 16-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matters which were not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The subject matters which are not in the original specification is as follows:
Regarding claims 1 and 11, the claims recite limitation “data object relationships” appears to constitute new matter. Applicant did not point out each, nor was Examiner able to find, any support for the limitation in the specification as originally filed. Applicant is required to cancel the new matter throughout the application in the reply to this Office Action.
Regarding claim 2, the claim recites limitation “generate recommendations for configuring the workspace component based on historical user interaction data and enterprise workflow efficiencies”, and “dynamically update the workspace component configuration in real-time based on predictive insights derived from ongoing data analysis”. The limitations appear to constitute new matter. Applicant did not point out each, nor was Examiner able to find, any support for these limitations in the specification as originally filed. Applicant is required to cancel the new matter throughout the application in the reply to this Office Action.
Regarding claim 21, the claim recites limitations “identifies patterns in data objects and data object relationships to recommend adjustments to the configuration of the workspace component for improving the enterprise function associated with the user account”. The limitations appear to constitute new matter. Applicant did not point out each, nor was Examiner able to find, any support for these limitations in the specification as originally filed. Applicant is required to cancel the new matter throughout the application in the reply to this Office Action.
Regarding claim 22, the claim recites “process historical user interaction data and real-time user behavior to dynamically adjust the workspace component”. The limitations appear to constitute new matter. Applicant did not point out each, nor was Examiner able to find, any support for these limitations in the specification as originally filed. Applicant is required to cancel the new matter throughout the application in the reply to this Office Action.
Regarding claim 23, the claim recites “predict optimal enterprise function performance metrics based on analysis of historical data trends”. The limitations appear to constitute new matter. Applicant did not point out each, nor was Examiner able to find, any support for these limitations in the specification as originally filed. Applicant is required to cancel the new matter throughout the application in the reply to this Office Action.
Regarding claim 24, the claim recites “identify inefficiencies in enterprise workflows by comparing real-time data from the enterprise data model with historical workflow data”, “reconfiguring workspace components to address identified inefficiencies”, “the inefficiencies are identified by analyzing deviations in predefined key performance indicators (KPIs)”. The limitations appear to constitute new matter. Applicant did not point out each, nor was Examiner able to find, any support for these limitations in the specification as originally filed. Applicant is required to cancel the new matter throughout the application in the reply to this Office Action.
Regarding claim 25, the claim recites “analyze user attributes, permissions, and prior interaction patterns stored in the enterprise data model”, and “the tailoring includes presenting prioritized data links in the workspace component based on predictive insights regarding user preferences and frequently accessed enterprise functions”. The limitations appear to constitute new matter. Applicant did not point out each, nor was Examiner able to find, any support for these limitations in the specification as originally filed. Applicant is required to cancel the new matter throughout the application in the reply to this Office Action.
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 and 16-25 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 are 21-25 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, determining a user account, receiving data representing a request to generate a workplace to display in the interface…, accessing an enterprise data model including one or more component data objects, receiving a data signal to select the component, configuring the component to form a portion of the workspace to adapted to the data representing the persona, generating code to identify another API to access data for performing the enterprise function, analyzing the enterprise data model to identify data objects and data object relationships, generating API endpoint code based on the identified data objects and data object relationships, applying the portion of the workspace as a portion of the computerized tool to improve the enterprise function associated with the user account, employing a machine learning algorithm to: analyze the enterprise data model to identify patterns among data objects and data object relationships relevant the enterprise function, generate recommendations for configuring the workspace component, update the workspace component configuration in real-time, receiving configuration data to configure the component to perform a 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 access 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”, “an application programming interface (“API”), “a machine learning algorithm”, and the term “automatically”. 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”, “an application programming interface (“API”), “a machine learning algorithm”, and the term “automatically”. 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 are 21-25 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 are 21-25 —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, 4, 6-11, 14, 16-23 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Reynolds et al., (US 2021/0390507, hereinafter: Reynolds), and in view of Sriharsha et al., (US 2022/0035775, hereinafter: Sriharsha), and further in view of Maloo et al., (US 2024/0080360, hereinafter: Maloo).
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);
determining, by the processor, a user account for which the computerized tool is implemented (see Abstract; ¶ 27, ¶ 30, ¶ 53, ¶ 59);
receiving, by the processor, data representing a request to generate a workspace to display in the interface, the request specifying a component within a plurality of components with which to include in the computerized tool (see Fig. 3, # 306; ¶ 25, ¶ 41-46), wherein at least one component includes a first portion of executable code accessible via an application programming interface ("API") from a networked computing platform and a second portion of executable code being associated with the enterprise data computing platform at which the plurality of components are associated (see ¶ 22, ¶ 27, ¶ 58-60);
accessing, by the processor, an enterprise data model including one or more component data objects configured to implement the component, the component being accessible based on data representing a persona associated with the user account (see Abstract; ¶ 41-43, ¶ 51);
configuring, by the processor, the component to form a portion of the workspace adapted to the data representing the persona (see ¶ 30-32, ¶ 45, ¶ 59, ¶ 72 );
applying, by the processor, the portion of the workspace as a portion of the computerized tool to improve the enterprise function associated with the user account (see ¶ 25-27, ¶ 43-45).
Reynolds discloses the computing device receiving a user input data or entry of an auxiliary query command into a query editor of a workspace (see ¶ 25).
Reynolds does not explicitly disclose the following limitations; however, Sriharsha in an analogous art of data field query system discloses
receiving, by the processor, a data signal to select the component (see ¶ 915, ¶ 921, claim 11);
the identified data object including one or more of tasks, projects, persona and permissions, the data object relationships pertinent to the enterprise including one or more of priority, persona associated with a user account, a permissions data model, personas, and permissions associated with one or more data objects (see ¶ 183, ¶ 316-317, ¶ 380, ¶ 571, ¶ 689-690, ¶ 698).
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 Sriharsha 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.
Reynolds discloses implementing 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 and Sriharsha do not explicitly disclose the following limitations; however, Maloo in an analogous art for managing data access operations from requests discloses
programmatically generating, by the processor, code to identify another API to access data for performing the enterprise function (see ¶ 8, ¶ 12, ¶ 58-59), wherein programmatically generating the code includes:
analyzing the enterprise data model to identify data objects and data object relationships pertinent to the enterprise function (see ¶ 12-14, ¶ 54-56, ¶ 111-113), and
automatically generating, by the processor, API endpoint code based on the identified data objects and data object relationships, the API endpoint code defining the another API for accessing the data (see ¶ 12, ¶ 56-58).
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 Sriharsha in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution for data access from external sources, 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.
In addition, the phrase(s) “the identified data object including one or more of tasks, projects, persona and permissions, the data object relationships pertinent to the enterprise including one or more of priority, persona associated with a user account, a permissions data model, personas, and permissions associated with one or more data objects” merely describing the type of information in the identified data object 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 4, Reynolds discloses the method of claim 1 wherein configuring the component to form the portion of the workspace comprises:
receiving configuration data to configure the component to perform a function (see Abstract; ¶ 43, ); and
generating code to identify another API to access data for which to perform the function (see ¶ 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 analyze and modify enterprise data (see Abstract; Fig. 3, # 302; ¶ 27, ¶ 30-31, ¶ 35, ¶ 46, ¶ 64);
determine a user account for which the computerized tool is implemented (see Abstract; ¶ 27, ¶ 30, ¶ 53, ¶ 59);
receive data representing a request to generate a workspace to display in the interface, the request specifying a component within a plurality of components with which to include in the computerized tool (see Fig. 3, # 306; ¶ 25, ¶ 41-46), wherein at least one component includes a first portion of executable code accessible via an application programming interface ("API") from a networked computing platform and a second portion of executable code being associated with the enterprise data computing platform at which the plurality of components are associated (see ¶ 22, ¶ 27, ¶ 58-60);
access an enterprise data model including one or more component data objects configured to implement the component, the component being accessible based on data representing a persona associated with the user account (see Abstract; ¶ 41-43, ¶ 51);
configure the component to form a portion of the workspace adapted to the data representing the persona (see ¶ 30-32, ¶ 45, ¶ 59, ¶ 72 );
apply the portion of the workspace as a portion of the computerized tool (see ¶ 25-27, ¶ 43-45).
Reynolds discloses the computing device receiving a user input data or entry of an auxiliary query command into a query editor of a workspace (see ¶ 25).
Reynolds does not explicitly disclose the following limitations; however, Sriharsha in an analogous art of data field query system discloses
receive a data signal to select the component (see ¶ 915, ¶ 921, claim 11) ;
the identified data object including one or more of tasks, projects, persona and permissions, the data object relationships pertinent to the enterprise including one or more of priority, persona associated with a user account, a permissions data model, personas, and permissions associated with one or more data objects (see ¶ 183, ¶ 316-317, ¶ 380, ¶ 571, ¶ 689-690, ¶ 698).
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 Sriharsha 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.
Reynolds discloses implementing 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 and Sriharsha do not explicitly disclose the following limitations; however, Maloo in an analogous art for managing data access operations from requests discloses
programmatically generating, by the processor, code to identify another API to access data for performing the enterprise function (see ¶ 8, ¶ 12, ¶ 58-59), wherein programmatically generating the code includes:
analyzing the enterprise data model to identify data objects and data object relationships pertinent to the enterprise function (see ¶ 12-14, ¶ 54-56, ¶ 111-113), and
automatically generating, by the processor, API endpoint code based on the identified data objects and data object relationships, the API endpoint code defining the another API for accessing the data (see ¶ 12, ¶ 56-58).
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 Sriharsha in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution for data access from external sources, 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.
In addition, the phrase(s) “the identified data object including one or more of tasks, projects, persona and permissions, the data object relationships pertinent to the enterprise including one or more of priority, persona associated with a user account, a permissions data model, personas, and permissions associated with one or more data objects” merely describing the type of information in the identified data object 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 14, Reynolds discloses the system of claim 11 wherein the processor configured to configure the component to form the portion of the workspace is further configured to:
receive configuration data to configure the component to perform a function (see Abstract; ¶ 43); and
generate code to identify another API to access data for which to perform the function (see ¶ 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, wherein the machine learning algorithm identifies patterns in data objects and data object relationships to recommend adjustments to the configuration of the workspace component for improving the enterprise function associated with the user account (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 configured to process historical user interaction data and real-time user behavior to dynamically adjust the workspace component configuration in response to changes in user actions or enterprise workflow requirements (see ¶ 28-30, ¶ 45, ¶ 53).
Regarding claim 23, Reynolds discloses the method of claim 21, wherein the machine learning algorithm includes a regression model configured to predict optimal enterprise function performance metrics based on analysis of historical data trends and current enterprise data streams (see ¶ 18, ¶ 23-25, ¶ 30, ¶ 46, ¶ 48).
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 algorithm configured to analyze user attributes, permissions, and prior interaction patterns stored in the enterprise data model, wherein the tailoring includes pre