DETAILED ACTION
This office action is responsive to the response filed 3/5/2026. The application contains claims 1-4, 6-12, 14-15, 17-20, 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 .
Claim Objections
Claim 17 objected to because of the following informalities: Claim 17 recite “the the second” instead of “the second”. 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.
Claims 19-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 19 recites the limitation " the second feature" in line 1 and 2. There is insufficient antecedent basis for this limitation in the claim. Dependent claim inherit the parent claim deficiency.
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 1-4, 6-12, 14-15, 17-20 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 1-4, 6-12, 14-15, 17-20 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,
“determine a first characteristic of an element of the training dataset based on analysis of the element”, “determine a second characteristic of the element of the training dataset, wherein the second characteristic is based on a time corresponding to the element“, “tag the element of the training dataset with: (a) a first attribute representing the first characteristic, and (b) a second attribute representing the 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” “determine an amount of drift for the AI or ML model trained with the subset of the plurality of elements“ (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”),
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));
Claim 15 recites a system comprising “A hardware memory device having program instructions stored thereon ” configured to perform the same method as set forth in claim 1, the added element of “A hardware memory device having program instructions stored thereon” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claim 15 is therefore rejected according to the same findings and rationale as provided above.
Claim 18 is similar in scope to claim 1; therefore it is rejected under similar rationale.
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 an image“, claim 3 disclose “first characteristic comprises a weather conditions observable in the image”, claim 4 disclose “the second characteristic comprises a weather condition report corresponding to a time and place corresponding to the image”. Therefore, claims 2-4 are 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 a location of collection of the element, and 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 14 and 20 disclose “determine the amount of 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, and wherein the prediction or inference norm is established in the absence of drift; and selecting 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 17 disclose “wherein the first characteristic comprises a weather condition observable from the element, and wherein the second characteristic comprises a weather condition report corresponding to a location and 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; claim 19 disclose “identifying the second feature using an extrinsic source based upon at (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 1-2, 6, 10, 12, 14-15 and 18 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, “ data minder module 300 receives training data that was used to train the machine learning model 120, and stores the training data in memory”, ¶83 “the training data may be made up of a plurality of individual training items (e.g. a plurality of images)”);
determine a first characteristic of an element of the training dataset based on analysis of 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);
determine a second characteristic of the element of 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”, ¶66, “ produce an unscaled similarity score which indicates how far the sample falls outside the manifold of the training data “),
tag the element of the training dataset with:
a first attribute representing the first characteristic (¶¶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 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”, ¶66, “ produce an unscaled similarity score which indicates how far the sample falls outside the manifold of the training data “); 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”, ¶61, “ data minder module 300 is configured to send sample data to data remapping module 400 if the similarity score is below the first predetermined similarity threshold but above or equal to a second predetermined similarity threshold”, ¶62, “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. Such sample data is sent to exception path”, ¶63, “ Image A has a similarity score which is above the first predetermined similarity threshold, since image A is the most similar to the training data. Accordingly, image A is sent to machine learning model 120 for processing. Image B has a similarity score which is below the first predetermined similarity threshold but above the second similarity threshold, since image B, since image B is similar to the training data but is blurred. Accordingly, image B is sent to data remapping module 400 to remove the blur. Image C has a similarity score which is below the second predetermined similarity threshold, since image C is not similar to the training data. Accordingly, image C is sent to exception path 150 for review by a human operator”, ¶85, “By “updated” it is meant that at least the machine learning model 120 is retrained using sample data”, ¶94, “more training data may become available over time, … update module 500 is further configured to trigger retraining of the machine learning model periodically and/or to include additional training data”);
and determine an amount of drift for the Al or ML model trained with the subset of the plurality of elements (Fig. 5, ¶86, “calculating a data drift for the machine learning model using the second performance data and the first performance data”, ¶¶89-90, “ Data drift identifies that the statistical properties of the sample data, which the machine learning model 120 is trying to predict, change over time in unforeseen ways. Data drift is also referred to as concept drift. The data drift is calculated based on the similarity score from data minder module 300 from the first and the second points in time in combination with the first and second performance metrics”, ¶91, “which causes steps 510, 520, 530 and 540 to be repeated continuously”).
Chris does not explicitly teach wherein the second characteristic is based on a time corresponding to the element.
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”);
determine a first characteristic of an element of the training dataset based on analysis of the element (¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”);
determine a second characteristic of the element of the training dataset, wherein the second characteristic is based on a time corresponding to the element (¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”, ¶64, “the set of input intent maps may be processed by an Application Programming Interface (API) corresponding to, but not limited to weather, flight, banking information, and so forth”, ¶65, “content dimension, for example, may be based on demographic details of the user, current location of the user, current temperature of the location where the user is present, current transportation being used by the user, time of the day, day of the week, or the current month”);
tag the 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 (¶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”, ¶64, “the set of input intent maps may be processed by an Application Programming Interface (API) corresponding to, but not limited to weather, flight, banking information, and so forth”, ¶65, “content dimension, for example, may be based on demographic details of the user, current location of the user, current temperature of the location where the user is present, current transportation being used by the user, time of the day, day of the week, or the current month”).
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 an image (Chris Fig. 4, ¶63, ¶6, “if the sample data is an image”, ¶81, “the training data may be made up of a plurality of individual training items (e.g. a plurality of images)”). The same motivation to combine for claim 1 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 10,
Chris-Trehan teach the IHS of claim 1 , wherein the program instructions, upon execution, cause the IHS to identify the second characteristic (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”, ¶66, “ produce an unscaled similarity score which indicates how far the sample falls outside the manifold of the training data“, Trehan, ¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”, ¶¶64-65), from an extrinsic source based upon a location of collection of the element and a time of collection of the element (Trehan, ¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”, ¶64, “the set of input intent maps may be processed by an Application Programming Interface (API) corresponding to, but not limited to weather, flight, banking information, and so forth”, ¶65, “content dimension, for example, may be based on demographic details of the user, current location of the user, current temperature of the location where the user is present, current transportation being used by the user, time of the day, day of the week, or the current month”).
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.
With regard to Claim 14,
Chris-Trehan teach the IHS of claim 1,wherein to determine the amount of 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 (Chris ¶60, “comparing, using a data representation model, the sample data to the training data to determine a similarity score“, ¶65, “the sample data is compared to the training data within this domain to determine a similarity score”, ¶66, “to produce an unscaled similarity score which indicates how far the sample falls outside the manifold of the training data”, ¶89, “Data drift identifies that the statistical properties of the sample data, which the machine learning model 120 is trying to predict, change over time in unforeseen ways. Data drift is also referred to as concept drift. The data drift is calculated based on the similarity score from data minder module 300 from the first and the second points in time in combination with the first and second performance metrics”); 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, and wherein the prediction or inference norm is established in the absence of drift.
With regard to Claim 15,
Claim 15 is similar in scope to claim 1; therefore it is rejected under similar rationale.
With regard to Claim 18,
Claim 18 is similar in scope to claim 1; therefore it is rejected under similar rationale.
Claims 3-4 and 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 BUENO BUORO et al. [US 20210270999 A1, hereinafter D1].
With regard to Claim 3,
Chris-Trehan teach the IHS of claim 2.
Chris-Trehan does not teach first characteristic comprises a weather conditions observable in the image.
D1 teach first characteristic comprises a weather conditions observable in the image (¶23, “the image processor retrieves images capturing the location”, “the image processor has access to recent images showing the weather conditions at the location”, ¶15, claim 7, “wherein the images are captured by at least one camera that has a view of weather conditions at the location”, ¶24, “At block 225, the image processor extracts attributes of interest from the images. The image processor can use any image processing technique (e.g., a ML algorithm or extraction algorithm) to identify the attributes of interest which can include rainfall, snowfall, fog, flooding, snow accumulation, temperature, and the like”, ¶26, “the attributes of interest extracted from the images are the actual conditions of the location … the image processor can identify the actual value or status of the location (e.g., whether it is raining/snowing, whether there is flooding or accumulation …”, ¶16, “ image processor 150 can use an image processing technique to extract weather conditions at the locations—i.e., a snow accumulation, flooding, precipitation (rain or snow), fog, wind gusts, and the like”).
Chris-Trehan and D1 are analogous art to the claimed invention because they are from a similar field of endeavor of using images processing and machine learning to analyze images and extract features, attributes, or characteristics represented in the images. 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 D1 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Chris-Trehan as described above to allow the usage of images captured at location to identify weather conditions at the location and thereby improve the accuracy of the weather forecasts (D1, ¶¶1, 16, 24). 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 4,
Chris-Trehan teach the IHS of claim 3, wherein the second characteristic comprises a weather condition report corresponding to a time and place corresponding to the image (D1, ¶23, “the image processor retrieves images capturing the location (e.g., the hyper-location)”, ¶15, “ images captured by a camera 170 at the hyper-location”, ¶12, “the request indicates a hyper-location—i.e., a geographic location where its weather conditions are captured by a plurality of images”, ¶¶23-24, “ each image retrieved at block 220 may have a timestamp …”, ¶39, “when the forecaster receives images that have timestamps that span an eight hour window”, ¶¶39-40, “the scenario generator generates a plurality of historical scenarios, as discussed in the method 200 for the time period corresponding to the timestamps of the images”, “these historical models can be compared to the actual conditions at the location for the same time window”, ¶35, “ the forecast is output on a GUI that indicates one or more future conditions at the location”). The same motivation to combine for claim 7 equally applies for current claim
With regard to Claim 17,
Claim 17 is similar in scope to claim 3 and 4; therefore it is rejected under similar rationale.
Claims 7-9 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).
Claim 11, 19-20 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 Yamamoto [US 2023/0289980 A1, hereinafter Yamamoto].
With regard to Claim 11,
Chris-Trehan teach the IHS of claim 10.
The same motivation to combine for claim 10 equally applies for current claim
Chris-Trehan 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 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).
With regard to Claim 19,
Chris-Trehan teach the method of claim 18, further comprising identifying the second 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”, ¶66, “ produce an unscaled similarity score which indicates how far the sample falls outside the manifold of the training data“, Trehan, ¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”, ¶¶64-65), feature using an extrinsic source based upon (a) a location of data collection, and (b) a time of data collection (Trehan, ¶6, “structure of a sentence is decomposed and analyzed to determine intent, nouns, location and time”, ¶64, “the set of input intent maps may be processed by an Application Programming Interface (API) corresponding to, but not limited to weather, flight, banking information, and so forth”, ¶65, “content dimension, for example, may be based on demographic details of the user, current location of the user, current temperature of the location where the user is present, current transportation being used by the user, time of the day, day of the week, or the current month”).
The same motivation to combine for claim 18 equally applies for current claim
Chris-Trehan does not explicitly teach wherein the second feature comprises a state of a vehicle configured to collect the element.
Yamamoto teach identifying the second feature using an extrinsic source based upon:(a) a location of data collection (¶¶62-63, “self-position estimation unit 71 estimates the self-position of the vehicle 1 on the basis of sensor data from the external recognition sensor 25 and the high-precision map stored in the map information storage unit 23. For example, the self-position estimation unit 71 estimates the self-position of the vehicle”, ¶74), and (b) a time of data collection (¶119, “a frame captured at a predetermined time is used as a reference”, ¶121, ¶129), wherein the second 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-Trehan 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 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 as described above by associating a state of a vehicle configured to collect the element to 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).
With regard to Claim 20,
Chris-Trehan-Yamamoto teach the method of claim 19, further comprising:
detecting drift by performing at least one of: (a) a pre-model analysis to calculate a first metric based, at least in part, upon a variance of the data with respect to a data norm, wherein the data norm is established in the absence of drift (Chris ¶60, “comparing, using a data representation model, the sample data to the training data to determine a similarity score“, ¶65, “the sample data is compared to the training data within this domain to determine a similarity score”, ¶66, “to produce an unscaled similarity score which indicates how far the sample falls outside the manifold of the training data”, ¶89, “Data drift identifies that the statistical properties of the sample data, which the machine learning model 120 is trying to predict, change over time in unforeseen ways. Data drift is also referred to as concept drift. The data drift is calculated based on the similarity score from data minder module 300 from the first and the second points in time in combination with the first and second performance metrics”); 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, and wherein the prediction or inference norm is established in the absence of drift; and
selecting the training dataset subset of the plurality of elements in response to the detection (Chris ¶91, “If the data drift is below the first predetermined drift threshold, then update module 500 continues to monitor the performance data by the loop 550”, “Then, update module 500 calculates the data drift using third performance data and the second performance data”, “This enables continuous monitoring of the machine learning model”, ¶92, “If the data drift is above the first predetermined drift threshold, then update module 500 in step 560 triggers retaining of the machine learning model 120 to form retained machine learning model 120”, ¶93, “Catastrophic forgetting refers to the loss of ability to cope with previously learned training data, whenever an attempt is made to train the network with new training data. To help avoid the catastrophic forgetting problem”).
Response to Arguments
Examiner respectfully withdraw 35 USC 112(b) for claim 4 based on the presented amended claims.
Applicant argue amended claims are eligible under § 101 at least because they
each recite features that are integrated into practical applications in such a way that the claims are not directed to a judicial exception.
Examiner respectfully disagree, analyzing data to detect drift is a mental process. And triggering training based on detected drift as a form of automating the model training as described in the specification is not an improvement to the functionality of AI as the analyses step and the determination step is mental process and the additional elements does not add what can be considered as activity that is not WURC activity. In addition the arguments does not clarify how the claims in view of the specifications do not clarify the state of the art at the time of filing and how the current claims is an improvement.
Applicant argue that the office action doe does not allege that the amended claims are taught or suggested by any reference.
Examiner notes a new mapping is provided for the amended claims. Examiner refer the applicant for the detailed rejection and mapping under 35 USC 103 rejection section.
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)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
