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 .
Claims 1-20 have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7/21/2023 and 3/3/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
The information disclosure statement filed 5/22/2024 fails to comply with 37 CFR 1.98(a)(3)(i) because it does not include a concise explanation of the relevance, as it is presently understood by the individual designated in 37 CFR 1.56(c) most knowledgeable about the content of the information, of each reference listed that is not in the English language. It has been placed in the application file, but the information referred to therein has not been considered.
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. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Claims 1-10
Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG” — see MPEP 2106.04(II) and 2106.04(d)), requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a machine.
Claim 1
Prong 1 of Step 2A of the 2019 PEG requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea underlined.
1. An apparatus for split processing of a model, the apparatus comprising: a memory comprising instructions; and a processor electrically connected to the memory and configured to execute the instructions, wherein, when the instructions are executed by the processor, the processor is configured to perform a plurality of operations, wherein the plurality of operations comprises:
obtaining information on a plurality of computing nodes that uses at least one layer among a plurality of layers of a model for an artificial intelligence (AI)-based service;
obtaining a requirement for the AI-based service; and
controlling split processing of the model based on the information and the requirement.
The broadest reasonable interpretation of the controlling split processing step is that the step falls within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. The claim does not provide any details about how the split processing is controlled, and the plain meaning of “controlling” encompasses mental observations or evaluations, e.g., a computer programmer’s decision to split a model.
Prong 2 of Step 2A of the 2019 PEG requires the examiner to determine if the claims recite additional element(s) or a combination of additional elements which integrate the abstract idea into a practical application. This requires additional element(s) in the claim to apply, rely on, or use the abstract idea in a manner that imposes a meaningful limit on the abstract idea, such that the claim is more than a drafting effort designed to monopolize the abstract idea. In Claim 1 above, the judicial exception is not integrated into a practical application.
The limitations “obtaining information …“ and “obtaining a requirement … “ are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05. The claim omits any details as to how the action solves a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to implement the abstract idea recited in step (b), which is equivalent to adding the words “apply it” to the recited judicial exception.
The claim includes the additional elements of computer memory and a processor for performing the operations. The computer is recited at a high level of generality. In limitations “obtaining information …“ and “obtaining a requirement … “, the computer is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). In limitation “controlling split processing,” the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Step 2B of the 2019 PEG requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself.
As explained with respect to Step 2A, Prong 2 above, there are two additional elements. Additional elements “obtaining information …“ and “obtaining a requirement … “ were both found to be insignificant extra-solution activity in Step 2A, Prong Two. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). The additional elements are recited at a high level of generality. These elements amount to receiving or transmitting data and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. As discussed in Step 2A, Prong 2 above, the recitation of a computer to perform limitations (a), (b), and (c) amounts to no more than mere instructions to apply the exception using a generic computer component
The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2A, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Therefore, claim 1 is rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Claims 2-3
Claims 2-3 are copied below.
2. The apparatus of claim 1, wherein the obtaining of the information comprises receiving at least one of first information on computing of the plurality of computing nodes or second information on a state of the plurality of computing nodes from the plurality of computing nodes.
3. The apparatus of claim 2, wherein the second information comprises information on mobility of the plurality of computing nodes.
These claims further describe the data gathering originally provided in claim 1 and are not integrated into a practical application and are not significantly more than the abstract idea itself for the same reasons provided above.
Claims 4-6
Claims 4-6 are copied below.
4. The apparatus of claim 1, wherein the requirement comprises at least one of computing latency or computing accuracy of the plurality of computing nodes that are required for the AI-based service.
5. The apparatus of claim 4, wherein the computing latency comprises at least one of computing latency of the plurality of computing nodes in a learning process or computing latency of the plurality of computing nodes in an inference process.
6. The apparatus of claim 4, wherein the computing accuracy comprises at least one of computing accuracy of the plurality of computing nodes in a learning process or computing accuracy of the plurality of computing nodes in an inference process.
These claims further describe the requirement limitation of claim 1 and are related to the data gathering originally provided in claim 1. These limitations merely indicate a field of use or technological environment in which the judicial exception is performed, and fails to add an inventive concept to the claims. See MPEP 2106.05(h). The claims are not integrated into a practical application and are not significantly more than the abstract idea itself for the same reasons provided above.
Claim 7
Claim 7 is copied below with the limitations belonging to an abstract idea underlined:
7. The apparatus of claim 1, wherein the controlling of the split processing of the model comprises determining a split point for the plurality of layers.
This limitation falls within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. The claim does not provide any details about how the split processing is determined, and the plain meaning of “determining” encompasses mental observations or evaluations, e.g., a computer programmer’s decision to split a model. No additional elements are provided. The judicial exception is not integrated into a practical application and the claim does not amount to significantly more than the abstract idea itself.
Claims 8-10
Claims 8-10 are copied below:
8. The apparatus of claim 1, wherein the controlling of the split processing of the model comprises transmitting data related to a first computing node that is included in the plurality of computing nodes to a second computing node that is not included in the plurality of computing nodes.
9. The apparatus of claim 8, wherein the transmitting of the data comprises transmitting data on the model of the first computing node to the second computing node.
10. The apparatus of claim 9, wherein the transmitting of the data on the model to the second computing node comprises: requesting data on the model from the first computing node based on information related to at least one computing node other than the first computing node among the plurality of computing nodes; and transmitting the data on the model to the second computing node.
These claims further describe the controlling of claim 1 by introducing the step of transmitting data. As similarly noted above with respect to claim 1, transmitting data is a form of data gathering and is not integrated into a practical application and is not significantly more than the abstract idea itself for the same reasons provided above.
Claims 11-20
Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG” — see MPEP 2106.04(II) and 2106.04(d)), requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process.
The remaining limitations of claim 11 are similar to those of claim 1. Similarly, claim 11 is directed to an abstract idea. The claim does not have additional elements that integrate the abstract idea into a practical application, and do not amount to significantly more than the judicial exception. The claim is not patent eligible for the same reasons noted above with respect to claim 1.
Claims 12-20 are dependent upon claim 11. They are similar to claims 2-10 and are rejected for the same reasons noted above with respect to those 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication 20220222584 by Nimmagadda et al. ("Nimmagadda").
In regard to claim 1, Nimmagadda discloses:
1. An apparatus for split processing of a model, the apparatus comprising: a memory comprising instructions; and a processor electrically connected to the memory and configured to execute the instructions, wherein, when the instructions are executed by the processor, the processor is configured to perform a plurality of operations, wherein the plurality of operations comprises: Nimmagadda, ¶ 0125, “an apparatus for heterogeneous compute-based artificial intelligence model partitioning comprising: at least one processor; and memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform operations …”
obtaining information on a plurality of computing nodes that uses at least one layer among a plurality of layers of a model for an artificial intelligence (AI)-based service; Nimmagadda Fig. 4 element 410, “fulfill requests and responses for various client endpoints 410.” ¶ 0106, “The compute normalizer 840 estimates and provides metrics including, by way of example and not limitation, precision factor, efficiency factor, and theoretical maximum compute.”
obtaining a requirement for the AI-based service; and Nimmagadda ¶ 0044, “… receiving key performance indicator (KPI) targets based on SLA contracts. “
controlling split processing of the model based on the information and the requirement. Nimmagadda ¶ 0043, “… respective edge slices 432, 434 are partitioned according to the needs of each container.” ¶ 0109, “The heterogeneous IR partitioner 835 takes input from the compute analyzer 820 and the compute normalizer 840 to identify optimal partitioning points of an AI model.”
In regard to claim 2, Nimmagadda also discloses:
2. The apparatus of claim 1, wherein the obtaining of the information comprises receiving at least one of first information on computing of the plurality of computing nodes or second information on a state of the plurality of computing nodes from the plurality of computing nodes. ¶ 0028, “varying requirements in terms of: (a) Priority (throughput and/or latency) and Quality of Service (QoS) (e.g., traffic for an autonomous car may have higher priority than a temperature sensor in terms of response time requirement; or, a performance sensitivity/bottleneck may exist at a compute/accelerator, memory, storage, or network resource, depending on the application); (b) Reliability and Resiliency (e.g., some input streams need to be acted upon and the traffic routed with mission-critical reliability, where as some other input streams may be tolerate an occasional failure, depending on the application); and (c) Physical constraints (e.g., power, cooling and form-factor).”
In regard to claim 3, Nimmagadda also discloses:
3. The apparatus of claim 2, wherein the second information comprises information on mobility of the plurality of computing nodes. ¶ 0050, e.g. “use cases involving mobility.” Also¶ 0053, “Further, the data for a specific client or application can move from edge to edge based on changing conditions (e.g., based on acceleration resource availability, following the car movement, etc.). For instance, based on the “rate of decay” of access, prediction can be made to identify the next owner to continue, or when the data or computational access will no longer be viable.”
In regard to claim 4, Nimmagadda also discloses:
4. The apparatus of claim 1, wherein the requirement comprises at least one of computing latency or computing accuracy of the plurality of computing nodes that are required for the AI-based service. Nimmagadda ¶ 0028, “The various use cases 205 may access resources under usage pressure from incoming streams, due to multiple services utilizing the edge cloud. To achieve results with low latency, the services executed within the edge cloud 110 balance varying requirements.” Also ¶ 0117, “A precision factor (e.g., the precision factor 845 as described in FIG. 8, etc.) may be calculated using the original processing cycles and the precision processing cycles (e.g., at operation 1030).”
In regard to claim 5, Nimmagadda also discloses:
5. The apparatus of claim 4, wherein the computing latency comprises at least one of computing latency of the plurality of computing nodes in a learning process or computing latency of the plurality of computing nodes in an inference process. ¶ 0025, “For example, edge gateway servers may be equipped with pools of memory and storage resources to perform computation in real-time for low latency use-cases (e.g., autonomous driving or video surveillance) for connected client devices.” ¶ 0084, “… AI processing (including machine learning, training, inferencing, and classification operations) …” “Optimizing the model during inference may improve the compute efficiency of the model and may allow for more effective partitioning of the model for distribution amongst devices having different hardware specifications, resources, types, etc.”
In regard to claim 6, Nimmagadda also discloses:
6. The apparatus of claim 4, wherein the computing accuracy comprises at least one of computing accuracy of the plurality of computing nodes in a learning process or computing accuracy of the plurality of computing nodes in an inference process. ¶ 0084, “… AI processing (including machine learning, training, inferencing, and classification operations) …” ¶ 0100, “Optimizing the model during inference may improve the compute efficiency of the model and may allow for more effective partitioning of the model for distribution amongst devices having different hardware specifications, resources, types, etc.” ¶ 0107, “… the compute may be lower because of the conversion to lower precision. … The precision factor 845 estimates how much compute reduction would be realized on average for a given hardware device.”
In regard to claim 7, Nimmagadda also discloses:
7. The apparatus of claim 1, wherein the controlling of the split processing of the model comprises determining a split point for the plurality of layers. ¶ 0021, “To address this technical challenge, the intermediate representation of the model graph is analyzed for compute and then partitions/subgraphs are identified to schedule across heterogeneous accelerator compute units present in the edge nodes.” ¶ 0100, “Input models may include a variety of machine learning models that may include layers, filters, nodes, and the like.” ¶ 0109, “The heterogeneous IR partitioner 835 takes input from the compute analyzer 820 and the compute normalizer 840 to identify optimal partitioning points of an AI model.”
In regard to claim 8, Nimmagadda also discloses:
8. The apparatus of claim 1, wherein the controlling of the split processing of the model comprises transmitting data related to a first computing node that is included in the plurality of computing nodes to a second computing node that is not included in the plurality of computing nodes. See Fig. 10, element 1005 and ¶ 0117, “Attributes of an input processing device (e.g., the input processing devices 895 as described in FIG. 8, etc.) may be obtained (e.g., by the compute normalizer 840 as described in FIG. 8, etc.) (e.g., at operation 1005).”
In regard to claim 9, Nimmagadda also discloses:
9. The apparatus of claim 8, wherein the transmitting of the data comprises transmitting data on the model of the first computing node to the second computing node. ¶ 0117, “An input model may be obtained (e.g., at operation 1010).”
In regard to claim 10, Nimmagadda also discloses:
10. The apparatus of claim 9, wherein the transmitting of the data on the model to the second computing node comprises: requesting data on the model from the first computing node based on information related to at least one computing node other than the first computing node among the plurality of computing nodes; and See Figs. 8 and 10 along with ¶ 0109 “If the overall compute (e.g., in FLOPs) present in the model is ‘f’ that needs to be partitioned across N input processing devices (e.g., device_1 880, device_2 885, and device_n 890), the flops ‘fn’ of the partitioned model are determined that need to be executed on an nth device using the formula fn=f*Cn/Σ(Ci), i=1 to N where fn is the FLOPs to be executed on the device, f is the total number of FLOPs of the model, Ci is the compute normalization factor of ith device, and N is the number of devices used for partitioning.”
transmitting the data on the model to the second computing node. ¶ 0117 as cited above.
In regard to claim 11, Nimmagadda discloses:
11. A method for split processing of a model, the method comprising: See Fig. 11, broadly depicting a method. All further limitations of claim 11 have been addressed in the above rejection of claim 1.
In regard to claims 12-20, parent claim 11 is addressed above.
All further limitations of claims 12-20 have been addressed in the above rejections of claims 2-10, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
"Latency-Driven Model Placement for Efficient Edge Intelligence Service" by Lin et al. See Abstract, “Model placement contains two procedures: model partition and sub-models assignment. In our method, we first convert the model into execution graphs and propose a novel latency-driven multilevel graph partition for the model. Then the partitioned sub-models are heuristically assigned to available processors.”
U.S. Patent Application Publication 20190286973 by Kovvuri et al. See ¶ 0035, “In some examples of the disclosed technology, a compiler is provided to partition a DNN model into a number of subgraphs. One or more, or all of the subgraphs can be run using specialized hardware to provide acceleration. Other subgraphs that are not mapped to specialized hardware can be implemented using a general-purpose processor”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James D Rutten whose telephone number is (571)272-3703. The examiner can normally be reached M-F 9:00-5:30 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James D. Rutten/Primary Examiner, Art Unit 2121
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