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 .
DETAILED ACTION
Status of Claims
This action is in response to the application filed on May 11, 2023.
This application claims priority to application 16/248,662 (now pat. 11,687,824), filed January 15, 2019.
Claims 1-21 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 11, 2023 has been considered by the examiner.
Claim Rejections - 35 USC § 112
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.
Claim 7 is 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. Claim 7 recites the limitation "the plurality of data clusters whose centroids are within a threshold distance from the query data instance" in line 1. There is insufficient antecedent basis for this limitation in the claim. Further, it is not clear how the centroids correspond to the data clusters as there is no previous discussion about calculating any centroid or determining a position of a data query instance, with renders the claim indefinite.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g. mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined that step 2A, Prong that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
According to Step 1 of the analysis, in the instant case claims 1-7 are directed to a method, claims 8-14 are directed to a non-transitory computer readable storage medium, and claims 15-21 are directed to a computer system. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Independent claim 1: considering Step 2A, Prong One, the limitations in claim 1 including: “identifying a data cluster in a plurality of data clusters to which the query data instance belongs, wherein each data cluster in the plurality of data clusters corresponds to a portion of a data space of the ML task, and wherein the identifying comprises determining that the query data instance is associated with the identified data cluster,” covers mental processes but for the recitation of generic computer components.
MPEP 2106.04.(a)(2)(III) details “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The claimed “identifying” is an observation, evaluation, and judgment.
Claim 1 contains additional elements “receiving, by a computer system that is part of a distributed machine learning system,” a plurality of ML workers,” transmitting, by the computer system, the query data instance to an ML worker in the plurality of ML workers that is assigned to the identified data cluster,” and receiving a decision or classification result for the query data instance from the ML worker.”
Considering Step 2A, Prong Two, the judicial exception in claim 1 is not integrated into a practical application. The “receiving” and “transmitting” limitations do not integrate the judicial exception into a practical application because the limitations are insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g). Further, the “computer system that is part of a distributed machine learning system” is a generic computer component that does not integrate the judicial exception into a practical application; see MPEP 2106.05(b).
Considering Step 2B, the “receiving” and “transmitting” limitations are well-understood, routine, conventional activity, receiving or transmitting data over a network; see MPEP 2106.05(d)(II). Further, the “computer system that is part of a distributed machine learning system” is a generic computer component that does not amount to significantly more; see MPEP 2106.05(b).
Therefore, claim 1 is ineligible.
Claims 2-4 recite only additional elements and are not rejected under 101. Streamlined analysis Step 2A, Prong One, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?” would be No; see MPEP 2106.04. If the language from these claims were to be incorporated into claim 1 the additional elements in claims 2-4 would be analyzed to determine whether they integrate the judicial exception into a practical application and/or amount to significantly more.
Claim 5, dependent on claim 1, recites “identifying a quorum of data clusters to which the query data instance belong” is a mental step, observation, evaluation, and judgment. Claim 5 does not include any new additional elements.
Claim 6, dependent on claim 5, recites “compiling the decision or classification result received from the ML worker and the one or more other decision or classification results received from the one or more other ML workers into a single final result” is a mental step, observation, evaluation, and judgment. Claim 6 does not include any new additional elements.
Claim 7 recites only additional elements and are not rejected under 101. Streamlined analysis Step 2A, Prong One, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?” would be No; see MPEP 2106.04. If the language from these claims were to be incorporated into claim 1 the additional elements in claim 7 would be analyzed to determine whether they integrate the judicial exception into a practical application and/or amount to significantly more.
Independent claim 8: considering Step 2A, Prong One, the limitations in claim 8 including: “identifying a data cluster in a plurality of data clusters to which the query data instance belongs, wherein each data cluster in the plurality of data clusters corresponds to a portion of a data space of the ML task, and wherein the identifying comprises determining that the query data instance is associated with the identified data cluster,” covers mental processes but for the recitation of generic computer components.
MPEP 2106.04.(a)(2)(III) details “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The claimed “identifying” is an observation, evaluation, and judgment.
Claim 8 contains additional elements “a non-transitory computer readable storage medium,” “receiving, by a computer system that is part of a distributed machine learning system,” a plurality of ML workers,” “transmitting, by the computer system, the query data instance to an ML worker in the plurality of ML workers that is assigned to the identified data cluster,” and receiving a decision or classification result for the query data instance from the ML worker.”
Considering Step 2A, Prong Two, the judicial exception in claim 1 is not integrated into a practical application. The “receiving” and “transmitting” limitations do not integrate the judicial exception into a practical application because the limitations are insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g). Further, the “non-transitory computer readable storage medium” and “computer system that is part of a distributed machine learning system” are generic computer components that do not integrate the judicial exception into a practical application; see MPEP 2106.05(b).
Considering Step 2B, the “receiving” and “transmitting” limitations are well-understood, routine, conventional activity, receiving or transmitting data over a network; see MPEP 2106.05(d)(II). Further, the “computer system that is part of a distributed machine learning system” is a generic computer component that does not amount to significantly more; see MPEP 2106.05(b).
Therefore, claim 8 is ineligible.
Claims 9-11 recite only additional elements and are not rejected under 101. Streamlined analysis Step 2A, Prong One, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?” would be No; see MPEP 2106.04. If the language from these claims were to be incorporated into claim 8 the additional elements in claims 9-11would be analyzed to determine whether they integrate the judicial exception into a practical application and/or amount to significantly more.
Claim 12, dependent on claim 8, recites “identifying a quorum of data clusters to which the query data instance belong” is a mental step, observation, evaluation, and judgment. Claim 12 does not include any new additional elements.
Claim 13, dependent on claim 12, recites “compiling the decision or classification result received from the ML worker and the one or more other decision or classification results received from the one or more other ML workers into a single final result” is a mental step, observation, evaluation, and judgment. Claim 13 does not include any new additional elements.
Claim 14 recites only additional elements and are not rejected under 101. Streamlined analysis Step 2A, Prong One, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?” would be No; see MPEP 2106.04. If the language from these claims were to be incorporated into claim 8 the additional elements in claim 14 would be analyzed to determine whether they integrate the judicial exception into a practical application and/or amount to significantly more.
Independent claim 15: considering Step 2A, Prong One, the limitations in claim 15 including: “identifying a data cluster in a plurality of data clusters to which the query data instance belongs, wherein each data cluster in the plurality of data clusters corresponds to a portion of a data space of the ML task, and wherein the identifying comprises determining that the query data instance is associated with the identified data cluster,” covers mental processes but for the recitation of generic computer components.
MPEP 2106.04.(a)(2)(III) details “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The claimed “identifying” is an observation, evaluation, and judgment.
Claim 15 contains additional elements “a computer system that is part of a distributed machine learning system,” “a processor,” “a non-transitory computer readable medium,” “receive a query data instance for an ML task,” “transmit the query data instance to an ML worker in the plurality of ML workers that is assigned to the identified data cluster,” and receive a decision or classification result for the query data instance from the ML worker.”
Considering Step 2A, Prong Two, the judicial exception in claim 1 is not integrated into a practical application. The “receive” and “transmit” limitations do not integrate the judicial exception into a practical application because the limitations are insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g). Further, the “computer system that is part of a distributed machine learning system,” “a processor,” and “non-transitory computer readable medium,” are generic computer components that do not integrate the judicial exception into a practical application; see MPEP 2106.05(b).
Considering Step 2B, the “receiving” and “transmitting” limitations are well-understood, routine, conventional activity, receiving or transmitting data over a network; see MPEP 2106.05(d)(II). Further, the “a computer system that is part of a distributed machine learning system,” “a processor,” “a non-transitory computer readable medium,” are generic computer component that do not amount to significantly more; see MPEP 2106.05(b).
Therefore, claim 15 is ineligible.
Claims 16-18 recite only additional elements and are not rejected under 101. Streamlined analysis Step 2A, Prong One, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?” would be No; see MPEP 2106.04. If the language from these claims were to be incorporated into claim 15 the additional elements in claims 16-18would be analyzed to determine whether they integrate the judicial exception into a practical application and/or amount to significantly more.
Claim 19, dependent on claim 15, recites “identifying a quorum of data clusters to which the query data instance belong” is a mental step, observation, evaluation, and judgment. Claim 8 does not include any new additional elements.
Claim 20, dependent on claim 19, recites “compiling the decision or classification result received from the ML worker and the one or more other decision or classification results received from the one or more other ML workers into a single final result” is a mental step, observation, evaluation, and judgment. Claim 20 does not include any new additional elements.
Claim 21 recites only additional elements and are not rejected under 101. Streamlined analysis Step 2A, Prong One, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?” would be No; see MPEP 2106.04. If the language from these claims were to be incorporated into claim 15 the additional elements in claim 21 would be analyzed to determine whether they integrate the judicial exception into a practical application and/or amount to significantly more.
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) 1-5, 8-12, and 15-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bendre et al., U.S. Patent 10,380,504 (Bendre).
With respect to independent claim 1 Bendre teaches:
A method comprising:
receiving, by a computer system that is part of a distributed machine learning (ML) system comprising a plurality of ML workers, a query data instance for an ML task (Bendre teaches ML models making ML predictions per customer instance basis; see 17:43-60. Bendre teaches receiving a prediction request from a device associated with a customer instance; see 17:43-60. Bendre also teaches receiving information indicating training data that is associated with the computing system and that is to be used as a basis for generating a machine learning model; see figure 7 and 32:32-43. Bendre further teaches a plurality of trainer devices (ML workers) in figure 7 and 32:32-47.);
identifying, by the computer system, a data cluster in a plurality of data clusters corresponds to a portion of a data space of the ML task, and wherein the identifying comprises determining that the query data instance is associated with the identified data cluster (Bendre teaches receiving a prediction request from a device associated with a customer instance and submitting the request for the network system to carry out the prediction using the generated ML model, where the ML model and ML prediction are accessible only to client devices associated with the particular customer instance; see 17:43-60. Bendre further teaches that a customer instances communicates with a scheduler and trainer to receive the solution definition; see 18:48-19:3. The scheduler receives an ML training request and triggers assignment of an ML trainer process to generate an ML model amongst a plurality of trainer devices each configured to execute one or more ML trainer processes; see 18:48-19:3 and 19:26-41. Bendre teaches receiving data and determining, using a scheduler and training controller, which ML trainer device (which could include server clusters) should be implemented to train the ML model. This is partitioning data space for an ML task, as claimed. The claim does not specify how the clustering algorithm determines which instance the training data belongs and the scheduler and trainer controller taught by Bendre are computer implemented modules processing program code (see 35:45-57), and considered algorithms.);
transmitting, by the computer system, the query data instance to an ML worker in the plurality of ML workers that is assigned to the identified data cluster (Bendre teaches a computing system that may be a cluster of computing devices, such as a server cluster, in figure 7 and 32:19-26. Bendre further teaches that server devices may be configured to transmit data to and receive data from cluster data storage 204; see figure 2 and 9:11-15. Further, Bendre teaches the ML trainer process can query the trainer device which may specify one or more ML trainer processes (see 24:30-40) and receiving a prediction request from a device associated with a customer instance and submitting the request for the network system to carry out the prediction using the generated ML model; see 17:43-60.); and
in response to the transmitting, receiving, by the computer system, a decision or classification result for the query data instance from the ML worker (Bendre teaches trained ML models are used to generate ML predictions on new data; see 17:16-27 and the example in 17:18-42.).
With respect to claim 2, the rejection of claim 1 is incorporated. Further, Bendre teaches:
wherein each ML worker in the plurality of ML workers maintains an ML model that is trained on training data from a corresponding data cluster in the plurality of data clusters (Bendre teaches receiving a solution definition that identifies training data to be used for generating an ML model and information specifying a target variable to be precited using the ML model; see 18:48-19:3. Bendre further teaches that a customer instances communicates with a scheduler and trainer to receive the solution definition; see 18:48-19:3. The scheduler receives an ML training request and triggers assignment of an ML trainer process to generate an ML model amongst a plurality of trainer devices each configured to execute one or more ML trainer processes; see 18:48-19:3 and 19:26-41.).
With respect to independent claim 8 Bendre teaches the limitations as disclosed in the rejection of claim 1 above. Further, Bendre teaches the additional limitations:
A non-transitory computer readable storage medium (Bendre teaches implementation details, including a non-transitory computer readable medium in 7:33-39.)
With respect to independent claim 15 Bendre teaches the limitations as disclosed in the rejection of claim 1 above. Further, Bendre teaches the additional limitations:
A computer system that is part of a distributed machine learning (ML) system comprising a plurality of ML workers (Figure 1 and 6:61+ of Bendre teach implementation of the disclosed methods via a computer.), the computer system comprising:
a processor (Bendre teaches a processor in 7:11-24.); and
a non-transitory computer readable medium having stored thereon program code (Bendre teaches implementation details, including a non-transitory computer readable medium in 7:33-39.) that, when executed, causes the processor to:
With respect to claims 3, 10, and 17, the rejections of claims 1, 8, and 15 are incorporated. Further, Bendre teaches:
wherein the ML worker maintains an ML model that is trained on training data from the identified data cluster, and wherein the ML worker generates the decision or classification result using the ML model (The scheduler receives an ML training request and triggers assignment of an ML trainer process to generate an ML model amongst a plurality of trainer devices each configured to execute one or more ML trainer processes; see 18:48-19:3 and 19:26-41.).
With respect to claims 4, 11, and 18, the rejections of claims 1, 8, and 15 are incorporated. Further, Bendre teaches:
forwarding the decision or classification result to an originator of the query data instance (Bendre teaches a computing system that may be a cluster of computing devices, such as a server cluster, in figure 7 and 32:19-26. Bendre further teaches that server devices may be configured to transmit data to and receive data from cluster data storage 204; see figure 2 and 9:11-15.).
With respect to claims 5, 12, and 19, the rejections of claims 1, 8 and 15 are incorporated. Further, Bendre teaches:
identifying a quorum of data clusters to which the query data instance belongs, the quorum comprising:
the identified data cluster (Bendre teaches this limitation; see the rejection of claim 1 above.); and
one or more other data clusters in the plurality of data clusters that are also
associated with the query data instance (Bendre teaches this limitation; see the rejection of claim 1 above.);
transmitting the query data instance to one or more other ML workers that are assigned to the one or more other data clusters respectively (Bendre teaches a computing system that may be a cluster of computing devices, such as a server cluster, in figure 7 and 32:19-26. Bendre further teaches that server devices may be configured to transmit data to and receive data from cluster data storage 204; see figure 2 and 9:11-15.); and
receiving, from the one or more other ML workers, one or more other decision or classification results for the query data instance (Bendre teaches receiving a prediction request from a device associated with a customer instance and submitting the request for the network system to carry out the prediction using the generated ML model, where the ML model and ML prediction are accessible only to client devices associated with the particular customer instance; see 17:43-60.).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 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.
Claim 6, 7, 13, 14, 20, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bendre et al., U.S. Patent 10,380,504 (Bendre); in view of Liu et al., “Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence” (Liu).
With respect to claims 6, 13, and 20, the rejections of claims 5, 12, and 19 are incorporated. Further Bendre does not explicitly disclose:
compiling the decision or classification result received from the ML worker and the one or more other decision or classification results received from the one or more other ML workers into a single final result.
However, Liu teaches this limitation:
compiling the decision or classification result received from the ML worker and the one or more other decision or classification results received from the one or more other ML workers into a single final result (Liu teaches spectral ensemble clustering that determines a consensus partition; see section 1. Liu determines, from multiple partitions, a consensus partition for a data element; see section 3. Determining a partition is a decision and the consensus partition is considered a single, final, result.).
Bendre and Liu are analogous art directed towards data classification. Bendre teaches methods of machine learning with distributed training and Liu teaches data clustering methods.
It would have been obvious for one of ordinary skill in data classification to incorporate Liu’s clustering methods into Bendre’s disclosed system at the time of filing. It would have been obvious because one of ordinary skill would be motivated to implement an efficient and robust clustering system, as discussed in section 1.
With respect to claims 7, 14, and 21, the rejections of claims 5, 12, and 19 are incorporated. Further, Liu teaches
wherein the quorum includes all data clusters in the plurality of data clusters whose centroids are within a threshold distance from the query data instance (Liu teaches measuring either squared Euclidean distance or cosine similarity to determine the proper cluster in section 7.1. Liu also teaches ensemble and k-means clustering in section 2. [0038] of the instant specification teaches that the quorum-based approach may use k-means clustering.).
See the rejection of claim 6 for the motivation to combine references.
Conclusion
Claims 1-21 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL T PELLETT whose telephone number is (571)270-7156. The examiner can normally be reached on Monday - Friday 9-5 EST.
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/DANIEL T PELLETT/Primary Examiner, Art Unit 2121
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