DETAILED ACTION
Claims 1-20 are presented for examination.
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.
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 and 4-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo et al (U.S. Pat. Pub. No. 2019/0156246 A1, hereinafter Kuo) in view of Chan (U.S. Pat. Pub. No. 2019/0228261 A1).
Kuo and Chan were cited in the IDS filed on 10/10/2023.
As per claim 1, Kuo teaches the limitations as claimed, including a method for modular model implementation, comprising, at an orchestration module:
receiving a set of requests identifying a set of models (Paragraph [0040], where the indication correlates to a request and includes a machine learning model);
for each model of the set of models, initializing an instance of the model using a model module, associated with the respective model, from a set of model modules (Paragraph [0042]; Paragraph [0019] demonstrates that the selected machine learning model is from a set of machine learning models).
Kuo does not expressly teach for each instance of a model of the set, executing a same series of standard submodules from the respective model module, wherein each standard submodule comprises standard model-specific logic, and wherein at least one standard submodule additionally comprises user-defined model-specific logic.
However, Chan teaches for each instance of a model of the set, executing a same series of standard submodules from the respective model module, wherein each standard submodule comprises standard model-specific logic, and wherein at least one standard submodule additionally comprises user-defined model-specific logic (Paragraphs [0011] and [0033]).
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine the teachings of Chan with those of Kuo in order to allow for Kuo’s method to ensure that all of the parts of the model were executed properly in order to increase scalability and usability among potential users.
As per claim 4, Kuo teaches that the model module further comprises a model submodule, wherein the model submodule comprises logic for a set of machine learning models (Paragraph [0052]).
As per claim 5, Kuo teaches that the model module further comprises an optimizer submodule associated with a set of optimizers, wherein the optimizer submodule comprises optimizer-specific logic for each optimizer of the set of optimizers (Paragraph [0050]).
As per claim 6, Kuo teaches that each request of the set of requests identifies a hardware type (Paragraph [0020]).
As per claim 7, Kuo teaches that the hardware type comprises at least one of a central processing unit (CPU), graphics processing unit (GPU), image processing unit (IPU), or tensor processing unit (TPU) (Paragraph [0020]).
As per claim 8, Kuo teaches for each request, initializing an instance of the respective hardware type using a hardware module, associated with the respective hardware type, from a set of hardware modules, wherein initializing the instance of the respective hardware type comprises executing a series of standard hardware submodules from the respective hardware module (Paragraph [0042]).
As per claim 9, Kuo teaches that each hardware module comprises the same series of standard hardware submodules, wherein each hardware module defines logic specific to the respective hardware type within the respective standard hardware submodules (Paragraph [0054]).
As per claim 10, Kuo teaches that for each request, the respective model module is executed on the respective instance of the hardware type (Paragraph [0054]).
Claims 2, 3, and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo in view of Chan and further in view of Klementiev et al (U.S. Pat. Pub. No. 2009/0094614 A1, hereinafter Klementiev).
As per claim 2, Kuo and Chan do not expressly teach a callback submodule, wherein the callback submodule comprises user- defined logic.
However, Klementiev teaches a callback submodule, wherein the callback submodule comprises user- defined logic (Paragraph [0035]).
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine the teachings of Klementiev with those of Kuo and Chan in order to allow for Kuo’s and Chan’s method to be more responsive to particular user needs by allowing for greater customization, which could increase the desirability of the system, thereby potentially increasing buy-in among prospective users.
As per claim 3, Kuo and Chan do not expressly teach a mechanism to override the standard model-specific logic and use the user-defined model- specific logic.
However, Klementiev teaches a mechanism to override the standard model-specific logic and use the user-defined model- specific logic (Paragraph [0035]).
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine the teachings of Klementiev with those of Kuo and Chan in order to allow for Kuo’s and Chan’s method to be more responsive to particular user needs by allowing for greater customization, which could increase the desirability of the system, thereby potentially increasing buy-in among prospective users.
As per claim 11, it is a system claim of method claim 1 with additional limitations. All corresponding limitations are rejected for the same reasons.
As to the additional limitations, Kuo and Chan do not expressly teach a standard hook associated with user-defined model-specific logic and executing the user-defined model-specific logic when the standard hook is triggered.
However, Klementiev teaches a standard hook associated with user-defined model-specific logic and executing the user-defined model-specific logic when the standard hook is triggered (Paragraph [0035]).
It would have been obvious to one of ordinary skill in the art at the time of the filing of the application to combine the teachings of Klementiev with those of Kuo and Chan in order to allow for Kuo’s and Chan’s system to be more responsive to particular user needs by allowing for greater customization, which could increase the desirability of the system, thereby potentially increasing buy-in among prospective users.
As per claim 12, Klementiev teaches that a model module further comprises a callback submodule, wherein the callback submodule further comprises user-defined logic (Paragraph [0035]).
As per claim 13, Klementiev teaches that the standard hook for the model module calls the callback submodule (Paragraph [0035]).
As per claims 14-20, they are system claims with no further limitations beyond those rejected above. Therefore, they are rejected for the same reasons.
Conclusion
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/GREGORY A KESSLER/Primary Examiner, Art Unit 2197