DETAILED ACTION
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 .
Response to Amendment
The amendment filed on February 27, 2026 was considered by the examiner. Claims 1-5, 7-15, and 17-20 are pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-5, 7-15, and 17-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “wherein the collateral process is identified in response to the model efficiency score of the first process model failing to satisfy a threshold” in lines 53-55. The specification of the present application details that “efficiency scored based on metrics may be performed using a plurality of approaches such as… threshold-based approach” and that a certain efficiency score may indicate operating with an error rate above a threshold (see ¶[0027]). There is no disclosure in the specification that “the collateral process is identified in response to the model efficiency score of the first process model failing to satisfy a threshold” as recited in the claim. As such, one of ordinary skill in the art would not have recognized Applicant was in possession of the claimed invention at the time the application was effectively filed.
Claims 2-5 and 7-10 are rejected by virtue of their dependence from claim 1.
Claim 11 recites “wherein the collateral process is identified in response to the model efficiency score of the first process model failing to satisfy a threshold” in lines 44-45. The specification of the present application details that “efficiency scored based on metrics may be performed using a plurality of approaches such as… threshold-based approach” and that a certain efficiency score may indicate operating with an error rate above a threshold (see ¶[0027]). There is no disclosure in the specification that “the collateral process is identified in response to the model efficiency score of the first process model failing to satisfy a threshold” as recited in the claim. As such, one of ordinary skill in the art would not have recognized Applicant was in possession of the claimed invention at the time the application was effectively filed.
Claims 12-15 and 17-20 are rejected by virtue of their dependence from claim 11.
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 1-5, 7-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract ideas without significantly more.
Claim 1 interpretation: Under the broadest reasonable interpretation (BRI), the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. Based on the specification, the recitation “generate reaction data as a function of the collected physiological responses of the user” (see specification ¶[0033]-[0034]) is being interpreted as judgements/observations (the labeling of events) and/or mathematical calculations/evaluations (the classifier). The recitation “generate a plurality of process data of a process, wherein the plurality of process data further comprises a plurality of process input data and a plurality of correlated process output data” (see specification ¶[0003]-[0004] the correlation of the input data to output data; and see specification ¶[0042], the (pre)processing and/or feature extraction) is being interpreted as judgements/observations and/or mathematical calculations/evaluations. The recitation “performing feature extraction on the physiological responses of the user” (see specification ¶[0042]) is being interpreted as judgements/observations and/or mathematical calculations/evaluations. The recitation “generating the cognitive data as a function of a cognition classifier” (see specification ¶[0032] and ¶[0080]) is being interpreted as judgements/observations (the labeling of events) and/or mathematical calculations/evaluations (the classifier). The recitation “determining the reaction data as a function of a user input and the cognitive data” (see specification ¶[0034]) is being interpreted as mathematical calculations/evaluations. The recitation “generate a first process model using the plurality of process data, wherein generating the first process model further comprises training the process model using the plurality of process input data and the plurality of correlated process output data” (see specification ¶[0025]) is being interpreted as mathematical calculations/evaluations. The recitation “train, using the plurality of metamodel training examples, a metamodel, wherein the metamodel is configured to receive process model measurements and output model efficiency scores” (see specification ¶[0026]-[0027]) is being interpreted as mathematical calculations/evaluations. The recitation “generate a measurement of the first process model” (see specification ¶[0028]) is being interpreted as mathematical calculations/evaluations. The recitation “identify a collateral process as a function of the metamodel, wherein the collateral process is identified in response to the model efficiency score of the first process model failing to satisfy a threshold, wherein the collateral process indicates an additional output identified by the metamodel as a function of the plurality of metamodel training examples correlated to historical data” (see specification ¶[0038]) is being interpreted as mathematical calculations/evaluations. The recitations are computer-implemented, as indicated in the specification (see ¶[0009]-[0010], ¶[0023], ¶[0042], and ¶[0070]), and in the claim, lines 4-6.
Claim 11 interpretation: Under the broadest reasonable interpretation (BRI), the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. Based on the specification, the recitation “generating a plurality of process data of a process, wherein the plurality of process data further comprises a plurality of process input data and a plurality of correlated process output data” (see specification ¶[0003]-[0004] the correlation of the input data to output data; and see specification ¶[0042], the (pre)processing and/or feature extraction) is being interpreted as judgements/observations and/or mathematical calculations/evaluations. The recitation “performing feature extraction on the physiological responses of the user” (see specification ¶[0042]) is being interpreted as judgements/observations and/or mathematical calculations/evaluations. The recitation “generating the cognitive data as a function of a cognition classifier” (see specification ¶[0032] and ¶[0080]) is being interpreted as judgements/observations (the labeling of events) and/or mathematical calculations/evaluations (the classifier). The recitation “determining the reaction data as a function of a user input and the cognitive data” (see specification ¶[0034]) is being interpreted as mathematical calculations/evaluations. The recitation “generating a first process model using the plurality of process data, wherein generating the first process model further comprises training the process model using the plurality of process input data and the plurality of correlated process output data” (see specification ¶[0025]) is being interpreted as mathematical calculations/evaluations. The recitation “training a metamodel using the plurality of metamodel training examples, wherein the metamodel is configured to receive process model measurements and output model efficiency scores” (see specification ¶[0026]-[0027]) is being interpreted as mathematical calculations/evaluations. The recitation “generating a measurement of the first process model” (see specification ¶[0028]) is being interpreted as mathematical calculations/evaluations. The recitation “identifying a collateral process as a function of the metamodel, wherein the collateral process is identified in response to the model efficiency score of the first process model failing to satisfy a threshold, wherein the collateral process indicates an additional output identified by the metamodel as a function of the plurality of metamodel training examples correlated to historical data” (see specification ¶[0038]) is being interpreted as mathematical calculations/evaluations. The recitations are computer-implemented, as indicated in the specification (see ¶[0009]-[0010], ¶[0023], ¶[0042], and ¶[0070]), and in the claim, line 4.
Step 1: This part of eligibility analysis evaluates whether the claim falls within any statutory category. MPEP 2106.03. Claim 1 is directed towards an apparatus for identifying collateral processes, which is directed towards a machine and/or a manufacture (a statutory category of invention). Claim 11 recites a method for identifying collateral processes, which is directed towards a process (a statutory category of invention). Step 1: YES.
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04(a)(2)(I). The courts consider mathematical calculations, when the claim is given its BRI in light of the specification, as falling within the “mathematical concept” grouping of abstract ideas. A claim does not have to recite “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using a mathematical method, or “performing” a mathematical operation, may also be considered a mathematical calculation when the BRI of the claim in light of the specification encompasses a mathematical calculation. As discussed in the claim interpretation section, the limitations include, under the BRI, various mathematical calculations/evaluations. Accordingly, the limitations as seen in claims 1 and 11 recite judicial exceptions (abstract ideas that fall within the mathematical calculations grouping of mathematical concepts).
Alternatively or additionally, these steps describe the concept of using implicit mathematical formulas (i.e., calculations to determine a likelihood score) to derive a conclusion based on input of data, which corresponds to concepts identified as abstract ideas by the courts (Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)). The concept of the recited limitations identified as mathematical concepts above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
Furthermore, as explained in MPEP 2106.04(a)(2)(III). The courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). The “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions. As discussed in the claim interpretation section, the limitations include, under the BRI, judgements/observations and/or mathematical calculations/evaluations. Accordingly, the limitations as seen in claims 1 and 11 recite judicial exceptions (abstract ideas that fall within the mental process grouping). No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice.
In particular, claim 1 recites the following elements, which are part of the abstract idea (i.e., the algorithm):
generate reaction data as a function of the collected physiological responses of the user;
generate a plurality of process data of a process as a function of outputs from the plurality of sensors, wherein the plurality of process data further comprises a plurality of process input data and a plurality of correlated process output data, wherein the plurality of process input data comprises cognitive data correlated to the reaction data, wherein the cognitive data comprises complexity data, wherein the complexity data comprises cognitive data related to overcoming a problem, wherein the complexity data further comprises a cognitive effect of at least a step to solve the problem, wherein cognitive data further comprises severity data, wherein the severity data comprises cognitive data related to at least an effect of the problem on a brain of the user, wherein generating the plurality of process data further comprises:
performing feature extraction on the physiological responses of the user collected using the plurality of sensors,
generating the cognitive data as a function of a cognition classifier; and
determining the reaction data as a function of a user input and the cognitive data;
generate a first process model using the plurality of process data, wherein generating the first process model further comprises training the process model using the plurality of process input data and the plurality of correlated process output data;
receive a plurality of metamodel training examples, wherein each metamodel training example of the plurality of metamodel training examples includes a plurality of model input examples and model output examples corresponding to an exemplary model, and wherein each model training example within the plurality of metamodel training examples further includes at least an efficiency score per the exemplary model relating to the plurality of model input examples and model output examples;
train, using the plurality of metamodel training examples, a metamodel, wherein the metamodel is configured to receive process model measurements and output model efficiency scores;
generate a measurement of the first process model;
output a model efficiency score of the first process model using the metamodel and the measurement of the first process model;
identify a collateral process as a function of the metamodel, wherein the collateral process is identified in response to the model efficiency score of the first process model failing to satisfy a threshold, wherein the collateral process indicates an additional output identified by the metamodel as a function of the plurality of metamodel training examples correlated to historical data;
transmit the collateral process.
Furthermore, claim 11 recites the following elements, which are part of the abstract idea (i.e., the algorithm):
a method for identifying collateral processes, the method comprising:
generating a plurality of process data of a process, wherein the plurality of process data further comprises a plurality of process input data and a plurality of correlated process output data, wherein the plurality of process input data comprises cognitive data correlated to reaction data, wherein the cognitive data comprises complexity data, wherein the complexity data comprises cognitive data related to overcoming a problem, wherein generating the plurality of process data further comprises:
performing feature extraction on the physiological responses of the user collected using the plurality of sensors,
generating the cognitive data as a function of a cognition classifier; and
determining the reaction data as a function of a user input and the cognitive data;
generating a first process model using the plurality of process data, wherein generating the first process model further comprises training the process model using the plurality of process input data and the plurality of correlated process output data;
receiving a plurality of metamodel training examples, wherein each metamodel training example of the plurality of metamodel training examples includes a plurality of model input examples and model output examples corresponding to an exemplary model, and wherein each metamodel training example within the plurality of metamodel training examples further includes at least an efficiency score per the exemplary model relating to the plurality of model input examples and model output examples;
training a metamodel using the plurality of metamodel training examples, wherein the metamodel is configured to receive process model measurements and output model efficiency scores;
generating a measurement of the first process model;
outputting a model efficiency score of the first process model using the metamodel and the measurement of the first process model;
identifying a collateral process as a function of the metamodel, wherein the collateral process is identified in response to the model efficiency score of the first process model failing to satisfy a threshold, wherein the collateral process indicates an additional output identified by the metamodel as a function of the plurality of metamodel training examples correlated to historical data;
transmitting the collateral process.
Step 2A Prong One: YES.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the judicial exceptions into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exceptions, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exceptions into a practical application. Claims 1 and 11 recite the additional elements of a processor, memory, and a user device (see specification ¶[0031] the user device is a generic computer, ¶[0046] the user interface is a GUI on the user device). The apparatus/method are merely instructions to implement an abstract idea on a generic computer or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
The claims further recite an additional element of a plurality of sensors. The plurality of sensors does not qualify as integration into a practical application because this limitation is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g) using generic components (i.e., the sensors are generic).
Step 2A Prong Two: NO.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05. As explained with Step 2A Prong Two, the claims recite additional elements which are directed towards the usage of a generic computer, and are at best the equivalent of merely adding the words “apply it” to the judicial exceptions. Mere instructions to apply an exception cannot provide an inventive concept. These elements/steps can be seen as well-understood, routine, and conventional individually and in combination. Claims 1 and 11 recite the additional elements of a processor, memory, and a user device (see specification ¶[0031] the user device is a generic computer, ¶[0046] the user interface is a GUI on the user device). Receiving further user input merely requires receiving data, which is just a function of a generic computer, and/or insignificant data gathering. Thus, the apparatus/method do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
The claims further recite an additional element of a plurality of sensors. The plurality of sensors does not qualify as significantly more because (1) this is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry and/or (2) this limitation is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g) using generic components (i.e., the sensors are generic). In this case, as the sensors are claimed generically, they may be considered generic computer components.
Alternatively and/or additionally, if the sensors were to include an eye-tracking sensor, Tomasi et al. (US Patent Application Publication 2019/0046029 – cited in prior action) teaches imaging the eyes of a subject (see abstract) utilizing conventional mobile device hardware, including the built-in camera (see ¶[0010]), so that different metrics may be obtained, including the movement of the eyes (see ¶[0039] and ¶[0111]-[0113]; Figs. 10A-11). Therefore, the plurality of sensors cannot be seen as significantly more.
The claims also recite a cognition classifier which is merely insignificant extrasolution activity applied to the judicial exception, e.g., mere data manipulation using a well-known element (i.e., the classifier) claimed generically. Machine learning, when claimed generically, is well-known, routine, and conventional in the art. For example, see Hu et al. (“Intelligent Sensor Networks The Integration of Sensor Networks, Signal Processing and Machine Learning”, CRC Press, 2012/10/23) teaches machine learning may utilize data from wireless sensor networks in supervised and unsupervised fashion (see pg. 3-7), and may be utilized for predictions (see pg. 153-180). See Huang et al. (“Kernal Based Algorithms for Mining Huge Data Sets Supervised, Semi-supervised, and Unsupervised Learning”, Springer, 2006) teaches about supervised learning, including for predicting labels of unseen data (see pg. 1-3). See Mitchell (“The Discipline of Machine Learning”, Machine Learning Department, Carnegie Mellon, July 2006) teaches about the state of machine learning, and including the usage in biological settings (see pg. 1-7). Therefore, the cognition classifier cannot be seen as significantly more.
Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Step 2B: NO.
Claims 1 and 11 are not eligible.
Claims 2-5 and 7-10; and 12-15 and 17-20 depend from claims 1 and 11, respectively, and merely further define the abstract ideas of claims 1 and 11 with no further element that integrates the abstract ideas into a practical application or that qualifies as being significantly more. Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome.
Claims 7 and 17 further contemplate a wearable device; however, the specification of the present application does not contemplate what the wearable device could be (see specification ¶[0081]). Therefore, the examiner is interpreting the wearable device as a generic smartphone which could be worn on a user. Therefore, the apparatus/method are merely instructions to implement an abstract idea on a generic computer (i.e., the generic smartphone) or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Further that, the claims recite additional elements which are directed towards the usage of a generic computer (i.e., the generic smartphone), and are at best the equivalent of merely adding the words “apply it” to the judicial exceptions. Mere instructions to apply an exception cannot provide an inventive concept. These elements/steps can be seen as well-understood, routine, and conventional individually and in combination. Thus, the apparatus/method do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Response to Arguments
Applicant’s arguments, 35 U.S.C. § 112
Applicant’s arguments, see pg. 1, filed February 27, 2026, with respect to the rejections of claims 1-5, 7-15, and 17-20 under 35 U.S.C. § 112 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn.
Applicant’s arguments, 35 U.S.C. § 112
Applicant’s arguments, see pg. 1, filed February 27, 2026, with respect to the rejections of claims 1-5, 7-15, and 17-20 under 35 U.S.C. § 112 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection are made that were necessitated by Applicant’s amendment filed on February 27, 2026.
Applicant’s arguments, 35 U.S.C. § 101
Applicant’s arguments, see pg. 1-13, filed February 27, 2026, with respect to the rejections of claims 1-5, 7-15, and 17-20 under 35 U.S.C. § 101 have been fully considered and are NOT persuasive. Applicant first argues that, with respect to Step 2A, prong one, the position that the pending claims do not recite a mental process or mathematical calculation is maintained. The examiner respectfully disagrees for the reasons as set forth in the prior actions. Therefore, Applicant’s arguments are not persuasive.
Next, Applicant argues that, with respect to Step 2A, prong two, the additional claim elements integrate any alleged judicial exception into a practical application, as “the Office’s analysis improperly discounts the technical significance of the additional claim elements by characterizing them as mere data gathering or as generic computer implementation… When properly construed claim 1 recites a concrete, multi-stage technical system that performs more than the mere collection, analysis, and display of information. Specifically, claim 1 recites a plurality of sensors configured to be placed on a user and used to collect physiological responses. These sensors are not claimed in isolation or appended to an abstract idea. Instead, the sensor outputs are transformed through feature extraction and classification into structured reaction data and cognitive data. This transformation is central to the claimed system and is not incidental data gathering. The sensor data directly drives downstream model training, evaluation, and collateral-process identification, and therefore constitutes an integral part of the claimed technological process under MPEP § 2106.04(d)”. The examiner respectfully disagrees. It is noted that there is no system claim.
Claim 1, as amended, requires only “a plurality of sensors, wherein the plurality of sensors are configured to be placed on a user; collect, using the plurality of sensors, physiological responses of the user”. Such data gathering is merely insignificant extra-solution activity. There are three considerations that may be utilized when determining if an element is insignificant extra-solution activity: (1) whether the extra-solution activity is well known, (2) whether the limitation is significant (i.e., it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention), and (3) whether the limitation amounts to necessary data gathering and outputting (i.e., all uses of the recited judicial exception require such data gathering or data output). See MPEP 2106.05(g).
In this case, as the claim only requires generic sensors (i.e., no specific type of sensor is recited) placed on a user to gather physiological responses, such activity is well known. For example, Tomasi et al. (US Patent Application Publication 2019/0046029 – cited in prior action) teaches imaging the eyes of a subject (see abstract) utilizing conventional mobile device hardware, including the built-in camera (see ¶[0010]), so that different metrics may be obtained, including the movement of the eyes (see ¶[0039] and ¶[0111]-[0113]; Figs. 10A-11). This element is furthermore not significant, as it only requires generic sensors to gather physiological data, not placing meaningful limits on the element, such as type(s) of sensors, sensor placement, type(s) of data gathered, specific sensor structure, etc. Lastly, such data gathering is necessary for the processing, as the sensor data is utilized in the generation of the process data. Therefore, such an element directed towards the plurality of sensors is mere insignificant extra-solution activity, which does not integrate any recited abstract ideas into a practical application. Therefore, Applicant’s arguments are not persuasive.
Next, Applicant argues that “generating a plurality of process data comprising correlated process input data and process output data, and training a first process model using those correlated datasets… This limitation defines a specific machine-learning training workflow in which model behavior is learned from structured, sensor-derived inputs correlated with observed outputs. The first process model is therefore a concrete computational artifact produced by the claimed system, not a mental process or abstract evaluation”. The examiner disagrees. The claim does not include “structed, sensor-derived inputs correlated with observed outputs”. As claimed, the process data is generated “as a function of outputs from the plurality of sensors” and “the plurality of process data further comprises a plurality of process input data and a plurality of correlated process output data”. There is no specific relation defined in the claim to indicate “structed, sensor-derived inputs correlated with observed outputs” as presently written. Furthermore, it is not clear what is meant by “a concrete computational artifact”. First, there is no claimed element directed to such a thing. Second, while a computational artifact includes physical or digital objects created using a computer, there is no physical objects (connotated by concrete) as presently claimed. Therefore, Applicant’s arguments are not persuasive. It is noted that there is no system claim.
Next, Applicant argues that the claims, as a whole, integrate the judicial exceptions into a practical application by when applied in a manner that meaningfully limits the judicial exception. In this case, “the metamodel improves system operation by enabling detection of globally suboptimal process models and by identifying collateral processes based on historical performance data, thereby improving robustness, reliability, and adaptability of the system’s model driven operation”. The examiner respectfully disagrees. It is noted that there is no system claim.
In order to be an improvement, any improvement must be disclosed “first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement” and that “if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.”
In this case, the specification only mentions that “Machine-learning models tend to be locally optimized to solutions presented by training examples and some random variation in stochastic algorithms or initial conditions. It is possible, however, for a model to be globally suboptimal. This can be difficult to detect using automated systems.” (see ¶[0002]); however, no disclosure is specifically provided that “enables detection of globally suboptimal process models”, and such elements are not recited in the claim.
The specification details that “As used herein, “process metamodel,” is a model configured to improve accuracy, efficiency and interpretability of outputs of machine learning models described in this disclosure” and “At each iteration of the comparison of first process model 124 to second process model 160 using metamodel 152, the functionality of apparatus 100 including processor 104 may be improved at least due to the improvements in the accuracy of the output generated by metamodel 152” (see ¶[0036] and ¶[0030]). Such an improvement is therefore directed towards merely improving the accuracy of the output of the model. As such, Applicant’s improvement does not lie in the functioning of the computer, rather, the improvement is directed towards the abstract ideas themselves (i.e., the improvement in the machine learning output, the identification of a collateral process).
An improved mental process is still a mental process even if such a mental process results in more accurate results.1,2 Also, having the claims focus on determining the collateral process is not itself limiting the claims to improving the technology because cases that involve practical, technological improvements extend beyond simply improving the accuracy of a prediction.3 See, e.g., McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016) (“The claimed process uses a combined order of specific rules that renders information into a specific format that is then used and applied to create desired results: a sequence of synchronized, animated characters.”); Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1304 (Fed. Cir. 2018) (finding patent eligible a claim drawn to a behavior-based virus scan that protects against viruses that have been “cosmetically modified to avoid detection by code-matching virus scans”); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1330, 1333 (Fed. Cir. 2016) (discussing patent eligible claims directed to “an innovative logical model for a computer database” that included a self-referential table allowing for greater flexibility in configuring databases, faster searching, and more effective storage); CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358, 1368 (Fed. Cir. 2020) (explaining that the claims at issue focus on a specific means for improving cardiac monitoring technology; they are not “directed to a result or effect that itself is the abstract idea and merely invoke generic processes and machinery” (quoting McRO, 837 F.3d at 1314)). Therefore, Applicant’s arguments are not persuasive.
Next, Applicant argues that, similar to Desjardins, “the present specification teaches concrete improvements to machine-learning technology itself, and not merely the use of machine learning as a generic analytical tool… the present invention improves machine-learning operation by introducing a metamodel that generates model efficiency scores from process model measurements and identifies collateral process when the efficiency score of the first process model fails to satisfy a threshold. This threshold-based identification of collateral processes enables the system to detect globally suboptimal or degraded model behavior and to adapt by identifying alternate or additional processes derived from historical model-performance patterns. The examiner respectfully disagrees. As described above, the specification only mentions that “Machine-learning models tend to be locally optimized to solutions presented by training examples and some random variation in stochastic algorithms or initial conditions. It is possible, however, for a model to be globally suboptimal. This can be difficult to detect using automated systems.” (see ¶[0002]); however, no disclosure is specifically provided that “enables detection of globally suboptimal process models”, and such elements are not recited in the claim. Furthermore, it is not clear what is referenced with “concrete improvements to machine-learning technology itself”.
Furthermore, the present application is not similar to Desjardins, except that machine learning is utilized, and Applicant has not explained how the present application is similar to Desjardins. The improvements in Desjardins provided an “improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification… allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system” and that “[s]uch improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation” (see December 05, 2025 USPTO Memo, revised second and last paragraphs of MPEP § 2106.04(d)(1)).
The improvement in Desjardins provides multiple key distinctions as compared to the present application; primarily, the improvements in Desjardins improved the functioning of a computer (i.e., allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system) and the improvement in Desjardins were not subsumed in the identified mathematical calculation (i.e., the identified abstract ideas). Conversely, the improvement of the claims of the present application does not improve any computer function, and lies in the machine learning output and the identification of a collateral process. As described above, any such improvements of the present application are directed towards abstract ideas themselves. Such that, in the present application as compared to Desjardins, such improvements are subsumed in the identified abstract ideas. Therefore, Applicant’s arguments are not persuasive.
Next, Applicant argues that, under Step 2B, as amended, “claim 1 recites additional elements that, when considered individually and as an ordered combination, amount to significantly more than any alleged judicial exception because the claim is tied to a specific, non-generic technical implementation that governs how the system evaluates, governs [sic], and adapts machine-learning process models”. The examiner respectfully disagrees. It is noted that there is no system claim. As discussed above, the machine learning portions (i.e., the cognition classifier, the process model, and the metamodel, and associated elements) are directed towards the abstract ideas, with the elements not directed towards the abstract ideas including the generic computers, displaying, user interface, and plurality of sensors. As explained with Step 2A Prong Two, the claims recite additional elements which are directed towards the usage of a generic computer, and are at best the equivalent of merely adding the words “apply it” to the judicial exceptions. Mere instructions to apply an exception cannot provide an inventive concept. These elements/steps can be seen as well-understood, routine, and conventional individually and in combination. Claims 1 and 11 recite the additional elements of a processor, memory, and a user device (see specification ¶[0031] the user device is a generic computer, ¶[0046] the user interface is a GUI on the user device). Receiving further user input merely requires receiving data, which is just a function of a generic computer, and/or insignificant data gathering. Thus, the apparatus/method do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
The claims further recite an additional element of a plurality of sensors. The plurality of sensors does not qualify as significantly more because (1) this is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry and/or (2) this limitation is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g) using generic components (i.e., the sensors are generic). In this case, as the sensors are claimed generically, they may be considered generic computer components.
Alternatively and/or additionally, if the sensors were to include an eye-tracking sensor, Tomasi et al. (US Patent Application Publication 2019/0046029 – cited in prior action) teaches imaging the eyes of a subject (see abstract) utilizing conventional mobile device hardware, including the built-in camera (see ¶[0010]), so that different metrics may be obtained, including the movement of the eyes (see ¶[0039] and ¶[0111]-[0113]; Figs. 10A-11). Therefore, the plurality of sensors cannot be seen as significantly more.
The claims also recite a cognition classifier which is merely insignificant extrasolution activity applied to the judicial exception, e.g., mere data manipulation using a well-known element (i.e., the classifier) claimed generically. Machine learning, when claimed generically, is well-known, routine, and conventional in the art. For example, see Hu et al. (“Intelligent Sensor Networks The Integration of Sensor Networks, Signal Processing and Machine Learning”, CRC Press, 2012/10/23) teaches machine learning may utilize data from wireless sensor networks in supervised and unsupervised fashion (see pg. 3-7), and may be utilized for predictions (see pg. 153-180). See Huang et al. (“Kernal Based Algorithms for Mining Huge Data Sets Supervised, Semi-supervised, and Unsupervised Learning”, Springer, 2006) teaches about supervised learning, including for predicting labels of unseen data (see pg. 1-3). See Mitchell (“The Discipline of Machine Learning”, Machine Learning Department, Carnegie Mellon, July 2006) teaches about the state of machine learning, and including the usage in biological settings (see pg. 1-7). Therefore, the cognition classifier cannot be seen as significantly more.
As such, Applicant’s arguments with respect to Step 2B are not persuasive. For commensurate reasons, Applicant’s arguments are not persuasive with respect to claim 11. The dependent claims remain rejected based on their dependence from a rejected base claim. Therefore, Applicant’s arguments are not persuasive and the rejections to the claims under 35 U.S.C. § 101 are maintained.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN D. MORONESO whose telephone number is (571)272-8055. The examiner can normally be reached M-F: 8:30AM - 6:00 PM, MST.
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/J.D.M./ Examiner, Art Unit 3791
/JENNIFER ROBERTSON/ Supervisory Patent Examiner, Art Unit 3791
1 “[T]he improvement in computational accuracy alleged here does not qualify as an improvement to a technological
process; rather, it is merely an enhancement to the abstract mathematical calculation of haplotype phase itself...The
different use of a mathematical calculation, even one that yields different or better results, does not render patent
eligible subject matter.” In re Board of Trustees of Leland Stanford Junior University, 991 F.3d 1245 (Fed. Cir.
2021).
2 “[A] claim for a new abstract idea is still an abstract idea.” Synopsys, Inc. v. Mentor Graphics Corp, 839 F.3d 1138
(Fec. Cir. 2016).
3 See In re Board of Trustees of Leland Stanford Junior University, 991 F.3d 1245 (Fed. Cir. 2021).
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