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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 recites “a method for..” which recites a series of steps and therefore is a process. Claim 16 recites “A system…” therefore is a machine. Claim 19 recites ”A non-transitory computer-readable medium” therefore is a manufacture.
Step 2A Prong One: Claims 1, 16, and 19 recite limitations “receiving” “causing” “causing”. These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting processor or a producer party, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “causing” in the context of this claim encompasses a user mentally, and with the aid of pen and paper writing the changes down on a sheet of paper and examine the list to identify the relevant ones
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim recites the additional elements "providing” “obtaining” this limitation amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g); and "obtaining"; this limitation is a mere generic response of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g). The elements are elements merely invoking a generic computer environment (processor, database, memory) and basic data-gathering or outputting functions (MPEP 21.96.05(f)) hence reciting insignificant extra solution activities.
The one or more hardware processors and one or more non-transitory computer-readable storage media in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations "providing” and "obtaining" are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner. No elements individually or in combination adds “significantly more” than the abstract idea hence are no more than well-understood, routine and conventional computer functions that merely apply the abstract idea on a generic computer. When viewed as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and do not add significantly more than the abstract idea itself.
Dependent claims are rejected for depending off independent claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 9, 15-17, 19, 20 is/are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Du et al. US2025/0094137
Regarding claim 1, Du teaches: providing a user interface depicting a graph representing a data processing pipeline; (Du see paragraph 0028 0030 graph of computational pipeline on user interface)
receiving, via the user interface, a request to activate a preview mode in association with a machine learning model interconnected with a node of the data processing pipeline; (Du see paragraph 0047 0048 0056-0058 preview panel to preview output of a node in a pipeline such that nodes represent machine learning algorithms and ml model and ml workflows)
causing first data generated by the node of the data processing pipeline to be routed to the machine learning model; (Du see paragraph 0028 0030 0047 0048 0056-0058 0087 0089 0248 0254 0257 0315 0316 in a pipeline the output of one node is the input to another such that nodes represent machine learning algorithms, output of one node is input to another ml model node)
obtaining output data generated by the machine learning model in response to the first data being provided as an input to the machine learning model; and (Du see paragraph 0028 0030 0047 0048 0056-0058 0087 0089 0248 0254 0257 0315 0316 in a pipeline the output of one node is the input to another such that nodes represent machine learning algorithms and ml model and ml workflows allowing user to upload datasets or nodes having input data and previewing the response of the ml model output)
causing the user interface to display a preview of a portion of the output data that comprises a sampling of one or more different types of data output by the machine learning model. (Du see paragraph 0047 0048 0056-0058 0253 preview panel to preview output of a node in a pipeline such that nodes represent machine learning algorithms and ml model and ml workflows and displaying output to be catered to different types of outcomes)
Regarding claim 2, Du teaches: wherein causing the user interface to display a preview further comprises causing the user interface to display the preview without writing the output data to at least one destination specified by the graph. (Du see paragraph 0253 real time changes outputting image on GUI, no mention of writing to destination specified on graph reads on negative limitation)
Regarding claim 3, Du teaches: further comprising retrieving input data from at least one source specified by the graph in response to the request to activate the preview mode. (Du see paragraphs 0055-0057 0087 0089 0239 0254 preview a node’s output based on modifications to nodes in a graph such that nodes to include live camera to capture video stream such that node is in graph)
Regarding claim 4, Du teaches: wherein the first data comprises live data streamed from a source specified by the graph. (Du see paragraph 0089 0287 0305 live camera stream, live image from camera, live video stream)
Regarding claim 5, Du teaches: retrieving input data from at least one source specified by the graph in response to the request to activate the preview mode; and (Du see paragraph 0315 0316 input data from computing device or platform or inputs)
causing the input data to be transformed according to the node of the data processing pipeline to generate the first data. (Du see paragraph 0047 0104 nodes to include data transformation, reformatting text input)
Regarding claim 9, Du teaches: wherein the first data comprises a stream of data items generated by the node of the data processing pipeline in sequence, and wherein applying the first data as an input to the machine learning model further comprises applying, in sequence, each of the data items of the stream of data items as an input to the machine learning model to generate the output data. (Du see paragraph 0028 0030 0047 0048 0056-0058 0089 0248 0254 0257 0287 0305 in a pipeline the output of one node is the input to another such that nodes represent machine learning algorithms and ml model and ml workflows allowing user to upload datasets and previewing the response of the ml model output such that inputs include live camera stream, live image from camera, live video stream which reads on sequence)
Regarding claim 15, Du teaches: wherein the sampling of the one or more different types of data output by the machine learning model comprises a sampling of one or more different label types found in the output data. (Du see paragraph 0058 different types of outcomes such as data visualizations, graphical simulations, text outputs)
Regarding claims 16, 17, 19, 20, note the rejection of claim(s) 1-5, 9, 15-17. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under same prior-art teachings.
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 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 of this title, 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.
Claim(s) 6-8, 18 are/is rejected under 35 U.S.C. 103 as being unpatentable over Du et al. US2025/0094137 in view of Floratou et al. US2021/0216905 in view of Numaflow “Blackhole Sink” 3/31/2023 https://numaflow.numaproj.io/user-guide/sinks/blackhole/
Regarding claim 6, Du does not teach: further comprising transmitting an abstract syntax tree (AST) of the data processing pipeline to an intake system, wherein the intake system produces an augmented AST by causing a function of the graph that writes to an external database to drop received data instead of writing the received data to the external database and by adding a preview node to the graph in association with the machine learning model.
However, Floratou teaches: further comprising transmitting an abstract syntax tree (AST) of the data processing pipeline to an intake system, wherein the intake system produces an augmented AST of the graph and by adding a preview node to the graph in association with the machine learning model. (Floratou see paragraph 0007 0008 0129-0132 0143 parsing ML model into a workflow representation and ML model building abstract syntax tree based on relationships between nodes of the AST in a directed 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 a computational pipeline as taught by Du as modified to include an AST as taught by Floratou for the predictable result of more efficiently organizing and managing data.
Du as modified does not teach: by causing a function that writes to an external database to drop received data instead of writing the received data to the external database
Numaflow teaches: by causing a function that writes to an external database to drop received data instead of writing the received data to the external database (Numaflow, blackhole sink where the output is drained without writing to any sink)
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 a computational pipeline as taught by Du as modified to include a blackhole sink as taught by Numaflow for the predictable result of more efficiently organizing and managing data.
Regarding claim 7, Du does not teach: further comprising transmitting an abstract syntax tree (AST) of the data processing pipeline to an intake system, wherein the intake system produces an augmented AST by causing a function of the graph that writes to an external database to drop received data instead of writing the received data to the external database and by adding a preview node to the graph in association with the machine learning model, and wherein the intake system runs a job using the augmented AST that results in the first data being transmitted to the preview node
However, Floratou teaches: further comprising transmitting an abstract syntax tree (AST) of the data processing pipeline to an intake system, wherein the intake system produces an augmented AST of the graph and by adding a preview node to the graph in association with the machine learning model, and wherein the intake system runs a job using the augmented AST that results in the first data being transmitted to the preview node. (Floratou see paragraph 0007 0008 0129-0132 0143 parsing ML model into a workflow representation and ML model building abstract syntax tree based on relationships between nodes of the AST in a directed 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 a computational pipeline as taught by Du as modified to include an AST as taught by Floratou for the predictable result of more efficiently organizing and managing data.
Du as modified does not teach: by causing a function that writes to an external database to drop received data instead of writing the received data to the external database
Numaflow teaches: by causing a function that writes to an external database to drop received data instead of writing the received data to the external database (Numaflow, blackhole sink where the output is drained without writing to any sink)
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 a computational pipeline as taught by Du as modified to include a blackhole sink as taught by Numaflow for the predictable result of more efficiently organizing and managing data.
Regarding claim 8, Du teaches: wherein applying the first data as an input to the machine learning model to generate output data further comprises applying, by the preview node, the first data as an input to the machine learning model to generate output data. (Du see paragraph 0028 0030 0047 0048 0056-0058 0087 0089 0248 0254 0257 0315 0316 in a pipeline the output of one node is the input to another such that nodes represent machine learning algorithms and ml model and ml workflows allowing user to upload datasets or nodes having input data and previewing the response of the ml model output)
Du does not teach: further comprising transmitting an abstract syntax tree (AST) of the data processing pipeline to an intake system, wherein the intake system produces an augmented AST by causing a function of the graph that writes to an external database to drop received data instead of writing the received data to the external database and by adding a preview node to the graph in association with the machine learning model, and wherein the intake system runs a job using the augmented AST that results in the first data being transmitted to the preview node and
However, Floratou teaches: further comprising transmitting an abstract syntax tree (AST) of the data processing pipeline to an intake system, wherein the intake system produces an augmented AST of the graph and by adding a preview node to the graph in association with the machine learning model, and wherein the intake system runs a job using the augmented AST that results in the first data being transmitted to the preview node and. (Floratou see paragraph 0007 0008 0129-0132 0143 parsing ML model into a workflow representation and ML model building abstract syntax tree based on relationships between nodes of the AST in a directed 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 a computational pipeline as taught by Du as modified to include an AST as taught by Floratou for the predictable result of more efficiently organizing and managing data.
Du as modified does not teach: by causing a function that writes to an external database to drop received data instead of writing the received data to the external database
Numaflow teaches: by causing a function that writes to an external database to drop received data instead of writing the received data to the external database (Numaflow, blackhole sink where the output is drained without writing to any sink)
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 a computational pipeline as taught by Du as modified to include a blackhole sink as taught by Numaflow for the predictable result of more efficiently organizing and managing data.
Regarding claim 18, see rejection of claim 8
Claim(s) 10-12 are/is rejected under 35 U.S.C. 103 as being unpatentable over Du et al. US2025/0094137 in view of Murakoshi US2009/0028025
Regarding claim 10, Du teaches: wherein applying the first data as an input to the machine learning model to generate output data further comprises applying the first data as the input to the machine learning model (Du see paragraph 0028 0030 0047 0048 0056-0058 0087 0089 0248 0254 0257 0315 0316 in a pipeline the output of one node is the input to another such that nodes represent machine learning algorithms and ml model and ml workflows allowing user to upload datasets or nodes having input data and previewing the response of the ml model output)
Du does not teach: for a first period of time
Murakoshi teaches: for a first period of time (Murakoshi see paragraph 0045 0046 input data supplied with continuous period of time based on characteristic quantity that is includes in a first period’s worth of input data and continuous input data for a second period of time longer than the first period of time)
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 a computational pipeline as taught by Du as modified to include input data based on time period as taught by Murakoshi for the predictable result of more efficiently organizing and managing data.
Regarding claim 11, Du teaches: wherein applying the first data as an input to the machine learning model to generate output data further comprises applying the first data as the input to the machine learning model (Du see paragraph 0028 0030 0047 0048 0056-0058 0087 0089 0248 0254 0257 0315 0316 in a pipeline the output of one node is the input to another such that nodes represent machine learning algorithms and ml model and ml workflows allowing user to upload datasets or nodes having input data and previewing the response of the ml model output)
Du does not teach: for a first period of time, and wherein the first data corresponds to a second period of time.
Murakoshi teaches: for a first period of time, and wherein the first data corresponds to a second period of time. (Murakoshi see paragraph 0045 0046 input data supplied with continuous period of time based on characteristic quantity that is includes in a first period’s worth of input data and continuous input data for a second period of time longer than the first period of time)
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 a computational pipeline as taught by Du as modified to include input data based on time period as taught by Murakoshi for the predictable result of more efficiently organizing and managing data.
Regarding claim 12, Du teaches: wherein applying the first data as an input to the machine learning model to generate output data further comprises applying the first data as the input to the machine learning model (Du see paragraph 0028 0030 0047 0048 0056-0058 0087 0089 0248 0254 0257 0315 0316 in a pipeline the output of one node is the input to another such that nodes represent machine learning algorithms and ml model and ml workflows allowing user to upload datasets or nodes having input data and previewing the response of the ml model output)
Du does not teach: for a first period of time, and wherein the first data corresponds to a second period of time greater than the first period of time
Murakoshi teaches: for a first period of time, and wherein the first data corresponds to a second period of time greater than the first period of time (Murakoshi see paragraph 0045 0046 input data supplied with continuous period of time based on characteristic quantity that is includes in a first period’s worth of input data and continuous input data for a second period of time longer than the first period of time)
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 a computational pipeline as taught by Du as modified to include input data based on time period as taught by Murakoshi for the predictable result of more efficiently organizing and managing data.
Claim(s) 13 and 14 are/is rejected under 35 U.S.C. 103 as being unpatentable over Du et al. US2025/0094137 in view of Crabtree et al. US2017/0124501
Regarding claim 13, Du teaches: wherein the first data comprises a stream of data items generated by the node of the data processing pipeline in sequence, wherein applying the first data as an input to the machine learning model to generate output data further comprises:
for each data item of the stream of data items in sequence, applying the respective data item as an input to the machine learning model to generate a portion of the output data; and (Du see paragraph 0028 0030 0047 0048 0056-0058 0089 0248 0254 0257 0287 0305 in a pipeline the output of one node is the input to another such that nodes represent machine learning algorithms and ml model and ml workflows allowing user to upload datasets and previewing the response of the ml model output such that inputs include live camera stream, live image from camera, live video stream which reads on sequence)
Du does not teach: determining, a first period of time after an initial portion of the output data is generated, that no portion of the output data corresponds to a first type of label.
Crabtree teaches: determining, a first period of time after an initial portion of the output data is generated, that no portion of the output data corresponds to a first type of label. (Crabtree see paragraph 0010 filtering data stream to remove records with absence of all information for data of a predetermined amount of time)
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 a computational pipeline as taught by Du as modified to include filtering data streams as taught by Crabtree for the predictable result of more efficiently organizing and managing data.
Regarding claim 14, Du teaches: wherein the first data comprises a stream of data items generated by the node of the data processing pipeline in sequence, wherein applying the first data as an input to the machine learning model to generate output data further comprises:
for each data item of the stream of data items in sequence, applying the respective data item as an input to the machine learning model to generate a portion of the output data;
to the machine learning model (Du see paragraph 0028 0030 0047 0048 0056-0058 0089 0248 0254 0257 0287 0305 in a pipeline the output of one node is the input to another such that nodes represent machine learning algorithms and ml model and ml workflows allowing user to upload datasets and previewing the response of the ml model output such that inputs include live camera stream, live image from camera, live video stream which reads on sequence)
Du does not teach: determining, a first period of time after an initial portion of the output data is generated, that no portion of the output data corresponds to a first type of label; and
stopping application of the stream of data items as an input
Crabtree teaches: determining, a first period of time after an initial portion of the output data is generated, that no portion of the output data corresponds to a first type of label; and
stopping application of the stream of data items as an input (Crabtree see paragraph 0010 filtering data stream to remove records with absence of all information for data of a predetermined amount of time)
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 a computational pipeline as taught by Du as modified to include filtering data streams as taught by Crabtree for the predictable result of more efficiently organizing and managing data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN S LIN whose telephone number is (571)270-0612. The examiner can normally be reached on M-F 9-5.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLEN S LIN/Primary Examiner, Art Unit 2153