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 .
Status of Claims
Claims 1-20 were rejected in the Non-Final Office action mailed on 11/17/2025. Applicant’s amended claimset, entered on 02/17/2026, amended Claims 1 and 12 and canceled Claim 6. Herein this Final Office Action, Claims 1-5 and 7-20 are rejected.
Response to Arguments
Applicant’s arguments filed 02/17/2026, with respect to Claim Interpretation, have been fully considered and are persuasive.
Examiner notes that the claim limitation of “training and/or testing the at least one AI/ML model” includes merely “testing” the “AI/ML model” without performing any “training” functionality. Thus, any machine learning or training to create the model is not necessarily a part of the scope of the claim because the testing is performed on the model itself. The signifier of “AI/ML” merely references the model that is to be tested. Therefore, the broadest reasonable interpretation of this limitation (“training and/or testing the at least one AI/ML model”) includes inputting a number into an equation (i.e. AI/ML model), and comparing the output number with an anticipated output (i.e. testing).
Applicant’s arguments filed 02/17/2026, with respect to Rejections under 35 U.S.C. 101 for Claims 1-5 and 7-20, have been fully considered and are not persuasive.
On Pages 12-13, Applicant argues “With respect to Step 2A, Prong One, the Office Action contends that the claimed invention is not directed to an abstract idea. The Office Action characterizes the claims as directed to commercial or legal interactions and mental processes. However, the claims as amended recite specific technical operations that are not capable of being performed in the human mind or through pen and paper. The claimed invention addresses technical problems identified in the specification. Paragraph [0005] of the specification explains that "data being utilized for training and/or testing the AI/ML models in the related art have a limitation on data relevancy and specificity" and that "overfitting of the AI/ML models with inappropriate data may lead to ineffective models with reduced performance and missed opportunities for leveraging the collected data to deliver meaningful insights or accurate predictions, eventually rendering the collected data unhelpful or useless." Paragraph [0006] of the specification states that "there is a need to provide a solution to effectively and efficiently collect the required amount of data with the required quality under the required conditions." These are technical problems relating to the performance and accuracy of AI/ML models, not commercial concerns.” Examiner does not materially disagree.
Examiner responds that issues such as “overfitting” and data quality could be a “technical problem” under MPEP 2106.05(a). However, the solution must also be a “technical” solution to provide a patent eligible improvement to the technology itself. For instance, if a hypothetical claim alleged a technical problem of high consumption of computer resources in performing a calculation, and a solution of not using the computer, but performing the calculation mentally, an Examiner could not consider that an improvement to the functioning of the computer because the solution was not a “technical” solution.
Examiner notes that this office action and the prior office action determined that the claims “recite” an abstract idea in Step 2A Prong One and the claims are “directed to” an abstract idea in Step 2A (as a whole).
On Pages 12-13, Applicant argues “The claims recite a technical solution to these technical problems. Claim 1 now recites "training and/or testing the at least one AI/ML model with the data" and "measuring one or more performance metrics of the trained and/or tested AI/ML model, wherein the one or more performance metrics comprise one or more of: accuracy, precision, F-score, and average precision." Training an AI/ML model is an inherently computer-implemented operation that cannot be performed mentally. As stated in paragraph [0045] of the specification, the "model trainer/tester module 110-3 may be configured to train and/or test one or more AI/ML models based on the validated data" using "various training algorithms ( e.g., parameter tuning, crossvalidation, training with mini-batches, transfer learning, federated learning, etc.) or testing algorithms ( e.g., determining evaluation metrics, error analysis, statistical significance testing, etc.)." These are computational processes requiring processor execution.” Examiner does not agree.
Although Examiner concedes that training is a computer operation, and would be an additional element, the scope of “training and/or testing” includes merely “testing” the model. The testing operations are a part of the abstract idea because they are at least “mathematical concepts” under MPEP 2106.04(a).
Additionally, Examiner notes that Specification ¶45 uses “various” training or testing algorithms, referencing several examples of algorithms not defined by the specification, but are known in the art. Thus, in order to comply with 35 U.S.C. 112(a), the application of these algorithms cannot provide the improvement because they are defined by what one of ordinary skill in the art would understand.
On Pages 13-14, Applicant argues “Claim 1 further recites "based on determining that the trained and/or tested AI/ML model has higher performance metrics than an untrained and/or untested AI/ML model, determining that the data contributes to the improvement of the performance of the at least one AI/ML model." This limitation finds support in paragraph [0101] of the specification, which states that "based on determining that the trained/tested AI/ML model has a higher accuracy than the untrained/untested AI/ML model, the at least one processor may determine that the data contributes to the improvement of the performance." This requires a quantitative comparison of model performance before and after training with the data, which is a technical operation performed by the processor based on measured metrics.” Examiner does not agree.
Examiner responds that measuring the performance of models, comparing them quantitatively, and selecting the model with a higher level of performance, are not “technical operations,” but a part of the abstract idea. Such operations cover at least “mathematical calculations” (i.e. calculating the performance and quantitatively comparing them), “observations” (i.e. measuring the performance), and “evaluations” or “judgements” (i.e. selecting the better model), which are a part of the abstract idea groupings of “mathematical concepts” and “mental processes” per MPEP 2106.04(a).
Put plainly, Applicant’s solution for improving the model seems to be measuring which model is better, and selecting that model. Such solution is not a technical improvement under MPEP 2106.05(a).
On Page 14, Applicant argues “Claim 1 additionally recites "generating a feature map based on existing data associated with the at least one AI/ML model" and "determining whether or not the data compensates to one or more areas of a feature map space that were previously sparse with existing datasets." Paragraph [0049] of the specification explains that "the module 110-3 and/or the module 110-5 may generate a feature map based on the data and then examine the feature map" and that "by examining the feature map, the module 110-3 and/or the module 110-5 can determine whether or not the data compensates to one or more areas of the feature map space that were previously sparse with the existing datasets." Feature map generation and sparse area analysis are specific machine learning techniques involving computational analysis of high-dimensional data representations. These operations require computer processing and are not mental processes.” Examiner does not agree.
Examiner responds that the generation of a “feature map” and determining whether or not data compensates an area of the feature map are a part of the abstract idea and do not require computer processing. Examiner notes that no machine learning is used in these limitations as the feature map is generated based on data “associated with the at least one AI/ML model.” As outlined in Fig. 3B and ¶49, a “feature map 310” merely includes “a plurality of feature points [312 and 316]” and “an area 314.” In accordance with a with the plain meaning of a “map,” this limitation merely relates to an understanding of where features are or where they are not, which is at least a “mental process” of “observation” per MPEP 2106.04(a).
On Pages 14-15, Applicant argues, “With respect to Step 2A, Prong Two, the claims integrate any alleged abstract idea into a practical application. The claims recite a specific technical implementation that improves AI/ML model training technology. Paragraph [0058] of the specification explains that "by determining the contribution of the data to the improvement of the AI/ML models, the compensation price can be appropriately determined, thereby avoiding the situations of over-compensating and under-compensating the data providers." Paragraph [0116] of the specification states that "the data may be validated and the contribution of the data in improving the performance of the AI/ML model may be determined, before compensating the user or storing the data into the database, thereby avoiding wastage of resources on invalid or low-quality data." The claimed invention provides a technical improvement to AI/ML training systems by ensuring that only data that measurably improves model performance, as determined through "measuring one or more performance metrics" and comparison between trained and untrained models, is incorporated into the training process. The "feature map" analysis further ensures that data addresses gaps in the existing training dataset by identifying "areas of a feature map space that were previously sparse with existing datasets." These technical improvements impose meaningful limits on the claims and represent more than mere instruction to apply an abstract idea using generic computer components. The ordered combination of elements in Claim 1, wherein data is received from a vehicle, validated based on a price list, used to train and/or test an AI/ML model, evaluated through specific performance metrics and feature map coverage analysis, and used to determine contribution based on quantitative comparison of model performance, constitutes a specific technical pipeline that is not merely linking an abstract idea to a particular technological environment.”” Examiner does not agree.
Examiner responds that “avoiding the situations of over-compensating and under-compensating the data providers” is not a technical problem, but a business problem which is a part of the abstract idea and cannot provide a patent eligible improvement under MPEP 2106.05(a).
Examiner responds that “avoiding wastage of resources on invalid or low-quality data” by measuring the quality of the data before storing the data does not give rise to a non-conclusory technical explanation as to how the system provides an improvement to the technology itself. A person of ordinary skill in the art would understand that training a ML model, which can be a part of the determination of whether the data contributes, can require significant resources, and that data would likely be stored in some form of memory before or during the training. In essence, Applicant appears to be arguing that the system trains the model to determine if the data improves a model, and if its determined that the data does not improve the model, the system saves computational resources by not using that data to train the model.
Examiner responds that merely identifying data that matches needed data is not an improvement to the functioning of a computer, but instead may provide a better “map,” which is an ineligible improvement in the abstract idea itself. MPEP 2106.05(a). Looking at the claims as a whole, the claims merely measure the usefulness of data (i.e. an “observation” or “mathematical calculation”), and then stores data determined to be useful on a computer. Such operations, as a whole, merely applies the abstract idea using generic computer components per MPEP 2106.05(f), and does not provide a patent eligible improvement to the function of the computer itself under MPEP 2106.05(a).
On Pages 15-16, Applicant argues “With respect to Step 2B, to the extent a separate analysis is required, the claims recite significantly more than any alleged abstract idea. The specific combination of "training and/or testing the at least one AI/ML model with the data," "measuring one or more performance metrics" comprising "accuracy, precision, F-score, and average precision," determining contribution "based on determining that the trained and/or tested AI/ML model has higher performance metrics than an untrained and/or untested AI/ML model," "generating a feature map based on existing data," and "determining whether or not the data compensates to one or more areas of a feature map space that were previously sparse with existing datasets" represents a specific technical approach to evaluating data contribution that is not well-understood, routine, or conventional in the field. As explained in paragraph [0005] of the specification, prior approaches suffered from the problem that "time and effort may be spent to collect a significant amount of data for the AI/ML models, but such data may not contribute to the improvement of the performance of the models due to, for example, overfitting." The claimed invention addresses this deficiency through the specific technical operations recited in the claims. The ordered combination of these elements provides an inventive concept that transforms any alleged abstract idea into patent-eligible subject matter. Independent Claim 12 recites corresponding limitations and is patentable for at least the same reasons as Claim 1. Claims 2-11 depend from Claim 1, and Claims 13-20 depend from Claim 12. These dependent claims are patentable at least by virtue of their dependency on patentable independent claims. In view of the foregoing amendments and remarks, withdrawal of the rejection of Claims 1-20 under 35 U.S.C. § 101 is respectfully requested.” Examiner does not agree.
Examiner responds that although collecting certain data to train an AI/ML model to prevent overfitting, could be considered a technical problem, the solution of merely testing and measuring the data is an abstract idea, and fails to provide a patent eligible improvement to the functioning of a computer. Such determination is sufficient to conclude that the claims do not provide “significantly more” at Step 2B. Thus, the claims are ineligible.
Applicant’s arguments filed 02/17/2026, with respect to Rejections under 35 U.S.C. 102 and 103 for Claims 1-5 and 7-20, have been fully considered and are moot in light of the additionally cited art of CN-115187850-A (“Fu”).
Claim Interpretation
Independent Claims 1 and 12 recite “a system for collecting data from a vehicle” in the first line of the claim. Because the “system” is “for collecting data from a vehicle,” the scope of the “system” does not include the “vehicle” itself. is 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.
Such interpretation of independent Claims 1 and 12 above is extended to dependent Claims 2-5, 7-11, and 13-20.
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 1-5 and 7-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites a first limitation of “wherein the one or more improvements comprise one or more of: an improvement in performance of the at least one AI/ML model, an improvement in test coverage, and an improvement in feature map coverage” (emphasis added) at Lines 8-10. Thus, the scope of this first limitation includes that “the” improvement can be any one of the three possible improvements, to the exclusion of the other two, e.g. “the one or more improvements comprise” “an improvement in test coverage,” but not “an improvement in performance of the at least one AI/ML model.” Claim 1 further recites a second limitation of “wherein determining whether or not the data contributes to the one or more improvements comprises: . . . determining that the data contributes to the improvement of the performance of the at least one AI/ML model; generating a feature map based on existing data associated with the at least one AI/ML model; and determining whether or not the data compensates to one or more areas of a feature map space that were previously sparse with existing datasets.” Thus, it appears as though the improvement must be at least “an improvement in performance of the at least one AI/ML model.” Because the relationship between the first limitation and the second limitation is unclear, the scope of the claim is indefinite. It is unclear as to whether the first limitation dictates the scope, and the second limitation is intended to be conditional to the applicable improvement of the first limitation, or the second limitation is intended to limit the scope of the first limitation, which would result in the language of the first limitation being immaterial, i.e. providing not further limitation on the scope of the claim. Solely for further examination purposes herein, the claim will be interpreted as if the first limitation is subordinate to the second limitation, i.e. as if the first limitation was deleted, e.g. “the one or more improvements comprise . . . an improvement in performance of the at least one AI/ML model . . .”
Claims 2-5 and 7-20 recite similar limitations (either directly or via dependency) as to the limitations of Claim 1 rejected above, and therefore are rejected under 35 U.S.C. 112(b) with a similar justification.
Claim 1 recites “generating a feature map based on existing data associated with the at least one AI/ML model; and determining whether or not the data compensates to one or more areas of a feature map space that were previously sparse with existing datasets” at the end of the claim. The first limitation introduces an “existing data,” i.e. “generating a feature map based on [an] existing data.” However, it is unclear as to the relationship between the “existing datasets” and the “existing data.” The “existing dataset” could be (1) referencing the “existing data” in its entirety, (2) referencing a larger data set that includes the “existing data” and other data, i.e. creating a “data set,” or (3) referencing “existing dataset” that is fully separate from the “existing data.” Additionally, although “sparse” could be considered a term of degree, in the context of the claim, the word “sparse” is not interpreted as providing some type of threshold of density, but merely provides a description of need, i.e. “determining whether or not the data compensates to one or more areas of a feature map space that were previously [needed by] existing datasets.” The limitation of “that were previously sparse with existing datasets” merely provides further description of what it means to “compensate” an area of a feature map space. Solely for further examination purposes herein, the claim will be interpreted as if the “existing dataset” references a new dataset, separate from the “existing data.”
Claims 2-5 and 7-20 recite similar limitations (either directly or via dependency) as to the limitations of Claim 1 rejected above, and therefore are rejected under 35 U.S.C. 112(b) with a similar justification.
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 an abstract idea without significantly more.
Step 1
Claims 1-12 recite a method (i.e. a process) and Claims 13-20 recite a system (i.e. a machine or manufacture. Therefore, Claims 1-20 all fall within the one of the four statutory categories of invention of 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 1 recites the abstract idea of “A method . . to collect data from a vehicle, the method comprising:”
“receiving data from the vehicle;
obtaining a price list;
validating the data based on the price list;
based on determining that the data is validated, determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model,
wherein the one or more improvements comprise one or more of: an improvement in performance of the at least one AI/ML model, an improvement in test coverage, and an improvement in feature map coverage;
based on determining that the data contributes to the one or more improvements, determining a price for compensating a user associated with the vehicle; and
compensating the user based on the determined price,
wherein determining whether or not the data contributes to the one or more improvements comprises:
training and/or testing the at least one AI/ML model with the data;
measuring one or more performance metrics of the trained and/or tested AI/ML model, wherein the one or more performance metrics comprise one or more of: accuracy, precision, F-score, and average precision;
based on determining that the trained and/or tested AI/ML model has higher performance metrics than an untrained and/or untested AI/ML model, determining that the data contributes to the improvement of the performance of the at least one AI/ML model;
generating a feature map based on existing data associated with the at least one AI/ML model; and
determining whether or not the data compensates to one or more areas of a feature map space that were previously sparse with existing datasets.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) receiving data from a certain source, (2) validating data based on an obtained price list, (3) determine whether data contributes to an improvement of a model by (3a)
testing the model (i.e. the broadest reasonable interpretation of “training and/or testing” includes only “testing,” which does not relate to the creation or ML aspects of the model), (3b) measuring certain performance metrics of the tested model, (3c) based on determining the performance metrics are higher than an untested model, determine that the data contributes to an improvement, (3d) generating a feature map based on existing data, and (3e) determine whether or not the data compensates to one or more areas of a feature map space, (4) determine a price for compensating a user, (5) compensating the user, all of which are: mathematical relationships (i.e. performance metrics of a model and determining if a metric is higher) and mathematical calculations (i.e. testing the model, calculating the certain performance metrics, and determining if a metric is higher), which are mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I, commercial or legal interactions (i.e. determining validity and quality of data to be purchased, determining price of data to be purchased, and purchasing data are at least “marketing or sales activities or behaviors”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II, and observations (i.e. receiving data, measuring performance metrics, and generating a feature map), evaluations (i.e. determining the validity and whether or not the data contributes to an improvement, determining whether a metric is higher), and judgement (i.e. determining whether data compensates a certain area of the map) which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III. The mere the recitation of generic computer components (i.e., the “at least one processor of a system”) implementing the identified abstract idea does prevent the claim from “reciting” the abstract idea of mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2106.04(d). Therefore, Claim 1 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) “at least one processor of a system” that performs the method.
The additional elements of (i) at least one processor of a system (Fig. 5 and ¶74 shows “the processor 520 may include a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), and/or another type of processing or computing component.”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 5 and ¶¶72-74 shows elements.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
The (i) processor of a system, when viewed as whole/ordered combination (Fig. 5 and ¶¶72-74 shows elements.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 5 and ¶¶72-74 shows elements.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) processor of a system, does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 5 and ¶¶72-74 shows elements.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 2-5 and 7-11 recite the abstract idea of:
“. . . wherein the validating the data comprises: performing an object recognition to identify an object included in the data; determining whether or not the identified object is associated with at least one object included in the price list; based on determining that the identified object is associated with the at least one object, determining whether or not the data fulfills at least one data requirement; and based on determining that the data fulfills the at least one data requirement, determining that the data is validated.” (Claim 2).
“. . . wherein the validating the data comprises: generating [information] including the information of the data; presenting the [information] to at least one human operator; receiving, from the [information], one or more feedbacks from the at least one human operator; and determining, based on the one or more feedbacks, whether or not the data is validated.” (Claim 3).
“. . . , wherein the data comprises an image captured . . . in the vehicle, and wherein the at least one data requirement comprises a resolution of the image.” (Claim 4).
“. . . , wherein the validating the data comprises: checking whether or not the data is real data which was captured by one or more onboard sensors in the vehicle or is fake data; and based on determining that the data fulfills at least one data requirement and is real data, determining that the data is validated.” (Claim 5).
“. . . , further comprising: based on determining that the data contributes to the improvement of the performance, computing a contribution score representing the contribution of the data to the improvement of the performance.” (Claim 7).
“. . . wherein the determining the price comprises: determining, from among a plurality of prices included in the price list, a base compensation price; and computing the price based on the base compensation price and the contribution score.” (Claim 8).
“. . . wherein the compensating the user comprises: providing payment information . . . , wherein the payment information comprises information of the user, a payment amount defined by the determined price, payment currency, and payment due date.” (Claim 9).
“. . . , further comprising: based on determining that the data contributes to the improvement of the performance, providing the data to . . . data associated with the at least one AI/ML model.” (Claim 10).
“. . . , wherein the vehicle is located at a region different from the system.” (Claim 11).
Dependent Claims 2-5 and 7-11, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 2-5 and 7-11 fail to establish claims that are not directed to an abstract idea because the further limitations (1) performing object recognition and determining if the object is on a list to validate data (Claim 2), (2) exchanging information with a user to validate data (Claim 3), (3) data including image and resolution (Claim 4), (4) checking the source of the data to validate data (Claim 5), (5) determining a contribution score (Claim 7), (6) determining the price based on a base price and contribution score (Claim 8), (7) providing certain payment information (Claim 9), (8) providing the date based on determining a contribution to the improvement (Claim 10), and (9) limiting the source of the data (Claim 11), which is a part of the abstract idea. The additional elements of Claims 2-5 and 7-11 (i.e. “at least one interface” in Claim 3, “an image sensor” in Claim 4, “a payment system” in Claim 9, and “a database” in Claim 10) fails to establish claims that are not directed to an abstract idea because the elements merely recite additional generic computer components (i.e. Fig. 4 and ¶61 shows “[UI] module 120-1 may generate one or more graphical user interfaces (GUIs), one or more voice user interfaces (VUIs), and/or the like, . . .” Fig. 6 and ¶88 shows “onboard sensor 650 may include . . . an image sensor (e.g., camera, infrared, etc.).” ¶57 shows “a payment system (e.g., a banking system associated with the user to be compensated, etc.).” Fig. 5 and ¶75 shows “the database module 110-4 (or one or more operations associated therewith) may be implemented by the memory 530 and/or the storage component 540.”) similar to the generic computer components of Claim 1 and generally link the abstract idea to a particular technology or field of use (i.e. computer environment) similar to Claim 1. The organization of the further limitations of Claims 2-5 and 7-11 fail to integrate an abstract idea into a practical application just as discussed above for Claim 1. Additionally, performing the abstract idea of Claim 1 as recited in each of the further limitations of Claims 2-5 and 7-11, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 1. Therefore, Claims 2-5 and 7-11 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 2-5 and 7-11 fail to establish that the claims provide an inventive concept, just as in Claim 1. Therefore, Claims 2-5 and 7-11 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claims 12-16 and 18-20 recite elements and limitations that are substantially similar to Claims 1-5 and 7-8. Claims 12-20 recite a method embodied by the elements and limitations of Claims 1-5 and 7-8. Therefore, Claims 12-16 and 18-20 are rejected under 35 U.S.C. 101 just as Claims 1-5 and 7-8 are rejected under 35 U.S.C. 101 as discussed above.
Dependent Claim 17 recite the abstract idea of: “. . . , wherein the determining whether or not the data contributes to the improvement of the performance comprises: . . . ; measuring one or more performance metrics of the trained AI/ML model; and determining, based on the one or more performance metrics, a contribution of the data in improving the performance of the at least one AI/ML model.”
Dependent Claim 17, has been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitations of Claim 17 fails to establish claims that are not directed to an abstract idea because the further limitations includes measuring the performance of the model to determine a contribution to the improvement (Claim 6), which is a part of the abstract idea. The additional elements of Claim 17 (i.e. “training the at least one AI/ML model with the data”) fails to establish claims that are not directed to an abstract idea because the elements merely recite additional generic computer components (i.e. ¶45 shows “. . . the module 110-3 may . . . train and/or test the AI/ML model(s) using various training algorithms (e.g., parameter tuning, cross-validation, training with mini-batches, transfer learning, federated learning, etc.) or testing algorithms (e.g., determining evaluation metrics, error analysis, statistical significance testing, etc.) . . .”) similar to the generic computer components of Claim 12 and generally link the abstract idea to a particular technology or field of use (i.e. computer environment) similar to Claim 12. The organization of the further limitations of Claim 17 fails to integrate an abstract idea into a practical application just as discussed above for Claim 12. Additionally, performing the abstract idea of Claim 12 as recited in each of the further limitations of Claim 17, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 12. Therefore, Claim 17 amounts to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claim 17 fails to establish that the claims provide an inventive concept, just as in Claim 12. Therefore, Claim 17 fails the Subject Matter Eligibility Test and is consequently rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 7-8, 10-12, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over FI-20207105-A1 (“Paju”) in view of CN-115187850-A (“Fu”).
Regarding Claim 1, Paju discloses “A method performed by at least one processor of a system to collect data from a vehicle” (Page 7 shows “The steps of the method are: (1) a map preparation approach that enables representing the routes where data collection needs to be done as a series of objects of monetary value (drawing 1), (2) a mobile phone application, (3) a database, and (4) a payment system.”), the method comprising:
“receiving data from the vehicle” (Page 2 shows “The mobile phone application that enables: . . . (ii) recording and upload of the videos by the user to the server; . . .” Page 3 discusses “Drawing 2” and shows that the user is driving a vehicle. Thus, Paju shows receiving data from the vehicle.);
“obtaining a price list” (Page 2 shows “Designing a map that represents the roads where data needs to be collected with objects of monetary value, such as coins, . . . The map can be modified at any point by the administrator, for example by placing new valuable items on the map, . . .” Drawings 1-2 shows an example map. Thus, Paju discloses that the system obtains a map of specific locations of where data needs to be collected that have a monetary value (i.e. obtaining a price list) from an administrator.);
“validating the data based on the price list” (Page 8 shows “The users need to record video footage through the application to collect these symbols of coins and other objects. When an object has been collected by one user, it cannot be collected anymore by other users. Each object is associated with video footage.” Page 8 shows “The geotagged videos are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps.” Page 5 defines “Geotagging” as “The identification or estimation of the real-world geographic location of, in this case, single video frames, expressed as a set of geographic coordinates.” Thus, Paju discloses that the system determines that the geotag of the video frame (i.e. data) matches the location of the object on the map (i.e. validating the data based on the price list).);
“based on determining that the data is validated, determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model, wherein the one or more improvements comprise one or more of: an improvement in performance of the at least one AI/ML model, an improvement in test coverage, and an improvement in feature map coverage” (Page 8 shows “The geotagged videos [(i.e. data determined to be validated)] are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check [(i.e. determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model)]. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Page 8 shows “The videos are then transferred by the administrator of the mobile phone application to an artificial intelligence model that has been trained to detect pavement defects from video footage that has location data combined to it. The artificial intelligence model analyses the video footage and produces information on the locations and severity of the pavement defects. This data is then transferred to any client that has agreed to buy the data related to the produced pavement defect data, to enable preventative pavement management.” Thus, the “quality check” discloses “determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model.” Page 7 shows that the “objects of monetary value” represents locations “where data collection needs to be done.” Therefore, the “quality check” of Paju shows at least “an improvement in feature map coverage” because upon determining sufficient quality to be useful to the artificial intelligence model the objects are permanently removed from the map. See also Page 8 showing “The artificial intelligence model analyses the video footage and produces information on the locations and severity of the pavement defects.”);
“based on determining that the data contributes to the one or more improvements, determining a price for compensating a user associated with the vehicle” (Page 8 shows “If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Dependent Claim 4 on Page 10 shows “for the collection of each object to be approved and the user paid, the video footage needs to undergo a quality check [(i.e. “determining that the data contributes to the one or more improvements”)]. . .” Thus, Paju shows that for acceptable quality video, the price for the data is determined to be greater than zero, and for unacceptable quality video, the price for the data is determined to be zero. Page 3 shows “Drawing 3 presents the screen that presents the users with the results on how many objects of monetary value they have collected. This information, along with the personal details and banking information, forms a basis for the payment system. The drawing 3 contains the following figures: (1) how many objects of monetary value did they collect; (2) what were their total earnings versus elapsed time; and (3) information on their drive, such as speed and total distance travelled.” Thus, Drawing 3 shows the determined price.); and
“compensating the user based on the determined price” (Page 2 shows “The mobile phone application that enables: . . . (ii) recording and upload of the videos by the user to the server; (iii) keeping track of the amount of money earned by each user for the payment system; A payment system through which the users are paid for their collection of video footage based on the value of the objects they have collected; . . .” See also Drawing 3 showing results of compensation for collected data and Page 5 defining a “Payment system” as “A combination of a database used to store user information and earnings information that are used as input to the payment processes, as well as a payment process which enables compensating users for their collection of video footage while fulfilling the legislative requirements related to such an act.”),
“wherein determining whether or not the data contributes to the one or more improvements comprises:” . . .
“generating a feature map based on existing data associated with the at least one AI/ML model” (Page 8 shows “The geotagged videos [(i.e. data determined to be validated)] are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check [(i.e. determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model)]. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Page 8 shows “The Client provides a GIS dataset of the centreline data for the roads. The centreline data [(i.e. existing data)] is transformed into a map presentation [(i.e. generating a feature map)], where the roads have been overlaid with objects of monetary value.”); and
“determining whether or not the data compensates to one or more areas of a feature map space that were previously sparse with existing datasets” (Pages 8-9 show “The Client provides a GIS dataset of the centreline data for the roads. The centreline data is transformed into a map presentation, where the roads have been overlaid with objects of monetary value. The objects of monetary value are placed in a way, that encourages data collection throughout the whole road network by the users of the mobile phone application. For example, more expensive objects are placed at the ends of dead-end roads [(i.e. one or more areas of a feature map space that were previously sparse with existing datasets)].”Page 8 shows “The geotagged videos [(i.e. data determined to be validated)] are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check [(i.e. determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model)]. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Thus, the determination to remove a more expensive object placed at a dead end in Paju teaches “determining whether or not the data compensates to one or more areas of a feature map space that were previously sparse with existing datasets.”).
Paju does not explicitly teach, but Fu further teaches that “determining whether or not the data contributes to the one or more improvements” (Page 2 shows “According to the preferred embodiment of the present invention, based on the to-be-checked scene image for image recognition, before obtaining the image recognition result, further comprising: performing image quality detection to the scene image to be checked [(i.e. determining whether or not the data contributes to an improvement)], judging whether the scene image to be checked meets the preset condition, . . .” (Emphasis added).) “comprises:”
“training and/or testing the at least one AI/ML model with the data” (Pages 9-10 show “In some embodiments, the preset neural network can be trained by the training sample to obtain the training to be detected object position and attribute information identification model, then can through the confusion matrix to be detected object position and the training process of the attribute information identification model to supervise. . . . the confusion matrix is a special list, with two dimensions (" actual " and " prediction ") and the same "class" set in two dimensions (each combination of the dimensions and classes is a variable in the table contingency.” Page 10 shows “when the application is implemented, training the model by training sample, using confusion matrix to evaluate the model performance to obtain the preset index value, so as to determine the performance of the model based on the preset index value, according to the preset index value, improving the efficiency of model optimization improvement.” (emphasis added).);
“measuring one or more performance metrics of the trained and/or tested AI/ML model, wherein the one or more performance metrics comprise one or more of: accuracy, precision, F-score, and average precision” (Page 10 shows “when the application is implemented, training the model by training sample, using confusion matrix to evaluate the model performance to obtain the preset index value, so as to determine the performance of the model based on the preset index value, according to the preset index value, improving the efficiency of model optimization improvement.” (Emphasis added). Page 10 shows “For the classification algorithm model, the evaluation index can be accuracy Precision, recall rate Recall, F score F-score, one of the area (Area Under Curve, abbreviated as AUC) surrounded by the receiver working characteristics (Receiver Operating Characteristic, ROC) and ROC curve and coordinate axis. As shown in Table 1, the confusion matrix gives the to-be-detected object position and attribute information identification model to obtain the prediction result and the type information of the real situation: Table 1 For example, the F score as the preset index value to evaluate the model performance of good or bad, wherein F score F-score calculation can be formula 1: formula 1 wherein, beta is the weight coefficient; P is the accuracy Precision, the calculation formula is P=TP/(TP + FP); R refers to recall rate Recall, the calculation formula is R=TP/ (TP + FN).” (Emphasis added).);
“based on determining that the trained and/or tested AI/ML model has higher performance metrics than an untrained and/or untested AI/ML model, determining that the data contributes to the improvement of the performance of the at least one AI/ML model” (The broadest reasonable interpretation of “an untrained and/or untested AI/ML model” refers to models that have not undergone “training and/or testing . . . with the data,” i.e. the “untrained and/or untested AI/ML model” may have been created with training or testing, but just have not used “the data” as claimed. Page 10 shows “the F score as the preset index value to evaluate the model performance of good or bad.” Page 10 shows “Illustratively, through the preset index value to evaluate the model performance of good or bad, such as: The higher the accuracy is, the better the performance of the model is.” Thus, Fu teaches that the model (i.e. trained or untrained) with higher accuracy is “better” (i.e. contributes to an improvement). See also Page 7 discussing training used to improve and optimize image analysis (“improve the accuracy of the image analysis” and “improve the precision of subsequent image recognition”).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fu with Paju because Fu teaches that an automated audit/checking of quality of uploaded data improves efficiency and accuracy (Page 2 states “Therefore, the buyer needs to audit the data uploaded by the third party platform, so as to ensure the authenticity of the data, and the existing technology by manual auditing, the auditing efficiency is low, and cannot ensure the accuracy of the auditing result. To sum up, for the data uploaded by the third party platform, the problem to be solved in the information verification is dependent on manual, but not with high efficiency and accuracy of the defect. Contents of the Invention In order to solve the above technical problem, the embodiment of the application provides an information checking method, device, device and medium, the data uploaded by the third party platform accurately and efficiently auditing.”). Thus, combining Fu with Paju furthers the interest taught in Fu, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 7, Paju and Fu teach “The method according to claim 1,” as described above.
Paju further teaches “based on determining that the data contributes to the improvement . . . , computing a contribution score representing the contribution of the data to the improvement . . .” (The broadest reasonable interpretation of a “Contribution Score” includes numerical tallies, e.g. a baseball team has a “score” of 4 runs by accumulating a single run four times and failing to accumulating a run any number of times. Paju Page 8 shows “The geotagged videos are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check [(i.e. determining whether or not the data contributes to one or more improvements)]. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Dependent Claim 4 on Page 10 shows “for the collection of each object to be approved and the user paid, the video footage needs to undergo a quality check [(i.e. “determining that the data contributes to the one or more improvements”)]. . .” Page 3 shows “Drawing 3 presents the screen that presents the users with the results on how many objects of monetary value they have collected. This information, along with the personal details and banking information, forms a basis for the payment system. The drawing 3 contains the following figures: (1) how many objects of monetary value did they collect [(i.e. contribution score)]; (2) what were their total earnings versus elapsed time; and (3) information on their drive, such as speed and total distance travelled.”).
Paju does not explicitly teach, but Fu teaches that “the improvement . . .” includes “the improvement of the performance” (Page 10 shows “the F score as the preset index value to evaluate the model performance of good or bad.” Page 10 shows “Illustratively, through the preset index value to evaluate the model performance of good or bad, such as: The higher the accuracy is, the better the performance of the model is.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fu with Paju because Fu teaches that an automated audit/checking of quality of uploaded data improves efficiency and accuracy (Page 2 states “Therefore, the buyer needs to audit the data uploaded by the third party platform, so as to ensure the authenticity of the data, and the existing technology by manual auditing, the auditing efficiency is low, and cannot ensure the accuracy of the auditing result. To sum up, for the data uploaded by the third party platform, the problem to be solved in the information verification is dependent on manual, but not with high efficiency and accuracy of the defect. Contents of the Invention In order to solve the above technical problem, the embodiment of the application provides an information checking method, device, device and medium, the data uploaded by the third party platform accurately and efficiently auditing.”). Thus, combining Fu with Paju furthers the interest taught in Fu, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 8, Paju and Fu teach “The method according to claim 7,” as described above.
Paju further teaches “wherein the determining the price comprises:”
“determining, from among a plurality of prices included in the price list, a base compensation price” (Page 3 shows “Drawing 1 presents an example of the map of the objects of monetary value that have been developed with Geographical Information System (GIS), and placing of which creates the technical effect of influencing the road user/application user behaviour. The drawing 1 contains the following figures: (1) objects of monetary value presenting the route where data collection is being done (1 =coin, 2=strawberry, 3=diamond); and (2) a basemap in the background.” Page 9 shows “For example, more expensive objects are placed at the ends of dead-end roads.” Therefore, each object on the map (i.e. price list) has a different price (i.e. base compensation price) that is determined by the system.); and
“computing the price based on the base compensation price and the contribution score” (The broadest reasonable interpretation of a “Contribution Score” includes numerical tallies, e.g. a baseball team has a “score” of 4 runs by accumulating a run four times. Dependent Claim 4 on Page 10 shows “for the collection of each object to be approved and the user paid, the video footage needs to undergo a quality check [(i.e. “determining that the data contributes to the one or more improvements”)]. . .” Page 3 shows “Drawing 3 presents the screen that presents the users with the results on how many objects of monetary value they have collected. This information, along with the personal details and banking information, forms a basis for the payment system. The drawing 3 contains the following figures: (1) how many objects [(i.e. contribution score)] of monetary value did they collect; (2) what were their total earnings versus elapsed time; and (3) information on their drive, such as speed and total distance travelled.” Thus, Paju shows that each object, which has an associated value (i.e. base compensation price), is submitted and then receives the quality check which either tallies (i.e. contribution score) the object as received or rejects the data and payment. Drawing 3 shows the price in element 2.).
Regarding Claim 10, Paju and Fu teach “The method according to claim 1,” as described above.
Paju further teaches “based on determining that the data contributes to the improvement of the performance, providing the data to a database storing data associated with the at least one AI/ML model” (Page 7 shows “The method for crowdsourcing road condition data collection for use in artificial intelligence based road condition analysis software, in this chapter referred to as the method, is used for creating the technical effect of crowdsourcing the data collection of road condition data, within a given road network, in the form of geotagged video footage, which can be stored in a database and used as an input for artificial intelligence models that have been trained to detect pavement defects from geotagged video footage.” Page 8 shows “The geotagged videos are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check [(i.e. “determining that the data contributes to the improvement of the performance”)]. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again. The videos are then [(i.e. “based on determining that the data contributes to the improvement of the performance”)] transferred by the administrator of the mobile phone application to [(i.e. “providing the data to”)] an artificial intelligence model [(i.e. “a database storing data associated with the at least one AI/ML model”)] that has been trained to detect pavement defects from video footage that has location data combined to it.”).
Regarding Claim 11, Paju and Fu teach “The method according to claim 1,” as described above.
Paju further teaches “wherein the vehicle is located at a region different from the system” (Page 8 shows “The geotagged videos are uploaded automatically to a cloud-based server containing the Database.” (Emphasis added). Because the server is “cloud-based” Paju discloses that the system is at a different location than the vehicle.).
Regarding Claim 12, Paju teaches “A system for collecting data from a vehicle, the system comprising: a memory storage storing computer-executable instructions; and at least one processor communicatively coupled to the memory storage, wherein the at least one processor is configured to execute the instructions to” (Page 7 shows “The steps of the method are: (1) a map preparation approach that enables representing the routes where data collection needs to be done as a series of objects of monetary value (drawing 1), (2) a mobile phone application, (3) a database, and (4) a payment system.” See also Page 8 showing “cloud-based server containing the database,” Page 4 showing “Database” includes “a structed set of data held in a computer,” and Page 5 showing “Mobile phone application” includes “a computer program or software application designed to run on a mobile device such as a phone.):
“receive data from the vehicle” (Page 2 shows “The mobile phone application that enables: . . . (ii) recording and upload of the videos by the user to the server; . . .” Page 3 discusses “Drawing 2” and shows that the user is driving a vehicle. Thus, Paju shows receiving data from the vehicle.);
“obtain a price list” (Page 2 shows “Designing a map that represents the roads where data needs to be collected with objects of monetary value, such as coins, . . . The map can be modified at any point by the administrator, for example by placing new valuable items on the map, . . .” Drawings 1-2 shows an example map. Thus, Paju discloses that the system obtains a map of specific locations of where data needs to be collected that have a monetary value (i.e. obtaining a price list) from an administrator.);
“validate the data based on the price list” (Page 8 shows “The users need to record video footage through the application to collect these symbols of coins and other objects. When an object has been collected by one user, it cannot be collected anymore by other users. Each object is associated with video footage.” Page 8 shows “The geotagged videos are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps.” Page 5 defines “Geotagging” as “The identification or estimation of the real-world geographic location of, in this case, single video frames, expressed as a set of geographic coordinates.” Thus, Paju discloses that the system determines that the geotag of the video frame (i.e. data) matches the location of the object on the map (i.e. validating the data based on the price list).);
“based on determining that the data is validated, determine whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model, wherein the one or more improvements comprise one or more of: an improvement in performance of the at least one AI/ML model, an improvement in test coverage, and an improvement in feature map coverage” (Page 8 shows “The geotagged videos [(i.e. data determined to be validated)] are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check [(i.e. determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model)]. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Page 8 shows “The videos are then transferred by the administrator of the mobile phone application to an artificial intelligence model that has been trained to detect pavement defects from video footage that has location data combined to it. The artificial intelligence model analyses the video footage and produces information on the locations and severity of the pavement defects. This data is then transferred to any client that has agreed to buy the data related to the produced pavement defect data, to enable preventative pavement management.” Thus, the “quality check” discloses “determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model.” Page 7 shows that the “objects of monetary value” represents locations “where data collection needs to be done.” Therefore, the “quality check” of Paju shows at least “an improvement in feature map coverage” because upon determining sufficient quality to be useful to the artificial intelligence model the objects are permanently removed from the map. See also Page 8 showing “The artificial intelligence model analyses the video footage and produces information on the locations and severity of the pavement defects.”);
“based on determining that the data contributes to the one or more improvements, determine a price for compensating a user associated with the vehicle” (Page 8 shows “If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Dependent Claim 4 on Page 10 shows “for the collection of each object to be approved and the user paid, the video footage needs to undergo a quality check [(i.e. “determining that the data contributes to the one or more improvements”)]. . .” Thus, Paju shows that for acceptable quality video, the price for the data is determined to be greater than zero, and for unacceptable quality video, the price for the data is determined to be zero. Page 3 shows “Drawing 3 presents the screen that presents the users with the results on how many objects of monetary value they have collected. This information, along with the personal details and banking information, forms a basis for the payment system. The drawing 3 contains the following figures: (1) how many objects of monetary value did they collect; (2) what were their total earnings versus elapsed time; and (3) information on their drive, such as speed and total distance travelled.” Thus, Drawing 3 shows the determined price.); and
“compensate the user based on the determined price” (Page 2 shows “The mobile phone application that enables: . . . (ii) recording and upload of the videos by the user to the server; (iii) keeping track of the amount of money earned by each user for the payment system; A payment system through which the users are paid for their collection of video footage based on the value of the objects they have collected; . . .” See also Drawing 3 showing results of compensation for collected data and Page 5 defining a “Payment system” as “A combination of a database used to store user information and earnings information that are used as input to the payment processes, as well as a payment process which enables compensating users for their collection of video footage while fulfilling the legislative requirements related to such an act.”),
“wherein determining whether or not the data contributes to the one or more improvements comprises:” . . .
“generating a feature map based on existing data associated with the at least one AI/ML model” (Page 8 shows “The geotagged videos [(i.e. data determined to be validated)] are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check [(i.e. determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model)]. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Page 8 shows “The Client provides a GIS dataset of the centreline data for the roads. The centreline data [(i.e. existing data)] is transformed into a map presentation [(i.e. generating a feature map)], where the roads have been overlaid with objects of monetary value.”); and
“determining whether or not the data compensates to one or more areas of a feature map space that were previously sparse with existing datasets” (Pages 8-9 show “The Client provides a GIS dataset of the centreline data for the roads. The centreline data is transformed into a map presentation, where the roads have been overlaid with objects of monetary value. The objects of monetary value are placed in a way, that encourages data collection throughout the whole road network by the users of the mobile phone application. For example, more expensive objects are placed at the ends of dead-end roads [(i.e. one or more areas of a feature map space that were previously sparse with existing datasets)].”Page 8 shows “The geotagged videos [(i.e. data determined to be validated)] are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check [(i.e. determining whether or not the data contributes to one or more improvements of at least one artificial intelligence (AI)/Machine Learning (ML) model)]. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Thus, the determination to remove a more expensive object placed at a dead end in Paju teaches “determining whether or not the data compensates to one or more areas of a feature map space that were previously sparse with existing datasets.”).
Paju does not explicitly teach, but Fu further teaches that “determining whether or not the data contributes to the one or more improvements” (Page 2 shows “According to the preferred embodiment of the present invention, based on the to-be-checked scene image for image recognition, before obtaining the image recognition result, further comprising: performing image quality detection to the scene image to be checked [(i.e. determining whether or not the data contributes to an improvement)], judging whether the scene image to be checked meets the preset condition, . . .” (Emphasis added).) “comprises:”
“training and/or testing the at least one AI/ML model with the data” (Pages 9-10 show “In some embodiments, the preset neural network can be trained by the training sample to obtain the training to be detected object position and attribute information identification model, then can through the confusion matrix to be detected object position and the training process of the attribute information identification model to supervise. . . . the confusion matrix is a special list, with two dimensions (" actual " and " prediction ") and the same "class" set in two dimensions (each combination of the dimensions and classes is a variable in the table contingency.” Page 10 shows “when the application is implemented, training the model by training sample, using confusion matrix to evaluate the model performance to obtain the preset index value, so as to determine the performance of the model based on the preset index value, according to the preset index value, improving the efficiency of model optimization improvement.” (emphasis added).);
“measuring one or more performance metrics of the trained and/or tested AI/ML model, wherein the one or more performance metrics comprise one or more of: accuracy, precision, F-score, and average precision” (Page 10 shows “when the application is implemented, training the model by training sample, using confusion matrix to evaluate the model performance to obtain the preset index value, so as to determine the performance of the model based on the preset index value, according to the preset index value, improving the efficiency of model optimization improvement.” (Emphasis added). Page 10 shows “For the classification algorithm model, the evaluation index can be accuracy Precision, recall rate Recall, F score F-score, one of the area (Area Under Curve, abbreviated as AUC) surrounded by the receiver working characteristics (Receiver Operating Characteristic, ROC) and ROC curve and coordinate axis. As shown in Table 1, the confusion matrix gives the to-be-detected object position and attribute information identification model to obtain the prediction result and the type information of the real situation: Table 1 For example, the F score as the preset index value to evaluate the model performance of good or bad, wherein F score F-score calculation can be formula 1: formula 1 wherein, beta is the weight coefficient; P is the accuracy Precision, the calculation formula is P=TP/(TP + FP); R refers to recall rate Recall, the calculation formula is R=TP/ (TP + FN).” (Emphasis added).);
“based on determining that the trained and/or tested AI/ML model has higher performance metrics than an untrained and/or untested AI/ML model, determining that the data contributes to the improvement of the performance of the at least one AI/ML model” (The broadest reasonable interpretation of “an untrained and/or untested AI/ML model” refers to models that have not undergone “training and/or testing . . . with the data,” i.e. the “untrained and/or untested AI/ML model” may have been created with training or testing, but just have not used “the data” as claimed. Page 10 shows “the F score as the preset index value to evaluate the model performance of good or bad.” Page 10 shows “Illustratively, through the preset index value to evaluate the model performance of good or bad, such as: The higher the accuracy is, the better the performance of the model is.” Thus, Fu teaches that the model (i.e. trained or untrained) with higher accuracy is “better” (i.e. contributes to an improvement). See also Page 7 discussing training used to improve and optimize image analysis (“improve the accuracy of the image analysis” and “improve the precision of subsequent image recognition”).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fu with Paju because Fu teaches that an automated audit/checking of quality of uploaded data improves efficiency and accuracy (Page 2 states “Therefore, the buyer needs to audit the data uploaded by the third party platform, so as to ensure the authenticity of the data, and the existing technology by manual auditing, the auditing efficiency is low, and cannot ensure the accuracy of the auditing result. To sum up, for the data uploaded by the third party platform, the problem to be solved in the information verification is dependent on manual, but not with high efficiency and accuracy of the defect. Contents of the Invention In order to solve the above technical problem, the embodiment of the application provides an information checking method, device, device and medium, the data uploaded by the third party platform accurately and efficiently auditing.”). Thus, combining Fu with Paju furthers the interest taught in Fu, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 17, Paju and Fu teaches “The system according to claim 12,” as described above.
Paju does not explicitly teach, but Fu teaches “wherein the at least one processor is configured to determine whether or not the data contributes to the improvement of the performance by:”
“training the at least one AI/ML model with the data” (Pages 9-10 show “In some embodiments, the preset neural network can be trained by the training sample to obtain the training to be detected object position and attribute information identification model, then can through the confusion matrix to be detected object position and the training process of the attribute information identification model to supervise. . . . the confusion matrix is a special list, with two dimensions (" actual " and " prediction ") and the same "class" set in two dimensions (each combination of the dimensions and classes is a variable in the table contingency.” Page 10 shows “when the application is implemented, training the model by training sample, using confusion matrix to evaluate the model performance to obtain the preset index value, so as to determine the performance of the model based on the preset index value, according to the preset index value, improving the efficiency of model optimization improvement.” (emphasis added).);
“measuring one or more performance metrics of the trained AI/ML model” (Page 10 shows “when the application is implemented, training the model by training sample, using confusion matrix to evaluate the model performance to obtain the preset index value, so as to determine the performance of the model based on the preset index value, according to the preset index value, improving the efficiency of model optimization improvement.” (Emphasis added). Page 10 shows “For the classification algorithm model, the evaluation index can be accuracy Precision, recall rate Recall, F score F-score, one of the area (Area Under Curve, abbreviated as AUC) surrounded by the receiver working characteristics (Receiver Operating Characteristic, ROC) and ROC curve and coordinate axis. As shown in Table 1, the confusion matrix gives the to-be-detected object position and attribute information identification model to obtain the prediction result and the type information of the real situation: Table 1 For example, the F score as the preset index value to evaluate the model performance of good or bad, wherein F score F-score calculation can be formula 1: formula 1 wherein, beta is the weight coefficient; P is the accuracy Precision, the calculation formula is P=TP/(TP + FP); R refers to recall rate Recall, the calculation formula is R=TP/ (TP + FN).” (Emphasis added).); and
“determining, based on the one or more performance metrics, a contribution of the data in improving the performance of the at least one AI/ML model” (Page 10 shows “the F score as the preset index value to evaluate the model performance of good or bad.” Page 10 shows “Illustratively, through the preset index value to evaluate the model performance of good or bad, such as: The higher the accuracy is, the better the performance of the model is.” Thus, Fu teaches that the model (i.e. trained or untrained) with higher accuracy is “better” (i.e. contributes to an improvement). See also Page 7 discussing training used to improve and optimize image analysis (“improve the accuracy of the image analysis” and “improve the precision of subsequent image recognition”).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fu with Paju because Fu teaches that an automated audit/checking of quality of uploaded data improves efficiency and accuracy (Page 2 states “Therefore, the buyer needs to audit the data uploaded by the third party platform, so as to ensure the authenticity of the data, and the existing technology by manual auditing, the auditing efficiency is low, and cannot ensure the accuracy of the auditing result. To sum up, for the data uploaded by the third party platform, the problem to be solved in the information verification is dependent on manual, but not with high efficiency and accuracy of the defect. Contents of the Invention In order to solve the above technical problem, the embodiment of the application provides an information checking method, device, device and medium, the data uploaded by the third party platform accurately and efficiently auditing.”). Thus, combining Fu with Paju furthers the interest taught in Fu, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 18, Paju and Fu teach “The system according to claim 12,” as described above.
Paju further teaches “wherein the at least one processor is further configured to: based on determining that the data contributes to the improvement . . . , compute a contribution score representing the contribution of the data to the improvement . . .” (The broadest reasonable interpretation of a “Contribution Score” includes numerical tallies, e.g. a baseball team has a “score” of 4 runs by accumulating a single run four times and failing to accumulating a run any number of times. Paju Page 8 shows “The geotagged videos are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check [(i.e. determining whether or not the data contributes to one or more improvements)]. If the video is up to the standards, as defined in the training video, the symbols of coins and other objects are permanently removed from the maps. If the videos are not up to the defined standards, they will be returned to the map and become available for collection by all users again.” Dependent Claim 4 on Page 10 shows “for the collection of each object to be approved and the user paid, the video footage needs to undergo a quality check [(i.e. “determining that the data contributes to the one or more improvements”)]. . .” Page 3 shows “Drawing 3 presents the screen that presents the users with the results on how many objects of monetary value they have collected. This information, along with the personal details and banking information, forms a basis for the payment system. The drawing 3 contains the following figures: (1) how many objects of monetary value did they collect [(i.e. contribution score)]; (2) what were their total earnings versus elapsed time; and (3) information on their drive, such as speed and total distance travelled.”).
Paju does not explicitly teach, but Fu teaches that “the improvement . . .” includes “the improvement of the performance” (Page 10 shows “the F score as the preset index value to evaluate the model performance of good or bad.” Page 10 shows “Illustratively, through the preset index value to evaluate the model performance of good or bad, such as: The higher the accuracy is, the better the performance of the model is.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fu with Paju because Fu teaches that an automated audit/checking of quality of uploaded data improves efficiency and accuracy (Page 2 states “Therefore, the buyer needs to audit the data uploaded by the third party platform, so as to ensure the authenticity of the data, and the existing technology by manual auditing, the auditing efficiency is low, and cannot ensure the accuracy of the auditing result. To sum up, for the data uploaded by the third party platform, the problem to be solved in the information verification is dependent on manual, but not with high efficiency and accuracy of the defect. Contents of the Invention In order to solve the above technical problem, the embodiment of the application provides an information checking method, device, device and medium, the data uploaded by the third party platform accurately and efficiently auditing.”). Thus, combining Fu with Paju furthers the interest taught in Fu, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 19, Paju and Fu teaches “The system according to claim 18,” as described above.
Paju further teaches “wherein the at least one processor is configured to determine the price by:”
“determining, from among a plurality of prices included in the price list, a base compensation price” (Page 3 shows “Drawing 1 presents an example of the map of the objects of monetary value that have been developed with Geographical Information System (GIS), and placing of which creates the technical effect of influencing the road user/application user behaviour. The drawing 1 contains the following figures: (1) objects of monetary value presenting the route where data collection is being done (1 =coin, 2=strawberry, 3=diamond); and (2) a basemap in the background.” Page 9 shows “For example, more expensive objects are placed at the ends of dead-end roads.” Therefore, each object on the map (i.e. price list) has a different price (i.e. base compensation price) that is determined by the system.); and
“computing the price based on the base compensation price and the contribution score” (The broadest reasonable interpretation of a “Contribution Score” includes numerical tallies, e.g. a baseball team has a “score” of 4 runs by accumulating a run four times. Dependent Claim 4 on Page 10 shows “for the collection of each object to be approved and the user paid, the video footage needs to undergo a quality check [(i.e. “determining that the data contributes to the one or more improvements”)]. . .” Page 3 shows “Drawing 3 presents the screen that presents the users with the results on how many objects of monetary value they have collected. This information, along with the personal details and banking information, forms a basis for the payment system. The drawing 3 contains the following figures: (1) how many objects [(i.e. contribution score)] of monetary value did they collect; (2) what were their total earnings versus elapsed time; and (3) information on their drive, such as speed and total distance travelled.” Thus, Paju shows that each object, which has an associated value (i.e. base compensation price), is submitted and then receives the quality check which either tallies (i.e. contribution score) the object as received or rejects the data and payment. Drawing 3 shows the price in element 2.).
Claims 2-3 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over FI-20207105-A1 (“Paju”) in view of CN-115187850-A (“Fu”) and US-20250232667-A1 (“Bai”).
Regarding Claim 2, Paju teaches “The method according to claim 1,” as described above.
Paju further teaches “wherein the validating the data comprises:”
“ . . . determining whether or not the data fulfills at least one data requirement” (Page 2 shows “Designing a map that represents the roads where data needs to be collected with objects of monetary value, such as coins, and which is used by the mobile phone application to show the users of the application where video footage needs to be collected and what reward each user will get for collecting videos in different locations.” (Emphasis added). Page 8 shows “The users need to record video footage through the application to collect these symbols of coins and other objects. When an object has been collected by one user, it cannot be collected anymore by other users. Each object is associated with video footage.” Page 5 defines “Geotagging” as “The identification or estimation of the real-world geographic location of, in this case, single video frames, expressed as a set of geographic coordinates.” Page 8 shows “The geotagged videos [(i.e. validated data)] are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check.” Thus, it is determined whether the geotag location of the video (i.e. the data) matches the location of a reward object (i.e. fulfilling data requirement).); and
“based on determining that the data fulfills the at least one data requirement, determining that the data is validated” (Page 8 shows “Each of the symbols of coins and other objects have a monetary value associated with them. The users need to record video footage through the application to collect these symbols of coins and other objects. When an object has been collected by one user, it cannot be collected anymore by other users. Each object is associated with video footage. Page 4 defines “Collect” as “The act of driving over the symbols that are free for collection within the map interface of the mobile phone application, while recording a video . . .” Therefore, Paju teaches that the video is “valid” based on fulfilling at least one data requirement (i.e. the geotag of the video matches the location of the reward object).).
Paju and Fu does not explicitly teach, but Bai teaches “wherein the validating the data comprises:”
“performing an object recognition to identify an object included in the data” (Fig. 2 and ¶37 shows “The roadway issue detecting system 200 is configured to automatically identify and report roadway issue 132 (i.e., identify and report the roadway issue 132 without assistance from driver 115 or any other occupant of vehicle 100). In at least some embodiments, the roadway issue detecting system 200 performs image recognition and machine learning to detect and classify the roadway issue 132 in video recordings and/or other types of data collected by sensors 105 (shown in FIG. 1 ). In an exemplary embodiment, vehicle controller 110 detects the roadway issue 132, generates classification data that defines what type of roadway issue was detected (e.g., temporary hazard, pothole, debris in the roadway, downed powerline etc.), and transmits roadway issue data that includes the roadway issue classification data and the GPS location data associated with the roadway issue.” (Emphasis added). Fig. 4 and ¶44 shows “In step S320, the vehicle controller 110 detects a roadway issue, for example, roadway issue 132 (shown in FIG. 2 ) and the GPS location of the roadway issue 132. In at least some embodiments, the vehicle controller 110 receives information from one or more sensors 105 and detects roadway issue 132 by analyzing the data received from sensors 105. In an alternative embodiment, vehicle controller 110 transmits the data received from sensors 105 to remote server 140, and remote server 140 detects roadway issue 132 by analyzing the data received from sensors 105.” (Emphasis added).);
“determining whether or not the identified object is associated with at least one object included in the price list” (Fig. 2 and ¶37 shows “The roadway issue detecting system 200 is configured to automatically identify and report roadway issue 132 (i.e., identify and report the roadway issue 132 without assistance from driver 115 or any other occupant of vehicle 100). In at least some embodiments, the roadway issue detecting system 200 performs image recognition and machine learning to detect and classify the roadway issue 132 in video recordings and/or other types of data collected by sensors 105 (shown in FIG. 1 ). In an exemplary embodiment, vehicle controller 110 detects the roadway issue 132, generates classification data that defines what type of roadway issue was detected (e.g., temporary hazard, pothole, debris in the roadway, downed powerline etc.), and transmits roadway issue data that includes the roadway issue classification data and the GPS location data associated with the roadway issue.” (Emphasis added). The classification of roadway issues teaches determining if the object is on a list (i.e. of the types of issues).); [and]
“based on determining that the identified object is associated with the at least one object, determining whether or not the data fulfills at least one data requirement” (Fig. 5 and ¶¶51-52 shows “[0051] In the exemplary embodiment, process 400 includes receiving 410 roadway data. Process 400 also includes determining 412 if the roadway data includes information to report to a roadway related entity. For example, vehicle controller 110 may receive 410 roadway data from sensors 105 and may determine 412, based on that roadway data, that a roadway issue is located nearby vehicle 100. Furthermore, vehicle controller 110 may determine 412, based on information from sensors 105, the GPS location of the detected roadway issue. [0052] Process 400 further includes determining 414 the proper roadway related entity assigned to repair or otherwise take care of a specific type of roadway issue at that GPS location, and transmitting 416 the roadway issue data to the proper roadway related entity. . . .” Therefore, the system determines whether or not the data fulfills the data fulfils a data requirement of each entity.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bai with Paju and Fu because Bai teaches that classifying the roadway issue with image recognition enables transmission to the entity responsible for fixing the issue (¶¶37-38 and ¶¶51-52). Thus, combining Bai with Paju and Fu furthers the interest taught in Bai, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 3, Paju teaches “The method according to claim 1,” as described above.
Paju and Fu do not explicitly teach, but Bai further teaches “wherein the validating the data comprises:”
“generating at least one interface including the information of the data” (¶37 shows “Vehicle controller 110 may optionally present the driver with a selection that allows the driver to manually confirm/verify the type of roadway issue (i.e., verify that the classification data is accurate), before vehicle controller transmits the roadway issue data.” ¶38 shows “In an exemplary embodiment, vehicle controller 110 detects the roadway issue 132, generates classification data that defines what type of roadway issue was detected (e.g., temporary hazard, pothole, debris in the roadway, downed powerline etc.), and transmits roadway issue data that includes the roadway issue classification data and the GPS location data associated with the roadway issue. Optionally, the roadway issue detecting and reporting system 200 may provide the driver with a selection that allows the driver to manually confirm/verify at least the type of roadway issue. For example, the system 200 may display a message stating that a first type of roadway issue has been detected, the system 200 is ready to report that issue, and request that the driver confirm that the system 200 accurately identified that issue by providing a predefined input.” (Emphasis added). Fig. 7 and ¶58 shows “User computer device 602 may include, but is not limited to, vehicle controller 110 (shown in FIG. 1 ), infotainment panel 120 (shown in FIG. 1 ), and user computer device 125.” Fig. 7 and ¶59 shows “User computer device 602 may also include at least one media output component 615 for presenting information to user 601.” Fig. 7 and ¶60 shows “media output component 615 may be configured to present a graphical user interface.”);
“presenting the at least one interface to at least one human operator” (¶38 shows “For example, the system 200 may display a message stating that a first type of roadway issue has been detected, the system 200 is ready to report that issue, and request that the driver confirm that the system 200 accurately identified that issue by providing a predefined input.” (Emphasis added). Fig. 7 and ¶59 shows “User computer device 602 may also include at least one media output component 615 for presenting information to user 601.” Fig. 7 and ¶60 shows “media output component 615 may be configured to present a graphical user interface.”);
“receiving, from the at least one interface, one or more feedbacks from the at least one human operator” (¶38 shows “For example, the system 200 may display a message stating that a first type of roadway issue has been detected, the system 200 is ready to report that issue, and request that the driver confirm that the system 200 accurately identified that issue by providing a predefined input.” (Emphasis added). Fig. 7 and ¶60 shows “In some embodiments, user computer device 602 may include an input device 620 for receiving input from user 601.” Fig. 7 and ¶61 shows “Input device 620 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touch screen), a gyroscope, an accelerometer, a position detector, a biometric input device, and/or an audio input device. A single component such as a touch screen may function as both an output device of media output component 615 and input device 620.”); and
“determining, based on the one or more feedbacks, whether or not the data is validated” (¶38 shows “In an exemplary embodiment, vehicle controller 110 detects the roadway issue 132, generates classification data that defines what type of roadway issue was detected (e.g., temporary hazard, pothole, debris in the roadway, downed powerline etc.), and transmits roadway issue data that includes the roadway issue classification data and the GPS location data associated with the roadway issue. Optionally, the roadway issue detecting and reporting system 200 may provide the driver with a selection that allows the driver to manually confirm/verify at least the type of roadway issue. For example, the system 200 may display a message stating that a first type of roadway issue has been detected, the system 200 is ready to report that issue, and request that the driver confirm that the system 200 accurately identified that issue by providing a predefined input.” (Emphasis added). Therefore, Bai teaches that the data is validated based on the feedback from the driver.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bai with Paju and Fu because Bai teaches that confirming the roadway issue with the driver improves the accuracy of issue detection (¶¶37-38). Thus, combining Bai with Paju and Fu furthers the interest taught in Bai, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 13, Paju and Fu teaches “The system according to claim 12,” as described above.
Paju further teaches “wherein the at least one processor is configured to validate the data by:”
“ . . . determining whether or not the data fulfills at least one data requirement” (Page 2 shows “Designing a map that represents the roads where data needs to be collected with objects of monetary value, such as coins, and which is used by the mobile phone application to show the users of the application where video footage needs to be collected and what reward each user will get for collecting videos in different locations.” (Emphasis added). Page 8 shows “The users need to record video footage through the application to collect these symbols of coins and other objects. When an object has been collected by one user, it cannot be collected anymore by other users. Each object is associated with video footage.” Page 5 defines “Geotagging” as “The identification or estimation of the real-world geographic location of, in this case, single video frames, expressed as a set of geographic coordinates.” Page 8 shows “The geotagged videos [(i.e. validated data)] are uploaded automatically to a cloud-based server containing the database, where they will undergo a quality check.” Thus, it is determined whether the geotag location of the video (i.e. the data) matches the location of a reward object (i.e. fulfilling data requirement).); and
“based on determining that the data fulfills the at least one data requirement, determining that the data is validated” (Page 8 shows “Each of the symbols of coins and other objects have a monetary value associated with them. The users need to record video footage through the application to collect these symbols of coins and other objects. When an object has been collected by one user, it cannot be collected anymore by other users. Each object is associated with video footage. Page 4 defines “Collect” as “The act of driving over the symbols that are free for collection within the map interface of the mobile phone application, while recording a video . . .” Therefore, Paju teaches that the video is “valid” based on fulfilling at least one data requirement (i.e. the geotag of the video matches the location of the reward object).).
Paju and Fu do not explicitly teach, but Bai teaches “wherein the at least one processor is configured to validate the data by:”
“performing an object recognition to identify an object included in the data” (Fig. 2 and ¶37 shows “The roadway issue detecting system 200 is configured to automatically identify and report roadway issue 132 (i.e., identify and report the roadway issue 132 without assistance from driver 115 or any other occupant of vehicle 100). In at least some embodiments, the roadway issue detecting system 200 performs image recognition and machine learning to detect and classify the roadway issue 132 in video recordings and/or other types of data collected by sensors 105 (shown in FIG. 1 ). In an exemplary embodiment, vehicle controller 110 detects the roadway issue 132, generates classification data that defines what type of roadway issue was detected (e.g., temporary hazard, pothole, debris in the roadway, downed powerline etc.), and transmits roadway issue data that includes the roadway issue classification data and the GPS location data associated with the roadway issue.” (Emphasis added). Fig. 4 and ¶44 shows “In step S320, the vehicle controller 110 detects a roadway issue, for example, roadway issue 132 (shown in FIG. 2 ) and the GPS location of the roadway issue 132. In at least some embodiments, the vehicle controller 110 receives information from one or more sensors 105 and detects roadway issue 132 by analyzing the data received from sensors 105. In an alternative embodiment, vehicle controller 110 transmits the data received from sensors 105 to remote server 140, and remote server 140 detects roadway issue 132 by analyzing the data received from sensors 105.” (Emphasis added).);
“determining whether or not the identified object is associated with at least one object included in the price list” (Fig. 2 and ¶37 shows “The roadway issue detecting system 200 is configured to automatically identify and report roadway issue 132 (i.e., identify and report the roadway issue 132 without assistance from driver 115 or any other occupant of vehicle 100). In at least some embodiments, the roadway issue detecting system 200 performs image recognition and machine learning to detect and classify the roadway issue 132 in video recordings and/or other types of data collected by sensors 105 (shown in FIG. 1 ). In an exemplary embodiment, vehicle controller 110 detects the roadway issue 132, generates classification data that defines what type of roadway issue was detected (e.g., temporary hazard, pothole, debris in the roadway, downed powerline etc.), and transmits roadway issue data that includes the roadway issue classification data and the GPS location data associated with the roadway issue.” (Emphasis added). The classification of roadway issues teaches determining if the object is on a list (i.e. of the types of issues).); [and]
“based on determining that the identified object is associated with the at least one object, determining whether or not the data fulfills at least one data requirement” (Fig. 5 and ¶¶51-52 shows “[0051] In the exemplary embodiment, process 400 includes receiving 410 roadway data. Process 400 also includes determining 412 if the roadway data includes information to report to a roadway related entity. For example, vehicle controller 110 may receive 410 roadway data from sensors 105 and may determine 412, based on that roadway data, that a roadway issue is located nearby vehicle 100. Furthermore, vehicle controller 110 may determine 412, based on information from sensors 105, the GPS location of the detected roadway issue. [0052] Process 400 further includes determining 414 the proper roadway related entity assigned to repair or otherwise take care of a specific type of roadway issue at that GPS location, and transmitting 416 the roadway issue data to the proper roadway related entity. . . .” Therefore, the system determines whether or not the data fulfills the data fulfils a data requirement of each entity.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bai with Paju and Fu because Bai teaches that classifying the roadway issue with image recognition enables transmission to the entity responsible for fixing the issue (¶¶37-38 and ¶¶51-52). Thus, combining Bai with Paju and Fu furthers the interest taught in Bai, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 14, Paju and Fu teaches “The method according to claim 1,” as described above.
Paju and Fu do not explicitly teach, but Bai teaches “wherein the at least one processor is configured to validate the data by:”
“generating at least one interface including the information of the data” (¶37 shows “Vehicle controller 110 may optionally present the driver with a selection that allows the driver to manually confirm/verify the type of roadway issue (i.e., verify that the classification data is accurate), before vehicle controller transmits the roadway issue data.” ¶38 shows “In an exemplary embodiment, vehicle controller 110 detects the roadway issue 132, generates classification data that defines what type of roadway issue was detected (e.g., temporary hazard, pothole, debris in the roadway, downed powerline etc.), and transmits roadway issue data that includes the roadway issue classification data and the GPS location data associated with the roadway issue. Optionally, the roadway issue detecting and reporting system 200 may provide the driver with a selection that allows the driver to manually confirm/verify at least the type of roadway issue. For example, the system 200 may display a message stating that a first type of roadway issue has been detected, the system 200 is ready to report that issue, and request that the driver confirm that the system 200 accurately identified that issue by providing a predefined input.” (Emphasis added). Fig. 7 and ¶58 shows “User computer device 602 may include, but is not limited to, vehicle controller 110 (shown in FIG. 1 ), infotainment panel 120 (shown in FIG. 1 ), and user computer device 125.” Fig. 7 and ¶59 shows “User computer device 602 may also include at least one media output component 615 for presenting information to user 601.” Fig. 7 and ¶60 shows “media output component 615 may be configured to present a graphical user interface.”);
“presenting the at least one interface to at least one human operator” (¶38 shows “For example, the system 200 may display a message stating that a first type of roadway issue has been detected, the system 200 is ready to report that issue, and request that the driver confirm that the system 200 accurately identified that issue by providing a predefined input.” (Emphasis added). Fig. 7 and ¶59 shows “User computer device 602 may also include at least one media output component 615 for presenting information to user 601.” Fig. 7 and ¶60 shows “media output component 615 may be configured to present a graphical user interface.”);
“receiving, from the at least one interface, one or more feedbacks from the at least one human operator” (¶38 shows “For example, the system 200 may display a message stating that a first type of roadway issue has been detected, the system 200 is ready to report that issue, and request that the driver confirm that the system 200 accurately identified that issue by providing a predefined input.” (Emphasis added). Fig. 7 and ¶60 shows “In some embodiments, user computer device 602 may include an input device 620 for receiving input from user 601.” Fig. 7 and ¶61 shows “Input device 620 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touch screen), a gyroscope, an accelerometer, a position detector, a biometric input device, and/or an audio input device. A single component such as a touch screen may function as both an output device of media output component 615 and input device 620.”); and
“determining, based on the one or more feedbacks, whether or not the data is validated” (¶38 shows “In an exemplary embodiment, vehicle controller 110 detects the roadway issue 132, generates classification data that defines what type of roadway issue was detected (e.g., temporary hazard, pothole, debris in the roadway, downed powerline etc.), and transmits roadway issue data that includes the roadway issue classification data and the GPS location data associated with the roadway issue. Optionally, the roadway issue detecting and reporting system 200 may provide the driver with a selection that allows the driver to manually confirm/verify at least the type of roadway issue. For example, the system 200 may display a message stating that a first type of roadway issue has been detected, the system 200 is ready to report that issue, and request that the driver confirm that the system 200 accurately identified that issue by providing a predefined input.” (Emphasis added). Therefore, Bai teaches that the data is validated based on the feedback from the driver.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bai with Paju and Fu because Bai teaches that confirming the roadway issue with the driver improves the accuracy of issue detection (¶¶37-38). Thus, combining Bai with Paju and Fu furthers the interest taught in Bai, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over FI-20207105-A1 (“Paju”) in view of CN-115187850-A (“Fu”), US-20250232667-A1 (“Bai”), and US-20240273695-A1 (“Wang”).
Regarding Claim 4, Paju, Fu, and Bai teaches “The method according to claim 2,” as described above.
Paju further teaches “wherein the data comprises an image captured by an image sensor in the vehicle” (Page 7 defines “Video footage” as “A recording of a series of moving visual images, i.e. frames through the mobile phone application.” Page 2 shows “The mobile phone application that enables: . . . (ii) recording and upload of the videos by the user to the server; . . .” Page 3 discusses “Drawing 2” and shows that the user is driving a vehicle. Thus, Paju shows that the date includes an image captured by an image sensor (i.e. mobile phone) in the vehicle.).
Paju, Fu, and Bai do not explicitly teach, but Wang further teaches “wherein the at least one data requirement comprises a resolution of the image” (¶44 shows “In the present invention, it is verified by experiments that moderately enlarging the original image pixel dimension of the focusing zone image will enhance the recognition accuracy of the focusing zone image in a subsequent object detection operation, especially ensure that both the length and width of an recognizable object are not less than a minimum recognizable resolution threshold (e.g., 15 pixels, but the present invention is not limited thereto). The minimum recognizable resolution threshold refers to a minimum resolution necessary for the resolution of any object feature to be recognized by a corresponding image analysis module/model in an object detection operation. In other words, when the focusing zone image has an object feature to be recognized, but if the resolution is insufficient (the resolution is less than the minimum recognizable resolution threshold) before enlarging, the object feature will not be recognized by the subsequent image recognition operation.” Therefore, there is a data requirement of a minimum resolution for object recognition.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wang with Paju, Fu, and Bai because Wang teaches that there is a minimum resolution for object recognition to be sufficiently accurately performed (¶44). Thus, combining Wang with Paju, Fu, and Bai furthers the interest taught in Wang, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 15, Paju, Fu, and Bai teaches “The system according to claim 13,” as described above.
Paju further teaches “wherein the data comprises an image captured by an image sensor in the vehicle” (Page 7 defines “Video footage” as “A recording of a series of moving visual images, i.e. frames through the mobile phone application.” Page 2 shows “The mobile phone application that enables: . . . (ii) recording and upload of the videos by the user to the server; . . .” Page 3 discusses “Drawing 2” and shows that the user is driving a vehicle. Thus, Paju shows that the date includes an image captured by an image sensor (i.e. mobile phone) in the vehicle.).
Paju, Fu, and Bai do not explicitly teach, but Wang further teaches “wherein the at least one data requirement comprises a resolution of the image” (¶44 shows “In the present invention, it is verified by experiments that moderately enlarging the original image pixel dimension of the focusing zone image will enhance the recognition accuracy of the focusing zone image in a subsequent object detection operation, especially ensure that both the length and width of an recognizable object are not less than a minimum recognizable resolution threshold (e.g., 15 pixels, but the present invention is not limited thereto). The minimum recognizable resolution threshold refers to a minimum resolution necessary for the resolution of any object feature to be recognized by a corresponding image analysis module/model in an object detection operation. In other words, when the focusing zone image has an object feature to be recognized, but if the resolution is insufficient (the resolution is less than the minimum recognizable resolution threshold) before enlarging, the object feature will not be recognized by the subsequent image recognition operation.” Therefore, there is a data requirement of a minimum resolution for object recognition.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wang with Paju, Fu, and Bai with because Wang teaches that there is a minimum resolution for object recognition to be sufficiently accurately performed (¶44). Thus, combining Wang with Paju, Fu, and Bai furthers the interest taught in Wang, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over FI-20207105-A1 (“Paju”) in view of CN-115187850-A (“Fu”), US-20250232667-A1 (“Bai”), and WO-2025133619-A1 (“Tocher” priority date of 12/21/2023).
Regarding Claim 5, Paju, Fu, and Bai teaches “The method according to claim 2,” as described above.
Paju further teaches “wherein the validating the data comprises: . . . based on determining that the data fulfills at least one data requirement . . . , determining that the data is validated” (Page 8 shows “Each of the symbols of coins and other objects have a monetary value associated with them. The users need to record video footage through the application to collect these symbols of coins and other objects. When an object has been collected by one user, it cannot be collected anymore by other users. Each object is associated with video footage. Page 4 defines “Collect” as “The act of driving over the symbols that are free for collection within the map interface of the mobile phone application, while recording a video . . .” Therefore, Paju teaches that the video is “valid” based on fulfilling at least one data requirement (i.e. the geotag of the video matches the location of the reward object).).
Paju, Fu, and Bai do not explicitly teach, but Tocher further teaches “wherein the validating the data comprises:”
“checking whether or not the data is real data which was captured by one or more onboard sensors in the vehicle or is fake data” (Section A.5.1 Data Ingestion on Page 24 shows a vehicle mounted camera that collects data. Section A.5.1 Data Ingestion on Pages 24-25 shows “Metadata like uploader, timestamp, spatial coordinates, and quality metrics are extracted and catalogued alongside the raw data. This metadata provides pivotal context for downstream analytics while aiding discovery, filtering, and governance of the repository contents. Submission occurs through a public API that handles transfer and validation. Before loading new data, checks verify the submitter is a trusted provider and that the data meets baseline standards. If questions emerge, the data can be held for manual review or submitted to the ingestion system's moderation workflow. This oversight prevents lapsed quality or malicious data [(i.e. fake data)] from polluting the repository. By focusing solely on intake and validation, the system maintains speed and adaptability as volumes and types escalate.”); and
“based on determining that the data . . . is real data, determining that the data is validated” (Section A.5.1 Data Ingestion on Pages 24-25 shows “Metadata like uploader, timestamp, spatial coordinates, and quality metrics are extracted and catalogued alongside the raw data. This metadata provides pivotal context for downstream analytics while aiding discovery, filtering, and governance of the repository contents. Submission occurs through a public API that handles transfer and validation. Before loading new data, checks verify the submitter is a trusted provider and that the data meets baseline standards. If questions emerge, the data can be held for manual review or submitted to the ingestion system's moderation workflow. This oversight prevents lapsed quality or malicious data [(i.e. fake data)] from polluting the repository. By focusing solely on intake and validation, the system maintains speed and adaptability as volumes and types escalate.” Therefore, Tocher teaches that only data that is determined to be not malicious data (i.e. fake data) by applying a data standard (i.e. real data) is transferred to the platform (i.e. determined to be valid).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tocher with Paju, Fu, and Bai because Tocher teaches that filtering out malicious data improves speed and volume of platform (Section A.5.1 Data Ingestion on Pages 24-25). Thus, combining Tocher with Paju, Fu, and Bai furthers the interest taught in Tocher, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 16, Paju, Fu, and Bai teaches “The system according to claim 13,” as described above.
Paju further teaches “wherein the validating the data comprises: . . . based on determining that the data fulfills at least one data requirement . . . , determining that the data is validated” (Page 8 shows “Each of the symbols of coins and other objects have a monetary value associated with them. The users need to record video footage through the application to collect these symbols of coins and other objects. When an object has been collected by one user, it cannot be collected anymore by other users. Each object is associated with video footage. Page 4 defines “Collect” as “The act of driving over the symbols that are free for collection within the map interface of the mobile phone application, while recording a video . . .” Therefore, Paju teaches that the video is “valid” based on fulfilling at least one data requirement (i.e. the geotag of the video matches the location of the reward object).).
Paju, Fu, and Bai do not explicitly teach, but Tocher further teaches “wherein the at least one processor is configured to validate the data by:”
“checking whether or not the data is real data which was captured by one or more onboard sensors in the vehicle or is fake data” (Section A.5.1 Data Ingestion on Page 24 shows a vehicle mounted camera that collects data. Section A.5.1 Data Ingestion on Pages 24-25 shows “Metadata like uploader, timestamp, spatial coordinates, and quality metrics are extracted and catalogued alongside the raw data. This metadata provides pivotal context for downstream analytics while aiding discovery, filtering, and governance of the repository contents. Submission occurs through a public API that handles transfer and validation. Before loading new data, checks verify the submitter is a trusted provider and that the data meets baseline standards. If questions emerge, the data can be held for manual review or submitted to the ingestion system's moderation workflow. This oversight prevents lapsed quality or malicious data [(i.e. fake data)] from polluting the repository. By focusing solely on intake and validation, the system maintains speed and adaptability as volumes and types escalate.”); and
“based on determining that the data . . . is real data, determining that the data is validated” (Section A.5.1 Data Ingestion on Pages 24-25 shows “Metadata like uploader, timestamp, spatial coordinates, and quality metrics are extracted and catalogued alongside the raw data. This metadata provides pivotal context for downstream analytics while aiding discovery, filtering, and governance of the repository contents. Submission occurs through a public API that handles transfer and validation. Before loading new data, checks verify the submitter is a trusted provider and that the data meets baseline standards. If questions emerge, the data can be held for manual review or submitted to the ingestion system's moderation workflow. This oversight prevents lapsed quality or malicious data [(i.e. fake data)] from polluting the repository. By focusing solely on intake and validation, the system maintains speed and adaptability as volumes and types escalate.” Therefore, Tocher teaches that only data that is determined to be not malicious data (i.e. fake data) by applying a data standard (i.e. real data) is transferred to the platform (i.e. determined to be valid).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tocher with Paju, Fu, and Bai because Tocher teaches that filtering out malicious data improves speed and volume of platform (Section A.5.1 Data Ingestion on Pages 24-25). Thus, combining Tocher with Paju, Fu, and Bai furthers the interest taught in Tocher, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claims 9 and 20 is rejected under 35 U.S.C. 103 as being unpatentable over FI-20207105-A1 (“Paju”) in view of CN-115187850-A (“Fu”) and “Tips for Scheduling Payments in Bill Pay” (“NuMark” Credit Union, 03/26/2021, https://www.numarkcu.org/blog/tips-for-scheduling-payments-in-bill-pay/).
Regarding Claim 9, Paju and Fu teaches “The method according to claim 8,” as described above.
Paju further teaches “wherein the compensating the user comprises: providing payment information to a payment system, wherein the payment information comprises information of the user, a payment amount defined by the determined price, [and] payment currency, . . . ” (Page 2 shows “A payment system through which the users are paid for their collection of video footage based on the value of the objects they have collected.” Page 5 defines “Payment system” as “A combination of a database used to store user information and earnings information that are used as input to the payment processes, as well as a payment process which enables compensating users for their collection of video footage while fulfilling the legislative requirements related to such an act.” Page 3 shows “Drawing 3 presents the screen that presents the users with the results on how many objects of monetary value they have collected. This information, along with the personal details and banking information [(i.e. information of the user)], forms a basis for the payment system. The drawing 3 contains the following figures: (1) how many objects of monetary value did they collect; (2) what were their total earnings [(i.e. “payment amount defined by the determined price)] versus elapsed time; and (3) information on their drive, such as speed and total distance travelled. Drawing 3 shows that “total earnings” in element 2 includes a Euro currency symbol.).
Paju and Fu do not explicitly teach, but NuMark further teaches “wherein the payment information comprises . . . payment due date” (Page 1 shows “Great news! NuMark members now have an upgraded Bill Pay service which allows you to pay your bills quickly and easily from your desktop or smart phone. When you are scheduling a payment, there are two dates to consider: the due date and the processdate. Due Date – this is the date you choose to have your payment arrive at the payee.” Page 1 further shows “Here’s an example. Your rent or mortgage is due on the first of the month, and you set the first of the month as your due date.” Therefore, NuMark teaches that an electronic a payment system receives a “due date” when processing a payment.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine NuMark with Paju and Fu because NuMark teaches that use of due date feature is an upgrade in payment services (Page 1). Thus, combining NuMark with Paju and Fu furthers the interest taught in NuMark, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 20, Paju and Fu teaches “The system according to claim 19,” as described above.
Paju further teaches “wherein the at least one processor is configured to compensate the user by: providing payment information to a payment system, wherein the payment information comprises information of the user, a payment amount defined by the determined price, [and] payment currency, . . .” (Page 2 shows “A payment system through which the users are paid for their collection of video footage based on the value of the objects they have collected.” Page 5 defines “Payment system” as “A combination of a database used to store user information and earnings information that are used as input to the payment processes, as well as a payment process which enables compensating users for their collection of video footage while fulfilling the legislative requirements related to such an act.” Page 3 shows “Drawing 3 presents the screen that presents the users with the results on how many objects of monetary value they have collected. This information, along with the personal details and banking information [(i.e. information of the user)], forms a basis for the payment system. The drawing 3 contains the following figures: (1) how many objects of monetary value did they collect; (2) what were their total earnings [(i.e. “payment amount defined by the determined price)] versus elapsed time; and (3) information on their drive, such as speed and total distance travelled. Drawing 3 shows that “total earnings” in element 2 includes a Euro currency symbol.).
Paju and Fu do not explicitly teach, but NuMark further teaches “wherein the payment information comprises . . . payment due date” (Page 1 shows “Great news! NuMark members now have an upgraded Bill Pay service which allows you to pay your bills quickly and easily from your desktop or smart phone. When you are scheduling a payment, there are two dates to consider: the due date and the processdate. Due Date – this is the date you choose to have your payment arrive at the payee.” Page 1 further shows “Here’s an example. Your rent or mortgage is due on the first of the month, and you set the first of the month as your due date.” Therefore, NuMark teaches that an electronic a payment system receives a “due date” when processing a payment.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine NuMark with Paju and Fu because NuMark teaches that use of due date feature is an upgrade in payment services (Page 1). Thus, combining NuMark with Paju and Fu furthers the interest taught in NuMark, and therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is as follows:
WO-2024202366-A1 (“Takaira”) shows testing data used in creating an AI model.
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/MATTHEW PARKER GOODMAN/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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