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 .
Information Disclosure Statement
The information disclosure statement filed December 1st, 2025 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because 37 CFR 1.98 requires that “Each publication listed in an information disclosure statement must be identified by publisher, author (if any), title, relevant pages of the publication, data, and place of publication.” Publication C1’s description does not appear to include any of those required elements.
It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
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 and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 1 further recites a step of constructing a data set using [] training data, which falls within the mental process grouping of abstract ideas (i.e. selecting or designating data as being part of a specific set). Therefore, the claim recites an abstract idea of constructing a data set from received data.
The claim does not recite any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
sending, by a first device, a first instruction to a second device, instructing the second device to collect and report training data for training a specific AI model and receiving, by the first device, training data reported by the second device which is insignificant extra-solution activity of data gathering, necessary for all uses of the abstract idea (see MPEP 2105.05(g))
training the specific AI model which is also insignificant extra-solution activity, by MPEP 2106.05(g), because MPEP 2106.05(g)(1), “the extra-solution limitation is well known,” because MPEP 2106.05(g)(2), the limitation does not impose meaningful limits on the claim (note that the model training, as recited, does not even require the use of the constructed data set), and because MPEP 2106.05(g)(3), all uses of the recited judicial exception requires such data output (there is no point in constructing a training data set without performing training)
the fact that the constructing is performed by a first device is mere instructions to perform the abstract idea using generic computer components, which by MPEP 2106.05(f)(2) cannot integrate the abstract idea into a practical application (“invoking computers or other machinery merely as a tool to perform an existing process”).
Thus, none of the additional elements integrate the abstract idea into a practical application and the claim is directed to the abstract idea of constructing a data set from received data.
Finally, the additional elements taken alone or in combination cannot provide an inventive concept nor significantly more than the abstract idea because, both alone and in combination, they are well-understood, routine, and conventional activity. This is demonstrated by Cheng, US Patent 11,809,480, which discusses “conventional federated learning” (column 17, lines 13-60) and describes it, as is well-known by one of ordinary skill in the art, as collecting data from a plurality of client devices by a server to create a dataset with which to train a machine learning model (e.g. lines 15-16, “trains machine learning models using decentralized data residing on end devices” & lines 38-43, “uploading gradients … in each round client systems are selected not uniformly at random”). Thus, the recited additional elements, taken alone or in combination, are identified as well-understood, routine, and conventional. Therefore, the claim is subject matter ineligible.
Claim 19 recites the first device, comprising a processor and a memory, to perform precisely the method of Claim 1. As performance of an abstract idea on generic computer components cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself, Claim 19 thus rejected for reasons set forth in the rejection of Claim 1.
Claim Rejections - 35 USC § 102
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.
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, 2, 6, 9-12, 15, 16, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mars, US Patent 10,296,848 (as provided by the applicant in the Information Disclosure Statement dated 12/1/2025).
Regarding Claim 1, Mars teaches a data collection method, comprising: sending, by a first device, a first instruction to a second device, instructing the second device to collect and report training data for training a specific AI model (Mars, Abstract, “transmitting the machine learning training data request to each of a plurality of external training data sources” e.g. column 3, line 22-23, “source from a number of data sources or users into the crowdsourcing data platform”); receiving, by the first device, training data reported by the second device (Mars, Abstract, “collecting and storing the machine learning training data from each of the plurality of external training data sources”); constructing, by the first device, a data set using the training data (Mars, column 3, lines 28-30, “process the raw training data samples collected from the plurality of external training data sources 180 into a refined or finished composition or list of training data samples”) and training the specific AI model (Mars, Claim 1, “training the machine learning classification model with the plurality of training data samples”).
Regarding Claim 2, Mars teaches the data collection according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Mars further teaches selecting, by the first device, N second devices from M candidate second devices according to a present first screening condition … wherein M and N are positive integers and N is less than or equal to M (Mars, column 10, lines 21-25, “receive an identification of one or more external training devices from which the user desires machine learning training responses … provide a dropdown menu from which an administrator may select the one or more external training data sources” denotes selecting only the subset that the user has designated) and unicasting the first instruction to the N second devices (Mars, column 10, lines 34-35, “transmit the machine learning training data request to a plurality of external machine learning training data sources” where the fact that each source requires its own format/template, see lines 50-52, “each of the plurality of external machine learning training data sources may have a different input template” denotes that the instruction is unicast, i.e. a different instruction to each external data source).
Regarding Claim 6, Mars teaches the data collection method according to Claim 2 (and thus the rejection of Claim 2 is incorporated). As Claim 2 was rejected by demonstrating that Mars teaches the unicast alternative of two alternative limitations, and as Claim 6 is further an alternative limitation, narrowing the broadcast first instruction of the unselected alternative limitation first parameters is irrelevant to showing that the Mars falls within the claim scope. That is, Mars teaches the data collection method of Claim 2 … wherein the broadcast first instruction comprises any of the recited limitations, as having a broadcast first instruction was optional in Claim 2 to begin with and not relied upon in the rejection.
Regarding Claim 9, Mars teaches the data collection according to Claim 1 (and thus the rejection of Claim 1 is incorporated). Mars further teaches wherein … the first device is a network-side device, and the second device is a network-side device (Mars, Fig. 1A, all devices are on the network, i.e. network-side devices).
Regarding Claim 10, Mars teaches a data collection method, comprising: receiving, by a second device, a first instruction from a first device, wherein the first instruction is used to instruct the second device to collect and report training data for training a specific AI model (Mars, Abstract, “transmitting the machine learning training data request to each of a plurality of external training data sources”) collecting, by the second device, training data and reporting the training data to the first device (Mars, column 3, line 22-23, “source from a number of data sources or users into the crowdsourcing data platform” & Abstract, “collecting and storing the machine learning training data from each of the plurality of external training data sources”).
Regarding Claim 11, Mars teaches the data collection method according to Claim 10 (and thus the rejection of Claim 10 is incorporated). Mars further teaches receiving, by the second device, the first instruction unicast by the first device (Mars, column 10, lines 34-35, “transmit the machine learning training data request to a plurality of external machine learning training data sources” where the fact that each source requires its own format/template, see lines 50-52, “each of the plurality of external machine learning training data sources may have a different input template” denotes that the instruction is unicast, i.e. a different instruction to each external data source), wherein the second device is a second device selected by the first device from candidate second devices according to a present first screening condition (Mars, column 10, lines 21-25, “receive an identification of one or more external training devices from which the user desires machine learning training responses … provide a dropdown menu from which an administrator may select the one or more external training data sources” denotes the second device has been selected).
Regarding Claim 12, Mars teaches the data collection method according to Claim 11 (and thus the rejection of Claim 11 is incorporated). Note that Mars further falls within the scope of the claim language, via the alternative limitation in a case that the second device has received the first instruction broadcast by the first device, collecting and reporting, by the second device, the training data because this limitation is contingent. By MPEP 2111.04, “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met”. The precedent condition the second device has received the first instruction broadcast by the first device is not met, thus the antecedent condition is not required.
Regarding Claim 12, Mars teaches the data collection method according to Claim 11 (and thus the rejection of Claim 11 is incorporated). Mars further teaches in the case that the second device has received the first instruction unicast by the first device, collecting and reporting, by the second device, the training data (Mars, Abstract, “collecting and storing the machine learning training data from each of the plurality of external training data sources”).
Regarding Claim 15, Mars teaches the data collection method according to Claim 10 (and thus the rejection of Claim 10 is incorporated). Mars further teaches before the reporting the training data to the first device, the method further comprises: sending, by the second device, a first request to the first device, requesting collection and reporting of training data (Mars, column 10, “each of the plurality of external machine learning training data sources may have a different template which may function to identify in advance of providing the machine learning training data request” that is, the first device has previously received a template appropriate for and from the second devices, where the template is a format of a request).
Regarding Claim 16, Mars teaches the data collection method according to Claim 11 (and thus the rejection of Claim 11 is incorporated). As Claim 11 was rejected by demonstrating that Mars teaches the unicast alternative of two alternative limitations, and as Claim 6 is further an alternative limitation, narrowing the broadcast first instruction of the unselected alternative limitation first parameters is irrelevant to showing that the Mars falls within the claim scope. That is, Mars teaches the data collection method of Claim 2 … wherein the broadcast first instruction comprises any of the recited limitations, as having a broadcast first instruction was optional in Claim 2 to begin with and not relied upon in the rejection.
Regarding Claim 18, Mars teaches the data collection according to Claim 10 (and thus the rejection of Claim 10 is incorporated). Mars further teaches wherein … the first device is a network-side device, and the second device is a network-side device (Mars, Fig. 1A, all devices are on the network, i.e. network-side devices).
Claim 19 recites a first device, comprising a processor and memory to perform precisely the method of Claim 1. As Mars teaches that their first device does so (Mars, Claim 1, “machine learning configuration and management console … comprises one or more computer processors and a non-transitory computer readable medium”), Claim 19 is rejected for reasons set forth in the rejection of Claim 1.
Claim 20 recites a second device, comprising a processor and memory to perform precisely the method of Claim 10. As Mars teaches that their second devices/“external training data sources” do so (Mars, column 3, “the plurality of external training data sources may include a crowdsourcing data platform, such as Amazon Mechanical Turk of the like”), Claim 19 is rejected for reasons set forth in the rejection of Claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
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.
Claims 3-6, 13, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mars, US Patent 10,296,848, in view of Wall, US PG Pub 2023/0306303.
Regarding Claim 3, Mars teaches the data collection method according to Claim 2 (and thus the rejection of Claim 2 is incorporated). Mars does not teach, but Wall, in the analogous art of collecting crowdsources data, does teach before the sending … : receiving, by the first device, first training data (Wall, Abstract, “particular crowdworkers who are able to perform with high accuracy and reliability (referred to herein as ‘super recognizers’) are identified” & Fig. 3, elements 320-340 & 350, where 330 “Obtain annotations from crowdworkers” happens before 360, “Provide unlabeled dataset to super recognizers” – performance on previous tasks is used to evaluate the crowdworkers and used to select good crowdworkers for later tasks). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to collect previous data from the crowdworkers, before sending a later request for more training data. The motivation to do so is identify the crowdworkers who provide the best training data (Wall, [0050], “metrics describing more efficient crowdworkers can be identified” and used [0051] “to further filter which crowdworkers … are appropriate to the requirements of the specific applications”).
Regarding Claim 4, the Mars/Wall combination of Claim 3 teaches the data collection method according to Claim 3 (and thus the rejection of Claim 3 is incorporated). The combination has already been shown to teach wherein the first device receives only the first training data reported by the candidate second devices and determines the first parameter based on the first training data (wherein the first parameter is a judgement parameter for the first screening condition) (in the combination, the first device computes a reliability score/parameter rather than receiving it directly, see Wall, [0050], “metrics describing more efficient crowdworkers can be identified” and used [0051] “to further filter which crowdworkers … are appropriate to the requirements of the specific applications”).
Regarding Claim 5, the Mars/Wall combination of Claim 3 teaches the data collection method according to Claim 3 (and thus the rejection of Claim 3 is incorporated). As Claim 3 was rejected by demonstrating that Mars/Wall teaches the first training data alternative of the alternative limitations first training data or a first parameter, Claim 5’s further narrowing of the unselected alternative limitation first parameters is irrelevant to showing that the Mars/Wall combination falls within the claim scope. The rejection of Claim 3 teaches the first training data alternative, and the other side of the or cannot effect that.
Regarding Claim 5, the Mars/Wall combination of Claim 3 teaches the data collection method according to Claim 3 (and thus the rejection of Claim 3 is incorporated). The combination can further teach wherein the first parameter comprises … data type of the candidate second device (Wall, Abstract, “super recognizer” is a data type of the candidate second device, since the data from a super recognizer is preferred).
Regarding Claim 6, Mars teaches the data collection method according to Claim 2 (and thus the rejection of Claim 2 is incorporated). Mars does not teach, but Wall, in the analogous art of crowdsourcing, teaches wherein the unicast first instruction comprises … number of samples of training data to be collected by the second device (Wall, [0054], “request to fill out the questionnaire for a minimum number of different videos … In numerous embodiments, the minimum number is 10” & [0056], “A second request is posted … schedules be completed for a number of different unlabeled videos”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to require a minimum number of training data samples to be collected from the second device, as does Wall, in the data-requesting invention of Mars. The motivation to do so is “as a precaution against luck” (Wall, [0054]) i.e. enough samples that the data being collected can be evaluated for quality control.
Regarding Claim 13, Mars teaches the data collection method according to Claim 11 (and thus the rejection of Claim 11 is incorporated). Mars does not teach, but Wall, in the analogous art of collecting crowdsources data, does teach before the receiving … : reporting, by the second candidate devices, first training data (Wall, Abstract, “particular crowdworkers who are able to perform with high accuracy and reliability (referred to herein as ‘super recognizers’) are identified” & Fig. 3, elements 320-340 & 350, where 330 “Obtain annotations from crowdworkers” happens before 360, “Provide unlabeled dataset to super recognizers” – performance on previous tasks is used to evaluate the crowdworkers and used to select good crowdworkers for later tasks). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to collect previous data from the crowdworkers, before sending a later request for more training data. The motivation to do so is identify the crowdworkers who provide the best training data (Wall, [0050], “metrics describing more efficient crowdworkers can be identified” and used [0051] “to further filter which crowdworkers … are appropriate to the requirements of the specific applications”).
Regarding Claim 14, the Mars/Wall combination of Claim 13 teaches the data collection method according to Claim 13 (and thus the rejection of Claim 13 is incorporated). As Claim 13 was rejected by demonstrating that Mars/Wall teaches the first training data alternative of the alternative limitations first training data or a first parameter, Claim 14’s further narrowing of the unselected alternative limitation first parameters is irrelevant to showing that the Mars/Wall combination falls within the claim scope. The rejection of Claim 13 teaches the first training data alternative, and the other side of the or cannot effect that.
Regarding Claim 14, the Mars/Wall combination of Claim 13 teaches the data collection method according to Claim 13 (and thus the rejection of Claim 13 is incorporated). The combination can further teach wherein the first parameter comprises … data type of the candidate second device (Wall, Abstract, “super recognizer” is a data type of the candidate second device, since the data from a super recognizer is preferred).
Regarding Claim 16, Mars teaches the data collection method according to Claim 11 (and thus the rejection of Claim 11 is incorporated). Mars does not teach, but Wall, in the analogous art of crowdsourcing, teaches wherein the unicast first instruction comprises … number of samples of training data to be collected by the second device (Wall, [0054], “request to fill out the questionnaire for a minimum number of different videos … In numerous embodiments, the minimum number is 10” & [0056], “A second request is posted … schedules be completed for a number of different unlabeled videos”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to require a minimum number of training data samples to be collected from the second device, as does Wall, in the data-requesting invention of Mars. The motivation to do so is “as a precaution against luck” (Wall, [0054]) i.e. enough samples that the data being collected can be evaluated for quality control.
Claims 7, 8, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mars, in view of Wall, and further in view of Appugliese, US PG Pub 2022/0237503.
Regarding Claim 7, the Mars/Wall combination of Claim 3 teaches the data collection method of Claim 3 (and thus the rejection of Claim 3 is incorporated). The Mars/Wall combination does not teach, but Appugliese teaches after training a specific AI model, sending, by a first device [that trained the model] (Appugliese, [0022], “Model training and validation outside a database system environment” denotes a first device training the model), the trained AI model and a hyperparameter to L inference devices, wherein L is greater than M, equal to M, or less than M (Appugliese, Abstract, “Model data comprising a model object and model metadata is extracted from a trained model” where “model metadata” that is transmitted to the database system environment along with the model is a hyperparameter, sent to at least one inference device/the database system environment). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, after training a model a la Mars/Wall, to send the model and a hyperparameter to an external inference device, as does Appugliese. The motivation to perform training and inference different devices is that it “allows for model development, training, and validation in an environment best suited to such activities” (Appugliese, [0022]); the motivation to send the hyperparameter/metadata as well is that the metadata “describes any data pre-processing needed to execute the model object” (Appugliese, [0023]), i.e. to allow the inference device to actually perform inference.
Regarding Claim 8, the Mars/Wall/Appugliese combination of Claim 7 teaches the data collection method of Claim 7 (and thus the rejection of Claim 7 is incorporated). The combination has already been shown to teach, via the metadata, wherein the hyperparameter comprises … inner iteration counts corresponding to … the inference devices (Appugliese, [0023], “data preprocessing or feature engineering steps … performed in series … the model metadata describes feature scaling, including normalization and standardization, of data input to the model” that is, a set of steps or iterations that need to be performed before the inference can occur, e.g. inner iteration counts corresponding to the inference devices).
Regarding Claim 17, the Mars/Wall combination of Claim 3 teaches the data collection method of Claim 13 (and thus the rejection of Claim 13 is incorporated). The Mars/Wall combination does not teach, but Appugliese teaches after the collecting, by the second device and reporting the training data to the first device, the method further comprises: receiving, by an inference device, the trained AI model and a hyperparameter sent by the first device (Appugliese, Abstract, “Model data comprising a model object and model metadata is extracted from a trained model” where “model metadata” that is transmitted to the database system environment along with the model is a hyperparameter, sent to at least one inference device/the database system environment) …. wherein the hyperparameter comprises … inner iteration counts corresponding to … the inference devices (Appugliese, [0023], “data preprocessing or feature engineering steps … performed in series … the model metadata describes feature scaling, including normalization and standardization, of data input to the model” that is, a set of steps or iterations that need to be performed before the inference can occur, e.g. inner iteration counts corresponding to the inference devices). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, after training a model a la Mars/Wall, to send the model and a hyperparameter to an external inference device, as does Appugliese. The motivation to perform training and inference different devices is that it “allows for model development, training, and validation in an environment best suited to such activities” (Appugliese, [0022]); the motivation to send the hyperparameter/ metadata as well is that the metadata “describes any data pre-processing needed to execute the model object” (Appugliese, [0023]), i.e. to allow the inference device to actually perform inference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Cheng, US Patent 11,809,480, appears to anticipate at least the independent claim.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN M SMITH whose telephone number is (469)295-9104. The examiner can normally be reached Monday - Friday, 8:00am - 4pm Pacific.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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