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 .
According to paper filed February 24th 2023, claims 1-25 are pending for examination with a February 24th 2022 priority date under 35 USC 119(e).
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 1-25 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-11 and 19-25 are directed to a method, and claims 12-18 are directed to a medium. Therefore, each of these claims is directed to one of the four statutory categories of patent eligible subject matter.
Step 2A Prong 1:
Claim 1 recites:
“adding the applied prototype to a catalog by populating the repository into a data structure that corresponds to the catalog, so as to make the applied prototype accessible to another user for implementation as part of another data science project”; adding and populating a catalog in a repository is an evaluation that can be carried out by a human in the mind or with pen and paper, and is thus a mental process.
Claim 12 recites:
“examining a metadata file that is maintained in the repository”; examining a metadata file in a repository is an evaluation that can be carried out by a human in the mind or with pen and paper, and is thus a mental process.
Claim 17 recites:
“determining whether alteration of the machine learning model is necessary for the new instance of the data science project to be suitable for analysis of the user-specific data, and in response to a determination that an alteration of the machine learning model is necessary, implementing the alteration on behalf of the user”; determining the alteration of machine learning models is an evaluation that can be carried out by a human in the mind or with pen and paper, and is thus a mental process.
Claim 19 recites:
“populating, in the human-readable configuration file, information related to each of the multiple applied prototypes”; populating a file with information related to applied prototype is an evaluation that can be carried out by a human in the mind or with pen and paper, and is thus a mental process.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements are as follows:
Claim 1 recites:
“receiving input that is indicative of a selection, by a user, of a data science project that utilizes a machine learning model trained to perform a task”; this limitation amounts to data gathering as per MPEP 2106.05(g).
“configuring an applied prototype that serves as a repository that includes code and information, if
any, that is needed to programmatically produce another instance of the data science project in such a manner that the machine learning model is extendable to a different user or a different dataset”; these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f).
claim 2 recites:
“wherein the repository is one of multiple repositories stored in the data structure, and wherein each of the multiple repositories is representative of a different one of multiple applied prototypes”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d) and mere instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f); further, this limitation is directed to limiting a judicial exception to a particular field of use as per MPEP 2106.05(h), as it merely limits the field of the data of the knowledge graph.
Claim 3 recites:
“receiving second input that is indicative of a selection, by a second user, of the applied prototype from among the multiple applied prototypes; and receiving third input that is indicative of a selection, by the second user, of data to be used in combination with the applied prototype”; this limitation is directed to mere data gathering which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity, as per MPEP 2106.05(g).
“deploying the applied prototype in the form of a new data science project in which the data is provided to the machine learning model as input”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 4 recites:
“wherein said deploying comprises: constructing the new data science project based on the code and
the information, if any, that is included in the repository corresponding to the applied prototype”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d), and a mere extension of claim 3 for “deploying”, as per MPEP 2106.05(f).
Claim 5 recites:
“wherein the computer program adjusts the new data science project on behalf of the second user, as
necessary, to accommodate the data”; this limitation fails to integrate the judicial exception
into a practical application, as per MPEP 2106.04(d).
Claim 6 recites:
“wherein each of the multiple applied prototypes is accompanied by a metadata file that defines an operational characteristic of the corresponding applied prototype”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 7 recites:
“wherein the operational characteristic is (i) computing resources needed by the corresponding applied prototype or (ii) setup steps for installing the corresponding applied prototype”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 8 recites:
in the applied prototype, the machine learning model is served as a representational state transfer (REST) endpoint with automated lineage building to allow for dynamic reconfiguration”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 9 recites:
“wherein the applied prototype is only available to other users that are part of a same organization as the user”; this limitation fails to integrate the judicial exception into a practical application,
as per MPEP 2106.04(d).
Claim 10 recites:
“receiving second input that is indicative of an approval, by an administrator, of the data science project; wherein said configuring is performed in response to receiving the second input”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d), and “receiving the second input” merely amounts to data gathering as per MPEP 2106.05(g).
Claim 11 recites:
“wherein the administrator is associated with an organization that operates the computer program and maintains the data structure that corresponds to the catalog”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 12 recites:
“receiving input that is indicative of a selection, by a user, of an applied prototype that serves as a repository for code corresponding to a data science project that utilizes a machine learning model trained to perform a task”; this limitation amounts to nothing more than data gathering as per MPEP 2106.05(g).
“a non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device; creating, in response to said receiving, a copy of the repository that corresponds to the applied prototype; initiating automatic execution of one or more steps specified in the metadata file to recreate the data science project in such a manner that the machine learning model is applicable to user-specified data”; this limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f).
Claim 13 recites:
“wherein the metadata file includes information regarding a parameter of the applied prototype”;
this limitation fails to integrate the judicial exception into a practical application, as per
MPEP 2106.04(d).
Claim 14 recites:
“wherein the parameter pertains to an environment variable, a software-implemented engine responsible for executing the code, or a runtime environment”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 15 recites:
“causing digital presentation of the information regarding the parameter of the applied prototype on an interface; and in response to receiving second input that is indicative of a confirmation, by the user, of the information regarding the parameter, constructing a new instance of the data science project using assets included in the copy of the repository”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 16 recites:
“wherein the assets include the code and information that is needed to programmatically recreate the new instance of the data science project”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 17 recites:
“determining whether alteration of the machine learning model is necessary for the new instance of the data science project to be suitable for analysis of the user-specific data, and in response to a determination that an alteration of the machine learning model is necessary, implementing the alteration on behalf of the user”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 18 recites:
“causing digital presentation of an indicium that visually illustrates progression as the new instance of
the data science project is being constructed”; this limitation fails to integrate the judicial
exception into a practical application, as per MPEP 2106.04(d).
Claim 19 recites:
“receiving first input that is indicative of a selection, by a user, of multiple applied prototypes from amongst a collection of applied prototypes, wherein each of the multiple applied prototypes serves as a repository that includes code that is needed to programmatically produce another instance of a corresponding data science project that utilizes a machine learning model trained to perform a task; receiving second input that is indicative of a request, from the user, to create a human-readable configuration file that identifies the multiple applied prototypes; causing the human-readable configuration file to be stored on a computer server”; this limitation amounts to mere data gathering as per MPEP 2106.05(g).
“creating the human-readable configuration file in a data-serialization language”; this limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f).
Claims 20 and 21 recite:
“wherein the computer server is a private computer server; wherein the computer server is a private computer server”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d), and this limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f).
Claim 22 recites:
“wherein said causing permits the human-readable configuration file to be accessed by other users who are members of a same group as the user”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 23 recites:
“wherein the user and the other users are employees of a same organization”; this limitation fails to integrate the judicial exception into a practical application, as per MPEP 2106.04(d).
Claim 25 recites:
“receiving third input that is indicative of a selection, by a second user, of the multiple applied prototypes from amongst a collection of applied prototypes; and forking the human-readable configuration file that is representative of an existing catalog, so as to create a new catalog that includes the multiple applied prototypes for the second user”; this limitation is directed to mere data gathering which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity, as per MPEP 2106.05(g).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are as follows:
Claim 1 recites:
“receiving input that is indicative of a selection, by a user, of a data science project that utilizes a machine learning model trained to perform a task”; this limitation recites a high level of generality and amounts to data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity, as per MPEP 2106.05(d)(II).
Claim 12 recites:
“receiving input that is indicative of a selection, by a user, of an applied prototype that serves as a repository for code corresponding to a data science project that utilizes a machine learning model trained to perform a task”; this limitation recites a high level of generality and amounts to data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity, as per MPEP 2106.05(d)(II).
Claim 19 recites:
“receiving first input that is indicative of a selection, by a user, of multiple applied prototypes from amongst a collection of applied prototypes, wherein each of the multiple applied prototypes serves as a repository that includes code that is needed to programmatically produce another instance of a corresponding data science project that utilizes a machine learning model trained to perform a task; receiving second input that is indicative of a request, from the user, to create a human-readable configuration file that identifies the multiple applied prototypes; causing the human-readable configuration file to be stored on a computer server”; these limitations recite a high level of generality and amounts to data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity, as per MPEP 2106.05(d)(II); and mere instructions to apply a judicial exception that cannot amount to significantly more than the judicial exception itself, as per MPEP 2106.05(f) and MPEP 2106.05(d).
Claims 1-11 and 19 recite:
“a computer program executing on a computing device”; this limitation amounts to nothing more than using a generic computer as a tool that cannot amount to significantly more than the judicial exception itself, as per MPEP 2106.05(f)(2) and MPEP 2106.05(d).
Claims 12-18 recite:
“a non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations”; this limitation amounts to nothing more than using a generic computer as a tool that cannot amount to significantly more than the judicial exception itself, as per MPEP 2106.05(f)(2) and MPEP 2106.05(d).
Dependent Claims
Claim 2 recites:
“wherein the repository is one of multiple repositories stored in the data structure, and wherein each of the multiple repositories is representative of a different one of multiple applied prototypes”; this
limitation merely limits a judicial exception to a particular field of use, as per MPEP 2106.05(h), cannot amount to significantly more than the judicial exception.
Claim 3 recites:
“receiving second input that is indicative of a selection, by a second user, of the applied prototype from among the multiple applied prototypes; and receiving third input that is indicative of a selection, by the second user, of data to be used in combination with the applied prototype”; this limitation amounts to well-understood, routine, and conventional activity as per MPEP 2106.05(d)(II), which cannot amount to significantly more than the judicial exception.
“deploying the applied prototype in the form of a new data science project in which the data is provided to the machine learning model as input”; this limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer as per MPEP 2106.05(f).
Claim 4 recites:
“wherein said deploying comprises: constructing the new data science project based on the code and the information, if any, that is included in the repository corresponding to the applied prototype”; this limitation amounts to well-understood, routine, conventional activity and insignificant extra solution activity, as per MPEP 2106.05(d) & MPEP2106.05(g).
Claim 5 recites:
“wherein the computer program adjusts the new data science project on behalf of the second user, as necessary, to accommodate the data”; this limitation amounts to insignificant extra solution activity, mere user permissibility, as per MPEP 2106.05(g).
Claim 6 recites:
“each of the multiple applied prototypes is accompanied by a metadata file that defines an
operational characteristic of the corresponding applied prototype”; this limitation amounts to well-understood, routine, conventional activity and insignificant extra solution activity, as per MPEP 2106.05(d) & MPEP 2106.05(g), and nothing more than the field of use, as per MPEP 2106.05(h).
Claim 7 recites:
“wherein the operational characteristic is (i) computing resources needed by the corresponding applied prototype or (ii) setup steps for installing the corresponding applied prototype”; this limitation amounts to insignificant extra solution activity, mere user permissibility and field of use, as per MPEP 2106.05(g) & MPEP 2106.05(h).
Claim 8 recites:
“the machine learning model is served as a representational state transfer (REST) endpoint with automated lineage building to allow for dynamic reconfiguration”; this limitation amounts to insignificant extra solution activity, mere user permissibility and field of use, as per MPEP 2106.05(g) & MPEP 2106.05(h).
Claim 9 recites:
“wherein the applied prototype is only available to other users that are part of a same organization as the user”; this limitation amounts to insignificant extra solution activity, mere user permissibility, as per MPEP 2106.05(g).
Claim 10 recites
“receiving second input that is indicative of an approval, by an administrator, of the data science project; wherein said configuring is performed in response to receiving the second input”; this limitation amounts to insignificant extra solution activity, mere user permissibility, as per MPEP 2106.05(g).
Claim 11 recites:
“wherein the administrator is associated with an organization that operates the computer program and maintains the data structure that corresponds to the catalog”; this limitation amounts to insignificant extra solution activity, mere user permissibility, as per MPEP 2106.05(g), and nothing more than field of use, as per MPEP 2106.05(h).
Claim 13 recites:
“wherein the metadata file includes information regarding a parameter of the applied prototype”; this limitation amounts to insignificant extra solution activity, as per MPEP 2106.05(g), and nothing more than the field of use as per MPEP 2106.05(h).
Claim 14 recites:
“wherein the parameter pertains to an environment variable, a software-implemented engine responsible for executing the code, or a runtime environment”; this limitation amounts to insignificant extra solution activity, as per MPEP 2106.05(g), and nothing more than the field
of use as per MPEP 2106.05(h).
Claim 15 recites:
“causing digital presentation of the information regarding the parameter of the applied prototype on an interface; and in response to receiving second input that is indicative of a confirmation, by the user, of the information regarding the parameter, constructing a new instance of the data science project using assets included in the copy of the repository”; this limitation amounts to insignificant extra solution activity, mere constructing new instance of data again, as per MPEP 2106.05(g).
Claim 16 recites:
“wherein the assets include the code and information that is needed to programmatically recreate the new instance of the data science project”; this limitation amounts to insignificant extra solution activity, mere data gathering, as per MPEP 2106.05(g).
Claim 17 recites:
“determining whether alteration of the machine learning model is necessary for the new instance of the data science project to be suitable for analysis of the user-specific data, and in response to a determination that an alteration of the machine learning model is necessary, implementing the alteration on behalf of the user”; this limitation amounts to insignificant extra solution activity, mere data gathering, as per MPEP 2106.05(g).
Claim 18 recites:
“causing digital presentation of an indicium that visually illustrates progression as the new instance of the data science project is being constructed”; this limitation amounts to well-understood, routine, conventional activity and insignificant extra solution activity, as per MPEP 2106.05(d) & MPEP 2106.05(g).
Claims 20 and 21 recite:
“wherein the computer server is a private computer server; wherein the computer server is a private
computer server”; this limitation amounts to insignificant extra solution activity, mere data
gathering, as per MPEP 2106.05(g), and to apply the abstract idea using a generic computer as per MPEP 2106.05(f).
Claim 22 recites:
“wherein said causing permits the human-readable configuration file to be accessed by other users
who are members of a same group as the user”; this limitation amounts to insignificant extra
solution activity, mere user permissibility, as per MPEP 2106.05(g), and nothing more than the field of use as per MPEP 2106.05(h).
Claim 23 recites:
“wherein the user and the other users are employees of a same organization”; this limitation amounts to insignificant extra solution activity, mere user permissibility, as per MPEP 2106.05(g), and nothing more than the field of use as per MPEP 2106.05(h).
Claim 25 recites:
“receiving third input that is indicative of a selection, by a second user, of the multiple applied
prototypes from amongst a collection of applied prototypes; and forking the human-readable configuration file that is representative of an existing catalog, so as to create a new catalog that includes the multiple applied prototypes for the second user”; these limitations is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity, as per MPEP 2106.05(d)(II), and amount to insignificant extra solution activity, mere user permissibility, as per MPEP 2106.05(g).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same
under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5 and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Lonial et al. (US 2020/0319857), hereinafter Lonial, and further in view of Cote et al. (US 2023/0076662), hereinafter Cote.
Claim 1
“receiving input that is indicative of a selection, by a user, of a data science project that utilizes a machine learning model trained to perform a task” Lonial [0072][0073] teaches a code notebook that allows data scientist to create and edit a collection of documentation including or bundled with modules for machine learning, and users may make a selection through a user interface;
“configuring an applied prototype that serves as a repository that includes code and information, if any, that is needed to programmatically produce another instance of the data science project in such a manner that the machine learning model is extendable to a different user or a different dataset” Lonial [0051][0066] teaches a repository of retail science-related data stored in its natural format, referred to as a retail science data lake, and data scientists and software engineers can refer to them to rapidly prototype application functionality including upload pre-built models to further built upon existing implementations;
“adding the applied prototype to a catalog by populating the repository into a data structure that corresponds to the catalog, so as to make the applied prototype accessible to another user for implementation as part of another data science project” Lonial [0141] teaches “the workspace can be shared with other users”, however, Lonial does not spelled out the catalog for applied prototype, said feature is taught in Cote,
Cote [0020][0073] teaches trained machine learning models to show/filter alarms, and a prototype applied to field data from production networks that can suppress in real-time 50% of all alarm; the preliminary categorical feature extraction results of a prototype are shown in Figure 7.
Lonial and Cote disclose analogous art. Cote is analogous art because it is in the field of training machine learning models and applying prototypes to field data from production networks. However, Lonial does not spell out the “catalog” as recited above. Said feature is taught in Cote. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Cote (Cote [0020][0073]: trained machine learning models to show/filter alarms, and a prototype applied to field data from production networks that can suppress in real-time 50% of all alarm, and Figure 7 depicts a catalog like table containing applied prototypes) into Lonial to enhance its applied prototype summarizing and illustration functions with a user friendly presentation of the various prototypes.
Claim 2
“wherein the repository is one of multiple repositories stored in the data structure, and wherein each of the multiple repositories is representative of a different one of multiple applied prototypes” Lonial [0050] teaches an innovation workbench system that has three broad categories of components: a retail science data model; data mining tools; and artificial intelligence and modeling libraries and components.
Claim 3
“receiving second input that is indicative of a selection, by a second user, of the applied prototype from among the
multiple applied prototypes” Lonial [0072][0073] teaches the claimed feature, which is construed and cited as user selection input as claimed in claim 1 because the claimed method step can be performed more than once, particularly, Lonial [0141] discloses “the workspace can be shared with other users”;
“receiving third input that is indicative of a selection, by the second user, of data to be used in combination with the applied prototype; and deploying the applied prototype in the form of a new data science project in which the data is provided to the machine learning model as input” Lonial [0072][0073] teaches the claimed feature, which is construed and cited as user selection input as claimed in claim 1 because the claimed method step can be performed more than once and utilized for more than one project, particularly, Lonial [0083] discloses “the retailer data may be exported or defined as REST data sources for access by other systems”.
Claim 4
“wherein said deploying comprises: constructing the new data science project based on the code and the information, if any, that is included in the repository corresponding to the applied prototype” Lonial [0076] teaches the retail data lake can enable a user to more rapidly construct custom functions and extensions.
Claim 5
“wherein the computer program adjusts the new data science project on behalf of the second user, as necessary, to accommodate the data” Lonial [0039] teaches a pre-configured command that records the locations of retail-specific information within a specific schema to expose and retrieve the retail-specific dataset and exclude information that is not retail-specific, which adjusts the data science project.
Claim 8
“wherein in the applied prototype, the machine learning model is served as a representational state transfer (REST)
endpoint with automated lineage building to allow for dynamic reconfiguration” Lonial [0083] discloses “the retailer data may be exported or defined as REST data sources for access by other systems”.
Claim 9
“wherein the applied prototype is only available to other users that are part of a same organization as the user” Lonial [0018] teaches a platform offers self-service tools that enable data scientists to employ a broad range of analyses, an organization can take advantage of the present platform’s capability to integrate a comprehensive set of services.
Claim 10
“receiving second input that is indicative of an approval, by an administrator, of the data science project; wherein said configuring is performed in response to receiving the second input” Lonial [0029] teaches a back-end data integration method, the initiation triggered by receiving a signal over a network, such as a user or administrator of a retailer system, and determining the method should begin.
Claim 11
“wherein the administrator is associated with an organization that operates the computer program and maintains the data structure that corresponds to the catalog” Lonial [0096] teaches a back-end data integration method, a signal received from an administrator over a network or parsing stored data indicates the system has entered a user input intended to select a code editor.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Lonial et al. (US 2020/0319857), hereinafter Lonial, and further in view of Cote et al. (US 2023/0076662), hereinafter Cote, and Wright et al. (US 2018/0276861), hereinafter Wright.
Claim 6
“wherein each of the multiple applied prototypes is accompanied by a metadata file that defines an operational
characteristic of the corresponding applied prototype” Wright [0159] teaches an event object packed of field values including metadata.
Lonial, Cote, and Wright disclose analogous art. Cote is analogous art because it is in the field of training machine learning models and applying prototypes to field data from production networks, and Wright is analogous art because it is in the field of data packing such as metadata. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Cote (Cote [0020][0073]: trained machine learning models to show/filter alarms, and a prototype applied to field data from production networks that can suppress in real-time 50% of all alarm, and Figure 7 depicts a catalog like table containing applied prototypes) into Lonial to enhance its applied prototype summarizing and illustration functions with a user friendly presentation of the various prototypes. However, Lonial still fails to spell out the “metadata” feature as recited above. Said feature is taught in Wright. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Wright (Wright [0159]: teaches event objects packeted as a collection of field values including metadata) into Lonial in combination of Cote to enhance its functions of defining and describing the characteristics of the dataset utilized in the creation of the various prototypes.
Claim 7
“wherein the operational characteristic is (i) computing resources needed by the corresponding applied prototype or (ii) setup steps for installing the corresponding applied prototype” Wright [0211] teaches a plotting system apply a k-prototype that includes storage space and computational resources to generate accurate plots.
Claims 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lonial et al. (US 2020/
0319857), hereinafter Lonial, and further in view of Wright et al. (US 2018/0276861), hereinafter Wright.
Claim 12
“receiving input that is indicative of a selection, by a user, of an applied prototype that serves as a repository for code corresponding to a data science project that utilizes a machine learning model trained to perform a task” Lonial [0072][0073] teaches a code notebook that allows data scientist to create and edit a collection of documentation including or bundled with modules for machine learning, and users may make a selection through a user interface;
“creating, in response to said receiving, a copy of the repository that corresponds to the applied prototype” Lonial [0051][0066] teaches a repository of retail science-related data stored in its natural format, referred to as a retail science data lake, and data scientists and software engineers can refer to them to rapidly prototype application functionality including upload pre-built models to further built upon existing implementations;
“examining a metadata file that is maintained in the repository” Wright [0159] teaches an event object packed of field values including metadata;
“initiating automatic execution of one or more steps specified in the metadata file to recreate the data science project in such a manner that the machine learning model is applicable to user-specific data” Lonial [0051][0066] teaches a repository of retail science-related data stored in its natural format, data scientists and software engineers can refer to them to rapidly prototype application functionality including upload pre-built models to further built upon existing implementations; the “further built” indicates “recreation”.
Lonial and Wright disclose analogous art. Wright is analogous art because it is in the field of data packing such as metadata. However, Lonial does not spell out the “metadata” feature as recited above. Said feature is taught in Wright. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Wright (Wright [0159]: teaches event objects packeted as a collection of field values including metadata) into Lonial to enhance its functions of defining and describing the characteristics of the dataset utilized in the creation of the various prototypes.
Claim 13
“wherein the metadata file includes information regarding a parameter of the applied prototype” Wright [0216][0217] teaches a data controller receiving user request indication that includes information and metadata, the data controller can also determine and obtain model score code with additional information and parameters.
Claim 14
“wherein the parameter pertains to an environment variable, a software-implemented engine responsible for executing the code, or a runtime environment” Wright Figures 7 & 8 depict Event Stream Processing Engine (ESPN), and Wright [0106] teaches computing environment includes automated network in a different environment and the data may be collected from various sensor to create parameters or other data.
Claim 15
“causing digital presentation of the information regarding the parameter of the applied prototype on an interface” Wright [0004][0005] teaches a graphical user interface presenting 2D and 2D plot display of k-prototype clustering,
“in response to receiving second input that is indicative of a confirmation, by the user, of the information regarding the parameter, constructing a new instance of the data science project using assets included in the copy of the repository” Lonial [0096] teaches a back-end data integration method, a signal received from an administrator over a network, and Lonial [0096] teaches a back-end data integration method, a signal received from an administrator over a network.
Claim 16
“wherein the assets include the code and information that is needed to programmatically recreate the new instance of the data science project” Lonial [0051][0066] teaches a repository of retail science-related data stored in its natural format, referred to as a retail science data lake, and data scientists and software engineers can refer to them to rapidly prototype application functionality including upload pre-built models to further built upon existing implementations.
Claim 17
“determining whether alteration of the machine learning model is necessary for the new instance of the data science project to be suitable for analysis of the user-specific data, and in response to a determination that an alteration of the machine learning model is necessary, implementing the alteration on behalf of the user” Wright [0189] teaches if the machine-learning model has an inadequate degree of accuracy for a particular task, it can be further trained or modified to improve accuracy.
Claim 18
“causing digital presentation of an indicium that visually illustrates progression as the new instance of the data science project is being constructed” Wright [0239][0240] & Figure 19A.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Lonial et al. (US 2020/0319857), hereinafter Lonial, and further in view of Sawant et al. (US 2020/0364606), hereinafter Sawant.
Claim 19
“receiving first input that is indicative of a selection, by a user, of multiple applied prototypes from amongst a collection of applied prototypes, wherein each of the multiple applied prototypes serves as a repository that includes code that is needed to programmatically produce another instance of a corresponding a data science project that utilizes a machine learning model trained to perform a task” Lonial [0072][0073] teaches a code notebook that allows data scientist to create and edit a collection of documentation including or bundled with modules for machine learning, and users may make a selection through a user interface; although the “multiple prototypes” is not spelled out, the user selection clearly indicates more than one prototype available;
“receiving second input that is indicative of a request, from the user, to create a human-readable configuration file that identifies the multiple applied prototypes” Lonial [0051][0066] teaches a repository of retail science-related data stored in its natural format, referred to as a retail science data lake, and data scientists and software engineers can refer to them to rapidly prototype application functionality including upload pre-built models to further built upon existing implementations; and Sawant [0056] teaches a system of building prototype machine learning models including building models in the ML platform by passing in a secure JASON Web Token (JWT); invention features like receiving inputs are construed as capable of performing more than once, receiving a second input is receiving an input again;
“creating the human-readable configuration file in a data-serialization language; populating, in the human-readable configuration file, information related to each of the multiple applied prototypes; and causing the human-readable configuration file to be stored on a computer server” Lonial [0051][0066] teaches a repository of retail science-related data stored in its natural format, referred to as a retail science data lake, and data scientists and software engineers can refer to them to rapidly prototype application functionality including upload pre-built models to further built upon existing implementations; and Sawant [0056] teaches a system of building prototype machine learning models including building models in the ML platform by passing in a secure JASON Web Token (JWT).
Lonial and Sawant disclose analogous art. Sawant is analogous art because it is in the field of building machine learning prototypes. However, Lonial fails to spell out the “human-readable configuration file” feature as recited above. Said feature is taught in Sawant. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Sawant (Sawant [0056]: teaches a data-serialization language, i.e., JASON Web Token (JWT) utilized in building prototype machine learning models) into Lonial to enhance its prototype machine learning model building functions by way of utilizing a data-serialization language.
Claims 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Lonial et al. (US 2020/
0319857), hereinafter Lonial, and further in view of Sawant et al. (US 2020/0364606), hereinafter Sawant, and Wright et al. (US 2018/0276861), hereinafter Wright.
Claim 20
“wherein the computer server is a private computer server” Wright [0089] teaches a private cloud that shares pool of configurable computing resources, e.g., servers.
Lonial, Sawant, and Wright disclose analogous art. Sawant is analogous art because it is in the field of building machine learning prototypes. Wright is analogous art because it is in the field of data packing such as metadata. Lonial fails to spell out the “human-readable configuration file” feature as recited above. Said feature is taught in Sawant. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Sawant (Sawant [0056]: teaches a data-serialization language, i.e., JASON Web Token (JWT) utilized in building prototype machine learning models) into Lonial to enhance its prototype machine learning model building functions by way of utilizing a data-serialization language. However, Lonial still fails to spell out the “private cloud” feature as recited above. Said feature is taught in Wright. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Wright (Wright [0089]: teaches a private cloud that shares pool of configurable computing resources, e.g., servers) into Lonial to enhance private and public storage functions.
Claim 21
“wherein the computer server is a public computer server” Wright [0089] teaches a public cloud that shares pool of configurable computing resources, e.g., servers.
Claim 22
“wherein said causing permits the human-readable configuration file to be accessed by other users who are members of a same group as the user” Lonial [0018] teaches a platform offers self-service tools that enable data scientists to employ a broad range of analyses, an organization can take advantage of the present platform’s capability to integrate a comprehensive set of services.
Claim 23
“wherein the user and the other users are employees of a same organization” Lonial [0018] teaches a platform
offers self-service tools that enable data scientists to employ a broad range of analyses, an organization can take advantage of the present platform’s capability to integrate a comprehensive set of services.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Lonial et al. (US 2020/0319857), hereinafter Lonial, and further in view of Sawant et al. (US 2020/0364606), hereinafter Sawant, and Cote et al. (US 2023/0076662), hereinafter Cote.
Claim 25
“receiving third input that is indicative of a selection, by a second user, of the multiple applied prototypes from amongst a collection of applied prototypes; and forking the human-readable configuration file that is representative of an existing catalog, so as to create a new catalog that includes the multiple applied prototypes for the second user” Lonial [0051][0066] teaches a repository of retail science-related data stored in its natural format, referred to as a retail science data lake, and data scientists and software engineers can refer to them to rapidly prototype application functionality including upload pre-built models to further built upon existing implementations, Lonial fails to spell out the recited “prototypes”, it is taught in Cote;
Cote Figure 7 depicts the preliminary categorical feature extraction results of a prototype. When a new prototype is built and added into the catalog, a new catalog is created.
Lonial, Sawant, and Cote disclose analogous art. Sawant is analogous art because it is in the field of building machine learning prototypes. Cote is analogous art because it is in the field of training machine learning models and applying prototypes to field data from production networks. Lonial fails to spell out the “human-readable configuration file” feature as recited above. Said feature is taught in Sawant. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Sawant (Sawant [0056]: teaches a data-serialization language, i.e., JASON Web Token (JWT) utilized in building prototype machine learning models) into Lonial to enhance its prototype machine learning model building functions by way of utilizing a data-serialization language. However, Lonial still fails to spell out the “a collection of applied prototypes” as claimed. Said feature is taught in Cole. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Cote (Cote [0020][0073]: trained machine learning models to show/filter alarms, and a prototype applied to field data from production networks that can suppress in real-time 50% of all alarm, and Figure 7 depicts a catalog like table containing applied prototypes) into Lonial in combination of Sawant to enhance its applied prototype summarizing and illustration functions with a user friendly presentation of the various prototypes.
Allowable Subject Matter
Claim 24 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 24 recites an allowable subject matter of a link from the human-readable configuration file to the corresponding repository of each of the multiple applied prototypes is not taught or suggested by any of the cited prior art references.
Conclusion
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/Ruay Ho/Examiner, Art Unit 2126