DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 10/22/2025. Claims 1-2,4-7,9-11,13-18 and 20 are pending in this Office 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 .
Response to Arguments
Applicant's arguments filed 10/22/2025 with respect to the 35 USC 112 rejection have been fully considered but they are not persuasive. The examiner respectfully traverses applicant’s arguments.
Applicant argues that par [0034] and [0053]-[0054] disclose the corresponding structures for the recited functions as recited in the claim 1.( Applicant’s arguments at page 7-8)
The examiner respectfully disagrees. At beast par [0052] discloses “The data analytics includes a ML feature pipeline 990 that includes data preparation 992, feature engineering 994, an offline features store 996, and an online features store 998”. However, none of them are corresponding hardware (structure) that perform the recited function (“perform feature engineering” or “process operational system information” as recited in claim 1. Accordingly, the examiner maintain the 35 USC 112 rejection.
Applicant's arguments filed 10/22/2025 with respect to the 35 USC 103 rejection have been fully considered and are moot in view of new ground of rejection.
Claim Interpretation
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function (bold and underlined emphasis added) and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “(b) a ML feature pipeline, in the data analytics organization, to perform feature engineering based on the data virtualization to create a ML model”;
“(c) a consumer layer, in an operational system organization of the enterprise and implemented via a microservice framework, to process operational system information utilizing a business rule engine” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph 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 the first paragraph of pre-AIA 35 U.S.C. 112:
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.
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 1-2,4-7 and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1:
Claim limitations " a ML feature pipeline, in the data analytics organization, to perform feature engineering based on the data virtualization to create a ML model” and “a consumer layer, in an operational system organization of the enterprise and implemented via a microservice framework, to process operational system information utilizing a business rule engine ” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The examiner is unable to find anywhere in the specification a corresponding structure, materials or acts of “a ML feature pipeline” that performs the function of “perform feature engineering based on the data virtualization to create a ML model” and a corresponding structure, materials or acts of “a consumer layer” that performs the function of “process operational system information utilizing a business rule engine” as recited in claim 1. Therefore, the claim 1 is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Due at least to their dependency upon Claim 1, claims 2,4-7 and 9 are also indefinite.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
4. Claims 1-2,4-7 and 9 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 claim(s) contains subject matter which was 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites “a ML feature pipeline” that performs the function of “perform feature engineering based on the data virtualization to create a ML model”; “a consumer layer” that performs the function of “process operational system information utilizing a business rule engine”. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Due at least to their dependency upon Claim 1, claims 2,4-7 and 9 also fail to comply with the written description requirement.
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 1-2,4-7,9-11,13-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Leung Pah Hang et al.(US Patent Application Publication 2023/0237366 A1, hereinafter “Hang”), further in view of Igure et al.(US Patent Application Publication 2023/0351224 A1, hereafter “Igure”), further in view of Sommers. (US Patent 11,386,111 B1, hereinafter “Sommers”) and further in view of Roy et al.(US Patent Application Publication 2022/0327012 A1, hereinafter “Roy”)
As to claim 1, Hang teaches a system associated with a Machine Learning ("ML") framework for an enterprise, comprising:
(a) an analytics data store, in a data analytics organization of the enterprise, containing electronic records associated with the enterprise (Hang par [0044] teaches feature store 502), each electronic record including an electronic record identifier and record characteristics to be processed by a data as a service layer to create data virtualization;(Hang par [0044] teaches feature store 502 may facilitate to store data such as training datasets, testing datasets , information pertaining to features of the ML model)
(b) a ML feature pipeline, in the data analytics organization, to perform feature engineering based on the data virtualization to create a ML model;(Hang par [0028] teaches the training may be performed based on the configuration artifacts related to training pipeline as stored in the data sources/ML workflows)
(c) a consumer layer, in an operational system organization of the enterprise and implemented via a microservice framework, to process operational system information utilizing a business rule engine (Hang par [0033] teaches rules engine storing set of rules including monitoring rules and validation rules) ;
(d) a real-time inference platform, in a data science organization of the enterprise, including: a computer processor, and a computer memory, coupled to the computer processor, storing instructions that, when executed by the computer processor cause the real-time inference platform to: (i) receive the ML model created by the ML feature pipeline, (Hang par [0030] teaches model plane 206 hosts the plurality of ML models that are stored in the model registry) (ii) receive data collected and prepared by the consumer layer utilizing the business rule engine (Hang par [0033] teaches user may define monitoring rule to trigger alert if there may be five or more consecutive time slots where a specific version of the ML model shows to have a consistent negative derivative and area under curve for receiver operating characteristic metric may be under 0.76) , and (iii) expose an Application Programming Interface ("API") endpoint associated with deployment of the ML model using the data received from the consumer layer (Hang par [0026] teaches the runtime plane 204 may mediate request to model proxy pertaining to a model endpoint of a suitable ML model via an application programming interface(API)); and
(e) a Continuous Integration/Continuous Delivery ("Cl/CD") platform, in the data science organization, to automatically provide feedback information to the ML feature pipeline regarding performance of the deployed ML model. (Hang par [0026] teaches the control plane 208 may assess the ground truth to evaluate one or more indictors that facilitate to identify instances/events such as drift in model performance and perform automated mitigation of the events). Examiner note: the various locations (in data analytics organization , in data science organization, in an operational system organization) are being interpreted as design choice.
Hang fails to expressly teach implemented via a microservice framework and the platform is a real-time inference platform.
However, Igure teaches implemented via a microservice framework and a real-time inference platform.(Igure par [0011] teaches Realtime inference. Igure par [0035] teaches the system architecture may use a microservice approach)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hang and Igure to achieve the claimed invention. One would have been motivated to make such combination to improve training and performance of the machine learning model.(Igure par [0005])
Hang and Igure fail to expressly teach wherein the analytics data store is associated with all of: (i) an on-premises enterprise database, (ii) an operational datastore, (iii) a data warehouse, (iv) third-party data, (v) a cloud data lake, and (vi) semantic data.
However, Sommers teaches wherein the analytics data store is associated with all of: (i) an on-premises enterprise database, (ii) an operational datastore, (iii) a data warehouse, (iv) third-party data, (v) a cloud data lake, and (vi) semantic data.(Sommers col 4, lines 1-22 teaches the Enterprise Data Warehouse 102 hosts an MPP database that stores a set of records from a set of data sources such as cloud, on-premise data files, databases, data warehouses, data lakes, data stores, data hubs, data marts, flat files, logs, applications, spreadsheets, file sharing services)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hang , Igure and Sommers to achieve the claimed invention. One would have been motivated to make such combination to minimize computationally intensive data conflicts while remaining compliant with the ACID principles and avoid specialized computer programming knowledge.(Sommers col 7, lines 15-20)
Hang ,Igure and Sommers fail to expressly teach wherein the received data comprises Representational State Transfer ("REST") information processed by an API gateway and an application load balancer in the data science organization.
However, Roy teaches wherein the received data comprises Representational State Transfer ("REST") information processed by an API gateway and an application load balancer in the data science organization.(Roy par [0043] teaches API gateway 140 and load balancer 143)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Hang , Igure, Sommers and Roy to achieve the claimed invention. One would have been motivated to make such combination to enable rapid, frequent and reliable delivery of large, complex applications and facilitate evolution of a technology stack of an enterprise.(Roy par [0044])
As to claim 2, Hang ,Igure , Sommers and Roy teach the system of claim 1, wherein the CI/CD platform is associated with at least one of: (i) a software development enterprise team, and (ii) an information technology operations enterprise team. (Hang par [0027] teaches user includes data scientists, engineers, ML engineers and other technical individuals )
As to claim 4, Hang ,Igure , Sommers and Roy teach the system of claim 1, wherein a continuous training process platform in the data science organization performs ML algorithm selection, ML model training, and ML model artifact detection. (Hang par [0028] teaches ML model training)
As to claim 5, Hang ,Igure , Sommers and Roy teach the system of claim 1, wherein a data indexing platform in the data science organization performs ML model monitoring and evaluation based on governance information and data from the real-time inference platform and transmits evaluation results to the CI/CD platform.(Hang par [0026] teaches the runtime plane 204 may include components that collect ground truth form the application and/or perform monitoring of the ML model)
As to claim 6, Hang ,Igure , Sommers and Roy teach the system of claim 5, wherein the governance information is associated with at least one of: (i) application logs, and (ii) a service registry. (Hang par [0030] teaches the model registry may be considered as a repository used to store the trained ML models)
As to claim 7, Hang ,Igure , Sommers and Roy teach the system of claim 1, wherein the ML feature pipeline includes an online features store and an offline features store. (Hang par [0027] teaches one or more data sources 302 and data source may be associated with a cloud based computing platform (online))
As to claim 9, Hang ,Igure , Sommers and Roy teach the system of claim 1, wherein the data as a service layer is associated with at least one of: (i) a batch layer, (ii) a curate layer, (iii) a heavy transform layer, and (iv) a real-time API layer.(Hang par [0032] teaches data batch process 328 pertaining to the data related to the ML model)
Claims 10-11 and 13-15 merely recite a method performed by the system of claims 1-2 and 4-6 respectively. Accordingly, Hang ,Igure, Sommers and Roy teach every limitation of claims 10-11 and 13-15as indicates in the above rejection of claims 1-2 and 4-6 respectively.
Claims 16-17 , 18 and 20 merely recite a non-transitory computer readable storing instructions executed by the system of claims 1-2 ,7 and 9 respectively. Accordingly, Hang ,Igure , Sommers and Roy teach every limitation of claims 16-17 , 18 and 20 as indicates in the above rejection of claims 1-2 ,7 and 9 respectively.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147