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 .
The following is in response to the amendment and remarks of 04/02/2026.
By the amendment, claims 1-5, 9-14 and 18-20 are amended.
Claims 1-20 are pending and have been considered below.
Response to Arguments/Amendment
The 35 USC 101 rejection of claims 1-20 have been withdrawn in light of the claims amendment and corresponding remarks.
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-20 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.
Regarding claims 1, 10 and 19, each of claims 1, 10 and 19 recite: “the machine learning pipeline comprising a plurality of non-operation-specific steps”. However, the disclosure does not describe the “non-operation-specific steps”. The specification discloses that a machine learning pipeline is constructed to perform one or more machine learning operations and may be executed to include additional operations or change the logic of existing operations (Specification ¶18) and that operations are “of the machine learning pipeline” (Specification ¶34); this conflicts with the subject matter claimed in the “the machine learning pipeline comprising a plurality of non-operation-specific steps” limitation. Accordingly, the subject matter is not described in such a way as to reasonably convey to one skilled in the art at the time that the inventor had possession of the claimed invention.
Claims 2-9, 11-18 and 20, which depend from their respective parent claims 1, 10 and 19, are similarly rejected.
Claims 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 10 and 19, each of claims 1, 10 and 19 recite: “the machine learning pipeline comprising a plurality of non-operation-specific steps”. However, the claims each further require that the machine learning pipeline is used to perform one or more operations. A broadest reasonable interpretation in light of the specification is that the machine learning pipeline is comprised of machine learning-specific steps to perform an operation (Specification ¶3, ¶18, ¶34). Accordingly, the machine learning pipeline being comprised of a plurality of non-operation-specific steps is indefinite as the BRI and claim require that the machine learning pipeline is comprised of at least machine learning-specific operations.
Claims 2-9, 11-18 and 20, which depend from their respective parent claims 1, 10 and 19, are similarly rejected.
Further regarding claims 9 and 18, each of claims 9 and 18 recite: “wherein the machine learning pipeline is configured to manage one or more machine learning operations independent of machine learning operation-specific steps corresponding to each machine learning operations”. However, as stated above, each of their respective parent claims are indefinite regarding the machine learning pipeline being both comprised of non-operation-specific steps and machine learning-specific operations. Similarly, claims 9 and 18 are indefinite because they require that the machine learning pipeline manages machine learning operations independent of executing the machine learning operations which are presumably comprised of operation-specific steps.
Further regarding claim 6, claim 6 recites the limitation "the execution representation" in line 2. There is insufficient antecedent basis for this limitation in the claim.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-2, 4, 6, 8-11, 13, 15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Katzenberger et al., US 2020/0348912 A1 [KATZENBERGER] in view of Tomioka et al., US 2021/0304066 A1 [TOMIOKA].
Regarding claim 1, KATZENBERGER discloses a method comprising:
generating a machine learning pipeline based on a received input (Fig. 1, ¶26-27: facilitate building machine learning framework, ¶30: using a model builder GUI, ¶Fig. 1 104: machine learning framework), the machine learning pipeline comprising a plurality of non-operation-specific steps (¶31-32: operator calls are done using function calls structured in a language that is non-specific to a building language, API using non-specific glue code);
receiving an indication of one or more operations to be performed using the machine learning pipeline (¶30-31: receive indication of operations to be performed by the pipeline);
generating executable code to use one or more machine learning executors, selected based, at least in part, on the indicated one or more operations (¶33-34: build execution graph 118 having a node for each indicated operator, ¶36: graph execution engine translates into execution graph 128 code which indicates each node executor); and
performing the executable code to implement the machine learning pipeline performing the one or more operations (¶37-38: graph execution engine executes the execution graph 128 code specifying the input calls and outputs of the operators and provides the results to application 126).
While KATZENBERGER discloses generally that the execution may use processors (¶59), KATZENBERGER fails to explicitly disclose wherein the performing the executable code uses one or more processors respectively corresponding to the one or more machine learning executors.
TOMIOKA discloses methods for executing pipelines when inference or training a machine learning model takes place (¶6). In particular, TOMIOKA discloses portioning, using a constraint generator configured to compute execution cost, a computation/execution graph such that each portion of the graph is executed by a respective processor (¶27, ¶38-40, ¶46, Fig. 2). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of KATZENBERGER and TOMIOKA before them before the effective filing of the claimed invention to combine the execution of each portion of an execution graph on a respective processor, as taught by TOMIOKA, with the execution of the portioned execution graph of KATZENBERGER. One would have been motivated to make this combination in order to increase efficiency in execution of machine learning pipelines, as suggested by TOMIOKA (¶2-3, ¶23-27).
Regarding claim 2, KATZENBERGER and TOMIOKA discloses the method of claim 1, and KATZENBERGER further discloses wherein the one or more operations comprise at least one of:
training one or more machine learning operations,
tuning one or more training parameters of the one or more machine learning operations,
predicting new data,
scoring a performance of a prediction result, and
interpreting a contribution level of different input data to prediction results (¶25, ¶30).
Regarding claim 4, KATZENBERGER and TOMIOKA discloses the method of claim 1, and TOMIOKA further discloses combining multiple vertices of an intermediate representation with compatible execution environments into a single vertex for execution (¶80-86: intermediate representation DAG comprising vertices, ¶109: when two vertices of identical computation assigned to same machine, they share code, broadly - combined for single execution).
Regarding claim 6, KATZENBERGER and TOMIOKA disclose the method of claim 1, and TOMIOKA further discloses:
dividing the execution representation into multiple parts for execution on different hardware (¶27, ¶38-40, ¶46, Fig. 2).
Regarding claim 8, KATZENBERGER and TOMIOKA discloses the method of Claim 1, and TOMIOKA further discloses:
composing multiple machine learning operations into a directed acyclic graph (DAG) machine learning pipeline (¶80).
Regarding claim 9, KATZENBERGER and TOMIOKA discloses the method of Claim 1, and KATZENBERGER further discloses wherein the authoring representation is configured to manage one or more machine learning operations independent of machine learning operation-specific steps corresponding to each machine learning operation (¶25).
Regarding claims 10-11, 13, 15 and 17-18, claims 10-11, 13, 15 and 17-18 recite limitations similar to claims 1-2, 4, 6 and 17-18, respectively, and are similarly rejected.
Regarding claims 19-20, claims 19-20 recite limitations similar to claims 1-2, respectively, and are similarly rejected.
Claims 3, 7, 12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over KATZENBERGER in view of TOMIOKA and in further view of Thomas et al., US 12,423,615 B1 [THOMAS].
Regarding claim 3, KATZENBERGER and TOMIOKA discloses method of claim 2, and TOMIOKA further discloses wherein the operation presents inference results of the machine model (¶31).
KATZENBERGER and TOMIOKA fail to disclose wherein the one or more operations comprise scoring the performance of the prediction result by scoring at least one of accuracy, precision, recall, and mean absolute error.
THOMAS discloses methods for utilizing machine learning pipelines to perform machine learning services (col 2 lines 36-44). In particular, THOMAS discloses machine learning pipelines having evaluation operations that return evaluation scores of inference predictions (col 7 lines 14-27). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of KATZENBERGER, TOMIOKA and THOMAS before them before the effective filing of the claimed invention to combine the machine learning pipeline evaluation operation scoring the performance of an inference result based on accuracy or precision metrics, as suggested by THOMAS, with the machine learning pipeline operations of KATZENBERGER and TOMIOKA. One would have been motivated to make this combination in order to provide improved debugging and deployment capabilities, directly improving efficient use of underlying computing resources, as suggested by THOMAS (col 4 lines 50-54).
Regarding claim 7, KATZENBERGER and TOMIOKA discloses the method of Claim 1, but fail to disclose wherein the machine learning pipeline comprises a previously-generated machine learning pipeline to which one or more pre-processing or post-processing operations have been subsequently added for a specific application.
THOMAS discloses methods for utilizing machine learning pipelines to perform machine learning services (col 2 lines 36-44). In particular, THOMAS discloses accessing prior machine pipeline workflows having pre-processed operations applied for a specific application (col 8 lines 46-62, col 9 lines 7-21, col 6 lines 13-18, col 7 lines 38-42). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of KATZENBERGER, TOMIOKA and THOMAS before them before the effective filing of the claimed invention to combine the machine learning pipeline including previously generated pre-processed pipeline data for a specific application, as suggested by THOMAS, with the machine learning pipeline of KATZENBERGER and TOMIOKA. One would have been motivated to make this combination in order to increased compute capacity efficiencies, as suggested by THOMAS (col 4 lines 44-50).
Regarding claims 12 and 16, claims 12 and 16 recite limitations similar to claims 3 and 7, respectively, and are similarly rejected.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over KATZENBERGER in view of TOMIOKA and in further view of MACIOCCO, US 2020/0142735 A1.
Regarding claim 5, KATZENBERGER and TOMIOKA disclose the method of claim 1, and TOMIOKA further discloses optimizing an intermediate representation comprising vertices (¶80-86).
KATZENBERGER and TOMIOKA fail to disclose wherein optimizing comprises dividing a single vertex into multiple vertices for concurrent execution.
MACIOCCO discloses methods for managing machine learning models in an edge environment (¶3, ¶13). In particular, MACIOCCO discloses optimizing an intermediate representation comprising dividing a single vertex into multiple vertices for concurrent execution (¶33: divide and conquer approach to parallel distribution of AI acceleration workloads across edge nodes). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of KATZENBERGER, TOMIOKA and MACIOCCO before them before the effective filing of the claimed invention to combine the optimizing an intermediate representation comprising dividing a single vertex into multiple vertices for concurrent execution, as suggested by MACIOCCO with the optimization of the intermediate representation of KATZENBERGER and TOMIOKA. One would have been motivated to make this combination in order to more effectively utilize resources of an edge platform, as suggested by MACIOCCO (¶29).
Regarding claim 14, claim 14 recites limitations similar to claim 5 and is similarly rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bhise et al.
US 11,989,627 B1
AUTOMATED MACHINE LEARNING PIPELINE GENERATION
Cervantes et al.
US 2022/0180184 A1
PROVIDING A LOCATION REPRESENTATION FOR MACHINE LEARNING TASKS
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 ANDREW L TANK whose telephone number is (571)270-1692. The examiner can normally be reached Monday-Thursday 9a-6p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at 571-270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW L TANK/ Primary Examiner, Art Unit 2141
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