DETAILED ACTION
Claim Status
This is first office action on the merits in response to the application filed on 6/28/2024.
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 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 6/28/2024 and 10/29/2025 is(are) in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 7-8 are objected to because of the following informalities:
In claim 7, line 6, “a time series prediction model” should read --the time series prediction model--.
In claim 8, line 6, “a time series prediction model” should read --the time series prediction model--.
Appropriate correction is required.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the Step 1 of the Section 101 analysis, Claims 1-17 are drawn to a method which is within the four statutory categories (i.e., a process), Claims 18-19 are drawn to a system which is within the four statutory categories (i.e. a machine), and Claim 20 is drawn to a non-transitory computer-readable medium which is within the four statutory categories (i.e., a manufacture).
Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). Based on consideration of all of the relevant factors with respect to the claim as a whole, claims 1-20 are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Regarding Claims 1 and 17-20:
Claims 1 and 17-20 are drawn to an abstract idea without significantly more. The claims recite “obtaining, by a first communication device, model updating information; and updating, by the first communication device, a time series prediction model used by the first communication device according to the model updating information, the time series prediction model being used for executing a prediction task, wherein the model updating information comprises at least one of the following: updating information of a kernel function; updating information of model hyper-parameters; updating information of a prediction mode; or updating information of a computing mode.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
For example, but for the “communication device”, “time series prediction model”, and “computing mode” language, the underlined limitations in the context of this claim encompass the human activity or mental processes. A person could obtain data or information such as updating information. The person could then update time series prediction with the obtained updating information. Finally, the person could execute a prediction task, as in a typical mental process.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “A method for updating a model, comprising:”, “A method for updating a model, comprising:”, “A communication device, comprising a processor and a memory, the memory storing a program or an instruction executable on the processor, the program or the instruction, when executed by the processor, causes the communication device to perform:”, “A communication device, comprising a processor and a memory, the memory storing a program or an instruction executable on the processor, the program or the instruction, when executed by the processor, implementing the steps of the method for updating a model”, “A non-transitory readable storage medium, storing a program or an instruction, the program or the instruction, when executed by a processor, implementing the steps of the method for updating a model”, “communication device”, “time series prediction model”, and “computing mode”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under the Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the process amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Regarding Claims 2-16:
Dependent claims 2-16 include additional limitations, for example, “communication device” and “model updating information” (claim 2); “communication device”, “model updating information”, and “model updating request information” (claim 3); “communication device”, “model updating request information”, and “time series prediction model” (claim 4); “communication device”, “model updating request information”, “model updating instruction information”, “time series prediction model”, and “computing delay.” (claim 5); “communication device”, “kernel function”, “model optimizer configuration”, “model training configuration”, and “model prediction configuration” (claim 6); “communication device”, “model configuration”, and “time series prediction model” (claim 7); “communication device”, “computing capacity”, “model configuration”, and “time series prediction model” (claim 8); “communication device”, “time series prediction model”, and “model updating information” (claim 9); “time series prediction model”, “model prediction errors”, and “communication device” (claim 10); “structure”, “time window”, “training window”, “prediction window”, and “time series prediction model” (claim 11); “communication device” and “prediction model list” (claim 12); “model updating information”, “computing mode”, “communication device”, “computing capacity information”, “storage capacity information”, and “computing unit configuration information” (claim 13); “communication device”, “cached”, “computing amount”, “computed”, and “parallel computing threads” (claim 14); “computing mode”, “time series prediction model”, “parallel computing mode”, “communication device”, “partitioning”, “optimization”, “model optimizer”, “fusion”, and “sub-model prediction results” (claim 15); and “model updating information”, “prediction”, “model”, and “timestamp” (claim 16), but none of these limitations are deemed significantly more than the abstract idea because, as stated above, they require no more than generic computer structures or signals to be executed, and do not recite any Improvements to the functioning of a computer, or Improvements to any other technology or technical field.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation or implementing the judicial exception on a generic computer.
Therefore, whether taken individually or as an ordered combination, claims 2-16 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 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.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walters (US 20200012900 A1; already of record in IDS) in view of Conort (US 20220076164 A1).
Regarding Claims 1 and 17-20, Walters teaches A method for updating a model, comprising (Walters: Abstract): A communication device, comprising a processor and a memory, the memory storing a program or an instruction executable on the processor, the program or the instruction, when executed by the processor, causes the communication device to perform (Walters: Abstract; Paragraph(s) 0012): A communication device, comprising a processor and a memory, the memory storing a program or an instruction executable on the processor, the program or the instruction, when executed by the processor, implementing the steps of the method for updating a model according to claim 17 (Walters: Abstract; Paragraph(s) 0012), A non-transitory readable storage medium, storing a program or an instruction, the program or the instruction, when executed by a processor, implementing the steps of the method for updating a model according to claim 1 (Walters: Abstract; Paragraph(s) 0012).
obtaining, by a first communication device, model updating information (Walters: Paragraph(s) 0005-0008 teach(es) actual data or synthetic data used in predictive models (e.g., forecasting models or classification models), and actual data may drift as real-world conditions change; the event data (e.g., actual time series data, future classification, or outcome data) and predicted data may diverge over time unless the predictive model is corrected); and
updating, by the first communication device, a [time series] prediction model used by the first communication device according to the model updating information, the [time series] prediction model being used for executing a prediction task (Walters: Paragraph(s) 0004-0008, 0010-0011, 0131, 0176-0183 teach(es) Data drift presents challenges to synthetic data models or predictive models. In some cases, synthetic data models may generate synthetic data based on actual data for training downstream predictive models, and the actual data may drift over time, resulting in drift in the synthetic data; when models are provided to users, the users may be unable to train or update the model based on drift without further intervention from the model provider, so the user depends on the provider to provide updated models), wherein
the model updating information comprises at least one of the following: updating information of a kernel function; updating information of model hyper-parameters; updating information of a prediction mode; or updating information of a computing mode (Walters: Abstract; Paragraph(s) 0049-0050, 0131, 0150 teach(es) kernel density estimator; prediction metrics; hyperparameter tuning functionality).
However, Walters does not explicitly teach a time series prediction model.
Conort from same or similar field of endeavor teaches a time series prediction model (Conort: Abstract; Paragraph(s) 0074, 0195 teach(es) Time-Series Predictive Data Analytics; The data indicative of the characteristics of a prediction problem may accommodate time-series prediction problems by indicating whether the prediction problem is a time-series prediction problem, and by identifying the time measurement variable in datasets corresponding to time-series prediction problems).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Walters to incorporate the teachings of Conort for a time series prediction model.
There is motivation to combine Conort into Walters because Conort s teachings of time-series prediction problems and Time-Series Predictive Data Analytics would facilitate updating a model (Conort: Abstract; Paragraph(s) 0074, 0195).
Regarding Claim 2, the combination of Walters and Conort teaches all the limitations of claim 1 above; and Walters further teaches wherein the obtaining, by a first communication device, model updating information comprises: receiving, by the first communication device, the model updating information from a second communication device (Walters: Paragraph(s) 0203, 0214 teach(es) the request is received from and/or is associated with a device (e.g., a client device, a server, a mobile device, a personal computer, or the like) or an account (e.g., an email account, a user account, or the like)).
Regarding Claim 3, the combination of Walters and Conort teaches all the limitations of claim 2 above; and Walters further teaches wherein before the receiving, by the first communication device, the model updating information from a second communication device, the method further comprises: transmitting, by the first communication device, model updating request information to the second communication device, the model updating request information being used for requesting to obtain first updating information, the model updating information comprising at least one of the following: the first updating information; or second updating information except the first updating information (Walters: Paragraph(s) 0173, 0197-0198 teach(es) model optimizer can update the model index to include the trained model. This updating can include creation of an entry in the index associating the model with the model characteristics for the model. In some embodiments, these model characteristics can include at least some of the one or more hyperparameter values used to generate the trained model).
Regarding Claim 4, the combination of Walters and Conort teaches all the limitations of claim 3 above; and Walters further teaches wherein the transmitting, by the first communication device, model updating request information to the second communication device comprises: transmitting, by the first communication device, the model updating request information to the second communication device according to first information, the first information comprising at least one of the following: prediction errors of the time series prediction model; statistical information of prediction errors of the time series prediction model; mobility information of the first communication device; statistical information of prediction results of the time series prediction model; or environmental perception information (Walters: Paragraph(s) 0061, 0184, 0196, 0041 teach(es) computing resources can be configured to train the data model received from model optimizer until some training criterion is satisfied. The training criterion can be, for example, a performance criterion (e.g., a Mean Absolute Error, Root Mean Squared Error, percent good classification, and the like), a convergence criterion (e.g., a minimum required improvement of a performance criterion over iterations or over time, a minimum required change in model parameters over iterations or over time), elapsed time or number of iterations, or the like; the data model can be configured to generate data matching statistical and content characteristics of a training dataset).
Regarding Claim 5, the combination of Walters and Conort teaches all the limitations of claim 3 above; and Walters further teaches wherein the transmitting, by the first communication device, model updating request information to the second communication device comprises: transmitting, by the first communication device, the model updating request information to the second communication device in a case that a first condition is satisfied, the first condition comprising at least one of the following: that the first communication device has received model updating instruction information transmitted by the second communication device; that a prediction task of the time series prediction model has been updated; or that a prediction performance requirement of the time series prediction model has been updated; wherein the prediction performance requirement comprises at least one of prediction accuracy or computing delay (Walters: Paragraph(s) 0134 teach(es) requests received from the interface can indicate a reference data stream. For example, such a request can identify streaming data source and/or specify a topic or subject (e.g., a Kafka topic or the like)).
Regarding Claim 6, the combination of Walters and Conort teaches all the limitations of claim 2 above; and Walters further teaches further comprising: transmitting, by the first communication device, first capacity information to the second communication device, the first capacity information being used for indicating at least one of the following: a kernel function supported by the first communication device; whether the first communication device has a prediction task; a model optimizer configuration supported by the first communication device; a model training configuration supported by the first communication device; or a model prediction configuration supported by the first communication device (Walters: Paragraph(s) 0043, 0046, 0049-0050 teach(es) Model optimizer can be configured to generate models based on instructions received from a user or another system; kernel density estimator).
Regarding Claim 7, the combination of Walters and Conort teaches all the limitations of claim 2 above; and Walters further teaches further comprising: receiving, by the first communication device, a first model configuration from the second communication device; and transmitting, by the first communication device, first feedback information to the second communication device, the first feedback information being used for indicating whether the first communication device supports a time series prediction model instructed by the first model configuration (Walters: Paragraph(s) 0043 teach(es) Model optimizer can be configured to select model training parameters. This selection can be based on model performance feedback received from computing resources. Model optimizer can be configured to provide trained models and descriptive information concerning the trained models to model storage).
Regarding Claim 8, the combination of Walters and Conort teaches all the limitations of claim 2 above; and Walters further teaches further comprising: transmitting, by the first communication device, computing capacity information to the second communication device, the computing capacity information being used for indicating a computing capacity of the first communication device; and receiving, by the first communication device, a second model configuration from the second communication device, a time series prediction model instructed by the second model configuration being determined according to at least one of the computing capacity, a processing delay, or a prediction performance requirement of the time series prediction model (Walters: Paragraph(s) 0039-0040 teach(es) Environment can include computing resources, dataset generator, database, model optimizer, model storage, model curator, and interface).
Regarding Claim 9, the combination of Walters and Conort teaches all the limitations of claim 2 above; however, the combination does not explicitly teach transmitting, by the first communication device, second information or updating recommendation information of the time series prediction model to the second communication device, the model updating information being determined according to the second information.
Conort further teaches further comprising: transmitting, by the first communication device, second information or updating recommendation information of the time series prediction model to the second communication device, the model updating information being determined according to the second information (Conort: Paragraph(s) 0342, 0327, 0329 teach(es) The predictive modeling system may provide key summary characteristics and offer recommendations for treatment of data anomalies, which the user is free to accept, decline, or request more information about).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Walters and Conort to incorporate the teachings of Conort for transmitting, by the first communication device, second information or updating recommendation information of the time series prediction model to the second communication device, the model updating information being determined according to the second information.
There is motivation to combine Conort into the combination of Walters and Conort because Conort s teachings of recommendations for treatment of data anomalies would facilitate updating a model (Conort: Paragraph(s) 0342, 0327, 0329).
Regarding Claim 10, the combination of Walters and Conort teaches all the limitations of claim 9 above; and Walters further teaches wherein the second information comprises at least one of the following: statistical information of prediction results of the time series prediction model; estimation errors of the time series prediction model; statistical information of estimation errors of the time series prediction model; model prediction errors of the time series prediction model; statistical information of model prediction errors of the time series prediction model; mobility information of the first communication device; statistical information of noise; or a prediction performance requirement of the time series prediction model (Walters: Paragraph(s) 0041, 0098 teach(es) the data model can be configured to generate data matching statistical and content characteristics of a training dataset; the similarity metric value can include at least one of a statistical correlation score (e.g., a score dependent on the covariances or univariate distributions of the synthetic data and the normalized reference dataset), a data similarity score (e.g., a score dependent on a number of matching or similar elements in the synthetic dataset and normalized reference dataset), or data quality score (e.g., a score dependent on at least one of a number of duplicate elements in each of the synthetic dataset and normalized reference dataset, a prevalence of the most common value in each of the synthetic dataset and normalized reference dataset, a maximum difference of rare values in each of the synthetic dataset and normalized reference dataset, the differences in schema between the synthetic dataset and normalized reference dataset, or the like)).
Regarding Claim 11, the combination of Walters and Conort teaches all the limitations of claim 1 above; and Walters further teaches wherein the prediction mode comprises a structure of a time window for the prediction task, and the structure of the time window comprises at least one of the following: a length of time units in a training window of the time series prediction model; a length of time units in a prediction window of the time series prediction model; an interval of adjacent time units in the training window of the time series prediction model; or an interval of adjacent time units in the prediction window of the time series prediction model (Walters: Paragraph(s) 0060 teach(es) computing resources can be configured to receive at least some training parameters from model optimizer (e.g., batch size, number of training batches, number of epochs, chunk size, time window, input noise dimension, or the like)).
Regarding Claim 12, the combination of Walters and Conort teaches all the limitations of claim 2 above; and Walters further teaches wherein the first communication device stores a same prediction model list as the second communication device, and the prediction model list comprises identities of prediction modes (Walters: Paragraph(s) 0162, 0166, 0157 teach(es) As a nonlimiting example of the use of an index of model characteristics, system can train a classification model to identify loans likely to be nonperforming based using a dataset of loan application data with a particular schema).
Regarding Claim 13, the combination of Walters and Conort teaches all the limitations of claim 2 above; and Walters further teaches wherein the model updating information comprises updating information of the computing mode; before the receiving, by the first communication device, the model updating information from a second communication device, the method further comprises: transmitting, by the first communication device, second capacity information to the second communication device, the second capacity information comprising at least one of the following: computing capacity information of the first communication device; storage capacity information of the first communication device; or computing unit configuration information of the first communication device (Walters: Paragraph(s) 0039-0040 teach(es) Environment can include computing resources, dataset generator, database, model optimizer, model storage, model curator, and interface).
Regarding Claim 14, the combination of Walters and Conort teaches all the limitations of claim 13 above; however, the combination does not explicitly teach wherein the second capacity information further comprises at least one of the following: a maximum data amount supported to be cached by the first communication device; a maximum computing amount supported to be computed by the first communication device; or a maximum number of parallel computing threads supported by the first communication device.
Conort further teaches wherein the second capacity information further comprises at least one of the following: a maximum data amount supported to be cached by the first communication device; a maximum computing amount supported to be computed by the first communication device; or a maximum number of parallel computing threads supported by the first communication device (Conort: Paragraph(s) 0355 teach(es) System hardware and software other than that specifically described herein may also be used, depending on the capacity of the device and the size of the user base).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Walters and Conort to incorporate the teachings of Conort for wherein the second capacity information further comprises at least one of the following: a maximum data amount supported to be cached by the first communication device; a maximum computing amount supported to be computed by the first communication device; or a maximum number of parallel computing threads supported by the first communication device.
There is motivation to combine Conort into the combination of Walters and Conort because Conort’s teachings of capacity of the device would facilitate updating a model (Conort: Paragraph(s) 0355).
Regarding Claim 15, the combination of Walters and Conort teaches all the limitations of claim 1 above; and Walters further teaches the method further comprises: receiving, by the first communication device, third information from a second communication device, the third information comprising at least one of the following: a partitioning manner of a dataset; selection of an optimization goal; selection of a model optimizer; an initial state of the model optimizer; or a fusion manner of a plurality of sub-model prediction results (Walters: Paragraph(s) 0060, 0066 teach(es) the data model can be at least partially initialized by model optimizer. For example, at least some of the initial weights and offsets of a neural network model received by computing resources can be set by model optimizer).
However, the combination of Walters and Conort does not explicitly teach wherein the updating information of the computing mode instructs to update the computing mode of the time series prediction model to a parallel computing mode.
Conort further teaches wherein the updating information of the computing mode instructs to update the computing mode of the time series prediction model to a parallel computing mode (Conort: Paragraph(s) 0161, 0189 teach(es) A processor including multiple processor cores and/or multiple processors multiple processors may provide functionality for parallel, simultaneous execution of instructions or for parallel, simultaneous execution of one instruction on more than one piece of data).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Walters and Conort to incorporate the teachings of Conort for wherein the updating information of the computing mode instructs to update the computing mode of the time series prediction model to a parallel computing mode.
There is motivation to combine Conort into the combination of Walters and Conort because Conort’s teachings of parallel and simultaneous execution would facilitate updating a model (Conort: Paragraph(s) 0161, 0189).
Regarding Claim 16, the combination of Walters and Conort teaches all the limitations of claim 1 above; and Walters further teaches wherein the model updating information further comprises at least one of the following: an identity of a prediction task to be updated; an identity of a model to be updated; or timestamp information for updating the model to be updated (Walters: Paragraph(s) 0166, 0157, 0162 teach(es) model optimizer can be configured to retrieve the stored model (and optionally the stored one or more stored hyperparameters) based on the model generation request and an index of stored models. The index of stored models can be maintained by model optimizer, model storage, or another component of system).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Breckenridge (US 8250009 B1) teaches Updateable Predictive Analytical Modeling, including updating and accuracy.
Song (US 11443237 B1) teaches Centralized Platform For Enhanced Automated Machine Learning Using Disparate Datasets, including updating the machine learning models periodically, and timestamp.
Ma (US 20220225126 A1) teaches Data Processing Method And Device In Wireless Communication Network, including index/identity of AI model, output parameters of the AI model, and predicting.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLAY LEE whose telephone number is (571)272-3309. The examiner can normally be reached Monday-Friday 8-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neha Patel can be reached at (571)270-1492. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLAY C LEE/Primary Examiner, Art Unit 3699
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