DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to claims filed 09/19/2022.
Claims 1-20 are pending.
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0049, line 1, “determine 702” should read “determine 704”.
Appropriate correction is required.
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: Supply definition for the term "one hot coding" as mentioned in Claims 8 and 18, line 2, or modify the claim language of Claims 8 and 18, line 2, "one hot coding" to "one hot encoding" .
Appropriate correction is required.
Claim Objections
Claims 7 and 17 are objected because of the following informalities: “requests in a load balancing queue” should read “requests in a physical machine queue” and “active virtual machines in the infrastructure” should read “active virtual machines on a physical machine”.
Appropriate correction is required.
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.
Claims 5 and 15 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth 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.
Claims 5 and 15 recites the limitation "the estimating engine" in line 1. There is insufficient antecedent basis for this limitation in the claim. Specifically, it is unclear in the claims if “the estimating engine” refers to “the first estimating engine”, or “the second estimating engine”, or both first and second estimating engines refereed in Claims 1 and 11. For the purpose of compact prosecution, the examiner will interpret the claim limitation as “the first and second estimating engines” mentioned in Claims 1 and 11.
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.
Claims 1, 2, 11, and 12 are rejected under 35 U.S.C 103 as being unpatentable by Colmenares Diaz et al. (US 20230141570 A1), hereinafter “Colmenares”, further in view of Liu et al. (US 20220237838 A1), hereinafter “Liu”:
Regarding Claim 1, Colmenares teaches
A method, comprising:
receiving a request at an infrastructure (The present disclosure generally relates to admission control for online data systems [0001]; Upon receiving a current server query from a client [Abstract]; New Query 205 and Admission Decision Based on Metrics and Objectives 210 and in Fig. 2. Examiner notes the online data systems is considered as an infrastructure.);
determining an occupancy status of a load balancing engine in the infrastructure (A current queue wait time is estimated based on a number of queries currently in the queue and the estimated processing times of query types for each of the queries currently in the queue [Abstract]; The broker host server(s) 145 receive query requests… and broadcast one or more sub-queries to the shard host servers [0022]; Fig. 2. Examiner notes that the broker host server is considered as the load balancer and the number of queries currently queued is considered as occupancy status of the load balancer because per Figure 2 where Query Queue 220 is within the broker host server.);
estimating a first metric based on the occupancy status of the load balancing engine with a first estimating engine (The admission controller estimates a current queue wait time based on a number of queries currently in the queue [0011]. Examiner notes that the admission controller is considered as a first estimating engine, and the queue wait time is considered as the first metric.-);
estimating a second metric with a second estimating engine (The admission controller estimates…the estimated processing times [0011]. Examiner notes that the admission controller is also considered as a second estimating engine, and the processing time is considered as the second metric.);
determining an estimated total metric from the first metric and the second metric (the response time of a query, RT(Q), is the sum of the processing time of the query, PT(Q), the wait time between enqueuing and dequeuing the query, WT(Q), and any additional time the server host takes to handle the query, which will be treated as negligible/zero for the sake of this illustration [0030].); and
performing an action when the estimated total metric is below a quality of service value (The server query is rejected from being added to the queue in response to determining the estimated response time does not satisfy a service level objective [Abstract]. Examiner notes the serviced level objective is considered as the quality of service value, and the rejection of adding the query to queue is the action performed).
Colmenares does not explicitly teach
determining an occupancy status of physical machines;
estimating a first metric based on a first noise vector with a first estimating engine;
estimating a second metric based on the occupancy status of the physical machines and a second noise vector with a second estimating engine.
However, Liu does teach
determining an occupancy status of physical machines (In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability [0365] Examiner notes that the resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability are considered as occupancy status of physical machines.);
estimating a first metric based on a first noise vector with a first estimating engine (In at least one embodiment, a set of structural feature vectors are obtained 402… these structural and appearance feature vectors are provided 406 as input to a slot attention transformer that is able to generate a set of transformed feature vectors from these input feature vectors… these transformed feature vectors are provided 408 as input to a generator, such as a generative adversarial network (GAN) [0056] Examiner notes that the transformed structural feature vectors are considered as a first noise vector, and generator is considered as the estimating engine.);
estimating a second metric based on the occupancy status of the physical machines and a second noise vector with a second estimating engine (In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability [0365]; a set of appearance feature vectors can also be obtained 404… these structural and appearance feature vectors are provided 406 as input to a slot attention transformer that is able to generate a set of transformed feature vectors from these input feature vectors… these transformed feature vectors are provided 408 as input to a generator, such as a generative adversarial network (GAN) [0056] Examiner notes that the resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability are considered as occupancy status of physical machines, same as above; the transformed appearance feature vectors are considered as a second noise vector; and generator is considered as the estimating engine.).
Colmenares and Liu are considered to be analogous to the claimed invention because both closely deal with load/resource availability and evaluate QOS metrics (Liu: a scheduler (and/or other component of application orchestration system 3228) may determine resource availability and distribution based on constraints imposed on a system (e.g., user constraints), such as quality of service (QoS) [0365]). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine the introduction of occupancy status of the physical machines and the noise vectors as inputs to determine the metrics presented in Colmenares. Specifically, supplying a noise vector as an input to estimate the first metric, and a second noise vector and occupancy status as inputs to estimate the second metric. This combination results in utilizing more information and introducing Generative Adversarial Network (GAN) models to help determine the metrics, given noise vectors are inputs that are particular to Machine Learning models like GAN. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of a more accurate estimation of the metrics by leveraging GAN through introducing inputs such as noise vectors. Specifically, improving the estimation that it is highly realistic and couldn’t be discriminated between real-world data through the feedback of the discriminator of GAN (Liu: this generator can be a generative adversarial network (GAN) trained to generate images that a discriminator of that GAN cannot determine to be synthetic and not a “real” image or representation. [0056]).
Regarding Claim 2, Colmenares in view of Liu teaches
The method of claim 1, wherein the first metric relates to a response time measured from receiving the request to assigning the request to a physical machine, wherein the request is moved from a queue of the load balancing engine to a queue of the physical machine (Colmenares: the wait time between enqueuing and dequeuing the query, WT(Q) [0030]; dequeued from the query queue 220 for processing by one or more shard hosts 160 [0032]. Examiner notes the time between enqueuing and dequeuing the query is considered as the time the request is moved from a queue of the load balancing engine to a queue of the physical machine because dequeuing means the request is moved to the queue of the physical machine per reference).
Regarding Claim 11, it is rejected for the same reasons as stated in the rejection of Claim 1 .
Further, Colmenares in view of Liu also teaches
A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising (Colmenares: carries out the computer-implemented methods 300 and 400 in response to its processor executing a computer program (e.g., a sequence of instructions) contained in a memory or other non-transitory machine-readable storage medium. [0086]):
Regarding Claim 12, it is rejected for the same reasons as stated in the rejection of Claim 2.
Claims 3-7 and 13-17 are rejected under 35 U.S.C 103 as being unpatentable by Colmenares in view of Liu, further in view of Ferguson et al. (US 5504894 A), hereinafter “Ferguson”:
Regarding Claim 3, Colmenares in view of Liu does not explicitly teach
The method of claim 1, further comprising wherein the second metric relates to a response time measured from receiving the request at the physical machine to assigning the request to a virtual machine operating on the physical machine or to sending a response to the request.
However, Ferguson does teach
The method of claim 1, further comprising wherein the second metric relates to a response time measured from receiving the request at the physical machine to assigning the request to a virtual machine operating on the physical machine or to sending a response to the request (This component predicts the effects of a proposed routing decision on the response times of all transactions currently in the system. [Col. 9, line 32-35]; response time for any particular completed transaction being a length of time which has elapsed from an arrival time when said any particular completed transaction arrived at said computer system for handling and a completion time when said any particular completed transaction was completed by said computer system [Claim 1]).
Colmenares in view of Liu, and Ferguson are considered to be analogous to the claimed invention because both closely deal with incoming requests’ QOS metric estimation (Ferguson: Furthermore, service level agreements are often specified in terms of required response times for different classes of work. [Col. 5, line 31-33]; This component predicts the effects of a proposed routing decision on the response times of all transactions currently in the system. [Col. 9, line 32-35]). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine the response time of the physical machine with the system and methods of Colmenares in view of Liu. This combination results in the total metric of Claim 1 being a metric of response time, which part of it is constituted by the time the physical machine takes to respond/process when the request arrives the physical machine. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of assuring the total estimated metric is not below the quality of service value through the estimated response time of each of the physical machines and evaluating each routing choices to the different physical machines (Ferguson: Whenever a transaction arrives, the workload manager considers a number of different possible transaction servers to which that arriving transaction could be routed and predicts estimated new values for the class performance indices for each of the considered routing choices. An overall goal satisfaction index is determined for each one and the routing choice corresponding to the best overall goal satisfaction index is selected as the routing choice. [Abstract]).
Regarding Claim 4, Colmenares in view of Liu and Ferguson teaches
The method of claim 1, wherein the first metric relates to a response time of the load balancing engine and the second metric relates to a response time of the physical machines (Colmenares: the wait time between enqueuing and dequeuing the query, WT(Q) [0030]; dequeued from the query queue 220 for processing by one or more shard hosts 160 [0032]. Ferguson: This component predicts the effects of a proposed routing decision on the response times of all transactions currently in the system. [Col. 9, line 32-35]; response time for any particular completed transaction being a length of time which has elapsed from an arrival time when said any particular completed transaction arrived at said computer system for handling and a completion time when said any particular completed transaction was completed by said computer system [Claim 1]).
Regarding Claim 5, Colmenares in view of Liu and Ferguson teaches
The method of claim 4, wherein the estimating engine comprises a load balancing generator and a physical machine generator, further comprising estimating the response time of the load balancing engine using the load balancing generator that has been trained in a load balancing model and estimating the response time of the physical machines using the physical machine generator that has been trained in a physical machine model (Liu: In at least one embodiment, these structural features and appearance features are provided as input to a generator 212, such as a generative neural network. In at least one embodiment, this generator 212 can include a transformer 214, or at least one conditional transformer layer or other attention mechanism, that can transform these appearance features using these structural features. In at least one embodiment, this generator can then infer features of an output image 216 to be generated, or synthesized, using these transformed features. [0049]; Examiner notes that infer features of an output image is considered as estimating response time as established by the combination of Colmenares and Liu above. this generator can be a generative adversarial network (GAN) trained to generate images [0050]; Examiner notes that the generative adversarial network is considered as the model which the generator has been trained. Further it would be obvious that both loading balancing and physical machine have model with a generator being trained, since they both have the same claim limitation and would receive the benefit or advantage instead of just one).
Regarding Claim 6, Colmenares in view of Liu and Ferguson teaches
The method of claim 5, wherein the load balancing model implicitly learns a distribution of real response times associated with occupancy values associated with the load balancing engine, the occupancy values including a number of requests in a load balancing queue and a number of active virtual machines in the infrastructure (Colmenares: The admission controller estimates a current queue wait time based on a number of queries currently in the queue [0011] Examiner notes the number of queries currently in the queue is considered as a number of requests in a load balancing queue which the occupancy values include. Separate query types often have different processing time distributions that vary over time. As such, admission controller 150 maintains approximations for these distributions in histograms…which admission controller 150 periodically updates at run time by tracking timing metrics during the processing of queries or similar measurements of processing times [0030]; As a result, the admission controller 150 can track/measure and update actual processing times for query types in corresponding histograms [0065] Examiner notes the histogram is considered as a distribution of real response times. Liu: In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability [0365], In at least one embodiment, these features can be provided to a generator 208 as discussed above to synthesize one or more output representations. In at least one embodiment, this generator can be a generative adversarial network (GAN) trained to generate image [0050] Examiner notes the resource requirements of applications or container, current usage or planned usage of these resources, and resource availability is considered as a number of active virtual machines in the infrastructure which the occupancy values include, and the GAN that has a generator trained to produce image through feature input is considered as a load balancing model).
Regarding Claim 7, Colmenares in view of Liu and Ferguson teaches
The method of claim 5, wherein the physical machine model implicitly learns a distribution of real response times associated with occupancy values associated with the physical machines, the occupancy values including a number of requests in a load balancing queue, a number of active virtual machines in the infrastructure, and size of each physical machine (Colmenares: The admission controller estimates a current queue wait time based on a number of queries currently in the queue [0011] Examiner notes the number of queries currently in the queue is considered as a number of requests in a load balancing queue which the occupancy values include. Separate query types often have different processing time distributions that vary over time. As such, admission controller 150 maintains approximations for these distributions in histograms…which admission controller 150 periodically updates at run time by tracking timing metrics during the processing of queries or similar measurements of processing times [0030]; As a result, the admission controller 150 can track/measure and update actual processing times for query types in corresponding histograms [0065] Examiner notes the histogram is considered as a distribution of real response times. Liu: In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability [0365], In at least one embodiment, these features can be provided to a generator 208 as discussed above to synthesize one or more output representations. In at least one embodiment, this generator can be a generative adversarial network (GAN) trained to generate image [0050] Examiner notes the resource requirements of applications or container, current usage or planned usage of these resources, and resource availability is considered as a number of active virtual machines in the infrastructure and size of the physical machine which the occupancy values include, and the GAN that has a generator trained to produce image through feature input is also considered as a physical machine model).
Regarding Claim 13, it is rejected for the same reasons as stated in the rejection of Claim 3.
Regarding Claim 14, it is rejected for the same reasons as stated in the rejection of Claim 4.
Regarding Claim 15, it is rejected for the same reasons as stated in the rejection of Claim 5.
Regarding Claim 16, it is rejected for the same reasons as stated in the rejection of Claim 6.
Regarding Claim 17, it is rejected for the same reasons as stated in the rejection of Claim 7.
Claims 8-9 and 18-19 are rejected under 35 U.S.C 103 as being unpatentable by Colmenares in view of Liu and Ferguson, further in view of Lu et al. (CN 110310344 A), hereinafter “Lu”:
Regarding Claim 8, Colmenares in view of Liu and Ferguson teaches
The method of claim 5,
wherein an input to the load balancing generator comprises a tensor including a noise vector, the number of requests in a load balancing queue and the number of active virtual machines (Colmenares: The admission controller estimates a current queue wait time based on a number of queries currently in the queue [0011] Examiner notes the number of queries currently in the queue is considered as the number of requests in a load balancing queue. Liu: In at least one embodiment, a set of structural feature vectors are obtained 402… these structural and appearance feature vectors are provided 406 as input to a slot attention transformer that is able to generate a set of transformed feature vectors from these input feature vectors… these transformed feature vectors are provided 408 as input to a generator, such as a generative adversarial network (GAN) [0056]; In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability [0365] Examiner notes the transformed feature vectors in considered as a tensor, the transformed structural vector is considered as a noise vector, the resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability is considered as number of active virtual machines, and the generator is considered as the load balancing generator),
wherein an input to the physical machine vector comprises a tensor including a noise vector, the number of requests in a physical machine queue, the number of active virtual machines on a physical machine, and the size of the physical machine (Ferguson: We now compute the expected queueing delays for the various transactions. Due to the priorities, a class C.sub.j transaction only queues behind transactions from class 1, 2, . . . , j-1. The response time of a transaction is determined by its service demands and the queueing delays it experiences. [Col. 11, line 27-33] Examiner notes the queueing delay is considered as the number of requests in a physical machine queue. Liu: a set of appearance feature vectors can also be obtained 404… these structural and appearance feature vectors are provided 406 as input to a slot attention transformer that is able to generate a set of transformed feature vectors from these input feature vectors… these transformed feature vectors are provided 408 as input to a generator, such as a generative adversarial network (GAN) [0056]; In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability [0365] Examiner notes the transformed feature vectors in considered as a tensor, the transformed appearance vector is considered as a noise vector, the resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability is considered as number of active virtual machines and the size of the physical machine, and the generator is also considered as the physical machine generator).
Colmenares in view of Liu and Ferguson does not explicitly teach
A one hot coding related to the number of requests in a load balancing queue and the number of active virtual machines, a one hot encoding related to the number of requests in a physical machine queue, the number of active virtual machines on a physical machine, and the size of the physical machine.
However, Lu does teach
A one hot coding related to the number of requests in a load balancing queue and the number of active virtual machines, a one hot encoding related to the number of requests in a physical machine queue, the number of active virtual machines on a physical machine, and the size of the physical machine (Step 4: being inputted in the generator after the first noise vector z ' and only hot vector C ' are spliced Decoder so that the generator export dummy copy collection. [0068] Examiner notes the only hot vector C’ is considered as a one-hot encoding vector.).
Colmenares in view of Liu and Ferguson, and Lu are considered to be analogous to the claimed invention because both closely deal with generating estimation base on given input using trained models (Lu: Piecing said first noise vector z’ and said one-hot vector C’ together and input into the decoder in the said generator, so the said generator output fake sample set [0068] ). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine a one-hot encoding format with the input fields of machine learning models of Colmenares in view of Liu and Ferguson. This combination results in the model able to receive its input fields, namely the number of requests in the load balancing and physical machine queue, the number of active virtual machines (on a physical machine), and the size of the physical machine as a one-hot encoding vector. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of classifying unclassified input data that traditional GAN models are not able to, and hence generate more controlled and accurate outputs of estimated response time (Lu: Specifically, traditional conditional GAN needs data with conditional tag (classification) to train the network, but the disclosed method automatically cluster the data with no label using Noise Characteristic jump and the amplification of one-hot encoding offset, and generate data with conditional tags [0073]; The original generative adversarial Nets are unable to control the content of generated sample when sampling, generated samples are completely are randomly generated.[0003]).
Regarding Claim 9, Colmenares in view of Liu and Ferguson, and further in view of Lu, teaches
The method of claim 8,
wherein the load balancing model comprises a load balancing discriminator configured to determine whether an input to the load balancing discriminator is real or fake and
wherein the physical machine model comprises a physical machine discriminator configured to determine whether an input to the physical machine discriminator is real or fake (Lu: arbiter D is to differentiate true sample set Xreal With dummy copy collection Xfake, to obtain the differentiated result. Wherein, arbiter can exactly determine an image input one as true sample set or dummy copy collection [0073]. Examiner notes arbiter D is considered as discriminator Liu: In at least one embodiment, this generator can be a generative adversarial network (GAN) trained to generate images that a discriminator of that GAN cannot determine to be synthetic and not a “real” image or representation. [0050] Examiner notes one reference is applied to both the load balancing model and the physical machine model because it would be obvious that both loading machine model and physical machine model have a discriminator, given they both have the same claim limitation and would receive the benefit or advantage instead of just one).
Regarding Claim 18, it is rejected for the same reasons as stated in the rejection of Claim 8.
Regarding Claim 19, it is rejected for the same reasons as stated in the rejection of Claim 9.
Claims 10 and 20 are rejected under 35 U.S.C 103 as being unpatentable by Colmenares in view of Liu, Ferguson, and Lu, further in view of Redford et al. (US 20230222336 A1), hereinafter “Redford”:
Regarding Claim 10,
Colmenares in view of Liu, Ferguson, and Lu teaches
The method of claim 9, further comprising training the load balancing model and the physical machine model using ground truth data (Colmenares: The admission controller estimates a current queue wait time based on a number of queries currently in the queue [0011]; Separate query types often have different processing time distributions that vary over time. As such, admission controller 150 maintains approximations for these distributions in histograms…which admission controller 150 periodically updates at run time by tracking timing metrics during the processing of queries or similar measurements of processing times [0030]; As a result, the admission controller 150 can track/measure and update actual processing times for query types in corresponding histograms [0065] Examiner notes the number of queries in the queue and the histogram is considered as ground truth data. Liu: In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability [0365], In at least one embodiment, these features can be provided to a generator 208 as discussed above to synthesize one or more output representations. In at least one embodiment, this generator can be a generative adversarial network (GAN) trained to generate image [0050] Examiner notes the resource requirements of applications or container, current usage or planned usage of these resources, and resource availability is considered as ground truth data and the GAN that has a generator trained to produce image through feature input is considered as a load balancing model and a physical machine model as demonstrated in claims above).
Colmenares in view of Liu, Ferguson, and Lu does not explicitly teach
ground truth data that is discretized and binned.
However, Redford does teach
ground truth data that is discretized and binned (This functional form can be approximated by discretising each confounder. In this representation, categorical confounders (such as vehicle type) are mapped to bins. Continuous confounders (such as distance from detector) are sliced into ranges and each range mapped to a bin. [0365]; Training a model requires ground truth and stack predictions (actual perception outputs), collected as described in Section 3.1.1. The mean and covariance of the normal distribution are fitted (e.g. using a maximum a posteriori method to incorporate a prior) to the observations in that bin. [0368] Examiner notes the continuous cofounders discretized and binned is considered as ground truth data).
Colmenares in view of Liu and Ferguson and Lu, and Redford are considered to be analogous to the claimed invention because both closely deal with generating estimation using ground truth with trained models (Redford: providing the sensor data of each input sample to the perception system to be modelled, wherein the perception system interprets the sensor data, in order to generate one or more actual perception outputs for the input sample; and training a function approximator to model the perception system by: for each input sample, inputting the training perception ground truths to the function approximator, wherein the function approximator computes one or more predicted perception values by processing the training perception ground truths [Abstract]). It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine discretize and bin the continuous input fields of machine learning models of Colmenares in view of Liu and Ferguson and Lu. This combination results in continuous input fields being classified and categorized and enables the input to be represented as one-hot encoding vectors. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of making the model interpretable and be analyzed and tuned to optimize the accuracy of the GAN model, further improving the response time prediction (Redford: The advantages of the PCM approach are that it accounts for global heteroskedasticity, gives a unified framework to capture confounders of different types, and it utilises simple probability distributions. In addition, the model is interpretable: the distribution in a bin can be examined, the training data can be directly inspected and there are no hidden transforms. Moreover, the parameters can be fitted analytically, meaning uncertainty from lack of convergence in optimisation routines can be avoided. [0372]).
Regarding Claim 20, it is rejected for the same reasons as stated in the rejection of Claim 10.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN ZIYANG YU whose telephone number is (571)272-7137. The examiner can normally be reached Monday - Friday 8:30-16:00.
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/A.Z.Y./Examiner, Art Unit 2197
/KENNETH TANG/Primary Examiner, Art Unit 2197
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