The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This office action is in response to Applicant’s submission filed on 13 July 2026. THIS ACTION IS NON-FINAL.
In response to the restriction requirement, Applicant’s election without traverse of the instant application in the reply filed on 13 July 2026 is acknowledged.
Status of Claims
Claims 1-9 are pending.
Claims 11-20 are cancelled.
Claims 1-9 are rejected under 35 U.S.C. 112(b) as indefinite.
Claim 1-9 are rejected under 35 U.S.C. 101 for being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is objected to.
Claim Objections
Claim 1 is objected to because of the following informalities: the claims contain grammatical error, “… to source one of the first and second dataset …”, “dataset” should be “datasets”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
A claim is indefinite if, when read in light of the specification, it fails to inform, with reasonable certainty, those skilled in the art about the scope of the invention. Nautilus, Inc. v. Biosig Instruments, Inc., 110 USPQ.2d 1688, U.S. Supreme Court (2014).
Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claim 1, "constructs a probabilistic affinity mapping between a first and a second dataset of the machine learning training data, which, when linked, comprises a plurality of historical queries and historical commands test-sampled from one or more production logs of a deployed dialogue system." It is unclear what the phrase "which, when linked" is intended to modify — i.e., whether "which" refers back to "a probabilistic affinity mapping," to "a first and a second dataset," or to some combination thereof — and it is further unclear what condition or state is denoted by "when linked" (e.g., whether this refers to the affinity mapping having been constructed, the two datasets having been combined, or some other event), the claim is therefore indefinite. For purposes of examination, the phrase has been interpreted as reciting that the first and second datasets, once linked via the affinity mapping, together comprise the recited historical queries and commands.
Regarding claims 2-9, which depend on above rejected claim 1, are rejected for the same reason.
Regarding claim 3,
(a)“the diversity metric value for each of the plurality of distinct affinity linked datasets within the machine learning training data”, lack of antecedent basis, the claim is therefor indefinite. For the purpose of applying prior art, this limitation is construed to be “a diversity metric value for each of the plurality of distinct affinity linked datasets within the machine learning training data”.
(b) “wherein the diversity metric value relates to …”, it is not clear which “diversity metric value” this element is refereeing to, is it “diversity metric value for each of the plurality of distinct affinity linked datasets within the machine learning training data …” in claim 3, or “diversity metric value of the machine learning data …” in claim 1? the claim is therefore indefinite. For the purpose of applying prior art, this limitation is construed to be “wherein the diversity metric value for each of the plurality of distinct affinity linked datasets within the machine learning data relates to …”.
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.
Judicial Exception
Claims 1-9 of the claimed invention are directed to a judicial exception, an abstract idea, without significantly more.
(Independent Claims) With regards to claim 1,
Step 1: The claim recites a machine, which falls into one of the statutory categories.
2A – Prong 1: the claim, in part, recites
(a)“constructs a probabilistic affinity mapping between a first and a second dataset of the machine learning training data, which, when linked, comprises a plurality of historical queries and historical commands test-sampled from one or more production logs of a deployed dialogue system…; calculates one or more validation metrics of the one of the first and second dataset of machine learning training data, including calculating one or more of a coverage metric value and a diversity metric value of the machine learning training data” (mental process and/or math concept ), as drafted, is a process that, under its broadest reasonable interpretation, covers mathematical concepts but for the recitation of generic computer components. That is, the steps of “constructs a probabilistic affinity mapping …”, “calculates …”, based on their broadest reasonable interpretation, describe mathematical calculations. If a claim limitation, under its broadest reasonable interpretation, covers mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
(b) “configures one or more training data sourcing parameters …” (mental process and/or math concept), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a computing device, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the language about generic computer elements, “configures … parameters”, in the limitation citied above encompasses evaluating and determining values of parameters, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
(c) “identifies whether to train at least one machine learning classifier based on one or more of the coverage metric value and the diversity metric value of the machine learning training data …” (mental process and/or math concept ), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a computing device, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the language about generic computer elements, “identifies whether …based on one or more of the coverage metric value”, in the limitation citied above encompasses evaluating and making decision, which is based on observation, evaluation, judgement, and/or opinion, that could be performed by human using paper / pen / calculator. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements: (a) “the system comprising: … one or more hardware computing devices implementing the diagnostic system”, “… to train the at least one machine learning classifier when the coverage metric value of the machine learning training data satisfies a minimum coverage metric value threshold”, which is mere instruction to apply an exception (see MPEP 2106.05(f)); (b) “one or more remote sources of machine learning training data… “, “to source one of the first and second dataset of the machine learning training data from the one or more remote sources of the machine learning training data …”, “collects the one of the first and second dataset of the machine learning training data …”, “transmits the one or more training data sourcing parameters to the one or more remote sources of the machine learning training data…”, which is extra-solution activity of pre-solution data gathering / post-solution data output (see MPEP.2106.05(g)); (c) “… deploys the at least one machine learning classifier to intake users' requests and provide relevant diagnostic analysis” which is field of use and technological environment (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, claim 1 recites the additional elements: (a) “the system comprising: … one or more hardware computing devices implementing the diagnostic system”, “… to train the at least one machine learning classifier when the coverage metric value of the machine learning training data satisfies a minimum coverage metric value threshold”, which is mere instruction to apply an exception (see MPEP 2106.05(f)); (b) “one or more remote sources of machine learning training data… “, “to source one of the first and second dataset of the machine learning training data from the one or more remote sources of the machine learning training data …”, “collects the one of the first and second dataset of the machine learning training data …”, “transmits the one or more training data sourcing parameters to the one or more remote sources of the machine learning training data…”, which is extra-solution activity of pre-solution data gathering / post-solution data output (see MPEP.2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"); (c) “… deploys the at least one machine learning classifier to intake users' requests and provide relevant diagnostic analysis” which is field of use and technological environment (see MPEP 2106.05(h)). Hence the additional elements do not add anything significant to the abstract idea. The claim is not patent eligible.
(Dependent claims)
Claims 2-9 are dependent on claim 1, and include all the limitations of claim 1. Therefore, claims 2-9 recite the same abstract ideas.
With regards to claim 2, the claim recites element of “wherein calculating the coverage metric value of the one of the first and second dataset of machine learning training data includes: calculating a distinct coverage metric value for each of a plurality of distinct affinity linked datasets within the machine learning training data, wherein the distinct coverage metric value relates to a measure indicating how well a distinct affinity linked dataset of the distinct affinity linked datasets covers a request expressed by a user; and calculating an aggregated coverage metric value for the machine learning training data based on the distinct coverage metric values for each of the plurality of distinct affinity linked datasets” (mental process and/or math concept), which provides further details on the mathematical calculations. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 3, the claim recites element of “wherein calculating one or more validation metrics of the machine learning training data includes: calculating a probabilistic affinity metric value for each of a plurality of indication to action data representations and a plurality of action to indication data representations within the machine learning training data; and calculating an aggregated diversity metric value for the machine learning training data based on the diversity metric value for each of the plurality of distinct affinity linked datasets within the machine learning training data; wherein the diversity metric value relates to a level of heterogeneity among the machine learning training data” (mental process and/or math concept), which provides further details on the mathematical calculations. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 4, The claim recites additional element of “the machine learning training data is defined by a plurality of distinct predictive probabilities; each of the plurality of distinct affinity linked datasets is associated with a distinct user request classification task of the deployed dialogue system; and each of the plurality of distinct affinity linked datasets includes at least one training and test subset of a plurality of diagnostic commands obtained from a priori diagnostic classification training”, which is characterization for the input data, which is extra-solution activity of pre-solution data gathering (see MPEP.2106.05(g)). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of “the machine learning training data is defined by a plurality of distinct predictive probabilities; each of the plurality of distinct affinity linked datasets is associated with a distinct user request classification task of the deployed dialogue system; and each of the plurality of distinct affinity linked datasets includes at least one training and test subset of a plurality of diagnostic commands obtained from a priori diagnostic classification training”, which is extra-solution activity of pre-solution data gathering (see MPEP.2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claim 5, the claim recites element of “… constructs each of the first and the second dataset of the machine learning training data using a plurality of engineered queries and engineered commands, each of the plurality of engineered queries and engineered commands being artificially generated for one or more identified intent classification tasks” (mental process and/or math concept), which is a data constructing process that can be performed by human. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 6, the claim recites element of “… constructs a composition of the each of the first and the second dataset of the machine learning training data to include a first predetermined ratio of historical queries and historical commands and a second predetermined ratio of engineered queries and engineered commands, and the first predetermined ratio of historical queries and historical commands has a value that is greater than the second predetermined ratio of engineered queries and/or engineered commands” (mental process and/or math concept), which is a data constructing process that can be performed by human. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 7, The claim recites additional element of “the machine learning training data comprises a plurality of distinct indications datasets, action datasets and affinity linked interaction datasets, each of the plurality of distinct indications datasets, action datasets and affinity linked interaction datasets being used for convergence of a machine learning system”, which is characterization for the input data, which is extra-solution activity of pre-solution data gathering (see MPEP.2106.05(g)). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of “the machine learning training data comprises a plurality of distinct indications datasets, action datasets and affinity linked interaction datasets, each of the plurality of distinct indications datasets, action datasets and affinity linked interaction datasets being used for convergence of a machine learning system”, which is extra-solution activity of pre-solution data gathering (see MPEP.2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The claim is not patent eligible.
With regards to claim 8, the claim recites element of “… generating a plurality of distinct sets of prompts for sourcing distinct indications datasets and action datasets for each of a plurality of objective classification tasks of the deployed dialogue system” (mental process and/or math concept), which is a prompt generation process that can be performed by human. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
With regards to claim 9, the claim recites element of “… generating the plurality of distinct sets of prompts is based on the plurality of historical queries and historical commands; and generating the plurality of distinct sets of prompts includes: test sampling the plurality of historical queries and historical commands from the one or more production logs of the deployed dialogue system, and converting the plurality of historical queries and historical commands into the plurality of distinct sets of prompts for sourcing raw machine learning training data” (mental process and/or math concept), which is a prompt generation process that can be performed by human. Except citing generic computer elements to implement the abstract idea, there is no additional element showing integration into a practical application or adding something significantly more to the abstract idea. The claim is not patent eligible.
Allowable Subject Matter
Claims 1-9 include allowable subject matter since when reading the claims in light of the specification, as per, MPEP §2111.01 or Toro Co. v. White Consolidated Industries Inc., 199F.3d 1295, 1301, 53 USPQ2d 1065, 1069, 1069 (Fed.Cir. 1999), none of the references of record alone or in combination disclose or suggest the combination of limitations specified in claims 1-9.
In interpreting the claims, in light of the specification filed on 13 July 2026, the Examiner finds the claimed invention to be patentably distinct from the prior arts of record.
Regarding the independent claim 1, the primary reason for the allowance is the inclusion of the specific system and process of constructing a probabilistic affinity mapping linking two historical query/command datasets before curating and threshold-gating training data via coverage and diversity metrics to train a diagnostic classifier.
Regarding the dependent claims, which include all the limitations of the independent claims, are also allowed.
The following are references close to the invention claimed:
Kang et al., US-PGPUB NO.20190294925A1 [hereafter Kang] teaches sourcing remote machine learning training data and calculating coverage and diversity metric values to gate training of a dialogue classifier. However, Kang does not teach the specific claimed elements combination of constructing a probabilistic affinity mapping linking two historical query/command datasets before curating and threshold-gating training data via coverage and diversity metrics to train a diagnostic classifier.
Lewis et al., US-PGPUB NO.20210125154A1 [hereafter Lewis] (assignee Snap-on Incorporated) teaches natural-language processing of vehicle repair-order data to cluster, classify, and extract facts for generating vehicle service content. However, Lewis does not teach the specific claimed elements combination of constructing a probabilistic affinity mapping linking two historical query/command datasets before curating and threshold-gating training data via coverage and diversity metrics to train a diagnostic classifier.
Olalere, US-PGPUB NO.20210398363A1 [hereafter Olalere] teaches capturing vehicle diagnostic data, including VIN and diagnostic trouble codes, over an OBD/CAN interface to identify vehicle problems. However, Olalere does not teach the specific claimed elements combination of constructing a probabilistic affinity mapping linking two historical query/command datasets before curating and threshold-gating training data via coverage and diversity metrics to train a diagnostic classifier.
Huang et al., "Text Mining with Application to Engineering Diagnostics," IEA/AIE 2006, Springer LNCS vol. 4031 (2006) [hereafter Huang] teaches text-mining classification mapping automotive problem descriptions to diagnostic categories using a trained classifier. However, Huang does not teach the specific claimed elements combination of constructing a probabilistic affinity mapping linking two historical query/command datasets before curating and threshold-gating training data via coverage and diversity metrics to train a diagnostic classifier.
Dong et al., "Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network," 2022 IEEE 25th conference on intelligent transportation systems (ITSC), Oct.8-12, 2022, Macau, China (2022) [hereafter Dong] teaches supervised and semi-supervised machine learning models analyzing in-vehicle CAN bus network data for anomaly detection. However, Dong does not teach the specific claimed elements combination of constructing a probabilistic affinity mapping linking two historical query/command datasets before curating and threshold-gating training data via coverage and diversity metrics to train a diagnostic classifier.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSU-CHANG LEE whose telephone number is 571-272-3567. The fax number is 571-273-3567.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas, can be reached 571-272-2589.
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/TSU-CHANG LEE/
Primary Examiner, Art Unit 2128
Prosecution Insights