The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Notice to Applicant
In response to the communication received on 09/30/2025, the following is a Final Office Action for Application No. 18618690.
Status of Claims
Claims 1-20 are pending.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of applicant’s claim for priority based on: 18618690 filed 03/27/2024 Claims Priority from Provisional Application 63454783, filed 03/27/2023.
Response to Amendments
Applicant’s amendments have been fully considered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment.
As per the arguments that are not moot, Applicant argues the following: overlay the processed set of data according to the visual format onto a map corresponding to the selected risk model. Examiner respectfully disagrees and provides Fig. 2 and ¶0056 teaches a “risk heat map” whereby the risk heat map may present information such that the remote party 208 may assess and better understand the severity of the risk of collisions with pedestrians, animals, or other vehicles leading to the alerts and actions taken by the system”.
As per the motivation to combine references, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for operating an aircraft for assessing operational risk of Ortlieb with the system for increasing the safety of voice conversations between drivers and remote parties of Sicconi for the following reasons: (1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Sicconi ¶0003 teaches that it is desirable to implement telematics systems to increase the safety of real-time voice conversations between a driver and a remote party; (2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Sicconi Abstract teaches system for increasing the safety of voice conversations between drivers and remote parties wherein the system includes an in-vehicle subsystem, and Ortlieb Abstract teaches operating the aircraft (AC) based on risk information provided in the mission risk map (RMA), preferably including minimizing a mission risk; and (3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Sicconi at least the above cited paragraphs, and Ortlieb at least the inclusively cited paragraphs. Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the system for operating an aircraft for assessing operational risk of Ortlieb with the system for increasing the safety of voice conversations between drivers and remote parties of Sicconi.
As per the 112(f) interpretation, Applicant argues that there does not exist functional language and that the claim does not explicitly state “means for.” Examiner respectfully disagrees and in particular, absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Here, a risk detection modeling system ultimately receives a set of data from the source of flight track data and is trained to identify potential risk precursors. There is insufficient structure, e.g. database and/or server/processor, for receiving data and is trained to identify potential risks. Thus, the risk detection modeling system is a non-structural term having no specific structural meaning that performs two functions, e.g., receiving data and is trained to identify potential risks, whereby the generic placeholder, e.g. risk detection modeling system, is not modified by sufficient structure, material, or acts for performing the claimed function. Thus, the 112(f) Claim Interpretation is maintained.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The risk detection modeling system and/or aircraft is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, risk detection modeling system and/or aircraft to inter alia perform the function of overlay the processed set of data according to the visual format onto a map corresponding to the selected risk model is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use a generic processing device and memory medium to inter alia perform the function of overlay the processed set of data according to the visual format onto a map corresponding to the selected risk model which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: risk detection modeling system and/or aircraft. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, risk detection modeling system and/or aircraft to inter alia perform the function of overlay the processed set of data according to the visual format onto a map corresponding to the selected risk model is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
In an effort to further expedite prosecution, see: July 2024 Subject Matter Eligibility Examples, Example 47. Anomaly Detection. Per the analysis of claim 2 Example 47, the analysis refers to MPEP 2106.05(f) which provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Although the additional elements, e.g. (per Example 47) “using a trained ANN”, limits the identified judicial exceptions, e.g. (per Example 47) “detecting one or more anomalies in a data set using the trained ANN” and, e.g. (per Example 47) “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment, e.g. (per Example 47: neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). As an exemplary direction for claim limitations to be eligible, see claims 1 and 3 of Example 47.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: risk detection modeling system in claims 1-20, particularly independent claims 1,8 and 14 and carried to dependent claims.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims fall within statutory class of process or machine; hence, the claims fall under statutory category of Step 1.
Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
a risk detection modeling system comprising a set of selectable risk models, wherein each selectable risk model receives a set of data from the source of flight track data and is trained to identify potential risk precursors to a predetermined type of risk condition;a risk determination application programming interface (API), wherein the risk determination API is configured to:access the risk detection modeling system to select a risk model from the set of the selectable risk models;cause the selected risk model to process the set of data based on the selected risk model;interpret the processed set of data to determine if any potential risk precursors were identified in the set of data for the selected risk model prior to conditions for any predetermined type of risk within the selected risk model being met by the flight track data; andconvert the interpreted processed set of data into a visual format according to the selected risk model to distinguish any potential risk precursors in the processed set of data from nominal data in the processed set of data; anda user interface in communication with the risk determination API, wherein the user interface is configured to:access the risk determination API to send a request causing the risk determination API to select the risk model;receive the processed set of data according to the visual format; andoverlay the processed set of data according to the visual format onto a map corresponding to the selected risk model.
[or]
providing a risk detection modeling system comprising a set of selectable risk models,wherein each selectable risk model receives a set of data from the data source comprising flight data for individual aircraft flown within a geographic region; andis trained to identify potential risk precursors to a predetermined type of risk condition;accessing the risk detection modeling system with a risk determination application programming interface (API) in response to a command from a user interface to select a risk model from the set of the selectable risk models; downloading the set of data from the data source via the risk detection modeling system and processing according to the selected risk model; interpreting the processed set of data via the risk determination API to determine if any potential risk precursors were identified in the set of processed data for the selected risk model prior to conditions for any predetermined type of risk within the selected risk model being met by the flight track data;converting the interpreted processed set of data into a visual format for display on the user interface according to the selected risk model to distinguish any potential risk precursors from nominal data; and overlaying the visual format onto a map displayed on the user interface, wherein the map corresponds to the selected risk model..
[or]
storing a set of selectable risk models within a risk detection modeling system in communication with the source of real-time flight track data, wherein each selectable risk model receives and analyzes a set of data from the source of real-time flight track data and is trained to identify potential risk precursors to a predetermined type of risk condition;accessing the risk detection modeling system with a risk determination application programming interface (API) configured to:select a risk model from the set of the selectable risk models;cause the selected risk model to process the received set of data based on the selected risk model;interpret the processed set of data to determine if any potential risk precursors were identified in the received set of data for the selected risk model prior to conditions for any predetermined type of risk within the selected risk model being met by the flight track data; andgenerate instructions to convert the interpreted processed set of data into a visual format according to the selected risk model to distinguish any potential risk precursors in the processed set of data from nominal data from the processed set of data; andaccessing the risk determination API with a user interface configured to:send a request causing the risk determination API to select the risk model;receive the processed set of data according to the generated instructions; andoverlay the processed set of data according to the visual format onto a map corresponding to the selected risk model..
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Mathematical Concepts. “[In a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Limitations are considered together as a single abstract idea for further analysis.
Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The risk detection modeling system and/or aircraft is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic risk detection modeling system and/or aircraft limitation is no more than mere instructions to apply the exception using a generic computer component. Further, overlay the processed set of data according to the visual format onto a map corresponding to the selected risk model by a risk detection modeling system and/or aircraft is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B.
Per Step 2B, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: risk detection modeling system and aircraft. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, overlay the processed set of data according to the visual format onto a map corresponding to the selected risk model by a risk detection modeling system and/or aircraft is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0022 wherein “the present technology may employ various memory devices, processors, servers, databases, network communication protocols, and encryption systems, which may carry out a variety of operations.” Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. 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, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims).
The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea to and including mathematical concepts without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Thus, 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sicconi et al. (US 20240112562 A1) hereinafter referred to as Sicconi in view of Ortlieb et al. (US 20210333808 A1) hereinafter referred to as Ortlieb in further view of Bertram et al. (US 9542849 B1) hereinafter referred to as Bertram.
Sicconi teaches:
Claim 1. A system for identifying aviation risk precursors from a source of flight track data, comprising:
a risk detection modeling system comprising a set of selectable risk models, wherein each selectable risk model is receives a set of data from the source of flight track data and is trained to identify potential risk precursors to a predetermined type of risk condition (¶0046 With continued to reference to FIG. 1, A risk machine learning model 136 may additionally receive input information comprising vehicle dynamics, traffic/weather/road conditions, GPS/route info, road facing camera to characterize risk and to escalate the urgency if the driver fails to act. Risk Machine learning and precomputed risk models are used to calibrate the risk estimation process to the skills and experience of the driver. Inputs into the main decision engine may include information about distraction and drowsiness levels, risk level, leveraging mandated behavior guidelines, considering user preferences, and relying on decision models and Risk Machine Learning model 136 to determine what messages to convey to the user. ¶0045 With continued reference to FIG. 1, risk level 116 may be calculated using a risk machine learning model 136. In embodiments, a risk machine learning model 136 may include a classifier, which may be consistent with the classifier disclosed with reference to FIG. 8. Inputs to the to the machine learning model may include a plurality of monitoring data 112, examples of other risk levels 116, examples of other monitoring data 112, road conditions, weather conditions, traffic conditions, vehicle conditions, physical condition of the driver, traffic events, and the like. The output of the machine learning model is a risk level 116 that reflects the current situation. Risk machine learning model 136 may be trained using risk training data. Risk training data is a plurality of data entries containing a plurality of inputs that are correlated to a plurality of outputs for training a computing device 104 by a machine-learning process. Risk training data may include past monitoring data, examples of other risk levels 116, examples of other monitoring data 112, and the like. Risk training data may include correlations between past monitoring data and past risk levels);
a risk determination application programming interface (API), wherein the risk determination API is configured to:access the risk detection modeling system to select a risk model from the set of the selectable risk models;cause the selected risk models to process the set of data based on the selected risk model (¶0046 With continued to reference to FIG. 1, A risk machine learning model 136 may additionally receive input information comprising vehicle dynamics, traffic/weather/road conditions, GPS/route info, road facing camera to characterize risk and to escalate the urgency if the driver fails to act. Risk Machine learning and precomputed risk models are used to calibrate the risk estimation process to the skills and experience of the driver. Inputs into the main decision engine may include information about distraction and drowsiness levels, risk level, leveraging mandated behavior guidelines, considering user preferences, and relying on decision models and Risk Machine Learning model 136 to determine what messages to convey to the user…The dialog interaction engine may use the blue LED light to generate brief timed blinking patterns as part of a mechanism to evaluate driver's alertness (e.g., mimicking light patterns with corresponding blinking of eyelids). ¶0120 memory 1208 may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof. ¶0122 Input device 1232 may include a touch screen interface that may be a part of or separate from display 1236, discussed further below. Input device 1232 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.);
interpret the processed set of data to determine if any potential risk precursors were identified in the set of data for the selected risk model prior to conditions for any predetermined type of risk within the selected risk model being met by the flight track data (¶0031 Still referring to FIG. 1, monitoring data 112 may include performing a voice analysis. The “voice analysis,” as described in this disclosure, refers to the process of processing audio and identifying patterns in the audio. In an embodiment, system's functionality may be augmented by its ability to discern potential mental health risks while driving within conversations. System may dynamically adjust and regulate the conversational style employed by the digital assistant as it may identify these potential risks…. if system detects an abrupt shift towards negative sentiment in a conversation, it can respond by providing empathetic language and suggesting stress-relief techniques or encouraging the user to reach out to support resources. In a further embodiment, system may incorporate cognitive load assessment techniques to detect mental health concerns. By monitoring user engagement, response times, and the complexity of the conversation, the digital assistant gauges the driver's cognitive load. Drastic deviations from baseline cognitive load levels may serve as indications of potential distress or mental fatigue. As a non-limiting example, system may observe a sharp decline in the driver's response times accompanied by simplified language use, it may interpret this as a possible sign of cognitive overload or emotional stress. In such instances, the system might adapt its communication style to reduce complexity and offer relaxation techniques. ¶0040 neural network may detect a driver expressing confusion or raises questions after a particular interaction, it might adapt by providing additional clarifications or simplifying the language in the ensuing responses. Similarly, if the network identifies signs of increased engagement or interest, it could delve deeper into the subject matter and offer more comprehensive explanations. These adjustments are based on predefined rules and learned patterns from the training data, allowing the neural network to fine-tune its responses in a manner that enhances clarity and relevance.); and
convert the interpreted processed set of data into a visual format according to the selected risk model to distinguish any potential risk precursors in the processed set of data from nominal data from the processed set of data (¶0031 if system detects an abrupt shift towards negative sentiment in a conversation, it can respond by providing empathetic language and suggesting stress-relief techniques or encouraging the user to reach out to support resources. In a further embodiment, system may incorporate cognitive load assessment techniques to detect mental health concerns. By monitoring user engagement, response times, and the complexity of the conversation, the digital assistant gauges the driver's cognitive load. Drastic deviations from baseline cognitive load levels may serve as indications of potential distress or mental fatigue. As a non-limiting example, system may observe a sharp decline in the driver's response times accompanied by simplified language use, it may interpret this as a possible sign of cognitive overload or emotional stress. In such instances, the system might adapt its communication style to reduce complexity and offer relaxation techniques. ¶0056 Still referring to FIG. 2, Remote subsystem 200 may also display other optionally received information such as a sub-screen with video from the driver's face and a heat-map illustrating road conditions. A “risk heat map” for the purposes of this disclosure, is a system that displays colored transparent overlays on top of objects (e.g., pedestrians, vehicles, road debris) in the road view video. In this manner, the risk heat map may present information such that the remote party 208 may assess and better understand the severity of the risk of collisions with pedestrians, animals, or other vehicles leading to the alerts and actions taken by the system); and
a user interface in communication with the risk determination API, wherein the user interface is configured to:access the risk determination API to send a request causing the risk determination API to select the risk model (¶0055 As used in the current disclosure, “display,” refers to a visual apparatus that is comprised of compact flat panel designs, liquid crystal display, organic light-emitting diode, or any combination thereof to present visual information superimposed on spaces. Display 212 may include a graphical user interface (GUI), multi-functional display (MFD), screens, touch screens, speakers, haptic feedback device, live feed, window, combination thereof, and the like. In a nonlimiting embodiment, display 212 may include a mobile computing device like a smartphone, tablet, computer, laptop, client device, server, a combination thereof, or another undisclosed display alone or in combination. Display 212 may be available for only the remote party. Alternatively or additionally, in-vehicle subsystem may include outputs for any or all elements of display 212 or any other elements described in reference to this figure. In embodiments, inclusion in-vehicle of display 212 and/or one or more elements, and/or use thereof, may be determined according to likelihood of distracting a driver of vehicle);
receive the processed set of data according to the visual format (Figs. 4, 8 and ¶0055 inclusion in-vehicle of display 212 and/or one or more elements, and/or use thereof, may be determined according to likelihood of distracting a driver of vehicle. For example, a video display may not be used to output an alert to a driver, to avoid visual distraction; instead, alert may be output using a combination of audio output, haptic output, and/or indicator lights. Still referring to FIG. 2, Remote subsystem 200 may also display other optionally received information such as a sub-screen with video from the driver's face and a heat-map illustrating road conditions. A “risk heat map” for the purposes of this disclosure, is a system that displays colored transparent overlays on top of objects (e.g., pedestrians, vehicles, road debris) in the road view video.); and
overlay the processed set of data according to the visual format onto a map corresponding to the selected risk model (Fig. 2 and ¶0056 Still referring to FIG. 2, Remote subsystem 200 may also display other optionally received information such as a sub-screen with video from the driver's face and a heat-map illustrating road conditions. A “risk heat map” for the purposes of this disclosure, is a system that displays colored transparent overlays on top of objects (e.g., pedestrians, vehicles, road debris) in the road view video. In this manner, the risk heat map may present information such that the remote party 208 may assess and better understand the severity of the risk of collisions with pedestrians, animals, or other vehicles leading to the alerts and actions taken by the system).
Although not explicitly taught by Sicconi, Ortlieb teaches in the analogous art of system for operating an aircraft for assessing operational risk:
a set of selectable risk models, wherein each selectable risk model is adapted to receive a set of data from the source of flight track data (¶0021 The method according to the present invention can offer risk-minimized operation with a modular data-driven approach. This can be achieved by building on dissimilarly acquired and heterogeneous geospatial data sets from which metrics for operational risk are derived. Aircraft and mission specific parameters as well as regulatory requirements can be modelled into each risk layer. In a corresponding embodiment of the invention, each risk model includes at least one of aircraft data and regulatory information on aircraft operation. This process allows for repeatable, precise, accurate and multi-dimensional models of risks associated with the intrinsic mission parameters and the vehicle's or aircraft's geometrical and performance characteristics. In the present description “vehicle” and “aircraft” are used as synonyms. ¶0027 Specific risk metrics are applied to each data layer (data set) individually to model the risk associated with each single layer. The actual risk model used (i.e., the cost function) may change with the data, for which it models the risk (for instance, the risk associated with population density will be modelled differently from the risk associated with a dump site for toxic waste). Following this process, one can obtain N (or n) three-dimensional risk models, where N (or n) is the number of applicable data layers. This yields a modular approach that allows to automatically update and inspect risk models individually according to their level of criticality and rate of change. ¶0052 FIG. 2 illustrates the superposition of multiple data layers DLa through DLh (cf. the example of DL2 in FIG. 1) to generate a 3D (three-dimensional) risk model, i.e., a model which indicates a risk value or risk assessment for a given point in 3D space. However, the invention is not limited to three dimensions. ¶0053 FIG. 3 illustrates the integration of risk models into a UAV system architecture comprising a Flight Management System FMS and an independent monitoring instance (“Monitor”). These entities access a maps database (cf. reference numeral DB and steps S8 to S10 in FIG. 1). Various geometrical and semantic data layers DL (cf. FIG. 2), DL′ are projected and fused together at step S′ to provide a complete risk map for path planning and decision making. Said integration of risk models takes place where the FMS and Monitor pull data from the database DB).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for operating an aircraft for assessing operational risk of Ortlieb with the system for increasing the safety of voice conversations between drivers and remote parties of Sicconi for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Sicconi ¶0003 teaches that it is desirable to implement telematics systems to increase the safety of real-time voice conversations between a driver and a remote party;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Sicconi Abstract teaches system for increasing the safety of voice conversations between drivers and remote parties wherein the system includes an in-vehicle subsystem, and Ortlieb Abstract teaches operating the aircraft (AC) based on risk information provided in the mission risk map (RMA), preferably including minimizing a mission risk; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Sicconi at least the above cited paragraphs, and Ortlieb at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the system for operating an aircraft for assessing operational risk of Ortlieb with the system for increasing the safety of voice conversations between drivers and remote parties of Sicconi. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Although not explicitly taught by Sicconi in view of Ortlieb, Bertram teaches in the analogous art of risk-based flight path data generating system:
interpret the processed set of data to determine if any potential risk precursors were identified in the set of data for the selected risk model prior to conditions for any predetermined type of risk within the selected risk model being met by the flight track data (C.4 L.38 The DRM data source 124 could include data associated with a DRM as disclosed herein. Referring now to FIG. 3A through 3C, exemplars of a DRM 208, a digital risk model of clearance altitudes (DRM-CA) 210, and a digital risk model of elevations (DRM-E) are illustrated. The DRM 208, the DRM-CA 210, and the DRM-E 212 could be developed by those concerned with risks and costs associated with operations of a UAV C.11 L.40 Referring now to FIG. 10, an altitude limitation or restriction could be set into place by one or more of those concerned with risks and costs associated with operations of a UAV using a digital risk model of ceilings (DRM-C) 256 comprised of a plurality of risk ceiling cells. Similar to a DRM-CA and a DRM-E, the DRM-C 256 could be developed to impose risk ceilings to impose a limitation or restriction of a maximum flying altitude. Risk ceilings could be employed where an aviation-governing authority has defined or established an airspace area above the risk ceiling. C.6 L.60 Although the DEM 200 is depicted through the use of rectangular cells and blocks, the inventive concepts disclosed herein are not limited to these shapes but could employ any shape(s) that could convey elevation information of a specified location).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the risk-based flight path data generating system of Bertram with the system for increasing the safety of voice conversations between drivers and remote parties of Sicconi in view of Ortlieb for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Sicconi ¶0003 teaches that it is desirable to implement telematics systems to increase the safety of real-time voice conversations between a driver and a remote party;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Sicconi Abstract teaches system for increasing the safety of voice conversations between drivers and remote parties wherein the system includes an in-vehicle subsystem, and Ortlieb Abstract teaches operating the aircraft (AC) based on risk information provided in the mission risk map (RMA), pref