DETAILED ACTION
This Office Action is in response to the remarks entered on 11/05/2025 .Claims 1, 11, 15 are amended. Claims 1-20 are pending.
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 Arguments
Applicant's arguments filed 10/22/2025 have been fully considered.
In reference to Applicant’s arguments:
- Claim interpretation under 35 USC 112(f).
Examiner’s response:
Interpretation is withdrawn in view of amendments and applicant’s arguments.
In reference to Applicant’s arguments:
- Claim rejections under 35 USC 103.
Examiner’s response:
Applicant’s main argument is directed to the amended limitation reciting a hierarchical cluster analyzer receiving a third set of data including personality trait information and performing clustering on personality trait information, asserting that the combination of Lo, Kim, Huang and Lu fails to teach it. Examiner respectfully disagrees, and would like to bring also the reference Garrity (previously used for dependent claim 4) to explain that the combination (emphasis added) of Lo, Kim, Huang, Lu and Garrity teaches the limitation, based on the broadest reasonable interpretation (BRI). The BRI of the limitation “a hierarchical cluster analyzer, implemented via the processor, receiving a third set of data including personality trait information and performing clustering on the personality trait information” is as follows: receiving personality trait information data, analyzing it and clustering it. The reference Lo teaches identifying factors affecting behavioral outcomes and casual relationship to model the interplay among an individual’s behavior, personal factors, and the environment, in order to gain a better understanding of human adaptation to environmental challenges (Lo at [Introduction]: “The focus of this paper is to help fill this void by proposing a dynamical systems approach to model the interplay among an individual’s behavior, personal factors, and the environment, in order to gain a better understanding of human adaptation to environmental challenges”). The reference Lu teaches performing clustering (using a clustering algorithm) by taking user features as inputs indicating which cluster or clusters within the model most closely match the user based on the user features (Lu [0030]: “Provider personalization system 110 may execute the same clustering algorithm used to generate the model, taking in at least the user features identified at 402 as inputs, and obtaining data indicating which cluster or clusters within the model most closely match the user based on the user features). Finally, the reference Garrity explicitly teaches the use of personality traits to predict either positive or negative driving behaviors (Garrity at p. 111: “the personality traits of neuroticism and openness to experience are predicted to be related to negative driving behaviors, and high scores on the trait of conscientiousness are expected to be related to positive driving behaviors”). For these reasons, Examiner understands that the combination (emphasis added) of Lo, Kim, Huang, Lu and Garrity still render obvious the limitations of independent claims 1, 11 and 15.
In reference to Applicant’s arguments:
- Claim rejections under 35 USC 101.
Examiner’s response:
Examiner respectfully disagrees. After careful reconsideration, Examiner still understands that the claims are directed to a judicial exception without significantly more.
First, Applicant asserts the claims do not recite abstract ideas, however, Examiner respectfully disagrees. Based on the broadest reasonable interpretation of the limitations recited in independent claims, the limitations are mainly directed to the observation of first, second and third data including personality trait information in order to analyze, correlate and judge a driver’s (user’s) state assessment. Analyzing two inputs of data in order to conclude a relationship for a third one amounts to observation, evaluation and judgment steps, being mental processes under the MPEP.
Applicant further asserts "One of the benefits of the profile modeling provided is that the prediction model may generate a prediction for profile modeling by receiving a first input and a second input and output the prediction for profile modeling having the same data type as the third set of data. In this way, the prediction may be flexible. In the case at hand, the claims improve the technical field of profile modeling by generating a model that receives a first input and a second input (which may have the same data type as the third set of data) and outputs the prediction for profile modeling "; however, Examiner still understands that this alleged improvement may be provided by the whole analysis and judgment performed, being abstract ideas. Examiner points to MPEP 2106.05 (a) which states “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements”, and this argument is directed to the alleged improvement in prediction (result of the analysis) modeling, which reasonably amounts to observation, evaluation and judgment steps. The main point of the limitations is directed to the prediction (assessment of a user state according to the instant application specification), therefore, these limitations cannot provide an improvement to a technology. The only additional elements recited at the claims are the use of a feature selector, a fuzzy logic inference system, a hierarchical cluster analyzer (all implemented by a processor), and these elements are recited at a high level of generality to perform the mental processes described at Step 2A Prong 1; therefore, this amounts to nothing more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP 2106.05(f)). Furthermore, the generation of a prediction model based on the three sets of data is also recited at a high level of generality, as no specific way of training the model is established and/or specific manner to deliver the prediction, which further amounts to nothing more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP 2106.05(f)).
Rejections are still maintained.
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 stand rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to systems and a method. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically the abstract idea of Mental Processes- “Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”.
Step 2A: Prong 1 analysis:
The claim(s) recite(s):
Claim 1:
“a feature selector, implemented via a processor, receiving a first set of data and performing feature selection on the first set of data” - this limitation recites receiving data and selecting features, which under broadest reasonable interpretation, amounts to observation, evaluation and judgment steps, being mental processes. The use of the feature selector, implemented via a processor, is discussed next at Prong 2;
“a fuzzy logic inference system, implemented via the processor, receiving a second set of data and performing classification on the second set of data” - this limitation recites receiving data and classifying it, which under broadest reasonable interpretation, amounts to observation, evaluation and judgment steps, being mental processes. The use of the fuzzy logic, implemented via the processor, is discussed next at Prong 2;
“a hierarchical cluster analyzer, implemented via the processor, receiving a third set of data including personality trait information and performing clustering on the personality trait information” - this limitation recites receiving data and clustering it, which under broadest reasonable interpretation, amounts to observation, evaluation and judgment steps, being mental processes. The use of the hierarchical cluster analyzer, implemented via the processor, is discussed next at Prong 2;
“wherein the prediction model generates a prediction for profile modeling by receiving a first input of the same data type as the first set of data, a second input of the same data type as the second set of data and outputting the prediction for profile modeling having the same data type as the third set of data” - this limitation recites performing a prediction for profile modeling, which under broadest reasonable interpretation, corresponds to analyzing two inputs of data in order to conclude a relationship for a third one, which amounts to observation, evaluation and judgment steps, being mental processes. The use of the prediction model is discussed next at Prong 2.
Step 2A: Prong 2 analysis:
This judicial exception is not integrated into a practical application because it only recites these additional elements:
“a feature selector, a fuzzy logic inference system, a hierarchical cluster analyzer (all implemented by a processor)” – these elements are recited at a high level of generality to perform the mental processes described at Prong 1, therefore, this amounts to nothing more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP 2106.05(f));
“a model generator, generating a prediction model based on the first set of data, the second set of data, and the third set of data” – this prediction model being generated is recited at a high level of generality, which is later used to perform a prediction (which is a mental processes as described at Prong 1); therefore, this amounts to nothing more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B analysis:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements explained above amount to nothing more than mere instructions to implement an abstract idea or other exception on a computer.
The claims are not patent eligible.
Independent claims 11 and 15 are analogous claims, therefore the same rejection and rationale applies to them, mutatis mutandis.
Dependent claim(s) 2-10, 12-14, 16-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The claims are reciting further embellishment of the judicial exception.
Claim 2: this limitation amounts to merely indicating a field of use or technological environment (see MPEP 2106.05(h)) and fails to integrate the judicial exception into a practical application.
Claim 3: this limitation amounts to merely indicating a field of use or technological environment (see MPEP 2106.05(h)) and fails to integrate the judicial exception into a practical application.
Claim 4: this limitation amounts to merely indicating a field of use or technological environment (see MPEP 2106.05(h)) and fails to integrate the judicial exception into a practical application.
Claim 5: this limitation recites further embellishment about the mental processes for evaluating an individual’s reaction to an event, being abstract ideas. The use of the fuzzy logic is recited at a high level of generality, which amounts to nothing more than mere instructions to perform the mental process.
Claim 6: this limitation amounts to merely indicating a field of use or technological environment (see MPEP 2106.05(h)) and fails to integrate the judicial exception into a practical application.
Claim 7: this limitation recites further embellishment about the mental processes for evaluating an individual’s reaction to an event, being abstract ideas.
Claim 8: this limitation recites the use of a NSGA-II, which is an algorithm, being a mathematical concept, abstract idea.
Claim 9: this limitation recites the use of a random decision forest, which is an algorithm, being a mathematical concept, abstract idea.
Claim 10: this limitation recites performing a prediction for profile modeling, which under broadest reasonable interpretation, corresponds to analyzing two inputs of data in order to conclude a relationship for a third one, which amounts to observation, evaluation and judgment steps, being mental processes. The use of the prediction model is recited at a high level of generality, which amounts to nothing more than mere instructions to perform the mental process.
Independent claims 11 and 15 are analogous claims to claim 1, as stated above.
Claims 12 and 16 are analogous to claim 2, therefore the same rationale applies to them.
Claims 13 and 17 are analogous to claim 3, therefore the same rationale applies to them.
Claims 14 and 18 are analogous to claim 4, therefore the same rationale applies to them.
Claim 19 is analogous to claim 5, therefore the same rationale applies to it.
Claim 20 is analogous to claim 6, therefore the same rationale applies to it.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lo Schiavo et al (NPL: “A dynamical systems approach to triadic reciprocal determinism of social cognitive theory”- hereinafter Lo) in view of Kim (US Pub. No. 2017/0339484 - hereinafter Kim), in view of Huang et al (US Pub. No. 2010/0019964- hereinafter Huang), further in view of Lu et al (US Pub. No. 2021/0125160 - hereinafter Lu), and further in view of Garrity et al (NPL: “Relations Among Personality Traits, Mood States, and Driving Behaviors”, as submitted in IDS 8/19/2022- hereinafter Garrity).
Referring to Claim 1, Lo teaches a system for profile modeling, comprising:
receiving a first set of data and performing feature selection on the first set of data (see Lo at 1. Introduction: “Although the statistical models are useful in identifying factors affecting behavioral outcomes and a causal relationship involving discrete time points, they do not allow a description of these variables as they influence each other and evolve over time. In spite of its obvious importance, very little research so far has attempted to operationalize the dynamical aspects of TRD, and a solid mathematical framework to describe how the variables in TRD evolve over time and influence each other is lacking. The focus of this paper is to help fill this void by proposing a dynamical systems approach to model the interplay among an individual’s behavior, personal factors, and the environment, in order to gain a better understanding of human adaptation to environmental challenges such as traumatic events and daily stressors”. Therefore, the behavior is interpreted as the first set of data for the model, as it is being received over time to model the relationship of the variables throughout time in a dynamic manner);
receiving a second set of data and performing classification on the second set of data (see Lo at 1. Introduction: “Although the statistical models are useful in identifying factors affecting behavioral outcomes and a causal relationship involving discrete time points, they do not allow a description of these variables as they influence each other and evolve over time. In spite of its obvious importance, very little research so far has attempted to operationalize the dynamical aspects of TRD, and a solid mathematical framework to describe how the variables in TRD evolve over time and influence each other is lacking. The focus of this paper is to help fill this void by proposing a dynamical systems approach to model the interplay among an individual’s behavior, personal factors, and the environment, in order to gain a better understanding of human adaptation to environmental challenges such as traumatic events and daily stressors”. Therefore, the environmental factor is interpreted as the second set of data for the model, as it is being received over time to model the relationship of the variables throughout time in a dynamic manner);
receiving a third set of data including personality trait information and performing clustering on the personality trait information (see Lo at 1. Introduction: “Although the statistical models are useful in identifying factors affecting behavioral outcomes and a causal relationship involving discrete time points, they do not allow a description of these variables as they influence each other and evolve over time. In spite of its obvious importance, very little research so far has attempted to operationalize the dynamical aspects of TRD, and a solid mathematical framework to describe how the variables in TRD evolve over time and influence each other is lacking. The focus of this paper is to help fill this void by proposing a dynamical systems approach to model the interplay among an individual’s behavior, personal factors, and the environment, in order to gain a better understanding of human adaptation to environmental challenges such as traumatic events and daily stressors”. Therefore, the personal factor and behavioral factor are interpreted as the third set of data for the model, as it is being received over time to model the relationship of the variables throughout time in a dynamic manner); and
a model generator, generating a prediction model based on the first set of data, the second set of data, and the third set of data (see Lo at Section 2. Development of a deterministic dynamical systems model: “The present study aims at modeling the relationship of these three elements by means of a set of coupled differential equations”),
wherein the prediction model generates a prediction for profile modeling by receiving a first input of the same data type as the first set of data, a second input of the same data type as the second set of data and outputting the prediction for profile modeling having the same data type as the third set of data (see Lo at Introduction: “Triadic reciprocal determinism (TRD) is often utilized as a conceptual and analytical model in studies using social cognitive theory (SCT) as a theoretical framework, representing bidirectional relationships among an individual’s behavior, personal factors, and the environment”, and “In this work we propose a deterministic dynamical systems approach to model TRD. As mentioned above, triadic reciprocal determinism is conceptualized as involving three components: personal factors, behavioral factors, and environmental factors, which influence and affect each other as the individual attempts to promote desired outcomes and reduce undesirable ones”, and Section 2. Development of a deterministic dynamical systems model: “The present study aims at modeling the relationship of these three elements by means of a set of coupled differential equations”. Therefore, by creating a model using behavior (first set of data), personal factor (third set of data), and environmental factors (second set of data), this is analogous to the claimed limitation).
However, Lo fails to explicitly teach:
a feature selector, implemented via a processor, receiving a first set of data and performing feature selection on the first set of data;
a fuzzy logic inference system, implemented via the processor, receiving a second set of data and performing classification on the second set of data;
a hierarchical cluster analyzer, implemented via the processor, receiving a third set of data including personality trait information and performing clustering on the personality trait information.
Kim teaches, in an analogous system, a feature selector, implemented via a processor, receiving a first set of data and performing feature selection on the first set of data (see Kim at [0079]: “Feature extraction techniques that exploit existing or recognized bio-signals can be applied to reduce processing but also general dimensionality reduction techniques may help, such as principal or independent component analysis”, and “a feature selection step 903 can be used to select a subset of relevant features from a larger feature set to remove redundant and irrelevant features, for example reducing one or more bio-signals from a bio-signal feature set, or one or more music attributes from a music attributes feature set, or one or more emotions/moods/preferences from a emotions/moods/preferences feature set”. Further at [0092-0093]: “Many of the functional units described in this specification have been labeled as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components… Modules may also be implemented in software for execution by various types of processors).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lo with the above teachings of Kim by receiving a first set of data, as taught by Lo, and performing feature selection, as taught by Kim. The modification would have been obvious because one of ordinary skill in the art would be motivated to deliver an optimal set of features into a classifier algorithm (see Kim at 0079).
Huang teaches, in an analogous system, a fuzzy logic inference system, implemented via the processor, receiving a second set of data and performing classification on the second set of data (see Huang at [Abstract]: “A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle”. Moreover, at [0006]: “A style characterization processor receives the maneuver identifier signals, the stored data from the data selection processor and the traffic and road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle”. Further at [0099]: “According to one embodiment of the present invention, the style characterization processor 52 classifies a driver's driving style based on discriminant features. Although various classification techniques, such as fuzzy logic, clustering, neural networks (NN), self-organizing maps (SOM), and even simple threshold-base logic can be used, it is an innovation of the present invention to utilize such techniques to characterize a drivers driving style”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo and Kim with the above teachings of Huang by receiving a first set of data and a second set of data, and performing feature selection on the first set of data, as taught by Lo and Kim, and further performing classification on the second set of data using fuzzy logic, as taught by Huang. The modification would have been obvious because one of ordinary skill in the art would be motivated to classify driving style of a user based on the signals from the maneuver identifier signals, the stored data from the data selection processor and the traffic and road condition signals (see Huang at 0006).
Lu teaches, in an analogous system, a hierarchical cluster analyzer, implemented via the processor, receiving a third set of data including personality trait information and performing clustering on the personality trait information (see Lu at [0030]: “Examples of clustering algorithms that may be used to generate such a model may include, but are not limited to, expectation maximization, K-means, and/or hierarchical clustering algorithms. Provider personalization system 110 may execute the same clustering algorithm used to generate the model, taking in at least the user features identified at 402 as inputs, and obtaining data indicating which cluster or clusters within the model most closely match the user based on the user features”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo, Kim and Huang with the above teachings of Lu by receiving a first, second and third sets of data, and performing feature selection on the first set of data and classification on the second set of data, as taught by Lo, Kim and Huang, and further performing clustering on the third set of data, as taught by Lu. The modification would have been obvious because one of ordinary skill in the art would be motivated to indicate which cluster or clusters within the model most closely match the user based on the user features (see Lu at 0030).
Even though Lo teaches the personality trait as explained above, Garrity further explicitly teaches it (see Garrity at p. 111 Hypotheses: “Specifically, high scores on the personality traits of neuroticism and openness to experience are predicted to be related to negative driving behaviors, and high scores on the trait of conscientiousness are expected to be related to positive driving behaviors”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo, Kim, Huang and Lu with the above teachings of Garrity by receiving a first, second and third sets of data, and performing feature selection on the first set of data, classification on the second set of data and clustering on the third set of data, as taught by Lo, Kim, Huang and Lu, wherein the third set of data includes personality trait information, as taught by Garrity. The modification would have been obvious because one of ordinary skill in the art would be motivated to assess relations among personality traits, mood states, and driving behaviors (see Garrity at Abstract).
Referring to Claim 2, the combination of Lo, Kim, Huang, Lu and Garrity teaches the system for profile modeling of claim 1, wherein the data type of the first set of data is mood state information associated with an individual, including anger, confusion, depression, fatigue, tension, or vigor (see Lo at Introduction: “In this work we propose a deterministic dynamical systems approach to model TRD. As mentioned above, triadic reciprocal determinism is conceptualized as involving three components: personal factors, behavioral factors, and environmental factors, which influence and affect each other as the individual attempts to promote desired outcomes and reduce undesirable ones”. Therefore, the behavioral factors are interpreted as the mood state. Furthermore, see Garrity at p. 109 Abstract: “One hundred sixty-three individuals participated in this study, which assessed relations among personality traits, mood states, and driving behaviors”, and p. 114 Table III left Column: Moods).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo, Kim, Huang and Lu with the above teachings of Garrity by receiving a first, second and third sets of data, and performing feature selection on the first set of data, classification on the second set of data and clustering on the third set of data, as taught by Lo, Kim, Huang and Lu, wherein the first set of data includes mood state information of a user, as taught by Garrity. The modification would have been obvious because one of ordinary skill in the art would be motivated to assess relations among personality traits, mood states, and driving behaviors (see Garrity at Abstract).
Referring to Claim 3, the combination of Lo, Kim, Huang, Lu and Garrity teaches the system for profile modeling of claim 1, wherein the data type of the second set of data is driving style information associated with an individual, including aggressive, anxious, keen, or sedate (see Huang at [Abstract]: “A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle”. Moreover, at [0006]: “A style characterization processor receives the maneuver identifier signals, the stored data from the data selection processor and the traffic and road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle”. Further at [0057]: “For example, a conservative driver driving aggressively may indicate that he or she is in a hurry or under stress. Similarly, a sporty driver driving conservatively may indicate that he or she is tired or drowsy”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo and Kim with the above teachings of Huang by receiving a first set of data and a second set of data, and performing feature selection on the first set of data, as taught by Lo and Kim, and further performing classification on the second set of data using fuzzy logic, as taught by Huang. The modification would have been obvious because one of ordinary skill in the art would be motivated to classify driving style of a user based on the signals from the maneuver identifier signals, the stored data from the data selection processor and the traffic and road condition signals (see Huang at 0006).
Referring to Claim 4, the combination of Lo, Kim, Huang, Lu and Garrity teaches the system for profile modeling of claim 1, wherein the data type of the third set of data is personality trait information associated with an individual, including neuroticism, extroversion, openness, agreeableness, or conscientiousness (see Lo at Introduction: “In this work we propose a deterministic dynamical systems approach to model TRD. As mentioned above, triadic reciprocal determinism is conceptualized as involving three components: personal factors, behavioral factors, and environmental factors, which influence and affect each other as the individual attempts to promote desired outcomes and reduce undesirable ones”. Therefore, the personal factors are interpreted as the personality trait. Furthermore, see Garrity at p. 111 Hypotheses: “Specifically, high scores on the personality traits of neuroticism and openness to experience are predicted to be related to negative driving behaviors, and high scores on the trait of conscientiousness are expected to be related to positive driving behaviors”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo, Kim, Huang and Lu with the above teachings of Garrity by receiving a first, second and third sets of data, and performing feature selection on the first set of data, classification on the second set of data and clustering on the third set of data, as taught by Lo, Kim, Huang and Lu, wherein the third set of data includes personality trait information, as taught by Garrity. The modification would have been obvious because one of ordinary skill in the art would be motivated to assess relations among personality traits, mood states, and driving behaviors (see Garrity at Abstract).
Referring to Claim 5, the combination of Lo, Kim, Huang, Lu and Garrity teaches the system for profile modeling of claim 1, wherein the fuzzy logic inference system performs classification on the second set of data by evaluating an individual's reaction to a defined event presented during simulation or a data collection phase (see Huang at [Abstract]: “A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle”. Moreover, at [0006]: “A style characterization processor receives the maneuver identifier signals, the stored data from the data selection processor and the traffic and road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle”. Further at [0057]: “For example, a conservative driver driving aggressively may indicate that he or she is in a hurry or under stress. Similarly, a sporty driver driving conservatively may indicate that he or she is tired or drowsy”. Therefore, these maneuvers detected based on traffic and road conditions are interpreted as the individual reaction to a defined event during data collection phase).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo and Kim with the above teachings of Huang by receiving a first set of data and a second set of data, and performing feature selection on the first set of data, as taught by Lo and Kim, and further performing classification on the second set of data using fuzzy logic, as taught by Huang. The modification would have been obvious because one of ordinary skill in the art would be motivated to classify driving style of a user based on the signals from the maneuver identifier signals, the stored data from the data selection processor and the traffic and road condition signals (see Huang at 0006).
Referring to Claim 6, the combination of Lo, Kim, Huang, Lu and Garrity teaches the system for profile modeling of claim 5, wherein the defined event is one of a normal driving scenario without surrounding vehicles, a vehicle following scenario, a stop sign scenario, or a lane change scenario within the simulation or the data collection phase (see Huang at [0114]: “The style characterization processor 52 can also use headway control behaviors to utilize the data corresponding to three of the five maneuvers, particularly, vehicle following, another vehicle cutting in, and preceding vehicle changing lanes. The other two maneuvers, no preceding vehicle and the subject vehicle changing lanes, are either of little concern or involve more complicated analysis”. See also [0250]: “According to another embodiment of the present invention, the maneuver identification processor 46 also identifies characteristic maneuvers of vehicles at highway on/off ramps. Typical highway on-ramps start with a short straight entry, continue to a relatively tight curve, and then end with a lane merging. Typical highway off-ramps start with a lane split as the entry portion, continue to a relatively tight curve, and then a short straight road portion and end at a traffic light or a stop sign”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo and Kim with the above teachings of Huang by receiving a first set of data and a second set of data, and performing feature selection on the first set of data, as taught by Lo and Kim, and further performing classification on the second set of data using fuzzy logic, as taught by Huang. The modification would have been obvious because one of ordinary skill in the art would be motivated to classify driving style of a user based on the signals from the maneuver identifier signals, the stored data from the data selection processor and the traffic and road condition signals (see Huang at 0006).
Referring to Claim 7, the combination of Lo, Kim, Huang, Lu and Garrity teaches the system for profile modeling of claim 5, wherein evaluating the individual's reaction to the defined event includes monitoring a speed near a speed limit sign, a minimum speed at a stop sign, a maximum acceleration after the stop sign, or a maximum deceleration near the stop sign within the simulation or the data collection phase (see Huang at [0056]: “The vehicle positioning processor 62 further determines vehicle location with regard to the EDMAP 66 and retrieves relevant local road/traffic information, such as road curvature, speed limit, number of lanes, etc.”. See also [0058]: “As mentioned above, various characteristic maneuvers can be used in the style characterization, such as vehicle headway control, vehicle launching, highway on/off ramp maneuvers, and steering-engaged maneuvers, which referred to maneuvers that involve a relatively large steering angle as and/or a relatively large vehicle yaw rate. The steering-engaged maneuvers may be further broken down into sub-categories, such as lane changes, left/right turns, U-turns and curve-handling maneuvers where a vehicle is negotiating a curve”. See also [0250]: “According to another embodiment of the present invention, the maneuver identification processor 46 also identifies characteristic maneuvers of vehicles at highway on/off ramps. Typical highway on-ramps start with a short straight entry, continue to a relatively tight curve, and then end with a lane merging. Typical highway off-ramps start with a lane split as the entry portion, continue to a relatively tight curve, and then a short straight road portion and end at a traffic light or a stop sign”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo and Kim with the above teachings of Huang by receiving a first set of data and a second set of data, and performing feature selection on the first set of data, as taught by Lo and Kim, and further performing classification on the second set of data using fuzzy logic, as taught by Huang. The modification would have been obvious because one of ordinary skill in the art would be motivated to classify driving style of a user based on the signals from the maneuver identifier signals, the stored data from the data selection processor and the traffic and road condition signals (see Huang at 0006).
Referring to Claim 10, the combination of Lo, Kim, Huang, Lu and Garrity teaches the system for profile modeling of claim 1, wherein the prediction model generates a second prediction for profile modeling by receiving the first input of the same data type as the first set of data, the second input of the same data type as the third set of data and outputting the prediction for profile modeling having the same data type as the second set of data (see Lo at Introduction: “Triadic reciprocal determinism (TRD) is often utilized as a conceptual and analytical model in studies using social cognitive theory (SCT) as a theoretical framework, representing bidirectional relationships among an individual’s behavior, personal factors, and the environment”, and “In this work we propose a deterministic dynamical systems approach to model TRD. As mentioned above, triadic reciprocal determinism is conceptualized as involving three components: personal factors, behavioral factors, and environmental factors, which influence and affect each other as the individual attempts to promote desired outcomes and reduce undesirable ones”. Therefore, since the three components influence and affect each other, two of them as inputs will output the third one (the first and second as inputs will output the third; the first and third as inputs will output the second; the second and third as inputs will output the first)).
Referring to independent Claim 11 and Claim 15, they are rejected on the same basis as independent claim 1 since they are analogous claims. Claim 15 only differs in the order of the data types recited in the last limitation, however, as explained at dependent claim 10, Lo teaches a “Triadic reciprocal determinism” which is often utilized as a conceptual and analytical model in studies using social cognitive theory (SCT) as a theoretical framework, representing bidirectional relationships among an individual’s behavior, personal factors, and the environment, wherein these three components influence and affect each other as the individual attempts to promote desired outcomes and reduce undesirable ones. Therefore, since the three components influence and affect each other, two of them as inputs will output the third one (the first and second as inputs will output the third; the first and third as inputs will output the second; the second and third as inputs will output the first)).
Referring to dependent Claim 12, it is rejected on the same basis as dependent claim 2 since they are analogous claims.
Referring to dependent Claim 13, it is rejected on the same basis as dependent claim 3 since they are analogous claims.
Referring to dependent Claim 14, it is rejected on the same basis as dependent claim 4 since they are analogous claims.
Referring to dependent Claim 16, it is rejected on the same basis as dependent claim 2 since they are analogous claims.
Referring to dependent Claim 17, it is rejected on the same basis as dependent claim 3 since they are analogous claims.
Referring to dependent Claim 18, it is rejected on the same basis as dependent claim 4 since they are analogous claims.
Referring to dependent Claim 19, it is rejected on the same basis as dependent claim 5 since they are analogous claims.
Referring to dependent Claim 20, it is rejected on the same basis as dependent claim 6 since they are analogous claims.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Lo Schiavo et al (NPL: “A dynamical systems approach to triadic reciprocal determinism of social cognitive theory”- hereinafter Lo) in view of Kim (US Pub. No. 2017/0339484 - hereinafter Kim), in view of Huang et al (US Pub. No. 2010/0019964- hereinafter Huang), in view of Lu et al (US Pub. No. 2021/0125160 - hereinafter Lu), in view of Garrity et al (NPL: “Relations Among Personality Traits, Mood States, and Driving Behaviors”, as submitted in IDS 8/19/2022- hereinafter Garrity), and further in view of Kishor et al (NPL: “Interactive fuzzy multiobjective reliability optimization using NSGA-II”- hereinafter Kishor).
Referring to Claim 8, the combination of Lo, Kim, Huang, Lu and Garrity teaches the system for profile modeling of claim 1, however, fails to teach wherein the fuzzy logic inference system performs classification on the second set of data based on a Non- dominated Sorting Genetic Algorithm II (NSGA-II) which optimizes weights for the classification.
Kishor teaches, in an analogous system, wherein the fuzzy logic inference system performs classification on the second set of data based on a Non- dominated Sorting Genetic Algorithm II (NSGA-II) which optimizes weights for the classification (see Kishor at p. 215: last sentence- p. 216: first paragraph: “The non-dominated sorting genetic algorithm (NSGA-II) [7] is a well known and extensively used algorithm based on its predecessor NSGA [8]. It is a fast and very efficient Multiobjective evolutionary algorithm (MOEA), which incorporates the features an elitist archive and a rule for adaptation assignment that takes into account both the rank and the distance of each solution regarding others”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo, Kim, Huang, Lu and Garrity with the above teachings of Kishor by receiving a first, second and third sets of data, and performing feature selection on the first set of data, classification on the second set of data and clustering on the third set of data, as taught by Lo, Kim, Huang, Lu and Garrity, wherein the classification on the second set of data is performed using fuzzy logic based on a NSGA-II, as taught by Kishor. The modification would have been obvious because one of ordinary skill in the art would be motivated to benefit from NSGA-II as it is a fast and very efficient Multiobjective evolutionary algorithm (MOEA), which incorporates the features an elitist archive and a rule for adaptation assignment that takes into account both the rank and the distance of each solution regarding others (see Kishor at Abstract and p.216).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Lo Schiavo et al (NPL: “A dynamical systems approach to triadic reciprocal determinism of social cognitive theory”- hereinafter Lo) in view of Kim (US Pub. No. 2017/0339484 - hereinafter Kim), in view of Huang et al (US Pub. No. 2010/0019964- hereinafter Huang), in view of Lu et al (US Pub. No. 2021/0125160 - hereinafter Lu), in view of Garrity et al (NPL: “Relations Among Personality Traits, Mood States, and Driving Behaviors”, as submitted in IDS 8/19/2022- hereinafter Garrity), and further in view of Pal (NPL: “Random Forest Classifier for Remote Sensing Classification”, as submitted in IDS 8/19/2022- hereinafter Pal).
Referring to Claim 9, the combination of Lo, Kim, Huang, Lu and Garrity teaches the system for profile modeling of claim 1, however, fails to teach wherein the model generator generates the prediction model based on random decision forest.
Pal teaches, in an analogous system, wherein the model generator generates the prediction model based on random decision forest (see Pal at Conclusion: “The results reported in section 3 suggest that the random forest classifier can achieve a classification accuracy which is comparable to that achieved by SVMs. Another advantage of the random forest classifier is that it requires setting of two parameters only whereas the SVMs require a number of user-defined parameters. The random forest classifier can handle categorical data, unbalanced data as well as the data with missing values, which is still not possible with SVMs. This classifier also provides the relative importance of different features during classification process, which can be useful in feature selection”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Lo, Kim, Huang, Lu and Garrity with the above teachings of Pal by receiving a first, second and third sets of data, and performing feature selection on the first set of data, classification on the second set of data and clustering on the third set of data, as taught by Lo, Kim, Huang, Lu and Garrity, wherein using all the sets for generating a model based on random decision forest, as taught by Pal. The modification would have been obvious because one of ordinary skill in the art would be motivated to benefit from a random decision forest as it can handle categorical data, unbalanced data as well as the data with missing values (see Pal at Conclusion).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/LUIS A SITIRICHE/Primary Examiner, Art Unit 2126