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
This action is responsive to the following communication: Non-Provisional Application filed Feb. 28, 2024.
Claims 1-11 are pending in the case. Claims 1, 9 and 10 are independent claims.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 U.S.C. § 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 1-6 and 8-9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “acquiring a feature value of data processed by a plurality of first learning models” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “performing learning of a second learning model that outputs information relating to an estimation result in a case where the feature value of data processed by the first learning model is input based on the acquired feature value” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “inputting the acquired feature value of data into the second learning model after learning to output an estimation result based on information obtained from the second learning model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
See Perkins US 2022/0253647 Para [38] – “the machine learning model and/or one or more sets of inference data may be reviewed manually to evaluate the machine learning model and/or the one or more sets of inference data and/or to determine whether the one or more sets of inference data are compatible with the machine learning model.”
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “inputting” and “output” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No
Claims 2-6 and 8 are dependent on claim 1 and includes all the limitations of claim 1. Therefore, claims 2-6 and 8 recite the same abstract idea of reading data (stored in a computer memory), performing some analysis on the data, and presenting the data on a user interface. The claims do not otherwise add any meaningful limits beyond the abstract idea.
As to claim 9:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “acquire a feature value of data processed by a plurality of first learning models” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “learning of a second learning model that outputs information relating to an estimation result in a case where the feature value of data processed by the first learning model is input based on the acquired feature value” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “input the acquired feature value of data into the second learning model after learning to output an estimation result based on information obtained from the second learning model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
See Perkins US 2022/0253647 Para [38] – “the machine learning model and/or one or more sets of inference data may be reviewed manually to evaluate the machine learning model and/or the one or more sets of inference data and/or to determine whether the one or more sets of inference data are compatible with the machine learning model.”
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “An information processing apparatus” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “a learner” and “an estimator” are additional elements that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No
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.
Claims 1-6, 8 and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Reisser et al. (hereinafter Reisser) U.S. Patent Publication No. 2023/0036702 (GR 20190100556).
With respect to independent claim 1, Reisser teaches an information processing method comprising: acquiring a feature value of data processed by a plurality of first learning models (see e.g., Fig. 1 Para [43][44] - “the maximum likelihood optimization method may be extended to be a mixture of K different predictive models, or “experts” … the K experts may refer to K different neural network models … model 106C on mobile device 102A may be considered a single model comprising a plurality of K mixture model components (e.g., experts) in the context of federated mixture model learning. Beneficially, a federated mixture model functions as a single model for providing input to and receiving output from an application using the model. “);
performing learning of a second learning model that outputs information relating to an estimation result in a case where the feature value of data processed by the first learning model is input based on the acquired feature value (see e.g., Para [57][58]-“the mapping needs to be parameterized and learned. In one embodiment, this may be accomplished by interpreting p(z=k|x) as the responsibilities of an (unsupervised) clustering problem … The parameters ϕ.sub.k are jointly optimized with w.sub.k as part of the same algorithmic formulation. In the same manner as described for w.sub.k in Algorithm 1, the parameters ϕ.sub.k are trained by performing local updates using local data and periodically sent to (e.g., synchronized with) the global server (e.g., global model coordinator 108 in FIG. 1).”); and
inputting the acquired feature value of data into the second learning model after learning to output an estimation result based on information obtained from the second learning model (see e.g., Para [61]-[63] -“processing, at the edge processing device, data stored locally on the edge processing device with respective machine learning model k … performing, at the edge processing device, an optimization of the respective machine learning model k based on the machine learning output y.sub.s,k and the user feedback associated with machine learning model output y.sub.s,k to generate locally updated machine learning model parameters w.sub.s,k.sup.t+τ. ”).
With respect to dependent claim 2, Reisser teaches acquiring a feature value processed by a plurality of the second learning models (see e.g., Para [43]-[46] - “model 106C on mobile device 102A may be considered a single model comprising a plurality of K mixture model components (e.g., experts) in the context of federated mixture model learning. Beneficially, a federated mixture model functions as a single model for providing input to and receiving output from an application using the model. “ “In a federated training context, data D={(x.sub.1,y.sub.1, . . . , (x.sub.N, y.sub.N) may be split across S different shards (or sets), such that each shard s owns N.sub.s data-points. It can further be assumed that the data across all S shards (e.g., D=D.sub.1 ∪+ . . . ∪D.sub.S) is drawn from K clusters, whose parameters w are shared across all shards in each individual cluster.”);
performing learning of a third learning model that outputs information relating to an estimation result in a case where the feature value of data processed by the second learning model is input based on the acquired feature value (see e.g., Para [58] -“ the parameters ϕ.sub.k are trained by performing local updates using local data and periodically sent to (e.g., synchronized with) the global server (e.g., global model coordinator 108 in FIG. 1)”); and
inputting the acquired feature value of data into the third learning model after learning to output an estimation result based on information obtained from the third learning model (see e.g., Para [61]-[66]- “to generate locally updated machine learning model parameters w.sub.s,k.sup.t+τ. Note that in some embodiments, the optimization depend on all other model outputs y.sub.s,k* for all other models k* in addition to y.sub.s,k for model k … for each respective machine learning model k of the plurality of machine learning models K: sending the locally updated machine learning model parameters w.sub.s,k.sup.T+τ to a remote processing device.“).
With respect to dependent claim 3, Reisser teaches outputting a relearning instruction of the first learning model based on the estimation result by the second learning model or the third learning model (see e.g., Para [63][69]-“ generate locally updated machine learning model parameters w.sub.s,k.sup.t+τ. Note that in some embodiments, the optimization depend on all other model outputs y.sub.s,k* for all other models k* in addition to y.sub.s,k for model k.” “the user feedback comprises an indication of the correctness of the machine learning model output.” – The process is repeated.).
With respect to dependent claim 4, Reisser teaches outputting a relearning instruction of the second learning model based on the estimation result by the third learning model (see e.g., Para [58][63][75] – iterative optimization of each model based on updated information is disclosed).
With respect to dependent claim 5, Reisser teaches outputting a correction value for correcting an arithmetic result by the first learning model based on an arithmetic result by the second learning model or the third learning model (see e.g., Para [49]-[53]-“ The global server then interprets these updated parameters by computing the “effective gradient” as the change towards the current global server parameters. ”).
With respect to dependent claim 6, Reisser teaches outputting a correction value for correcting an arithmetic result by the second learning model based on an arithmetic result by the third learning model (see e.g., Para [43]-[46][78]-[80]-“for each respective model k of the plurality of models K: determining a corresponding density estimator p(x|ϕ.sub.k) parameterized by weighting parameters ϕ.sub.k for the respective model k. ”).
With respect to dependent claim 8, Reisser teaches the feature value is an arithmetic result obtained by the first learning model or the second learning model or data extracted in a middle process (see e.g., Para [25][44][90] - “Let Z be a collection of all z.sub.s,i, where there is a z for every data point (y.sub.s,i,x.sub.s,i, Then, z.sub.s,i indicates which of the K experts (e.g., neural networks in this example) is chosen to model a particular data point (y.sub.s,i,x.sub.s,i).”).
With respect to independent claim 9, Reisser teaches an information processing apparatus comprising: an acquisitor configured to acquire a feature value of data processed by a plurality of first learning models (see e.g., Para [60]-[63]-“receiving, at an edge processing device s, a set of global parameters w.sub.k.sup.t for each machine learning model k of a plurality of machine learning models K”); a learner configured to perform learning of a second learning model that outputs information relating to an estimation result in a case where the feature value of data processed by the first learning model is input based on the acquired feature value (see e.g., Para [63]-“performing, at the edge processing device, an optimization of the respective machine learning model k based on the machine learning output y.sub.s,k and the user feedback associated with machine learning model output y.sub.s,k to generate locally updated machine learning model parameters w.sub.s,k.sup.t+τ.”); and an estimator configured to input the acquired feature value of data into the second learning model after learning to output an estimation result based on information obtained from the second learning model (see e.g., Para [61][65]- process data with the model, generate output and receiving updated global parameters).
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 7, 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Reisser in view of Kiritoshi et al. (hereinafter Kiritoshi) U.S. Patent Publication No. 2022/0101137.
With respect to dependent claim 7, Reisser does not expressly show the data is time series data output from a plurality of types of sensors having different sampling periods, and the method further comprises performing learning of the second learning model using a feature value extracted from a plurality of time series data having different sampling periods for each sensor. However, Kiritoshi teaches the above feature (see e.g., Fig. 6 Para [32][57] -“The collection unit 12a collects a plurality of data. For example, the collection unit 12a collects a plurality of sensor data that are acquired in a monitoring target facility. Specifically, the collection unit 12a periodically (for example, every minute) receives multivariate and time-series numerical data from a sensor that is placed on a monitoring target facility such as a factory or a plant and stores them in the data storage unit 13a.”” Then, FIG. 6 illustrates transition of process data that are collected from each of sensor A to sensor E by the collection unit 12a where the learning unit 12c learns a model so as to produce a learned model as explained in the first embodiment. Then, the prediction unit 12e predicts anomaly after a certain period of time by using a learned model. Then, the visualization unit 12f outputs time-series data of a calculated degree of anomaly as a chart screen.”). Both Reisser and Kiritoshi are directed to device learning methods. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Reisser and Kiritoshi in front of them to further modify the modified system of Reisser to include the above feature. The motivation to combine Reisser and Kiritoshi comes from Kiritoshi. Kiritoshi discloses the motivation to improve learning by extracting a relationship between an input and an output (see e.g., Kiritoshi Para [3]-[6][32]). This motivation for combination also applies to claims 10 and 11 below.
With respect to independent claim 10, Reisser-Kiritoshi teaches an information processing system comprising:
a plurality of information processing apparatuses that includes an edge device connected to a sensor and a host device connected to the edge device (see e.g., Para [38]-[40]-“ mobile devices 102A-C, which are examples of edge processing devices, each have a local data store 104A-C, respectively, and a local machine learning model instance 106A-C, respectively. For example, mobile device 102A includes an initial machine learning model instance 106A, which it may receive from, for example, global machine learning model coordinator 108, which may be a software provider in some examples. Each of mobile devices 102A-C may use its respective machine learning model instance (106A-C) for some useful task, such as processing local data 104A-C, and further perform local training and optimization of its respective machine learning model instance (106A-C).”); and
an apparatus group server communicatively connected to the plurality of information processing apparatuses (see e.g., Para [40] – “Global model coordinator 108 may use all of the local model updates to determine a global (or consensus) model update, which may then be distributed to mobile devices 102A-C.”), wherein the edge device includes an acquisitor configured to acquire time series data from the sensor (see e.g., Kiritoshi Para [3]-[6][32]), a first learner configured to perform learning of a first learning model that outputs information relating to the information processing apparatus on which the sensor is provided in a case where the time series data from the sensor (see e.g., Kiritoshi Para [3]-[6][32]) is input based on the acquired time series data, a first estimator configured to input the time series data from the sensor into the first learning model after learning to output an estimation result based on information obtained from the first learning model, and an output configured to output a first feature value extracted from the time series data to the host device, the host device includes a first feature value storage configured to store the first feature value input from the edge device, a second learner configured to perform learning of a second learning model that outputs the information relating to the information processing apparatus in a case where the first feature value is input based on the stored first feature value (see e.g., Para [60]-[63] [73]-[77]), a second estimator configured to input a newly acquired first feature value into the second learning model after learning to output an estimation result based on information obtained from the second learning model, and a transmitter configured to transmit a second feature value of the time series data extracted for each information processing apparatus to the apparatus group server, and the apparatus group server includes a second feature value storage configured to store the second feature value received from the host device (see e.g., Para [43]-[46] - “model 106C on mobile device 102A may be considered a single model comprising a plurality of K mixture model components (e.g., experts) in the context of federated mixture model learning. Beneficially, a federated mixture model functions as a single model for providing input to and receiving output from an application using the model. “ “In a federated training context, data D={(x.sub.1,y.sub.1, . . . , (x.sub.N, y.sub.N) may be split across S different shards (or sets), such that each shard s owns N.sub.s data-points. It can further be assumed that the data across all S shards (e.g., D=D.sub.1 ∪+ . . . ∪D.sub.S) is drawn from K clusters, whose parameters w are shared across all shards in each individual cluster.”), a third learner configured to perform learning of a third learning model that outputs the information relating to the information processing apparatus in a case where the second feature value is input based on the stored second feature value, and a third estimator configured to input a newly acquired second feature value into the third learning model after learning to output an estimation result based on information obtained from the third learning model (see e.g., Para [61]-[66][73]-[79]- “to generate locally updated machine learning model parameters w.sub.s,k.sup.t+τ. Note that in some embodiments, the optimization depend on all other model outputs y.sub.s,k* for all other models k* in addition to y.sub.s,k for model k … for each respective machine learning model k of the plurality of machine learning models K: sending the locally updated machine learning model parameters w.sub.s,k.sup.T+τ to a remote processing device.“ - iterative optimization of each model based on updated information is disclosed).
With respect to dependent claim 11, the modified Reisser teaches the host device and the apparatus group server include a determiner configured to determine whether it is necessary to update the first learning model based on the estimation result by the learning model provided in each of the host device and the apparatus group server (see e.g. Para [62][63][69][75][77]- “eceiving, at the edge processing device, user feedback regarding machine learning model output y.sub.s,k.”” performing, at the edge processing device, an optimization of the respective machine learning model k based on the machine learning output y.sub.s,k and the user feedback associated with machine learning model output y.sub.s,k to generate locally updated machine learning model parameters w.sub.s,k.sup.t+τ””the user feedback comprises an indication of the correctness of the machine learning model output”), and an instructor configured to instruct the edge device to relearn the first learning model in a case where it is determined that an update is necessary (see e.g. Para [62][63][69][75][77]).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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