DETAILED ACTION
This office action is responsive to the response filed 3/5/2026. The application contains claims 1-20, all examined and rejected.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/5/2026 has been entered.
Claim Rejections - 35 USC § 112
Claim 4 recites the limitation "the object" in line 1. There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner consider the presence of an object as one of the possible first characteristics.
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 non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 15, 18 are each directed to a statutory category, it recites a series of steps pertaining to analyze received data, tag data, and select subset of the received data (mental process, mathematical concept).
Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process, manufacture, and machine as in independent Claim 1, 15, and 18, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed tag data and select subset of the data based on the created tags which is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
For claim 1, “tag an element of the training dataset with: (a) a first attribute representing a first characteristic detectable in the element, and (b) a second attribute representing a second characteristic that has previously not been detected in the element”, and “select a subset of the plurality of elements to train an Artificial Intelligence (AD) or Machine Learning (ML) model based, at least in part, upon the tag” (Mental process, observation, evaluation and judgment) (Mental process, observation, evaluation and judgment);
For claim 15, “select a subset of a plurality of elements of a training dataset to re-train the AI/ML model based, at least in part, upon an attribute of each element of the subset that represents a characteristic not observable in the input data”, “identify the attribute, wherein to identify the attribute”, “in response to a determination that a drift confidence score output by the drift detector is smaller than another drift confidence score output by the another drift detector, identify the input data as having the characteristic“ (Mental process, observation, evaluation and judgment) (Mental process, observation, evaluation and judgment);
For claim 18, “selecting a training dataset based, at least in part, upon an attribute representing a feature that has previously not been observable, detectable, or ascertainable from the training dataset” (Mental process, observation, evaluation and judgment) (Mental process, observation, evaluation and judgment);
The claims recites additional elements as
Claim 1
“An Information Handling System (IHS), comprising: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to” (“Using a computer as a tool to perform a mental process” MPEP 2106.04(a)(2)(III)(C);
“receive a training dataset comprising a plurality of elements” (insignificant extra-solution activity”),
“wherein the second characteristic comprises at least one of: a weather condition or an intent“ (data description directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)));
Claim 15:
“hardware memory device having program instructions stored thereon that, upon execution”, (“Using a computer as a tool to perform a mental process” MPEP 2106.04(a)(2)(III)(C));
“receive a request to re-train an Artificial Intelligence (AI) or Machine Learning (ML) model where drift is detected with respect to input data”, (insignificant extra solution activity of data transfer. MPEP 2106.05(g));
“wherein the characteristic comprises at least one of: a weather condition or an intent“ (data description directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)));
“identify the attribute, wherein to identify the attribute, the program instructions, upon execution, further cause the IHS to: provide the input data to a drift detector and to another drift detector (insignificant extra solution activity of data transfer. MPEP 2106.05(g)), wherein the drift detector is associated with the characteristic and the another drift detector is associated with another characteristic (data description directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)));
Claim 18:
“wherein the feature comprises at least one of: a weather condition or an intent“ (data description directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)))
“re-training an Artificial Intelligence (AI) or Machine Learning (ML) model based upon the training dataset” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)) and training a system is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f))
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“An Information Handling System (IHS), comprising: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to” (“Using a computer as a tool to perform a mental process”, (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2));
“receive a training dataset comprising a plurality of elements” (insignificant extra-solution activity”, "well-understood, routine, or conventional" (WURC), sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i));
“wherein the second characteristic comprises at least one of: a weather condition or an intent“ (data description directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h))).
Claim 15:
“hardware memory device having program instructions stored thereon that, upon execution” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)),
“receive a request to re-train an Artificial Intelligence (AI) or Machine Learning (ML) model where drift is detected with respect to input data” ("well-understood, routine, or conventional" (WURC), sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i));
“wherein the characteristic comprises at least one of: a weather condition or an intent“ (data description directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)));
“identify the attribute, wherein to identify the attribute, the program instructions, upon execution, further cause the IHS to: provide the input data to a drift detector and to another drift detector ("well-understood, routine, or conventional" (WURC), sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), wherein the drift detector is associated with the characteristic and the another drift detector is associated with another characteristic (data description directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)));
Claim 18:
“wherein the feature comprises at least one of: a weather condition or an intent“ (data description directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)))
“re-training an Artificial Intelligence (AI) or Machine Learning (ML) model based upon the training dataset” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
In the instant case, Claim 1, 15, and 18 are directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose, “element comprises at least one of: an image, a video, an audio signal, or text “, claim 3 disclose “first characteristic comprises at least one of: an object, a place, an utterance, or a word”, claim 4 disclose “the object comprises a Light Detection and Ranging (LIDAR) obstacle”. Therefore, claims 2-4 directed to the description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 6 “tag the element with the second attribute, the program instructions, upon execution, cause the IHS to: provide the element to a drift detector associated with the second characteristic; receive a drift confidence score from the drift detector Sending data, receiving a score (insignificant extra-solution activity”, WURC, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i))); and in response to a determination that the drift confidence score is below a threshold value, identify the element as having the second characteristic (mental process (evaluation and judgment) and mathematical concept)”. These limitations does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, Claim 7, disclose provide the element to a first drift detector and to a second drift detector, wherein the first drift detector is associated with the second characteristic and the second drift detector is associated with a third characteristic is Sending data, (insignificant extra-solution activity”, WURC, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i))), wherein the third characteristic has previously not been detected in the element (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h)); (a) in response to a determination that a first drift confidence score output by the first drift detector is smaller than a second drift confidence score output by the second drift detector, identify the element as having the second characteristic; or (b) in response to a determination that the second drift confidence score output by the second drift detector is smaller than the first drift confidence score output by the first drift detector, identify the element as having the third characteristic (Mental process, mathematical concept), in addition using a model for drift detection (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)) It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea;, Claims 8 disclose “second characteristic comprises a weather condition, and wherein the third characteristic comprises another weather condition“ (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h)) It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; Claims 9 disclose “the second characteristic comprises an intent, and wherein the third characteristic comprises another intent “ (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; Claims 10 disclose “program instructions, upon execution, cause the IHS to identify the second characteristic from an extrinsic source based upon at least one of: a location of collection of the element, or a time of collection of the element“ (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h)) It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; Claims 11 disclose “the extrinsic source comprises a Controller Area Network (CAN) bus message, and wherein the second characteristic comprises a state of a vehicle configured to collect the element“ (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h)) It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 12 disclose “wherein the AI/ML model comprises at least one of: a Linear Regression model, a Deep Neural Network model, a Logistic Regression model, a Decision Tree model, a Linear Discriminant Analysis model, a Naive Bayes model, a Support Vector Machines model, a Learning Vector Quantization model, a K-nearest Neighbors model, a Transformer model, or a Random Forest model” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 13 disclose detect drift in the AI/ML model; and select the subset of the plurality of elements in response to the detection (mental process, mathematical concept), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 14 and 20 disclose a pre-model analysis to calculate a first metric based, at least in part, upon a variance of input data with respect to an input data norm, wherein the input data norm is established in the absence of drift; or (b) a post-model analysis to calculate a second metric based, at least in part, upon a variance of prediction or inference results with respect to a prediction or inference norm, wherein the prediction or inference norm is established in the absence of drift (mental process, mathematical concept), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 17 disclose “wherein the characteristic further comprises a weather condition, and wherein the another characteristic comprises another weather condition” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h)) It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 19 disclose “identifying the feature using an extrinsic source based upon at least one of: (a) a location of data collection, or (b) a time of data collection, wherein the feature indicates a state of a vehicle configured to perform the collection” (mental process), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of independent claims; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6, 12 are rejected35 U.S.C. 103 as being unpatentable over Christiansen et al . [US 2021/0125104 A1, hereinafter Chris] in view of Trehan [US 2021/0365642 A1].
With regard to Claim 1,
Chris disclose an Information Handling System (IHS), comprising: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to (Fig. 12, ¶20, ¶¶111-112):
receive a training dataset comprising a plurality of elements (Fig. 4, 310, ¶59, ¶60, “data minder module 300 is configured to perform the following steps: receiving training data, wherein the training data is the data used to train the machine learning model 120 (step 310)”, ¶64);
tag an element of the training dataset with:
a first attribute representing a first characteristic detectable in the element (¶¶38-39, “Machine learning model 120 preferably takes the form of a deep neural network. A deep neural network is a class of machine learning model that uses multiple layers to progressively extract higher level features from the sample data. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts such as digits, letters, or faces”, ¶45), and
(b) a second attribute representing a second characteristic that has previously not been detected in the element (¶59, ¶60, ” comparing, using a data representation model, the sample data to the training data to determine a similarity score”, ¶65, “data minder module 300 compares, using a data representation model, the sample data to the training data to determine a similarity score”, ¶61, ¶¶38-39, “Machine learning model 120 preferably takes the form of a deep neural network. A deep neural network is a class of machine learning model that uses multiple layers to progressively extract higher level features from the sample data. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts such as digits, letters, or faces”, ¶45 “similarity score and any of the features is a second characteristic has not been detected before processing); and
select a subset of the plurality of elements to train an Artificial Intelligence (AD) or Machine Learning (ML) model based, at least in part, upon the tag (¶45, ¶60, “if the similarity score is above or equal to a first predetermined similarity threshold, sending the sample data to the machine learning model for processing”, ¶63).
The Examiner further notes that the data type is non-functional descriptive material and is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 218 USPQ 401, 403 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). However, in effort to expedite persecution and as Chris does not explicitly teach that the second characteristic comprises at least one of: a weather condition or an intent.
Trehan teach an Information Handling System (IHS), comprising: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to (¶12)
receive a training dataset comprising a plurality of elements (¶4, “ML approach enables the chatbot to learn responses and probable intents from a corpus of training data”, ¶6, “machine learning using large sample sets of fragments”, “ access to large training data samples are appropriately tagged with intents”);
tag an element of the training dataset (¶6, “ training data may be re-tagged to resolve boundary cases, over fit cases and under fit cases“, “training data samples are appropriately tagged with intents”) with:
a first attribute representing a first characteristic detectable in the element (¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”), and
a second attribute representing a second characteristic, wherein the second characteristics comprises at least one of: a weather condition or an intent (¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”, ¶59, “mapped to a predefined intent and a predetermined response in the intermediate language. One of the plurality of pre-stored sets of intent maps”).
Chris and Trehan are analogous art to the claimed invention because they are from a similar field of endeavor of feature extraction for machine learning models. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris resulting in resolutions as disclosed by Nara with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris as described above to enable enables the chatbot to learn responses and probable intents from a corpus of training data, this will broaden the applications that Chris teaching could be used for (Trehan, ¶4, “enables the chatbot to learn responses and probable intents from a corpus of training data. Once the intent is understood, the chatbot may respond by providing information from known data sources”). This modification is simple substitution of one known element for another to obtain predictable results (MPEP 2143).
With regard to Claim 2,
Chris-Trehan disclose the IHS of claim 1, wherein the element comprises at least one of: an image (Chris Fig. 4, ¶63, ¶6, “if the sample data is an image”), a video, an audio signal, or text. The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 3,
Chris-Trehan disclose the IHS of claim 1, wherein the first characteristic comprises at least one of: a place or an utterance (Chris ¶37, “sample data for use with the invention includes images containing other objects for detection, for example letters of the digits, letters, a traffic sign, an animal, etc.”, Trehan, ¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”). The same motivation to combine for claim 1 equally applies for current claim.
The Examiner further notes that the data type is non-functional descriptive material and is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 218 USPQ 401, 403 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
With regard to Claim 4,
Chris-Trehan disclose the IHS of claim 3, wherein the object comprises a Light Detection and Ranging (LIDAR) obstacle (Because this limitation merely elaborates on a conditional limitation of a parent claim, the prior art of record is deemed to meet this limitation by virtue of meeting an alternative condition in the parent claim). The same motivation to combine for claim 3 equally applies for current claim.
With regard to Claim 6,
Chris-Trehan disclose the IHS of claim 1, wherein to tag the element with the second attribute, the program instructions, upon execution, cause the IHS to:
provide the element to a drift detector associated with the second characteristic (Chris ¶60, “data minder module 300 is configured to perform the following steps: receiving training data, wherein the training data is the data used to train the machine learning model 120 (step 310); receiving the sample data prior to the machine learning model 120 (step 320); comparing, using a data representation model, the sample data to the training data to determine a similarity score”, data minder module is a draft detector);
receive a drift confidence score from the drift detector (Chris ¶60, “similarity score” similarity score is drift confidence score); and
in response to a determination that the drift confidence score is below a threshold value, identify the element as having the second characteristic (Chris ¶62, “Data minder module 300 also identifies sample data that has a lower similarity score that the second predetermined similarity threshold. This is because, for a machine learning model 120 which is a classifier, sample data falling below the second predetermined similarity threshold are typically have extremely poor image quality or do not contain the object to be classified. This causes the sample data to fall significantly outside of the classification manifold, ¶¶63-65). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 12,
Chris-Trehan disclose the IHS of claim 1, wherein the AI/ML model comprises at least one of: a Linear Regression model, a Deep Neural Network model, a Logistic Regression model, a Decision Tree model, a Linear Discriminant Analysis model, a Naive Bayes model, a Support Vector Machines model, a Learning Vector Quantization model, a K-nearest Neighbors model, a Transformer model, or a Random Forest model (¶39). The same motivation to combine for claim 1 equally applies for current claim.
Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Christiansen et al . [US 2021/0125104 A1, hereinafter Chris] in view of Trehan [US 2021/0365642 A1] in view of Narayanaswamy et al. [US 2015/0253463 A1, hereinafter Nara].
With regard to Claim 7,
Chris-Trehan teach the IHS of claim 1, wherein to tag the element with the second attribute, the program instructions, upon execution, cause the IHS to:
provide the element to a first drift detector, wherein the first drift detector is associated with the second characteristic (Chris, ¶60, “data minder module 300 is configured to perform the following steps: receiving training data, wherein the training data is the data used to train the machine learning model 120 (step 310); receiving the sample data prior to the machine learning model 120 (step 320); comparing, using a data representation model, the sample data to the training data to determine a similarity score”, data minder module is a draft detector), a first drift confidence score output by the first drift detector (Chris, ¶60, “similarity score” similarity score is drift confidence score).
Chris-Trehan does not explicitly teach a second drift detector, the second drift detector is associated with a third characteristic, and wherein the third characteristic has previously not been detected in the element; and at least one of: (a) in response to a determination that a first drift confidence score output by the first drift detector is smaller than a second drift confidence score output by the second drift detector, identify the element as having the second characteristic; or (b) in response to a determination that the second drift confidence score output by the second drift detector is smaller than the first drift confidence score output by the first drift detector, identify the element as having the third characteristic.
Nara teach provide the element to a first drift detector and to a second drift detector (¶13, ¶23, “accuracy estimation engine 114, … computes an accuracy score measuring the accuracy of the specific run (for example, by computing the Root Mean Squared Error (RMSE) over the predictions for all of the observations)”, ¶35, “determining an ensemble of one or more of the multiple forecasting models to apply to the given environmental event based on (i) said estimated accuracy value for each of the multiple forecasting models,”), wherein the first drift detector is associated with the second characteristic and the second drift detector is associated with a third characteristic, and wherein the third characteristic has previously not been detected in the element (¶¶17-18, “event classifier component 108 associates a label with the forecast output along with the time of the event occurrence. The output of the event classifier component 108 includes an event list comprising of [event label, event time] pairs. Examples of event labels can include (but are not limited to) “Heavy rainfall,” “Hurricane,” “Thunder storms,” “Cyclones,” “Strong winds,””); and
at least one of:
(a) in response to a determination that a first drift confidence score output by the first drift detector is smaller than a second drift confidence score output by the second drift detector, identify the element as having the second characteristic (Fig. 2, 208, ¶23, “accuracy estimation engine 114, for every model and event type, scans previous forecasts and filters those runs for which the corresponding observed values indicate occurrence of the selected event. For these runs of the model, the accuracy estimation engine 114 compares the model output parameters with the observable parameters. Based on this comparison, the accuracy estimation engine 114 computes an accuracy score measuring the accuracy of the specific run (for example, by computing the Root Mean Squared Error (RMSE) over the predictions for all of the observations), 31, “the model and parameter selection engine 110 determines, based on the identified event (from the event classifier 108), the estimated accuracy for the event (from the accuracy estimation engine 114) … an ensemble of models and parameterizations that are suited for the given event”, ¶35, “determining an ensemble of one or more of the multiple forecasting models … based on (i) said estimated accuracy value for each of the multiple forecasting models”, accuracy scores calculated by accuracy estimation engine for every forecast model for the input data (element) act as drift confidence scores as it reflects how well each detector model matches the data (higher similarity represent lower drift)); or
(b) in response to a determination that the second drift confidence score output by the second drift detector is smaller than the first drift confidence score output by the first drift detector, identify the element as having the third characteristic.
Chris-Trehan and Nara are analogous art to the claimed invention because they are from a similar field of endeavor of modeling and detecting data similarity. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Trehan resulting in resolutions as disclosed by Nara with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Trehan as described above to provide a higher ability to match data by aligning the elements with the most related characteristics, and this help in prioritizing the most likely scenario. This modification is simply combining elements according to known methods to yield predictable results, and usage of known a technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 8,
Chris-Trehan-Nara teach the IHS of claim 7, wherein the second characteristic comprises a weather condition, and wherein the third characteristic comprises another weather condition (Nara ¶¶17-18, “event classifier component 108 associates a label with the forecast output along with the time of the event occurrence. The output of the event classifier component 108 includes an event list comprising of [event label, event time] pairs. Examples of event labels can include (but are not limited to) “Heavy rainfall,” “Hurricane,” “Thunder storms,” “Cyclones,” “Strong winds,””).
The same motivation to combine for claim 7 equally applies for current claim
The Examiner further notes that the data type is non-functional descriptive material and is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 218 USPQ 401, 403 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
With regard to Claim 9,
Chris-Trehan-Nara teach the HS of claim 7, wherein the second characteristic further comprises an intent, and wherein the third characteristic comprises another intent (Trehan, ¶4, “learn responses and probable intents from a corpus of training data “, ¶6, “large sample sets of fragments, which are then mapped to intents“, “training data samples are appropriately tagged with intents”, ¶59, “mapped to a predefined intent“).The same motivation to combine for claim 7 equally applies for current claim
The Examiner further notes that the data type is non-functional descriptive material and is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 218 USPQ 401, 403 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
With regard to Claim 10,
Chris-Trehan teach the IHS of claim 1, wherein the program instructions, upon execution, cause the IHS to identify the second characteristic (¶59, ¶60, ” comparing, using a data representation model, the sample data to the training data to determine a similarity score”, ¶65, “data minder module 300 compares, using a data representation model, the sample data to the training data to determine a similarity score”, ¶61, “similarity score is a second characteristic not detectable in the element as it is a quantitative measure that cannot be detected in the element itself). The same motivation to combine for claim 1 equally applies for current claim
Chris-Trehan does not explicitly teach
Nara teach identify the second characteristic from an extrinsic source based upon at least one of: a location of collection of the element, or a time of collection of the element (¶17, “output of the event classifier component 108 includes an event list comprising of [event label, event time] pairs. Examples of event labels can include (but are not limited to) “Heavy rainfall,” “Hurricane,” “Thunder storms,” “Cyclones,” “Strong winds,” etc.”).
Chris-Trehan and Nara are analogous art to the claimed invention because they are from a similar field of endeavor of modeling and detecting data similarity. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris resulting in resolutions as disclosed by Nara with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris as described above to provide a high contextual accuracy as weather data changes rapidly, also this would prevent misapplying weather data as errors could occur within large data collection. Therefore this generally increase data value as user could analyze it, use in a more personalized form (e.g. weather in specific location). This modification is simply combining elements according to known methods to yield predictable results, and usage of known a technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Christiansen et al. [US 2021/0125104 A1, hereinafter Chris] in view of Trehan [US 2021/0365642 A1] in view of Narayanaswamy et al. [US 2015/0253463 A1, hereinafter Nara] in view of Yamamoto [US 2023/0289980 A1, hereinafter Yamamoto]
With regard to Claim 11,
Chris-Trehan-Nara teach the IHS of claim 10.
The same motivation to combine for claim 10 equally applies for current claim
Chris-Trehan-Nara does not explicitly teach the extrinsic source comprises a Controller Area Network (CAN) bus message, and wherein the second characteristic comprises a state of a vehicle configured to collect the element.
Yamamoto teach extrinsic source comprises a Controller Area Network (CAN) bus message (¶38, “communication network 41 is formed by, for example, an in-vehicle communication network, a bus, or the like that conforms to any standard such as controller area network (CAN)”), and wherein the second characteristic comprises a state of a vehicle configured to collect the element (¶207, “data transmission unit 323 of the information processing device 311 transmits the image and the recognition result to the server 312. The data transmission unit 323 of the information processing device 311 transmits at least data and a frame related to the recognition result recognized by the recognition processing unit 322. A mechanism for transmitting a vehicle speed, a frame rate, and the like as necessary may be employed”).
Chris-Trehan-Nara and Yamamoto are analogous art to the claimed invention because they are from a similar field of endeavor of collecting and analyzing data to detect objects within data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Trehan-Nara resulting in resolutions as disclosed by Yamamoto with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Trehan-Nara as described above as using Controller Area Network (CAN) to reduce Wiring Complexity and Cost as replaces the need for numerous point-to-point connections with a single multiplex wire that connects all devices in a system with high robustness and reliability while providing efficient data exchange in real time which is essential for applications that require quick responses. In addition associating a state of a vehicle configured to collect the element would improve the system ability to understand the data context which improve the data value as such information would improve the understanding of driving scenarios which also enhance machine learning model robustness and precision. This modification is simply combining elements according to known methods to yield predictable results, and usage of known a technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Claims 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Christiansen et al . [US 2021/0125104 A1, hereinafter Chris] in view of Trehan [US 2021/0365642 A1] in view of Calmon et al. [US 2023/0004854 A1, hereinafter Calmon].
With regard to Claim 13,
Chris-Trehan teach the IHS of claim 1, wherein the program instructions, upon execution, cause the IHS to:
detect drift in the AI/ML model (¶19, “calculating a data drift for the machine learning model using the second performance data and the first performance data; only if the data drift is above a first predetermined drift threshold, triggering retraining of the machine learning model. In this way, the machine learning inference system is able to automatically retrain the machine learning model by identifying when changes in performance data and data drifts are above expected levels”).
Chris-Trehan does not explicitly teach select the subset of the plurality of elements in response to the detection.
Calmon teach detect drift in the AI/ML model (¶22, “if the confidence value for the ML model at an edge node falls below the confidence threshold, then drift has occurred for the ML model. In one or more embodiments, in response to detecting that drift has occurred, the edge node sends a drift signal to the central node”, ¶24, “once the central node has determined that the ML model has drifted, the central node updates the model in the shared communication layer to be associated with a drifted indication instead of a fresh indication”); and select the subset of the plurality of elements in response to the detection (¶25, “once enough new data has been received from the edge nodes, the central node retrains the ML model using, at least in part, the new data. Any amount of new data from any number of edge nodes may be considered enough data to trigger retraining of the ML model. All or any portion of the new data may be used in a new training data set, which may or may not be combined with all or any portion of the previous training set to obtain a new training set to retrain the ML mode”).
Chris-Trehan and Calmon are analogous art to the claimed invention because they are from a similar field of endeavor of updating ML models based on drift detection. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Trehan resulting in resolutions as disclosed by Calmon with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Trehan as described above to optimize retraining of model as by efficiently allocate resources to retraining or adapting only on the most relevant data, rather than retraining on a large dataset with minimal changes which save time and resources and improve the model accuracy. This modification is simply combining elements according to known methods to yield predictable results, and usage of known a technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 18,
Chris teach a method, comprising: selecting a training dataset based, at least in part, upon an attribute representing a feature that has previously not been observable, detectable, or ascertainable from the training dataset (¶59, ¶60, ” comparing, using a data representation model, the sample data to the training data to determine a similarity score”, ¶65, “data minder module 300 compares, using a data representation model, the sample data to the training data to determine a similarity score”, ¶61, “similarity score is a second characteristic not detectable in the element as it is a quantitative measure that cannot be detected in the element itself, ¶60, “if the similarity score is above or equal to a first predetermined similarity threshold, sending the sample data to the machine learning model for processing”, ¶63, ¶¶38-39).
The Examiner further notes that the data type is non-functional descriptive material and is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 218 USPQ 401, 403 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). However, in effort to expedite persecution and as Chris does not explicitly teach the feature comprises at least one of: a weather condition or an intent.
Trehan teach the feature comprises at least one of: a weather condition or an intent (¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”, ¶59, “mapped to a predefined intent and a predetermined response in the intermediate language. One of the plurality of pre-stored sets of intent maps”).
Chris and Trehan are analogous art to the claimed invention because they are from a similar field of endeavor of feature extraction for machine learning models. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris resulting in resolutions as disclosed by Nara with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris as described above to enable enables the chatbot to learn responses and probable intents from a corpus of training data, this will broaden the applications that Chris teaching could be used for (Trehan, ¶4, “enables the chatbot to learn responses and probable intents from a corpus of training data. Once the intent is understood, the chatbot may respond by providing information from known data sources”). This modification is simple substitution of one known element for another to obtain predictable results (MPEP 2143).
Chris-Trehan re-training an Artificial Intelligence (AI) or Machine Learning (ML) model based upon the training dataset
Calmon teach selecting a training dataset; and re-training an Artificial Intelligence (AI) or Machine Learning (ML) model based upon the training dataset (¶25, “once enough new data has been received from the edge nodes, the central node retrains the ML model using, at least in part, the new data. Any amount of new data from any number of edge nodes may be considered enough data to trigger retraining of the ML model. All or any portion of the new data may be used in a new training data set, which may or may not be combined with all or any portion of the previous training set to obtain a new training set to retrain the ML mode”).
Chris-Trehan and Calmon are analogous art to the claimed invention because they are from a similar field of endeavor of updating ML models based on drift detection. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Trehan resulting in resolutions as disclosed by Calmon with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Trehan as described above to optimize retraining of model as by efficiently allocate resources to retraining or adapting only on the most relevant data, rather than retraining on a large dataset with minimal changes which save time and resources and improve the model accuracy. This modification is simply combining elements according to known methods to yield predictable results, and usage of known a technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Christiansen et al . [US 2021/0125104 A1, hereinafter Chris] in view of Trehan [US 2021/0365642 A1] in view of Calmon et al. [US 2023/0004854 A1, hereinafter Calmon] in view of Erlandson et al. [US 2019/0147357 A1, hereinafter Erlandson].
With regard to Claim 14,
Chris-Trehan-Calmon teach the IHS of claim 13.
The same motivation to combine for claim 13 equally applies for current claim
Chris-Trehan-Calmon does not explicitly teach wherein to detect the drift, the program instructions, upon execution, further cause the IHS to perform at least one of:
(a) a pre-model analysis to calculate a first metric based, at least in part, upon a variance of input data with respect to an input data norm, wherein the input data norm is established in the absence of drift; or
(b) a post-model analysis to calculate a second metric based, at least in part, upon a variance of prediction or inference results with respect to a prediction or inference norm, wherein the prediction or inference norm is established in the absence of drift.
Elandson teach wherein to detect the drift, the program instructions, upon execution, further cause the IHS to perform at least one of:
a pre-model analysis to calculate a first metric based, at least in part, upon a variance of input data with respect to an input data norm, wherein the input data norm is established in the absence of drift; or
a post-model analysis to calculate a second metric based, at least in part, upon a variance of prediction or inference results with respect to a prediction or inference norm, wherein the prediction or inference norm is established in the absence of drift (¶22, “predictive learning model is trained on a training data set that represents a particular snapshot in time of an ongoing data stream. After the predictive learning model is trained and deployed in operation, the data stream, referred to herein as operational data, will often continue to evolve. When the operational data changes sufficiently relative to the original training data set, the predictive performance (aka “inference”) of the predictive learning model degrades because the operational data is from regions of the larger feature space that the predictive learning model never encountered through the training data set. This phenomenon is sometimes referred to as “learning model drift,” although in fact it is the operational data, not the predictive learning model, that is drifting”).
Chris-Trehan-Calmon and Erlandson are analogous art to the claimed invention because they are from a similar field of endeavor of drift detection for machine learning models. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Trehan-Calmon resulting in resolutions as disclosed by Erlandson with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Trehan-Calmon as described above to detect the drift of the operational data as it occurs. Detecting the drift of the operational data may be useful, for example, to determine when a predictive learning model should be retrained on current operational data (Erlandson, ¶23, “Detecting learning model drift directly by comparing the output of the predictive learning model against ground truth is almost always impossible, … it is desirable to detect the drift of the operational data as it occurs. Detecting the drift of the operational data may be useful, for example, to determine when a predictive learning model should be retrained on current operational data”).
Claim 15, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Christiansen et al . [US 2021/0125104 A1, hereinafter Chris] in view of Trehan [US 2021/0365642 A1] in view of Calmon et al. [US 2023/0004854 A1, hereinafter Calmon] in view of Narayanaswamy et al. [US 2015/0253463 A1, hereinafter Nara].
With regard to Claim 15,
Chris teach a hardware memory device having program instructions stored thereon that, upon execution, cause an Information Handling System (IHS) (Fig. 12, ¶20, ¶¶111-112) to:
receive a request to re-train an Artificial Intelligence (AI) or Machine Learning (ML) model where drift is detected with respect to input data (¶19, “calculating a data drift for the machine learning model using the second performance data and the first performance data; only if the data drift is above a first predetermined drift threshold, triggering retraining of the machine learning model. In this way, the machine learning inference system is able to automatically retrain the machine learning model by identifying when changes in performance data and data drifts are above expected levels”);
select a subset of a plurality of elements (¶59, ¶60, ” comparing, using a data representation model, the sample data to the training data to determine a similarity score”, ¶65, “data minder module 300 compares, using a data representation model, the sample data to the training data to determine a similarity score”, ¶61, “similarity score is a second characteristic not detectable in the element as it is a quantitative measure that cannot be detected in the element itself, ¶60, “if the similarity score is above or equal to a first predetermined similarity threshold, sending the sample data to the machine learning model for processing”, ¶63, ¶¶38-39); and
identify the attribute, and wherein to identify the attribute, the program instructions, upon execution, further cause the IHS to:
provide the input data to a drift detector, wherein the drift detector is associated with the characteristic (Chris, ¶60, “data minder module 300 is configured to perform the following steps: receiving training data, wherein the training data is the data used to train the machine learning model 120 (step 310); receiving the sample data prior to the machine learning model 120 (step 320); comparing, using a data representation model, the sample data to the training data to determine a similarity score”, data minder module is a draft detector), a first drift confidence score output by the first drift detector (¶60, “similarity score” similarity score is drift confidence score).
The Examiner further notes that the data type is non-functional descriptive material and is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 218 USPQ 401, 403 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). However, in effort to expedite persecution and as Chris does not explicitly teach characteristic comprises at least one of: a weather condition or an intent.
Trehan teach a hardware memory device having program instructions stored thereon that, upon execution, cause an Information Handling System (IHS) to: (¶12)
characteristic comprises at least one of: a weather condition or an intent (¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”, ¶59, “mapped to a predefined intent and a predetermined response in the intermediate language. One of the plurality of pre-stored sets of intent maps”).
Chris and Trehan are analogous art to the claimed invention because they are from a similar field of endeavor of feature extraction for machine learning models. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris resulting in resolutions as disclosed by Nara with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris as described above to enable enables the chatbot to learn responses and probable intents from a corpus of training data, this will broaden the applications that Chris teaching could be used for (Trehan, ¶4, “enables the chatbot to learn responses and probable intents from a corpus of training data. Once the intent is understood, the chatbot may respond by providing information from known data sources”). This modification is simple substitution of one known element for another to obtain predictable results (MPEP 2143).
Chris-Trehan does not explicitly teach
Calmon teach receive a request to re-train an Artificial Intelligence (AI) or Machine Learning (ML) model where drift is detected with respect to input data (¶22, “if the confidence value for the ML model at an edge node falls below the confidence threshold, then drift has occurred for the ML model. In one or more embodiments, in response to detecting that drift has occurred, the edge node sends a drift signal to the central node”, ¶24, “once the central node has determined that the ML model has drifted, the central node updates the model in the shared communication layer to be associated with a drifted indication instead of a fresh indication”); and
in response to the request, select a subset of a plurality of elements of a training dataset to re-train the AI/ML model (¶25, “once enough new data has been received from the edge nodes, the central node retrains the ML model using, at least in part, the new data. Any amount of new data from any number of edge nodes may be considered enough data to trigger retraining of the ML model. All or any portion of the new data may be used in a new training data set, which may or may not be combined with all or any portion of the previous training set to obtain a new training set to retrain the ML mode”).
Chris-Trehan and Calmon are analogous art to the claimed invention because they are from a similar field of endeavor of updating ML models based on drift detection. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Trehan resulting in resolutions as disclosed by Calmon with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Trehan as described above to optimize retraining of model as by efficiently allocate resources to retraining or adapting only on the most relevant data, rather than retraining on a large dataset with minimal changes which save time and resources and improve the model accuracy. This modification is simply combining elements according to known methods to yield predictable results, and usage of known a technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Chris-Trehan-Calmon does not explicitly teach another drift detector, and the other drift detector is associated with another characteristic and in response to a determination that a drift confidence score output by the drift detector is smaller than another drift confidence score output by the other drift detector, identify the input data as having the characteristic.
Nara teach provide the input data to a drift detector and to another drift detector (¶13, ¶23, “accuracy estimation engine 114, … computes an accuracy score measuring the accuracy of the specific run (for example, by computing the Root Mean Squared Error (RMSE) over the predictions for all of the observations)”, ¶35, “determining an ensemble of one or more of the multiple forecasting models to apply to the given environmental event based on (i) said estimated accuracy value for each of the multiple forecasting models,”), wherein the drift detector is associated with the characteristic and the other drift detector is associated with another characteristic (¶¶17-18, “event classifier component 108 associates a label with the forecast output along with the time of the event occurrence. The output of the event classifier component 108 includes an event list comprising of [event label, event time] pairs. Examples of event labels can include (but are not limited to) “Heavy rainfall,” “Hurricane,” “Thunder storms,” “Cyclones,” “Strong winds,””); and
in response to a determination that a drift confidence score output by the drift detector is smaller than another drift confidence score output by the other drift detector, identify the input data as having the characteristic (Fig. 2, 208, ¶23, “accuracy estimation engine 114, for every model and event type, scans previous forecasts and filters those runs for which the corresponding observed values indicate occurrence of the selected event. For these runs of the model, the accuracy estimation engine 114 compares the model output p(parameters with the observable parameters. Based on this comparison, the accuracy estimation engine 114 computes an accuracy score measuring the accuracy of the specific run (for example, by computing the Root Mean Squared Error (RMSE) over the predictions for all of the observations), 31, “the model and parameter selection engine 110 determines, based on the identified event (from the event classifier 108), the estimated accuracy for the event (from the accuracy estimation engine 114) … an ensemble of models and parameterizations that are suited for the given event”, ¶35, “determining an ensemble of one or more of the multiple forecasting models … based on (i) said estimated accuracy value for each of the multiple forecasting models”).
Chris-Trehan-Calmon and Nara are analogous art to the claimed invention because they are from a similar field of endeavor of modeling and detecting data similarity. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Calmon resulting in resolutions as disclosed by Nara with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Calmon as described above to provide a higher ability to match data by aligning the elements with the most related characteristics, and this help in prioritizing the most likely scenario. This modification is simply combining elements according to known methods to yield predictable results, and usage of known a technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 17,
Chris-Trehan-Calmon-Nara teach the hardware memory of claim 15.
The same motivation to combine for claim 15 equally applies for current claim
Chris-Trehan-Calmon does not explicitly teach second characteristic comprises a weather condition, and wherein the third characteristic comprises another weather condition.
Nara teach second characteristic comprises a weather condition, and wherein the third characteristic comprises another weather condition (Nara ¶¶17-18, “event classifier component 108 associates a label with the forecast output along with the time of the event occurrence. The output of the event classifier component 108 includes an event list comprising of [event label, event time] pairs. Examples of event labels can include (but are not limited to) “Heavy rainfall,” “Hurricane,” “Thunder storms,” “Cyclones,” “Strong winds,””).
Chris-Trehan-Calmon and Nara are analogous art to the claimed invention because they are from a similar field of endeavor of modeling and detecting data similarity. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Trehan-Calmon resulting in resolutions as disclosed by Nara with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Trehan-Calmon as described above to provide a higher ability to match data by aligning the elements with the most related characteristics, and this help in prioritizing the most likely scenario. This modification is simply combining elements according to known methods to yield predictable results, and usage of known a technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
The same motivation to combine for claim 16 equally applies for current claim
The Examiner further notes that the data type is non-functional descriptive material and is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 218 USPQ 401, 403 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Claim 19 are rejected under 35 U.S.C. 103 as being unpatentable over Christiansen et al . [US 2021/0125104 A1, hereinafter Chris] in view of Trehan [US 2021/0365642 A1] in view of Calmon et al. [US 2023/0004854 A1, hereinafter Calmon] in view of Yamamoto [US 2023/0289980 A1, hereinafter Yamamoto].
With regard to Claim 19,
Chris-Calmon teach the method of claim 18, further comprising identifying the hidden feature (Chris, ¶59, ¶60, ” comparing, using a data representation model, the sample data to the training data to determine a similarity score”, ¶65, “data minder module 300 compares, using a data representation model, the sample data to the training data to determine a similarity score”, ¶61, “similarity score is a hidden feature in the element as it is a quantitative measure that cannot be detected in the element itself, ¶60, “if the similarity score is above or equal to a first predetermined similarity threshold, sending the sample data to the machine learning model for processing”, ¶63).
The same motivation to combine for claim 18 equally applies for current claim
Chris-Calmon does not explicitly teach extrinsic source comprises a Controller Area Network (CAN) bus message, and wherein the second characteristic comprises a state of a vehicle configured to collect the element.
Yamamoto teach extrinsic source comprises a Controller Area Network (CAN) bus message (¶38, “communication network 41 is formed by, for example, an in-vehicle communication network, a bus, or the like that conforms to any standard such as controller area network (CAN)”), and wherein the hidden feature indicates a state of a vehicle configured to perform the collection (¶207, “data transmission unit 323 of the information processing device 311 transmits the image and the recognition result to the server 312. The data transmission unit 323 of the information processing device 311 transmits at least data and a frame related to the recognition result recognized by the recognition processing unit 322. A mechanism for transmitting a vehicle speed, a frame rate, and the like as necessary may be employed”).
Chris-Calmon and Yamamoto are analogous art to the claimed invention because they are from a similar field of endeavor of collecting and analyzing data to detect objects within data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Calmon resulting in resolutions as disclosed by Yamamoto with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Calmon as described above as using Controller Area Network (CAN) to reduce Wiring Complexity and Cost as replaces the need for numerous point-to-point connections with a single multiplex wire that connects all devices in a system with high robustness and reliability while providing efficient data exchange in real time which is essential for applications that require quick responses. In addition associating a state of a vehicle configured to collect the element would improve the system ability to understand the data context which improve the data value as such information would improve the understanding of driving scenarios which also enhance machine learning model robustness and precision. This modification is simply combining elements according to known methods to yield predictable results, and usage of known a technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Christiansen et al . [US 2021/0125104 A1, hereinafter Chris] in view of Trehan [US 2021/0365642 A1] in view of Calmon et al. [US 2023/0004854 A1, hereinafter Calmon] in view of Yamamoto [US 2023/0289980 A1, hereinafter Yamamoto] in view of Erlandson et al. [US 2019/0147357 A1, hereinafter Erlandson].
With regard to Claim 20,
Chris-Trehan-Calmon-Yamamoto teach the method of claim 19.
The same motivation to combine for claim 19 equally applies for current claim
Chris-Calmon-Yamamoto does not explicitly teach wherein to detect the drift, the program instructions, upon execution, further cause the IHS to perform at least one of:
(a) a pre-model analysis to calculate a first metric based, at least in part, upon a variance of input data with respect to an input data norm, wherein the input data norm is established in the absence of drift; or
(b) a post-model analysis to calculate a second metric based, at least in part, upon a variance of prediction or inference results with respect to a prediction or inference norm, wherein the prediction or inference norm is established in the absence of drift.
Elandson teach wherein to detect the drift, the program instructions, upon execution, further cause the IHS to perform at least one of:
a pre-model analysis to calculate a first metric based, at least in part, upon a variance of input data with respect to an input data norm, wherein the input data norm is established in the absence of drift; or (b) a post-model analysis to calculate a second metric based, at least in part, upon a variance of prediction or inference results with respect to a prediction or inference norm, wherein the prediction or inference norm is established in the absence of drift (¶22, “predictive learning model is trained on a training data set that represents a particular snapshot in time of an ongoing data stream. After the predictive learning model is trained and deployed in operation, the data stream, referred to herein as operational data, will often continue to evolve. When the operational data changes sufficiently relative to the original training data set, the predictive performance (aka “inference”) of the predictive learning model degrades because the operational data is from regions of the larger feature space that the predictive learning model never encountered through the training data set. This phenomenon is sometimes referred to as “learning model drift,” although in fact it is the operational data, not the predictive learning model, that is drifting”).
Chris-Calmon-Yamamoto and Erlandson are analogous art to the claimed invention because they are from a similar field of endeavor of drift detection for machine learning models. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Chris-Calmon-Yamamoto resulting in resolutions as disclosed by Erlandson with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Calmon-Yamamoto as described above to detect the drift of the operational data as it occurs. Detecting the drift of the operational data may be useful, for example, to determine when a predictive learning model should be retrained on current operational data (Erlandson, ¶23, “Detecting learning model drift directly by comparing the output of the predictive learning model against ground truth is almost always impossible, … it is desirable to detect the drift of the operational data as it occurs. Detecting the drift of the operational data may be useful, for example, to determine when a predictive learning model should be retrained on current operational data”).
Response to Arguments
Applicant argue that the current amendments provide improvement to technology and are therefore directed to patent eligible subject matter.
Examiner respectfully disagrees, the provided argument is a conclusionary statement that does not provide any clear details of how the amended claims reflect that improvement that was disclosed in the specifications.
Applicant’s arguments with respect to claim(s) 1, 15, 18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
As to the remaining dependent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the aforementioned Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this “Response to Arguments” section in this office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 2023/0144809 filed by Tajima et al. discloses a method for detecting concept drift and causing a learning model to relearn using post-drift process data See at least ¶3, ¶76
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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