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 action is responsive to pending claims 1, 3-8, 12-13, 16-17, 19-20 filed 7/22/2026.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: processing unit, communication unit (claim 13, 16); module (claim 16);
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
The 101 rejections are withdrawn.
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.
Claim(s) 1, 3, 5-7, 13, 16-17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng (CN 114579548 A) in view of Druzhynin (WO 2023107128 A1).
For claim 1, Zheng discloses: a method, in a server-client scenario comprising a service provider and a client associated with a terminal device (p.3:25-40 discloses overall network architecture including data server service provider, edge processor client device, and sensor collector terminal device, see p.3:39-43), the method comprising:
determining, by the service provider, a set of rules for preparing input data (p.4:30-40: establishing cleaning rules based on incidence relation between client and terminal);
receiving, by the client, from the service provider the set of rules for preparing input data for the service provider (ibid: established rules are received);
preparing, by the client, based on raw data from the terminal device, input data for the service provider according to the set of rules to provide prepared input data (p.4:45-50: preparing raw data according to rules, hence, generating prepared data for input to further processes);
providing, by the client, the prepared input data to the service provider (p.3:30-50, p.4:40-43: data server service provider stores the cleaned analysis data according to cleaning rules);
updating, by the service provider, the set of rules (p.4:40-45, p.5:10-15, );
providing, by the service provider, the updated set of rules to the client (ibid: rules are provided to the client).
Zheng does not disclose: wherein the method is for training a machine learning (ML) model; wherein the scenario is a ML scenario; wherein the service provider is a ML service provider;
training, by the ML service provider, the ML model based on the prepared input data;
wherein the updating is based on an evolution of the training of the ML model to provide an updated set of rules;
training, by the ML service provider, an updated version of the trained ML model based on input data prepared according to the updated set of rules.
Druzhynin discloses: wherein the method is for training a machine learning (ML) model (fig.1 gives network overview, with fig.2 0021 disclosing receiving data from edge at the deployment engine / server to train ML models, such as based on receive sensor data, see 0023, 0025); wherein the scenario is a ML scenario (fig.1 gives overview of networking scenario for machine learning); wherein the service provider is a ML service provider (fig.2, 0021, as above: service provider / deployment engine (fig.1:104) for providing ML models);
training, by the ML service provider, the ML model based on the prepared input data (fig. 2, 0021, 0023, 0025).
wherein the updating is based on an evolution of the training of the ML model to provide an updated set of rules model (Druzhynin 0025, 0028: updating configurations and training based on monitored data, newly attached devices, etc., hence, combination with Zheng yielding updating of cleaning configurations of client);
training, by the ML service provider, an updated version of the trained ML model based on input data prepared according to the updated set of rules (Druzhynin 0025, 0028).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zheng by incorporating the ML training configuration of Druzhynin. Both concern the art of server and edge device data collection and monitoring, and the incorporation would have, according to Druzhynin, allow deployment of ML models that are the best suited for the particular time, context, environment, etc. of the sensor network system (0017).
For claim 3, Zheng modified by Druzhynin discloses the method of claim 1, as described above. Zheng modified by Druzhynin further discloses: at least one of: providing, by the ML service provider, the trained ML model to the client (fig.2, 0021: deployment); or providing, by the ML service provider, the trained ML model to a further device (ibid: provision to further clients).
For claim 5, Zheng modified by Druzhynin discloses the method of claim 1, as described above. Zheng modified by Druzhynin further discloses: the client accesses the ML service provider with an identity stored in the client or obtained from the terminal device (Druzhynin figs.3a-b shows secure communication between client and server via VPN, hence, the client storing various VPN authentication parameters and identities, e.g., certificates, authentication data, private network addresses, etc.); and
the ML service provider performs secure authentication based on the identity provided from the client (ibid: VPN constitutes encryption and tunneling security based on private network identity and authorizations).
For claim 6, Zheng modified by Druzhynin discloses the method of claim 1, as described above. Zheng modified by Druzhynin further discloses: wherein the set of rules for preparing input data includes at least one of: missing data management, data formatting, filtering, normalization, discretization, transformation (Zheng p.1:45-55, p.2:1-5 discloses improvement of data cleaning effect from sensors, IOT devices, hence, data transformation), scaling, augmentation, anonymization, or labelling.
For claim 7, Zheng modified by Druzhynin discloses the method of claim 1, as described above. Zheng modified by Druzhynin further discloses: wherein the raw data are collected by the terminal device from an external device (Zheng p.3:39-43 contemplates collecting data from environment, hence, lighting device, agricultural devices, etc.).
For claim 13, Zheng discloses: an apparatus configured to:
determine a set of rules for preparing input data (p.4:30-40: establishing cleaning rules based on incidence relation between client and terminal);
provide, to a client associated with a terminal device, the set of rules (ibid: established rules are received);
receive, from the client, input data prepared according to the set of rules (p.3:30-50, p.4:40-43: data server service provider stores the cleaned analysis data according to cleaning rules).
Zheng does not disclose: wherein the apparatus is a computing apparatus, comprising:
a processing unit; and
a communication unit;
wherein the rules are for training a machine learning (ML) model;
train the ML model based on the input data prepared according to the set of rules;
provide the trained ML model to at least one of the client or a further device;
provide to the client an updated set of rules based on an evolution of the training of the ML model;
train an updated version of the trained ML model based on input data prepared according to the updated set of rules.
Druzhynin discloses: wherein the apparatus is a computing apparatus, comprising:
a processing unit (0031); and
a communication unit (0020);
wherein the rules are for training a machine learning (ML) model (fig.2, 0021: training and deploying a ML model based on sensor data, hence, combination with Zheng yielding a technique where the data rules facilitate model training);
train the ML model based on the input data prepared according to the set of rules (ibid: combination with updating of rules of Zheng (p.4:40-45, p.5:10-15) yielding training based on the data received according to new rules, see Druzhynin 0025, 0028).
provide the trained ML model to at least one of the client or a further device (fig.2, 0021: deployment to one client device or another);
provide to the client an updated set of rules based on an evolution of the training of the ML model (Zheng p.4:40-45: updating cleaning rules by the server; Druzhynin 0025, 0028: updating configurations and training based on monitored data, newly attached devices, etc., hence, combination with Zheng yielding updating of cleaning configurations of client);
train an updated version of the trained ML model based on input data prepared according to the updated set of rules (Druzhynin 0025, 0028, 0021).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zheng by incorporating the ML training configuration of Druzhynin. Both concern the art of server and edge device data collection and monitoring, and the incorporation would have, according to Druzhynin, allow deployment of ML models that are the best suited for the particular time, context, environment, etc. of the sensor network system (0017).
For claim 16, Zheng discloses: a system comprising:
a module (p.3:33-38: edge processor constitutes a module with various computing components, e.g., communications component, storage component, etc.); and
a computing apparatus (p.3:30-35: data server constitutes a computing apparatus),
wherein the module is operable to:
receive, from a service provider, a set of rules for preparing input data for the ML service provider (p.4:30-40: establishing cleaning rules based on incidence relation between client and terminal);
prepare, based on raw data from a terminal device, input data for the service provider according to the set of rules to provide prepared input data (p.4:45-50: preparing raw data according to rules); and
provide the prepared input data to the ML service provider (p.3:30-50, p.4:40-43: data server service provider stores the cleaned analysis data according to cleaning rules), and wherein the computing apparatus includes a communication unit (p.3:30-40: communication of rules, data occurs via communication module) and is operable to:
determine a set of rules (p.4:30-40: establishing cleaning rules based on incidence relation between client and terminal, the rules determined for communication to client) for preparing input data for training a ML model;
provide, to a client associated with the terminal device, the set of rules (ibid: established rules are received);
receive, from the client, the input data prepared according to the set of rules (p.3:30-50, p.4:40-43: data server service provider receives and stores the cleaned analysis data according to cleaning rules);
provide, by the service provider, the updated set of rules to the client (p.4:40-45, 50-60, pg.5:10-15).
Zheng does not disclose: wherein the service provider is a machine learning service provider; train the ML model based on the prepared input data;
update, by the ML service provider, the set of rules based on an evolution of the training of the ML model to provide an updated set of rules;
train, by the ML service provider, an updated version of the trained ML model based on input data prepared according to the updated set of rules.
Druzhynin discloses: wherein the service provider is a machine learning service provider (fig.2, 0021: training and deploying a ML model based on sensor data, hence, combination with Zheng yielding a technique where the data rules facilitate model training);
train the ML model based on the prepared input data (fig.2, 0021);
update, by the ML service provider, the set of rules based on an evolution of the training of the ML model to provide an updated set of rules (Druzhynin 0025, 0028: updating configurations and training based on monitored data, newly attached devices, etc., hence, combination with Zheng yielding updating of cleaning configurations of client);
train, by the ML service provider, an updated version of the trained ML model based on input data prepared according to the updated set of rules (Druzhynin 0025, 0028, 0021).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zheng by incorporating the ML training configuration of Druzhynin. Both concern the art of server and edge device data collection and monitoring, and the incorporation would have, according to Druzhynin, allow deployment of ML models that are the best suited for the particular time, context, environment, etc. of the sensor network system (0017).
For claim 17, Zheng discloses: determining, by a service provider, a set of rules for preparing input data (p.4:30-40: establishing cleaning rules based on incidence relation between client and terminal);
receiving, by a client, from the service provider the set of rules for preparing input data for the service provider (ibid: established rules are received);
preparing, by the client, based on raw data from a terminal device, input data for the service provider according to the set of rules to provide prepared input data (p.4:45-50: preparing raw data according to rules);
providing, by the client, the prepared input data to the service provider (p.3:30-50, p.4:40-43: data server service provider stores the cleaned analysis data according to cleaning rules).
providing, by the service provider, the updated set of rules to the client (p.4:40-45, 50-60, p.5:10-15).
Zheng does not disclose: one or more tangible, non-transitory, computer-readable media storing instructions which, when executed on two or more processors of at least a computing device and a computing apparatus, cause the two or more processors to perform operations;
wherein the service provider is a machine learning service provider;
training, by the ML service provider, a ML model based on the prepared input data;
updating, by the ML service provider, the set of rules based on an evolution of the training of the ML model to provide an updated set of rules;
training, by the ML service provider, an updated version of the trained ML model based on input data prepared according to the updated set of rules.
Druzhynin discloses: one or more tangible, non-transitory, computer-readable media storing instructions which, when executed on two or more processors of at least a computing device and a computing apparatus, cause the two or more processors to perform operations (0031-32);
wherein the service provider is a machine learning service provider (fig.2, 0021: training and deploying a ML model based on sensor data, hence, combination with Zheng yielding a technique where the data rules facilitate model training);
training, by the ML service provider, a ML model based on the prepared input data (fig.2:,1, 0025);
updating, by the ML service provider, the set of rules based on an evolution of the training of the ML model to provide an updated set of rules (Druzhynin 0025, 0028: updating configurations and training based on monitored data, newly attached devices, etc., hence, combination with Zheng yielding updating of cleaning configurations of client);
training, by the ML service provider, an updated version of the trained ML model based on input data prepared according to the updated set of rules (Druzhynin 0025, 0028, 0021).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zheng by incorporating the ML training configuration of Druzhynin. Both concern the art of server and edge device data collection and monitoring, and the incorporation would have, according to Druzhynin, allow deployment of ML models that are the best suited for the particular time, context, environment, etc. of the sensor network system (0017).
For claim 19, Zheng modified by Druzhynin discloses the method of claim 17, as described above. Zheng modified by Druzhynin further discloses: wherein the operations include at least one of:
providing, by the ML service provider, the trained ML model to the client (Druzhynin fig.2, 0021: deployment); or
providing, by the ML service provider, the trained ML model to a further device (ibid: deployment to further devices).
Claim(s) 4, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng (CN 114579548 A) in view of Druzhynin (WO 2023107128 A1) in view of Ma Explore ("Exploring edge computing for multitier industrial control", published 2020).
For claim 4, Zheng modified by Druzhynin discloses the method of claim 3, as described above. Zheng modified by Druzhynin further discloses: after providing the trained ML model to the client:
providing, by the client, the trained ML model to the terminal device; or
performing, by the client, inference application of the trained ML model on further raw data from the terminal device (Druzhynin fig.3d, 0025: provision of inference / output, such as based on sensor data input).
Zheng modified by Druzhynin does not disclose: providing, by the client, output of the inference application to the terminal device.
Ma discloses: : providing, by the client, output of the inference application to the terminal device (fig.1-2, §II.A “System Model” contemplates control of industrial equipment via an edge controller collecting sensor data and running a relatively more sophisticated control algorithm, hence, combination with the machine learning edge control techniques of Druzhynin fig.3 yielding a technique where a machine learning model provides inference output as control signals to the terminal device).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zheng modified by Druzhynin by incorporating the edge controlling technique of Ma. Both concern the art of edge computing for sensor data collection, and the incorporation would have, according to Ma, provide increase flexibility and greater computational power in an industrial setting (§I ¶1, II.A ¶1).
For claim 20, Zheng modified by Druzhynin discloses the method of claim 19, as described above. Zheng modified by Druzhynin further discloses: wherein the operations include, after providing the trained ML model to the client:
providing, by the client, the trained ML model to the terminal device; or
performing, by the client, inference application of the trained ML model on further raw data from the terminal device (Druzhynin fig.3d, 0025: provision of inference / output, such as based on sensor data input).
Zheng modified by Druzhynin does not disclose: providing, by the client, output of the inference application to the terminal device.
Ma discloses: : providing, by the client, output of the inference application to the terminal device (fig.1-2, §II.A “System Model” contemplates control of industrial equipment via an edge controller collecting sensor data and running a relatively more sophisticated control algorithm, hence, combination with the machine learning edge control techniques of Druzhynin fig.3 yielding a technique where a machine learning model provides inference output as control signals to the terminal device).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zheng modified by Druzhynin by incorporating the edge controlling technique of Ma. Both concern the art of edge computing for sensor data collection, and the incorporation would have, according to Ma, provide increase flexibility and greater computational power in an industrial setting (§I ¶1, II.A ¶1).
Claim(s) 8, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng (CN 114579548 A) in view of Druzhynin (WO 2023107128 A1) in view of Tripathi ("Channel-adaptive transmission protocols for smart grid IoT communication", published 2020).
For claim 8, Zheng discloses: a module perform operations including:
in response to receiving from a service provider a set of rules for preparing input data for the ML service provider (p.3:25-40 discloses overall network architecture including data server service provider, edge processor client device, and sensor collector terminal device, see p.3:39-43; with p.4:30-40: establishing cleaning rules based on incidence relation between client and terminal), preparing, based on raw data from a terminal device, input data for the service provider according to the set of rules to provide prepared input data (p.4:45-50: preparing raw data according to rules);
providing the prepared input data to the service provider (p.3:30-50, p.4:40-43: data server service provider receives from the client and stores the cleaned analysis data according to cleaning rules);
in response to receiving from the service provider an updated set of rules, preparing updated input data according to the updated set of rules (p.4:40-45, p.5:10-15);
providing the updated input data prepared according to the updated set of rules to the service provider (p.4:40-45: updating cleaning rules by the server, hence, server being provided new cleaned data based on revised rules);
Zheng does not disclose: the module being for machine learning (ML) operations, the module comprising:
at least one processor; and
a machine-readable storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform steps;
the service provider being a ML service provider;
wherein the updated set of rules is based on an evolution of training of an ML model; wherein the updated set of rules are based on an objective function that determines an optimum between transmission bandwidth and transmission power;
wherein the providing is for training an updated version of the trained ML model;
receive from the ML service provider the updated version of the trained ML model; and
providing the updated version of the trained ML model to the terminal device, or
performing inference application of the updated version of the trained ML model on further raw data from the terminal device, and providing output of the inference application to the terminal device.
Druzhynin discloses: wherein the module is for training a machine learning (ML) operations (fig.1 gives network overview, with fig.2 0021 disclosing receiving data from edge at the deployment engine / server to train ML models, such as based on receive sensor data, see 0023, 0025); the service provider is a ML service provider, the module being for ML operations (fig.2, 0021, as above: service provider / edge device client (fig.1:104) for providing ML models); the module comprising:
at least one processor (0031); and
a machine-readable storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform steps (0032).
service provider being a ML service provider (fig.2, 0021, as above: service provider / deployment engine (fig.1:104) for providing ML models);
wherein the updated set of rules is based on an evolution of training of an ML model (Druzhynin 0025, 0028: updating configurations and training based on monitored data, newly attached devices, etc., hence, combination with Zheng yielding updating of cleaning configurations of client);
wherein the providing is for training an updated version of the trained ML model Druzhynin 0025, 0028);
receive from the ML service provider the updated version of the trained ML model (Druzhynin fig.2, 0021);
performing, by the client, inference application of the trained ML model on further raw data from the terminal device (Druzhynin fig.3d, 0025: provision of inference / output, such as based on sensor data input).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zheng by incorporating the ML training configuration of Druzhynin. Both concern the art of server and edge device data collection and monitoring, and the incorporation would have, according to Druzhynin, allow deployment of ML models that are the best suited for the particular time, context, environment, etc. of the sensor network system (0017).
Zheng modified by Druzhynin does not disclose: wherein the updated set of rules are based on an objective function that determines an optimum between transmission bandwidth and transmission power;
providing output of the inference application to the terminal device.
Ma discloses: : providing, by the client, output of the inference application to the terminal device (fig.1-2, §II.A “System Model” contemplates control of industrial equipment via an edge controller collecting sensor data and running a relatively more sophisticated control algorithm, hence, combination with the machine learning edge control techniques of Druzhynin fig.3 yielding a technique where a machine learning model provides inference output as control signals to the terminal device).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zheng modified by Druzhynin by incorporating the edge controlling technique of Ma. Both concern the art of edge computing for sensor data collection, and the incorporation would have, according to Ma, provide increase flexibility and greater computational power in an industrial setting (§I ¶1, II.A ¶1).
Zheng modified by Druzhynin modified by Ma does not disclose: wherein the updated set of rules are based on an objective function that determines an optimum between transmission bandwidth and transmission power (fig.2 gives overview of selection of Reed Solomon coding base on channel state, see §V¶1-2 characterizing channel state based on various SNR thresholds and characterized by frequency, hence, updating set of rules (encoding and decoding Reed Solomon protocol) based on a selection function that determines an optimum between transmission bandwidth metrics (frequency) and power (SNR predictions)).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify the method of Zheng modified by Druzhynin modified by Ma does by incorporating the adaptive channel encoding technique of Tripathi. Both concern the art of IoT based network communications, and the incorporation would have, according to Tripathi, improve IOT transmissions by lowering loss and increasing efficiency (§I:B) for modern IOT networks (§I ¶1).
For claim 12, Zheng modified by Druzhynin modified by Ma modified by Tripathi discloses the method of claim 8, as described above. Druzhynin further discloses: wherein the module comprises a communication unit that includes at least one of a WiFi module, a Bluetooth module, or a 3GPP-compliant mobile communication module (Druzhynin 0020).
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, Applicant argued:
1. The claims are statutory.
Examiner agrees.
2. Cited art does not disclose an update due to an evolution of an ML model, as Druzhynin discloses the updating of the models themselves, not the evolution of a ML model.
Examiner submits that the disclosure of updating based on retraining and reoptimizing would be recognized by a POSITA as an evolution of the model. Hence, further clarification is needed to reach Applicant’s intended scope.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gao (US 20220172111 A1) discloses a GUI workflow for data preparation for machine learning.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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/LIANG LI/
Primary examiner AU 2143
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