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 .
DETAILED ACTION
This action is in response to the application filed on 11/10/2023.
Claims 1-20 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “machine learning” is recited in the independent claims multiple times at different limitations, it is unclear if all the limitations are using the same “machine learning” algorithms or are different. The examiner recommends amending the limitation to clarify the intent of the invention. For purposes of rejection, the examiner is interpreting all the limitations to use the same “machine learning”.
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.
Claim 1, this claim is within at least one of the four categories of patent eligible subject matter as it is directing to a system claim under Step 1.
However, the limitation “establish compatibility of the set of computer-executable operations responsive to the modification; and construct, responsive to establishment of the compatibility, the set of computer- executable operations for use with machine learning”, as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation as drafted, is a function that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitation encompass a human mind carrying out the functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas under Prong 1 Step 2A.
Under Prong 2 Step 2A, this judicial exception is not integrated into a practical application. The claim recites the following additional elements “data processing system”, “processors”, “memory” and “receive, from a client device via a network, a request to establish computer-executable operations for use with machine learning on a data set; provide, for display via a graphical user interface on the client device, an indication of a set of computer-executable operations generated automatically for machine learning on the data set by the data processing system responsive to the request; receive, from the client device via the graphical user interface, an indication to modify the set of computer-executable operations;” The “data processing system”, “processors” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception. The additional elements “receive, from a client device via a network, a request to establish computer-executable operations for use with machine learning on a data set; provide, for display via a graphical user interface on the client device, an indication of a set of computer-executable operations generated automatically for machine learning on the data set by the data processing system responsive to the request; receive, from the client device via the graphical user interface, an indication to modify the set of computer-executable operations” do nothing more than add insignificant extra solution activity to the judicial exception of merely sending, receiving, and displaying data/information. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the “data processing system”, “processors”, “memory”” are merely a generic computer or generic computer components to apply the judicial exception which cannot provide an inventive concept. Furthermore, the limitations “receive, from a client device via a network, a request to establish computer-executable operations for use with machine learning on a data set; provide, for display via a graphical user interface on the client device, an indication of a set of computer-executable operations generated automatically for machine learning on the data set by the data processing system responsive to the request; receive, from the client device via the graphical user interface, an indication to modify the set of computer-executable operations;” the courts have identified mere data gathering and merely displaying data/information on a display is well-understood, routine and conventional activity. See MPEP 2106.05(d). Mere instructions to apply an exception cannot provide an inventive concept. Accordingly, the claim does not appear to be patent eligible under 35 USC 101.
Claim 2 further recites selecting operations that map to an attribute which recites additional mental step while the presenting and receiving limitations which are insignificant extra solution activity and WURC akin to mere data gathering and displaying. Claim 3 further recites receiving limitation which is insignificant extra solution activity and WURC akin to mere data gathering along with determining, modifying, and constructing which are additional mental steps. Claim 4 further recites establishing compatibility which recites additional mental step. Claims 5 recites non-functional descriptive language that does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim 6 further recites automatically modifying operations which recites additional mental step. Claim 7 recites providing a prompt which is insignificant extra solution activity and WURC akin to displaying data. Claim 8 recites “apply it” steps which are mere instructions to apply an exception cannot provide an inventive concept. Claim 9 further recites modifying to add operations to extract the feature from the input data which recites additional mental step. Claims 10-11 recites providing, via gui, visual representation of data which is insignificant extra solution activity and WURC akin to displaying data. Claim 12 recites non-functional descriptive language that does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim 13 recites sharing operations with second client which is insignificant extra solution activity and WURC akin to mere data gathering.
Claim set 14-18 and claim set 19-20 are also rejected under the same rationale as claim set 1-13 for having similar limitations.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8, 10, 12-15, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nookula et al. (US 11,853,401 B1) hereinafter Nookula in view of Siracusa et al. (US 2020/0380415 A1) hereinafter Siracusa.
Regarding claim 1,
A system, comprising:
a data processing system comprising one or more processors, coupled with memory (Nookula Fig. 9 illustrates system 900 comprising processors 910 coupled to memory 920), to:
receive, from a client device via a network, a request to establish computer-executable operations for use with machine learning on a data set (Nookula [col. 4, lines 40-57] discloses request sent to construct ML model using identified data set through model construction service 110 in which the ML model includes code/logic from block library 112 illustrated in Fig. 4.);
provide, for display via a graphical user interface on the client device, an indication of a set of computer-executable operations generated automatically for machine learning on the data set by the data processing system responsive to the request (Nookula [col. 4, lines 49-57] discloses “the model construction service 110 may identify the identifiers of the one or more aspects in the request, and obtain code/logic for the corresponding ML models (e.g., from a block library 112, which could store pre-trained models 113A, model portions 114A), ML model portions, etc., and configure the ML model(s) and/or model portions based on the other aspects/information—e.g., orderings, settings, hyperparameter values, etc.”. The examiner would like to point out that “for display via a graphical user interface on the client device” is intended use language thus, no rejection provided);
receive, from the client device via the graphical user interface, an indication to modify the set of computer-executable operations (Nookula [col. 5, lines 8-16] dislcoses some pre-trained ML models can be “customized” for particular use cases by re-training them with case-specific training data sets 128—for example, a generic object detection model (that has already been trained to detect some number of objects, e.g., 10,000 different objects) can be retrained to identify new types of objects (e.g., the presence of a drone, multiple brands of shoes, the existence of a hot dog) by retraining the model with a data set 128 of those specific objects. Nookula [col. 6, lines 60-67] and [col. 7, lines 1-2] discloses customizing ML model using blocks 114/operations as illustrated in Fig. 4);
establish [compatibility] of the set of computer-executable operations responsive to the modification (Nookula [col. 8, lines 39-59] discloses validating the ML model after the blocks form the ML model and/or after training the ML model); and
Nookula lacks explicitly
construct, responsive to establishment of the compatibility, the set of computer- executable operations for use with machine learning.
Siracusa teaches
construct, responsive to establishment of the compatibility, the set of computer- executable operations for use with machine learning (Siracusa [0043] teaches creating an ML model that is compatible with model specification).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nookula to incorporate the teachings of Siracusa to “construct, responsive to establishment of the compatibility, the set of computer- executable operations for use with machine learning” in order to efficiently confirm that customized addition works with existing operations and prevent the system from halting.
Regarding claim 2,
The system of claim 1, wherein the data processing system is further configured to:
select a plurality of computer-executable operations that map to an attribute of the data set (Nookula [col. 4, lines 49-57] discloses “the model construction service 110 may identify the identifiers of the one or more aspects in the request, and obtain code/logic for the corresponding ML models (e.g., from a block library 112, which could store pre-trained models 113A, model portions 114A), ML model portions, etc., and configure the ML model(s) and/or model portions based on the other aspects/information—e.g., orderings, settings, hyperparameter values, etc.”);
present, via the graphical user interface, an indication of the plurality of computer- executable operations (Nookula Fig. 4 illustrates in the GUI 104 the blocks 114); and
receive, from the client device, an instruction to replace at least one of the plurality of computer-executable operations with at least one of a computer-executable operation at least partially coded by a user via a software development kit (Nookula [col. 6, lines 60-65] discloses customizing the ML model using blocks 114 as illustrated in Fig. 4) or selected by the user via a catalog of computer-executable operations (No rejection required due to “or” language).
Regarding claim 8, Nookula discloses
The system of claim 1, wherein the data processing system is further configured to:
execute the constructed set of computer-executable operations to generate a model based on the data set via machine learning (Nookula [col. 8, lines 60-64] discloses training the first ML model using the data set via ML Training Containers 630 illustrated in Fig. 6); and
deploy the model to make predictions based on an input data stream different from the data set (Nookula [col. 1, lines 14-17] and [col. 9, lines 10-29] discloses deploying ML models to make predictions for other data).
Regarding claim 10, Nookula further discloses
The system of claim 1, wherein the data processing system is further configured to:
provide, upon execution of the constructed set of computer-executable operations, via the graphical user interface, a first visual representation of data generated subsequent to execution of a first computer-executable operation of the set of computer-executable operations (Nookula [col. 8, lines 65-67] and [col. 9, lines 1-3] disclose “The operations 500 include, at block 530, causing a result of the training to be presented to the user. Block 530 in some embodiments includes transmitting the result via one or more HTTP messages to the electronic device. The result may include one or more of: an accuracy of one or more ML models, output generated by training the models, etc.”); and
provide, via the graphical user interface, a second visual representation of data generated subsequent to execution of a second computer-executable operation of the set of computer- executable operations (Nookula [col. 8, lines 65-67] and [col. 9, lines 1-3] disclose “The operations 500 include, at block 530, causing a result of the training to be presented to the user. Block 530 in some embodiments includes transmitting the result via one or more HTTP messages to the electronic device. The result may include one or more of: an accuracy of one or more ML models, output generated by training the models, etc.” Where the training may be conceptually similar to the second execution).
Regarding claim 12, Nookula further discloses The system of claim 1,
wherein the set of computer-executable operations comprise at least one of a data transform (No rejection required due to “or” language) or a prediction (Nookula [col. 17, lines 38-40] disclose “. Execution of the code 656 results in the generation of outputs (e.g., predicted results)”).
Regarding claim 13, Nookula further discloses The system of claim 12, wherein the data processing system is further configured to:
share at least a portion of the set of computer-executable operations with a second client device for inclusion in a second set of computer-executable operations established via the second client device (Nookula [col. 5, lines 33-50] disclose providing the model to one or more electronic devices 102B as illustrated in Fig. 3. The examiner would like to point out that “for inclusion in a second set of computer-executable operations established via the second client device” is being analyzed as intended use thus no explicit rejection provided).
Regarding claim 14, it’s directed to a method having similar limitations cited in claim 1. Thus claim 14 is also rejected under the same rationale as cited in the rejection of claim 1 above.
Regarding claim 15, it’s directed to a method having similar limitations cited in claim 2. Thus claim 15 is also rejected under the same rationale as cited in the rejection of claim 2 above.
Regarding claim 19, it’s directed to a system having similar limitations cited in claim 1. Thus claim 19 is also rejected under the same rationale as cited in the rejection of claim 1 above.
Claims 3, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nookula et al. (US 11,853,401 B1) hereinafter Nookula in view of Siracusa et al. (US 2020/0380415 A1) hereinafter Siracusa and further in view of Walsh (US 2022/0035688 A1).
Regarding claim 3, the combination teaches
The system of claim 1, wherein the data processing system is further configured to:
Siracusa further teaches
determine the custom computer-executable operation is incompatible with the set of computer-executable operations (Siracusa [0041] teaches determining whether an existing model is missing data/objects required for conversion and determine whether the existing model is not valid and/or consistent generally (e.g., incorrect data, operation(s) performed by the model not consistent with purported goal or result, etc.).);
modify, responsive to the determination of incompatibility, a computer-executable operation of the set of computer-executable operations (Siracusa [0041] teaches in response to non-conformance, providing an error statement); and
the combination lacks explicitly
receive, from the client device, an indication to add a custom computer-executable operation in the set of computer-executable operations, the custom computer-executable operation comprising code generated by a user via a software development kit and uploaded to the data processing system;
construct, responsive to the modification, the set of computer-executable operations with the custom computer-executable operation;
Walsh teaches
receive, from the client device, an indication to add a custom computer-executable operation in the set of computer-executable operations, the custom computer-executable operation comprising code generated by a user via a software development kit and uploaded to the data processing system (Walsh [0032] teaches These visual elements or shapes may be customizable such as via an SDK. The SDK for customizing the visual elements or shape operation may include various plug-ins such as that described in embodiments of the present disclosure. For example, an intelligent listener API plug-in according to some embodiments herein may be utilized with a shape such as start shape of a modeled integration process. Further, the customized elements may be provided via graphical user interface 112 thus, uploaded to processing system);
construct, responsive to the modification, the set of computer-executable operations with the custom computer-executable operation (Walsh [0032] and [0054] teach building the process which would include customized elements as illustrated in Fig. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination to incorporate the teachings of Walsh to “receive, from the client device, an indication to add a custom computer-executable operation in the set of computer-executable operations, the custom computer-executable operation comprising code generated by a user via a software development kit and uploaded to the data processing system; construct, responsive to the modification, the set of computer-executable operations with the custom computer-executable operation;” in order to improve data integrity and reduce errors, optimize conflict resolution and reduce debugging, improve validation of data dependencies, and enhance interoperability.
Regarding claim 16, it’s directed to a method having similar limitations cited in claim 3. Thus claim 16 is also rejected under the same rationale as cited in the rejection of claim 3 above.
Regarding claim 20, it’s directed to a system having similar limitations cited in claim 3 in addition to the following. Thus claim 20 is also rejected under the same rationale as cited in the rejection of claim 3 above and
generate, via the software development kit, a plurality of blueprints based at least in part on the modification (Walsh [0032] teaches The information handling system 100 may further include a graphical user interface 112. The graphical user interface 112 in an embodiment may provide a visual designer environment permitting a user to define integration process flows between applications/systems, such as between trading partner and enterprise systems, and to model a customized business integration process. The graphical user interface 112 in an embodiment may provide a menu of pre-defined user-selectable visual elements or shapes and permit the user to arrange them as appropriate to model a process and may be displayed on the video display 110. The elements may include visual, drag-and-drop icons representing specific units of work required as part of the integration process, such as invoking an application-specific connector, transforming data from one format to another, routing data down multiple paths of execution by examining the contents of the data, business logic validation of the data being processed, etc. These visual elements or shapes may be customizable such as via an SDK. The SDK for customizing the visual elements or shape operation may include various plug-ins such as that described in embodiments of the present disclosure. For example, an intelligent listener API plug-in according to some embodiments herein may be utilized with a shape such as start shape of a modeled integration process.); and
present a visualization for the plurality of blueprints via the graphical user interface (Walsh Fig. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination to incorporate the teachings of Walsh to “generate, via the software development kit, a plurality of blueprints based at least in part on the modification; present a visualization for the plurality of blueprints via the graphical user interface” in order to improve data integrity and reduce errors, optimize conflict resolution and reduce debugging, improve validation of data dependencies, and enhance interoperability.
Claims 4-6, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nookula et al. (US 11,853,401 B1) hereinafter Nookula in view of Siracusa et al. (US 2020/0380415 A1) hereinafter Siracusa and further in view of Gschwind (US 2015/0278112 A1).
Regarding claim 4, the combination teaches
The system of claim 1, wherein the data processing system is further configured to:
the combination lacks explicitly
establish the compatibility of the set of computer-executable operations based on a comparison of an attribute of an output value of a first computer-executable operation of the set of computer-executable operations with an attribute of an input value of a second computer- executable operation
Gschwind teaches
establish the compatibility of the set of computer-executable operations based on a comparison of an attribute of an output value of a first computer-executable operation of the set of computer-executable operations with an attribute of an input value of a second computer- executable operation (Gschwind [0155] teaches “the same (or compatible) associated output attribute information is to be entered for the other attribute, the entering can merge two distinct entries with separate I/D access attributes but similar input and output addresses. (Output attributes are for example compatible when one output attribute only applies to a specific one type of access corresponding to an input attribute.)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination to incorporate the teachings of Gschwind to “establish the compatibility of the set of computer-executable operations based on a comparison of an attribute of an output value of a first computer-executable operation of the set of computer-executable operations with an attribute of an input value of a second computer- executable operation” in order to improve data integrity and reduce errors, optimize conflict resolution and reduce debugging, improve validation of data dependencies, and enhance interoperability.
Regarding claim 5, the combination teaches
The system of claim 4,
the combination lacks explicitly
wherein the attribute corresponds to at least one of a data type, a data sparsity, a binary representation of data, a shape of data, or missing values.
Siracusa further teaches
wherein the attribute corresponds to at least one of a data type (Siracusa [0032] teaches variables may include data types), a data sparsity, a binary representation of data, a shape of data, or missing values (No rejection required due to “or” language).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nookula to incorporate the teachings of Siracusa to “wherein the attribute corresponds to at least one of a data type” in order to improve data integrity, optimize performance, and reduce complexity.
Regarding claim 6, the combination teaches The system of claim 1, wherein the data processing system is further configured to:
Siracusa further teaches
automatically modify a computer-executable operation of the set of computer-executable operations to establish the compatibility (Siracusa [0043] teaches the specification converter 220 transforms the ML model into the particular model specification and provides a transformed ML model that is therefore compatible with the particular model specification (304)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nookula to incorporate the teachings of Siracusa to “automatically modify a computer-executable operation of the set of computer-executable operations to establish the compatibility” in order to efficiently perform the modification while minimizing error likelihood and wasted computing resources from manual modification with errors.
Regarding claim 17, it’s directed to a method having similar limitations cited in claim 4. Thus claim 17 is also rejected under the same rationale as cited in the rejection of claim 4 above.
Regarding claim 18, it’s directed to a method having similar limitations cited in claim 5. Thus claim 18 is also rejected under the same rationale as cited in the rejection of claim 5 above.
Claim 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nookula et al. (US 11,853,401 B1) hereinafter Nookula in view of Siracusa et al. (US 2020/0380415 A1) hereinafter Siracusa and further in view of Gschwind (US 2015/0278112 A1) and further in view of Moolman et al. (US 2018/0247243 A1) hereinafter Moolman.
Regarding claim 7, the combination teaches
The system of claim 6, wherein the data processing system is further configured to:
the combination lacks explicitly
provide a prompt via the graphical user interface indicating the automatic modification.
Moolman teaches
provide a prompt via the graphical user interface indicating the automatic modification (Moolman [0088] teaches interface 220 receives instructions indicative of changes or modifications made to workflow projects).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination to incorporate the teachings of Moolman to “automatically modify a computer-executable operation of the set of computer-executable operations to establish the compatibility” in order to increase transparency and trust, reduce errors and increase accuracy, ease of learning and use, improve workflow control, and efficiently provide real-time feedback.
Claim 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nookula et al. (US 11,853,401 B1) hereinafter Nookula in view of Siracusa et al. (US 2020/0380415 A1) hereinafter Siracusa and further in view of Suri (US 2018/0004508 A1).
Regarding claim 9, the combination teaches
The system of claim 1,
the combination lacks explicitly
wherein prior to the modification, the set of computer-executable operations automatically generated by the data processing system lacks a configuration to extract a feature from input data, and subsequent to modification and establishment of the compatibility, the set of computer-executable operations is configured to extract the feature from the input data
Suri teaches
wherein prior to the modification, the set of computer-executable operations automatically generated by the data processing system lacks a configuration to extract a feature from input data, and subsequent to modification and establishment of the compatibility, the set of computer-executable operations is configured to extract the feature from the input data (Suri claim 1 teaches “the first code set having at least one difference from the second code set, the at least one difference relating to addition, removal, or modification of code of the first code set in comparison to code of the second code set, and the first code set and the second code set relating to one or more of: extracting input data from a source file, transforming the input data to form output data, or storing the output data in a target file”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination to incorporate the teachings of Suri to “wherein prior to the modification, the set of computer-executable operations automatically generated by the data processing system lacks a configuration to extract a feature from input data, and subsequent to modification and establishment of the compatibility, the set of computer-executable operations is configured to extract the feature from the input data” in order to improve accuracy, enhance performance, increase automation, reduce noise, and improve efficiency.
Claim 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nookula et al. (US 11,853,401 B1) hereinafter Nookula in view of Siracusa et al. (US 2020/0380415 A1) hereinafter Siracusa and further in view of Lodhia et al. (US 10,838,698 B2) hereinafter Lodhia.
Regarding claim 11, the combination teaches
The system of claim 1, wherein the data processing system is further configured to:
the combination lacks explicitly
present, via the graphical user interface, the set of computer-executable operations as a directed acyclic graph
Lodhia teaches
present, via the graphical user interface, the set of computer-executable operations as a directed acyclic graph (Lodhia [col. 4, lines 15-20] teaches present, via the graphical user interface, the set of computer-executable operations as a directed acyclic graph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination to incorporate the teachings of Lodhia to “present, via the graphical user interface, the set of computer-executable operations as a directed acyclic graph” in order to improve comprehension and visualization, enhance operational efficiency, and increase reliability and maintainability.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Noor Alkhateeb whose telephone number is (313)446-4909. The examiner can normally be reached Monday-Friday from 9:00AM ET to 5:00PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat do, can be reached at telephone number (571) 272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NOOR ALKHATEEB/Primary Examiner, Art Unit 2193